diff --git a/cv/ocr/dbnetpp/paddlepaddle/README.md b/cv/ocr/dbnetpp/paddlepaddle/README.md index 8e45f5619d4ba0f4a094c7a10ddc753da5c6d018..01affd485769359e8c92d92785cab033eff203dd 100644 --- a/cv/ocr/dbnetpp/paddlepaddle/README.md +++ b/cv/ocr/dbnetpp/paddlepaddle/README.md @@ -5,19 +5,24 @@ Recently, segmentation-based scene text detection methods have drawn extensive attention in the scene text detection field, because of their superiority in detecting the text instances of arbitrary shapes and extreme aspect ratios, profiting from the pixel-level descriptions. However, the vast majority of the existing segmentation-based approaches are limited to their complex post-processing algorithms and the scale robustness of their segmentation models, where the post-processing algorithms are not only isolated to the model optimization but also time-consuming and the scale robustness is usually strengthened by fusing multi-scale feature maps directly. In this paper, we propose a Differentiable Binarization (DB) module that integrates the binarization process, one of the most important steps in the post-processing procedure, into a segmentation network. Optimized along with the proposed DB module, the segmentation network can produce more accurate results, which enhances the accuracy of text detection with a simple pipeline. Furthermore, an efficient Adaptive Scale Fusion (ASF) module is proposed to improve the scale robustness by fusing features of different scales adaptively. By incorporating the proposed DB and ASF with the segmentation network, our proposed scene text detector consistently achieves state-of-the-art results, in terms of both detection accuracy and speed, on five standard benchmarks. ## Step 1: Installation + ```bash -git clone -b release/2.6 https://github.com/PaddlePaddle/PaddleOCR.git +# Clone PaddleOCR, branch: release/2.5 +git clone -b release/2.5 https://github.com/PaddlePaddle/PaddleOCR.git + +# Copy PaddleOCR 2.5 patch from toolbox +yes | cp -rf ../../../../toolbox/PaddleOCR/* PaddleOCR/ cd PaddleOCR -pip3 install -r requirements.txt +# install requirements. pip3 install protobuf==3.20.3 urllib3==1.26.6 yum install -y mesa-libGL -python3 setup.py develop +bash ../init.sh ``` ## Step 2: Preparing datasets -Download the [ICDAR2015 Dataset](https://deepai.org/dataset/icdar-2015) +Download the [ICDAR2015 Dataset](https://deepai.org/dataset/icdar-2015) ```bash # ICDAR2015 PATH as follow: @@ -32,27 +37,34 @@ drwxr-xr-x 2 root root 24576 Jul 21 15:53 icdar_c4_train_imgs -rw-r--r-- 1 root root 468453 Jul 21 15:54 test_icdar2015_label.txt -rw-r--r-- 1 root root 1063118 Jul 21 15:54 train_icdar2015_label.txt +# Prepare datasets +mkdir train_data pretrain_models +ln -s /path/to/icdar2015/ train_data/icdar2015 + +# Pretrain +wget -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/pretrained/MobileNetV3_large_x0_5_pretrained.pdparams ``` ## Step 3: Training ```bash -# Notice: modify "configs/det/det_r50_db++_icdar15.yml" file, set the datasets path as yours. -wget -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/ResNet50_dcn_asf_synthtext_pretrained.pdparams - +# run training export FLAGS_cudnn_exhaustive_search=True export FLAGS_cudnn_batchnorm_spatial_persistent=True export CUDA_VISIBLE_DEVICES=0,1,2,3 - -python3 -u -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/det/det_r50_db++_icdar15.yml -o Global.use_visualdl=True \ ->train.log 2>&1 & +python3 -m paddle.distributed.launch --gpus $CUDA_VISIBLE_DEVICES \ + tools/train.py \ + -c configs/det/det_mv3_db.yml \ + -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained ``` ## Results -GPUs|IPS|ACC -----|---|--- -BI-V100 x8|5.46 samples/s|precision: 0.9062 + +| GPUs | IPS | ACC | +| ------------ | ---------------- | ------------------- | +| BI-V100 x8 | 5.46 samples/s | precision: 0.9062 | ## Reference + - [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR.git) diff --git a/cv/ocr/dbnetpp/paddlepaddle/init.sh b/cv/ocr/dbnetpp/paddlepaddle/init.sh new file mode 100644 index 0000000000000000000000000000000000000000..166b9195f7ce22038d9c092a57545c4624aaf8a8 --- /dev/null +++ b/cv/ocr/dbnetpp/paddlepaddle/init.sh @@ -0,0 +1,26 @@ +#!/bin/bash +# Copyright (c) 2024, Shanghai Iluvatar CoreX Semiconductor Co., Ltd. +# All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); you may +# not use this file except in compliance with the License. You may obtain +# a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the +# License for the specific language governing permissions and limitations +# under the License. + +pip3 install -r requirements.txt +PY_VERSION=$(python3 -V 2>&1 | awk '{print $2}' | awk -F '.' '{print $2}') +if [ "$PY_VERSION" == "10" ]; then + pip3 install numpy==1.22.4 + pip3 install opencv-contrib-python +else + pip3 install opencv-contrib-python==4.4.0.46 +fi + +python3 setup.py develop diff --git a/toolbox/PaddleOCR/PPOCRLabel/PPOCRLabel.py b/toolbox/PaddleOCR/PPOCRLabel/PPOCRLabel.py new file mode 100644 index 0000000000000000000000000000000000000000..5f67b5f17b37455d008d8e1bcb729e3bfd801090 --- /dev/null +++ b/toolbox/PaddleOCR/PPOCRLabel/PPOCRLabel.py @@ -0,0 +1,2707 @@ +# Copyright (c) <2015-Present> Tzutalin +# Copyright (c) 2024, Shanghai Iluvatar CoreX Semiconductor Co., Ltd. +# All Rights Reserved. +# Copyright (C) 2013 MIT, Computer Science and Artificial Intelligence Laboratory. Bryan Russell, Antonio Torralba, +# William T. Freeman. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and +# associated documentation files (the "Software"), to deal in the Software without restriction, including without +# limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the +# Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: +# The above copyright notice and this permission notice shall be included in all copies or substantial portions of +# the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT +# NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT +# SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF +# CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +# THE SOFTWARE. +# !/usr/bin/env python +# -*- coding: utf-8 -*- +# pyrcc5 -o libs/resources.py resources.qrc + +import argparse +import ast +import codecs +import json +import os.path +import platform +import subprocess +import sys +import xlrd +from functools import partial + +from PyQt5.QtCore import QSize, Qt, QPoint, QByteArray, QTimer, QFileInfo, QPointF, QProcess +from PyQt5.QtGui import QImage, QCursor, QPixmap, QImageReader +from PyQt5.QtWidgets import QMainWindow, QListWidget, QVBoxLayout, QToolButton, QHBoxLayout, QDockWidget, QWidget, \ + QSlider, QGraphicsOpacityEffect, QMessageBox, QListView, QScrollArea, QWidgetAction, QApplication, QLabel, QGridLayout, \ + QFileDialog, QListWidgetItem, QComboBox, QDialog + +__dir__ = os.path.dirname(os.path.abspath(__file__)) + +sys.path.append(__dir__) +sys.path.append(os.path.abspath(os.path.join(__dir__, '../..'))) +sys.path.append(os.path.abspath(os.path.join(__dir__, '../PaddleOCR'))) +sys.path.append("..") + +from paddleocr import PaddleOCR, PPStructure +from libs.constants import * +from libs.utils import * +from libs.labelColor import label_colormap +from libs.settings import Settings +from libs.shape import Shape, DEFAULT_LINE_COLOR, DEFAULT_FILL_COLOR, DEFAULT_LOCK_COLOR +from libs.stringBundle import StringBundle +from libs.canvas import Canvas +from libs.zoomWidget import ZoomWidget +from libs.autoDialog import AutoDialog +from libs.labelDialog import LabelDialog +from libs.colorDialog import ColorDialog +from libs.ustr import ustr +from libs.hashableQListWidgetItem import HashableQListWidgetItem +from libs.editinlist import EditInList +from libs.unique_label_qlist_widget import UniqueLabelQListWidget +from libs.keyDialog import KeyDialog + +__appname__ = 'PPOCRLabel' + +LABEL_COLORMAP = label_colormap() + + +class MainWindow(QMainWindow): + FIT_WINDOW, FIT_WIDTH, MANUAL_ZOOM = list(range(3)) + + def __init__(self, + lang="ch", + gpu=False, + kie_mode=False, + default_filename=None, + default_predefined_class_file=None, + default_save_dir=None): + super(MainWindow, self).__init__() + self.setWindowTitle(__appname__) + self.setWindowState(Qt.WindowMaximized) # set window max + self.activateWindow() # PPOCRLabel goes to the front when activate + + # Load setting in the main thread + self.settings = Settings() + self.settings.load() + settings = self.settings + self.lang = lang + + # Load string bundle for i18n + if lang not in ['ch', 'en']: + lang = 'en' + self.stringBundle = StringBundle.getBundle(localeStr='zh-CN' if lang == 'ch' else 'en') # 'en' + getStr = lambda strId: self.stringBundle.getString(strId) + + # KIE setting + self.kie_mode = kie_mode + self.key_previous_text = "" + self.existed_key_cls_set = set() + self.key_dialog_tip = getStr('keyDialogTip') + + self.defaultSaveDir = default_save_dir + self.ocr = PaddleOCR(use_pdserving=False, + use_angle_cls=True, + det=True, + cls=True, + use_gpu=gpu, + lang=lang, + show_log=False) + self.table_ocr = PPStructure(use_pdserving=False, + use_gpu=gpu, + lang=lang, + layout=False, + show_log=False) + + if os.path.exists('./data/paddle.png'): + result = self.ocr.ocr('./data/paddle.png', cls=True, det=True) + result = self.table_ocr('./data/paddle.png', return_ocr_result_in_table=True) + + # For loading all image under a directory + self.mImgList = [] + self.mImgList5 = [] + self.dirname = None + self.labelHist = [] + self.lastOpenDir = None + self.result_dic = [] + self.result_dic_locked = [] + self.changeFileFolder = False + self.haveAutoReced = False + self.labelFile = None + self.currIndex = 0 + + # Whether we need to save or not. + self.dirty = False + + self._noSelectionSlot = False + self._beginner = True + self.screencastViewer = self.getAvailableScreencastViewer() + self.screencast = "https://github.com/PaddlePaddle/PaddleOCR" + + # Load predefined classes to the list + self.loadPredefinedClasses(default_predefined_class_file) + + # Main widgets and related state. + self.labelDialog = LabelDialog(parent=self, listItem=self.labelHist) + self.autoDialog = AutoDialog(parent=self) + + self.itemsToShapes = {} + self.shapesToItems = {} + self.itemsToShapesbox = {} + self.shapesToItemsbox = {} + self.prevLabelText = getStr('tempLabel') + self.noLabelText = getStr('nullLabel') + self.model = 'paddle' + self.PPreader = None + self.autoSaveNum = 5 + + # ================== File List ================== + + filelistLayout = QVBoxLayout() + filelistLayout.setContentsMargins(0, 0, 0, 0) + + self.fileListWidget = QListWidget() + self.fileListWidget.itemClicked.connect(self.fileitemDoubleClicked) + self.fileListWidget.setIconSize(QSize(25, 25)) + filelistLayout.addWidget(self.fileListWidget) + + fileListContainer = QWidget() + fileListContainer.setLayout(filelistLayout) + self.fileListName = getStr('fileList') + self.fileDock = QDockWidget(self.fileListName, self) + self.fileDock.setObjectName(getStr('files')) + self.fileDock.setWidget(fileListContainer) + self.addDockWidget(Qt.LeftDockWidgetArea, self.fileDock) + + # ================== Key List ================== + if self.kie_mode: + self.keyList = UniqueLabelQListWidget() + + # set key list height + key_list_height = int(QApplication.desktop().height() // 4) + if key_list_height < 50: + key_list_height = 50 + self.keyList.setMaximumHeight(key_list_height) + + self.keyListDockName = getStr('keyListTitle') + self.keyListDock = QDockWidget(self.keyListDockName, self) + self.keyListDock.setWidget(self.keyList) + self.keyListDock.setFeatures(QDockWidget.NoDockWidgetFeatures) + filelistLayout.addWidget(self.keyListDock) + + self.AutoRecognition = QToolButton() + self.AutoRecognition.setToolButtonStyle(Qt.ToolButtonTextBesideIcon) + self.AutoRecognition.setIcon(newIcon('Auto')) + autoRecLayout = QHBoxLayout() + autoRecLayout.setContentsMargins(0, 0, 0, 0) + autoRecLayout.addWidget(self.AutoRecognition) + autoRecContainer = QWidget() + autoRecContainer.setLayout(autoRecLayout) + filelistLayout.addWidget(autoRecContainer) + + # ================== Right Area ================== + listLayout = QVBoxLayout() + listLayout.setContentsMargins(0, 0, 0, 0) + + # Buttons + self.editButton = QToolButton() + self.reRecogButton = QToolButton() + self.reRecogButton.setIcon(newIcon('reRec', 30)) + self.reRecogButton.setToolButtonStyle(Qt.ToolButtonTextBesideIcon) + + self.tableRecButton = QToolButton() + self.tableRecButton.setToolButtonStyle(Qt.ToolButtonTextBesideIcon) + + self.newButton = QToolButton() + self.newButton.setToolButtonStyle(Qt.ToolButtonTextBesideIcon) + self.createpolyButton = QToolButton() + self.createpolyButton.setToolButtonStyle(Qt.ToolButtonTextBesideIcon) + + self.SaveButton = QToolButton() + self.SaveButton.setToolButtonStyle(Qt.ToolButtonTextBesideIcon) + self.DelButton = QToolButton() + self.DelButton.setToolButtonStyle(Qt.ToolButtonTextBesideIcon) + + leftTopToolBox = QGridLayout() + leftTopToolBox.addWidget(self.newButton, 0, 0, 1, 1) + leftTopToolBox.addWidget(self.createpolyButton, 0, 1, 1, 1) + leftTopToolBox.addWidget(self.reRecogButton, 1, 0, 1, 1) + leftTopToolBox.addWidget(self.tableRecButton, 1, 1, 1, 1) + + leftTopToolBoxContainer = QWidget() + leftTopToolBoxContainer.setLayout(leftTopToolBox) + listLayout.addWidget(leftTopToolBoxContainer) + + # ================== Label List ================== + # Create and add a widget for showing current label items + self.labelList = EditInList() + labelListContainer = QWidget() + labelListContainer.setLayout(listLayout) + self.labelList.itemSelectionChanged.connect(self.labelSelectionChanged) + self.labelList.clicked.connect(self.labelList.item_clicked) + + # Connect to itemChanged to detect checkbox changes. + self.labelList.itemChanged.connect(self.labelItemChanged) + self.labelListDockName = getStr('recognitionResult') + self.labelListDock = QDockWidget(self.labelListDockName, self) + self.labelListDock.setWidget(self.labelList) + self.labelListDock.setFeatures(QDockWidget.NoDockWidgetFeatures) + listLayout.addWidget(self.labelListDock) + + # ================== Detection Box ================== + self.BoxList = QListWidget() + + # self.BoxList.itemActivated.connect(self.boxSelectionChanged) + self.BoxList.itemSelectionChanged.connect(self.boxSelectionChanged) + self.BoxList.itemDoubleClicked.connect(self.editBox) + # Connect to itemChanged to detect checkbox changes. + self.BoxList.itemChanged.connect(self.boxItemChanged) + self.BoxListDockName = getStr('detectionBoxposition') + self.BoxListDock = QDockWidget(self.BoxListDockName, self) + self.BoxListDock.setWidget(self.BoxList) + self.BoxListDock.setFeatures(QDockWidget.NoDockWidgetFeatures) + listLayout.addWidget(self.BoxListDock) + + # ================== Lower Right Area ================== + leftbtmtoolbox = QHBoxLayout() + leftbtmtoolbox.addWidget(self.SaveButton) + leftbtmtoolbox.addWidget(self.DelButton) + leftbtmtoolboxcontainer = QWidget() + leftbtmtoolboxcontainer.setLayout(leftbtmtoolbox) + listLayout.addWidget(leftbtmtoolboxcontainer) + + self.dock = QDockWidget(getStr('boxLabelText'), self) + self.dock.setObjectName(getStr('labels')) + self.dock.setWidget(labelListContainer) + + # ================== Zoom Bar ================== + self.imageSlider = QSlider(Qt.Horizontal) + self.imageSlider.valueChanged.connect(self.CanvasSizeChange) + self.imageSlider.setMinimum(-9) + self.imageSlider.setMaximum(510) + self.imageSlider.setSingleStep(1) + self.imageSlider.setTickPosition(QSlider.TicksBelow) + self.imageSlider.setTickInterval(1) + + op = QGraphicsOpacityEffect() + op.setOpacity(0.2) + self.imageSlider.setGraphicsEffect(op) + + self.imageSlider.setStyleSheet("background-color:transparent") + self.imageSliderDock = QDockWidget(getStr('ImageResize'), self) + self.imageSliderDock.setObjectName(getStr('IR')) + self.imageSliderDock.setWidget(self.imageSlider) + self.imageSliderDock.setFeatures(QDockWidget.DockWidgetFloatable) + self.imageSliderDock.setAttribute(Qt.WA_TranslucentBackground) + self.addDockWidget(Qt.RightDockWidgetArea, self.imageSliderDock) + + self.zoomWidget = ZoomWidget() + self.colorDialog = ColorDialog(parent=self) + self.zoomWidgetValue = self.zoomWidget.value() + + self.msgBox = QMessageBox() + + # ================== Thumbnail ================== + hlayout = QHBoxLayout() + m = (0, 0, 0, 0) + hlayout.setSpacing(0) + hlayout.setContentsMargins(*m) + self.preButton = QToolButton() + self.preButton.setIcon(newIcon("prev", 40)) + self.preButton.setIconSize(QSize(40, 100)) + self.preButton.clicked.connect(self.openPrevImg) + self.preButton.setStyleSheet('border: none;') + self.preButton.setShortcut('a') + self.iconlist = QListWidget() + self.iconlist.setViewMode(QListView.IconMode) + self.iconlist.setFlow(QListView.TopToBottom) + self.iconlist.setSpacing(10) + self.iconlist.setIconSize(QSize(50, 50)) + self.iconlist.setMovement(QListView.Static) + self.iconlist.setResizeMode(QListView.Adjust) + self.iconlist.itemClicked.connect(self.iconitemDoubleClicked) + self.iconlist.setStyleSheet("QListWidget{ background-color:transparent; border: none;}") + self.iconlist.setHorizontalScrollBarPolicy(Qt.ScrollBarAlwaysOff) + self.nextButton = QToolButton() + self.nextButton.setIcon(newIcon("next", 40)) + self.nextButton.setIconSize(QSize(40, 100)) + self.nextButton.setStyleSheet('border: none;') + self.nextButton.clicked.connect(self.openNextImg) + self.nextButton.setShortcut('d') + + hlayout.addWidget(self.preButton) + hlayout.addWidget(self.iconlist) + hlayout.addWidget(self.nextButton) + + iconListContainer = QWidget() + iconListContainer.setLayout(hlayout) + iconListContainer.setFixedHeight(100) + + # ================== Canvas ================== + self.canvas = Canvas(parent=self) + self.canvas.zoomRequest.connect(self.zoomRequest) + self.canvas.setDrawingShapeToSquare(settings.get(SETTING_DRAW_SQUARE, False)) + + scroll = QScrollArea() + scroll.setWidget(self.canvas) + scroll.setWidgetResizable(True) + self.scrollBars = { + Qt.Vertical: scroll.verticalScrollBar(), + Qt.Horizontal: scroll.horizontalScrollBar() + } + self.scrollArea = scroll + self.canvas.scrollRequest.connect(self.scrollRequest) + + self.canvas.newShape.connect(partial(self.newShape, False)) + self.canvas.shapeMoved.connect(self.updateBoxlist) # self.setDirty + self.canvas.selectionChanged.connect(self.shapeSelectionChanged) + self.canvas.drawingPolygon.connect(self.toggleDrawingSensitive) + + centerLayout = QVBoxLayout() + centerLayout.setContentsMargins(0, 0, 0, 0) + centerLayout.addWidget(scroll) + centerLayout.addWidget(iconListContainer, 0, Qt.AlignCenter) + centerContainer = QWidget() + centerContainer.setLayout(centerLayout) + + self.setCentralWidget(centerContainer) + self.addDockWidget(Qt.RightDockWidgetArea, self.dock) + + self.dock.setFeatures(QDockWidget.DockWidgetClosable | QDockWidget.DockWidgetFloatable) + self.fileDock.setFeatures(QDockWidget.NoDockWidgetFeatures) + + # ================== Actions ================== + action = partial(newAction, self) + quit = action(getStr('quit'), self.close, + 'Ctrl+Q', 'quit', getStr('quitApp')) + + opendir = action(getStr('openDir'), self.openDirDialog, + 'Ctrl+u', 'open', getStr('openDir')) + + open_dataset_dir = action(getStr('openDatasetDir'), self.openDatasetDirDialog, + 'Ctrl+p', 'open', getStr('openDatasetDir'), enabled=False) + + save = action(getStr('save'), self.saveFile, + 'Ctrl+V', 'verify', getStr('saveDetail'), enabled=False) + + alcm = action(getStr('choosemodel'), self.autolcm, + 'Ctrl+M', 'next', getStr('tipchoosemodel')) + + deleteImg = action(getStr('deleteImg'), self.deleteImg, 'Ctrl+Shift+D', 'close', getStr('deleteImgDetail'), + enabled=True) + + resetAll = action(getStr('resetAll'), self.resetAll, None, 'resetall', getStr('resetAllDetail')) + + color1 = action(getStr('boxLineColor'), self.chooseColor, + 'Ctrl+L', 'color_line', getStr('boxLineColorDetail')) + + createMode = action(getStr('crtBox'), self.setCreateMode, + 'w', 'new', getStr('crtBoxDetail'), enabled=False) + editMode = action('&Edit\nRectBox', self.setEditMode, + 'Ctrl+J', 'edit', u'Move and edit Boxs', enabled=False) + + create = action(getStr('crtBox'), self.createShape, + 'w', 'objects', getStr('crtBoxDetail'), enabled=False) + + delete = action(getStr('delBox'), self.deleteSelectedShape, + 'backspace', 'delete', getStr('delBoxDetail'), enabled=False) + + copy = action(getStr('dupBox'), self.copySelectedShape, + 'Ctrl+C', 'copy', getStr('dupBoxDetail'), + enabled=False) + + hideAll = action(getStr('hideBox'), partial(self.togglePolygons, False), + 'Ctrl+H', 'hide', getStr('hideAllBoxDetail'), + enabled=False) + showAll = action(getStr('showBox'), partial(self.togglePolygons, True), + 'Ctrl+A', 'hide', getStr('showAllBoxDetail'), + enabled=False) + + help = action(getStr('tutorial'), self.showTutorialDialog, None, 'help', getStr('tutorialDetail')) + showInfo = action(getStr('info'), self.showInfoDialog, None, 'help', getStr('info')) + showSteps = action(getStr('steps'), self.showStepsDialog, None, 'help', getStr('steps')) + showKeys = action(getStr('keys'), self.showKeysDialog, None, 'help', getStr('keys')) + + zoom = QWidgetAction(self) + zoom.setDefaultWidget(self.zoomWidget) + self.zoomWidget.setWhatsThis( + u"Zoom in or out of the image. Also accessible with" + " %s and %s from the canvas." % (fmtShortcut("Ctrl+[-+]"), + fmtShortcut("Ctrl+Wheel"))) + self.zoomWidget.setEnabled(False) + + zoomIn = action(getStr('zoomin'), partial(self.addZoom, 10), + 'Ctrl++', 'zoom-in', getStr('zoominDetail'), enabled=False) + zoomOut = action(getStr('zoomout'), partial(self.addZoom, -10), + 'Ctrl+-', 'zoom-out', getStr('zoomoutDetail'), enabled=False) + zoomOrg = action(getStr('originalsize'), partial(self.setZoom, 100), + 'Ctrl+=', 'zoom', getStr('originalsizeDetail'), enabled=False) + fitWindow = action(getStr('fitWin'), self.setFitWindow, + 'Ctrl+F', 'fit-window', getStr('fitWinDetail'), + checkable=True, enabled=False) + fitWidth = action(getStr('fitWidth'), self.setFitWidth, + 'Ctrl+Shift+F', 'fit-width', getStr('fitWidthDetail'), + checkable=True, enabled=False) + # Group zoom controls into a list for easier toggling. + zoomActions = (self.zoomWidget, zoomIn, zoomOut, + zoomOrg, fitWindow, fitWidth) + self.zoomMode = self.MANUAL_ZOOM + self.scalers = { + self.FIT_WINDOW: self.scaleFitWindow, + self.FIT_WIDTH: self.scaleFitWidth, + # Set to one to scale to 100% when loading files. + self.MANUAL_ZOOM: lambda: 1, + } + + # ================== New Actions ================== + + edit = action(getStr('editLabel'), self.editLabel, + 'Ctrl+E', 'edit', getStr('editLabelDetail'), enabled=False) + + AutoRec = action(getStr('autoRecognition'), self.autoRecognition, + '', 'Auto', getStr('autoRecognition'), enabled=False) + + reRec = action(getStr('reRecognition'), self.reRecognition, + 'Ctrl+Shift+R', 'reRec', getStr('reRecognition'), enabled=False) + + singleRere = action(getStr('singleRe'), self.singleRerecognition, + 'Ctrl+R', 'reRec', getStr('singleRe'), enabled=False) + + createpoly = action(getStr('creatPolygon'), self.createPolygon, + 'q', 'new', getStr('creatPolygon'), enabled=False) + + tableRec = action(getStr('TableRecognition'), self.TableRecognition, + '', 'Auto', getStr('TableRecognition'), enabled=False) + + cellreRec = action(getStr('cellreRecognition'), self.cellreRecognition, + '', 'reRec', getStr('cellreRecognition'), enabled=False) + + saveRec = action(getStr('saveRec'), self.saveRecResult, + '', 'save', getStr('saveRec'), enabled=False) + + saveLabel = action(getStr('saveLabel'), self.saveLabelFile, # + 'Ctrl+S', 'save', getStr('saveLabel'), enabled=False) + + exportJSON = action(getStr('exportJSON'), self.exportJSON, + '', 'save', getStr('exportJSON'), enabled=False) + + undoLastPoint = action(getStr("undoLastPoint"), self.canvas.undoLastPoint, + 'Ctrl+Z', "undo", getStr("undoLastPoint"), enabled=False) + + rotateLeft = action(getStr("rotateLeft"), partial(self.rotateImgAction, 1), + 'Ctrl+Alt+L', "rotateLeft", getStr("rotateLeft"), enabled=False) + + rotateRight = action(getStr("rotateRight"), partial(self.rotateImgAction, -1), + 'Ctrl+Alt+R', "rotateRight", getStr("rotateRight"), enabled=False) + + undo = action(getStr("undo"), self.undoShapeEdit, + 'Ctrl+Z', "undo", getStr("undo"), enabled=False) + + change_cls = action(getStr("keyChange"), self.change_box_key, + 'Ctrl+X', "edit", getStr("keyChange"), enabled=False) + + lock = action(getStr("lockBox"), self.lockSelectedShape, + None, "lock", getStr("lockBoxDetail"), enabled=False) + + self.editButton.setDefaultAction(edit) + self.newButton.setDefaultAction(create) + self.createpolyButton.setDefaultAction(createpoly) + self.DelButton.setDefaultAction(deleteImg) + self.SaveButton.setDefaultAction(save) + self.AutoRecognition.setDefaultAction(AutoRec) + self.reRecogButton.setDefaultAction(reRec) + self.tableRecButton.setDefaultAction(tableRec) + # self.preButton.setDefaultAction(openPrevImg) + # self.nextButton.setDefaultAction(openNextImg) + + # ================== Zoom layout ================== + zoomLayout = QHBoxLayout() + zoomLayout.addStretch() + self.zoominButton = QToolButton() + self.zoominButton.setToolButtonStyle(Qt.ToolButtonTextBesideIcon) + self.zoominButton.setDefaultAction(zoomIn) + self.zoomoutButton = QToolButton() + self.zoomoutButton.setToolButtonStyle(Qt.ToolButtonTextBesideIcon) + self.zoomoutButton.setDefaultAction(zoomOut) + self.zoomorgButton = QToolButton() + self.zoomorgButton.setToolButtonStyle(Qt.ToolButtonTextBesideIcon) + self.zoomorgButton.setDefaultAction(zoomOrg) + zoomLayout.addWidget(self.zoominButton) + zoomLayout.addWidget(self.zoomorgButton) + zoomLayout.addWidget(self.zoomoutButton) + + zoomContainer = QWidget() + zoomContainer.setLayout(zoomLayout) + zoomContainer.setGeometry(0, 0, 30, 150) + + shapeLineColor = action(getStr('shapeLineColor'), self.chshapeLineColor, + icon='color_line', tip=getStr('shapeLineColorDetail'), + enabled=False) + shapeFillColor = action(getStr('shapeFillColor'), self.chshapeFillColor, + icon='color', tip=getStr('shapeFillColorDetail'), + enabled=False) + + # Label list context menu. + labelMenu = QMenu() + addActions(labelMenu, (edit, delete)) + + self.labelList.setContextMenuPolicy(Qt.CustomContextMenu) + self.labelList.customContextMenuRequested.connect(self.popLabelListMenu) + + # Draw squares/rectangles + self.drawSquaresOption = QAction(getStr('drawSquares'), self) + self.drawSquaresOption.setCheckable(True) + self.drawSquaresOption.setChecked(settings.get(SETTING_DRAW_SQUARE, False)) + self.drawSquaresOption.triggered.connect(self.toogleDrawSquare) + + # Store actions for further handling. + self.actions = struct(save=save, resetAll=resetAll, deleteImg=deleteImg, + lineColor=color1, create=create, createpoly=createpoly, tableRec=tableRec, delete=delete, edit=edit, copy=copy, + saveRec=saveRec, singleRere=singleRere, AutoRec=AutoRec, reRec=reRec, cellreRec=cellreRec, + createMode=createMode, editMode=editMode, + shapeLineColor=shapeLineColor, shapeFillColor=shapeFillColor, + zoom=zoom, zoomIn=zoomIn, zoomOut=zoomOut, zoomOrg=zoomOrg, + fitWindow=fitWindow, fitWidth=fitWidth, + zoomActions=zoomActions, saveLabel=saveLabel, change_cls=change_cls, + undo=undo, undoLastPoint=undoLastPoint, open_dataset_dir=open_dataset_dir, + rotateLeft=rotateLeft, rotateRight=rotateRight, lock=lock, exportJSON=exportJSON, + fileMenuActions=(opendir, open_dataset_dir, saveLabel, exportJSON, resetAll, quit), + beginner=(), advanced=(), + editMenu=(createpoly, edit, copy, delete, singleRere, cellreRec, None, undo, undoLastPoint, + None, rotateLeft, rotateRight, None, color1, self.drawSquaresOption, lock, + None, change_cls), + beginnerContext=( + create, createpoly, edit, copy, delete, singleRere, cellreRec, rotateLeft, rotateRight, lock, change_cls), + advancedContext=(createMode, editMode, edit, copy, + delete, shapeLineColor, shapeFillColor), + onLoadActive=(create, createpoly, createMode, editMode), + onShapesPresent=(hideAll, showAll)) + + # menus + self.menus = struct( + file=self.menu('&' + getStr('mfile')), + edit=self.menu('&' + getStr('medit')), + view=self.menu('&' + getStr('mview')), + autolabel=self.menu('&PaddleOCR'), + help=self.menu('&' + getStr('mhelp')), + recentFiles=QMenu('Open &Recent'), + labelList=labelMenu) + + self.lastLabel = None + # Add option to enable/disable labels being displayed at the top of bounding boxes + self.displayLabelOption = QAction(getStr('displayLabel'), self) + self.displayLabelOption.setShortcut("Ctrl+Shift+P") + self.displayLabelOption.setCheckable(True) + self.displayLabelOption.setChecked(settings.get(SETTING_PAINT_LABEL, False)) + self.displayLabelOption.triggered.connect(self.togglePaintLabelsOption) + + self.labelDialogOption = QAction(getStr('labelDialogOption'), self) + self.labelDialogOption.setShortcut("Ctrl+Shift+L") + self.labelDialogOption.setCheckable(True) + self.labelDialogOption.setChecked(settings.get(SETTING_PAINT_LABEL, False)) + self.labelDialogOption.triggered.connect(self.speedChoose) + + self.autoSaveOption = QAction(getStr('autoSaveMode'), self) + self.autoSaveOption.setCheckable(True) + self.autoSaveOption.setChecked(settings.get(SETTING_PAINT_LABEL, False)) + self.autoSaveOption.triggered.connect(self.autoSaveFunc) + + addActions(self.menus.file, + (opendir, open_dataset_dir, None, saveLabel, saveRec, exportJSON, self.autoSaveOption, None, resetAll, deleteImg, + quit)) + + addActions(self.menus.help, (showKeys, showSteps, showInfo)) + addActions(self.menus.view, ( + self.displayLabelOption, self.labelDialogOption, + None, + hideAll, showAll, None, + zoomIn, zoomOut, zoomOrg, None, + fitWindow, fitWidth)) + + addActions(self.menus.autolabel, (AutoRec, reRec, cellreRec, alcm, None, help)) + + self.menus.file.aboutToShow.connect(self.updateFileMenu) + + # Custom context menu for the canvas widget: + addActions(self.canvas.menus[0], self.actions.beginnerContext) + + self.statusBar().showMessage('%s started.' % __appname__) + self.statusBar().show() + + # Application state. + self.image = QImage() + self.filePath = ustr(default_filename) + self.lastOpenDir = None + self.recentFiles = [] + self.maxRecent = 7 + self.lineColor = None + self.fillColor = None + self.zoom_level = 100 + self.fit_window = False + # Add Chris + self.difficult = False + + # Fix the compatible issue for qt4 and qt5. Convert the QStringList to python list + if settings.get(SETTING_RECENT_FILES): + if have_qstring(): + recentFileQStringList = settings.get(SETTING_RECENT_FILES) + self.recentFiles = [ustr(i) for i in recentFileQStringList] + else: + self.recentFiles = recentFileQStringList = settings.get(SETTING_RECENT_FILES) + + size = settings.get(SETTING_WIN_SIZE, QSize(1200, 800)) + + position = QPoint(0, 0) + saved_position = settings.get(SETTING_WIN_POSE, position) + # Fix the multiple monitors issue + for i in range(QApplication.desktop().screenCount()): + if QApplication.desktop().availableGeometry(i).contains(saved_position): + position = saved_position + break + self.resize(size) + self.move(position) + saveDir = ustr(settings.get(SETTING_SAVE_DIR, None)) + self.lastOpenDir = ustr(settings.get(SETTING_LAST_OPEN_DIR, None)) + + self.restoreState(settings.get(SETTING_WIN_STATE, QByteArray())) + Shape.line_color = self.lineColor = QColor(settings.get(SETTING_LINE_COLOR, DEFAULT_LINE_COLOR)) + Shape.fill_color = self.fillColor = QColor(settings.get(SETTING_FILL_COLOR, DEFAULT_FILL_COLOR)) + self.canvas.setDrawingColor(self.lineColor) + # Add chris + Shape.difficult = self.difficult + + # ADD: + # Populate the File menu dynamically. + self.updateFileMenu() + + # Since loading the file may take some time, make sure it runs in the background. + if self.filePath and os.path.isdir(self.filePath): + self.queueEvent(partial(self.importDirImages, self.filePath or "")) + elif self.filePath: + self.queueEvent(partial(self.loadFile, self.filePath or "")) + + self.keyDialog = None + + # Callbacks: + self.zoomWidget.valueChanged.connect(self.paintCanvas) + + self.populateModeActions() + + # Display cursor coordinates at the right of status bar + self.labelCoordinates = QLabel('') + self.statusBar().addPermanentWidget(self.labelCoordinates) + + # Open Dir if deafult file + if self.filePath and os.path.isdir(self.filePath): + self.openDirDialog(dirpath=self.filePath, silent=True) + + def menu(self, title, actions=None): + menu = self.menuBar().addMenu(title) + if actions: + addActions(menu, actions) + return menu + + def keyReleaseEvent(self, event): + if event.key() == Qt.Key_Control: + self.canvas.setDrawingShapeToSquare(False) + + def keyPressEvent(self, event): + if event.key() == Qt.Key_Control: + # Draw rectangle if Ctrl is pressed + self.canvas.setDrawingShapeToSquare(True) + + def noShapes(self): + return not self.itemsToShapes + + def populateModeActions(self): + self.canvas.menus[0].clear() + addActions(self.canvas.menus[0], self.actions.beginnerContext) + self.menus.edit.clear() + actions = (self.actions.create,) # if self.beginner() else (self.actions.createMode, self.actions.editMode) + addActions(self.menus.edit, actions + self.actions.editMenu) + + def setDirty(self): + self.dirty = True + self.actions.save.setEnabled(True) + + def setClean(self): + self.dirty = False + self.actions.save.setEnabled(False) + self.actions.create.setEnabled(True) + self.actions.createpoly.setEnabled(True) + + def toggleActions(self, value=True): + """Enable/Disable widgets which depend on an opened image.""" + for z in self.actions.zoomActions: + z.setEnabled(value) + for action in self.actions.onLoadActive: + action.setEnabled(value) + + def queueEvent(self, function): + QTimer.singleShot(0, function) + + def status(self, message, delay=5000): + self.statusBar().showMessage(message, delay) + + def resetState(self): + self.itemsToShapes.clear() + self.shapesToItems.clear() + self.itemsToShapesbox.clear() # ADD + self.shapesToItemsbox.clear() + self.labelList.clear() + self.BoxList.clear() + self.filePath = None + self.imageData = None + self.labelFile = None + self.canvas.resetState() + self.labelCoordinates.clear() + # self.comboBox.cb.clear() + self.result_dic = [] + + def currentItem(self): + items = self.labelList.selectedItems() + if items: + return items[0] + return None + + def currentBox(self): + items = self.BoxList.selectedItems() + if items: + return items[0] + return None + + def addRecentFile(self, filePath): + if filePath in self.recentFiles: + self.recentFiles.remove(filePath) + elif len(self.recentFiles) >= self.maxRecent: + self.recentFiles.pop() + self.recentFiles.insert(0, filePath) + + def beginner(self): + return self._beginner + + def advanced(self): + return not self.beginner() + + def getAvailableScreencastViewer(self): + osName = platform.system() + + if osName == 'Windows': + return ['C:\\Program Files\\Internet Explorer\\iexplore.exe'] + elif osName == 'Linux': + return ['xdg-open'] + elif osName == 'Darwin': + return ['open'] + + ## Callbacks ## + def showTutorialDialog(self): + subprocess.Popen(self.screencastViewer + [self.screencast]) + + def showInfoDialog(self): + from libs.__init__ import __version__ + msg = u'Name:{0} \nApp Version:{1} \n{2} '.format(__appname__, __version__, sys.version_info) + QMessageBox.information(self, u'Information', msg) + + def showStepsDialog(self): + msg = stepsInfo(self.lang) + QMessageBox.information(self, u'Information', msg) + + def showKeysDialog(self): + msg = keysInfo(self.lang) + QMessageBox.information(self, u'Information', msg) + + def createShape(self): + assert self.beginner() + self.canvas.setEditing(False) + self.actions.create.setEnabled(False) + self.actions.createpoly.setEnabled(False) + self.canvas.fourpoint = False + + def createPolygon(self): + assert self.beginner() + self.canvas.setEditing(False) + self.canvas.fourpoint = True + self.actions.create.setEnabled(False) + self.actions.createpoly.setEnabled(False) + self.actions.undoLastPoint.setEnabled(True) + + def rotateImg(self, filename, k, _value): + self.actions.rotateRight.setEnabled(_value) + pix = cv2.imread(filename) + pix = np.rot90(pix, k) + cv2.imwrite(filename, pix) + self.canvas.update() + self.loadFile(filename) + + def rotateImgWarn(self): + if self.lang == 'ch': + self.msgBox.warning(self, "提示", "\n 该图片已经有标注框,旋转操作会打乱标注,建议清除标注框后旋转。") + else: + self.msgBox.warning(self, "Warn", "\n The picture already has a label box, " + "and rotation will disrupt the label. " + "It is recommended to clear the label box and rotate it.") + + def rotateImgAction(self, k=1, _value=False): + + filename = self.mImgList[self.currIndex] + + if os.path.exists(filename): + if self.itemsToShapesbox: + self.rotateImgWarn() + else: + self.saveFile() + self.dirty = False + self.rotateImg(filename=filename, k=k, _value=True) + else: + self.rotateImgWarn() + self.actions.rotateRight.setEnabled(False) + self.actions.rotateLeft.setEnabled(False) + + def toggleDrawingSensitive(self, drawing=True): + """In the middle of drawing, toggling between modes should be disabled.""" + self.actions.editMode.setEnabled(not drawing) + if not drawing and self.beginner(): + # Cancel creation. + print('Cancel creation.') + self.canvas.setEditing(True) + self.canvas.restoreCursor() + self.actions.create.setEnabled(True) + self.actions.createpoly.setEnabled(True) + + def toggleDrawMode(self, edit=True): + self.canvas.setEditing(edit) + self.actions.createMode.setEnabled(edit) + self.actions.editMode.setEnabled(not edit) + + def setCreateMode(self): + assert self.advanced() + self.toggleDrawMode(False) + + def setEditMode(self): + assert self.advanced() + self.toggleDrawMode(True) + self.labelSelectionChanged() + + def updateFileMenu(self): + currFilePath = self.filePath + + def exists(filename): + return os.path.exists(filename) + + menu = self.menus.recentFiles + menu.clear() + files = [f for f in self.recentFiles if f != + currFilePath and exists(f)] + for i, f in enumerate(files): + icon = newIcon('labels') + action = QAction( + icon, '&%d %s' % (i + 1, QFileInfo(f).fileName()), self) + action.triggered.connect(partial(self.loadRecent, f)) + menu.addAction(action) + + def popLabelListMenu(self, point): + self.menus.labelList.exec_(self.labelList.mapToGlobal(point)) + + def editLabel(self): + if not self.canvas.editing(): + return + item = self.currentItem() + if not item: + return + text = self.labelDialog.popUp(item.text()) + if text is not None: + item.setText(text) + # item.setBackground(generateColorByText(text)) + self.setDirty() + self.updateComboBox() + + # =================== detection box related functions =================== + def boxItemChanged(self, item): + shape = self.itemsToShapesbox[item] + + box = ast.literal_eval(item.text()) + # print('shape in labelItemChanged is',shape.points) + if box != [(int(p.x()), int(p.y())) for p in shape.points]: + # shape.points = box + shape.points = [QPointF(p[0], p[1]) for p in box] + + # QPointF(x,y) + # shape.line_color = generateColorByText(shape.label) + self.setDirty() + else: # User probably changed item visibility + self.canvas.setShapeVisible(shape, True) # item.checkState() == Qt.Checked + + def editBox(self): # ADD + if not self.canvas.editing(): + return + item = self.currentBox() + if not item: + return + text = self.labelDialog.popUp(item.text()) + + imageSize = str(self.image.size()) + width, height = self.image.width(), self.image.height() + if text: + try: + text_list = eval(text) + except: + msg_box = QMessageBox(QMessageBox.Warning, 'Warning', 'Please enter the correct format') + msg_box.exec_() + return + if len(text_list) < 4: + msg_box = QMessageBox(QMessageBox.Warning, 'Warning', 'Please enter the coordinates of 4 points') + msg_box.exec_() + return + for box in text_list: + if box[0] > width or box[0] < 0 or box[1] > height or box[1] < 0: + msg_box = QMessageBox(QMessageBox.Warning, 'Warning', 'Out of picture size') + msg_box.exec_() + return + + item.setText(text) + # item.setBackground(generateColorByText(text)) + self.setDirty() + self.updateComboBox() + + def updateBoxlist(self): + self.canvas.selectedShapes_hShape = [] + if self.canvas.hShape != None: + self.canvas.selectedShapes_hShape = self.canvas.selectedShapes + [self.canvas.hShape] + else: + self.canvas.selectedShapes_hShape = self.canvas.selectedShapes + for shape in self.canvas.selectedShapes_hShape: + item = self.shapesToItemsbox[shape] # listitem + text = [(int(p.x()), int(p.y())) for p in shape.points] + item.setText(str(text)) + self.actions.undo.setEnabled(True) + self.setDirty() + + def indexTo5Files(self, currIndex): + if currIndex < 2: + return self.mImgList[:5] + elif currIndex > len(self.mImgList) - 3: + return self.mImgList[-5:] + else: + return self.mImgList[currIndex - 2: currIndex + 3] + + # Tzutalin 20160906 : Add file list and dock to move faster + def fileitemDoubleClicked(self, item=None): + self.currIndex = self.mImgList.index(ustr(os.path.join(os.path.abspath(self.dirname), item.text()))) + filename = self.mImgList[self.currIndex] + if filename: + self.mImgList5 = self.indexTo5Files(self.currIndex) + # self.additems5(None) + self.loadFile(filename) + + def iconitemDoubleClicked(self, item=None): + self.currIndex = self.mImgList.index(ustr(os.path.join(item.toolTip()))) + filename = self.mImgList[self.currIndex] + if filename: + self.mImgList5 = self.indexTo5Files(self.currIndex) + # self.additems5(None) + self.loadFile(filename) + + def CanvasSizeChange(self): + if len(self.mImgList) > 0 and self.imageSlider.hasFocus(): + self.zoomWidget.setValue(self.imageSlider.value()) + + def shapeSelectionChanged(self, selected_shapes): + self._noSelectionSlot = True + for shape in self.canvas.selectedShapes: + shape.selected = False + self.labelList.clearSelection() + self.canvas.selectedShapes = selected_shapes + for shape in self.canvas.selectedShapes: + shape.selected = True + self.shapesToItems[shape].setSelected(True) + self.shapesToItemsbox[shape].setSelected(True) + + self.labelList.scrollToItem(self.currentItem()) # QAbstractItemView.EnsureVisible + self.BoxList.scrollToItem(self.currentBox()) + + if self.kie_mode: + if len(self.canvas.selectedShapes) == 1 and self.keyList.count() > 0: + selected_key_item_row = self.keyList.findItemsByLabel(self.canvas.selectedShapes[0].key_cls, + get_row=True) + if isinstance(selected_key_item_row, list) and len(selected_key_item_row) == 0: + key_text = self.canvas.selectedShapes[0].key_cls + item = self.keyList.createItemFromLabel(key_text) + self.keyList.addItem(item) + rgb = self._get_rgb_by_label(key_text, self.kie_mode) + self.keyList.setItemLabel(item, key_text, rgb) + selected_key_item_row = self.keyList.findItemsByLabel(self.canvas.selectedShapes[0].key_cls, + get_row=True) + + self.keyList.setCurrentRow(selected_key_item_row) + + self._noSelectionSlot = False + n_selected = len(selected_shapes) + self.actions.singleRere.setEnabled(n_selected) + self.actions.cellreRec.setEnabled(n_selected) + self.actions.delete.setEnabled(n_selected) + self.actions.copy.setEnabled(n_selected) + self.actions.edit.setEnabled(n_selected == 1) + self.actions.lock.setEnabled(n_selected) + self.actions.change_cls.setEnabled(n_selected) + + def addLabel(self, shape): + shape.paintLabel = self.displayLabelOption.isChecked() + item = HashableQListWidgetItem(shape.label) + item.setFlags(item.flags() | Qt.ItemIsUserCheckable) + item.setCheckState(Qt.Unchecked) if shape.difficult else item.setCheckState(Qt.Checked) + # Checked means difficult is False + # item.setBackground(generateColorByText(shape.label)) + self.itemsToShapes[item] = shape + self.shapesToItems[shape] = item + self.labelList.addItem(item) + # print('item in add label is ',[(p.x(), p.y()) for p in shape.points], shape.label) + + # ADD for box + item = HashableQListWidgetItem(str([(int(p.x()), int(p.y())) for p in shape.points])) + self.itemsToShapesbox[item] = shape + self.shapesToItemsbox[shape] = item + self.BoxList.addItem(item) + for action in self.actions.onShapesPresent: + action.setEnabled(True) + self.updateComboBox() + + # update show counting + self.BoxListDock.setWindowTitle(self.BoxListDockName + f" ({self.BoxList.count()})") + self.labelListDock.setWindowTitle(self.labelListDockName + f" ({self.labelList.count()})") + + def remLabels(self, shapes): + if shapes is None: + # print('rm empty label') + return + for shape in shapes: + item = self.shapesToItems[shape] + self.labelList.takeItem(self.labelList.row(item)) + del self.shapesToItems[shape] + del self.itemsToShapes[item] + self.updateComboBox() + + # ADD: + item = self.shapesToItemsbox[shape] + self.BoxList.takeItem(self.BoxList.row(item)) + del self.shapesToItemsbox[shape] + del self.itemsToShapesbox[item] + self.updateComboBox() + + def loadLabels(self, shapes): + s = [] + for label, points, line_color, key_cls, difficult in shapes: + shape = Shape(label=label, line_color=line_color, key_cls=key_cls) + for x, y in points: + + # Ensure the labels are within the bounds of the image. If not, fix them. + x, y, snapped = self.canvas.snapPointToCanvas(x, y) + if snapped: + self.setDirty() + + shape.addPoint(QPointF(x, y)) + shape.difficult = difficult + # shape.locked = False + shape.close() + s.append(shape) + + self._update_shape_color(shape) + self.addLabel(shape) + + self.updateComboBox() + self.canvas.loadShapes(s) + + def singleLabel(self, shape): + if shape is None: + # print('rm empty label') + return + item = self.shapesToItems[shape] + item.setText(shape.label) + self.updateComboBox() + + # ADD: + item = self.shapesToItemsbox[shape] + item.setText(str([(int(p.x()), int(p.y())) for p in shape.points])) + self.updateComboBox() + + def updateComboBox(self): + # Get the unique labels and add them to the Combobox. + itemsTextList = [str(self.labelList.item(i).text()) for i in range(self.labelList.count())] + + uniqueTextList = list(set(itemsTextList)) + # Add a null row for showing all the labels + uniqueTextList.append("") + uniqueTextList.sort() + + # self.comboBox.update_items(uniqueTextList) + + def saveLabels(self, annotationFilePath, mode='Auto'): + # Mode is Auto means that labels will be loaded from self.result_dic totally, which is the output of ocr model + annotationFilePath = ustr(annotationFilePath) + + def format_shape(s): + # print('s in saveLabels is ',s) + return dict(label=s.label, # str + line_color=s.line_color.getRgb(), + fill_color=s.fill_color.getRgb(), + points=[(int(p.x()), int(p.y())) for p in s.points], # QPonitF + difficult=s.difficult, + key_cls=s.key_cls) # bool + + if mode == 'Auto': + shapes = [] + else: + shapes = [format_shape(shape) for shape in self.canvas.shapes if shape.line_color != DEFAULT_LOCK_COLOR] + # Can add differrent annotation formats here + for box in self.result_dic: + trans_dic = {"label": box[1][0], "points": box[0], "difficult": False} + if self.kie_mode: + trans_dic.update({"key_cls": "None"}) + if trans_dic["label"] == "" and mode == 'Auto': + continue + shapes.append(trans_dic) + + try: + trans_dic = [] + for box in shapes: + trans_dict = {"transcription": box['label'], "points": box['points'], "difficult": box['difficult']} + if self.kie_mode: + trans_dict.update({"key_cls": box['key_cls']}) + trans_dic.append(trans_dict) + self.PPlabel[annotationFilePath] = trans_dic + if mode == 'Auto': + self.Cachelabel[annotationFilePath] = trans_dic + + # else: + # self.labelFile.save(annotationFilePath, shapes, self.filePath, self.imageData, + # self.lineColor.getRgb(), self.fillColor.getRgb()) + # print('Image:{0} -> Annotation:{1}'.format(self.filePath, annotationFilePath)) + return True + except: + self.errorMessage(u'Error saving label data', u'Error saving label data') + return False + + def copySelectedShape(self): + for shape in self.canvas.copySelectedShape(): + self.addLabel(shape) + # fix copy and delete + # self.shapeSelectionChanged(True) + + def labelSelectionChanged(self): + if self._noSelectionSlot: + return + if self.canvas.editing(): + selected_shapes = [] + for item in self.labelList.selectedItems(): + selected_shapes.append(self.itemsToShapes[item]) + if selected_shapes: + self.canvas.selectShapes(selected_shapes) + else: + self.canvas.deSelectShape() + + def boxSelectionChanged(self): + if self._noSelectionSlot: + # self.BoxList.scrollToItem(self.currentBox(), QAbstractItemView.PositionAtCenter) + return + if self.canvas.editing(): + selected_shapes = [] + for item in self.BoxList.selectedItems(): + selected_shapes.append(self.itemsToShapesbox[item]) + if selected_shapes: + self.canvas.selectShapes(selected_shapes) + else: + self.canvas.deSelectShape() + + def labelItemChanged(self, item): + shape = self.itemsToShapes[item] + label = item.text() + if label != shape.label: + shape.label = item.text() + # shape.line_color = generateColorByText(shape.label) + self.setDirty() + elif not ((item.checkState() == Qt.Unchecked) ^ (not shape.difficult)): + shape.difficult = True if item.checkState() == Qt.Unchecked else False + self.setDirty() + else: # User probably changed item visibility + self.canvas.setShapeVisible(shape, True) # item.checkState() == Qt.Checked + # self.actions.save.setEnabled(True) + + # Callback functions: + def newShape(self, value=True): + """Pop-up and give focus to the label editor. + + position MUST be in global coordinates. + """ + if len(self.labelHist) > 0: + self.labelDialog = LabelDialog(parent=self, listItem=self.labelHist) + + if value: + text = self.labelDialog.popUp(text=self.prevLabelText) + self.lastLabel = text + else: + text = self.prevLabelText + + if text is not None: + self.prevLabelText = self.stringBundle.getString('tempLabel') + + shape = self.canvas.setLastLabel(text, None, None, None) # generate_color, generate_color + if self.kie_mode: + key_text, _ = self.keyDialog.popUp(self.key_previous_text) + if key_text is not None: + shape = self.canvas.setLastLabel(text, None, None, key_text) # generate_color, generate_color + self.key_previous_text = key_text + if not self.keyList.findItemsByLabel(key_text): + item = self.keyList.createItemFromLabel(key_text) + self.keyList.addItem(item) + rgb = self._get_rgb_by_label(key_text, self.kie_mode) + self.keyList.setItemLabel(item, key_text, rgb) + + self._update_shape_color(shape) + self.keyDialog.addLabelHistory(key_text) + + self.addLabel(shape) + if self.beginner(): # Switch to edit mode. + self.canvas.setEditing(True) + self.actions.create.setEnabled(True) + self.actions.createpoly.setEnabled(True) + self.actions.undoLastPoint.setEnabled(False) + self.actions.undo.setEnabled(True) + else: + self.actions.editMode.setEnabled(True) + self.setDirty() + + else: + # self.canvas.undoLastLine() + self.canvas.resetAllLines() + + def _update_shape_color(self, shape): + r, g, b = self._get_rgb_by_label(shape.key_cls, self.kie_mode) + shape.line_color = QColor(r, g, b) + shape.vertex_fill_color = QColor(r, g, b) + shape.hvertex_fill_color = QColor(255, 255, 255) + shape.fill_color = QColor(r, g, b, 128) + shape.select_line_color = QColor(255, 255, 255) + shape.select_fill_color = QColor(r, g, b, 155) + + def _get_rgb_by_label(self, label, kie_mode): + shift_auto_shape_color = 2 # use for random color + if kie_mode and label != "None": + item = self.keyList.findItemsByLabel(label)[0] + label_id = self.keyList.indexFromItem(item).row() + 1 + label_id += shift_auto_shape_color + return LABEL_COLORMAP[label_id % len(LABEL_COLORMAP)] + else: + return (0, 255, 0) + + def scrollRequest(self, delta, orientation): + units = - delta / (8 * 15) + bar = self.scrollBars[orientation] + bar.setValue(bar.value() + bar.singleStep() * units) + + def setZoom(self, value): + self.actions.fitWidth.setChecked(False) + self.actions.fitWindow.setChecked(False) + self.zoomMode = self.MANUAL_ZOOM + self.zoomWidget.setValue(value) + + def addZoom(self, increment=10): + self.setZoom(self.zoomWidget.value() + increment) + self.imageSlider.setValue(self.zoomWidget.value() + increment) # set zoom slider value + + def zoomRequest(self, delta): + # get the current scrollbar positions + # calculate the percentages ~ coordinates + h_bar = self.scrollBars[Qt.Horizontal] + v_bar = self.scrollBars[Qt.Vertical] + + # get the current maximum, to know the difference after zooming + h_bar_max = h_bar.maximum() + v_bar_max = v_bar.maximum() + + # get the cursor position and canvas size + # calculate the desired movement from 0 to 1 + # where 0 = move left + # 1 = move right + # up and down analogous + cursor = QCursor() + pos = cursor.pos() + relative_pos = QWidget.mapFromGlobal(self, pos) + + cursor_x = relative_pos.x() + cursor_y = relative_pos.y() + + w = self.scrollArea.width() + h = self.scrollArea.height() + + # the scaling from 0 to 1 has some padding + # you don't have to hit the very leftmost pixel for a maximum-left movement + margin = 0.1 + move_x = (cursor_x - margin * w) / (w - 2 * margin * w) + move_y = (cursor_y - margin * h) / (h - 2 * margin * h) + + # clamp the values from 0 to 1 + move_x = min(max(move_x, 0), 1) + move_y = min(max(move_y, 0), 1) + + # zoom in + units = delta / (8 * 15) + scale = 10 + self.addZoom(scale * units) + + # get the difference in scrollbar values + # this is how far we can move + d_h_bar_max = h_bar.maximum() - h_bar_max + d_v_bar_max = v_bar.maximum() - v_bar_max + + # get the new scrollbar values + new_h_bar_value = h_bar.value() + move_x * d_h_bar_max + new_v_bar_value = v_bar.value() + move_y * d_v_bar_max + + h_bar.setValue(new_h_bar_value) + v_bar.setValue(new_v_bar_value) + + def setFitWindow(self, value=True): + if value: + self.actions.fitWidth.setChecked(False) + self.zoomMode = self.FIT_WINDOW if value else self.MANUAL_ZOOM + self.adjustScale() + + def setFitWidth(self, value=True): + if value: + self.actions.fitWindow.setChecked(False) + self.zoomMode = self.FIT_WIDTH if value else self.MANUAL_ZOOM + self.adjustScale() + + def togglePolygons(self, value): + for item, shape in self.itemsToShapes.items(): + self.canvas.setShapeVisible(shape, value) + + def loadFile(self, filePath=None): + """Load the specified file, or the last opened file if None.""" + if self.dirty: + self.mayContinue() + self.resetState() + self.canvas.setEnabled(False) + if filePath is None: + filePath = self.settings.get(SETTING_FILENAME) + + # Make sure that filePath is a regular python string, rather than QString + filePath = ustr(filePath) + # Fix bug: An index error after select a directory when open a new file. + unicodeFilePath = ustr(filePath) + # unicodeFilePath = os.path.abspath(unicodeFilePath) + # Tzutalin 20160906 : Add file list and dock to move faster + # Highlight the file item + + if unicodeFilePath and self.fileListWidget.count() > 0: + if unicodeFilePath in self.mImgList: + index = self.mImgList.index(unicodeFilePath) + fileWidgetItem = self.fileListWidget.item(index) + print('unicodeFilePath is', unicodeFilePath) + fileWidgetItem.setSelected(True) + self.iconlist.clear() + self.additems5(None) + + for i in range(5): + item_tooltip = self.iconlist.item(i).toolTip() + # print(i,"---",item_tooltip) + if item_tooltip == ustr(filePath): + titem = self.iconlist.item(i) + titem.setSelected(True) + self.iconlist.scrollToItem(titem) + break + else: + self.fileListWidget.clear() + self.mImgList.clear() + self.iconlist.clear() + + # if unicodeFilePath and self.iconList.count() > 0: + # if unicodeFilePath in self.mImgList: + + if unicodeFilePath and os.path.exists(unicodeFilePath): + self.canvas.verified = False + cvimg = cv2.imdecode(np.fromfile(unicodeFilePath, dtype=np.uint8), 1) + height, width, depth = cvimg.shape + cvimg = cv2.cvtColor(cvimg, cv2.COLOR_BGR2RGB) + image = QImage(cvimg.data, width, height, width * depth, QImage.Format_RGB888) + + if image.isNull(): + self.errorMessage(u'Error opening file', + u"

Make sure %s is a valid image file." % unicodeFilePath) + self.status("Error reading %s" % unicodeFilePath) + return False + self.status("Loaded %s" % os.path.basename(unicodeFilePath)) + self.image = image + self.filePath = unicodeFilePath + self.canvas.loadPixmap(QPixmap.fromImage(image)) + + if self.validFilestate(filePath) is True: + self.setClean() + else: + self.dirty = False + self.actions.save.setEnabled(True) + if len(self.canvas.lockedShapes) != 0: + self.actions.save.setEnabled(True) + self.setDirty() + self.canvas.setEnabled(True) + self.adjustScale(initial=True) + self.paintCanvas() + self.addRecentFile(self.filePath) + self.toggleActions(True) + + self.showBoundingBoxFromPPlabel(filePath) + + self.setWindowTitle(__appname__ + ' ' + filePath) + + # Default : select last item if there is at least one item + if self.labelList.count(): + self.labelList.setCurrentItem(self.labelList.item(self.labelList.count() - 1)) + self.labelList.item(self.labelList.count() - 1).setSelected(True) + + # show file list image count + select_indexes = self.fileListWidget.selectedIndexes() + if len(select_indexes) > 0: + self.fileDock.setWindowTitle(self.fileListName + f" ({select_indexes[0].row() + 1}" + f"/{self.fileListWidget.count()})") + # update show counting + self.BoxListDock.setWindowTitle(self.BoxListDockName + f" ({self.BoxList.count()})") + self.labelListDock.setWindowTitle(self.labelListDockName + f" ({self.labelList.count()})") + + self.canvas.setFocus(True) + return True + return False + + def showBoundingBoxFromPPlabel(self, filePath): + width, height = self.image.width(), self.image.height() + imgidx = self.getImglabelidx(filePath) + shapes = [] + # box['ratio'] of the shapes saved in lockedShapes contains the ratio of the + # four corner coordinates of the shapes to the height and width of the image + for box in self.canvas.lockedShapes: + key_cls = 'None' if not self.kie_mode else box['key_cls'] + if self.canvas.isInTheSameImage: + shapes.append((box['transcription'], [[s[0] * width, s[1] * height] for s in box['ratio']], + DEFAULT_LOCK_COLOR, key_cls, box['difficult'])) + else: + shapes.append(('锁定框:待检测', [[s[0] * width, s[1] * height] for s in box['ratio']], + DEFAULT_LOCK_COLOR, key_cls, box['difficult'])) + if imgidx in self.PPlabel.keys(): + for box in self.PPlabel[imgidx]: + key_cls = 'None' if not self.kie_mode else box.get('key_cls', 'None') + shapes.append((box['transcription'], box['points'], None, key_cls, box.get('difficult', False))) + + self.loadLabels(shapes) + self.canvas.verified = False + + def validFilestate(self, filePath): + if filePath not in self.fileStatedict.keys(): + return None + elif self.fileStatedict[filePath] == 1: + return True + else: + return False + + def resizeEvent(self, event): + if self.canvas and not self.image.isNull() \ + and self.zoomMode != self.MANUAL_ZOOM: + self.adjustScale() + super(MainWindow, self).resizeEvent(event) + + def paintCanvas(self): + assert not self.image.isNull(), "cannot paint null image" + self.canvas.scale = 0.01 * self.zoomWidget.value() + self.canvas.adjustSize() + self.canvas.update() + + def adjustScale(self, initial=False): + value = self.scalers[self.FIT_WINDOW if initial else self.zoomMode]() + self.zoomWidget.setValue(int(100 * value)) + self.imageSlider.setValue(self.zoomWidget.value()) # set zoom slider value + + def scaleFitWindow(self): + """Figure out the size of the pixmap in order to fit the main widget.""" + e = 2.0 # So that no scrollbars are generated. + w1 = self.centralWidget().width() - e + h1 = self.centralWidget().height() - e - 110 + a1 = w1 / h1 + # Calculate a new scale value based on the pixmap's aspect ratio. + w2 = self.canvas.pixmap.width() - 0.0 + h2 = self.canvas.pixmap.height() - 0.0 + a2 = w2 / h2 + return w1 / w2 if a2 >= a1 else h1 / h2 + + def scaleFitWidth(self): + # The epsilon does not seem to work too well here. + w = self.centralWidget().width() - 2.0 + return w / self.canvas.pixmap.width() + + def closeEvent(self, event): + if not self.mayContinue(): + event.ignore() + else: + settings = self.settings + # If it loads images from dir, don't load it at the beginning + if self.dirname is None: + settings[SETTING_FILENAME] = self.filePath if self.filePath else '' + else: + settings[SETTING_FILENAME] = '' + + settings[SETTING_WIN_SIZE] = self.size() + settings[SETTING_WIN_POSE] = self.pos() + settings[SETTING_WIN_STATE] = self.saveState() + settings[SETTING_LINE_COLOR] = self.lineColor + settings[SETTING_FILL_COLOR] = self.fillColor + settings[SETTING_RECENT_FILES] = self.recentFiles + settings[SETTING_ADVANCE_MODE] = not self._beginner + if self.defaultSaveDir and os.path.exists(self.defaultSaveDir): + settings[SETTING_SAVE_DIR] = ustr(self.defaultSaveDir) + else: + settings[SETTING_SAVE_DIR] = '' + + if self.lastOpenDir and os.path.exists(self.lastOpenDir): + settings[SETTING_LAST_OPEN_DIR] = self.lastOpenDir + else: + settings[SETTING_LAST_OPEN_DIR] = '' + + settings[SETTING_PAINT_LABEL] = self.displayLabelOption.isChecked() + settings[SETTING_DRAW_SQUARE] = self.drawSquaresOption.isChecked() + settings.save() + try: + self.saveLabelFile() + except: + pass + + def loadRecent(self, filename): + if self.mayContinue(): + print(filename, "======") + self.loadFile(filename) + + def scanAllImages(self, folderPath): + extensions = ['.%s' % fmt.data().decode("ascii").lower() for fmt in QImageReader.supportedImageFormats()] + images = [] + + for file in os.listdir(folderPath): + if file.lower().endswith(tuple(extensions)): + relativePath = os.path.join(folderPath, file) + path = ustr(os.path.abspath(relativePath)) + images.append(path) + natural_sort(images, key=lambda x: x.lower()) + return images + + def openDirDialog(self, _value=False, dirpath=None, silent=False): + if not self.mayContinue(): + return + + defaultOpenDirPath = dirpath if dirpath else '.' + if self.lastOpenDir and os.path.exists(self.lastOpenDir): + defaultOpenDirPath = self.lastOpenDir + else: + defaultOpenDirPath = os.path.dirname(self.filePath) if self.filePath else '.' + if silent != True: + targetDirPath = ustr(QFileDialog.getExistingDirectory(self, + '%s - Open Directory' % __appname__, + defaultOpenDirPath, + QFileDialog.ShowDirsOnly | QFileDialog.DontResolveSymlinks)) + else: + targetDirPath = ustr(defaultOpenDirPath) + self.lastOpenDir = targetDirPath + self.importDirImages(targetDirPath) + + def openDatasetDirDialog(self): + if self.lastOpenDir and os.path.exists(self.lastOpenDir): + if platform.system() == 'Windows': + os.startfile(self.lastOpenDir) + else: + os.system('open ' + os.path.normpath(self.lastOpenDir)) + defaultOpenDirPath = self.lastOpenDir + + else: + if self.lang == 'ch': + self.msgBox.warning(self, "提示", "\n 原文件夹已不存在,请从新选择数据集路径!") + else: + self.msgBox.warning(self, "Warn", + "\n The original folder no longer exists, please choose the data set path again!") + + self.actions.open_dataset_dir.setEnabled(False) + defaultOpenDirPath = os.path.dirname(self.filePath) if self.filePath else '.' + + def init_key_list(self, label_dict): + if not self.kie_mode: + return + # load key_cls + for image, info in label_dict.items(): + for box in info: + if "key_cls" not in box: + box.update({"key_cls": "None"}) + self.existed_key_cls_set.add(box["key_cls"]) + if len(self.existed_key_cls_set) > 0: + for key_text in self.existed_key_cls_set: + if not self.keyList.findItemsByLabel(key_text): + item = self.keyList.createItemFromLabel(key_text) + self.keyList.addItem(item) + rgb = self._get_rgb_by_label(key_text, self.kie_mode) + self.keyList.setItemLabel(item, key_text, rgb) + + if self.keyDialog is None: + # key list dialog + self.keyDialog = KeyDialog( + text=self.key_dialog_tip, + parent=self, + labels=self.existed_key_cls_set, + sort_labels=True, + show_text_field=True, + completion="startswith", + fit_to_content={'column': True, 'row': False}, + flags=None + ) + + def importDirImages(self, dirpath, isDelete=False): + if not self.mayContinue() or not dirpath: + return + if self.defaultSaveDir and self.defaultSaveDir != dirpath: + self.saveLabelFile() + + if not isDelete: + self.loadFilestate(dirpath) + self.PPlabelpath = dirpath + '/Label.txt' + self.PPlabel = self.loadLabelFile(self.PPlabelpath) + self.Cachelabelpath = dirpath + '/Cache.cach' + self.Cachelabel = self.loadLabelFile(self.Cachelabelpath) + if self.Cachelabel: + self.PPlabel = dict(self.Cachelabel, **self.PPlabel) + + self.init_key_list(self.PPlabel) + + self.lastOpenDir = dirpath + self.dirname = dirpath + + self.defaultSaveDir = dirpath + self.statusBar().showMessage('%s started. Annotation will be saved to %s' % + (__appname__, self.defaultSaveDir)) + self.statusBar().show() + + self.filePath = None + self.fileListWidget.clear() + self.mImgList = self.scanAllImages(dirpath) + self.mImgList5 = self.mImgList[:5] + self.openNextImg() + doneicon = newIcon('done') + closeicon = newIcon('close') + for imgPath in self.mImgList: + filename = os.path.basename(imgPath) + if self.validFilestate(imgPath) is True: + item = QListWidgetItem(doneicon, filename) + else: + item = QListWidgetItem(closeicon, filename) + self.fileListWidget.addItem(item) + + print('DirPath in importDirImages is', dirpath) + self.iconlist.clear() + self.additems5(dirpath) + self.changeFileFolder = True + self.haveAutoReced = False + self.AutoRecognition.setEnabled(True) + self.reRecogButton.setEnabled(True) + self.tableRecButton.setEnabled(True) + self.actions.AutoRec.setEnabled(True) + self.actions.reRec.setEnabled(True) + self.actions.tableRec.setEnabled(True) + self.actions.open_dataset_dir.setEnabled(True) + self.actions.rotateLeft.setEnabled(True) + self.actions.rotateRight.setEnabled(True) + + self.fileListWidget.setCurrentRow(0) # set list index to first + self.fileDock.setWindowTitle(self.fileListName + f" (1/{self.fileListWidget.count()})") # show image count + + def openPrevImg(self, _value=False): + if len(self.mImgList) <= 0: + return + + if self.filePath is None: + return + + currIndex = self.mImgList.index(self.filePath) + self.mImgList5 = self.mImgList[:5] + if currIndex - 1 >= 0: + filename = self.mImgList[currIndex - 1] + self.mImgList5 = self.indexTo5Files(currIndex - 1) + if filename: + self.loadFile(filename) + + def openNextImg(self, _value=False): + if not self.mayContinue(): + return + + if len(self.mImgList) <= 0: + return + + filename = None + if self.filePath is None: + filename = self.mImgList[0] + self.mImgList5 = self.mImgList[:5] + else: + currIndex = self.mImgList.index(self.filePath) + if currIndex + 1 < len(self.mImgList): + filename = self.mImgList[currIndex + 1] + self.mImgList5 = self.indexTo5Files(currIndex + 1) + else: + self.mImgList5 = self.indexTo5Files(currIndex) + if filename: + print('file name in openNext is ', filename) + self.loadFile(filename) + + def updateFileListIcon(self, filename): + pass + + def saveFile(self, _value=False, mode='Manual'): + # Manual mode is used for users click "Save" manually,which will change the state of the image + if self.filePath: + imgidx = self.getImglabelidx(self.filePath) + self._saveFile(imgidx, mode=mode) + + def saveLockedShapes(self): + self.canvas.lockedShapes = [] + self.canvas.selectedShapes = [] + for s in self.canvas.shapes: + if s.line_color == DEFAULT_LOCK_COLOR: + self.canvas.selectedShapes.append(s) + self.lockSelectedShape() + for s in self.canvas.shapes: + if s.line_color == DEFAULT_LOCK_COLOR: + self.canvas.selectedShapes.remove(s) + self.canvas.shapes.remove(s) + + def _saveFile(self, annotationFilePath, mode='Manual'): + if len(self.canvas.lockedShapes) != 0: + self.saveLockedShapes() + + if mode == 'Manual': + self.result_dic_locked = [] + img = cv2.imread(self.filePath) + width, height = self.image.width(), self.image.height() + for shape in self.canvas.lockedShapes: + box = [[int(p[0] * width), int(p[1] * height)] for p in shape['ratio']] + # assert len(box) == 4 + result = [(shape['transcription'], 1)] + result.insert(0, box) + self.result_dic_locked.append(result) + self.result_dic += self.result_dic_locked + self.result_dic_locked = [] + if annotationFilePath and self.saveLabels(annotationFilePath, mode=mode): + self.setClean() + self.statusBar().showMessage('Saved to %s' % annotationFilePath) + self.statusBar().show() + currIndex = self.mImgList.index(self.filePath) + item = self.fileListWidget.item(currIndex) + item.setIcon(newIcon('done')) + + self.fileStatedict[self.filePath] = 1 + if len(self.fileStatedict) % self.autoSaveNum == 0: + self.saveFilestate() + self.savePPlabel(mode='Auto') + + self.fileListWidget.insertItem(int(currIndex), item) + if not self.canvas.isInTheSameImage: + self.openNextImg() + self.actions.saveRec.setEnabled(True) + self.actions.saveLabel.setEnabled(True) + self.actions.exportJSON.setEnabled(True) + + elif mode == 'Auto': + if annotationFilePath and self.saveLabels(annotationFilePath, mode=mode): + self.setClean() + self.statusBar().showMessage('Saved to %s' % annotationFilePath) + self.statusBar().show() + + def closeFile(self, _value=False): + if not self.mayContinue(): + return + self.resetState() + self.setClean() + self.toggleActions(False) + self.canvas.setEnabled(False) + self.actions.saveAs.setEnabled(False) + + def deleteImg(self): + deletePath = self.filePath + if deletePath is not None: + deleteInfo = self.deleteImgDialog() + if deleteInfo == QMessageBox.Yes: + if platform.system() == 'Windows': + from win32com.shell import shell, shellcon + shell.SHFileOperation((0, shellcon.FO_DELETE, deletePath, None, + shellcon.FOF_SILENT | shellcon.FOF_ALLOWUNDO | shellcon.FOF_NOCONFIRMATION, + None, None)) + # linux + elif platform.system() == 'Linux': + cmd = 'trash ' + deletePath + os.system(cmd) + # macOS + elif platform.system() == 'Darwin': + import subprocess + absPath = os.path.abspath(deletePath).replace('\\', '\\\\').replace('"', '\\"') + cmd = ['osascript', '-e', + 'tell app "Finder" to move {the POSIX file "' + absPath + '"} to trash'] + print(cmd) + subprocess.call(cmd, stdout=open(os.devnull, 'w')) + + if self.filePath in self.fileStatedict.keys(): + self.fileStatedict.pop(self.filePath) + imgidx = self.getImglabelidx(self.filePath) + if imgidx in self.PPlabel.keys(): + self.PPlabel.pop(imgidx) + self.openNextImg() + self.importDirImages(self.lastOpenDir, isDelete=True) + + def deleteImgDialog(self): + yes, cancel = QMessageBox.Yes, QMessageBox.Cancel + msg = u'The image will be deleted to the recycle bin' + return QMessageBox.warning(self, u'Attention', msg, yes | cancel) + + def resetAll(self): + self.settings.reset() + self.close() + proc = QProcess() + proc.startDetached(os.path.abspath(__file__)) + + def mayContinue(self): # + if not self.dirty: + return True + else: + discardChanges = self.discardChangesDialog() + if discardChanges == QMessageBox.No: + return True + elif discardChanges == QMessageBox.Yes: + self.canvas.isInTheSameImage = True + self.saveFile() + self.canvas.isInTheSameImage = False + return True + else: + return False + + def discardChangesDialog(self): + yes, no, cancel = QMessageBox.Yes, QMessageBox.No, QMessageBox.Cancel + if self.lang == 'ch': + msg = u'您有未保存的变更, 您想保存再继续吗?\n点击 "No" 丢弃所有未保存的变更.' + else: + msg = u'You have unsaved changes, would you like to save them and proceed?\nClick "No" to undo all changes.' + return QMessageBox.warning(self, u'Attention', msg, yes | no | cancel) + + def errorMessage(self, title, message): + return QMessageBox.critical(self, title, + '

%s

%s' % (title, message)) + + def currentPath(self): + return os.path.dirname(self.filePath) if self.filePath else '.' + + def chooseColor(self): + color = self.colorDialog.getColor(self.lineColor, u'Choose line color', + default=DEFAULT_LINE_COLOR) + if color: + self.lineColor = color + Shape.line_color = color + self.canvas.setDrawingColor(color) + self.canvas.update() + self.setDirty() + + def deleteSelectedShape(self): + self.remLabels(self.canvas.deleteSelected()) + self.actions.undo.setEnabled(True) + self.setDirty() + if self.noShapes(): + for action in self.actions.onShapesPresent: + action.setEnabled(False) + self.BoxListDock.setWindowTitle(self.BoxListDockName + f" ({self.BoxList.count()})") + self.labelListDock.setWindowTitle(self.labelListDockName + f" ({self.labelList.count()})") + + def chshapeLineColor(self): + color = self.colorDialog.getColor(self.lineColor, u'Choose line color', + default=DEFAULT_LINE_COLOR) + if color: + for shape in self.canvas.selectedShapes: shape.line_color = color + self.canvas.update() + self.setDirty() + + def chshapeFillColor(self): + color = self.colorDialog.getColor(self.fillColor, u'Choose fill color', + default=DEFAULT_FILL_COLOR) + if color: + for shape in self.canvas.selectedShapes: shape.fill_color = color + self.canvas.update() + self.setDirty() + + def copyShape(self): + self.canvas.endMove(copy=True) + self.addLabel(self.canvas.selectedShape) + self.setDirty() + + def moveShape(self): + self.canvas.endMove(copy=False) + self.setDirty() + + def loadPredefinedClasses(self, predefClassesFile): + if os.path.exists(predefClassesFile) is True: + with codecs.open(predefClassesFile, 'r', 'utf8') as f: + for line in f: + line = line.strip() + if self.labelHist is None: + self.labelHist = [line] + else: + self.labelHist.append(line) + + def togglePaintLabelsOption(self): + for shape in self.canvas.shapes: + shape.paintLabel = self.displayLabelOption.isChecked() + + def toogleDrawSquare(self): + self.canvas.setDrawingShapeToSquare(self.drawSquaresOption.isChecked()) + + def additems(self, dirpath): + for file in self.mImgList: + pix = QPixmap(file) + _, filename = os.path.split(file) + filename, _ = os.path.splitext(filename) + item = QListWidgetItem(QIcon(pix.scaled(100, 100, Qt.IgnoreAspectRatio, Qt.FastTransformation)), + filename[:10]) + item.setToolTip(file) + self.iconlist.addItem(item) + + def additems5(self, dirpath): + for file in self.mImgList5: + pix = QPixmap(file) + _, filename = os.path.split(file) + filename, _ = os.path.splitext(filename) + pfilename = filename[:10] + if len(pfilename) < 10: + lentoken = 12 - len(pfilename) + prelen = lentoken // 2 + bfilename = prelen * " " + pfilename + (lentoken - prelen) * " " + # item = QListWidgetItem(QIcon(pix.scaled(100, 100, Qt.KeepAspectRatio, Qt.SmoothTransformation)),filename[:10]) + item = QListWidgetItem(QIcon(pix.scaled(100, 100, Qt.IgnoreAspectRatio, Qt.FastTransformation)), pfilename) + # item.setForeground(QBrush(Qt.white)) + item.setToolTip(file) + self.iconlist.addItem(item) + owidth = 0 + for index in range(len(self.mImgList5)): + item = self.iconlist.item(index) + itemwidget = self.iconlist.visualItemRect(item) + owidth += itemwidget.width() + self.iconlist.setMinimumWidth(owidth + 50) + + def gen_quad_from_poly(self, poly): + """ + Generate min area quad from poly. + """ + point_num = poly.shape[0] + min_area_quad = np.zeros((4, 2), dtype=np.float32) + rect = cv2.minAreaRect(poly.astype( + np.int32)) # (center (x,y), (width, height), angle of rotation) + box = np.array(cv2.boxPoints(rect)) + + first_point_idx = 0 + min_dist = 1e4 + for i in range(4): + dist = np.linalg.norm(box[(i + 0) % 4] - poly[0]) + \ + np.linalg.norm(box[(i + 1) % 4] - poly[point_num // 2 - 1]) + \ + np.linalg.norm(box[(i + 2) % 4] - poly[point_num // 2]) + \ + np.linalg.norm(box[(i + 3) % 4] - poly[-1]) + if dist < min_dist: + min_dist = dist + first_point_idx = i + for i in range(4): + min_area_quad[i] = box[(first_point_idx + i) % 4] + + bbox_new = min_area_quad.tolist() + bbox = [] + + for box in bbox_new: + box = list(map(int, box)) + bbox.append(box) + + return bbox + + def getImglabelidx(self, filePath): + if platform.system() == 'Windows': + spliter = '\\' + else: + spliter = '/' + filepathsplit = filePath.split(spliter)[-2:] + return filepathsplit[0] + '/' + filepathsplit[1] + + def autoRecognition(self): + assert self.mImgList is not None + print('Using model from ', self.model) + + uncheckedList = [i for i in self.mImgList if i not in self.fileStatedict.keys()] + self.autoDialog = AutoDialog(parent=self, ocr=self.ocr, mImgList=uncheckedList, lenbar=len(uncheckedList)) + self.autoDialog.popUp() + self.currIndex = len(self.mImgList) - 1 + self.loadFile(self.filePath) # ADD + self.haveAutoReced = True + self.AutoRecognition.setEnabled(False) + self.actions.AutoRec.setEnabled(False) + self.setDirty() + self.saveCacheLabel() + + self.init_key_list(self.Cachelabel) + + def reRecognition(self): + img = cv2.imread(self.filePath) + # org_box = [dic['points'] for dic in self.PPlabel[self.getImglabelidx(self.filePath)]] + if self.canvas.shapes: + self.result_dic = [] + self.result_dic_locked = [] # result_dic_locked stores the ocr result of self.canvas.lockedShapes + rec_flag = 0 + for shape in self.canvas.shapes: + box = [[int(p.x()), int(p.y())] for p in shape.points] + + if len(box) > 4: + box = self.gen_quad_from_poly(np.array(box)) + assert len(box) == 4 + + img_crop = get_rotate_crop_image(img, np.array(box, np.float32)) + if img_crop is None: + msg = 'Can not recognise the detection box in ' + self.filePath + '. Please change manually' + QMessageBox.information(self, "Information", msg) + return + result = self.ocr.ocr(img_crop, cls=True, det=False) + if result[0][0] != '': + if shape.line_color == DEFAULT_LOCK_COLOR: + shape.label = result[0][0] + result.insert(0, box) + self.result_dic_locked.append(result) + else: + result.insert(0, box) + self.result_dic.append(result) + else: + print('Can not recognise the box') + if shape.line_color == DEFAULT_LOCK_COLOR: + shape.label = result[0][0] + self.result_dic_locked.append([box, (self.noLabelText, 0)]) + else: + self.result_dic.append([box, (self.noLabelText, 0)]) + try: + if self.noLabelText == shape.label or result[1][0] == shape.label: + print('label no change') + else: + rec_flag += 1 + except IndexError as e: + print('Can not recognise the box') + if (len(self.result_dic) > 0 and rec_flag > 0) or self.canvas.lockedShapes: + self.canvas.isInTheSameImage = True + self.saveFile(mode='Auto') + self.loadFile(self.filePath) + self.canvas.isInTheSameImage = False + self.setDirty() + elif len(self.result_dic) == len(self.canvas.shapes) and rec_flag == 0: + if self.lang == 'ch': + QMessageBox.information(self, "Information", "识别结果保持一致!") + else: + QMessageBox.information(self, "Information", "The recognition result remains unchanged!") + else: + print('Can not recgonise in ', self.filePath) + else: + QMessageBox.information(self, "Information", "Draw a box!") + + def singleRerecognition(self): + img = cv2.imread(self.filePath) + for shape in self.canvas.selectedShapes: + box = [[int(p.x()), int(p.y())] for p in shape.points] + if len(box) > 4: + box = self.gen_quad_from_poly(np.array(box)) + assert len(box) == 4 + img_crop = get_rotate_crop_image(img, np.array(box, np.float32)) + if img_crop is None: + msg = 'Can not recognise the detection box in ' + self.filePath + '. Please change manually' + QMessageBox.information(self, "Information", msg) + return + result = self.ocr.ocr(img_crop, cls=True, det=False) + if result[0][0] != '': + result.insert(0, box) + print('result in reRec is ', result) + if result[1][0] == shape.label: + print('label no change') + else: + shape.label = result[1][0] + else: + print('Can not recognise the box') + if self.noLabelText == shape.label: + print('label no change') + else: + shape.label = self.noLabelText + self.singleLabel(shape) + self.setDirty() + + def TableRecognition(self): + ''' + Table Recegnition + ''' + from paddleocr.ppstructure.table.predict_table import to_excel + + import time + + start = time.time() + img = cv2.imread(self.filePath) + res = self.table_ocr(img, return_ocr_result_in_table=True) + + TableRec_excel_dir = self.lastOpenDir + '/tableRec_excel_output/' + os.makedirs(TableRec_excel_dir, exist_ok=True) + filename, _ = os.path.splitext(os.path.basename(self.filePath)) + + excel_path = TableRec_excel_dir + '{}.xlsx'.format(filename) + + if res is None: + msg = 'Can not recognise the table in ' + self.filePath + '. Please change manually' + QMessageBox.information(self, "Information", msg) + to_excel('', excel_path) # create an empty excel + return + + # save res + # ONLY SUPPORT ONE TABLE in one image + hasTable = False + for region in res: + if region['type'] == 'Table': + if region['res']['boxes'] is None: + msg = 'Can not recognise the detection box in ' + self.filePath + '. Please change manually' + QMessageBox.information(self, "Information", msg) + to_excel('', excel_path) # create an empty excel + return + hasTable = True + # save table ocr result on PPOCRLabel + # clear all old annotaions before saving result + self.itemsToShapes.clear() + self.shapesToItems.clear() + self.itemsToShapesbox.clear() # ADD + self.shapesToItemsbox.clear() + self.labelList.clear() + self.BoxList.clear() + self.result_dic = [] + self.result_dic_locked = [] + + shapes = [] + result_len = len(region['res']['boxes']) + for i in range(result_len): + bbox = np.array(region['res']['boxes'][i]) + rec_text = region['res']['rec_res'][i][0] + + # polys to rectangles + x1, y1 = np.min(bbox[:, 0]), np.min(bbox[:, 1]) + x2, y2 = np.max(bbox[:, 0]), np.max(bbox[:, 1]) + rext_bbox = [[x1, y1], [x2, y1], [x2, y2], [x1, y2]] + + # save bbox to shape + shape = Shape(label=rec_text, line_color=DEFAULT_LINE_COLOR, key_cls=None) + for point in rext_bbox: + x, y = point + # Ensure the labels are within the bounds of the image. + # If not, fix them. + x, y, snapped = self.canvas.snapPointToCanvas(x, y) + shape.addPoint(QPointF(x, y)) + shape.difficult = False + # shape.locked = False + shape.close() + self.addLabel(shape) + shapes.append(shape) + self.setDirty() + self.canvas.loadShapes(shapes) + + # save HTML result to excel + try: + to_excel(region['res']['html'], excel_path) + except: + print('Can not save excel file, maybe Permission denied (.xlsx is being occupied)') + break + + if not hasTable: + msg = 'Can not recognise the table in ' + self.filePath + '. Please change manually' + QMessageBox.information(self, "Information", msg) + to_excel('', excel_path) # create an empty excel + return + + # automatically open excel annotation file + if platform.system() == 'Windows': + try: + import win32com.client + except: + print("CANNOT OPEN .xlsx. It could be one of the following reasons: " \ + "Only support Windows | No python win32com") + + try: + xl = win32com.client.Dispatch("Excel.Application") + xl.Visible = True + xl.Workbooks.Open(excel_path) + # excelEx = "You need to show the excel executable at this point" + # subprocess.Popen([excelEx, excel_path]) + + # os.startfile(excel_path) + except: + print("CANNOT OPEN .xlsx. It could be the following reasons: " \ + ".xlsx is not existed") + else: + os.system('open ' + os.path.normpath(excel_path)) + + print('time cost: ', time.time() - start) + + def cellreRecognition(self): + ''' + re-recognise text in a cell + ''' + img = cv2.imread(self.filePath) + for shape in self.canvas.selectedShapes: + box = [[int(p.x()), int(p.y())] for p in shape.points] + + if len(box) > 4: + box = self.gen_quad_from_poly(np.array(box)) + assert len(box) == 4 + + # pad around bbox for better text recognition accuracy + _box = boxPad(box, img.shape, 6) + img_crop = get_rotate_crop_image(img, np.array(_box, np.float32)) + if img_crop is None: + msg = 'Can not recognise the detection box in ' + self.filePath + '. Please change manually' + QMessageBox.information(self, "Information", msg) + return + + # merge the text result in the cell + texts = '' + probs = 0. # the probability of the cell is avgerage prob of every text box in the cell + bboxes = self.ocr.ocr(img_crop, det=True, rec=False, cls=False) + if len(bboxes) > 0: + bboxes.reverse() # top row text at first + for _bbox in bboxes: + patch = get_rotate_crop_image(img_crop, np.array(_bbox, np.float32)) + rec_res = self.ocr.ocr(patch, det=False, rec=True, cls=False) + text = rec_res[0][0] + if text != '': + texts += text + ('' if text[0].isalpha() else ' ') # add space between english word + probs += rec_res[0][1] + probs = probs / len(bboxes) + result = [(texts.strip(), probs)] + + if result[0][0] != '': + result.insert(0, box) + print('result in reRec is ', result) + if result[1][0] == shape.label: + print('label no change') + else: + shape.label = result[1][0] + else: + print('Can not recognise the box') + if self.noLabelText == shape.label: + print('label no change') + else: + shape.label = self.noLabelText + self.singleLabel(shape) + self.setDirty() + + def exportJSON(self): + ''' + export PPLabel and CSV to JSON (PubTabNet) + ''' + import pandas as pd + from libs.dataPartitionDialog import DataPartitionDialog + + # data partition user input + partitionDialog = DataPartitionDialog(parent=self) + partitionDialog.exec() + if partitionDialog.getStatus() == False: + return + + # automatically save annotations + self.saveFilestate() + self.savePPlabel(mode='auto') + + # load box annotations + labeldict = {} + if not os.path.exists(self.PPlabelpath): + msg = 'ERROR, Can not find Label.txt' + QMessageBox.information(self, "Information", msg) + return + else: + with open(self.PPlabelpath, 'r', encoding='utf-8') as f: + data = f.readlines() + for each in data: + file, label = each.split('\t') + if label: + label = label.replace('false', 'False') + label = label.replace('true', 'True') + labeldict[file] = eval(label) + else: + labeldict[file] = [] + + # if len(labeldict) != len(csv_paths): + # msg = 'ERROR, box label and excel label are not in the same number\n' + \ + # 'box label: ' + str(len(labeldict)) + '\n' + \ + # 'excel label: ' + str(len(csv_paths)) + '\n' + \ + # 'Please check the label.txt and tableRec_excel_output\n' + # QMessageBox.information(self, "Information", msg) + # return + train_split, val_split, test_split = partitionDialog.getDataPartition() + # check validate + if train_split + val_split + test_split > 100: + msg = "The sum of training, validation and testing data should be less than 100%" + QMessageBox.information(self, "Information", msg) + return + print(train_split, val_split, test_split) + train_split, val_split, test_split = float(train_split) / 100., float(val_split) / 100., float(test_split) / 100. + train_id = int(len(labeldict) * train_split) + val_id = int(len(labeldict) * (train_split + val_split)) + print('Data partition: train:', train_id, + 'validation:', val_id - train_id, + 'test:', len(labeldict) - val_id) + + TableRec_excel_dir = os.path.join(self.lastOpenDir, 'tableRec_excel_output') + json_results = [] + imgid = 0 + for image_path in labeldict.keys(): + # load csv annotations + filename, _ = os.path.splitext(os.path.basename(image_path)) + csv_path = os.path.join(TableRec_excel_dir, filename + '.xlsx') + if not os.path.exists(csv_path): + msg = 'ERROR, Can not find ' + csv_path + QMessageBox.information(self, "Information", msg) + return + + # read xlsx file, convert to HTML + # xd = pd.ExcelFile(csv_path) + # df = xd.parse() + # structure = df.to_html(index = False) + excel = xlrd.open_workbook(csv_path) + sheet0 = excel.sheet_by_index(0) # only sheet 0 + merged_cells = sheet0.merged_cells # (0,1,1,3) start row, end row, start col, end col + + html_list = [['td'] * sheet0.ncols for i in range(sheet0.nrows)] + + for merged in merged_cells: + html_list = expand_list(merged, html_list) + + token_list = convert_token(html_list) + + + # load box annotations + cells = [] + for anno in labeldict[image_path]: + tokens = list(anno['transcription']) + obb = anno['points'] + hbb = OBB2HBB(np.array(obb)).tolist() + cells.append({'tokens': tokens, 'bbox': hbb}) + + # data split + if imgid < train_id: + split = 'train' + elif imgid < val_id: + split = 'val' + else: + split = 'test' + + # save dict + html = {'structure': {'tokens': token_list}, 'cell': cells} + json_results.append({'filename': os.path.basename(image_path), 'split': split, 'imgid': imgid, 'html': html}) + imgid += 1 + + # save json + with open("{}/annotation.json".format(self.lastOpenDir), "w", encoding='utf-8') as fid: + fid.write(json.dumps(json_results, ensure_ascii=False)) + + msg = 'JSON sucessfully saved in {}/annotation.json'.format(self.lastOpenDir) + QMessageBox.information(self, "Information", msg) + + def autolcm(self): + vbox = QVBoxLayout() + hbox = QHBoxLayout() + self.panel = QLabel() + self.panel.setText(self.stringBundle.getString('choseModelLg')) + self.panel.setAlignment(Qt.AlignLeft) + self.comboBox = QComboBox() + self.comboBox.setObjectName("comboBox") + self.comboBox.addItems(['Chinese & English', 'English', 'French', 'German', 'Korean', 'Japanese']) + vbox.addWidget(self.panel) + vbox.addWidget(self.comboBox) + self.dialog = QDialog() + self.dialog.resize(300, 100) + self.okBtn = QPushButton(self.stringBundle.getString('ok')) + self.cancelBtn = QPushButton(self.stringBundle.getString('cancel')) + + self.okBtn.clicked.connect(self.modelChoose) + self.cancelBtn.clicked.connect(self.cancel) + self.dialog.setWindowTitle(self.stringBundle.getString('choseModelLg')) + + hbox.addWidget(self.okBtn) + hbox.addWidget(self.cancelBtn) + + vbox.addWidget(self.panel) + vbox.addLayout(hbox) + self.dialog.setLayout(vbox) + self.dialog.setWindowModality(Qt.ApplicationModal) + self.dialog.exec_() + if self.filePath: + self.AutoRecognition.setEnabled(True) + self.actions.AutoRec.setEnabled(True) + + def modelChoose(self): + print(self.comboBox.currentText()) + lg_idx = {'Chinese & English': 'ch', 'English': 'en', 'French': 'french', 'German': 'german', + 'Korean': 'korean', 'Japanese': 'japan'} + del self.ocr + self.ocr = PaddleOCR(use_pdserving=False, use_angle_cls=True, det=True, cls=True, use_gpu=False, + lang=lg_idx[self.comboBox.currentText()]) + del self.table_ocr + self.table_ocr = PPStructure(use_pdserving=False, + use_gpu=False, + lang=lg_idx[self.comboBox.currentText()], + layout=False, + show_log=False) + self.dialog.close() + + def cancel(self): + self.dialog.close() + + def loadFilestate(self, saveDir): + self.fileStatepath = saveDir + '/fileState.txt' + self.fileStatedict = {} + if not os.path.exists(self.fileStatepath): + f = open(self.fileStatepath, 'w', encoding='utf-8') + else: + with open(self.fileStatepath, 'r', encoding='utf-8') as f: + states = f.readlines() + for each in states: + file, state = each.split('\t') + self.fileStatedict[file] = 1 + self.actions.saveLabel.setEnabled(True) + self.actions.saveRec.setEnabled(True) + self.actions.exportJSON.setEnabled(True) + + def saveFilestate(self): + with open(self.fileStatepath, 'w', encoding='utf-8') as f: + for key in self.fileStatedict: + f.write(key + '\t') + f.write(str(self.fileStatedict[key]) + '\n') + + def loadLabelFile(self, labelpath): + labeldict = {} + if not os.path.exists(labelpath): + f = open(labelpath, 'w', encoding='utf-8') + + else: + with open(labelpath, 'r', encoding='utf-8') as f: + data = f.readlines() + for each in data: + file, label = each.split('\t') + if label: + label = label.replace('false', 'False') + label = label.replace('true', 'True') + labeldict[file] = eval(label) + else: + labeldict[file] = [] + return labeldict + + def savePPlabel(self, mode='Manual'): + savedfile = [self.getImglabelidx(i) for i in self.fileStatedict.keys()] + with open(self.PPlabelpath, 'w', encoding='utf-8') as f: + for key in self.PPlabel: + if key in savedfile and self.PPlabel[key] != []: + f.write(key + '\t') + f.write(json.dumps(self.PPlabel[key], ensure_ascii=False) + '\n') + + if mode == 'Manual': + if self.lang == 'ch': + msg = '已将检查过的图片标签保存在 ' + self.PPlabelpath + " 文件中" + else: + msg = 'Images that have been checked are saved in ' + self.PPlabelpath + QMessageBox.information(self, "Information", msg) + + def saveCacheLabel(self): + with open(self.Cachelabelpath, 'w', encoding='utf-8') as f: + for key in self.Cachelabel: + f.write(key + '\t') + f.write(json.dumps(self.Cachelabel[key], ensure_ascii=False) + '\n') + + def saveLabelFile(self): + self.saveFilestate() + self.savePPlabel() + + def saveRecResult(self): + if {} in [self.PPlabelpath, self.PPlabel, self.fileStatedict]: + QMessageBox.information(self, "Information", "Check the image first") + return + + rec_gt_dir = os.path.dirname(self.PPlabelpath) + '/rec_gt.txt' + crop_img_dir = os.path.dirname(self.PPlabelpath) + '/crop_img/' + ques_img = [] + if not os.path.exists(crop_img_dir): + os.mkdir(crop_img_dir) + + with open(rec_gt_dir, 'w', encoding='utf-8') as f: + for key in self.fileStatedict: + idx = self.getImglabelidx(key) + try: + img = cv2.imread(key) + for i, label in enumerate(self.PPlabel[idx]): + if label['difficult']: + continue + img_crop = get_rotate_crop_image(img, np.array(label['points'], np.float32)) + img_name = os.path.splitext(os.path.basename(idx))[0] + '_crop_' + str(i) + '.jpg' + cv2.imwrite(crop_img_dir + img_name, img_crop) + f.write('crop_img/' + img_name + '\t') + f.write(label['transcription'] + '\n') + except Exception as e: + ques_img.append(key) + print("Can not read image ", e) + if ques_img: + QMessageBox.information(self, + "Information", + "The following images can not be saved, please check the image path and labels.\n" + + "".join(str(i) + '\n' for i in ques_img)) + QMessageBox.information(self, "Information", "Cropped images have been saved in " + str(crop_img_dir)) + + def speedChoose(self): + if self.labelDialogOption.isChecked(): + self.canvas.newShape.disconnect() + self.canvas.newShape.connect(partial(self.newShape, True)) + + else: + self.canvas.newShape.disconnect() + self.canvas.newShape.connect(partial(self.newShape, False)) + + def autoSaveFunc(self): + if self.autoSaveOption.isChecked(): + self.autoSaveNum = 1 # Real auto_Save + try: + self.saveLabelFile() + except: + pass + print('The program will automatically save once after confirming an image') + else: + self.autoSaveNum = 5 # Used for backup + print('The program will automatically save once after confirming 5 images (default)') + + def change_box_key(self): + if not self.kie_mode: + return + key_text, _ = self.keyDialog.popUp(self.key_previous_text) + if key_text is None: + return + self.key_previous_text = key_text + for shape in self.canvas.selectedShapes: + shape.key_cls = key_text + if not self.keyList.findItemsByLabel(key_text): + item = self.keyList.createItemFromLabel(key_text) + self.keyList.addItem(item) + rgb = self._get_rgb_by_label(key_text, self.kie_mode) + self.keyList.setItemLabel(item, key_text, rgb) + + self._update_shape_color(shape) + self.keyDialog.addLabelHistory(key_text) + + def undoShapeEdit(self): + self.canvas.restoreShape() + self.labelList.clear() + self.BoxList.clear() + self.loadShapes(self.canvas.shapes) + self.actions.undo.setEnabled(self.canvas.isShapeRestorable) + + def loadShapes(self, shapes, replace=True): + self._noSelectionSlot = True + for shape in shapes: + self.addLabel(shape) + self.labelList.clearSelection() + self._noSelectionSlot = False + self.canvas.loadShapes(shapes, replace=replace) + print("loadShapes") # 1 + + def lockSelectedShape(self): + """lock the selected shapes. + + Add self.selectedShapes to lock self.canvas.lockedShapes, + which holds the ratio of the four coordinates of the locked shapes + to the width and height of the image + """ + width, height = self.image.width(), self.image.height() + + def format_shape(s): + return dict(label=s.label, # str + line_color=s.line_color.getRgb(), + fill_color=s.fill_color.getRgb(), + ratio=[[int(p.x()) / width, int(p.y()) / height] for p in s.points], # QPonitF + difficult=s.difficult, # bool + key_cls=s.key_cls, # bool + ) + + # lock + if len(self.canvas.lockedShapes) == 0: + for s in self.canvas.selectedShapes: + s.line_color = DEFAULT_LOCK_COLOR + s.locked = True + shapes = [format_shape(shape) for shape in self.canvas.selectedShapes] + trans_dic = [] + for box in shapes: + trans_dict = {"transcription": box['label'], "ratio": box['ratio'], "difficult": box['difficult']} + if self.kie_mode: + trans_dict.update({"key_cls": box["key_cls"]}) + trans_dic.append(trans_dict) + self.canvas.lockedShapes = trans_dic + self.actions.save.setEnabled(True) + + # unlock + else: + for s in self.canvas.shapes: + s.line_color = DEFAULT_LINE_COLOR + self.canvas.lockedShapes = [] + self.result_dic_locked = [] + self.setDirty() + self.actions.save.setEnabled(True) + + +def inverted(color): + return QColor(*[255 - v for v in color.getRgb()]) + + +def read(filename, default=None): + try: + with open(filename, 'rb') as f: + return f.read() + except: + return default + + +def str2bool(v): + return v.lower() in ("true", "t", "1") + + +def get_main_app(argv=[]): + """ + Standard boilerplate Qt application code. + Do everything but app.exec_() -- so that we can test the application in one thread + """ + app = QApplication(argv) + app.setApplicationName(__appname__) + app.setWindowIcon(newIcon("app")) + # Tzutalin 201705+: Accept extra arguments to change predefined class file + arg_parser = argparse.ArgumentParser() + arg_parser.add_argument("--lang", type=str, default='en', nargs="?") + arg_parser.add_argument("--gpu", type=str2bool, default=True, nargs="?") + arg_parser.add_argument("--kie", type=str2bool, default=False, nargs="?") + arg_parser.add_argument("--predefined_classes_file", + default=os.path.join(os.path.dirname(__file__), "data", "predefined_classes.txt"), + nargs="?") + args = arg_parser.parse_args(argv[1:]) + + win = MainWindow(lang=args.lang, + gpu=args.gpu, + kie_mode=args.kie, + default_predefined_class_file=args.predefined_classes_file) + win.show() + return app, win + + +def main(): + """construct main app and run it""" + app, _win = get_main_app(sys.argv) + return app.exec_() + + +if __name__ == '__main__': + + resource_file = './libs/resources.py' + if not os.path.exists(resource_file): + output = os.system('pyrcc5 -o libs/resources.py resources.qrc') + assert output == 0, "operate the cmd have some problems ,please check whether there is a in the lib " \ + "directory resources.py " + + sys.exit(main()) diff --git a/toolbox/PaddleOCR/PPOCRLabel/libs/resources.py b/toolbox/PaddleOCR/PPOCRLabel/libs/resources.py new file mode 100644 index 0000000000000000000000000000000000000000..7635de537117884e4936d4f077d655f57443867c --- /dev/null +++ b/toolbox/PaddleOCR/PPOCRLabel/libs/resources.py @@ -0,0 +1,11678 @@ +# -*- coding: utf-8 -*- +# Copyright (c) 2024, Shanghai Iluvatar CoreX Semiconductor Co., Ltd. +# All Rights Reserved. +# +# Resource object code +# +# Created by: The Resource Compiler for PyQt5 (Qt v5.15.2) +# +# WARNING! All changes made in this file will be lost! +# + +from PyQt5 import QtCore + +qt_resource_data = b"\ +\x00\x00\x10\x78\ +\x89\ +\x50\x4e\x47\x0d\x0a\x1a\x0a\x00\x00\x00\x0d\x49\x48\x44\x52\x00\ +\x00\x00\xc8\x00\x00\x00\xc8\x08\x06\x00\x00\x00\xad\x58\xae\x9e\ +\x00\x00\x10\x3f\x49\x44\x41\x54\x78\x5e\xed\x9d\x0b\xac\x1e\x45\ +\x15\xc7\xcf\xd9\x7b\xdb\x42\x20\x42\x89\x06\x15\x09\x04\x08\x12\ +\xde\xa2\x3c\x0c\x82\x10\x04\xc4\x08\x04\x63\x45\xe5\x91\x02\x16\ +\xa9\xb6\x77\x67\xbf\xde\x46\x5e\x89\x25\x18\x21\xb5\x37\x3b\x7b\ +\xfb\x00\x0a\x3e\x80\x04\x4c\x4d\x90\x48\x22\x0a\x28\x68\x10\x91\ +\x00\x51\x8b\x12\x83\x84\x87\x8f\x08\x11\x83\x3c\xe5\xb6\x77\xc7\ +\x0c\x7e\x90\x4b\xbd\x8f\xdd\xb3\x33\xfb\xed\xec\xfe\x27\x21\x97\ +\xa4\x73\xce\xcc\xfc\xce\xfe\xf2\xed\xf7\xd8\x19\x26\x34\x10\x00\ +\x81\x19\x09\x30\xd8\x80\x00\x08\xcc\x4c\x00\x82\xe0\xea\x00\x81\ +\x59\x08\x40\x10\x5c\x1e\x20\x00\x41\x70\x0d\x80\x80\x8c\x00\x5e\ +\x41\x64\xdc\x10\xd5\x11\x02\x10\xa4\x23\x85\xc6\x32\x65\x04\x20\ 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@@ -0,0 +1,320 @@ +# Copyright (c) <2015-Present> Tzutalin +# Copyright (c) 2024, Shanghai Iluvatar CoreX Semiconductor Co., Ltd. +# All Rights Reserved. +# Copyright (C) 2013 MIT, Computer Science and Artificial Intelligence Laboratory. Bryan Russell, Antonio Torralba, +# William T. Freeman. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and +# associated documentation files (the "Software"), to deal in the Software without restriction, including without +# limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the +# Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: +# The above copyright notice and this permission notice shall be included in all copies or substantial portions of +# the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT +# NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT +# SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF +# CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +# THE SOFTWARE. +import hashlib +import os +import re +import sys +from math import sqrt + +import cv2 +import numpy as np +from PyQt5.QtCore import QRegExp, QT_VERSION_STR +from PyQt5.QtGui import QIcon, QRegExpValidator, QColor +from PyQt5.QtWidgets import QPushButton, QAction, QMenu +from libs.ustr import ustr + +__dir__ = os.path.dirname(os.path.abspath(__file__)) # 获取本程序文件路径 +__iconpath__ = os.path.abspath(os.path.join(__dir__, '../resources/icons')) + + +def newIcon(icon, iconSize=None): + if iconSize is not None: + return QIcon(QIcon(__iconpath__ + "/" + icon + ".png").pixmap(iconSize, iconSize)) + else: + return QIcon(__iconpath__ + "/" + icon + ".png") + + +def newButton(text, icon=None, slot=None): + b = QPushButton(text) + if icon is not None: + b.setIcon(newIcon(icon)) + if slot is not None: + b.clicked.connect(slot) + return b + + +def newAction(parent, text, slot=None, shortcut=None, icon=None, + tip=None, checkable=False, enabled=True, iconSize=None): + """Create a new action and assign callbacks, shortcuts, etc.""" + a = QAction(text, parent) + if icon is not None: + if iconSize is not None: + a.setIcon(newIcon(icon, iconSize)) + else: + a.setIcon(newIcon(icon)) + if shortcut is not None: + if isinstance(shortcut, (list, tuple)): + a.setShortcuts(shortcut) + else: + a.setShortcut(shortcut) + if tip is not None: + a.setToolTip(tip) + a.setStatusTip(tip) + if slot is not None: + a.triggered.connect(slot) + if checkable: + a.setCheckable(True) + a.setEnabled(enabled) + return a + + +def addActions(widget, actions): + for action in actions: + if action is None: + widget.addSeparator() + elif isinstance(action, QMenu): + widget.addMenu(action) + else: + widget.addAction(action) + + +def labelValidator(): + return QRegExpValidator(QRegExp(r'^[^ \t].+'), None) + + +class struct(object): + + def __init__(self, **kwargs): + self.__dict__.update(kwargs) + + +def distance(p): + return sqrt(p.x() * p.x() + p.y() * p.y()) + + +def fmtShortcut(text): + mod, key = text.split('+', 1) + return '%s+%s' % (mod, key) + + +def generateColorByText(text): + s = ustr(text) + hashCode = int(hashlib.sha256(s.encode('utf-8')).hexdigest(), 16) + r = int((hashCode / 255) % 255) + g = int((hashCode / 65025) % 255) + b = int((hashCode / 16581375) % 255) + return QColor(r, g, b, 100) + + +def have_qstring(): + '''p3/qt5 get rid of QString wrapper as py3 has native unicode str type''' + return not (sys.version_info.major >= 3 or QT_VERSION_STR.startswith('5.')) + + +def natural_sort(list, key=lambda s: s): + """ + Sort the list into natural alphanumeric order. + """ + + def get_alphanum_key_func(key): + convert = lambda text: int(text) if text.isdigit() else text + return lambda s: [convert(c) for c in re.split('([0-9]+)', key(s))] + + sort_key = get_alphanum_key_func(key) + list.sort(key=sort_key) + + +def get_rotate_crop_image(img, points): + # Use Green's theory to judge clockwise or counterclockwise + # author: biyanhua + d = 0.0 + for index in range(-1, 3): + d += -0.5 * (points[index + 1][1] + points[index][1]) * ( + points[index + 1][0] - points[index][0]) + if d < 0: # counterclockwise + tmp = np.array(points) + points[1], points[3] = tmp[3], tmp[1] + + try: + img_crop_width = int( + max( + np.linalg.norm(points[0] - points[1]), + np.linalg.norm(points[2] - points[3]))) + img_crop_height = int( + max( + np.linalg.norm(points[0] - points[3]), + np.linalg.norm(points[1] - points[2]))) + pts_std = np.float32([[0, 0], [img_crop_width, 0], + [img_crop_width, img_crop_height], + [0, img_crop_height]]) + M = cv2.getPerspectiveTransform(points, pts_std) + dst_img = cv2.warpPerspective( + img, + M, (img_crop_width, img_crop_height), + borderMode=cv2.BORDER_REPLICATE, + flags=cv2.INTER_CUBIC) + dst_img_height, dst_img_width = dst_img.shape[0:2] + if dst_img_height * 1.0 / dst_img_width >= 1.5: + dst_img = np.rot90(dst_img) + return dst_img + except Exception as e: + print(e) + + +def boxPad(box, imgShape, pad : int) -> np.array: + """ + Pad a box with [pad] pixels on each side. + """ + box = np.array(box, dtype=np.int32) + box[0][0], box[0][1] = box[0][0] - pad, box[0][1] - pad + box[1][0], box[1][1] = box[1][0] + pad, box[1][1] - pad + box[2][0], box[2][1] = box[2][0] + pad, box[2][1] + pad + box[3][0], box[3][1] = box[3][0] - pad, box[3][1] + pad + h, w, _ = imgShape + box[:,0] = np.clip(box[:,0], 0, w) + box[:,1] = np.clip(box[:,1], 0, h) + return box + + +def OBB2HBB(obb) -> np.array: + """ + Convert Oriented Bounding Box to Horizontal Bounding Box. + """ + hbb = np.zeros(4, dtype=np.int32) + hbb[0] = min(obb[:, 0]) + hbb[1] = min(obb[:, 1]) + hbb[2] = max(obb[:, 0]) + hbb[3] = max(obb[:, 1]) + return hbb + + +def expand_list(merged, html_list): + ''' + Fill blanks according to merged cells + ''' + sr, er, sc, ec = merged + for i in range(sr, er): + for j in range(sc, ec): + html_list[i][j] = None + html_list[sr][sc] = '' + if ec - sc > 1: + html_list[sr][sc] += " colspan={}".format(ec - sc) + if er - sr > 1: + html_list[sr][sc] += " rowspan={}".format(er - sr) + return html_list + + +def convert_token(html_list): + ''' + Convert raw html to label format + ''' + token_list = [""] + # final html list: + for row in html_list: + token_list.append("") + for col in row: + if col == None: + continue + elif col == 'td': + token_list.extend(["", ""]) + else: + token_list.append("", ""]) + token_list.append("") + token_list.append("") + + return token_list + + +def stepsInfo(lang='en'): + if lang == 'ch': + msg = "1. 安装与运行:使用上述命令安装与运行程序。\n" \ + "2. 打开文件夹:在菜单栏点击 “文件” - 打开目录 选择待标记图片的文件夹.\n" \ + "3. 自动标注:点击 ”自动标注“,使用PPOCR超轻量模型对图片文件名前图片状态为 “X” 的图片进行自动标注。\n" \ + "4. 手动标注:点击 “矩形标注”(推荐直接在英文模式下点击键盘中的 “W”),用户可对当前图片中模型未检出的部分进行手动" \ + "绘制标记框。点击键盘P,则使用四点标注模式(或点击“编辑” - “四点标注”),用户依次点击4个点后,双击左键表示标注完成。\n" \ + "5. 标记框绘制完成后,用户点击 “确认”,检测框会先被预分配一个 “待识别” 标签。\n" \ + "6. 重新识别:将图片中的所有检测画绘制/调整完成后,点击 “重新识别”,PPOCR模型会对当前图片中的**所有检测框**重新识别。\n" \ + "7. 内容更改:双击识别结果,对不准确的识别结果进行手动更改。\n" \ + "8. 保存:点击 “保存”,图片状态切换为 “√”,跳转至下一张。\n" \ + "9. 删除:点击 “删除图像”,图片将会被删除至回收站。\n" \ + "10. 标注结果:关闭应用程序或切换文件路径后,手动保存过的标签将会被存放在所打开图片文件夹下的" \ + "*Label.txt*中。在菜单栏点击 “PaddleOCR” - 保存识别结果后,会将此类图片的识别训练数据保存在*crop_img*文件夹下," \ + "识别标签保存在*rec_gt.txt*中。\n" + + else: + msg = "1. Build and launch using the instructions above.\n" \ + "2. Click 'Open Dir' in Menu/File to select the folder of the picture.\n" \ + "3. Click 'Auto recognition', use PPOCR model to automatically annotate images which marked with 'X' before the file name." \ + "4. Create Box:\n" \ + "4.1 Click 'Create RectBox' or press 'W' in English keyboard mode to draw a new rectangle detection box. Click and release left mouse to select a region to annotate the text area.\n" \ + "4.2 Press 'P' to enter four-point labeling mode which enables you to create any four-point shape by clicking four points with the left mouse button in succession and DOUBLE CLICK the left mouse as the signal of labeling completion.\n" \ + "5. After the marking frame is drawn, the user clicks 'OK', and the detection frame will be pre-assigned a TEMPORARY label.\n" \ + "6. Click re-Recognition, model will rewrite ALL recognition results in ALL detection box.\n" \ + "7. Double click the result in 'recognition result' list to manually change inaccurate recognition results.\n" \ + "8. Click 'Save', the image status will switch to '√',then the program automatically jump to the next.\n" \ + "9. Click 'Delete Image' and the image will be deleted to the recycle bin.\n" \ + "10. Labeling result: After closing the application or switching the file path, the manually saved label will be stored in *Label.txt* under the opened picture folder.\n" \ + " Click PaddleOCR-Save Recognition Results in the menu bar, the recognition training data of such pictures will be saved in the *crop_img* folder, and the recognition label will be saved in *rec_gt.txt*.\n" + + return msg + + +def keysInfo(lang='en'): + if lang == 'ch': + msg = "快捷键\t\t\t说明\n" \ + "———————————————————————\n" \ + "Ctrl + shift + R\t\t对当前图片的所有标记重新识别\n" \ + "W\t\t\t新建矩形框\n" \ + "Q\t\t\t新建四点框\n" \ + "Ctrl + E\t\t编辑所选框标签\n" \ + "Ctrl + R\t\t重新识别所选标记\n" \ + "Ctrl + C\t\t复制并粘贴选中的标记框\n" \ + "Ctrl + 鼠标左键\t\t多选标记框\n" \ + "Backspace\t\t删除所选框\n" \ + "Ctrl + V\t\t确认本张图片标记\n" \ + "Ctrl + Shift + d\t删除本张图片\n" \ + "D\t\t\t下一张图片\n" \ + "A\t\t\t上一张图片\n" \ + "Ctrl++\t\t\t缩小\n" \ + "Ctrl--\t\t\t放大\n" \ + "↑→↓←\t\t\t移动标记框\n" \ + "———————————————————————\n" \ + "注:Mac用户Command键替换上述Ctrl键" + + else: + msg = "Shortcut Keys\t\tDescription\n" \ + "———————————————————————\n" \ + "Ctrl + shift + R\t\tRe-recognize all the labels\n" \ + "\t\t\tof the current image\n" \ + "\n" \ + "W\t\t\tCreate a rect box\n" \ + "Q\t\t\tCreate a four-points box\n" \ + "Ctrl + E\t\tEdit label of the selected box\n" \ + "Ctrl + R\t\tRe-recognize the selected box\n" \ + "Ctrl + C\t\tCopy and paste the selected\n" \ + "\t\t\tbox\n" \ + "\n" \ + "Ctrl + Left Mouse\tMulti select the label\n" \ + "Button\t\t\tbox\n" \ + "\n" \ + "Backspace\t\tDelete the selected box\n" \ + "Ctrl + V\t\tCheck image\n" \ + "Ctrl + Shift + d\tDelete image\n" \ + "D\t\t\tNext image\n" \ + "A\t\t\tPrevious image\n" \ + "Ctrl++\t\t\tZoom in\n" \ + "Ctrl--\t\t\tZoom out\n" \ + "↑→↓←\t\t\tMove selected box" \ + "———————————————————————\n" \ + "Notice:For Mac users, use the 'Command' key instead of the 'Ctrl' key" + + return msg diff --git a/toolbox/PaddleOCR/PPOCRLabel/resources/strings/strings-en.properties b/toolbox/PaddleOCR/PPOCRLabel/resources/strings/strings-en.properties new file mode 100644 index 0000000000000000000000000000000000000000..7ba9af4c33ebe8130b5ce529e01270361c5f11d6 --- /dev/null +++ b/toolbox/PaddleOCR/PPOCRLabel/resources/strings/strings-en.properties @@ -0,0 +1,115 @@ +openFile=Open +openFileDetail=Open image or label file +quit=Quit +quitApp=Quit application +openDir=Open Dir +openDatasetDir=Open DatasetDir +copyPrevBounding=Copy previous Bounding Boxes in the current image +changeSavedAnnotationDir=Change default saved Annotation dir +openAnnotation=Open Annotation +openAnnotationDetail=Open an annotation file +changeSaveDir=Change Save Dir +nextImg=Next Image +nextImgDetail=Open the next Image +prevImg=Prev Image +prevImgDetail=Open the previous Image +verifyImg=Verify Image +verifyImgDetail=Verify Image +save=Check +saveDetail=Save the labels to a file +changeSaveFormat=Change save format +saveAs=Save As +saveAsDetail=Save the labels to a different file +closeCur=Close +closeCurDetail=Close the current file +deleteImg=Delete current image +deleteImgDetail=Delete the current image +resetAll=Reset Interface and Save Dir +resetAllDetail=Reset All +boxLineColor=Box Line Color +boxLineColorDetail=Choose Box line color +crtBox=Create RectBox +crtBoxDetail=Draw a new box +delBox=Delete RectBox +delBoxDetail=Remove the box +dupBox=Duplicate RectBox +dupBoxDetail=Create a duplicate of the selected box +tutorial=PaddleOCR url +tutorialDetail=Show demo +info=Information +zoomin=Zoom In +zoominDetail=Increase zoom level +zoomout=Zoom Out +zoomoutDetail=Decrease zoom level +originalsize=Original size +originalsizeDetail=Zoom to original size +fitWin=Fit Window +fitWinDetail=Zoom follows window size +fitWidth=Fit Width +fitWidthDetail=Zoom follows window width +editLabel=Edit Label +editLabelDetail=Modify the label of the selected Box +shapeLineColor=Shape Line Color +shapeLineColorDetail=Change the line color for this specific shape +shapeFillColor=Shape Fill Color +shapeFillColorDetail=Change the fill color for this specific shape +showHide=Show/Hide Label Panel +useDefaultLabel=Use default label +useDifficult=Difficult +boxLabelText=Box Labels +labels=Labels +autoSaveMode=Auto Save mode +singleClsMode=Single Class Mode +displayLabel=Display Labels +fileList=File List +files=Files +advancedMode=Advanced Mode +advancedModeDetail=Swtich to advanced mode +showAllBoxDetail=Show all bounding boxes +hideAllBoxDetail=Hide all bounding boxes +annoPanel=anno Panel +anno=anno +addNewBbox=new bbox +reLabel=reLabel +choosemodel=Choose OCR model +tipchoosemodel=Choose OCR model from dir +ImageResize=Image Resize +IR=Image Resize +autoRecognition=Auto Recognition +reRecognition=Re-recognition +mfile=File +medit=Edit +mview=View +mhelp=Help +iconList=Icon List +detectionBoxposition=Detection box position +recognitionResult=Recognition result +creatPolygon=Create PolygonBox +rotateLeft=Left turn 90 degrees +rotateRight=Right turn 90 degrees +drawSquares=Draw Squares +saveRec=Export Recognition Result +tempLabel=TEMPORARY +nullLabel=NULL +steps=Steps +keys=Shortcut Keys +choseModelLg=Choose Model Language +cancel=Cancel +ok=OK +autolabeling=Automatic Labeling +hideBox=Hide All Box +showBox=Show All Box +saveLabel=Export Label +singleRe=Re-recognition RectBox +labelDialogOption=Pop-up Label Input Dialog +undo=Undo +undoLastPoint=Undo Last Point +autoSaveMode=Auto Export Label Mode +lockBox=Lock selected box/Unlock all box +lockBoxDetail=Lock selected box/Unlock all box +keyListTitle=Key List +keyDialogTip=Enter object label +keyChange=Change Box Key +TableRecognition=Table Recognition +cellreRecognition=Cell Re-Recognition +exportJSON=export JSON(PubTabNet) diff --git a/toolbox/PaddleOCR/PPOCRLabel/resources/strings/strings-zh-CN.properties b/toolbox/PaddleOCR/PPOCRLabel/resources/strings/strings-zh-CN.properties new file mode 100644 index 0000000000000000000000000000000000000000..308974ef29fac367bccd5f0e97926b2087a9f8df --- /dev/null +++ b/toolbox/PaddleOCR/PPOCRLabel/resources/strings/strings-zh-CN.properties @@ -0,0 +1,115 @@ +saveAsDetail=將标签保存到其他文件 +changeSaveDir=改变存放目录 +openFile=打开文件 +shapeLineColorDetail=更改线条颜色 +resetAll=重置界面与保存地址 +crtBox=矩形标注 +crtBoxDetail=创建一个新的区块 +dupBoxDetail=复制区块 +verifyImg=验证图像 +zoominDetail=放大 +verifyImgDetail=验证图像 +saveDetail=保存标签文件 +openFileDetail=打开图像文件 +fitWidthDetail=调整宽度适应到窗口宽度 +tutorial=PaddleOCR地址 +editLabel=编辑标签 +openAnnotationDetail=打开标签文件 +quit=退出 +shapeFillColorDetail=更改填充颜色 +closeCurDetail=关闭当前文件 +closeCur=关闭文件 +deleteImg=删除图像 +deleteImgDetail=删除当前图像 +fitWin=调整到窗口大小 +delBox=删除选择的区块 +boxLineColorDetail=选择线框颜色 +originalsize=原始大小 +resetAllDetail=重置所有设定 +zoomoutDetail=放大画面 +save=确认 +saveAs=另存为 +fitWinDetail=缩放到当前窗口大小 +openDir=打开目录 +openDatasetDir=打开数据集路径 +copyPrevBounding=复制当前图像中的上一个边界框 +showHide=显示/隐藏标签 +changeSaveFormat=更改存储格式 +shapeFillColor=填充颜色 +quitApp=退出程序 +dupBox=复制区块 +delBoxDetail=删除区块 +zoomin=放大画面 +info=信息 +openAnnotation=开启标签 +prevImgDetail=上一个图像 +fitWidth=缩放到当前画面宽度 +zoomout=缩小画面 +changeSavedAnnotationDir=更改保存标签文件的预设目录 +nextImgDetail=下一个图像 +originalsizeDetail=放大到原始大小 +prevImg=上一张 +tutorialDetail=显示示范内容 +shapeLineColor=形状线条颜色 +boxLineColor=区块线条颜色 +editLabelDetail=修改当前所选的区块颜色 +nextImg=下一张 +useDefaultLabel=使用预设标签 +useDifficult=有难度的 +boxLabelText=区块的标签 +labels=标签 +autoSaveMode=自动保存模式 +singleClsMode=单一类别模式 +displayLabel=显示类别 +fileList=文件列表 +files=文件 +advancedMode=专家模式 +advancedModeDetail=切换到专家模式 +showAllBoxDetail=显示所有区块 +hideAllBoxDetail=隐藏所有区块 +annoPanel=标注面板 +anno=标注 +addNewBbox=新框 +reLabel=重标注 +choosemodel=选择模型 +tipchoosemodel=选择OCR模型 +ImageResize=图片缩放 +IR=图片缩放 +autoRecognition=自动标注 +reRecognition=重新识别 +mfile=文件 +medit=编辑 +mview=视图 +mhelp=帮助 +iconList=缩略图 +detectionBoxposition=检测框位置 +recognitionResult=识别结果 +creatPolygon=多边形标注 +drawSquares=正方形标注 +rotateLeft=图片左旋转90度 +rotateRight=图片右旋转90度 +saveRec=导出识别结果 +tempLabel=待识别 +nullLabel=无法识别 +steps=操作步骤 +keys=快捷键 +choseModelLg=选择模型语言 +cancel=取消 +ok=确认 +autolabeling=自动标注中 +hideBox=隐藏所有标注 +showBox=显示所有标注 +saveLabel=导出标记结果 +singleRe=重识别此区块 +labelDialogOption=弹出标记输入框 +undo=撤销 +undoLastPoint=撤销上个点 +autoSaveMode=自动导出标记结果 +lockBox=锁定框/解除锁定框 +lockBoxDetail=若当前没有框处于锁定状态则锁定选中的框,若存在锁定框则解除所有锁定框的锁定状态 +keyListTitle=关键词列表 +keyDialogTip=请输入类型名称 +keyChange=更改Box关键字类别 +TableRecognition=表格识别 +cellreRecognition=单元格重识别 +exportJSON=导出表格JSON标注 \ No newline at end of file diff --git a/toolbox/PaddleOCR/configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_dml.yml b/toolbox/PaddleOCR/configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_dml.yml new file mode 100644 index 0000000000000000000000000000000000000000..f3ad966488dbc2f6f7ca12033bc4a3d35e1b3bd7 --- /dev/null +++ b/toolbox/PaddleOCR/configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_dml.yml @@ -0,0 +1,178 @@ +Global: + use_gpu: true + epoch_num: 1200 + log_smooth_window: 20 + print_batch_step: 2 + save_model_dir: ./output/ch_db_mv3/ + save_epoch_step: 1200 + # evaluation is run every 5000 iterations after the 4000th iteration + eval_batch_step: [3000, 2000] + cal_metric_during_train: False + pretrained_model: ./pretrain_models/MobileNetV3_large_x0_5_pretrained + checkpoints: + save_inference_dir: + use_visualdl: False + infer_img: doc/imgs_en/img_10.jpg + save_res_path: ./output/det_db/predicts_db.txt + +Architecture: + name: DistillationModel + algorithm: Distillation + model_type: det + Models: + Student: + pretrained: ./pretrain_models/MobileNetV3_large_x0_5_pretrained + freeze_params: false + return_all_feats: false + model_type: det + algorithm: DB + Backbone: + name: MobileNetV3 + scale: 0.5 + model_name: large + disable_se: True + Neck: + name: DBFPN + out_channels: 96 + Head: + name: DBHead + k: 50 + Teacher: + pretrained: ./pretrain_models/MobileNetV3_large_x0_5_pretrained + freeze_params: false + return_all_feats: false + model_type: det + algorithm: DB + Transform: + Backbone: + name: MobileNetV3 + scale: 0.5 + model_name: large + disable_se: True + Neck: + name: DBFPN + out_channels: 96 + Head: + name: DBHead + k: 50 + + +Loss: + name: CombinedLoss + loss_config_list: + - DistillationDMLLoss: + model_name_pairs: + - ["Student", "Teacher"] + maps_name: "thrink_maps" + weight: 1.0 + act: "softmax" + model_name_pairs: ["Student", "Teacher"] + key: maps + - DistillationDBLoss: + weight: 1.0 + model_name_list: ["Student", "Teacher"] + # key: maps + name: DBLoss + balance_loss: true + main_loss_type: DiceLoss + alpha: 5 + beta: 10 + ohem_ratio: 3 + + +Optimizer: + name: Adam + beta1: 0.9 + beta2: 0.999 + lr: + name: Cosine + learning_rate: 0.001 + warmup_epoch: 2 + regularizer: + name: 'L2' + factor: 0 + +PostProcess: + name: DistillationDBPostProcess + model_name: ["Student", "Teacher"] + key: head_out + thresh: 0.3 + box_thresh: 0.6 + max_candidates: 1000 + unclip_ratio: 1.5 + +Metric: + name: DistillationMetric + base_metric_name: DetMetric + main_indicator: hmean + key: "Student" + +Train: + dataset: + name: SimpleDataSet + data_dir: ./train_data/icdar2015/text_localization/ + label_file_list: + - ./train_data/icdar2015/text_localization/train_icdar2015_label.txt + ratio_list: [1.0] + transforms: + - DecodeImage: # load image + img_mode: BGR + channel_first: False + - DetLabelEncode: # Class handling label + - CopyPaste: + - IaaAugment: + augmenter_args: + - { 'type': Fliplr, 'args': { 'p': 0.5 } } + - { 'type': Affine, 'args': { 'rotate': [-10, 10] } } + - { 'type': Resize, 'args': { 'size': [0.5, 3] } } + - EastRandomCropData: + size: [960, 960] + max_tries: 50 + keep_ratio: true + - MakeBorderMap: + shrink_ratio: 0.4 + thresh_min: 0.3 + thresh_max: 0.7 + - MakeShrinkMap: + shrink_ratio: 0.4 + min_text_size: 8 + - NormalizeImage: + scale: 1./255. + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + keep_keys: ['image', 'threshold_map', 'threshold_mask', 'shrink_map', 'shrink_mask'] # the order of the dataloader list + loader: + shuffle: True + drop_last: False + batch_size_per_card: 8 + num_workers: 4 + +Eval: + dataset: + name: SimpleDataSet + data_dir: ./train_data/icdar2015/text_localization/ + label_file_list: + - ./train_data/icdar2015/text_localization/test_icdar2015_label.txt + transforms: + - DecodeImage: # load image + img_mode: BGR + channel_first: False + - DetLabelEncode: # Class handling label + - DetResizeForTest: +# image_shape: [736, 1280] + - NormalizeImage: + scale: 1./255. + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + keep_keys: ['image', 'shape', 'polys', 'ignore_tags'] + loader: + shuffle: False + drop_last: False + batch_size_per_card: 1 # must be 1 + num_workers: 2 diff --git a/toolbox/PaddleOCR/configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_cml.yml b/toolbox/PaddleOCR/configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_cml.yml new file mode 100644 index 0000000000000000000000000000000000000000..3e77577c17abe2111c501d96ce6b1087ac44f8d6 --- /dev/null +++ b/toolbox/PaddleOCR/configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_cml.yml @@ -0,0 +1,234 @@ +Global: + debug: false + use_gpu: true + epoch_num: 500 + log_smooth_window: 20 + print_batch_step: 10 + save_model_dir: ./output/ch_PP-OCR_v3_det/ + save_epoch_step: 100 + eval_batch_step: + - 0 + - 400 + cal_metric_during_train: false + pretrained_model: null + checkpoints: null + save_inference_dir: null + use_visualdl: false + infer_img: doc/imgs_en/img_10.jpg + save_res_path: ./checkpoints/det_db/predicts_db.txt + distributed: true + +Architecture: + name: DistillationModel + algorithm: Distillation + model_type: det + Models: + Student: + model_type: det + algorithm: DB + Transform: null + Backbone: + name: MobileNetV3 + scale: 0.5 + model_name: large + disable_se: true + Neck: + name: RSEFPN + out_channels: 96 + shortcut: True + Head: + name: DBHead + k: 50 + Student2: + model_type: det + algorithm: DB + Transform: null + Backbone: + name: MobileNetV3 + scale: 0.5 + model_name: large + disable_se: true + Neck: + name: RSEFPN + out_channels: 96 + shortcut: True + Head: + name: DBHead + k: 50 + Teacher: + freeze_params: true + return_all_feats: false + model_type: det + algorithm: DB + Backbone: + name: ResNet + in_channels: 3 + layers: 50 + Neck: + name: LKPAN + out_channels: 256 + Head: + name: DBHead + kernel_list: [7,2,2] + k: 50 + +Loss: + name: CombinedLoss + loss_config_list: + - DistillationDilaDBLoss: + weight: 1.0 + model_name_pairs: + - ["Student", "Teacher"] + - ["Student2", "Teacher"] + key: maps + balance_loss: true + main_loss_type: DiceLoss + alpha: 5 + beta: 10 + ohem_ratio: 3 + - DistillationDMLLoss: + model_name_pairs: + - ["Student", "Student2"] + maps_name: "thrink_maps" + weight: 1.0 + # act: None + model_name_pairs: ["Student", "Student2"] + key: maps + - DistillationDBLoss: + weight: 1.0 + model_name_list: ["Student", "Student2"] + # key: maps + # name: DBLoss + balance_loss: true + main_loss_type: DiceLoss + alpha: 5 + beta: 10 + ohem_ratio: 3 + +Optimizer: + name: Adam + beta1: 0.9 + beta2: 0.999 + lr: + name: Cosine + learning_rate: 0.001 + warmup_epoch: 2 + regularizer: + name: L2 + factor: 5.0e-05 + +PostProcess: + name: DistillationDBPostProcess + model_name: ["Student"] + key: head_out + thresh: 0.3 + box_thresh: 0.6 + max_candidates: 1000 + unclip_ratio: 1.5 + +Metric: + name: DistillationMetric + base_metric_name: DetMetric + main_indicator: hmean + key: "Student" + +Train: + dataset: + name: SimpleDataSet + data_dir: ./train_data/icdar2015/text_localization/ + label_file_list: + - ./train_data/icdar2015/text_localization/train_icdar2015_label.txt + ratio_list: [1.0] + transforms: + - DecodeImage: + img_mode: BGR + channel_first: false + - DetLabelEncode: null + - CopyPaste: + - IaaAugment: + augmenter_args: + - type: Fliplr + args: + p: 0.5 + - type: Affine + args: + rotate: + - -10 + - 10 + - type: Resize + args: + size: + - 0.5 + - 3 + - EastRandomCropData: + size: + - 960 + - 960 + max_tries: 50 + keep_ratio: true + - MakeBorderMap: + shrink_ratio: 0.4 + thresh_min: 0.3 + thresh_max: 0.7 + - MakeShrinkMap: + shrink_ratio: 0.4 + min_text_size: 8 + - NormalizeImage: + scale: 1./255. + mean: + - 0.485 + - 0.456 + - 0.406 + std: + - 0.229 + - 0.224 + - 0.225 + order: hwc + - ToCHWImage: null + - KeepKeys: + keep_keys: + - image + - threshold_map + - threshold_mask + - shrink_map + - shrink_mask + loader: + shuffle: true + drop_last: false + batch_size_per_card: 8 + num_workers: 4 +Eval: + dataset: + name: SimpleDataSet + data_dir: ./train_data/icdar2015/text_localization/ + label_file_list: + - ./train_data/icdar2015/text_localization/test_icdar2015_label.txt + transforms: + - DecodeImage: + img_mode: BGR + channel_first: false + - DetLabelEncode: null + - DetResizeForTest: null + - NormalizeImage: + scale: 1./255. + mean: + - 0.485 + - 0.456 + - 0.406 + std: + - 0.229 + - 0.224 + - 0.225 + order: hwc + - ToCHWImage: null + - KeepKeys: + keep_keys: + - image + - shape + - polys + - ignore_tags + loader: + shuffle: false + drop_last: false + batch_size_per_card: 1 + num_workers: 2 diff --git a/toolbox/PaddleOCR/configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_dml.yml b/toolbox/PaddleOCR/configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_dml.yml new file mode 100644 index 0000000000000000000000000000000000000000..3f30ada13f2f1c3c01ba8886bbfba006da516f17 --- /dev/null +++ b/toolbox/PaddleOCR/configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_dml.yml @@ -0,0 +1,173 @@ +Global: + use_gpu: true + epoch_num: 1200 + log_smooth_window: 20 + print_batch_step: 2 + save_model_dir: ./output/ch_db_mv3/ + save_epoch_step: 1200 + # evaluation is run every 5000 iterations after the 4000th iteration + eval_batch_step: [3000, 2000] + cal_metric_during_train: False + pretrained_model: ./pretrain_models/MobileNetV3_large_x0_5_pretrained + checkpoints: + save_inference_dir: + use_visualdl: False + infer_img: doc/imgs_en/img_10.jpg + save_res_path: ./output/det_db/predicts_db.txt + +Architecture: + name: DistillationModel + algorithm: Distillation + model_type: det + Models: + Student: + return_all_feats: false + model_type: det + algorithm: DB + Backbone: + name: ResNet + in_channels: 3 + layers: 50 + Neck: + name: LKPAN + out_channels: 256 + Head: + name: DBHead + kernel_list: [7,2,2] + k: 50 + Student2: + return_all_feats: false + model_type: det + algorithm: DB + Backbone: + name: ResNet + in_channels: 3 + layers: 50 + Neck: + name: LKPAN + out_channels: 256 + Head: + name: DBHead + kernel_list: [7,2,2] + k: 50 + + +Loss: + name: CombinedLoss + loss_config_list: + - DistillationDMLLoss: + model_name_pairs: + - ["Student", "Student2"] + maps_name: "thrink_maps" + weight: 1.0 + act: "softmax" + model_name_pairs: ["Student", "Student2"] + key: maps + - DistillationDBLoss: + weight: 1.0 + model_name_list: ["Student", "Student2"] + # key: maps + name: DBLoss + balance_loss: true + main_loss_type: DiceLoss + alpha: 5 + beta: 10 + ohem_ratio: 3 + + +Optimizer: + name: Adam + beta1: 0.9 + beta2: 0.999 + lr: + name: Cosine + learning_rate: 0.001 + warmup_epoch: 2 + regularizer: + name: 'L2' + factor: 0 + +PostProcess: + name: DistillationDBPostProcess + model_name: ["Student", "Student2"] + key: head_out + thresh: 0.3 + box_thresh: 0.6 + max_candidates: 1000 + unclip_ratio: 1.5 + +Metric: + name: DistillationMetric + base_metric_name: DetMetric + main_indicator: hmean + key: "Student" + +Train: + dataset: + name: SimpleDataSet + data_dir: ./train_data/icdar2015/text_localization/ + label_file_list: + - ./train_data/icdar2015/text_localization/train_icdar2015_label.txt + ratio_list: [1.0] + transforms: + - DecodeImage: # load image + img_mode: BGR + channel_first: False + - DetLabelEncode: # Class handling label + - CopyPaste: + - IaaAugment: + augmenter_args: + - { 'type': Fliplr, 'args': { 'p': 0.5 } } + - { 'type': Affine, 'args': { 'rotate': [-10, 10] } } + - { 'type': Resize, 'args': { 'size': [0.5, 3] } } + - EastRandomCropData: + size: [960, 960] + max_tries: 50 + keep_ratio: true + - MakeBorderMap: + shrink_ratio: 0.4 + thresh_min: 0.3 + thresh_max: 0.7 + - MakeShrinkMap: + shrink_ratio: 0.4 + min_text_size: 8 + - NormalizeImage: + scale: 1./255. + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + keep_keys: ['image', 'threshold_map', 'threshold_mask', 'shrink_map', 'shrink_mask'] # the order of the dataloader list + loader: + shuffle: True + drop_last: False + batch_size_per_card: 8 + num_workers: 4 + +Eval: + dataset: + name: SimpleDataSet + data_dir: ./train_data/icdar2015/text_localization/ + label_file_list: + - ./train_data/icdar2015/text_localization/test_icdar2015_label.txt + transforms: + - DecodeImage: # load image + img_mode: BGR + channel_first: False + - DetLabelEncode: # Class handling label + - DetResizeForTest: +# image_shape: [736, 1280] + - NormalizeImage: + scale: 1./255. + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + keep_keys: ['image', 'shape', 'polys', 'ignore_tags'] + loader: + shuffle: False + drop_last: False + batch_size_per_card: 1 # must be 1 + num_workers: 2 diff --git a/toolbox/PaddleOCR/configs/det/det_mv3_db.yml b/toolbox/PaddleOCR/configs/det/det_mv3_db.yml new file mode 100644 index 0000000000000000000000000000000000000000..cb16c5edc020b005f068f3bb5f8e745a91f71a18 --- /dev/null +++ b/toolbox/PaddleOCR/configs/det/det_mv3_db.yml @@ -0,0 +1,132 @@ +Global: + use_gpu: true + use_xpu: false + epoch_num: 300 + log_smooth_window: 20 + print_batch_step: 2 + save_model_dir: ./output/db_mv3/ + save_epoch_step: 400 + # evaluation is run every 2000 iterations + eval_batch_step: [0, 400] + cal_metric_during_train: False + pretrained_model: ./pretrain_models/MobileNetV3_large_x0_5_pretrained + checkpoints: + save_inference_dir: + use_visualdl: False + infer_img: doc/imgs_en/img_10.jpg + save_res_path: ./output/det_db/predicts_db.txt + +Architecture: + model_type: det + algorithm: DB + Transform: + Backbone: + name: MobileNetV3 + scale: 0.5 + model_name: large + Neck: + name: DBFPN + out_channels: 256 + Head: + name: DBHead + k: 50 + +Loss: + name: DBLoss + balance_loss: true + main_loss_type: DiceLoss + alpha: 5 + beta: 10 + ohem_ratio: 3 + +Optimizer: + name: Adam + beta1: 0.9 + beta2: 0.999 + lr: + learning_rate: 0.001 + regularizer: + name: 'L2' + factor: 0 + +PostProcess: + name: DBPostProcess + thresh: 0.3 + box_thresh: 0.6 + max_candidates: 1000 + unclip_ratio: 1.5 + +Metric: + name: DetMetric + main_indicator: hmean + +Train: + dataset: + name: SimpleDataSet + data_dir: ./train_data/icdar2015/text_localization/ + label_file_list: + - ./train_data/icdar2015/text_localization/train_icdar2015_label.txt + ratio_list: [1.0] + transforms: + - DecodeImage: # load image + img_mode: BGR + channel_first: False + - DetLabelEncode: # Class handling label + - IaaAugment: + augmenter_args: + - { 'type': Fliplr, 'args': { 'p': 0.5 } } + - { 'type': Affine, 'args': { 'rotate': [-10, 10] } } + - { 'type': Resize, 'args': { 'size': [0.5, 3] } } + - EastRandomCropData: + size: [640, 640] + max_tries: 50 + keep_ratio: true + - MakeBorderMap: + shrink_ratio: 0.4 + thresh_min: 0.3 + thresh_max: 0.7 + - MakeShrinkMap: + shrink_ratio: 0.4 + min_text_size: 8 + - NormalizeImage: + scale: 1./255. + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + keep_keys: ['image', 'threshold_map', 'threshold_mask', 'shrink_map', 'shrink_mask'] # the order of the dataloader list + loader: + shuffle: True + drop_last: False + batch_size_per_card: 16 + num_workers: 8 + use_shared_memory: True + +Eval: + dataset: + name: SimpleDataSet + data_dir: ./train_data/icdar2015/text_localization/ + label_file_list: + - ./train_data/icdar2015/text_localization/test_icdar2015_label.txt + transforms: + - DecodeImage: # load image + img_mode: BGR + channel_first: False + - DetLabelEncode: # Class handling label + - DetResizeForTest: + image_shape: [736, 1280] + - NormalizeImage: + scale: 1./255. + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + keep_keys: ['image', 'shape', 'polys', 'ignore_tags'] + loader: + shuffle: False + drop_last: False + batch_size_per_card: 1 # must be 1 + num_workers: 8 + use_shared_memory: True diff --git a/toolbox/PaddleOCR/configs/kie/kie_unet_sdmgr.yml b/toolbox/PaddleOCR/configs/kie/kie_unet_sdmgr.yml new file mode 100644 index 0000000000000000000000000000000000000000..a6968aaa3aa7a717a848416efc5ccc567f774b4d --- /dev/null +++ b/toolbox/PaddleOCR/configs/kie/kie_unet_sdmgr.yml @@ -0,0 +1,111 @@ +Global: + use_gpu: True + epoch_num: 60 + log_smooth_window: 20 + print_batch_step: 50 + save_model_dir: ./output/kie_5/ + save_epoch_step: 50 + # evaluation is run every 5000 iterations after the 4000th iteration + eval_batch_step: [ 0, 80 ] + # 1. If pretrained_model is saved in static mode, such as classification pretrained model + # from static branch, load_static_weights must be set as True. + # 2. If you want to finetune the pretrained models we provide in the docs, + # you should set load_static_weights as False. + load_static_weights: False + cal_metric_during_train: False + pretrained_model: + checkpoints: + save_inference_dir: + use_visualdl: False + class_path: ./train_data/wildreceipt/class_list.txt + infer_img: ./train_data/wildreceipt/1.txt + save_res_path: ./output/sdmgr_kie/predicts_kie.txt + img_scale: [ 1024, 512 ] + +Architecture: + model_type: kie + algorithm: SDMGR + Transform: + Backbone: + name: Kie_backbone + Head: + name: SDMGRHead + +Loss: + name: SDMGRLoss + +Optimizer: + name: Adam + beta1: 0.9 + beta2: 0.999 + lr: + name: Piecewise + learning_rate: 0.001 + decay_epochs: [ 60, 80, 100] + values: [ 0.001, 0.0001, 0.00001] + warmup_epoch: 2 + regularizer: + name: 'L2' + factor: 0.00005 + +PostProcess: + name: None + +Metric: + name: KIEMetric + main_indicator: hmean + +Train: + dataset: + name: SimpleDataSet + data_dir: ./train_data/wildreceipt/ + label_file_list: [ './train_data/wildreceipt/wildreceipt_train.txt' ] + ratio_list: [ 1.0 ] + transforms: + - DecodeImage: # load image + img_mode: RGB + channel_first: False + - NormalizeImage: + scale: 1 + mean: [ 123.675, 116.28, 103.53 ] + std: [ 58.395, 57.12, 57.375 ] + order: 'hwc' + - KieLabelEncode: # Class handling label + character_dict_path: ./train_data/wildreceipt/dict.txt + - KieResize: + - ToCHWImage: + - KeepKeys: + keep_keys: [ 'image', 'relations', 'texts', 'points', 'labels', 'tag', 'shape'] # dataloader will return list in this order + loader: + shuffle: True + drop_last: False + batch_size_per_card: 4 + num_workers: 4 + +Eval: + dataset: + name: SimpleDataSet + data_dir: ./train_data/wildreceipt + label_file_list: + - ./train_data/wildreceipt/wildreceipt_test.txt + # - /paddle/data/PaddleOCR/train_data/wildreceipt/1.txt + transforms: + - DecodeImage: # load image + img_mode: RGB + channel_first: False + - KieLabelEncode: # Class handling label + character_dict_path: ./train_data/wildreceipt/dict.txt + - KieResize: + - NormalizeImage: + scale: 1 + mean: [ 123.675, 116.28, 103.53 ] + std: [ 58.395, 57.12, 57.375 ] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + keep_keys: [ 'image', 'relations', 'texts', 'points', 'labels', 'tag', 'ori_image', 'ori_boxes', 'shape'] + loader: + shuffle: False + drop_last: False + batch_size_per_card: 1 # must be 1 + num_workers: 4 diff --git a/toolbox/PaddleOCR/configs/rec/rec_svtrnet.yml b/toolbox/PaddleOCR/configs/rec/rec_svtrnet.yml new file mode 100644 index 0000000000000000000000000000000000000000..233d5e276577cad0144456ef7df1e20de99891f9 --- /dev/null +++ b/toolbox/PaddleOCR/configs/rec/rec_svtrnet.yml @@ -0,0 +1,117 @@ +Global: + use_gpu: True + epoch_num: 20 + log_smooth_window: 20 + print_batch_step: 10 + save_model_dir: ./output/rec/svtr/ + save_epoch_step: 1 + # evaluation is run every 2000 iterations after the 0th iteration + eval_batch_step: [0, 2000] + cal_metric_during_train: True + pretrained_model: + checkpoints: + save_inference_dir: + use_visualdl: False + infer_img: doc/imgs_words_en/word_10.png + # for data or label process + character_dict_path: + character_type: en + max_text_length: 25 + infer_mode: False + use_space_char: False + save_res_path: ./output/rec/predicts_svtr_tiny.txt + + +Optimizer: + name: AdamW + beta1: 0.9 + beta2: 0.99 + epsilon: 0.00000008 + weight_decay: 0.05 + no_weight_decay_name: norm pos_embed + one_dim_param_no_weight_decay: true + lr: + name: Cosine + learning_rate: 0.0005 + warmup_epoch: 2 + +Architecture: + model_type: rec + algorithm: SVTR + Transform: + name: STN_ON + tps_inputsize: [32, 64] + tps_outputsize: [32, 100] + num_control_points: 20 + tps_margins: [0.05,0.05] + stn_activation: none + Backbone: + name: SVTRNet + img_size: [32, 100] + out_char_num: 25 + out_channels: 192 + patch_merging: 'Conv' + embed_dim: [64, 128, 256] + depth: [3, 6, 3] + num_heads: [2, 4, 8] + mixer: ['Local','Local','Local','Local','Local','Local','Global','Global','Global','Global','Global','Global'] + local_mixer: [[7, 11], [7, 11], [7, 11]] + last_stage: True + prenorm: false + Neck: + name: SequenceEncoder + encoder_type: reshape + Head: + name: CTCHead + +Loss: + name: CTCLoss + +PostProcess: + name: CTCLabelDecode + +Metric: + name: RecMetric + main_indicator: acc + +Train: + dataset: + name: LMDBDataSet + data_dir: ./train_data/data_lmdb_release/training/ + transforms: + - DecodeImage: # load image + img_mode: BGR + channel_first: False + - CTCLabelEncode: # Class handling label + - RecResizeImg: + character_dict_path: + image_shape: [3, 64, 256] + padding: False + - KeepKeys: + keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order + loader: + shuffle: True + batch_size_per_card: 512 + drop_last: True + num_workers: 4 + +Eval: + dataset: + name: LMDBDataSet + data_dir: ./train_data/data_lmdb_release/validation/ + transforms: + - DecodeImage: # load image + img_mode: BGR + channel_first: False + - CTCLabelEncode: # Class handling label + - RecResizeImg: + character_dict_path: + image_shape: [3, 64, 256] + padding: False + - KeepKeys: + keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order + loader: + shuffle: False + drop_last: False + batch_size_per_card: 256 + num_workers: 2 diff --git a/toolbox/PaddleOCR/deploy/android_demo/app/src/main/cpp/native.cpp b/toolbox/PaddleOCR/deploy/android_demo/app/src/main/cpp/native.cpp new file mode 100644 index 0000000000000000000000000000000000000000..2834765a9d2165029062268609f93dc7030bbd2e --- /dev/null +++ b/toolbox/PaddleOCR/deploy/android_demo/app/src/main/cpp/native.cpp @@ -0,0 +1,121 @@ +// +// Created by fujiayi on 2020/7/5. +// +// Copyright (c) 2024, Shanghai Iluvatar CoreX Semiconductor Co., Ltd. +// All Rights Reserved. + +#include "native.h" +#include "ocr_ppredictor.h" +#include +#include +#include + +static paddle::lite_api::PowerMode str_to_cpu_mode(const std::string &cpu_mode); + +extern "C" JNIEXPORT jlong JNICALL +Java_com_baidu_paddle_lite_demo_ocr_OCRPredictorNative_init( + JNIEnv *env, jobject thiz, jstring j_det_model_path, + jstring j_rec_model_path, jstring j_cls_model_path, jint j_use_opencl, jint j_thread_num, + jstring j_cpu_mode) { + std::string det_model_path = jstring_to_cpp_string(env, j_det_model_path); + std::string rec_model_path = jstring_to_cpp_string(env, j_rec_model_path); + std::string cls_model_path = jstring_to_cpp_string(env, j_cls_model_path); + int thread_num = j_thread_num; + std::string cpu_mode = jstring_to_cpp_string(env, j_cpu_mode); + ppredictor::OCR_Config conf; + conf.use_opencl = j_use_opencl; + conf.thread_num = thread_num; + conf.mode = str_to_cpu_mode(cpu_mode); + ppredictor::OCR_PPredictor *orc_predictor = + new ppredictor::OCR_PPredictor{conf}; + orc_predictor->init_from_file(det_model_path, rec_model_path, cls_model_path); + return reinterpret_cast(orc_predictor); +} + +/** + * "LITE_POWER_HIGH" convert to paddle::lite_api::LITE_POWER_HIGH + * @param cpu_mode + * @return + */ +static paddle::lite_api::PowerMode +str_to_cpu_mode(const std::string &cpu_mode) { + static std::map cpu_mode_map{ + {"LITE_POWER_HIGH", paddle::lite_api::LITE_POWER_HIGH}, + {"LITE_POWER_LOW", paddle::lite_api::LITE_POWER_HIGH}, + {"LITE_POWER_FULL", paddle::lite_api::LITE_POWER_FULL}, + {"LITE_POWER_NO_BIND", paddle::lite_api::LITE_POWER_NO_BIND}, + {"LITE_POWER_RAND_HIGH", paddle::lite_api::LITE_POWER_RAND_HIGH}, + {"LITE_POWER_RAND_LOW", paddle::lite_api::LITE_POWER_RAND_LOW}}; + std::string upper_key; + std::transform(cpu_mode.cbegin(), cpu_mode.cend(), upper_key.begin(), + ::toupper); + auto index = cpu_mode_map.find(upper_key); + if (index == cpu_mode_map.end()) { + LOGE("cpu_mode not found %s", upper_key.c_str()); + return paddle::lite_api::LITE_POWER_HIGH; + } else { + return index->second; + } +} + +extern "C" JNIEXPORT jfloatArray JNICALL +Java_com_baidu_paddle_lite_demo_ocr_OCRPredictorNative_forward( + JNIEnv *env, jobject thiz, jlong java_pointer, jobject original_image,jint j_max_size_len, jint j_run_det, jint j_run_cls, jint j_run_rec) { + LOGI("begin to run native forward"); + if (java_pointer == 0) { + LOGE("JAVA pointer is NULL"); + return cpp_array_to_jfloatarray(env, nullptr, 0); + } + + cv::Mat origin = bitmap_to_cv_mat(env, original_image); + if (origin.size == 0) { + LOGE("origin bitmap cannot convert to CV Mat"); + return cpp_array_to_jfloatarray(env, nullptr, 0); + } + + int max_size_len = j_max_size_len; + int run_det = j_run_det; + int run_cls = j_run_cls; + int run_rec = j_run_rec; + + ppredictor::OCR_PPredictor *ppredictor = + (ppredictor::OCR_PPredictor *)java_pointer; + std::vector dims_arr; + std::vector results = + ppredictor->infer_ocr(origin, max_size_len, run_det, run_cls, run_rec); + LOGI("infer_ocr finished with boxes %ld", results.size()); + + // 这里将std::vector 序列化成 + // float数组,传输到java层再反序列化 + std::vector float_arr; + for (const ppredictor::OCRPredictResult &r : results) { + float_arr.push_back(r.points.size()); + float_arr.push_back(r.word_index.size()); + float_arr.push_back(r.score); + // add det point + for (const std::vector &point : r.points) { + float_arr.push_back(point.at(0)); + float_arr.push_back(point.at(1)); + } + // add rec word idx + for (int index : r.word_index) { + float_arr.push_back(index); + } + // add cls result + float_arr.push_back(r.cls_label); + float_arr.push_back(r.cls_score); + } + return cpp_array_to_jfloatarray(env, float_arr.data(), float_arr.size()); +} + +extern "C" JNIEXPORT void JNICALL +Java_com_baidu_paddle_lite_demo_ocr_OCRPredictorNative_release( + JNIEnv *env, jobject thiz, jlong java_pointer) { + if (java_pointer == 0) { + LOGE("JAVA pointer is NULL"); + return; + } + ppredictor::OCR_PPredictor *ppredictor = + (ppredictor::OCR_PPredictor *)java_pointer; + delete ppredictor; +} diff --git a/toolbox/PaddleOCR/deploy/android_demo/app/src/main/java/com/baidu/paddle/lite/demo/ocr/OCRPredictorNative.java b/toolbox/PaddleOCR/deploy/android_demo/app/src/main/java/com/baidu/paddle/lite/demo/ocr/OCRPredictorNative.java new file mode 100644 index 0000000000000000000000000000000000000000..69f58bc59321034d0d38e09bbf204aad5df8cd9c --- /dev/null +++ b/toolbox/PaddleOCR/deploy/android_demo/app/src/main/java/com/baidu/paddle/lite/demo/ocr/OCRPredictorNative.java @@ -0,0 +1,107 @@ +// Copyright (c) 2024, Shanghai Iluvatar CoreX Semiconductor Co., Ltd. +// All Rights Reserved. +package com.baidu.paddle.lite.demo.ocr; + +import android.graphics.Bitmap; +import android.util.Log; + +import java.util.ArrayList; +import java.util.concurrent.atomic.AtomicBoolean; + +public class OCRPredictorNative { + + private static final AtomicBoolean isSOLoaded = new AtomicBoolean(); + + public static void loadLibrary() throws RuntimeException { + if (!isSOLoaded.get() && isSOLoaded.compareAndSet(false, true)) { + try { + System.loadLibrary("Native"); + } catch (Throwable e) { + RuntimeException exception = new RuntimeException( + "Load libNative.so failed, please check it exists in apk file.", e); + throw exception; + } + } + } + + private Config config; + + private long nativePointer = 0; + + public OCRPredictorNative(Config config) { + this.config = config; + loadLibrary(); + nativePointer = init(config.detModelFilename, config.recModelFilename, config.clsModelFilename, config.useOpencl, + config.cpuThreadNum, config.cpuPower); + Log.i("OCRPredictorNative", "load success " + nativePointer); + + } + + + public ArrayList runImage(Bitmap originalImage, int max_size_len, int run_det, int run_cls, int run_rec) { + Log.i("OCRPredictorNative", "begin to run image "); + float[] rawResults = forward(nativePointer, originalImage, max_size_len, run_det, run_cls, run_rec); + ArrayList results = postprocess(rawResults); + return results; + } + + public static class Config { + public int useOpencl; + public int cpuThreadNum; + public String cpuPower; + public String detModelFilename; + public String recModelFilename; + public String clsModelFilename; + + } + + public void destory() { + if (nativePointer > 0) { + release(nativePointer); + nativePointer = 0; + } + } + + protected native long init(String detModelPath, String recModelPath, String clsModelPath, int useOpencl, int threadNum, String cpuMode); + + protected native float[] forward(long pointer, Bitmap originalImage,int max_size_len, int run_det, int run_cls, int run_rec); + + protected native void release(long pointer); + + private ArrayList postprocess(float[] raw) { + ArrayList results = new ArrayList(); + int begin = 0; + + while (begin < raw.length) { + int point_num = Math.round(raw[begin]); + int word_num = Math.round(raw[begin + 1]); + OcrResultModel res = parse(raw, begin + 2, point_num, word_num); + begin += 2 + 1 + point_num * 2 + word_num + 2; + results.add(res); + } + + return results; + } + + private OcrResultModel parse(float[] raw, int begin, int pointNum, int wordNum) { + int current = begin; + OcrResultModel res = new OcrResultModel(); + res.setConfidence(raw[current]); + current++; + for (int i = 0; i < pointNum; i++) { + res.addPoints(Math.round(raw[current + i * 2]), Math.round(raw[current + i * 2 + 1])); + } + current += (pointNum * 2); + for (int i = 0; i < wordNum; i++) { + int index = Math.round(raw[current + i]); + res.addWordIndex(index); + } + current += wordNum; + res.setClsIdx(raw[current]); + res.setClsConfidence(raw[current + 1]); + Log.i("OCRPredictorNative", "word finished " + wordNum); + return res; + } + + +} diff --git a/toolbox/PaddleOCR/deploy/cpp_infer/CMakeLists.txt b/toolbox/PaddleOCR/deploy/cpp_infer/CMakeLists.txt new file mode 100644 index 0000000000000000000000000000000000000000..8270734bf53d9bf5c24799099c1d639653ba7929 --- /dev/null +++ b/toolbox/PaddleOCR/deploy/cpp_infer/CMakeLists.txt @@ -0,0 +1,230 @@ +# Copyright (c) 2024, Shanghai Iluvatar CoreX Semiconductor Co., Ltd. +# All Rights Reserved. + +project(ppocr CXX C) +cmake_minimum_required(VERSION 3.14) + +option(WITH_MKL "Compile demo with MKL/OpenBlas support, default use MKL." ON) +option(WITH_GPU "Compile demo with GPU/CPU, default use CPU." OFF) +option(WITH_STATIC_LIB "Compile demo with static/shared library, default use static." ON) +option(WITH_TENSORRT "Compile demo with TensorRT." OFF) + +SET(PADDLE_LIB "" CACHE PATH "Location of libraries") +SET(OPENCV_DIR "" CACHE PATH "Location of libraries") +SET(CUDA_LIB "" CACHE PATH "Location of libraries") +SET(CUDNN_LIB "" CACHE PATH "Location of libraries") +SET(TENSORRT_DIR "" CACHE PATH "Compile demo with TensorRT") + +set(DEMO_NAME "ppocr") + + +macro(safe_set_static_flag) + foreach(flag_var + CMAKE_CXX_FLAGS CMAKE_CXX_FLAGS_DEBUG CMAKE_CXX_FLAGS_RELEASE + CMAKE_CXX_FLAGS_MINSIZEREL CMAKE_CXX_FLAGS_RELWITHDEBINFO) + if(${flag_var} MATCHES "/MD") + string(REGEX REPLACE "/MD" "/MT" ${flag_var} "${${flag_var}}") + endif(${flag_var} MATCHES "/MD") + endforeach(flag_var) +endmacro() + +if (WITH_MKL) + ADD_DEFINITIONS(-DUSE_MKL) +endif() + +if(NOT DEFINED PADDLE_LIB) + message(FATAL_ERROR "please set PADDLE_LIB with -DPADDLE_LIB=/path/paddle/lib") +endif() + +if(NOT DEFINED OPENCV_DIR) + message(FATAL_ERROR "please set OPENCV_DIR with -DOPENCV_DIR=/path/opencv") +endif() + + +if (WIN32) + include_directories("${PADDLE_LIB}/paddle/include") + link_directories("${PADDLE_LIB}/paddle/lib") + find_package(OpenCV REQUIRED PATHS ${OPENCV_DIR}/build/ NO_DEFAULT_PATH) + +else () + find_package(OpenCV REQUIRED PATHS ${OPENCV_DIR}/share/OpenCV NO_DEFAULT_PATH) + include_directories("${PADDLE_LIB}/paddle/include") + link_directories("${PADDLE_LIB}/paddle/lib") +endif () +include_directories(${OpenCV_INCLUDE_DIRS}) + +if (WIN32) + add_definitions("/DGOOGLE_GLOG_DLL_DECL=") + if(WITH_MKL) + set(FLAG_OPENMP "/openmp") + endif() + set(CMAKE_C_FLAGS_DEBUG "${CMAKE_C_FLAGS_DEBUG} /bigobj /MTd ${FLAG_OPENMP}") + set(CMAKE_C_FLAGS_RELEASE "${CMAKE_C_FLAGS_RELEASE} /bigobj /MT ${FLAG_OPENMP}") + set(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} /bigobj /MTd ${FLAG_OPENMP}") + set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} /bigobj /MT ${FLAG_OPENMP}") + if (WITH_STATIC_LIB) + safe_set_static_flag() + add_definitions(-DSTATIC_LIB) + endif() + message("cmake c debug flags " ${CMAKE_C_FLAGS_DEBUG}) + message("cmake c release flags " ${CMAKE_C_FLAGS_RELEASE}) + message("cmake cxx debug flags " ${CMAKE_CXX_FLAGS_DEBUG}) + message("cmake cxx release flags " ${CMAKE_CXX_FLAGS_RELEASE}) +else() + if(WITH_MKL) + set(FLAG_OPENMP "-fopenmp") + endif() + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -g -o3 ${FLAG_OPENMP} -std=c++11") + set(CMAKE_STATIC_LIBRARY_PREFIX "") + message("cmake cxx flags" ${CMAKE_CXX_FLAGS}) +endif() + +if (WITH_GPU) + if (NOT DEFINED CUDA_LIB OR ${CUDA_LIB} STREQUAL "") + message(FATAL_ERROR "please set CUDA_LIB with -DCUDA_LIB=/path/cuda-8.0/lib64") + endif() + if (NOT WIN32) + if (NOT DEFINED CUDNN_LIB) + message(FATAL_ERROR "please set CUDNN_LIB with -DCUDNN_LIB=/path/cudnn_v7.4/cuda/lib64") + endif() + endif(NOT WIN32) +endif() + +include_directories("${PADDLE_LIB}/third_party/install/protobuf/include") +include_directories("${PADDLE_LIB}/third_party/install/glog/include") +include_directories("${PADDLE_LIB}/third_party/install/gflags/include") +include_directories("${PADDLE_LIB}/third_party/install/xxhash/include") +include_directories("${PADDLE_LIB}/third_party/install/zlib/include") +include_directories("${PADDLE_LIB}/third_party/boost") +include_directories("${PADDLE_LIB}/third_party/eigen3") + +include_directories("${CMAKE_SOURCE_DIR}/") + +if (NOT WIN32) + if (WITH_TENSORRT AND WITH_GPU) + include_directories("${TENSORRT_DIR}/include") + link_directories("${TENSORRT_DIR}/lib") + endif() +endif(NOT WIN32) + +link_directories("${PADDLE_LIB}/third_party/install/zlib/lib") + +link_directories("${PADDLE_LIB}/third_party/install/protobuf/lib") +link_directories("${PADDLE_LIB}/third_party/install/glog/lib") +link_directories("${PADDLE_LIB}/third_party/install/gflags/lib") +link_directories("${PADDLE_LIB}/third_party/install/xxhash/lib") +link_directories("${PADDLE_LIB}/paddle/lib") + + +if(WITH_MKL) + include_directories("${PADDLE_LIB}/third_party/install/mklml/include") + if (WIN32) + set(MATH_LIB ${PADDLE_LIB}/third_party/install/mklml/lib/mklml.lib + ${PADDLE_LIB}/third_party/install/mklml/lib/libiomp5md.lib) + else () + set(MATH_LIB ${PADDLE_LIB}/third_party/install/mklml/lib/libmklml_intel${CMAKE_SHARED_LIBRARY_SUFFIX} + ${PADDLE_LIB}/third_party/install/mklml/lib/libiomp5${CMAKE_SHARED_LIBRARY_SUFFIX}) + execute_process(COMMAND cp -r ${PADDLE_LIB}/third_party/install/mklml/lib/libmklml_intel${CMAKE_SHARED_LIBRARY_SUFFIX} /usr/lib) + endif () + set(MKLDNN_PATH "${PADDLE_LIB}/third_party/install/mkldnn") + if(EXISTS ${MKLDNN_PATH}) + include_directories("${MKLDNN_PATH}/include") + if (WIN32) + set(MKLDNN_LIB ${MKLDNN_PATH}/lib/mkldnn.lib) + else () + set(MKLDNN_LIB ${MKLDNN_PATH}/lib/libmkldnn.so.0) + endif () + endif() +else() + if (WIN32) + set(MATH_LIB ${PADDLE_LIB}/third_party/install/openblas/lib/openblas${CMAKE_STATIC_LIBRARY_SUFFIX}) + else () + set(MATH_LIB ${PADDLE_LIB}/third_party/install/openblas/lib/libopenblas${CMAKE_STATIC_LIBRARY_SUFFIX}) + endif () +endif() + +# Note: libpaddle_inference_api.so/a must put before libpaddle_inference.so/a +if(WITH_STATIC_LIB) + if(WIN32) + set(DEPS + ${PADDLE_LIB}/paddle/lib/paddle_inference${CMAKE_STATIC_LIBRARY_SUFFIX}) + else() + set(DEPS + ${PADDLE_LIB}/paddle/lib/libpaddle_inference${CMAKE_STATIC_LIBRARY_SUFFIX}) + endif() +else() + if(WIN32) + set(DEPS + ${PADDLE_LIB}/paddle/lib/paddle_inference${CMAKE_SHARED_LIBRARY_SUFFIX}) + else() + set(DEPS + ${PADDLE_LIB}/paddle/lib/libpaddle_inference${CMAKE_SHARED_LIBRARY_SUFFIX}) + endif() +endif(WITH_STATIC_LIB) + +if (NOT WIN32) + set(DEPS ${DEPS} + ${MATH_LIB} ${MKLDNN_LIB} + glog gflags protobuf z xxhash + ) + if(EXISTS "${PADDLE_LIB}/third_party/install/snappystream/lib") + set(DEPS ${DEPS} snappystream) + endif() + if (EXISTS "${PADDLE_LIB}/third_party/install/snappy/lib") + set(DEPS ${DEPS} snappy) + endif() +else() + set(DEPS ${DEPS} + ${MATH_LIB} ${MKLDNN_LIB} + glog gflags_static libprotobuf xxhash) + set(DEPS ${DEPS} libcmt shlwapi) + if (EXISTS "${PADDLE_LIB}/third_party/install/snappy/lib") + set(DEPS ${DEPS} snappy) + endif() + if(EXISTS "${PADDLE_LIB}/third_party/install/snappystream/lib") + set(DEPS ${DEPS} snappystream) + endif() +endif(NOT WIN32) + + +if(WITH_GPU) + if(NOT WIN32) + if (WITH_TENSORRT) + set(DEPS ${DEPS} ${TENSORRT_DIR}/lib/libnvinfer${CMAKE_SHARED_LIBRARY_SUFFIX}) + set(DEPS ${DEPS} ${TENSORRT_DIR}/lib/libnvinfer_plugin${CMAKE_SHARED_LIBRARY_SUFFIX}) + endif() + set(DEPS ${DEPS} ${CUDA_LIB}/libcudart${CMAKE_SHARED_LIBRARY_SUFFIX}) + set(DEPS ${DEPS} ${CUDNN_LIB}/libcudnn${CMAKE_SHARED_LIBRARY_SUFFIX}) + else() + set(DEPS ${DEPS} ${CUDA_LIB}/cudart${CMAKE_STATIC_LIBRARY_SUFFIX} ) + set(DEPS ${DEPS} ${CUDA_LIB}/cublas${CMAKE_STATIC_LIBRARY_SUFFIX} ) + set(DEPS ${DEPS} ${CUDNN_LIB}/cudnn${CMAKE_STATIC_LIBRARY_SUFFIX}) + endif() +endif() + + +if (NOT WIN32) + set(EXTERNAL_LIB "-ldl -lrt -lgomp -lz -lm -lpthread") + set(DEPS ${DEPS} ${EXTERNAL_LIB}) +endif() + +set(DEPS ${DEPS} ${OpenCV_LIBS}) + +include(FetchContent) +include(external-cmake/auto-log.cmake) +include_directories(${FETCHCONTENT_BASE_DIR}/extern_autolog-src) + +AUX_SOURCE_DIRECTORY(./src SRCS) +add_executable(${DEMO_NAME} ${SRCS}) +target_link_libraries(${DEMO_NAME} ${DEPS}) + +if (WIN32 AND WITH_MKL) + add_custom_command(TARGET ${DEMO_NAME} POST_BUILD + COMMAND ${CMAKE_COMMAND} -E copy_if_different ${PADDLE_LIB}/third_party/install/mklml/lib/mklml.dll ./mklml.dll + COMMAND ${CMAKE_COMMAND} -E copy_if_different ${PADDLE_LIB}/third_party/install/mklml/lib/libiomp5md.dll ./libiomp5md.dll + COMMAND ${CMAKE_COMMAND} -E copy_if_different ${PADDLE_LIB}/third_party/install/mkldnn/lib/mkldnn.dll ./mkldnn.dll + COMMAND ${CMAKE_COMMAND} -E copy_if_different ${PADDLE_LIB}/third_party/install/mklml/lib/mklml.dll ./release/mklml.dll + COMMAND ${CMAKE_COMMAND} -E copy_if_different ${PADDLE_LIB}/third_party/install/mklml/lib/libiomp5md.dll ./release/libiomp5md.dll + COMMAND ${CMAKE_COMMAND} -E copy_if_different ${PADDLE_LIB}/third_party/install/mkldnn/lib/mkldnn.dll ./release/mkldnn.dll + ) +endif() diff --git a/toolbox/PaddleOCR/deploy/cpp_infer/src/args.cpp b/toolbox/PaddleOCR/deploy/cpp_infer/src/args.cpp new file mode 100644 index 0000000000000000000000000000000000000000..f108d0c6b5b67fab49a9f01227fbb750cf239f8c --- /dev/null +++ b/toolbox/PaddleOCR/deploy/cpp_infer/src/args.cpp @@ -0,0 +1,58 @@ +// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. +// Copyright (c) 2024, Shanghai Iluvatar CoreX Semiconductor Co., Ltd. +// All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include + +// common args +DEFINE_bool(use_gpu, false, "Infering with GPU or CPU."); +DEFINE_bool(use_tensorrt, false, "Whether use tensorrt."); +DEFINE_int32(gpu_id, 0, "Device id of GPU to execute."); +DEFINE_int32(gpu_mem, 4000, "GPU id when infering with GPU."); +DEFINE_int32(cpu_threads, 10, "Num of threads with CPU."); +DEFINE_bool(enable_mkldnn, false, "Whether use mkldnn with CPU."); +DEFINE_string(precision, "fp32", "Precision be one of fp32/fp16/int8"); +DEFINE_bool(benchmark, false, "Whether use benchmark."); +DEFINE_string(output, "./output/", "Save benchmark log path."); +DEFINE_string(image_dir, "", "Dir of input image."); +DEFINE_string( + type, "ocr", + "Perform ocr or structure, the value is selected in ['ocr','structure']."); +// detection related +DEFINE_string(det_model_dir, "", "Path of det inference model."); +DEFINE_int32(max_side_len, 960, "max_side_len of input image."); +DEFINE_double(det_db_thresh, 0.3, "Threshold of det_db_thresh."); +DEFINE_double(det_db_box_thresh, 0.6, "Threshold of det_db_box_thresh."); +DEFINE_double(det_db_unclip_ratio, 1.5, "Threshold of det_db_unclip_ratio."); +DEFINE_bool(use_dilation, false, "Whether use the dilation on output map."); +DEFINE_string(det_db_score_mode, "slow", "Whether use polygon score."); +DEFINE_bool(visualize, true, "Whether show the detection results."); +// classification related +DEFINE_bool(use_angle_cls, false, "Whether use use_angle_cls."); +DEFINE_string(cls_model_dir, "", "Path of cls inference model."); +DEFINE_double(cls_thresh, 0.9, "Threshold of cls_thresh."); +DEFINE_int32(cls_batch_num, 1, "cls_batch_num."); +// recognition related +DEFINE_string(rec_model_dir, "", "Path of rec inference model."); +DEFINE_int32(rec_batch_num, 6, "rec_batch_num."); +DEFINE_string(rec_char_dict_path, "../../ppocr/utils/ppocr_keys_v1.txt", + "Path of dictionary."); +DEFINE_int32(rec_img_h, 32, "rec image height"); +DEFINE_int32(rec_img_w, 320, "rec image width"); + +// ocr forward related +DEFINE_bool(det, true, "Whether use det in forward."); +DEFINE_bool(rec, true, "Whether use rec in forward."); +DEFINE_bool(cls, false, "Whether use cls in forward."); diff --git a/toolbox/PaddleOCR/deploy/cpp_infer/src/ocr_rec.cpp b/toolbox/PaddleOCR/deploy/cpp_infer/src/ocr_rec.cpp new file mode 100644 index 0000000000000000000000000000000000000000..913c4deeda44dd56e7eccad26d3883d21b18dd8b --- /dev/null +++ b/toolbox/PaddleOCR/deploy/cpp_infer/src/ocr_rec.cpp @@ -0,0 +1,187 @@ +// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. +// Copyright (c) 2024, Shanghai Iluvatar CoreX Semiconductor Co., Ltd. +// All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include + +namespace PaddleOCR { + +void CRNNRecognizer::Run(std::vector img_list, + std::vector &rec_texts, + std::vector &rec_text_scores, + std::vector ×) { + std::chrono::duration preprocess_diff = + std::chrono::steady_clock::now() - std::chrono::steady_clock::now(); + std::chrono::duration inference_diff = + std::chrono::steady_clock::now() - std::chrono::steady_clock::now(); + std::chrono::duration postprocess_diff = + std::chrono::steady_clock::now() - std::chrono::steady_clock::now(); + + int img_num = img_list.size(); + std::vector width_list; + for (int i = 0; i < img_num; i++) { + width_list.push_back(float(img_list[i].cols) / img_list[i].rows); + } + std::vector indices = Utility::argsort(width_list); + + for (int beg_img_no = 0; beg_img_no < img_num; + beg_img_no += this->rec_batch_num_) { + auto preprocess_start = std::chrono::steady_clock::now(); + int end_img_no = min(img_num, beg_img_no + this->rec_batch_num_); + int batch_num = end_img_no - beg_img_no; + int imgH = this->rec_image_shape_[1]; + int imgW = this->rec_image_shape_[2]; + float max_wh_ratio = imgW * 1.0 / imgH; + for (int ino = beg_img_no; ino < end_img_no; ino++) { + int h = img_list[indices[ino]].rows; + int w = img_list[indices[ino]].cols; + float wh_ratio = w * 1.0 / h; + max_wh_ratio = max(max_wh_ratio, wh_ratio); + } + + int batch_width = imgW; + std::vector norm_img_batch; + for (int ino = beg_img_no; ino < end_img_no; ino++) { + cv::Mat srcimg; + img_list[indices[ino]].copyTo(srcimg); + cv::Mat resize_img; + this->resize_op_.Run(srcimg, resize_img, max_wh_ratio, + this->use_tensorrt_, this->rec_image_shape_); + this->normalize_op_.Run(&resize_img, this->mean_, this->scale_, + this->is_scale_); + norm_img_batch.push_back(resize_img); + batch_width = max(resize_img.cols, batch_width); + } + + std::vector input(batch_num * 3 * imgH * batch_width, 0.0f); + this->permute_op_.Run(norm_img_batch, input.data()); + auto preprocess_end = std::chrono::steady_clock::now(); + preprocess_diff += preprocess_end - preprocess_start; + // Inference. + auto input_names = this->predictor_->GetInputNames(); + auto input_t = this->predictor_->GetInputHandle(input_names[0]); + input_t->Reshape({batch_num, 3, imgH, batch_width}); + auto inference_start = std::chrono::steady_clock::now(); + input_t->CopyFromCpu(input.data()); + this->predictor_->Run(); + + std::vector predict_batch; + auto output_names = this->predictor_->GetOutputNames(); + auto output_t = this->predictor_->GetOutputHandle(output_names[0]); + auto predict_shape = output_t->shape(); + + int out_num = std::accumulate(predict_shape.begin(), predict_shape.end(), 1, + std::multiplies()); + predict_batch.resize(out_num); + + output_t->CopyToCpu(predict_batch.data()); + auto inference_end = std::chrono::steady_clock::now(); + inference_diff += inference_end - inference_start; + // ctc decode + auto postprocess_start = std::chrono::steady_clock::now(); + for (int m = 0; m < predict_shape[0]; m++) { + std::string str_res; + int argmax_idx; + int last_index = 0; + float score = 0.f; + int count = 0; + float max_value = 0.0f; + + for (int n = 0; n < predict_shape[1]; n++) { + argmax_idx = int(Utility::argmax( + &predict_batch[(m * predict_shape[1] + n) * predict_shape[2]], + &predict_batch[(m * predict_shape[1] + n + 1) * predict_shape[2]])); + max_value = float(*std::max_element( + &predict_batch[(m * predict_shape[1] + n) * predict_shape[2]], + &predict_batch[(m * predict_shape[1] + n + 1) * predict_shape[2]])); + + if (argmax_idx > 0 && (!(n > 0 && argmax_idx == last_index))) { + score += max_value; + count += 1; + str_res += label_list_[argmax_idx]; + } + last_index = argmax_idx; + } + score /= count; + if (isnan(score)) { + continue; + } + rec_texts[indices[beg_img_no + m]] = str_res; + rec_text_scores[indices[beg_img_no + m]] = score; + } + auto postprocess_end = std::chrono::steady_clock::now(); + postprocess_diff += postprocess_end - postprocess_start; + } + times.push_back(double(preprocess_diff.count() * 1000)); + times.push_back(double(inference_diff.count() * 1000)); + times.push_back(double(postprocess_diff.count() * 1000)); +} + +void CRNNRecognizer::LoadModel(const std::string &model_dir) { + // AnalysisConfig config; + paddle_infer::Config config; + config.SetModel(model_dir + "/inference.pdmodel", + model_dir + "/inference.pdiparams"); + + if (this->use_gpu_) { + config.EnableUseGpu(this->gpu_mem_, this->gpu_id_); + if (this->use_tensorrt_) { + auto precision = paddle_infer::Config::Precision::kFloat32; + if (this->precision_ == "fp16") { + precision = paddle_infer::Config::Precision::kHalf; + } + if (this->precision_ == "int8") { + precision = paddle_infer::Config::Precision::kInt8; + } + config.EnableTensorRtEngine(1 << 20, 10, 3, precision, false, false); + int imgH = this->rec_image_shape_[1]; + int imgW = this->rec_image_shape_[2]; + std::map> min_input_shape = { + {"x", {1, 3, imgH, 10}}, {"lstm_0.tmp_0", {10, 1, 96}}}; + std::map> max_input_shape = { + {"x", {1, 3, imgH, 2000}}, {"lstm_0.tmp_0", {1000, 1, 96}}}; + std::map> opt_input_shape = { + {"x", {1, 3, imgH, imgW}}, {"lstm_0.tmp_0", {25, 1, 96}}}; + + config.SetTRTDynamicShapeInfo(min_input_shape, max_input_shape, + opt_input_shape); + } + } else { + config.DisableGpu(); + if (this->use_mkldnn_) { + config.EnableMKLDNN(); + // cache 10 different shapes for mkldnn to avoid memory leak + config.SetMkldnnCacheCapacity(10); + } + config.SetCpuMathLibraryNumThreads(this->cpu_math_library_num_threads_); + } + + // get pass_builder object + auto pass_builder = config.pass_builder(); + // delete "matmul_transpose_reshape_fuse_pass" + pass_builder->DeletePass("matmul_transpose_reshape_fuse_pass"); + config.SwitchUseFeedFetchOps(false); + // true for multiple input + config.SwitchSpecifyInputNames(true); + + config.SwitchIrOptim(true); + + config.EnableMemoryOptim(); + // config.DisableGlogInfo(); + + this->predictor_ = CreatePredictor(config); +} + +} // namespace PaddleOCR diff --git a/toolbox/PaddleOCR/deploy/hubserving/ocr_det/params.py b/toolbox/PaddleOCR/deploy/hubserving/ocr_det/params.py new file mode 100644 index 0000000000000000000000000000000000000000..190f59d8197fbb4ca6c6cae66829e89b99fb1328 --- /dev/null +++ b/toolbox/PaddleOCR/deploy/hubserving/ocr_det/params.py @@ -0,0 +1,50 @@ +# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. +# Copyright (c) 2024, Shanghai Iluvatar CoreX Semiconductor Co., Ltd. +# All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + + +class Config(object): + pass + + +def read_params(): + cfg = Config() + + #params for text detector + cfg.det_algorithm = "DB" + cfg.det_model_dir = "./inference/ch_PP-OCRv3_det_infer/" + cfg.det_limit_side_len = 960 + cfg.det_limit_type = 'max' + + #DB parmas + cfg.det_db_thresh = 0.3 + cfg.det_db_box_thresh = 0.5 + cfg.det_db_unclip_ratio = 1.6 + cfg.use_dilation = False + cfg.det_db_score_mode = "fast" + + # #EAST parmas + # cfg.det_east_score_thresh = 0.8 + # cfg.det_east_cover_thresh = 0.1 + # cfg.det_east_nms_thresh = 0.2 + + cfg.use_pdserving = False + cfg.use_tensorrt = False + + return cfg diff --git a/toolbox/PaddleOCR/deploy/lite/config.txt b/toolbox/PaddleOCR/deploy/lite/config.txt new file mode 100644 index 0000000000000000000000000000000000000000..738051384db7722d767ecb757e94e8fdb78a401d --- /dev/null +++ b/toolbox/PaddleOCR/deploy/lite/config.txt @@ -0,0 +1,7 @@ +max_side_len 960 +det_db_thresh 0.3 +det_db_box_thresh 0.5 +det_db_unclip_ratio 1.6 +det_db_use_dilate 0 +det_use_polygon_score 1 +use_direction_classify 1 diff --git a/toolbox/PaddleOCR/deploy/lite/crnn_process.cc b/toolbox/PaddleOCR/deploy/lite/crnn_process.cc new file mode 100644 index 0000000000000000000000000000000000000000..c04d91024bf3d6cf486380257923169049f46a00 --- /dev/null +++ b/toolbox/PaddleOCR/deploy/lite/crnn_process.cc @@ -0,0 +1,117 @@ +// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. +// Copyright (c) 2024, Shanghai Iluvatar CoreX Semiconductor Co., Ltd. +// All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "crnn_process.h" //NOLINT +#include +#include +#include + +const std::vector rec_image_shape{3, 32, 320}; + +cv::Mat CrnnResizeImg(cv::Mat img, float wh_ratio) { + int imgC, imgH, imgW; + imgC = rec_image_shape[0]; + imgW = rec_image_shape[2]; + imgH = rec_image_shape[1]; + + imgW = int(32 * wh_ratio); + + float ratio = static_cast(img.cols) / static_cast(img.rows); + int resize_w, resize_h; + if (ceilf(imgH * ratio) > imgW) + resize_w = imgW; + else + resize_w = static_cast(ceilf(imgH * ratio)); + cv::Mat resize_img; + cv::resize(img, resize_img, cv::Size(resize_w, imgH), 0.f, 0.f, + cv::INTER_LINEAR); + + return resize_img; +} + +std::vector ReadDict(std::string path) { + std::ifstream in(path); + std::string filename; + std::string line; + std::vector m_vec; + if (in) { + while (getline(in, line)) { + m_vec.push_back(line); + } + } else { + std::cout << "no such file" << std::endl; + } + return m_vec; +} + +cv::Mat GetRotateCropImage(cv::Mat srcimage, + std::vector> box) { + cv::Mat image; + srcimage.copyTo(image); + std::vector> points = box; + + int x_collect[4] = {box[0][0], box[1][0], box[2][0], box[3][0]}; + int y_collect[4] = {box[0][1], box[1][1], box[2][1], box[3][1]}; + int left = int(*std::min_element(x_collect, x_collect + 4)); + int right = int(*std::max_element(x_collect, x_collect + 4)); + int top = int(*std::min_element(y_collect, y_collect + 4)); + int bottom = int(*std::max_element(y_collect, y_collect + 4)); + + cv::Mat img_crop; + image(cv::Rect(left, top, right - left, bottom - top)).copyTo(img_crop); + + for (int i = 0; i < points.size(); i++) { + points[i][0] -= left; + points[i][1] -= top; + } + + int img_crop_width = + static_cast(sqrt(pow(points[0][0] - points[1][0], 2) + + pow(points[0][1] - points[1][1], 2))); + int img_crop_height = + static_cast(sqrt(pow(points[0][0] - points[3][0], 2) + + pow(points[0][1] - points[3][1], 2))); + + cv::Point2f pts_std[4]; + pts_std[0] = cv::Point2f(0., 0.); + pts_std[1] = cv::Point2f(img_crop_width, 0.); + pts_std[2] = cv::Point2f(img_crop_width, img_crop_height); + pts_std[3] = cv::Point2f(0.f, img_crop_height); + + cv::Point2f pointsf[4]; + pointsf[0] = cv::Point2f(points[0][0], points[0][1]); + pointsf[1] = cv::Point2f(points[1][0], points[1][1]); + pointsf[2] = cv::Point2f(points[2][0], points[2][1]); + pointsf[3] = cv::Point2f(points[3][0], points[3][1]); + + cv::Mat M = cv::getPerspectiveTransform(pointsf, pts_std); + + cv::Mat dst_img; + cv::warpPerspective(img_crop, dst_img, M, + cv::Size(img_crop_width, img_crop_height), + cv::BORDER_REPLICATE); + + const float ratio = 1.5; + if (static_cast(dst_img.rows) >= + static_cast(dst_img.cols) * ratio) { + cv::Mat srcCopy = cv::Mat(dst_img.rows, dst_img.cols, dst_img.depth()); + cv::transpose(dst_img, srcCopy); + cv::flip(srcCopy, srcCopy, 0); + return srcCopy; + } else { + return dst_img; + } +} diff --git a/toolbox/PaddleOCR/deploy/lite/crnn_process.h b/toolbox/PaddleOCR/deploy/lite/crnn_process.h new file mode 100644 index 0000000000000000000000000000000000000000..5d8741d844de3661dbf2de416ddc6221c3e15d0d --- /dev/null +++ b/toolbox/PaddleOCR/deploy/lite/crnn_process.h @@ -0,0 +1,40 @@ +// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. +// Copyright (c) 2024, Shanghai Iluvatar CoreX Semiconductor Co., Ltd. +// All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#pragma once + +#include +#include +#include +#include +#include +#include + +#include "math.h" //NOLINT +#include "opencv2/core.hpp" +#include "opencv2/imgcodecs.hpp" +#include "opencv2/imgproc.hpp" + +cv::Mat CrnnResizeImg(cv::Mat img, float wh_ratio); + +std::vector ReadDict(std::string path); + +cv::Mat GetRotateCropImage(cv::Mat srcimage, std::vector> box); + +template +inline size_t Argmax(ForwardIterator first, ForwardIterator last) { + return std::distance(first, std::max_element(first, last)); +} diff --git a/toolbox/PaddleOCR/deploy/lite/ocr_db_crnn.cc b/toolbox/PaddleOCR/deploy/lite/ocr_db_crnn.cc new file mode 100644 index 0000000000000000000000000000000000000000..c9ed0730b44ec906a02fd885a59e6361a35f1e71 --- /dev/null +++ b/toolbox/PaddleOCR/deploy/lite/ocr_db_crnn.cc @@ -0,0 +1,677 @@ +// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. +// Copyright (c) 2024, Shanghai Iluvatar CoreX Semiconductor Co., Ltd. +// All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include +#include "paddle_api.h" // NOLINT +#include "paddle_place.h" + +#include "cls_process.h" +#include "crnn_process.h" +#include "db_post_process.h" +#include "AutoLog/auto_log/lite_autolog.h" + +using namespace paddle::lite_api; // NOLINT +using namespace std; + +// fill tensor with mean and scale and trans layout: nhwc -> nchw, neon speed up +void NeonMeanScale(const float *din, float *dout, int size, + const std::vector mean, + const std::vector scale) { + if (mean.size() != 3 || scale.size() != 3) { + std::cerr << "[ERROR] mean or scale size must equal to 3" << std::endl; + exit(1); + } + float32x4_t vmean0 = vdupq_n_f32(mean[0]); + float32x4_t vmean1 = vdupq_n_f32(mean[1]); + float32x4_t vmean2 = vdupq_n_f32(mean[2]); + float32x4_t vscale0 = vdupq_n_f32(scale[0]); + float32x4_t vscale1 = vdupq_n_f32(scale[1]); + float32x4_t vscale2 = vdupq_n_f32(scale[2]); + + float *dout_c0 = dout; + float *dout_c1 = dout + size; + float *dout_c2 = dout + size * 2; + + int i = 0; + for (; i < size - 3; i += 4) { + float32x4x3_t vin3 = vld3q_f32(din); + float32x4_t vsub0 = vsubq_f32(vin3.val[0], vmean0); + float32x4_t vsub1 = vsubq_f32(vin3.val[1], vmean1); + float32x4_t vsub2 = vsubq_f32(vin3.val[2], vmean2); + float32x4_t vs0 = vmulq_f32(vsub0, vscale0); + float32x4_t vs1 = vmulq_f32(vsub1, vscale1); + float32x4_t vs2 = vmulq_f32(vsub2, vscale2); + vst1q_f32(dout_c0, vs0); + vst1q_f32(dout_c1, vs1); + vst1q_f32(dout_c2, vs2); + + din += 12; + dout_c0 += 4; + dout_c1 += 4; + dout_c2 += 4; + } + for (; i < size; i++) { + *(dout_c0++) = (*(din++) - mean[0]) * scale[0]; + *(dout_c1++) = (*(din++) - mean[1]) * scale[1]; + *(dout_c2++) = (*(din++) - mean[2]) * scale[2]; + } +} + +// resize image to a size multiple of 32 which is required by the network +cv::Mat DetResizeImg(const cv::Mat img, int max_size_len, + std::vector &ratio_hw) { + int w = img.cols; + int h = img.rows; + + float ratio = 1.f; + int max_wh = w >= h ? w : h; + if (max_wh > max_size_len) { + if (h > w) { + ratio = static_cast(max_size_len) / static_cast(h); + } else { + ratio = static_cast(max_size_len) / static_cast(w); + } + } + + int resize_h = static_cast(float(h) * ratio); + int resize_w = static_cast(float(w) * ratio); + if (resize_h % 32 == 0) + resize_h = resize_h; + else if (resize_h / 32 < 1 + 1e-5) + resize_h = 32; + else + resize_h = (resize_h / 32 - 1) * 32; + + if (resize_w % 32 == 0) + resize_w = resize_w; + else if (resize_w / 32 < 1 + 1e-5) + resize_w = 32; + else + resize_w = (resize_w / 32 - 1) * 32; + + cv::Mat resize_img; + cv::resize(img, resize_img, cv::Size(resize_w, resize_h)); + + ratio_hw.push_back(static_cast(resize_h) / static_cast(h)); + ratio_hw.push_back(static_cast(resize_w) / static_cast(w)); + return resize_img; +} + +cv::Mat RunClsModel(cv::Mat img, std::shared_ptr predictor_cls, + const float thresh = 0.9) { + std::vector mean = {0.5f, 0.5f, 0.5f}; + std::vector scale = {1 / 0.5f, 1 / 0.5f, 1 / 0.5f}; + + cv::Mat srcimg; + img.copyTo(srcimg); + cv::Mat crop_img; + img.copyTo(crop_img); + cv::Mat resize_img; + + int index = 0; + float wh_ratio = + static_cast(crop_img.cols) / static_cast(crop_img.rows); + + resize_img = ClsResizeImg(crop_img); + resize_img.convertTo(resize_img, CV_32FC3, 1 / 255.f); + + const float *dimg = reinterpret_cast(resize_img.data); + + std::unique_ptr input_tensor0(std::move(predictor_cls->GetInput(0))); + input_tensor0->Resize({1, 3, resize_img.rows, resize_img.cols}); + auto *data0 = input_tensor0->mutable_data(); + + NeonMeanScale(dimg, data0, resize_img.rows * resize_img.cols, mean, scale); + // Run CLS predictor + predictor_cls->Run(); + + // Get output and run postprocess + std::unique_ptr softmax_out( + std::move(predictor_cls->GetOutput(0))); + auto *softmax_scores = softmax_out->mutable_data(); + auto softmax_out_shape = softmax_out->shape(); + float score = 0; + int label = 0; + for (int i = 0; i < softmax_out_shape[1]; i++) { + if (softmax_scores[i] > score) { + score = softmax_scores[i]; + label = i; + } + } + if (label % 2 == 1 && score > thresh) { + cv::rotate(srcimg, srcimg, 1); + } + return srcimg; +} + +void RunRecModel(std::vector>> boxes, cv::Mat img, + std::shared_ptr predictor_crnn, + std::vector &rec_text, + std::vector &rec_text_score, + std::vector charactor_dict, + std::shared_ptr predictor_cls, + int use_direction_classify, + std::vector *times) { + std::vector mean = {0.5f, 0.5f, 0.5f}; + std::vector scale = {1 / 0.5f, 1 / 0.5f, 1 / 0.5f}; + + cv::Mat srcimg; + img.copyTo(srcimg); + cv::Mat crop_img; + cv::Mat resize_img; + + int index = 0; + + std::vector time_info = {0, 0, 0}; + for (int i = boxes.size() - 1; i >= 0; i--) { + auto preprocess_start = std::chrono::steady_clock::now(); + crop_img = GetRotateCropImage(srcimg, boxes[i]); + if (use_direction_classify >= 1) { + crop_img = RunClsModel(crop_img, predictor_cls); + } + float wh_ratio = + static_cast(crop_img.cols) / static_cast(crop_img.rows); + + resize_img = CrnnResizeImg(crop_img, wh_ratio); + resize_img.convertTo(resize_img, CV_32FC3, 1 / 255.f); + + const float *dimg = reinterpret_cast(resize_img.data); + + std::unique_ptr input_tensor0( + std::move(predictor_crnn->GetInput(0))); + input_tensor0->Resize({1, 3, resize_img.rows, resize_img.cols}); + auto *data0 = input_tensor0->mutable_data(); + + NeonMeanScale(dimg, data0, resize_img.rows * resize_img.cols, mean, scale); + auto preprocess_end = std::chrono::steady_clock::now(); + //// Run CRNN predictor + auto inference_start = std::chrono::steady_clock::now(); + predictor_crnn->Run(); + + // Get output and run postprocess + std::unique_ptr output_tensor0( + std::move(predictor_crnn->GetOutput(0))); + auto *predict_batch = output_tensor0->data(); + auto predict_shape = output_tensor0->shape(); + auto inference_end = std::chrono::steady_clock::now(); + + // ctc decode + auto postprocess_start = std::chrono::steady_clock::now(); + std::string str_res; + int argmax_idx; + int last_index = 0; + float score = 0.f; + int count = 0; + float max_value = 0.0f; + + for (int n = 0; n < predict_shape[1]; n++) { + argmax_idx = int(Argmax(&predict_batch[n * predict_shape[2]], + &predict_batch[(n + 1) * predict_shape[2]])); + max_value = + float(*std::max_element(&predict_batch[n * predict_shape[2]], + &predict_batch[(n + 1) * predict_shape[2]])); + if (argmax_idx > 0 && (!(n > 0 && argmax_idx == last_index))) { + score += max_value; + count += 1; + str_res += charactor_dict[argmax_idx]; + } + last_index = argmax_idx; + } + score /= count; + rec_text.push_back(str_res); + rec_text_score.push_back(score); + auto postprocess_end = std::chrono::steady_clock::now(); + + std::chrono::duration preprocess_diff = preprocess_end - preprocess_start; + time_info[0] += double(preprocess_diff.count() * 1000); + std::chrono::duration inference_diff = inference_end - inference_start; + time_info[1] += double(inference_diff.count() * 1000); + std::chrono::duration postprocess_diff = postprocess_end - postprocess_start; + time_info[2] += double(postprocess_diff.count() * 1000); + + } + +times->push_back(time_info[0]); +times->push_back(time_info[1]); +times->push_back(time_info[2]); +} + +std::vector>> +RunDetModel(std::shared_ptr predictor, cv::Mat img, + std::map Config, std::vector *times) { + // Read img + int max_side_len = int(Config["max_side_len"]); + int det_db_use_dilate = int(Config["det_db_use_dilate"]); + + cv::Mat srcimg; + img.copyTo(srcimg); + + auto preprocess_start = std::chrono::steady_clock::now(); + std::vector ratio_hw; + img = DetResizeImg(img, max_side_len, ratio_hw); + cv::Mat img_fp; + img.convertTo(img_fp, CV_32FC3, 1.0 / 255.f); + + // Prepare input data from image + std::unique_ptr input_tensor0(std::move(predictor->GetInput(0))); + input_tensor0->Resize({1, 3, img_fp.rows, img_fp.cols}); + auto *data0 = input_tensor0->mutable_data(); + + std::vector mean = {0.485f, 0.456f, 0.406f}; + std::vector scale = {1 / 0.229f, 1 / 0.224f, 1 / 0.225f}; + const float *dimg = reinterpret_cast(img_fp.data); + NeonMeanScale(dimg, data0, img_fp.rows * img_fp.cols, mean, scale); + auto preprocess_end = std::chrono::steady_clock::now(); + + // Run predictor + auto inference_start = std::chrono::steady_clock::now(); + predictor->Run(); + + // Get output and post process + std::unique_ptr output_tensor( + std::move(predictor->GetOutput(0))); + auto *outptr = output_tensor->data(); + auto shape_out = output_tensor->shape(); + auto inference_end = std::chrono::steady_clock::now(); + + // Save output + auto postprocess_start = std::chrono::steady_clock::now(); + float pred[shape_out[2] * shape_out[3]]; + unsigned char cbuf[shape_out[2] * shape_out[3]]; + + for (int i = 0; i < int(shape_out[2] * shape_out[3]); i++) { + pred[i] = static_cast(outptr[i]); + cbuf[i] = static_cast((outptr[i]) * 255); + } + + cv::Mat cbuf_map(shape_out[2], shape_out[3], CV_8UC1, + reinterpret_cast(cbuf)); + cv::Mat pred_map(shape_out[2], shape_out[3], CV_32F, + reinterpret_cast(pred)); + + const double threshold = double(Config["det_db_thresh"]) * 255; + const double max_value = 255; + cv::Mat bit_map; + cv::threshold(cbuf_map, bit_map, threshold, max_value, cv::THRESH_BINARY); + if (det_db_use_dilate == 1) { + cv::Mat dilation_map; + cv::Mat dila_ele = + cv::getStructuringElement(cv::MORPH_RECT, cv::Size(2, 2)); + cv::dilate(bit_map, dilation_map, dila_ele); + bit_map = dilation_map; + } + auto boxes = BoxesFromBitmap(pred_map, bit_map, Config); + + std::vector>> filter_boxes = + FilterTagDetRes(boxes, ratio_hw[0], ratio_hw[1], srcimg); + auto postprocess_end = std::chrono::steady_clock::now(); + + std::chrono::duration preprocess_diff = preprocess_end - preprocess_start; + times->push_back(double(preprocess_diff.count() * 1000)); + std::chrono::duration inference_diff = inference_end - inference_start; + times->push_back(double(inference_diff.count() * 1000)); + std::chrono::duration postprocess_diff = postprocess_end - postprocess_start; + times->push_back(double(postprocess_diff.count() * 1000)); + + return filter_boxes; +} + +std::shared_ptr loadModel(std::string model_file, int num_threads) { + MobileConfig config; + config.set_model_from_file(model_file); + + config.set_threads(num_threads); + std::shared_ptr predictor = + CreatePaddlePredictor(config); + return predictor; +} + +cv::Mat Visualization(cv::Mat srcimg, + std::vector>> boxes) { + cv::Point rook_points[boxes.size()][4]; + for (int n = 0; n < boxes.size(); n++) { + for (int m = 0; m < boxes[0].size(); m++) { + rook_points[n][m] = cv::Point(static_cast(boxes[n][m][0]), + static_cast(boxes[n][m][1])); + } + } + cv::Mat img_vis; + srcimg.copyTo(img_vis); + for (int n = 0; n < boxes.size(); n++) { + const cv::Point *ppt[1] = {rook_points[n]}; + int npt[] = {4}; + cv::polylines(img_vis, ppt, npt, 1, 1, CV_RGB(0, 255, 0), 2, 8, 0); + } + + cv::imwrite("./vis.jpg", img_vis); + std::cout << "The detection visualized image saved in ./vis.jpg" << std::endl; + return img_vis; +} + +std::vector split(const std::string &str, + const std::string &delim) { + std::vector res; + if ("" == str) + return res; + char *strs = new char[str.length() + 1]; + std::strcpy(strs, str.c_str()); + + char *d = new char[delim.length() + 1]; + std::strcpy(d, delim.c_str()); + + char *p = std::strtok(strs, d); + while (p) { + string s = p; + res.push_back(s); + p = std::strtok(NULL, d); + } + + return res; +} + +std::map LoadConfigTxt(std::string config_path) { + auto config = ReadDict(config_path); + + std::map dict; + for (int i = 0; i < config.size(); i++) { + std::vector res = split(config[i], " "); + dict[res[0]] = stod(res[1]); + } + return dict; +} + +void check_params(int argc, char **argv) { + if (argc<=1 || (strcmp(argv[1], "det")!=0 && strcmp(argv[1], "rec")!=0 && strcmp(argv[1], "system")!=0)) { + std::cerr << "Please choose one mode of [det, rec, system] !" << std::endl; + exit(1); + } + if (strcmp(argv[1], "det") == 0) { + if (argc < 9){ + std::cerr << "[ERROR] usage:" << argv[0] + << " det det_model runtime_device num_threads batchsize img_dir det_config lite_benchmark_value" << std::endl; + exit(1); + } + } + + if (strcmp(argv[1], "rec") == 0) { + if (argc < 9){ + std::cerr << "[ERROR] usage:" << argv[0] + << " rec rec_model runtime_device num_threads batchsize img_dir key_txt lite_benchmark_value" << std::endl; + exit(1); + } + } + + if (strcmp(argv[1], "system") == 0) { + if (argc < 12){ + std::cerr << "[ERROR] usage:" << argv[0] + << " system det_model rec_model clas_model runtime_device num_threads batchsize img_dir det_config key_txt lite_benchmark_value" << std::endl; + exit(1); + } + } +} + +void system(char **argv){ + std::string det_model_file = argv[2]; + std::string rec_model_file = argv[3]; + std::string cls_model_file = argv[4]; + std::string runtime_device = argv[5]; + std::string precision = argv[6]; + std::string num_threads = argv[7]; + std::string batchsize = argv[8]; + std::string img_dir = argv[9]; + std::string det_config_path = argv[10]; + std::string dict_path = argv[11]; + + if (strcmp(argv[6], "FP32") != 0 && strcmp(argv[6], "INT8") != 0) { + std::cerr << "Only support FP32 or INT8." << std::endl; + exit(1); + } + + std::vector cv_all_img_names; + cv::glob(img_dir, cv_all_img_names); + + //// load config from txt file + auto Config = LoadConfigTxt(det_config_path); + int use_direction_classify = int(Config["use_direction_classify"]); + + auto charactor_dict = ReadDict(dict_path); + charactor_dict.insert(charactor_dict.begin(), "#"); // blank char for ctc + charactor_dict.push_back(" "); + + auto det_predictor = loadModel(det_model_file, std::stoi(num_threads)); + auto rec_predictor = loadModel(rec_model_file, std::stoi(num_threads)); + auto cls_predictor = loadModel(cls_model_file, std::stoi(num_threads)); + + std::vector det_time_info = {0, 0, 0}; + std::vector rec_time_info = {0, 0, 0}; + + for (int i = 0; i < cv_all_img_names.size(); ++i) { + std::cout << "The predict img: " << cv_all_img_names[i] << std::endl; + cv::Mat srcimg = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR); + + if (!srcimg.data) { + std::cerr << "[ERROR] image read failed! image path: " << cv_all_img_names[i] << std::endl; + exit(1); + } + + std::vector det_times; + auto boxes = RunDetModel(det_predictor, srcimg, Config, &det_times); + + std::vector rec_text; + std::vector rec_text_score; + + std::vector rec_times; + RunRecModel(boxes, srcimg, rec_predictor, rec_text, rec_text_score, + charactor_dict, cls_predictor, use_direction_classify, &rec_times); + + //// visualization + auto img_vis = Visualization(srcimg, boxes); + + //// print recognized text + for (int i = 0; i < rec_text.size(); i++) { + std::cout << i << "\t" << rec_text[i] << "\t" << rec_text_score[i] + << std::endl; + + } + + det_time_info[0] += det_times[0]; + det_time_info[1] += det_times[1]; + det_time_info[2] += det_times[2]; + rec_time_info[0] += rec_times[0]; + rec_time_info[1] += rec_times[1]; + rec_time_info[2] += rec_times[2]; + } + if (strcmp(argv[12], "True") == 0) { + AutoLogger autolog_det(det_model_file, + runtime_device, + std::stoi(num_threads), + std::stoi(batchsize), + "dynamic", + precision, + det_time_info, + cv_all_img_names.size()); + AutoLogger autolog_rec(rec_model_file, + runtime_device, + std::stoi(num_threads), + std::stoi(batchsize), + "dynamic", + precision, + rec_time_info, + cv_all_img_names.size()); + + autolog_det.report(); + std::cout << std::endl; + autolog_rec.report(); + } +} + +void det(int argc, char **argv) { + std::string det_model_file = argv[2]; + std::string runtime_device = argv[3]; + std::string precision = argv[4]; + std::string num_threads = argv[5]; + std::string batchsize = argv[6]; + std::string img_dir = argv[7]; + std::string det_config_path = argv[8]; + + if (strcmp(argv[4], "FP32") != 0 && strcmp(argv[4], "INT8") != 0) { + std::cerr << "Only support FP32 or INT8." << std::endl; + exit(1); + } + + std::vector cv_all_img_names; + cv::glob(img_dir, cv_all_img_names); + + //// load config from txt file + auto Config = LoadConfigTxt(det_config_path); + + auto det_predictor = loadModel(det_model_file, std::stoi(num_threads)); + + std::vector time_info = {0, 0, 0}; + for (int i = 0; i < cv_all_img_names.size(); ++i) { + std::cout << "The predict img: " << cv_all_img_names[i] << std::endl; + cv::Mat srcimg = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR); + + if (!srcimg.data) { + std::cerr << "[ERROR] image read failed! image path: " << cv_all_img_names[i] << std::endl; + exit(1); + } + + std::vector times; + auto boxes = RunDetModel(det_predictor, srcimg, Config, ×); + + //// visualization + auto img_vis = Visualization(srcimg, boxes); + std::cout << boxes.size() << " bboxes have detected:" << std::endl; + + for (int i=0; i cv_all_img_names; + cv::glob(img_dir, cv_all_img_names); + + auto charactor_dict = ReadDict(dict_path); + charactor_dict.insert(charactor_dict.begin(), "#"); // blank char for ctc + charactor_dict.push_back(" "); + + auto rec_predictor = loadModel(rec_model_file, std::stoi(num_threads)); + + std::shared_ptr cls_predictor; + + std::vector time_info = {0, 0, 0}; + for (int i = 0; i < cv_all_img_names.size(); ++i) { + std::cout << "The predict img: " << cv_all_img_names[i] << std::endl; + cv::Mat srcimg = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR); + + if (!srcimg.data) { + std::cerr << "[ERROR] image read failed! image path: " << cv_all_img_names[i] << std::endl; + exit(1); + } + + int width = srcimg.cols; + int height = srcimg.rows; + std::vector upper_left = {0, 0}; + std::vector upper_right = {width, 0}; + std::vector lower_right = {width, height}; + std::vector lower_left = {0, height}; + std::vector> box = {upper_left, upper_right, lower_right, lower_left}; + std::vector>> boxes = {box}; + + std::vector rec_text; + std::vector rec_text_score; + std::vector times; + RunRecModel(boxes, srcimg, rec_predictor, rec_text, rec_text_score, + charactor_dict, cls_predictor, 0, ×); + + //// print recognized text + for (int i = 0; i < rec_text.size(); i++) { + std::cout << i << "\t" << rec_text[i] << "\t" << rec_text_score[i] + << std::endl; + } + time_info[0] += times[0]; + time_info[1] += times[1]; + time_info[2] += times[2]; + } + // TODO: support autolog + if (strcmp(argv[9], "True") == 0) { + AutoLogger autolog(rec_model_file, + runtime_device, + std::stoi(num_threads), + std::stoi(batchsize), + "dynamic", + precision, + time_info, + cv_all_img_names.size()); + autolog.report(); + } +} + +int main(int argc, char **argv) { + check_params(argc, argv); + std::cout << "mode: " << argv[1] << endl; + + if (strcmp(argv[1], "system") == 0) { + system(argv); + } + + if (strcmp(argv[1], "det") == 0) { + det(argc, argv); + } + + if (strcmp(argv[1], "rec") == 0) { + rec(argc, argv); + } + + return 0; +} diff --git a/toolbox/PaddleOCR/deploy/pdserving/ocr_cpp_client.py b/toolbox/PaddleOCR/deploy/pdserving/ocr_cpp_client.py new file mode 100644 index 0000000000000000000000000000000000000000..1320f4d221e9cca3ffad8fe9607e8d21abd39387 --- /dev/null +++ b/toolbox/PaddleOCR/deploy/pdserving/ocr_cpp_client.py @@ -0,0 +1,56 @@ +# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. +# Copyright (c) 2024, Shanghai Iluvatar CoreX Semiconductor Co., Ltd. +# All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# pylint: disable=doc-string-missing + +from paddle_serving_client import Client +import sys +import numpy as np +import base64 +import os +import cv2 +from paddle_serving_app.reader import Sequential, URL2Image, ResizeByFactor +from paddle_serving_app.reader import Div, Normalize, Transpose +from ocr_reader import OCRReader + +client = Client() +# TODO:load_client need to load more than one client model. +# this need to figure out some details. +client.load_client_config(sys.argv[1:]) +client.connect(["127.0.0.1:9293"]) + +import paddle +test_img_dir = "test_img/" + +ocr_reader = OCRReader(char_dict_path="../../ppocr/utils/ppocr_keys_v1.txt") + + +def cv2_to_base64(image): + return base64.b64encode(image).decode( + 'utf8') #data.tostring()).decode('utf8') + + +for img_file in os.listdir(test_img_dir): + with open(os.path.join(test_img_dir, img_file), 'rb') as file: + image_data = file.read() + image = cv2_to_base64(image_data) + res_list = [] + fetch_map = client.predict( + feed={"x": image}, fetch=["save_infer_model/scale_0.tmp_1"], batch=True) + one_batch_res = ocr_reader.postprocess(fetch_map, with_score=True) + for res in one_batch_res: + res_list.append(res[0]) + res = {"res": str(res_list)} + print(res) diff --git a/toolbox/PaddleOCR/deploy/pdserving/ocr_reader.py b/toolbox/PaddleOCR/deploy/pdserving/ocr_reader.py new file mode 100644 index 0000000000000000000000000000000000000000..305e795aee032cd9f1890325ea63f190d6cfeec9 --- /dev/null +++ b/toolbox/PaddleOCR/deploy/pdserving/ocr_reader.py @@ -0,0 +1,461 @@ +# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +# Copyright (c) 2024, Shanghai Iluvatar CoreX Semiconductor Co., Ltd. +# All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import cv2 +import copy +import numpy as np +import math +import re +import sys +import argparse +import string +from copy import deepcopy + + +class DetResizeForTest(object): + def __init__(self, **kwargs): + super(DetResizeForTest, self).__init__() + self.resize_type = 0 + if 'image_shape' in kwargs: + self.image_shape = kwargs['image_shape'] + self.resize_type = 1 + elif 'limit_side_len' in kwargs: + self.limit_side_len = kwargs['limit_side_len'] + self.limit_type = kwargs.get('limit_type', 'min') + elif 'resize_short' in kwargs: + self.limit_side_len = 736 + self.limit_type = 'min' + else: + self.resize_type = 2 + self.resize_long = kwargs.get('resize_long', 960) + + def __call__(self, data): + img = deepcopy(data) + src_h, src_w, _ = img.shape + + if self.resize_type == 0: + img, [ratio_h, ratio_w] = self.resize_image_type0(img) + elif self.resize_type == 2: + img, [ratio_h, ratio_w] = self.resize_image_type2(img) + else: + img, [ratio_h, ratio_w] = self.resize_image_type1(img) + + return img + + def resize_image_type1(self, img): + resize_h, resize_w = self.image_shape + ori_h, ori_w = img.shape[:2] # (h, w, c) + ratio_h = float(resize_h) / ori_h + ratio_w = float(resize_w) / ori_w + img = cv2.resize(img, (int(resize_w), int(resize_h))) + return img, [ratio_h, ratio_w] + + def resize_image_type0(self, img): + """ + resize image to a size multiple of 32 which is required by the network + args: + img(array): array with shape [h, w, c] + return(tuple): + img, (ratio_h, ratio_w) + """ + limit_side_len = self.limit_side_len + h, w, _ = img.shape + + # limit the max side + if self.limit_type == 'max': + if max(h, w) > limit_side_len: + if h > w: + ratio = float(limit_side_len) / h + else: + ratio = float(limit_side_len) / w + else: + ratio = 1. + else: + if min(h, w) < limit_side_len: + if h < w: + ratio = float(limit_side_len) / h + else: + ratio = float(limit_side_len) / w + else: + ratio = 1. + resize_h = int(h * ratio) + resize_w = int(w * ratio) + + resize_h = int(round(resize_h / 32) * 32) + resize_w = int(round(resize_w / 32) * 32) + + try: + if int(resize_w) <= 0 or int(resize_h) <= 0: + return None, (None, None) + img = cv2.resize(img, (int(resize_w), int(resize_h))) + except: + print(img.shape, resize_w, resize_h) + sys.exit(0) + ratio_h = resize_h / float(h) + ratio_w = resize_w / float(w) + # return img, np.array([h, w]) + return img, [ratio_h, ratio_w] + + def resize_image_type2(self, img): + h, w, _ = img.shape + + resize_w = w + resize_h = h + + # Fix the longer side + if resize_h > resize_w: + ratio = float(self.resize_long) / resize_h + else: + ratio = float(self.resize_long) / resize_w + + resize_h = int(resize_h * ratio) + resize_w = int(resize_w * ratio) + + max_stride = 128 + resize_h = (resize_h + max_stride - 1) // max_stride * max_stride + resize_w = (resize_w + max_stride - 1) // max_stride * max_stride + img = cv2.resize(img, (int(resize_w), int(resize_h))) + ratio_h = resize_h / float(h) + ratio_w = resize_w / float(w) + + return img, [ratio_h, ratio_w] + + +class BaseRecLabelDecode(object): + """ Convert between text-label and text-index """ + + def __init__(self, config): + support_character_type = [ + 'ch', 'en', 'EN_symbol', 'french', 'german', 'japan', 'korean', + 'it', 'xi', 'pu', 'ru', 'ar', 'ta', 'ug', 'fa', 'ur', 'rs', 'oc', + 'rsc', 'bg', 'uk', 'be', 'te', 'ka', 'chinese_cht', 'hi', 'mr', + 'ne', 'EN' + ] + character_type = config['character_type'] + character_dict_path = config['character_dict_path'] + use_space_char = True + assert character_type in support_character_type, "Only {} are supported now but get {}".format( + support_character_type, character_type) + + self.beg_str = "sos" + self.end_str = "eos" + + if character_type == "en": + self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz" + dict_character = list(self.character_str) + elif character_type == "EN_symbol": + # same with ASTER setting (use 94 char). + self.character_str = string.printable[:-6] + dict_character = list(self.character_str) + elif character_type in support_character_type: + self.character_str = "" + assert character_dict_path is not None, "character_dict_path should not be None when character_type is {}".format( + character_type) + with open(character_dict_path, "rb") as fin: + lines = fin.readlines() + for line in lines: + line = line.decode('utf-8').strip("\n").strip("\r\n") + self.character_str += line + if use_space_char: + self.character_str += " " + dict_character = list(self.character_str) + + else: + raise NotImplementedError + self.character_type = character_type + dict_character = self.add_special_char(dict_character) + self.dict = {} + for i, char in enumerate(dict_character): + self.dict[char] = i + self.character = dict_character + + def add_special_char(self, dict_character): + return dict_character + + def decode(self, text_index, text_prob=None, is_remove_duplicate=False): + """ convert text-index into text-label. """ + result_list = [] + ignored_tokens = self.get_ignored_tokens() + batch_size = len(text_index) + for batch_idx in range(batch_size): + char_list = [] + conf_list = [] + for idx in range(len(text_index[batch_idx])): + if text_index[batch_idx][idx] in ignored_tokens: + continue + if is_remove_duplicate: + # only for predict + if idx > 0 and text_index[batch_idx][idx - 1] == text_index[ + batch_idx][idx]: + continue + char_list.append(self.character[int(text_index[batch_idx][ + idx])]) + if text_prob is not None: + conf_list.append(text_prob[batch_idx][idx]) + else: + conf_list.append(1) + text = ''.join(char_list) + result_list.append((text, np.mean(conf_list))) + return result_list + + def get_ignored_tokens(self): + return [0] # for ctc blank + + +class CTCLabelDecode(BaseRecLabelDecode): + """ Convert between text-label and text-index """ + + def __init__( + self, + config, + #character_dict_path=None, + #character_type='ch', + #use_space_char=False, + **kwargs): + super(CTCLabelDecode, self).__init__(config) + + def __call__(self, preds, label=None, *args, **kwargs): + preds_idx = preds.argmax(axis=2) + preds_prob = preds.max(axis=2) + text = self.decode(preds_idx, preds_prob, is_remove_duplicate=True) + if label is None: + return text + label = self.decode(label) + return text, label + + def add_special_char(self, dict_character): + dict_character = ['blank'] + dict_character + return dict_character + + +class CharacterOps(object): + """ Convert between text-label and text-index """ + + def __init__(self, config): + self.character_type = config['character_type'] + self.loss_type = config['loss_type'] + if self.character_type == "en": + self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz" + dict_character = list(self.character_str) + elif self.character_type == "ch": + character_dict_path = config['character_dict_path'] + self.character_str = "" + with open(character_dict_path, "rb") as fin: + lines = fin.readlines() + for line in lines: + line = line.decode('utf-8').strip("\n").strip("\r\n") + self.character_str += line + dict_character = list(self.character_str) + elif self.character_type == "en_sensitive": + # same with ASTER setting (use 94 char). + self.character_str = string.printable[:-6] + dict_character = list(self.character_str) + else: + self.character_str = None + assert self.character_str is not None, \ + "Nonsupport type of the character: {}".format(self.character_str) + self.beg_str = "sos" + self.end_str = "eos" + if self.loss_type == "attention": + dict_character = [self.beg_str, self.end_str] + dict_character + self.dict = {} + for i, char in enumerate(dict_character): + self.dict[char] = i + self.character = dict_character + + def encode(self, text): + """convert text-label into text-index. + input: + text: text labels of each image. [batch_size] + + output: + text: concatenated text index for CTCLoss. + [sum(text_lengths)] = [text_index_0 + text_index_1 + ... + text_index_(n - 1)] + length: length of each text. [batch_size] + """ + if self.character_type == "en": + text = text.lower() + + text_list = [] + for char in text: + if char not in self.dict: + continue + text_list.append(self.dict[char]) + text = np.array(text_list) + return text + + def decode(self, text_index, is_remove_duplicate=False): + """ convert text-index into text-label. """ + char_list = [] + char_num = self.get_char_num() + + if self.loss_type == "attention": + beg_idx = self.get_beg_end_flag_idx("beg") + end_idx = self.get_beg_end_flag_idx("end") + ignored_tokens = [beg_idx, end_idx] + else: + ignored_tokens = [char_num] + + for idx in range(len(text_index)): + if text_index[idx] in ignored_tokens: + continue + if is_remove_duplicate: + if idx > 0 and text_index[idx - 1] == text_index[idx]: + continue + char_list.append(self.character[text_index[idx]]) + text = ''.join(char_list) + return text + + def get_char_num(self): + return len(self.character) + + def get_beg_end_flag_idx(self, beg_or_end): + if self.loss_type == "attention": + if beg_or_end == "beg": + idx = np.array(self.dict[self.beg_str]) + elif beg_or_end == "end": + idx = np.array(self.dict[self.end_str]) + else: + assert False, "Unsupport type %s in get_beg_end_flag_idx"\ + % beg_or_end + return idx + else: + err = "error in get_beg_end_flag_idx when using the loss %s"\ + % (self.loss_type) + assert False, err + + +class OCRReader(object): + def __init__(self, + algorithm="CRNN", + image_shape=[3, 32, 320], + char_type="ch", + batch_num=1, + char_dict_path="./ppocr_keys_v1.txt"): + self.rec_image_shape = image_shape + self.character_type = char_type + self.rec_batch_num = batch_num + char_ops_params = {} + char_ops_params["character_type"] = char_type + char_ops_params["character_dict_path"] = char_dict_path + char_ops_params['loss_type'] = 'ctc' + self.char_ops = CharacterOps(char_ops_params) + self.label_ops = CTCLabelDecode(char_ops_params) + + def resize_norm_img(self, img, max_wh_ratio): + imgC, imgH, imgW = self.rec_image_shape + if self.character_type == "ch": + imgW = int(32 * max_wh_ratio) + h = img.shape[0] + w = img.shape[1] + ratio = w / float(h) + if math.ceil(imgH * ratio) > imgW: + resized_w = imgW + else: + resized_w = int(math.ceil(imgH * ratio)) + resized_image = cv2.resize(img, (resized_w, imgH)) + resized_image = resized_image.astype('float32') + resized_image = resized_image.transpose((2, 0, 1)) / 255 + resized_image -= 0.5 + resized_image /= 0.5 + padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32) + + padding_im[:, :, 0:resized_w] = resized_image + return padding_im + + def preprocess(self, img_list): + img_num = len(img_list) + norm_img_batch = [] + max_wh_ratio = 0 + for ino in range(img_num): + h, w = img_list[ino].shape[0:2] + wh_ratio = w * 1.0 / h + max_wh_ratio = max(max_wh_ratio, wh_ratio) + + for ino in range(img_num): + norm_img = self.resize_norm_img(img_list[ino], max_wh_ratio) + norm_img = norm_img[np.newaxis, :] + norm_img_batch.append(norm_img) + norm_img_batch = np.concatenate(norm_img_batch) + norm_img_batch = norm_img_batch.copy() + + return norm_img_batch[0] + + def postprocess(self, outputs, with_score=False): + preds = list(outputs.values())[0] + try: + preds = preds.numpy() + except: + pass + preds_idx = preds.argmax(axis=2) + preds_prob = preds.max(axis=2) + text = self.label_ops.decode( + preds_idx, preds_prob, is_remove_duplicate=True) + return text + + +from argparse import ArgumentParser, RawDescriptionHelpFormatter +import yaml + + +class ArgsParser(ArgumentParser): + def __init__(self): + super(ArgsParser, self).__init__( + formatter_class=RawDescriptionHelpFormatter) + self.add_argument("-c", "--config", help="configuration file to use") + self.add_argument( + "-o", "--opt", nargs='+', help="set configuration options") + + def parse_args(self, argv=None): + args = super(ArgsParser, self).parse_args(argv) + assert args.config is not None, \ + "Please specify --config=configure_file_path." + args.conf_dict = self._parse_opt(args.opt, args.config) + print("args config:", args.conf_dict) + return args + + def _parse_helper(self, v): + if v.isnumeric(): + if "." in v: + v = float(v) + else: + v = int(v) + elif v == "True" or v == "False": + v = (v == "True") + return v + + def _parse_opt(self, opts, conf_path): + f = open(conf_path) + config = yaml.load(f, Loader=yaml.Loader) + if not opts: + return config + for s in opts: + s = s.strip() + k, v = s.split('=') + v = self._parse_helper(v) + print(k, v, type(v)) + cur = config + parent = cur + for kk in k.split("."): + if kk not in cur: + cur[kk] = {} + parent = cur + cur = cur[kk] + else: + parent = cur + cur = cur[kk] + parent[k.split(".")[-1]] = v + return config diff --git a/toolbox/PaddleOCR/deploy/pdserving/web_service.py b/toolbox/PaddleOCR/deploy/pdserving/web_service.py new file mode 100644 index 0000000000000000000000000000000000000000..382d6f85ea8b90f039b52639d71b7c22549b8db9 --- /dev/null +++ b/toolbox/PaddleOCR/deploy/pdserving/web_service.py @@ -0,0 +1,170 @@ +# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +# Copyright (c) 2024, Shanghai Iluvatar CoreX Semiconductor Co., Ltd. +# All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from paddle_serving_server.web_service import WebService, Op + +import logging +import numpy as np +import copy +import cv2 +import base64 +# from paddle_serving_app.reader import OCRReader +from ocr_reader import OCRReader, DetResizeForTest +from paddle_serving_app.reader import Sequential, ResizeByFactor +from paddle_serving_app.reader import Div, Normalize, Transpose +from paddle_serving_app.reader import DBPostProcess, FilterBoxes, GetRotateCropImage, SortedBoxes + +_LOGGER = logging.getLogger() + + +class DetOp(Op): + def init_op(self): + self.det_preprocess = Sequential([ + DetResizeForTest(), Div(255), + Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), Transpose( + (2, 0, 1)) + ]) + self.filter_func = FilterBoxes(10, 10) + self.post_func = DBPostProcess({ + "thresh": 0.3, + "box_thresh": 0.6, + "max_candidates": 1000, + "unclip_ratio": 1.5, + "min_size": 3 + }) + + def preprocess(self, input_dicts, data_id, log_id): + (_, input_dict), = input_dicts.items() + data = base64.b64decode(input_dict["image"].encode('utf8')) + self.raw_im = data + data = np.fromstring(data, np.uint8) + # Note: class variables(self.var) can only be used in process op mode + im = cv2.imdecode(data, cv2.IMREAD_COLOR) + self.ori_h, self.ori_w, _ = im.shape + det_img = self.det_preprocess(im) + _, self.new_h, self.new_w = det_img.shape + return {"x": det_img[np.newaxis, :].copy()}, False, None, "" + + def postprocess(self, input_dicts, fetch_dict, data_id, log_id): + det_out = list(fetch_dict.values())[0] + ratio_list = [ + float(self.new_h) / self.ori_h, float(self.new_w) / self.ori_w + ] + dt_boxes_list = self.post_func(det_out, [ratio_list]) + dt_boxes = self.filter_func(dt_boxes_list[0], [self.ori_h, self.ori_w]) + out_dict = {"dt_boxes": dt_boxes, "image": self.raw_im} + + return out_dict, None, "" + + +class RecOp(Op): + def init_op(self): + self.ocr_reader = OCRReader( + char_dict_path="../../ppocr/utils/ppocr_keys_v1.txt") + + self.get_rotate_crop_image = GetRotateCropImage() + self.sorted_boxes = SortedBoxes() + + def preprocess(self, input_dicts, data_id, log_id): + (_, input_dict), = input_dicts.items() + raw_im = input_dict["image"] + data = np.frombuffer(raw_im, np.uint8) + im = cv2.imdecode(data, cv2.IMREAD_COLOR) + self.dt_list = input_dict["dt_boxes"] + self.dt_list = self.sorted_boxes(self.dt_list) + # deepcopy to save origin dt_boxes + dt_boxes = copy.deepcopy(self.dt_list) + feed_list = [] + img_list = [] + max_wh_ratio = 0 + ## Many mini-batchs, the type of feed_data is list. + max_batch_size = 6 # len(dt_boxes) + + # If max_batch_size is 0, skipping predict stage + if max_batch_size == 0: + return {}, True, None, "" + boxes_size = len(dt_boxes) + batch_size = boxes_size // max_batch_size + rem = boxes_size % max_batch_size + for bt_idx in range(0, batch_size + 1): + imgs = None + boxes_num_in_one_batch = 0 + if bt_idx == batch_size: + if rem == 0: + continue + else: + boxes_num_in_one_batch = rem + elif bt_idx < batch_size: + boxes_num_in_one_batch = max_batch_size + else: + _LOGGER.error("batch_size error, bt_idx={}, batch_size={}". + format(bt_idx, batch_size)) + break + + start = bt_idx * max_batch_size + end = start + boxes_num_in_one_batch + img_list = [] + for box_idx in range(start, end): + boximg = self.get_rotate_crop_image(im, dt_boxes[box_idx]) + img_list.append(boximg) + h, w = boximg.shape[0:2] + wh_ratio = w * 1.0 / h + max_wh_ratio = max(max_wh_ratio, wh_ratio) + _, w, h = self.ocr_reader.resize_norm_img(img_list[0], + max_wh_ratio).shape + + imgs = np.zeros((boxes_num_in_one_batch, 3, w, h)).astype('float32') + for id, img in enumerate(img_list): + norm_img = self.ocr_reader.resize_norm_img(img, max_wh_ratio) + imgs[id] = norm_img + feed = {"x": imgs.copy()} + feed_list.append(feed) + return feed_list, False, None, "" + + def postprocess(self, input_dicts, fetch_data, data_id, log_id): + rec_list = [] + dt_num = len(self.dt_list) + if isinstance(fetch_data, dict): + if len(fetch_data) > 0: + rec_batch_res = self.ocr_reader.postprocess( + fetch_data, with_score=True) + for res in rec_batch_res: + rec_list.append(res) + elif isinstance(fetch_data, list): + for one_batch in fetch_data: + one_batch_res = self.ocr_reader.postprocess( + one_batch, with_score=True) + for res in one_batch_res: + rec_list.append(res) + result_list = [] + for i in range(dt_num): + text = rec_list[i] + dt_box = self.dt_list[i] + result_list.append([text, dt_box.tolist()]) + res = {"result": str(result_list)} + return res, None, "" + + +class OcrService(WebService): + def get_pipeline_response(self, read_op): + det_op = DetOp(name="det", input_ops=[read_op]) + rec_op = RecOp(name="rec", input_ops=[det_op]) + return rec_op + + +uci_service = OcrService(name="ocr") +uci_service.prepare_pipeline_config("config.yml") +uci_service.run_service() diff --git a/toolbox/PaddleOCR/paddleocr.py b/toolbox/PaddleOCR/paddleocr.py new file mode 100644 index 0000000000000000000000000000000000000000..fa18124d9bfc0a22adf4e8ece8d6b32b20de52f5 --- /dev/null +++ b/toolbox/PaddleOCR/paddleocr.py @@ -0,0 +1,596 @@ +# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. +# Copyright (c) 2024, Shanghai Iluvatar CoreX Semiconductor Co., Ltd. +# All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import sys +import importlib + +__dir__ = os.path.dirname(__file__) + +import paddle + +sys.path.append(os.path.join(__dir__, '')) + +import cv2 +import logging +import numpy as np +from pathlib import Path + +tools = importlib.import_module('.', 'tools') +ppocr = importlib.import_module('.', 'ppocr') +ppstructure = importlib.import_module('.', 'ppstructure') + +from tools.infer import predict_system +from ppocr.utils.logging import get_logger + +logger = get_logger() +from ppocr.utils.utility import check_and_read_gif, get_image_file_list +from ppocr.utils.network import maybe_download, download_with_progressbar, is_link, confirm_model_dir_url +from tools.infer.utility import draw_ocr, str2bool, check_gpu +from ppstructure.utility import init_args, draw_structure_result +from ppstructure.predict_system import StructureSystem, save_structure_res + +__all__ = [ + 'PaddleOCR', 'PPStructure', 'draw_ocr', 'draw_structure_result', + 'save_structure_res', 'download_with_progressbar' +] + +SUPPORT_DET_MODEL = ['DB'] +VERSION = '2.5.0.1' +SUPPORT_REC_MODEL = ['CRNN'] +BASE_DIR = os.path.expanduser("~/.paddleocr/") + +DEFAULT_OCR_MODEL_VERSION = 'PP-OCRv3' +SUPPORT_OCR_MODEL_VERSION = ['PP-OCR', 'PP-OCRv2', 'PP-OCRv3'] +DEFAULT_STRUCTURE_MODEL_VERSION = 'PP-STRUCTURE' +SUPPORT_STRUCTURE_MODEL_VERSION = ['PP-STRUCTURE'] +MODEL_URLS = { + 'OCR': { + 'PP-OCRv3': { + 'det': { + 'ch': { + 'url': + 'https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar', + }, + 'en': { + 'url': + 'https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_det_infer.tar', + }, + 'ml': { + 'url': + 'https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/Multilingual_PP-OCRv3_det_infer.tar' + } + }, + 'rec': { + 'ch': { + 'url': + 'https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar', + 'dict_path': './ppocr/utils/ppocr_keys_v1.txt' + }, + 'en': { + 'url': + 'https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_infer.tar', + 'dict_path': './ppocr/utils/en_dict.txt' + }, + 'korean': { + 'url': + 'https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/korean_PP-OCRv3_rec_infer.tar', + 'dict_path': './ppocr/utils/dict/korean_dict.txt' + }, + 'japan': { + 'url': + 'https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/japan_PP-OCRv3_rec_infer.tar', + 'dict_path': './ppocr/utils/dict/japan_dict.txt' + }, + 'chinese_cht': { + 'url': + 'https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/chinese_cht_PP-OCRv3_rec_infer.tar', + 'dict_path': './ppocr/utils/dict/chinese_cht_dict.txt' + }, + 'ta': { + 'url': + 'https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/ta_PP-OCRv3_rec_infer.tar', + 'dict_path': './ppocr/utils/dict/ta_dict.txt' + }, + 'te': { + 'url': + 'https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/te_PP-OCRv3_rec_infer.tar', + 'dict_path': './ppocr/utils/dict/te_dict.txt' + }, + 'ka': { + 'url': + 'https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/ka_PP-OCRv3_rec_infer.tar', + 'dict_path': './ppocr/utils/dict/ka_dict.txt' + }, + 'latin': { + 'url': + 'https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/latin_PP-OCRv3_rec_infer.tar', + 'dict_path': './ppocr/utils/dict/latin_dict.txt' + }, + 'arabic': { + 'url': + 'https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/arabic_PP-OCRv3_rec_infer.tar', + 'dict_path': './ppocr/utils/dict/arabic_dict.txt' + }, + 'cyrillic': { + 'url': + 'https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/cyrillic_PP-OCRv3_rec_infer.tar', + 'dict_path': './ppocr/utils/dict/cyrillic_dict.txt' + }, + 'devanagari': { + 'url': + 'https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/devanagari_PP-OCRv3_rec_infer.tar', + 'dict_path': './ppocr/utils/dict/devanagari_dict.txt' + }, + }, + 'cls': { + 'ch': { + 'url': + 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar', + } + }, + }, + 'PP-OCRv2': { + 'det': { + 'ch': { + 'url': + 'https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar', + }, + }, + 'rec': { + 'ch': { + 'url': + 'https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar', + 'dict_path': './ppocr/utils/ppocr_keys_v1.txt' + } + } + }, + 'PP-OCR': { + 'det': { + 'ch': { + 'url': + 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar', + }, + 'en': { + 'url': + 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/en_ppocr_mobile_v2.0_det_infer.tar', + }, + 'structure': { + 'url': + 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_det_infer.tar' + } + }, + 'rec': { + 'ch': { + 'url': + 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar', + 'dict_path': './ppocr/utils/ppocr_keys_v1.txt' + }, + 'en': { + 'url': + 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/en_number_mobile_v2.0_rec_infer.tar', + 'dict_path': './ppocr/utils/en_dict.txt' + }, + 'french': { + 'url': + 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/french_mobile_v2.0_rec_infer.tar', + 'dict_path': './ppocr/utils/dict/french_dict.txt' + }, + 'german': { + 'url': + 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/german_mobile_v2.0_rec_infer.tar', + 'dict_path': './ppocr/utils/dict/german_dict.txt' + }, + 'korean': { + 'url': + 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_mobile_v2.0_rec_infer.tar', + 'dict_path': './ppocr/utils/dict/korean_dict.txt' + }, + 'japan': { + 'url': + 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/japan_mobile_v2.0_rec_infer.tar', + 'dict_path': './ppocr/utils/dict/japan_dict.txt' + }, + 'chinese_cht': { + 'url': + 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/chinese_cht_mobile_v2.0_rec_infer.tar', + 'dict_path': './ppocr/utils/dict/chinese_cht_dict.txt' + }, + 'ta': { + 'url': + 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ta_mobile_v2.0_rec_infer.tar', + 'dict_path': './ppocr/utils/dict/ta_dict.txt' + }, + 'te': { + 'url': + 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/te_mobile_v2.0_rec_infer.tar', + 'dict_path': './ppocr/utils/dict/te_dict.txt' + }, + 'ka': { + 'url': + 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ka_mobile_v2.0_rec_infer.tar', + 'dict_path': './ppocr/utils/dict/ka_dict.txt' + }, + 'latin': { + 'url': + 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/latin_ppocr_mobile_v2.0_rec_infer.tar', + 'dict_path': './ppocr/utils/dict/latin_dict.txt' + }, + 'arabic': { + 'url': + 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/arabic_ppocr_mobile_v2.0_rec_infer.tar', + 'dict_path': './ppocr/utils/dict/arabic_dict.txt' + }, + 'cyrillic': { + 'url': + 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/cyrillic_ppocr_mobile_v2.0_rec_infer.tar', + 'dict_path': './ppocr/utils/dict/cyrillic_dict.txt' + }, + 'devanagari': { + 'url': + 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/devanagari_ppocr_mobile_v2.0_rec_infer.tar', + 'dict_path': './ppocr/utils/dict/devanagari_dict.txt' + }, + 'structure': { + 'url': + 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_rec_infer.tar', + 'dict_path': 'ppocr/utils/dict/table_dict.txt' + } + }, + 'cls': { + 'ch': { + 'url': + 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar', + } + }, + } + }, + 'STRUCTURE': { + 'PP-STRUCTURE': { + 'table': { + 'en': { + 'url': + 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar', + 'dict_path': 'ppocr/utils/dict/table_structure_dict.txt' + } + } + } + } +} + + +def parse_args(mMain=True): + import argparse + parser = init_args() + parser.add_help = mMain + parser.add_argument("--lang", type=str, default='ch') + parser.add_argument("--det", type=str2bool, default=True) + parser.add_argument("--rec", type=str2bool, default=True) + parser.add_argument("--type", type=str, default='ocr') + parser.add_argument( + "--ocr_version", + type=str, + choices=SUPPORT_OCR_MODEL_VERSION, + default='PP-OCRv3', + help='OCR Model version, the current model support list is as follows: ' + '1. PP-OCRv3 Support Chinese and English detection and recognition model, and direction classifier model' + '2. PP-OCRv2 Support Chinese detection and recognition model. ' + '3. PP-OCR support Chinese detection, recognition and direction classifier and multilingual recognition model.' + ) + parser.add_argument( + "--structure_version", + type=str, + choices=SUPPORT_STRUCTURE_MODEL_VERSION, + default='PP-STRUCTURE', + help='Model version, the current model support list is as follows:' + ' 1. STRUCTURE Support en table structure model.') + + for action in parser._actions: + if action.dest in ['rec_char_dict_path', 'table_char_dict_path']: + action.default = None + if mMain: + return parser.parse_args() + else: + inference_args_dict = {} + for action in parser._actions: + inference_args_dict[action.dest] = action.default + return argparse.Namespace(**inference_args_dict) + + +def parse_lang(lang): + latin_lang = [ + 'af', 'az', 'bs', 'cs', 'cy', 'da', 'de', 'es', 'et', 'fr', 'ga', 'hr', + 'hu', 'id', 'is', 'it', 'ku', 'la', 'lt', 'lv', 'mi', 'ms', 'mt', 'nl', + 'no', 'oc', 'pi', 'pl', 'pt', 'ro', 'rs_latin', 'sk', 'sl', 'sq', 'sv', + 'sw', 'tl', 'tr', 'uz', 'vi', 'french', 'german' + ] + arabic_lang = ['ar', 'fa', 'ug', 'ur'] + cyrillic_lang = [ + 'ru', 'rs_cyrillic', 'be', 'bg', 'uk', 'mn', 'abq', 'ady', 'kbd', 'ava', + 'dar', 'inh', 'che', 'lbe', 'lez', 'tab' + ] + devanagari_lang = [ + 'hi', 'mr', 'ne', 'bh', 'mai', 'ang', 'bho', 'mah', 'sck', 'new', 'gom', + 'sa', 'bgc' + ] + if lang in latin_lang: + lang = "latin" + elif lang in arabic_lang: + lang = "arabic" + elif lang in cyrillic_lang: + lang = "cyrillic" + elif lang in devanagari_lang: + lang = "devanagari" + assert lang in MODEL_URLS['OCR'][DEFAULT_OCR_MODEL_VERSION][ + 'rec'], 'param lang must in {}, but got {}'.format( + MODEL_URLS['OCR'][DEFAULT_OCR_MODEL_VERSION]['rec'].keys(), lang) + if lang == "ch": + det_lang = "ch" + elif lang == 'structure': + det_lang = 'structure' + elif lang in ["en", "latin"]: + det_lang = "en" + else: + det_lang = "ml" + return lang, det_lang + + +def get_model_config(type, version, model_type, lang): + if type == 'OCR': + DEFAULT_MODEL_VERSION = DEFAULT_OCR_MODEL_VERSION + elif type == 'STRUCTURE': + DEFAULT_MODEL_VERSION = DEFAULT_STRUCTURE_MODEL_VERSION + else: + raise NotImplementedError + + model_urls = MODEL_URLS[type] + if version not in model_urls: + version = DEFAULT_MODEL_VERSION + if model_type not in model_urls[version]: + if model_type in model_urls[DEFAULT_MODEL_VERSION]: + version = DEFAULT_MODEL_VERSION + else: + logger.error('{} models is not support, we only support {}'.format( + model_type, model_urls[DEFAULT_MODEL_VERSION].keys())) + sys.exit(-1) + + if lang not in model_urls[version][model_type]: + if lang in model_urls[DEFAULT_MODEL_VERSION][model_type]: + version = DEFAULT_MODEL_VERSION + else: + logger.error( + 'lang {} is not support, we only support {} for {} models'. + format(lang, model_urls[DEFAULT_MODEL_VERSION][model_type].keys( + ), model_type)) + sys.exit(-1) + return model_urls[version][model_type][lang] + + +class PaddleOCR(predict_system.TextSystem): + def __init__(self, **kwargs): + """ + paddleocr package + args: + **kwargs: other params show in paddleocr --help + """ + params = parse_args(mMain=False) + params.__dict__.update(**kwargs) + assert params.ocr_version in SUPPORT_OCR_MODEL_VERSION, "ocr_version must in {}, but get {}".format( + SUPPORT_OCR_MODEL_VERSION, params.ocr_version) + params.use_gpu = check_gpu(params.use_gpu) + + if not params.show_log: + logger.setLevel(logging.INFO) + self.use_angle_cls = params.use_angle_cls + lang, det_lang = parse_lang(params.lang) + + # init model dir + det_model_config = get_model_config('OCR', params.ocr_version, 'det', + det_lang) + params.det_model_dir, det_url = confirm_model_dir_url( + params.det_model_dir, + os.path.join(BASE_DIR, 'whl', 'det', det_lang), + det_model_config['url']) + rec_model_config = get_model_config('OCR', params.ocr_version, 'rec', + lang) + params.rec_model_dir, rec_url = confirm_model_dir_url( + params.rec_model_dir, + os.path.join(BASE_DIR, 'whl', 'rec', lang), rec_model_config['url']) + cls_model_config = get_model_config('OCR', params.ocr_version, 'cls', + 'ch') + params.cls_model_dir, cls_url = confirm_model_dir_url( + params.cls_model_dir, + os.path.join(BASE_DIR, 'whl', 'cls'), cls_model_config['url']) + if params.ocr_version == 'PP-OCRv3': + params.rec_image_shape = "3, 48, 320" + else: + params.rec_image_shape = "3, 32, 320" + # download model + maybe_download(params.det_model_dir, det_url) + maybe_download(params.rec_model_dir, rec_url) + maybe_download(params.cls_model_dir, cls_url) + + if params.det_algorithm not in SUPPORT_DET_MODEL: + logger.error('det_algorithm must in {}'.format(SUPPORT_DET_MODEL)) + sys.exit(0) + if params.rec_algorithm not in SUPPORT_REC_MODEL: + logger.error('rec_algorithm must in {}'.format(SUPPORT_REC_MODEL)) + sys.exit(0) + + if params.rec_char_dict_path is None: + params.rec_char_dict_path = str( + Path(__file__).parent / rec_model_config['dict_path']) + + logger.debug(params) + # init det_model and rec_model + super().__init__(params) + + def ocr(self, img, det=True, rec=True, cls=True): + """ + ocr with paddleocr + args: + img: img for ocr, support ndarray, img_path and list or ndarray + det: use text detection or not. If false, only rec will be exec. Default is True + rec: use text recognition or not. If false, only det will be exec. Default is True + cls: use angle classifier or not. Default is True. If true, the text with rotation of 180 degrees can be recognized. If no text is rotated by 180 degrees, use cls=False to get better performance. Text with rotation of 90 or 270 degrees can be recognized even if cls=False. + """ + assert isinstance(img, (np.ndarray, list, str)) + if isinstance(img, list) and det == True: + logger.error('When input a list of images, det must be false') + exit(0) + if cls == True and self.use_angle_cls == False: + logger.warning( + 'Since the angle classifier is not initialized, the angle classifier will not be uesd during the forward process' + ) + + if isinstance(img, str): + # download net image + if img.startswith('http'): + download_with_progressbar(img, 'tmp.jpg') + img = 'tmp.jpg' + image_file = img + img, flag = check_and_read_gif(image_file) + if not flag: + with open(image_file, 'rb') as f: + np_arr = np.frombuffer(f.read(), dtype=np.uint8) + img = cv2.imdecode(np_arr, cv2.IMREAD_COLOR) + if img is None: + logger.error("error in loading image:{}".format(image_file)) + return None + if isinstance(img, np.ndarray) and len(img.shape) == 2: + img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) + if det and rec: + dt_boxes, rec_res = self.__call__(img, cls) + return [[box.tolist(), res] for box, res in zip(dt_boxes, rec_res)] + elif det and not rec: + dt_boxes, elapse = self.text_detector(img) + if dt_boxes is None: + return None + return [box.tolist() for box in dt_boxes] + else: + if not isinstance(img, list): + img = [img] + if self.use_angle_cls and cls: + img, cls_res, elapse = self.text_classifier(img) + if not rec: + return cls_res + rec_res, elapse = self.text_recognizer(img) + return rec_res + + +class PPStructure(StructureSystem): + def __init__(self, **kwargs): + params = parse_args(mMain=False) + params.__dict__.update(**kwargs) + assert params.structure_version in SUPPORT_STRUCTURE_MODEL_VERSION, "structure_version must in {}, but get {}".format( + SUPPORT_STRUCTURE_MODEL_VERSION, params.structure_version) + params.use_gpu = check_gpu(params.use_gpu) + + if not params.show_log: + logger.setLevel(logging.INFO) + lang, det_lang = parse_lang(params.lang) + + # init model dir + det_model_config = get_model_config('OCR', params.ocr_version, 'det', + det_lang) + params.det_model_dir, det_url = confirm_model_dir_url( + params.det_model_dir, + os.path.join(BASE_DIR, 'whl', 'det', det_lang), + det_model_config['url']) + rec_model_config = get_model_config('OCR', params.ocr_version, 'rec', + lang) + params.rec_model_dir, rec_url = confirm_model_dir_url( + params.rec_model_dir, + os.path.join(BASE_DIR, 'whl', 'rec', lang), rec_model_config['url']) + table_model_config = get_model_config( + 'STRUCTURE', params.structure_version, 'table', 'en') + params.table_model_dir, table_url = confirm_model_dir_url( + params.table_model_dir, + os.path.join(BASE_DIR, 'whl', 'table'), table_model_config['url']) + # download model + maybe_download(params.det_model_dir, det_url) + maybe_download(params.rec_model_dir, rec_url) + maybe_download(params.table_model_dir, table_url) + + if params.rec_char_dict_path is None: + params.rec_char_dict_path = str( + Path(__file__).parent / rec_model_config['dict_path']) + if params.table_char_dict_path is None: + params.table_char_dict_path = str( + Path(__file__).parent / table_model_config['dict_path']) + + logger.debug(params) + super().__init__(params) + + def __call__(self, img, return_ocr_result_in_table=False): + if isinstance(img, str): + # download net image + if img.startswith('http'): + download_with_progressbar(img, 'tmp.jpg') + img = 'tmp.jpg' + image_file = img + img, flag = check_and_read_gif(image_file) + if not flag: + with open(image_file, 'rb') as f: + np_arr = np.frombuffer(f.read(), dtype=np.uint8) + img = cv2.imdecode(np_arr, cv2.IMREAD_COLOR) + if img is None: + logger.error("error in loading image:{}".format(image_file)) + return None + if isinstance(img, np.ndarray) and len(img.shape) == 2: + img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) + + res = super().__call__(img, return_ocr_result_in_table) + return res + + +def main(): + # for cmd + args = parse_args(mMain=True) + image_dir = args.image_dir + if is_link(image_dir): + download_with_progressbar(image_dir, 'tmp.jpg') + image_file_list = ['tmp.jpg'] + else: + image_file_list = get_image_file_list(args.image_dir) + if len(image_file_list) == 0: + logger.error('no images find in {}'.format(args.image_dir)) + return + if args.type == 'ocr': + engine = PaddleOCR(**(args.__dict__)) + elif args.type == 'structure': + engine = PPStructure(**(args.__dict__)) + else: + raise NotImplementedError + + for img_path in image_file_list: + img_name = os.path.basename(img_path).split('.')[0] + logger.info('{}{}{}'.format('*' * 10, img_path, '*' * 10)) + if args.type == 'ocr': + result = engine.ocr(img_path, + det=args.det, + rec=args.rec, + cls=args.use_angle_cls) + if result is not None: + for line in result: + logger.info(line) + elif args.type == 'structure': + result = engine(img_path) + save_structure_res(result, args.output, img_name) + + for item in result: + item.pop('img') + logger.info(item) diff --git a/toolbox/PaddleOCR/ppocr/data/imaug/__init__.py b/toolbox/PaddleOCR/ppocr/data/imaug/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b7bd1041169d845c0ea72d13d05567d3f86deb4f --- /dev/null +++ b/toolbox/PaddleOCR/ppocr/data/imaug/__init__.py @@ -0,0 +1,77 @@ +# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve. +# Copyright (c) 2024, Shanghai Iluvatar CoreX Semiconductor Co., Ltd. +# All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +from __future__ import unicode_literals + +from .iaa_augment import IaaAugment +from .make_border_map import MakeBorderMap +from .make_shrink_map import MakeShrinkMap +from .random_crop_data import EastRandomCropData, RandomCropImgMask +from .make_pse_gt import MakePseGt + +from .rec_img_aug import RecAug, RecConAug, RecResizeImg, ClsResizeImg, \ + SRNRecResizeImg, NRTRRecResizeImg, SARRecResizeImg, PRENResizeImg +from .ssl_img_aug import SSLRotateResize +from .randaugment import RandAugment +from .copy_paste import CopyPaste +from .ColorJitter import ColorJitter +from .operators import * +from .label_ops import * + +from .east_process import * +from .sast_process import * +from .pg_process import * +from .gen_table_mask import * + +from .vqa import * + +from .fce_aug import * +from .fce_targets import FCENetTargets + + +def transform(data, ops=None): + """ transform """ + if ops is None: + ops = [] + for op in ops: + data = op(data) + if data is None: + return None + return data + + +def create_operators(op_param_list, global_config=None): + """ + create operators based on the config + + Args: + params(list): a dict list, used to create some operators + """ + assert isinstance(op_param_list, list), ('operator config should be a list') + ops = [] + for operator in op_param_list: + assert isinstance(operator, + dict) and len(operator) == 1, "yaml format error" + op_name = list(operator)[0] + param = {} if operator[op_name] is None else operator[op_name] + if global_config is not None: + param.update(global_config) + op = eval(op_name)(**param) + ops.append(op) + return ops diff --git a/toolbox/PaddleOCR/ppocr/data/imaug/copy_paste.py b/toolbox/PaddleOCR/ppocr/data/imaug/copy_paste.py new file mode 100644 index 0000000000000000000000000000000000000000..cac3976840b3e38a3409e2ba9f0913455291a89d --- /dev/null +++ b/toolbox/PaddleOCR/ppocr/data/imaug/copy_paste.py @@ -0,0 +1,173 @@ +# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve. +# Copyright (c) 2024, Shanghai Iluvatar CoreX Semiconductor Co., Ltd. +# All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import copy +import cv2 +import random +import numpy as np +from PIL import Image +from shapely.geometry import Polygon + +from ppocr.data.imaug.iaa_augment import IaaAugment +from ppocr.data.imaug.random_crop_data import is_poly_outside_rect +from tools.infer.utility import get_rotate_crop_image + + +class CopyPaste(object): + def __init__(self, objects_paste_ratio=0.2, limit_paste=True, **kwargs): + self.ext_data_num = 1 + self.objects_paste_ratio = objects_paste_ratio + self.limit_paste = limit_paste + augmenter_args = [{'type': 'Resize', 'args': {'size': [0.5, 3]}}] + self.aug = IaaAugment(augmenter_args) + + def __call__(self, data): + point_num = data['polys'].shape[1] + src_img = data['image'] + src_polys = data['polys'].tolist() + src_ignores = data['ignore_tags'].tolist() + ext_data = data['ext_data'][0] + ext_image = ext_data['image'] + ext_polys = ext_data['polys'] + ext_ignores = ext_data['ignore_tags'] + + indexs = [i for i in range(len(ext_ignores)) if not ext_ignores[i]] + select_num = max( + 1, min(int(self.objects_paste_ratio * len(ext_polys)), 30)) + + random.shuffle(indexs) + select_idxs = indexs[:select_num] + select_polys = ext_polys[select_idxs] + select_ignores = ext_ignores[select_idxs] + + src_img = cv2.cvtColor(src_img, cv2.COLOR_BGR2RGB) + ext_image = cv2.cvtColor(ext_image, cv2.COLOR_BGR2RGB) + src_img = Image.fromarray(src_img).convert('RGBA') + for poly, tag in zip(select_polys, select_ignores): + box_img = get_rotate_crop_image(ext_image, poly) + + src_img, box = self.paste_img(src_img, box_img, src_polys) + if box is not None: + box = box.tolist() + for _ in range(len(box), point_num): + box.append(box[-1]) + src_polys.append(box) + src_ignores.append(tag) + src_img = cv2.cvtColor(np.array(src_img), cv2.COLOR_RGB2BGR) + h, w = src_img.shape[:2] + src_polys = np.array(src_polys) + src_polys[:, :, 0] = np.clip(src_polys[:, :, 0], 0, w) + src_polys[:, :, 1] = np.clip(src_polys[:, :, 1], 0, h) + data['image'] = src_img + data['polys'] = src_polys + data['ignore_tags'] = np.array(src_ignores) + return data + + def paste_img(self, src_img, box_img, src_polys): + box_img_pil = Image.fromarray(box_img).convert('RGBA') + src_w, src_h = src_img.size + box_w, box_h = box_img_pil.size + + angle = np.random.randint(0, 360) + box = np.array([[[0, 0], [box_w, 0], [box_w, box_h], [0, box_h]]]) + box = rotate_bbox(box_img, box, angle)[0] + box_img_pil = box_img_pil.rotate(angle, expand=1) + box_w, box_h = box_img_pil.width, box_img_pil.height + if src_w - box_w < 0 or src_h - box_h < 0: + return src_img, None + + paste_x, paste_y = self.select_coord(src_polys, box, src_w - box_w, + src_h - box_h) + if paste_x is None: + return src_img, None + box[:, 0] += paste_x + box[:, 1] += paste_y + r, g, b, A = box_img_pil.split() + src_img.paste(box_img_pil, (paste_x, paste_y), mask=A) + + return src_img, box + + def select_coord(self, src_polys, box, endx, endy): + if self.limit_paste: + xmin, ymin, xmax, ymax = box[:, 0].min(), box[:, 1].min( + ), box[:, 0].max(), box[:, 1].max() + for _ in range(50): + paste_x = random.randint(0, endx) + paste_y = random.randint(0, endy) + xmin1 = xmin + paste_x + xmax1 = xmax + paste_x + ymin1 = ymin + paste_y + ymax1 = ymax + paste_y + + num_poly_in_rect = 0 + for poly in src_polys: + if not is_poly_outside_rect(poly, xmin1, ymin1, + xmax1 - xmin1, ymax1 - ymin1): + num_poly_in_rect += 1 + break + if num_poly_in_rect == 0: + return paste_x, paste_y + return None, None + else: + paste_x = random.randint(0, endx) + paste_y = random.randint(0, endy) + return paste_x, paste_y + + +def get_union(pD, pG): + return Polygon(pD).union(Polygon(pG)).area + + +def get_intersection_over_union(pD, pG): + return get_intersection(pD, pG) / get_union(pD, pG) + + +def get_intersection(pD, pG): + return Polygon(pD).intersection(Polygon(pG)).area + + +def rotate_bbox(img, text_polys, angle, scale=1): + """ + from https://github.com/WenmuZhou/DBNet.pytorch/blob/master/data_loader/modules/augment.py + Args: + img: np.ndarray + text_polys: np.ndarray N*4*2 + angle: int + scale: int + + Returns: + + """ + w = img.shape[1] + h = img.shape[0] + + rangle = np.deg2rad(angle) + nw = (abs(np.sin(rangle) * h) + abs(np.cos(rangle) * w)) + nh = (abs(np.cos(rangle) * h) + abs(np.sin(rangle) * w)) + rot_mat = cv2.getRotationMatrix2D((nw * 0.5, nh * 0.5), angle, scale) + rot_move = np.dot(rot_mat, np.array([(nw - w) * 0.5, (nh - h) * 0.5, 0])) + rot_mat[0, 2] += rot_move[0] + rot_mat[1, 2] += rot_move[1] + + # ---------------------- rotate box ---------------------- + rot_text_polys = list() + for bbox in text_polys: + point1 = np.dot(rot_mat, np.array([bbox[0, 0], bbox[0, 1], 1])) + point2 = np.dot(rot_mat, np.array([bbox[1, 0], bbox[1, 1], 1])) + point3 = np.dot(rot_mat, np.array([bbox[2, 0], bbox[2, 1], 1])) + point4 = np.dot(rot_mat, np.array([bbox[3, 0], bbox[3, 1], 1])) + rot_text_polys.append([point1, point2, point3, point4]) + return np.array(rot_text_polys, dtype=np.float32) diff --git a/toolbox/PaddleOCR/ppocr/data/imaug/fce_targets.py b/toolbox/PaddleOCR/ppocr/data/imaug/fce_targets.py new file mode 100644 index 0000000000000000000000000000000000000000..dbbd806c2369c7889e8505a67a7e0bcf24da4d59 --- /dev/null +++ b/toolbox/PaddleOCR/ppocr/data/imaug/fce_targets.py @@ -0,0 +1,660 @@ +# copyright (c) 2022 PaddlePaddle Authors. All Rights Reserve. +# Copyright (c) 2024, Shanghai Iluvatar CoreX Semiconductor Co., Ltd. +# All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +This code is refer from: +https://github.com/open-mmlab/mmocr/blob/main/mmocr/datasets/pipelines/textdet_targets/fcenet_targets.py +""" + +import cv2 +import numpy as np +from numpy.fft import fft +from numpy.linalg import norm +import sys + + +class FCENetTargets: + """Generate the ground truth targets of FCENet: Fourier Contour Embedding + for Arbitrary-Shaped Text Detection. + + [https://arxiv.org/abs/2104.10442] + + Args: + fourier_degree (int): The maximum Fourier transform degree k. + resample_step (float): The step size for resampling the text center + line (TCL). It's better not to exceed half of the minimum width. + center_region_shrink_ratio (float): The shrink ratio of text center + region. + level_size_divisors (tuple(int)): The downsample ratio on each level. + level_proportion_range (tuple(tuple(int))): The range of text sizes + assigned to each level. + """ + + def __init__(self, + fourier_degree=5, + resample_step=4.0, + center_region_shrink_ratio=0.3, + level_size_divisors=(8, 16, 32), + level_proportion_range=((0, 0.25), (0.2, 0.65), (0.55, 1.0)), + orientation_thr=2.0, + **kwargs): + + super().__init__() + assert isinstance(level_size_divisors, tuple) + assert isinstance(level_proportion_range, tuple) + assert len(level_size_divisors) == len(level_proportion_range) + self.fourier_degree = fourier_degree + self.resample_step = resample_step + self.center_region_shrink_ratio = center_region_shrink_ratio + self.level_size_divisors = level_size_divisors + self.level_proportion_range = level_proportion_range + + self.orientation_thr = orientation_thr + + def vector_angle(self, vec1, vec2): + if vec1.ndim > 1: + unit_vec1 = vec1 / (norm(vec1, axis=-1) + 1e-8).reshape((-1, 1)) + else: + unit_vec1 = vec1 / (norm(vec1, axis=-1) + 1e-8) + if vec2.ndim > 1: + unit_vec2 = vec2 / (norm(vec2, axis=-1) + 1e-8).reshape((-1, 1)) + else: + unit_vec2 = vec2 / (norm(vec2, axis=-1) + 1e-8) + return np.arccos( + np.clip( + np.sum(unit_vec1 * unit_vec2, axis=-1), -1.0, 1.0)) + + def resample_line(self, line, n): + """Resample n points on a line. + + Args: + line (ndarray): The points composing a line. + n (int): The resampled points number. + + Returns: + resampled_line (ndarray): The points composing the resampled line. + """ + + assert line.ndim == 2 + assert line.shape[0] >= 2 + assert line.shape[1] == 2 + assert isinstance(n, int) + assert n > 0 + + length_list = [ + norm(line[i + 1] - line[i]) for i in range(len(line) - 1) + ] + total_length = sum(length_list) + length_cumsum = np.cumsum([0.0] + length_list) + delta_length = total_length / (float(n) + 1e-8) + + current_edge_ind = 0 + resampled_line = [line[0]] + + for i in range(1, n): + current_line_len = i * delta_length + + while current_line_len >= length_cumsum[current_edge_ind + 1]: + current_edge_ind += 1 + current_edge_end_shift = current_line_len - length_cumsum[ + current_edge_ind] + end_shift_ratio = current_edge_end_shift / length_list[ + current_edge_ind] + current_point = line[current_edge_ind] + (line[current_edge_ind + 1] + - line[current_edge_ind] + ) * end_shift_ratio + resampled_line.append(current_point) + + resampled_line.append(line[-1]) + resampled_line = np.array(resampled_line) + + return resampled_line + + def reorder_poly_edge(self, points): + """Get the respective points composing head edge, tail edge, top + sideline and bottom sideline. + + Args: + points (ndarray): The points composing a text polygon. + + Returns: + head_edge (ndarray): The two points composing the head edge of text + polygon. + tail_edge (ndarray): The two points composing the tail edge of text + polygon. + top_sideline (ndarray): The points composing top curved sideline of + text polygon. + bot_sideline (ndarray): The points composing bottom curved sideline + of text polygon. + """ + + assert points.ndim == 2 + assert points.shape[0] >= 4 + assert points.shape[1] == 2 + + head_inds, tail_inds = self.find_head_tail(points, self.orientation_thr) + head_edge, tail_edge = points[head_inds], points[tail_inds] + + pad_points = np.vstack([points, points]) + if tail_inds[1] < 1: + tail_inds[1] = len(points) + sideline1 = pad_points[head_inds[1]:tail_inds[1]] + sideline2 = pad_points[tail_inds[1]:(head_inds[1] + len(points))] + sideline_mean_shift = np.mean( + sideline1, axis=0) - np.mean( + sideline2, axis=0) + + if sideline_mean_shift[1] > 0: + top_sideline, bot_sideline = sideline2, sideline1 + else: + top_sideline, bot_sideline = sideline1, sideline2 + + return head_edge, tail_edge, top_sideline, bot_sideline + + def find_head_tail(self, points, orientation_thr): + """Find the head edge and tail edge of a text polygon. + + Args: + points (ndarray): The points composing a text polygon. + orientation_thr (float): The threshold for distinguishing between + head edge and tail edge among the horizontal and vertical edges + of a quadrangle. + + Returns: + head_inds (list): The indexes of two points composing head edge. + tail_inds (list): The indexes of two points composing tail edge. + """ + + assert points.ndim == 2 + assert points.shape[0] >= 4 + assert points.shape[1] == 2 + assert isinstance(orientation_thr, float) + + if len(points) > 4: + pad_points = np.vstack([points, points[0]]) + edge_vec = pad_points[1:] - pad_points[:-1] + + theta_sum = [] + adjacent_vec_theta = [] + for i, edge_vec1 in enumerate(edge_vec): + adjacent_ind = [x % len(edge_vec) for x in [i - 1, i + 1]] + adjacent_edge_vec = edge_vec[adjacent_ind] + temp_theta_sum = np.sum( + self.vector_angle(edge_vec1, adjacent_edge_vec)) + temp_adjacent_theta = self.vector_angle(adjacent_edge_vec[0], + adjacent_edge_vec[1]) + theta_sum.append(temp_theta_sum) + adjacent_vec_theta.append(temp_adjacent_theta) + theta_sum_score = np.array(theta_sum) / np.pi + adjacent_theta_score = np.array(adjacent_vec_theta) / np.pi + poly_center = np.mean(points, axis=0) + edge_dist = np.maximum( + norm( + pad_points[1:] - poly_center, axis=-1), + norm( + pad_points[:-1] - poly_center, axis=-1)) + dist_score = edge_dist / np.max(edge_dist) + position_score = np.zeros(len(edge_vec)) + score = 0.5 * theta_sum_score + 0.15 * adjacent_theta_score + score += 0.35 * dist_score + if len(points) % 2 == 0: + position_score[(len(score) // 2 - 1)] += 1 + position_score[-1] += 1 + score += 0.1 * position_score + pad_score = np.concatenate([score, score]) + score_matrix = np.zeros((len(score), len(score) - 3)) + x = np.arange(len(score) - 3) / float(len(score) - 4) + gaussian = 1. / (np.sqrt(2. * np.pi) * 0.5) * np.exp(-np.power( + (x - 0.5) / 0.5, 2.) / 2) + gaussian = gaussian / np.max(gaussian) + for i in range(len(score)): + score_matrix[i, :] = score[i] + pad_score[(i + 2):(i + len( + score) - 1)] * gaussian * 0.3 + + head_start, tail_increment = np.unravel_index(score_matrix.argmax(), + score_matrix.shape) + tail_start = (head_start + tail_increment + 2) % len(points) + head_end = (head_start + 1) % len(points) + tail_end = (tail_start + 1) % len(points) + + if head_end > tail_end: + head_start, tail_start = tail_start, head_start + head_end, tail_end = tail_end, head_end + head_inds = [head_start, head_end] + tail_inds = [tail_start, tail_end] + else: + if self.vector_slope(points[1] - points[0]) + self.vector_slope( + points[3] - points[2]) < self.vector_slope(points[ + 2] - points[1]) + self.vector_slope(points[0] - points[ + 3]): + horizontal_edge_inds = [[0, 1], [2, 3]] + vertical_edge_inds = [[3, 0], [1, 2]] + else: + horizontal_edge_inds = [[3, 0], [1, 2]] + vertical_edge_inds = [[0, 1], [2, 3]] + + vertical_len_sum = norm(points[vertical_edge_inds[0][0]] - points[ + vertical_edge_inds[0][1]]) + norm(points[vertical_edge_inds[1][ + 0]] - points[vertical_edge_inds[1][1]]) + horizontal_len_sum = norm(points[horizontal_edge_inds[0][ + 0]] - points[horizontal_edge_inds[0][1]]) + norm(points[ + horizontal_edge_inds[1][0]] - points[horizontal_edge_inds[1] + [1]]) + + if vertical_len_sum > horizontal_len_sum * orientation_thr: + head_inds = horizontal_edge_inds[0] + tail_inds = horizontal_edge_inds[1] + else: + head_inds = vertical_edge_inds[0] + tail_inds = vertical_edge_inds[1] + + return head_inds, tail_inds + + def resample_sidelines(self, sideline1, sideline2, resample_step): + """Resample two sidelines to be of the same points number according to + step size. + + Args: + sideline1 (ndarray): The points composing a sideline of a text + polygon. + sideline2 (ndarray): The points composing another sideline of a + text polygon. + resample_step (float): The resampled step size. + + Returns: + resampled_line1 (ndarray): The resampled line 1. + resampled_line2 (ndarray): The resampled line 2. + """ + + assert sideline1.ndim == sideline2.ndim == 2 + assert sideline1.shape[1] == sideline2.shape[1] == 2 + assert sideline1.shape[0] >= 2 + assert sideline2.shape[0] >= 2 + assert isinstance(resample_step, float) + + length1 = sum([ + norm(sideline1[i + 1] - sideline1[i]) + for i in range(len(sideline1) - 1) + ]) + length2 = sum([ + norm(sideline2[i + 1] - sideline2[i]) + for i in range(len(sideline2) - 1) + ]) + + total_length = (length1 + length2) / 2 + resample_point_num = max(int(float(total_length) / resample_step), 1) + + resampled_line1 = self.resample_line(sideline1, resample_point_num) + resampled_line2 = self.resample_line(sideline2, resample_point_num) + + return resampled_line1, resampled_line2 + + def generate_center_region_mask(self, img_size, text_polys): + """Generate text center region mask. + + Args: + img_size (tuple): The image size of (height, width). + text_polys (list[list[ndarray]]): The list of text polygons. + + Returns: + center_region_mask (ndarray): The text center region mask. + """ + + assert isinstance(img_size, tuple) + # assert check_argument.is_2dlist(text_polys) + + h, w = img_size + + center_region_mask = np.zeros((h, w), np.uint8) + + center_region_boxes = [] + for poly in text_polys: + # assert len(poly) == 1 + polygon_points = poly.reshape(-1, 2) + _, _, top_line, bot_line = self.reorder_poly_edge(polygon_points) + resampled_top_line, resampled_bot_line = self.resample_sidelines( + top_line, bot_line, self.resample_step) + resampled_bot_line = resampled_bot_line[::-1] + center_line = (resampled_top_line + resampled_bot_line) / 2 + + line_head_shrink_len = norm(resampled_top_line[0] - + resampled_bot_line[0]) / 4.0 + line_tail_shrink_len = norm(resampled_top_line[-1] - + resampled_bot_line[-1]) / 4.0 + head_shrink_num = int(line_head_shrink_len // self.resample_step) + tail_shrink_num = int(line_tail_shrink_len // self.resample_step) + if len(center_line) > head_shrink_num + tail_shrink_num + 2: + center_line = center_line[head_shrink_num:len(center_line) - + tail_shrink_num] + resampled_top_line = resampled_top_line[head_shrink_num:len( + resampled_top_line) - tail_shrink_num] + resampled_bot_line = resampled_bot_line[head_shrink_num:len( + resampled_bot_line) - tail_shrink_num] + + for i in range(0, len(center_line) - 1): + tl = center_line[i] + (resampled_top_line[i] - center_line[i] + ) * self.center_region_shrink_ratio + tr = center_line[i + 1] + (resampled_top_line[i + 1] - + center_line[i + 1] + ) * self.center_region_shrink_ratio + br = center_line[i + 1] + (resampled_bot_line[i + 1] - + center_line[i + 1] + ) * self.center_region_shrink_ratio + bl = center_line[i] + (resampled_bot_line[i] - center_line[i] + ) * self.center_region_shrink_ratio + current_center_box = np.vstack([tl, tr, br, + bl]).astype(np.int32) + center_region_boxes.append(current_center_box) + + cv2.fillPoly(center_region_mask, center_region_boxes, 1) + return center_region_mask + + def resample_polygon(self, polygon, n=400): + """Resample one polygon with n points on its boundary. + + Args: + polygon (list[float]): The input polygon. + n (int): The number of resampled points. + Returns: + resampled_polygon (list[float]): The resampled polygon. + """ + length = [] + + for i in range(len(polygon)): + p1 = polygon[i] + if i == len(polygon) - 1: + p2 = polygon[0] + else: + p2 = polygon[i + 1] + length.append(((p1[0] - p2[0])**2 + (p1[1] - p2[1])**2)**0.5) + + total_length = sum(length) + n_on_each_line = (np.array(length) / (total_length + 1e-8)) * n + n_on_each_line = n_on_each_line.astype(np.int32) + new_polygon = [] + + for i in range(len(polygon)): + num = n_on_each_line[i] + p1 = polygon[i] + if i == len(polygon) - 1: + p2 = polygon[0] + else: + p2 = polygon[i + 1] + + if num == 0: + continue + + dxdy = (p2 - p1) / num + for j in range(num): + point = p1 + dxdy * j + new_polygon.append(point) + + return np.array(new_polygon) + + def normalize_polygon(self, polygon): + """Normalize one polygon so that its start point is at right most. + + Args: + polygon (list[float]): The origin polygon. + Returns: + new_polygon (lost[float]): The polygon with start point at right. + """ + temp_polygon = polygon - polygon.mean(axis=0) + x = np.abs(temp_polygon[:, 0]) + y = temp_polygon[:, 1] + index_x = np.argsort(x) + index_y = np.argmin(y[index_x[:8]]) + index = index_x[index_y] + new_polygon = np.concatenate([polygon[index:], polygon[:index]]) + return new_polygon + + def poly2fourier(self, polygon, fourier_degree): + """Perform Fourier transformation to generate Fourier coefficients ck + from polygon. + + Args: + polygon (ndarray): An input polygon. + fourier_degree (int): The maximum Fourier degree K. + Returns: + c (ndarray(complex)): Fourier coefficients. + """ + points = polygon[:, 0] + polygon[:, 1] * 1j + c_fft = fft(points) / len(points) + c = np.hstack((c_fft[-fourier_degree:], c_fft[:fourier_degree + 1])) + return c + + def clockwise(self, c, fourier_degree): + """Make sure the polygon reconstructed from Fourier coefficients c in + the clockwise direction. + + Args: + polygon (list[float]): The origin polygon. + Returns: + new_polygon (lost[float]): The polygon in clockwise point order. + """ + if np.abs(c[fourier_degree + 1]) > np.abs(c[fourier_degree - 1]): + return c + elif np.abs(c[fourier_degree + 1]) < np.abs(c[fourier_degree - 1]): + return c[::-1] + else: + if np.abs(c[fourier_degree + 2]) > np.abs(c[fourier_degree - 2]): + return c + else: + return c[::-1] + + def cal_fourier_signature(self, polygon, fourier_degree): + """Calculate Fourier signature from input polygon. + + Args: + polygon (ndarray): The input polygon. + fourier_degree (int): The maximum Fourier degree K. + Returns: + fourier_signature (ndarray): An array shaped (2k+1, 2) containing + real part and image part of 2k+1 Fourier coefficients. + """ + resampled_polygon = self.resample_polygon(polygon) + resampled_polygon = self.normalize_polygon(resampled_polygon) + + fourier_coeff = self.poly2fourier(resampled_polygon, fourier_degree) + fourier_coeff = self.clockwise(fourier_coeff, fourier_degree) + + real_part = np.real(fourier_coeff).reshape((-1, 1)) + image_part = np.imag(fourier_coeff).reshape((-1, 1)) + fourier_signature = np.hstack([real_part, image_part]) + + return fourier_signature + + def generate_fourier_maps(self, img_size, text_polys): + """Generate Fourier coefficient maps. + + Args: + img_size (tuple): The image size of (height, width). + text_polys (list[list[ndarray]]): The list of text polygons. + + Returns: + fourier_real_map (ndarray): The Fourier coefficient real part maps. + fourier_image_map (ndarray): The Fourier coefficient image part + maps. + """ + + assert isinstance(img_size, tuple) + + h, w = img_size + k = self.fourier_degree + real_map = np.zeros((k * 2 + 1, h, w), dtype=np.float32) + imag_map = np.zeros((k * 2 + 1, h, w), dtype=np.float32) + + for poly in text_polys: + mask = np.zeros((h, w), dtype=np.uint8) + polygon = np.array(poly).reshape((1, -1, 2)) + cv2.fillPoly(mask, polygon.astype(np.int32), 1) + fourier_coeff = self.cal_fourier_signature(polygon[0], k) + for i in range(-k, k + 1): + if i != 0: + real_map[i + k, :, :] = mask * fourier_coeff[i + k, 0] + ( + 1 - mask) * real_map[i + k, :, :] + imag_map[i + k, :, :] = mask * fourier_coeff[i + k, 1] + ( + 1 - mask) * imag_map[i + k, :, :] + else: + yx = np.argwhere(mask > 0.5) + k_ind = np.ones((len(yx)), dtype=np.int64) * k + y, x = yx[:, 0], yx[:, 1] + real_map[k_ind, y, x] = fourier_coeff[k, 0] - x + imag_map[k_ind, y, x] = fourier_coeff[k, 1] - y + + return real_map, imag_map + + def generate_text_region_mask(self, img_size, text_polys): + """Generate text center region mask and geometry attribute maps. + + Args: + img_size (tuple): The image size (height, width). + text_polys (list[list[ndarray]]): The list of text polygons. + + Returns: + text_region_mask (ndarray): The text region mask. + """ + + assert isinstance(img_size, tuple) + + h, w = img_size + text_region_mask = np.zeros((h, w), dtype=np.uint8) + + for poly in text_polys: + polygon = np.array(poly, dtype=np.int32).reshape((1, -1, 2)) + cv2.fillPoly(text_region_mask, polygon, 1) + + return text_region_mask + + def generate_effective_mask(self, mask_size: tuple, polygons_ignore): + """Generate effective mask by setting the ineffective regions to 0 and + effective regions to 1. + + Args: + mask_size (tuple): The mask size. + polygons_ignore (list[[ndarray]]: The list of ignored text + polygons. + + Returns: + mask (ndarray): The effective mask of (height, width). + """ + + mask = np.ones(mask_size, dtype=np.uint8) + + for poly in polygons_ignore: + instance = poly.reshape(-1, 2).astype(np.int32).reshape(1, -1, 2) + cv2.fillPoly(mask, instance, 0) + + return mask + + def generate_level_targets(self, img_size, text_polys, ignore_polys): + """Generate ground truth target on each level. + + Args: + img_size (list[int]): Shape of input image. + text_polys (list[list[ndarray]]): A list of ground truth polygons. + ignore_polys (list[list[ndarray]]): A list of ignored polygons. + Returns: + level_maps (list(ndarray)): A list of ground target on each level. + """ + h, w = img_size + lv_size_divs = self.level_size_divisors + lv_proportion_range = self.level_proportion_range + lv_text_polys = [[] for i in range(len(lv_size_divs))] + lv_ignore_polys = [[] for i in range(len(lv_size_divs))] + level_maps = [] + for poly in text_polys: + polygon = np.array(poly, dtype=np.int).reshape((1, -1, 2)) + _, _, box_w, box_h = cv2.boundingRect(polygon) + proportion = max(box_h, box_w) / (h + 1e-8) + + for ind, proportion_range in enumerate(lv_proportion_range): + if proportion_range[0] < proportion < proportion_range[1]: + lv_text_polys[ind].append(poly / lv_size_divs[ind]) + + for ignore_poly in ignore_polys: + polygon = np.array(ignore_poly, dtype=np.int).reshape((1, -1, 2)) + _, _, box_w, box_h = cv2.boundingRect(polygon) + proportion = max(box_h, box_w) / (h + 1e-8) + + for ind, proportion_range in enumerate(lv_proportion_range): + if proportion_range[0] < proportion < proportion_range[1]: + lv_ignore_polys[ind].append(ignore_poly / lv_size_divs[ind]) + + for ind, size_divisor in enumerate(lv_size_divs): + current_level_maps = [] + level_img_size = (h // size_divisor, w // size_divisor) + + text_region = self.generate_text_region_mask( + level_img_size, lv_text_polys[ind])[None] + current_level_maps.append(text_region) + + center_region = self.generate_center_region_mask( + level_img_size, lv_text_polys[ind])[None] + current_level_maps.append(center_region) + + effective_mask = self.generate_effective_mask( + level_img_size, lv_ignore_polys[ind])[None] + current_level_maps.append(effective_mask) + + fourier_real_map, fourier_image_maps = self.generate_fourier_maps( + level_img_size, lv_text_polys[ind]) + current_level_maps.append(fourier_real_map) + current_level_maps.append(fourier_image_maps) + + level_maps.append(np.concatenate(current_level_maps)) + + return level_maps + + def generate_targets(self, results): + """Generate the ground truth targets for FCENet. + + Args: + results (dict): The input result dictionary. + + Returns: + results (dict): The output result dictionary. + """ + + assert isinstance(results, dict) + image = results['image'] + polygons = results['polys'] + ignore_tags = results['ignore_tags'] + h, w, _ = image.shape + + polygon_masks = [] + polygon_masks_ignore = [] + for tag, polygon in zip(ignore_tags, polygons): + if tag is True: + polygon_masks_ignore.append(polygon) + else: + polygon_masks.append(polygon) + + level_maps = self.generate_level_targets((h, w), polygon_masks, + polygon_masks_ignore) + + mapping = { + 'p3_maps': level_maps[0], + 'p4_maps': level_maps[1], + 'p5_maps': level_maps[2] + } + for key, value in mapping.items(): + results[key] = value + + return results + + def __call__(self, results): + results = self.generate_targets(results) + return results diff --git a/toolbox/PaddleOCR/ppocr/data/imaug/label_ops.py b/toolbox/PaddleOCR/ppocr/data/imaug/label_ops.py new file mode 100644 index 0000000000000000000000000000000000000000..0d39bab5c904a0609c644b31caeb3f2ff344e8cc --- /dev/null +++ b/toolbox/PaddleOCR/ppocr/data/imaug/label_ops.py @@ -0,0 +1,1043 @@ +# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve. +# Copyright (c) 2024, Shanghai Iluvatar CoreX Semiconductor Co., Ltd. +# All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +from __future__ import unicode_literals + +import copy +import numpy as np +import string +from shapely.geometry import LineString, Point, Polygon +import json +import copy + +from ppocr.utils.logging import get_logger + + +class ClsLabelEncode(object): + def __init__(self, label_list, **kwargs): + self.label_list = label_list + + def __call__(self, data): + label = data['label'] + if label not in self.label_list: + return None + label = self.label_list.index(label) + data['label'] = label + return data + + +class DetLabelEncode(object): + def __init__(self, **kwargs): + pass + + def __call__(self, data): + label = data['label'] + label = json.loads(label) + nBox = len(label) + boxes, txts, txt_tags = [], [], [] + for bno in range(0, nBox): + box = label[bno]['points'] + txt = label[bno]['transcription'] + boxes.append(box) + txts.append(txt) + if txt in ['*', '###']: + txt_tags.append(True) + else: + txt_tags.append(False) + if len(boxes) == 0: + return None + boxes = self.expand_points_num(boxes) + boxes = np.array(boxes, dtype=np.float32) + txt_tags = np.array(txt_tags, dtype=np.bool) + + data['polys'] = boxes + data['texts'] = txts + data['ignore_tags'] = txt_tags + return data + + def order_points_clockwise(self, pts): + rect = np.zeros((4, 2), dtype="float32") + s = pts.sum(axis=1) + rect[0] = pts[np.argmin(s)] + rect[2] = pts[np.argmax(s)] + diff = np.diff(pts, axis=1) + rect[1] = pts[np.argmin(diff)] + rect[3] = pts[np.argmax(diff)] + return rect + + def expand_points_num(self, boxes): + max_points_num = 0 + for box in boxes: + if len(box) > max_points_num: + max_points_num = len(box) + ex_boxes = [] + for box in boxes: + ex_box = box + [box[-1]] * (max_points_num - len(box)) + ex_boxes.append(ex_box) + return ex_boxes + + +class BaseRecLabelEncode(object): + """ Convert between text-label and text-index """ + + def __init__(self, + max_text_length, + character_dict_path=None, + use_space_char=False): + + self.max_text_len = max_text_length + self.beg_str = "sos" + self.end_str = "eos" + self.lower = False + + if character_dict_path is None: + logger = get_logger() + logger.warning( + "The character_dict_path is None, model can only recognize number and lower letters" + ) + self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz" + dict_character = list(self.character_str) + self.lower = True + else: + self.character_str = [] + with open(character_dict_path, "rb") as fin: + lines = fin.readlines() + for line in lines: + line = line.decode('utf-8').strip("\n").strip("\r\n") + self.character_str.append(line) + if use_space_char: + self.character_str.append(" ") + dict_character = list(self.character_str) + dict_character = self.add_special_char(dict_character) + self.dict = {} + for i, char in enumerate(dict_character): + self.dict[char] = i + self.character = dict_character + + def add_special_char(self, dict_character): + return dict_character + + def encode(self, text): + """convert text-label into text-index. + input: + text: text labels of each image. [batch_size] + + output: + text: concatenated text index for CTCLoss. + [sum(text_lengths)] = [text_index_0 + text_index_1 + ... + text_index_(n - 1)] + length: length of each text. [batch_size] + """ + if len(text) == 0 or len(text) > self.max_text_len: + return None + if self.lower: + text = text.lower() + text_list = [] + for char in text: + if char not in self.dict: + # logger = get_logger() + # logger.warning('{} is not in dict'.format(char)) + continue + text_list.append(self.dict[char]) + if len(text_list) == 0: + return None + return text_list + + +class NRTRLabelEncode(BaseRecLabelEncode): + """ Convert between text-label and text-index """ + + def __init__(self, + max_text_length, + character_dict_path=None, + use_space_char=False, + **kwargs): + + super(NRTRLabelEncode, self).__init__( + max_text_length, character_dict_path, use_space_char) + + def __call__(self, data): + text = data['label'] + text = self.encode(text) + if text is None: + return None + if len(text) >= self.max_text_len - 1: + return None + data['length'] = np.array(len(text)) + text.insert(0, 2) + text.append(3) + text = text + [0] * (self.max_text_len - len(text)) + data['label'] = np.array(text) + return data + + def add_special_char(self, dict_character): + dict_character = ['blank', '', '', ''] + dict_character + return dict_character + + +class CTCLabelEncode(BaseRecLabelEncode): + """ Convert between text-label and text-index """ + + def __init__(self, + max_text_length, + character_dict_path=None, + use_space_char=False, + **kwargs): + super(CTCLabelEncode, self).__init__( + max_text_length, character_dict_path, use_space_char) + + def __call__(self, data): + text = data['label'] + text = self.encode(text) + if text is None: + return None + data['length'] = np.array(len(text)) + text = text + [0] * (self.max_text_len - len(text)) + data['label'] = np.array(text) + + label = [0] * len(self.character) + for x in text: + label[x] += 1 + data['label_ace'] = np.array(label) + return data + + def add_special_char(self, dict_character): + dict_character = ['blank'] + dict_character + return dict_character + + +class E2ELabelEncodeTest(BaseRecLabelEncode): + def __init__(self, + max_text_length, + character_dict_path=None, + use_space_char=False, + **kwargs): + super(E2ELabelEncodeTest, self).__init__( + max_text_length, character_dict_path, use_space_char) + + def __call__(self, data): + import json + padnum = len(self.dict) + label = data['label'] + label = json.loads(label) + nBox = len(label) + boxes, txts, txt_tags = [], [], [] + for bno in range(0, nBox): + box = label[bno]['points'] + txt = label[bno]['transcription'] + boxes.append(box) + txts.append(txt) + if txt in ['*', '###']: + txt_tags.append(True) + else: + txt_tags.append(False) + boxes = np.array(boxes, dtype=np.float32) + txt_tags = np.array(txt_tags, dtype=np.bool) + data['polys'] = boxes + data['ignore_tags'] = txt_tags + temp_texts = [] + for text in txts: + text = text.lower() + text = self.encode(text) + if text is None: + return None + text = text + [padnum] * (self.max_text_len - len(text) + ) # use 36 to pad + temp_texts.append(text) + data['texts'] = np.array(temp_texts) + return data + + +class E2ELabelEncodeTrain(object): + def __init__(self, **kwargs): + pass + + def __call__(self, data): + import json + label = data['label'] + label = json.loads(label) + nBox = len(label) + boxes, txts, txt_tags = [], [], [] + for bno in range(0, nBox): + box = label[bno]['points'] + txt = label[bno]['transcription'] + boxes.append(box) + txts.append(txt) + if txt in ['*', '###']: + txt_tags.append(True) + else: + txt_tags.append(False) + boxes = np.array(boxes, dtype=np.float32) + txt_tags = np.array(txt_tags, dtype=np.bool) + + data['polys'] = boxes + data['texts'] = txts + data['ignore_tags'] = txt_tags + return data + + +class KieLabelEncode(object): + def __init__(self, character_dict_path, norm=10, directed=False, **kwargs): + super(KieLabelEncode, self).__init__() + self.dict = dict({'': 0}) + with open(character_dict_path, 'r', encoding='utf-8') as fr: + idx = 1 + for line in fr: + char = line.strip() + self.dict[char] = idx + idx += 1 + self.norm = norm + self.directed = directed + + def compute_relation(self, boxes): + """Compute relation between every two boxes.""" + x1s, y1s = boxes[:, 0:1], boxes[:, 1:2] + x2s, y2s = boxes[:, 4:5], boxes[:, 5:6] + ws, hs = x2s - x1s + 1, np.maximum(y2s - y1s + 1, 1) + dxs = (x1s[:, 0][None] - x1s) / self.norm + dys = (y1s[:, 0][None] - y1s) / self.norm + xhhs, xwhs = hs[:, 0][None] / hs, ws[:, 0][None] / hs + whs = ws / hs + np.zeros_like(xhhs) + relations = np.stack([dxs, dys, whs, xhhs, xwhs], -1) + bboxes = np.concatenate([x1s, y1s, x2s, y2s], -1).astype(np.float32) + return relations, bboxes + + def pad_text_indices(self, text_inds): + """Pad text index to same length.""" + max_len = 300 + recoder_len = max([len(text_ind) for text_ind in text_inds]) + padded_text_inds = -np.ones((len(text_inds), max_len), np.int32) + for idx, text_ind in enumerate(text_inds): + padded_text_inds[idx, :len(text_ind)] = np.array(text_ind) + return padded_text_inds, recoder_len + + def list_to_numpy(self, ann_infos): + """Convert bboxes, relations, texts and labels to ndarray.""" + boxes, text_inds = ann_infos['points'], ann_infos['text_inds'] + boxes = np.array(boxes, np.int32) + relations, bboxes = self.compute_relation(boxes) + + labels = ann_infos.get('labels', None) + if labels is not None: + labels = np.array(labels, np.int32) + edges = ann_infos.get('edges', None) + if edges is not None: + labels = labels[:, None] + edges = np.array(edges) + edges = (edges[:, None] == edges[None, :]).astype(np.int32) + if self.directed: + edges = (edges & labels == 1).astype(np.int32) + np.fill_diagonal(edges, -1) + labels = np.concatenate([labels, edges], -1) + padded_text_inds, recoder_len = self.pad_text_indices(text_inds) + max_num = 300 + temp_bboxes = np.zeros([max_num, 4]) + h, _ = bboxes.shape + temp_bboxes[:h, :] = bboxes + + temp_relations = np.zeros([max_num, max_num, 5]) + temp_relations[:h, :h, :] = relations + + temp_padded_text_inds = np.zeros([max_num, max_num]) + temp_padded_text_inds[:h, :] = padded_text_inds + + temp_labels = np.zeros([max_num, max_num]) + temp_labels[:h, :h + 1] = labels + + tag = np.array([h, recoder_len]) + return dict( + image=ann_infos['image'], + points=temp_bboxes, + relations=temp_relations, + texts=temp_padded_text_inds, + labels=temp_labels, + tag=tag) + + def convert_canonical(self, points_x, points_y): + + assert len(points_x) == 4 + assert len(points_y) == 4 + + points = [Point(points_x[i], points_y[i]) for i in range(4)] + + polygon = Polygon([(p.x, p.y) for p in points]) + min_x, min_y, _, _ = polygon.bounds + points_to_lefttop = [ + LineString([points[i], Point(min_x, min_y)]) for i in range(4) + ] + distances = np.array([line.length for line in points_to_lefttop]) + sort_dist_idx = np.argsort(distances) + lefttop_idx = sort_dist_idx[0] + + if lefttop_idx == 0: + point_orders = [0, 1, 2, 3] + elif lefttop_idx == 1: + point_orders = [1, 2, 3, 0] + elif lefttop_idx == 2: + point_orders = [2, 3, 0, 1] + else: + point_orders = [3, 0, 1, 2] + + sorted_points_x = [points_x[i] for i in point_orders] + sorted_points_y = [points_y[j] for j in point_orders] + + return sorted_points_x, sorted_points_y + + def sort_vertex(self, points_x, points_y): + + assert len(points_x) == 4 + assert len(points_y) == 4 + + x = np.array(points_x) + y = np.array(points_y) + center_x = np.sum(x) * 0.25 + center_y = np.sum(y) * 0.25 + + x_arr = np.array(x - center_x) + y_arr = np.array(y - center_y) + + angle = np.arctan2(y_arr, x_arr) * 180.0 / np.pi + sort_idx = np.argsort(angle) + + sorted_points_x, sorted_points_y = [], [] + for i in range(4): + sorted_points_x.append(points_x[sort_idx[i]]) + sorted_points_y.append(points_y[sort_idx[i]]) + + return self.convert_canonical(sorted_points_x, sorted_points_y) + + def __call__(self, data): + import json + label = data['label'] + annotations = json.loads(label) + boxes, texts, text_inds, labels, edges = [], [], [], [], [] + for ann in annotations: + box = ann['points'] + x_list = [box[i][0] for i in range(4)] + y_list = [box[i][1] for i in range(4)] + sorted_x_list, sorted_y_list = self.sort_vertex(x_list, y_list) + sorted_box = [] + for x, y in zip(sorted_x_list, sorted_y_list): + sorted_box.append(x) + sorted_box.append(y) + boxes.append(sorted_box) + text = ann['transcription'] + texts.append(ann['transcription']) + text_ind = [self.dict[c] for c in text if c in self.dict] + text_inds.append(text_ind) + labels.append(ann['label']) + edges.append(ann.get('edge', 0)) + ann_infos = dict( + image=data['image'], + points=boxes, + texts=texts, + text_inds=text_inds, + edges=edges, + labels=labels) + + return self.list_to_numpy(ann_infos) + + +class AttnLabelEncode(BaseRecLabelEncode): + """ Convert between text-label and text-index """ + + def __init__(self, + max_text_length, + character_dict_path=None, + use_space_char=False, + **kwargs): + super(AttnLabelEncode, self).__init__( + max_text_length, character_dict_path, use_space_char) + + def add_special_char(self, dict_character): + self.beg_str = "sos" + self.end_str = "eos" + dict_character = [self.beg_str] + dict_character + [self.end_str] + return dict_character + + def __call__(self, data): + text = data['label'] + text = self.encode(text) + if text is None: + return None + if len(text) >= self.max_text_len: + return None + data['length'] = np.array(len(text)) + text = [0] + text + [len(self.character) - 1] + [0] * (self.max_text_len + - len(text) - 2) + data['label'] = np.array(text) + return data + + def get_ignored_tokens(self): + beg_idx = self.get_beg_end_flag_idx("beg") + end_idx = self.get_beg_end_flag_idx("end") + return [beg_idx, end_idx] + + def get_beg_end_flag_idx(self, beg_or_end): + if beg_or_end == "beg": + idx = np.array(self.dict[self.beg_str]) + elif beg_or_end == "end": + idx = np.array(self.dict[self.end_str]) + else: + assert False, "Unsupport type %s in get_beg_end_flag_idx" \ + % beg_or_end + return idx + + +class SEEDLabelEncode(BaseRecLabelEncode): + """ Convert between text-label and text-index """ + + def __init__(self, + max_text_length, + character_dict_path=None, + use_space_char=False, + **kwargs): + super(SEEDLabelEncode, self).__init__( + max_text_length, character_dict_path, use_space_char) + + def add_special_char(self, dict_character): + self.padding = "padding" + self.end_str = "eos" + self.unknown = "unknown" + dict_character = dict_character + [ + self.end_str, self.padding, self.unknown + ] + return dict_character + + def __call__(self, data): + text = data['label'] + text = self.encode(text) + if text is None: + return None + if len(text) >= self.max_text_len: + return None + data['length'] = np.array(len(text)) + 1 # conclude eos + text = text + [len(self.character) - 3] + [len(self.character) - 2] * ( + self.max_text_len - len(text) - 1) + data['label'] = np.array(text) + return data + + +class SRNLabelEncode(BaseRecLabelEncode): + """ Convert between text-label and text-index """ + + def __init__(self, + max_text_length=25, + character_dict_path=None, + use_space_char=False, + **kwargs): + super(SRNLabelEncode, self).__init__( + max_text_length, character_dict_path, use_space_char) + + def add_special_char(self, dict_character): + dict_character = dict_character + [self.beg_str, self.end_str] + return dict_character + + def __call__(self, data): + text = data['label'] + text = self.encode(text) + char_num = len(self.character) + if text is None: + return None + if len(text) > self.max_text_len: + return None + data['length'] = np.array(len(text)) + text = text + [char_num - 1] * (self.max_text_len - len(text)) + data['label'] = np.array(text) + return data + + def get_ignored_tokens(self): + beg_idx = self.get_beg_end_flag_idx("beg") + end_idx = self.get_beg_end_flag_idx("end") + return [beg_idx, end_idx] + + def get_beg_end_flag_idx(self, beg_or_end): + if beg_or_end == "beg": + idx = np.array(self.dict[self.beg_str]) + elif beg_or_end == "end": + idx = np.array(self.dict[self.end_str]) + else: + assert False, "Unsupport type %s in get_beg_end_flag_idx" \ + % beg_or_end + return idx + + +class TableLabelEncode(object): + """ Convert between text-label and text-index """ + + def __init__(self, + max_text_length, + max_elem_length, + max_cell_num, + character_dict_path, + span_weight=1.0, + **kwargs): + self.max_text_length = max_text_length + self.max_elem_length = max_elem_length + self.max_cell_num = max_cell_num + list_character, list_elem = self.load_char_elem_dict( + character_dict_path) + list_character = self.add_special_char(list_character) + list_elem = self.add_special_char(list_elem) + self.dict_character = {} + for i, char in enumerate(list_character): + self.dict_character[char] = i + self.dict_elem = {} + for i, elem in enumerate(list_elem): + self.dict_elem[elem] = i + self.span_weight = span_weight + + def load_char_elem_dict(self, character_dict_path): + list_character = [] + list_elem = [] + with open(character_dict_path, "rb") as fin: + lines = fin.readlines() + substr = lines[0].decode('utf-8').strip("\r\n").split("\t") + character_num = int(substr[0]) + elem_num = int(substr[1]) + for cno in range(1, 1 + character_num): + character = lines[cno].decode('utf-8').strip("\r\n") + list_character.append(character) + for eno in range(1 + character_num, 1 + character_num + elem_num): + elem = lines[eno].decode('utf-8').strip("\r\n") + list_elem.append(elem) + return list_character, list_elem + + def add_special_char(self, list_character): + self.beg_str = "sos" + self.end_str = "eos" + list_character = [self.beg_str] + list_character + [self.end_str] + return list_character + + def get_span_idx_list(self): + span_idx_list = [] + for elem in self.dict_elem: + if 'span' in elem: + span_idx_list.append(self.dict_elem[elem]) + return span_idx_list + + def __call__(self, data): + cells = data['cells'] + structure = data['structure']['tokens'] + structure = self.encode(structure, 'elem') + if structure is None: + return None + elem_num = len(structure) + structure = [0] + structure + [len(self.dict_elem) - 1] + structure = structure + [0] * (self.max_elem_length + 2 - len(structure) + ) + structure = np.array(structure) + data['structure'] = structure + elem_char_idx1 = self.dict_elem[''] + elem_char_idx2 = self.dict_elem[' 0: + span_weight = len(td_idx_list) * 1.0 / len(span_idx_list) + span_weight = min(max(span_weight, 1.0), self.span_weight) + for cno in range(len(cells)): + if 'bbox' in cells[cno]: + bbox = cells[cno]['bbox'].copy() + bbox[0] = bbox[0] * 1.0 / img_width + bbox[1] = bbox[1] * 1.0 / img_height + bbox[2] = bbox[2] * 1.0 / img_width + bbox[3] = bbox[3] * 1.0 / img_height + td_idx = td_idx_list[cno] + bbox_list[td_idx] = bbox + bbox_list_mask[td_idx] = 1.0 + cand_span_idx = td_idx + 1 + if cand_span_idx < (self.max_elem_length + 2): + if structure[cand_span_idx] in span_idx_list: + structure_mask[cand_span_idx] = span_weight + + data['bbox_list'] = bbox_list + data['bbox_list_mask'] = bbox_list_mask + data['structure_mask'] = structure_mask + char_beg_idx = self.get_beg_end_flag_idx('beg', 'char') + char_end_idx = self.get_beg_end_flag_idx('end', 'char') + elem_beg_idx = self.get_beg_end_flag_idx('beg', 'elem') + elem_end_idx = self.get_beg_end_flag_idx('end', 'elem') + data['sp_tokens'] = np.array([ + char_beg_idx, char_end_idx, elem_beg_idx, elem_end_idx, + elem_char_idx1, elem_char_idx2, self.max_text_length, + self.max_elem_length, self.max_cell_num, elem_num + ]) + return data + + def encode(self, text, char_or_elem): + """convert text-label into text-index. + """ + if char_or_elem == "char": + max_len = self.max_text_length + current_dict = self.dict_character + else: + max_len = self.max_elem_length + current_dict = self.dict_elem + if len(text) > max_len: + return None + if len(text) == 0: + if char_or_elem == "char": + return [self.dict_character['space']] + else: + return None + text_list = [] + for char in text: + if char not in current_dict: + return None + text_list.append(current_dict[char]) + if len(text_list) == 0: + if char_or_elem == "char": + return [self.dict_character['space']] + else: + return None + return text_list + + def get_ignored_tokens(self, char_or_elem): + beg_idx = self.get_beg_end_flag_idx("beg", char_or_elem) + end_idx = self.get_beg_end_flag_idx("end", char_or_elem) + return [beg_idx, end_idx] + + def get_beg_end_flag_idx(self, beg_or_end, char_or_elem): + if char_or_elem == "char": + if beg_or_end == "beg": + idx = np.array(self.dict_character[self.beg_str]) + elif beg_or_end == "end": + idx = np.array(self.dict_character[self.end_str]) + else: + assert False, "Unsupport type %s in get_beg_end_flag_idx of char" \ + % beg_or_end + elif char_or_elem == "elem": + if beg_or_end == "beg": + idx = np.array(self.dict_elem[self.beg_str]) + elif beg_or_end == "end": + idx = np.array(self.dict_elem[self.end_str]) + else: + assert False, "Unsupport type %s in get_beg_end_flag_idx of elem" \ + % beg_or_end + else: + assert False, "Unsupport type %s in char_or_elem" \ + % char_or_elem + return idx + + +class SARLabelEncode(BaseRecLabelEncode): + """ Convert between text-label and text-index """ + + def __init__(self, + max_text_length, + character_dict_path=None, + use_space_char=False, + **kwargs): + super(SARLabelEncode, self).__init__( + max_text_length, character_dict_path, use_space_char) + + def add_special_char(self, dict_character): + beg_end_str = "" + unknown_str = "" + padding_str = "" + dict_character = dict_character + [unknown_str] + self.unknown_idx = len(dict_character) - 1 + dict_character = dict_character + [beg_end_str] + self.start_idx = len(dict_character) - 1 + self.end_idx = len(dict_character) - 1 + dict_character = dict_character + [padding_str] + self.padding_idx = len(dict_character) - 1 + + return dict_character + + def __call__(self, data): + text = data['label'] + text = self.encode(text) + if text is None: + return None + if len(text) >= self.max_text_len - 1: + return None + data['length'] = np.array(len(text)) + target = [self.start_idx] + text + [self.end_idx] + padded_text = [self.padding_idx for _ in range(self.max_text_len)] + + padded_text[:len(target)] = target + data['label'] = np.array(padded_text) + return data + + def get_ignored_tokens(self): + return [self.padding_idx] + + +class PRENLabelEncode(BaseRecLabelEncode): + def __init__(self, + max_text_length, + character_dict_path, + use_space_char=False, + **kwargs): + super(PRENLabelEncode, self).__init__( + max_text_length, character_dict_path, use_space_char) + + def add_special_char(self, dict_character): + padding_str = '' # 0 + end_str = '' # 1 + unknown_str = '' # 2 + + dict_character = [padding_str, end_str, unknown_str] + dict_character + self.padding_idx = 0 + self.end_idx = 1 + self.unknown_idx = 2 + + return dict_character + + def encode(self, text): + if len(text) == 0 or len(text) >= self.max_text_len: + return None + if self.lower: + text = text.lower() + text_list = [] + for char in text: + if char not in self.dict: + text_list.append(self.unknown_idx) + else: + text_list.append(self.dict[char]) + text_list.append(self.end_idx) + if len(text_list) < self.max_text_len: + text_list += [self.padding_idx] * ( + self.max_text_len - len(text_list)) + return text_list + + def __call__(self, data): + text = data['label'] + encoded_text = self.encode(text) + if encoded_text is None: + return None + data['label'] = np.array(encoded_text) + return data + + +class VQATokenLabelEncode(object): + """ + Label encode for NLP VQA methods + """ + + def __init__(self, + class_path, + contains_re=False, + add_special_ids=False, + algorithm='LayoutXLM', + infer_mode=False, + ocr_engine=None, + **kwargs): + super(VQATokenLabelEncode, self).__init__() + from paddlenlp.transformers import LayoutXLMTokenizer, LayoutLMTokenizer, LayoutLMv2Tokenizer + from ppocr.utils.utility import load_vqa_bio_label_maps + tokenizer_dict = { + 'LayoutXLM': { + 'class': LayoutXLMTokenizer, + 'pretrained_model': 'layoutxlm-base-uncased' + }, + 'LayoutLM': { + 'class': LayoutLMTokenizer, + 'pretrained_model': 'layoutlm-base-uncased' + }, + 'LayoutLMv2': { + 'class': LayoutLMv2Tokenizer, + 'pretrained_model': 'layoutlmv2-base-uncased' + } + } + self.contains_re = contains_re + tokenizer_config = tokenizer_dict[algorithm] + self.tokenizer = tokenizer_config['class'].from_pretrained( + tokenizer_config['pretrained_model']) + self.label2id_map, id2label_map = load_vqa_bio_label_maps(class_path) + self.add_special_ids = add_special_ids + self.infer_mode = infer_mode + self.ocr_engine = ocr_engine + + def __call__(self, data): + # load bbox and label info + ocr_info = self._load_ocr_info(data) + + height, width, _ = data['image'].shape + + words_list = [] + bbox_list = [] + input_ids_list = [] + token_type_ids_list = [] + segment_offset_id = [] + gt_label_list = [] + + entities = [] + + # for re + train_re = self.contains_re and not self.infer_mode + if train_re: + relations = [] + id2label = {} + entity_id_to_index_map = {} + empty_entity = set() + + data['ocr_info'] = copy.deepcopy(ocr_info) + + for info in ocr_info: + if train_re: + # for re + if len(info["text"]) == 0: + empty_entity.add(info["id"]) + continue + id2label[info["id"]] = info["label"] + relations.extend([tuple(sorted(l)) for l in info["linking"]]) + # smooth_box + bbox = self._smooth_box(info["bbox"], height, width) + + text = info["text"] + encode_res = self.tokenizer.encode( + text, pad_to_max_seq_len=False, return_attention_mask=True) + + if not self.add_special_ids: + # TODO: use tok.all_special_ids to remove + encode_res["input_ids"] = encode_res["input_ids"][1:-1] + encode_res["token_type_ids"] = encode_res["token_type_ids"][1: + -1] + encode_res["attention_mask"] = encode_res["attention_mask"][1: + -1] + # parse label + if not self.infer_mode: + label = info['label'] + gt_label = self._parse_label(label, encode_res) + + # construct entities for re + if train_re: + if gt_label[0] != self.label2id_map["O"]: + entity_id_to_index_map[info["id"]] = len(entities) + label = label.upper() + entities.append({ + "start": len(input_ids_list), + "end": + len(input_ids_list) + len(encode_res["input_ids"]), + "label": label.upper(), + }) + else: + entities.append({ + "start": len(input_ids_list), + "end": len(input_ids_list) + len(encode_res["input_ids"]), + "label": 'O', + }) + input_ids_list.extend(encode_res["input_ids"]) + token_type_ids_list.extend(encode_res["token_type_ids"]) + bbox_list.extend([bbox] * len(encode_res["input_ids"])) + words_list.append(text) + segment_offset_id.append(len(input_ids_list)) + if not self.infer_mode: + gt_label_list.extend(gt_label) + + data['input_ids'] = input_ids_list + data['token_type_ids'] = token_type_ids_list + data['bbox'] = bbox_list + data['attention_mask'] = [1] * len(input_ids_list) + data['labels'] = gt_label_list + data['segment_offset_id'] = segment_offset_id + data['tokenizer_params'] = dict( + padding_side=self.tokenizer.padding_side, + pad_token_type_id=self.tokenizer.pad_token_type_id, + pad_token_id=self.tokenizer.pad_token_id) + data['entities'] = entities + + if train_re: + data['relations'] = relations + data['id2label'] = id2label + data['empty_entity'] = empty_entity + data['entity_id_to_index_map'] = entity_id_to_index_map + return data + + def _load_ocr_info(self, data): + def trans_poly_to_bbox(poly): + x1 = np.min([p[0] for p in poly]) + x2 = np.max([p[0] for p in poly]) + y1 = np.min([p[1] for p in poly]) + y2 = np.max([p[1] for p in poly]) + return [x1, y1, x2, y2] + + if self.infer_mode: + ocr_result = self.ocr_engine.ocr(data['image'], cls=False) + ocr_info = [] + for res in ocr_result: + ocr_info.append({ + "text": res[1][0], + "bbox": trans_poly_to_bbox(res[0]), + "poly": res[0], + }) + return ocr_info + else: + info = data['label'] + # read text info + info_dict = json.loads(info) + return info_dict["ocr_info"] + + def _smooth_box(self, bbox, height, width): + bbox[0] = int(bbox[0] * 1000.0 / width) + bbox[2] = int(bbox[2] * 1000.0 / width) + bbox[1] = int(bbox[1] * 1000.0 / height) + bbox[3] = int(bbox[3] * 1000.0 / height) + return bbox + + def _parse_label(self, label, encode_res): + gt_label = [] + if label.lower() == "other": + gt_label.extend([0] * len(encode_res["input_ids"])) + else: + gt_label.append(self.label2id_map[("b-" + label).upper()]) + gt_label.extend([self.label2id_map[("i-" + label).upper()]] * + (len(encode_res["input_ids"]) - 1)) + return gt_label + + +class MultiLabelEncode(BaseRecLabelEncode): + def __init__(self, + max_text_length, + character_dict_path=None, + use_space_char=False, + **kwargs): + super(MultiLabelEncode, self).__init__( + max_text_length, character_dict_path, use_space_char) + + self.ctc_encode = CTCLabelEncode(max_text_length, character_dict_path, + use_space_char, **kwargs) + self.sar_encode = SARLabelEncode(max_text_length, character_dict_path, + use_space_char, **kwargs) + + def __call__(self, data): + + data_ctc = copy.deepcopy(data) + data_sar = copy.deepcopy(data) + data_out = dict() + data_out['img_path'] = data.get('img_path', None) + data_out['image'] = data['image'] + ctc = self.ctc_encode.__call__(data_ctc) + sar = self.sar_encode.__call__(data_sar) + if ctc is None or sar is None: + return None + data_out['label_ctc'] = ctc['label'] + data_out['label_sar'] = sar['label'] + data_out['length'] = ctc['length'] + return data_out diff --git a/toolbox/PaddleOCR/ppocr/data/imaug/rec_img_aug.py b/toolbox/PaddleOCR/ppocr/data/imaug/rec_img_aug.py new file mode 100644 index 0000000000000000000000000000000000000000..50f63452ad56db42b15153dfcfdc6347e4263220 --- /dev/null +++ b/toolbox/PaddleOCR/ppocr/data/imaug/rec_img_aug.py @@ -0,0 +1,603 @@ +# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve. +# Copyright (c) 2024, Shanghai Iluvatar CoreX Semiconductor Co., Ltd. +# All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +import cv2 +import numpy as np +import random +import copy +from PIL import Image +from .text_image_aug import tia_perspective, tia_stretch, tia_distort + + +class RecAug(object): + def __init__(self, use_tia=True, aug_prob=0.4, **kwargs): + self.use_tia = use_tia + self.aug_prob = aug_prob + + def __call__(self, data): + img = data['image'] + img = warp(img, 10, self.use_tia, self.aug_prob) + data['image'] = img + return data + + +class RecConAug(object): + def __init__(self, + prob=0.5, + image_shape=(32, 320, 3), + max_text_length=25, + ext_data_num=1, + **kwargs): + self.ext_data_num = ext_data_num + self.prob = prob + self.max_text_length = max_text_length + self.image_shape = image_shape + self.max_wh_ratio = self.image_shape[1] / self.image_shape[0] + + def merge_ext_data(self, data, ext_data): + ori_w = round(data['image'].shape[1] / data['image'].shape[0] * + self.image_shape[0]) + ext_w = round(ext_data['image'].shape[1] / ext_data['image'].shape[0] * + self.image_shape[0]) + data['image'] = cv2.resize(data['image'], (ori_w, self.image_shape[0])) + ext_data['image'] = cv2.resize(ext_data['image'], + (ext_w, self.image_shape[0])) + data['image'] = np.concatenate( + [data['image'], ext_data['image']], axis=1) + data["label"] += ext_data["label"] + return data + + def __call__(self, data): + rnd_num = random.random() + if rnd_num > self.prob: + return data + for idx, ext_data in enumerate(data["ext_data"]): + if len(data["label"]) + len(ext_data[ + "label"]) > self.max_text_length: + break + concat_ratio = data['image'].shape[1] / data['image'].shape[ + 0] + ext_data['image'].shape[1] / ext_data['image'].shape[0] + if concat_ratio > self.max_wh_ratio: + break + data = self.merge_ext_data(data, ext_data) + data.pop("ext_data") + return data + + +class ClsResizeImg(object): + def __init__(self, image_shape, **kwargs): + self.image_shape = image_shape + + def __call__(self, data): + img = data['image'] + norm_img, _ = resize_norm_img(img, self.image_shape) + data['image'] = norm_img + return data + + +class NRTRRecResizeImg(object): + def __init__(self, image_shape, resize_type, padding=False, **kwargs): + self.image_shape = image_shape + self.resize_type = resize_type + self.padding = padding + + def __call__(self, data): + img = data['image'] + img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) + image_shape = self.image_shape + if self.padding: + imgC, imgH, imgW = image_shape + # todo: change to 0 and modified image shape + h = img.shape[0] + w = img.shape[1] + ratio = w / float(h) + if math.ceil(imgH * ratio) > imgW: + resized_w = imgW + else: + resized_w = int(math.ceil(imgH * ratio)) + resized_image = cv2.resize(img, (resized_w, imgH)) + norm_img = np.expand_dims(resized_image, -1) + norm_img = norm_img.transpose((2, 0, 1)) + resized_image = norm_img.astype(np.float32) / 128. - 1. + padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32) + padding_im[:, :, 0:resized_w] = resized_image + data['image'] = padding_im + return data + if self.resize_type == 'PIL': + image_pil = Image.fromarray(np.uint8(img)) + img = image_pil.resize(self.image_shape, Image.ANTIALIAS) + img = np.array(img) + if self.resize_type == 'OpenCV': + img = cv2.resize(img, self.image_shape) + norm_img = np.expand_dims(img, -1) + norm_img = norm_img.transpose((2, 0, 1)) + data['image'] = norm_img.astype(np.float32) / 128. - 1. + return data + + +class RecResizeImg(object): + def __init__(self, + image_shape, + infer_mode=False, + character_dict_path='./ppocr/utils/ppocr_keys_v1.txt', + padding=True, + **kwargs): + self.image_shape = image_shape + self.infer_mode = infer_mode + self.character_dict_path = character_dict_path + self.padding = padding + + def __call__(self, data): + img = data['image'] + if self.infer_mode and self.character_dict_path is not None: + norm_img, valid_ratio = resize_norm_img_chinese(img, + self.image_shape) + else: + norm_img, valid_ratio = resize_norm_img(img, self.image_shape, + self.padding) + data['image'] = norm_img + data['valid_ratio'] = valid_ratio + return data + + +class SRNRecResizeImg(object): + def __init__(self, image_shape, num_heads, max_text_length, **kwargs): + self.image_shape = image_shape + self.num_heads = num_heads + self.max_text_length = max_text_length + + def __call__(self, data): + img = data['image'] + norm_img = resize_norm_img_srn(img, self.image_shape) + data['image'] = norm_img + [encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1, gsrm_slf_attn_bias2] = \ + srn_other_inputs(self.image_shape, self.num_heads, self.max_text_length) + + data['encoder_word_pos'] = encoder_word_pos + data['gsrm_word_pos'] = gsrm_word_pos + data['gsrm_slf_attn_bias1'] = gsrm_slf_attn_bias1 + data['gsrm_slf_attn_bias2'] = gsrm_slf_attn_bias2 + return data + + +class SARRecResizeImg(object): + def __init__(self, image_shape, width_downsample_ratio=0.25, **kwargs): + self.image_shape = image_shape + self.width_downsample_ratio = width_downsample_ratio + + def __call__(self, data): + img = data['image'] + norm_img, resize_shape, pad_shape, valid_ratio = resize_norm_img_sar( + img, self.image_shape, self.width_downsample_ratio) + data['image'] = norm_img + data['resized_shape'] = resize_shape + data['pad_shape'] = pad_shape + data['valid_ratio'] = valid_ratio + return data + + +class PRENResizeImg(object): + def __init__(self, image_shape, **kwargs): + """ + Accroding to original paper's realization, it's a hard resize method here. + So maybe you should optimize it to fit for your task better. + """ + self.dst_h, self.dst_w = image_shape + + def __call__(self, data): + img = data['image'] + resized_img = cv2.resize( + img, (self.dst_w, self.dst_h), interpolation=cv2.INTER_LINEAR) + resized_img = resized_img.transpose((2, 0, 1)) / 255 + resized_img -= 0.5 + resized_img /= 0.5 + data['image'] = resized_img.astype(np.float32) + return data + + +def resize_norm_img_sar(img, image_shape, width_downsample_ratio=0.25): + imgC, imgH, imgW_min, imgW_max = image_shape + h = img.shape[0] + w = img.shape[1] + valid_ratio = 1.0 + # make sure new_width is an integral multiple of width_divisor. + width_divisor = int(1 / width_downsample_ratio) + # resize + ratio = w / float(h) + resize_w = math.ceil(imgH * ratio) + if resize_w % width_divisor != 0: + resize_w = round(resize_w / width_divisor) * width_divisor + if imgW_min is not None: + resize_w = max(imgW_min, resize_w) + if imgW_max is not None: + valid_ratio = min(1.0, 1.0 * resize_w / imgW_max) + resize_w = min(imgW_max, resize_w) + resized_image = cv2.resize(img, (resize_w, imgH)) + resized_image = resized_image.astype('float32') + # norm + if image_shape[0] == 1: + resized_image = resized_image / 255 + resized_image = resized_image[np.newaxis, :] + else: + resized_image = resized_image.transpose((2, 0, 1)) / 255 + resized_image -= 0.5 + resized_image /= 0.5 + resize_shape = resized_image.shape + padding_im = -1.0 * np.ones((imgC, imgH, imgW_max), dtype=np.float32) + padding_im[:, :, 0:resize_w] = resized_image + pad_shape = padding_im.shape + + return padding_im, resize_shape, pad_shape, valid_ratio + + +def resize_norm_img(img, image_shape, padding=True): + imgC, imgH, imgW = image_shape + h = img.shape[0] + w = img.shape[1] + if not padding: + resized_image = cv2.resize( + img, (imgW, imgH), interpolation=cv2.INTER_LINEAR) + resized_w = imgW + else: + ratio = w / float(h) + if math.ceil(imgH * ratio) > imgW: + resized_w = imgW + else: + resized_w = int(math.ceil(imgH * ratio)) + resized_image = cv2.resize(img, (resized_w, imgH)) + resized_image = resized_image.astype('float32') + if image_shape[0] == 1: + resized_image = resized_image / 255 + resized_image = resized_image[np.newaxis, :] + else: + resized_image = resized_image.transpose((2, 0, 1)) / 255 + resized_image -= 0.5 + resized_image /= 0.5 + padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32) + padding_im[:, :, 0:resized_w] = resized_image + valid_ratio = min(1.0, float(resized_w / imgW)) + return padding_im, valid_ratio + + +def resize_norm_img_chinese(img, image_shape): + imgC, imgH, imgW = image_shape + # todo: change to 0 and modified image shape + max_wh_ratio = imgW * 1.0 / imgH + h, w = img.shape[0], img.shape[1] + ratio = w * 1.0 / h + max_wh_ratio = max(max_wh_ratio, ratio) + imgW = int(imgH * max_wh_ratio) + if math.ceil(imgH * ratio) > imgW: + resized_w = imgW + else: + resized_w = int(math.ceil(imgH * ratio)) + resized_image = cv2.resize(img, (resized_w, imgH)) + resized_image = resized_image.astype('float32') + if image_shape[0] == 1: + resized_image = resized_image / 255 + resized_image = resized_image[np.newaxis, :] + else: + resized_image = resized_image.transpose((2, 0, 1)) / 255 + resized_image -= 0.5 + resized_image /= 0.5 + padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32) + padding_im[:, :, 0:resized_w] = resized_image + valid_ratio = min(1.0, float(resized_w / imgW)) + return padding_im, valid_ratio + + +def resize_norm_img_srn(img, image_shape): + imgC, imgH, imgW = image_shape + + img_black = np.zeros((imgH, imgW)) + im_hei = img.shape[0] + im_wid = img.shape[1] + + if im_wid <= im_hei * 1: + img_new = cv2.resize(img, (imgH * 1, imgH)) + elif im_wid <= im_hei * 2: + img_new = cv2.resize(img, (imgH * 2, imgH)) + elif im_wid <= im_hei * 3: + img_new = cv2.resize(img, (imgH * 3, imgH)) + else: + img_new = cv2.resize(img, (imgW, imgH)) + + img_np = np.asarray(img_new) + img_np = cv2.cvtColor(img_np, cv2.COLOR_BGR2GRAY) + img_black[:, 0:img_np.shape[1]] = img_np + img_black = img_black[:, :, np.newaxis] + + row, col, c = img_black.shape + c = 1 + + return np.reshape(img_black, (c, row, col)).astype(np.float32) + + +def srn_other_inputs(image_shape, num_heads, max_text_length): + + imgC, imgH, imgW = image_shape + feature_dim = int((imgH / 8) * (imgW / 8)) + + encoder_word_pos = np.array(range(0, feature_dim)).reshape( + (feature_dim, 1)).astype('int64') + gsrm_word_pos = np.array(range(0, max_text_length)).reshape( + (max_text_length, 1)).astype('int64') + + gsrm_attn_bias_data = np.ones((1, max_text_length, max_text_length)) + gsrm_slf_attn_bias1 = np.triu(gsrm_attn_bias_data, 1).reshape( + [1, max_text_length, max_text_length]) + gsrm_slf_attn_bias1 = np.tile(gsrm_slf_attn_bias1, + [num_heads, 1, 1]) * [-1e9] + + gsrm_slf_attn_bias2 = np.tril(gsrm_attn_bias_data, -1).reshape( + [1, max_text_length, max_text_length]) + gsrm_slf_attn_bias2 = np.tile(gsrm_slf_attn_bias2, + [num_heads, 1, 1]) * [-1e9] + + return [ + encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1, + gsrm_slf_attn_bias2 + ] + + +def flag(): + """ + flag + """ + return 1 if random.random() > 0.5000001 else -1 + + +def cvtColor(img): + """ + cvtColor + """ + hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) + delta = 0.001 * random.random() * flag() + hsv[:, :, 2] = hsv[:, :, 2] * (1 + delta) + new_img = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR) + return new_img + + +def blur(img): + """ + blur + """ + h, w, _ = img.shape + if h > 10 and w > 10: + return cv2.GaussianBlur(img, (5, 5), 1) + else: + return img + + +def jitter(img): + """ + jitter + """ + w, h, _ = img.shape + if h > 10 and w > 10: + thres = min(w, h) + s = int(random.random() * thres * 0.01) + src_img = img.copy() + for i in range(s): + img[i:, i:, :] = src_img[:w - i, :h - i, :] + return img + else: + return img + + +def add_gasuss_noise(image, mean=0, var=0.1): + """ + Gasuss noise + """ + + noise = np.random.normal(mean, var**0.5, image.shape) + out = image + 0.5 * noise + out = np.clip(out, 0, 255) + out = np.uint8(out) + return out + + +def get_crop(image): + """ + random crop + """ + h, w, _ = image.shape + top_min = 1 + top_max = 8 + top_crop = int(random.randint(top_min, top_max)) + top_crop = min(top_crop, h - 1) + crop_img = image.copy() + ratio = random.randint(0, 1) + if ratio: + crop_img = crop_img[top_crop:h, :, :] + else: + crop_img = crop_img[0:h - top_crop, :, :] + return crop_img + + +class Config: + """ + Config + """ + + def __init__(self, use_tia): + self.anglex = random.random() * 30 + self.angley = random.random() * 15 + self.anglez = random.random() * 10 + self.fov = 42 + self.r = 0 + self.shearx = random.random() * 0.3 + self.sheary = random.random() * 0.05 + self.borderMode = cv2.BORDER_REPLICATE + self.use_tia = use_tia + + def make(self, w, h, ang): + """ + make + """ + self.anglex = random.random() * 5 * flag() + self.angley = random.random() * 5 * flag() + self.anglez = -1 * random.random() * int(ang) * flag() + self.fov = 42 + self.r = 0 + self.shearx = 0 + self.sheary = 0 + self.borderMode = cv2.BORDER_REPLICATE + self.w = w + self.h = h + + self.perspective = self.use_tia + self.stretch = self.use_tia + self.distort = self.use_tia + + self.crop = True + self.affine = False + self.reverse = True + self.noise = True + self.jitter = True + self.blur = True + self.color = True + + +def rad(x): + """ + rad + """ + return x * np.pi / 180 + + +def get_warpR(config): + """ + get_warpR + """ + anglex, angley, anglez, fov, w, h, r = \ + config.anglex, config.angley, config.anglez, config.fov, config.w, config.h, config.r + if w > 69 and w < 112: + anglex = anglex * 1.5 + + z = np.sqrt(w**2 + h**2) / 2 / np.tan(rad(fov / 2)) + # Homogeneous coordinate transformation matrix + rx = np.array([[1, 0, 0, 0], + [0, np.cos(rad(anglex)), -np.sin(rad(anglex)), 0], [ + 0, + -np.sin(rad(anglex)), + np.cos(rad(anglex)), + 0, + ], [0, 0, 0, 1]], np.float32) + ry = np.array([[np.cos(rad(angley)), 0, np.sin(rad(angley)), 0], + [0, 1, 0, 0], [ + -np.sin(rad(angley)), + 0, + np.cos(rad(angley)), + 0, + ], [0, 0, 0, 1]], np.float32) + rz = np.array([[np.cos(rad(anglez)), np.sin(rad(anglez)), 0, 0], + [-np.sin(rad(anglez)), np.cos(rad(anglez)), 0, 0], + [0, 0, 1, 0], [0, 0, 0, 1]], np.float32) + r = rx.dot(ry).dot(rz) + # generate 4 points + pcenter = np.array([h / 2, w / 2, 0, 0], np.float32) + p1 = np.array([0, 0, 0, 0], np.float32) - pcenter + p2 = np.array([w, 0, 0, 0], np.float32) - pcenter + p3 = np.array([0, h, 0, 0], np.float32) - pcenter + p4 = np.array([w, h, 0, 0], np.float32) - pcenter + dst1 = r.dot(p1) + dst2 = r.dot(p2) + dst3 = r.dot(p3) + dst4 = r.dot(p4) + list_dst = np.array([dst1, dst2, dst3, dst4]) + org = np.array([[0, 0], [w, 0], [0, h], [w, h]], np.float32) + dst = np.zeros((4, 2), np.float32) + # Project onto the image plane + dst[:, 0] = list_dst[:, 0] * z / (z - list_dst[:, 2]) + pcenter[0] + dst[:, 1] = list_dst[:, 1] * z / (z - list_dst[:, 2]) + pcenter[1] + + warpR = cv2.getPerspectiveTransform(org, dst) + + dst1, dst2, dst3, dst4 = dst + r1 = int(min(dst1[1], dst2[1])) + r2 = int(max(dst3[1], dst4[1])) + c1 = int(min(dst1[0], dst3[0])) + c2 = int(max(dst2[0], dst4[0])) + + try: + ratio = min(1.0 * h / (r2 - r1), 1.0 * w / (c2 - c1)) + + dx = -c1 + dy = -r1 + T1 = np.float32([[1., 0, dx], [0, 1., dy], [0, 0, 1.0 / ratio]]) + ret = T1.dot(warpR) + except: + ratio = 1.0 + T1 = np.float32([[1., 0, 0], [0, 1., 0], [0, 0, 1.]]) + ret = T1 + return ret, (-r1, -c1), ratio, dst + + +def get_warpAffine(config): + """ + get_warpAffine + """ + anglez = config.anglez + rz = np.array([[np.cos(rad(anglez)), np.sin(rad(anglez)), 0], + [-np.sin(rad(anglez)), np.cos(rad(anglez)), 0]], np.float32) + return rz + + +def warp(img, ang, use_tia=True, prob=0.4): + """ + warp + """ + h, w, _ = img.shape + config = Config(use_tia=use_tia) + config.make(w, h, ang) + new_img = img + + if config.distort: + img_height, img_width = img.shape[0:2] + if random.random() <= prob and img_height >= 20 and img_width >= 20: + new_img = tia_distort(new_img, random.randint(3, 6)) + + if config.stretch: + img_height, img_width = img.shape[0:2] + if random.random() <= prob and img_height >= 20 and img_width >= 20: + new_img = tia_stretch(new_img, random.randint(3, 6)) + + if config.perspective: + if random.random() <= prob: + new_img = tia_perspective(new_img) + + if config.crop: + img_height, img_width = img.shape[0:2] + if random.random() <= prob and img_height >= 20 and img_width >= 20: + new_img = get_crop(new_img) + + if config.blur: + if random.random() <= prob: + new_img = blur(new_img) + if config.color: + if random.random() <= prob: + new_img = cvtColor(new_img) + if config.jitter: + new_img = jitter(new_img) + if config.noise: + if random.random() <= prob: + new_img = add_gasuss_noise(new_img) + if config.reverse: + if random.random() <= prob: + new_img = 255 - new_img + return new_img diff --git a/toolbox/PaddleOCR/ppocr/data/lmdb_dataset.py b/toolbox/PaddleOCR/ppocr/data/lmdb_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..a858690549bff940c550875a27ada6e9c6971130 --- /dev/null +++ b/toolbox/PaddleOCR/ppocr/data/lmdb_dataset.py @@ -0,0 +1,121 @@ +# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve. +# Copyright (c) 2024, Shanghai Iluvatar CoreX Semiconductor Co., Ltd. +# All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import numpy as np +import os +from paddle.io import Dataset +import lmdb +import cv2 + +from .imaug import transform, create_operators + + +class LMDBDataSet(Dataset): + def __init__(self, config, mode, logger, seed=None): + super(LMDBDataSet, self).__init__() + + global_config = config['Global'] + dataset_config = config[mode]['dataset'] + loader_config = config[mode]['loader'] + batch_size = loader_config['batch_size_per_card'] + data_dir = dataset_config['data_dir'] + self.do_shuffle = loader_config['shuffle'] + + self.lmdb_sets = self.load_hierarchical_lmdb_dataset(data_dir) + logger.info("Initialize indexs of datasets:%s" % data_dir) + self.data_idx_order_list = self.dataset_traversal() + if self.do_shuffle: + np.random.shuffle(self.data_idx_order_list) + self.ops = create_operators(dataset_config['transforms'], global_config) + + ratio_list = dataset_config.get("ratio_list", [1.0]) + self.need_reset = True in [x < 1 for x in ratio_list] + + def load_hierarchical_lmdb_dataset(self, data_dir): + lmdb_sets = {} + dataset_idx = 0 + for dirpath, dirnames, filenames in os.walk(data_dir + '/'): + if not dirnames: + env = lmdb.open( + dirpath, + max_readers=32, + readonly=True, + lock=False, + readahead=False, + meminit=False) + txn = env.begin(write=False) + num_samples = int(txn.get('num-samples'.encode())) + lmdb_sets[dataset_idx] = {"dirpath":dirpath, "env":env, \ + "txn":txn, "num_samples":num_samples} + dataset_idx += 1 + return lmdb_sets + + def dataset_traversal(self): + lmdb_num = len(self.lmdb_sets) + total_sample_num = 0 + for lno in range(lmdb_num): + total_sample_num += self.lmdb_sets[lno]['num_samples'] + data_idx_order_list = np.zeros((total_sample_num, 2)) + beg_idx = 0 + for lno in range(lmdb_num): + tmp_sample_num = self.lmdb_sets[lno]['num_samples'] + end_idx = beg_idx + tmp_sample_num + data_idx_order_list[beg_idx:end_idx, 0] = lno + data_idx_order_list[beg_idx:end_idx, 1] \ + = list(range(tmp_sample_num)) + data_idx_order_list[beg_idx:end_idx, 1] += 1 + beg_idx = beg_idx + tmp_sample_num + return data_idx_order_list + + def get_img_data(self, value): + """get_img_data""" + if not value: + return None + imgdata = np.frombuffer(value, dtype='uint8') + if imgdata is None: + return None + imgori = cv2.imdecode(imgdata, 1) + if imgori is None: + return None + return imgori + + def get_lmdb_sample_info(self, txn, index): + label_key = 'label-%09d'.encode() % index + label = txn.get(label_key) + if label is None: + return None + label = label.decode('utf-8') + img_key = 'image-%09d'.encode() % index + imgbuf = txn.get(img_key) + return imgbuf, label + + def __getitem__(self, idx): + lmdb_idx, file_idx = self.data_idx_order_list[idx] + lmdb_idx = int(lmdb_idx) + file_idx = int(file_idx) + sample_info = self.get_lmdb_sample_info(self.lmdb_sets[lmdb_idx]['txn'], + file_idx) + if sample_info is None: + return self.__getitem__(np.random.randint(self.__len__())) + img, label = sample_info + data = {'image': img, 'label': label} + outs = transform(data, self.ops) + if outs is None: + return self.__getitem__(np.random.randint(self.__len__())) + return outs + + def __len__(self): + return self.data_idx_order_list.shape[0] diff --git a/toolbox/PaddleOCR/ppocr/data/simple_dataset.py b/toolbox/PaddleOCR/ppocr/data/simple_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..b7b48f16445caab78c4d43ba04ff46d5e004d59c --- /dev/null +++ b/toolbox/PaddleOCR/ppocr/data/simple_dataset.py @@ -0,0 +1,154 @@ +# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve. +# Copyright (c) 2024, Shanghai Iluvatar CoreX Semiconductor Co., Ltd. +# All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import numpy as np +import os +import json +import random +import traceback +from paddle.io import Dataset +from .imaug import transform, create_operators + + +class SimpleDataSet(Dataset): + def __init__(self, config, mode, logger, seed=None): + super(SimpleDataSet, self).__init__() + self.logger = logger + self.mode = mode.lower() + + global_config = config['Global'] + dataset_config = config[mode]['dataset'] + loader_config = config[mode]['loader'] + + self.delimiter = dataset_config.get('delimiter', '\t') + label_file_list = dataset_config.pop('label_file_list') + data_source_num = len(label_file_list) + ratio_list = dataset_config.get("ratio_list", [1.0]) + if isinstance(ratio_list, (float, int)): + ratio_list = [float(ratio_list)] * int(data_source_num) + + assert len( + ratio_list + ) == data_source_num, "The length of ratio_list should be the same as the file_list." + self.data_dir = dataset_config['data_dir'] + self.do_shuffle = loader_config['shuffle'] + self.seed = seed + logger.info("Initialize indexs of datasets:%s" % label_file_list) + self.data_lines = self.get_image_info_list(label_file_list, ratio_list) + self.data_idx_order_list = list(range(len(self.data_lines))) + if self.mode == "train" and self.do_shuffle: + self.shuffle_data_random() + self.ops = create_operators(dataset_config['transforms'], global_config) + self.ext_op_transform_idx = dataset_config.get("ext_op_transform_idx", + 2) + self.need_reset = True in [x < 1 for x in ratio_list] + + def get_image_info_list(self, file_list, ratio_list): + if isinstance(file_list, str): + file_list = [file_list] + data_lines = [] + for idx, file in enumerate(file_list): + with open(file, "rb") as f: + lines = f.readlines() + if self.mode == "train" or ratio_list[idx] < 1.0: + random.seed(self.seed) + lines = random.sample(lines, + round(len(lines) * ratio_list[idx])) + data_lines.extend(lines) + return data_lines + + def shuffle_data_random(self): + random.seed(self.seed) + random.shuffle(self.data_lines) + return + + def _try_parse_filename_list(self, file_name): + # multiple images -> one gt label + if len(file_name) > 0 and file_name[0] == "[": + try: + info = json.loads(file_name) + file_name = random.choice(info) + except: + pass + return file_name + + def get_ext_data(self): + ext_data_num = 0 + for op in self.ops: + if hasattr(op, 'ext_data_num'): + ext_data_num = getattr(op, 'ext_data_num') + break + load_data_ops = self.ops[:self.ext_op_transform_idx] + ext_data = [] + + while len(ext_data) < ext_data_num: + file_idx = self.data_idx_order_list[np.random.randint(self.__len__( + ))] + data_line = self.data_lines[file_idx] + data_line = data_line.decode('utf-8') + substr = data_line.strip("\n").split(self.delimiter) + file_name = substr[0] + file_name = self._try_parse_filename_list(file_name) + label = substr[1] + img_path = os.path.join(self.data_dir, file_name) + data = {'img_path': img_path, 'label': label} + if not os.path.exists(img_path): + continue + with open(data['img_path'], 'rb') as f: + img = f.read() + data['image'] = img + data = transform(data, load_data_ops) + + if data is None: + continue + if 'polys' in data.keys(): + if data['polys'].shape[1] != 4: + continue + ext_data.append(data) + return ext_data + + def __getitem__(self, idx): + file_idx = self.data_idx_order_list[idx] + data_line = self.data_lines[file_idx] + try: + data_line = data_line.decode('utf-8') + substr = data_line.strip("\n").split(self.delimiter) + file_name = substr[0] + file_name = self._try_parse_filename_list(file_name) + label = substr[1] + img_path = os.path.join(self.data_dir, file_name) + data = {'img_path': img_path, 'label': label} + if not os.path.exists(img_path): + raise Exception("{} does not exist!".format(img_path)) + with open(data['img_path'], 'rb') as f: + img = f.read() + data['image'] = img + data['ext_data'] = self.get_ext_data() + outs = transform(data, self.ops) + except: + self.logger.error( + "When parsing line {}, error happened with msg: {}".format( + data_line, traceback.format_exc())) + outs = None + if outs is None: + # during evaluation, we should fix the idx to get same results for many times of evaluation. + rnd_idx = np.random.randint(self.__len__( + )) if self.mode == "train" else (idx + 1) % self.__len__() + return self.__getitem__(rnd_idx) + return outs + + def __len__(self): + return len(self.data_idx_order_list) diff --git a/toolbox/PaddleOCR/ppocr/losses/basic_loss.py b/toolbox/PaddleOCR/ppocr/losses/basic_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..fe5f89361f4a2e37152fa56cd9d2befdfbfd66bc --- /dev/null +++ b/toolbox/PaddleOCR/ppocr/losses/basic_loss.py @@ -0,0 +1,157 @@ +#copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve. +# Copyright (c) 2024, Shanghai Iluvatar CoreX Semiconductor Co., Ltd. +# All Rights Reserved. +# +#Licensed under the Apache License, Version 2.0 (the "License"); +#you may not use this file except in compliance with the License. +#You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +#Unless required by applicable law or agreed to in writing, software +#distributed under the License is distributed on an "AS IS" BASIS, +#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +#See the License for the specific language governing permissions and +#limitations under the License. + +import paddle +import paddle.nn as nn +import paddle.nn.functional as F + +from paddle.nn import L1Loss +from paddle.nn import MSELoss as L2Loss +from paddle.nn import SmoothL1Loss + + +class CELoss(nn.Layer): + def __init__(self, epsilon=None): + super().__init__() + if epsilon is not None and (epsilon <= 0 or epsilon >= 1): + epsilon = None + self.epsilon = epsilon + + def _labelsmoothing(self, target, class_num): + if target.shape[-1] != class_num: + one_hot_target = F.one_hot(target, class_num) + else: + one_hot_target = target + soft_target = F.label_smooth(one_hot_target, epsilon=self.epsilon) + soft_target = paddle.reshape(soft_target, shape=[-1, class_num]) + return soft_target + + def forward(self, x, label): + loss_dict = {} + if self.epsilon is not None: + class_num = x.shape[-1] + label = self._labelsmoothing(label, class_num) + x = -F.log_softmax(x, axis=-1) + loss = paddle.sum(x * label, axis=-1) + else: + if label.shape[-1] == x.shape[-1]: + label = F.softmax(label, axis=-1) + soft_label = True + else: + soft_label = False + loss = F.cross_entropy(x, label=label, soft_label=soft_label) + return loss + + +class KLJSLoss(object): + def __init__(self, mode='kl'): + assert mode in ['kl', 'js', 'KL', 'JS' + ], "mode can only be one of ['kl', 'js', 'KL', 'JS']" + self.mode = mode + + def __call__(self, p1, p2, reduction="mean"): + + loss = paddle.multiply(p2, paddle.log((p2 + 1e-5) / (p1 + 1e-5) + 1e-5)) + + if self.mode.lower() == "js": + loss += paddle.multiply( + p1, paddle.log((p1 + 1e-5) / (p2 + 1e-5) + 1e-5)) + loss *= 0.5 + if reduction == "mean": + loss = paddle.mean(loss, axis=[1, 2]) + elif reduction == "none" or reduction is None: + return loss + else: + loss = paddle.sum(loss, axis=[1, 2]) + + return loss + + +class DMLLoss(nn.Layer): + """ + DMLLoss + """ + + def __init__(self, act=None, use_log=False): + super().__init__() + if act is not None: + assert act in ["softmax", "sigmoid"] + if act == "softmax": + self.act = nn.Softmax(axis=-1) + elif act == "sigmoid": + self.act = nn.Sigmoid() + else: + self.act = None + + self.use_log = use_log + self.jskl_loss = KLJSLoss(mode="js") + + def _kldiv(self, x, target): + eps = 1.0e-10 + loss = target * (paddle.log(target + eps) - x) + # batch mean loss + loss = paddle.sum(loss) / loss.shape[0] + return loss + + def forward(self, out1, out2): + if self.act is not None: + out1 = self.act(out1) + 1e-10 + out2 = self.act(out2) + 1e-10 + if self.use_log: + # for recognition distillation, log is needed for feature map + log_out1 = paddle.log(out1) + log_out2 = paddle.log(out2) + loss = ( + self._kldiv(log_out1, out2) + self._kldiv(log_out2, out1)) / 2.0 + else: + # for detection distillation log is not needed + loss = self.jskl_loss(out1, out2) + return loss + + +class DistanceLoss(nn.Layer): + """ + DistanceLoss: + mode: loss mode + """ + + def __init__(self, mode="l2", **kargs): + super().__init__() + assert mode in ["l1", "l2", "smooth_l1"] + if mode == "l1": + self.loss_func = nn.L1Loss(**kargs) + elif mode == "l2": + self.loss_func = nn.MSELoss(**kargs) + elif mode == "smooth_l1": + self.loss_func = nn.SmoothL1Loss(**kargs) + + def forward(self, x, y): + return self.loss_func(x, y) + + +class LossFromOutput(nn.Layer): + def __init__(self, key='loss', reduction='none'): + super().__init__() + self.key = key + self.reduction = reduction + + def forward(self, predicts, batch): + loss = predicts[self.key] + if self.reduction == 'mean': + loss = paddle.mean(loss) + elif self.reduction == 'sum': + loss = paddle.sum(loss) + return {'loss': loss} diff --git a/toolbox/PaddleOCR/ppocr/losses/rec_aster_loss.py b/toolbox/PaddleOCR/ppocr/losses/rec_aster_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..6f4b83a8341365118af083660a458b8ac37310c9 --- /dev/null +++ b/toolbox/PaddleOCR/ppocr/losses/rec_aster_loss.py @@ -0,0 +1,101 @@ +# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve. +# Copyright (c) 2024, Shanghai Iluvatar CoreX Semiconductor Co., Ltd. +# All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import paddle +from paddle import nn + + +class CosineEmbeddingLoss(nn.Layer): + def __init__(self, margin=0.): + super(CosineEmbeddingLoss, self).__init__() + self.margin = margin + self.epsilon = 1e-12 + + def forward(self, x1, x2, target): + similarity = paddle.fluid.layers.reduce_sum( + x1 * x2, dim=-1) / (paddle.norm( + x1, axis=-1) * paddle.norm( + x2, axis=-1) + self.epsilon) + one_list = paddle.full_like(target, fill_value=1) + out = paddle.fluid.layers.reduce_mean( + paddle.where( + paddle.equal(target, one_list), 1. - similarity, + paddle.maximum( + paddle.zeros_like(similarity), similarity - self.margin))) + + return out + + +class AsterLoss(nn.Layer): + def __init__(self, + weight=None, + size_average=True, + ignore_index=-100, + sequence_normalize=False, + sample_normalize=True, + **kwargs): + super(AsterLoss, self).__init__() + self.weight = weight + self.size_average = size_average + self.ignore_index = ignore_index + self.sequence_normalize = sequence_normalize + self.sample_normalize = sample_normalize + self.loss_sem = CosineEmbeddingLoss() + self.is_cosin_loss = True + self.loss_func_rec = nn.CrossEntropyLoss(weight=None, reduction='none') + + def forward(self, predicts, batch): + targets = batch[1].astype("int64") + label_lengths = batch[2].astype('int64') + sem_target = batch[3].astype('float32') + embedding_vectors = predicts['embedding_vectors'] + rec_pred = predicts['rec_pred'] + + if not self.is_cosin_loss: + sem_loss = paddle.sum(self.loss_sem(embedding_vectors, sem_target)) + else: + label_target = paddle.ones([embedding_vectors.shape[0]]) + sem_loss = paddle.sum( + self.loss_sem(embedding_vectors, sem_target, label_target)) + + # rec loss + batch_size, def_max_length = targets.shape[0], targets.shape[1] + + mask = paddle.zeros([batch_size, def_max_length]) + for i in range(batch_size): + mask[i, :label_lengths[i]] = 1 + mask = paddle.cast(mask, "float32") + max_length = max(label_lengths) + assert max_length == rec_pred.shape[1] + targets = targets[:, :max_length] + mask = mask[:, :max_length] + rec_pred = paddle.reshape(rec_pred, [-1, rec_pred.shape[2]]) + input = nn.functional.log_softmax(rec_pred, axis=1) + targets = paddle.reshape(targets, [-1, 1]) + mask = paddle.reshape(mask, [-1, 1]) + output = -paddle.index_sample(input, index=targets) * mask + output = paddle.sum(output) + if self.sequence_normalize: + output = output / paddle.sum(mask) + if self.sample_normalize: + output = output / batch_size + + loss = output + sem_loss * 0.1 + return {'loss': loss} diff --git a/toolbox/PaddleOCR/ppocr/losses/table_att_loss.py b/toolbox/PaddleOCR/ppocr/losses/table_att_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..b10a1748527bae53c2813d35b66b9cf02690cf2d --- /dev/null +++ b/toolbox/PaddleOCR/ppocr/losses/table_att_loss.py @@ -0,0 +1,111 @@ +# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve. +# Copyright (c) 2024, Shanghai Iluvatar CoreX Semiconductor Co., Ltd. +# All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import paddle +from paddle import nn +from paddle.nn import functional as F +from paddle import fluid + +class TableAttentionLoss(nn.Layer): + def __init__(self, structure_weight, loc_weight, use_giou=False, giou_weight=1.0, **kwargs): + super(TableAttentionLoss, self).__init__() + self.loss_func = nn.CrossEntropyLoss(weight=None, reduction='none') + self.structure_weight = structure_weight + self.loc_weight = loc_weight + self.use_giou = use_giou + self.giou_weight = giou_weight + + def giou_loss(self, preds, bbox, eps=1e-7, reduction='mean'): + ''' + :param preds:[[x1,y1,x2,y2], [x1,y1,x2,y2],,,] + :param bbox:[[x1,y1,x2,y2], [x1,y1,x2,y2],,,] + :return: loss + ''' + ix1 = fluid.layers.elementwise_max(preds[:, 0], bbox[:, 0]) + iy1 = fluid.layers.elementwise_max(preds[:, 1], bbox[:, 1]) + ix2 = fluid.layers.elementwise_min(preds[:, 2], bbox[:, 2]) + iy2 = fluid.layers.elementwise_min(preds[:, 3], bbox[:, 3]) + + iw = fluid.layers.clip(ix2 - ix1 + 1e-3, 0., 1e10) + ih = fluid.layers.clip(iy2 - iy1 + 1e-3, 0., 1e10) + + # overlap + inters = iw * ih + + # union + uni = (preds[:, 2] - preds[:, 0] + 1e-3) * (preds[:, 3] - preds[:, 1] + 1e-3 + ) + (bbox[:, 2] - bbox[:, 0] + 1e-3) * ( + bbox[:, 3] - bbox[:, 1] + 1e-3) - inters + eps + + # ious + ious = inters / uni + + ex1 = fluid.layers.elementwise_min(preds[:, 0], bbox[:, 0]) + ey1 = fluid.layers.elementwise_min(preds[:, 1], bbox[:, 1]) + ex2 = fluid.layers.elementwise_max(preds[:, 2], bbox[:, 2]) + ey2 = fluid.layers.elementwise_max(preds[:, 3], bbox[:, 3]) + ew = fluid.layers.clip(ex2 - ex1 + 1e-3, 0., 1e10) + eh = fluid.layers.clip(ey2 - ey1 + 1e-3, 0., 1e10) + + # enclose erea + enclose = ew * eh + eps + giou = ious - (enclose - uni) / enclose + + loss = 1 - giou + + if reduction == 'mean': + loss = paddle.mean(loss) + elif reduction == 'sum': + loss = paddle.sum(loss) + else: + raise NotImplementedError + return loss + + def forward(self, predicts, batch): + structure_probs = predicts['structure_probs'] + structure_targets = batch[1].astype("int64") + structure_targets = structure_targets[:, 1:] + if len(batch) == 6: + structure_mask = batch[5].astype("int64") + structure_mask = structure_mask[:, 1:] + structure_mask = paddle.reshape(structure_mask, [-1]) + structure_probs = paddle.reshape(structure_probs, [-1, structure_probs.shape[-1]]) + structure_targets = paddle.reshape(structure_targets, [-1]) + structure_loss = self.loss_func(structure_probs, structure_targets) + + if len(batch) == 6: + structure_loss = structure_loss * structure_mask + +# structure_loss = paddle.sum(structure_loss) * self.structure_weight + structure_loss = paddle.mean(structure_loss) * self.structure_weight + + loc_preds = predicts['loc_preds'] + loc_targets = batch[2].astype("float32") + loc_targets_mask = batch[4].astype("float32") + loc_targets = loc_targets[:, 1:, :] + loc_targets_mask = loc_targets_mask[:, 1:, :] + loc_loss = F.mse_loss(loc_preds * loc_targets_mask, loc_targets) * self.loc_weight + if self.use_giou: + loc_loss_giou = self.giou_loss(loc_preds * loc_targets_mask, loc_targets) * self.giou_weight + total_loss = structure_loss + loc_loss + loc_loss_giou + return {'loss':total_loss, "structure_loss":structure_loss, "loc_loss":loc_loss, "loc_loss_giou":loc_loss_giou} + else: + total_loss = structure_loss + loc_loss + return {'loss':total_loss, "structure_loss":structure_loss, "loc_loss":loc_loss} diff --git a/toolbox/PaddleOCR/ppocr/metrics/kie_metric.py b/toolbox/PaddleOCR/ppocr/metrics/kie_metric.py new file mode 100644 index 0000000000000000000000000000000000000000..3142597025c735e39dcde36e4966e5bf04140294 --- /dev/null +++ b/toolbox/PaddleOCR/ppocr/metrics/kie_metric.py @@ -0,0 +1,73 @@ +# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +# Copyright (c) 2024, Shanghai Iluvatar CoreX Semiconductor Co., Ltd. +# All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# The code is refer from: https://github.com/open-mmlab/mmocr/blob/main/mmocr/core/evaluation/kie_metric.py + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np +import paddle + +__all__ = ['KIEMetric'] + + +class KIEMetric(object): + def __init__(self, main_indicator='hmean', **kwargs): + self.main_indicator = main_indicator + self.reset() + self.node = [] + self.gt = [] + + def __call__(self, preds, batch, **kwargs): + nodes, _ = preds + gts, tag = batch[4].squeeze(0), batch[5].tolist()[0] + gts = gts[:tag[0], :1].reshape([-1]) + self.node.append(nodes.numpy()) + self.gt.append(gts) + # result = self.compute_f1_score(nodes, gts) + # self.results.append(result) + + def compute_f1_score(self, preds, gts): + ignores = [0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 25] + C = preds.shape[1] + classes = np.array(sorted(set(range(C)) - set(ignores))) + hist = np.bincount( + (gts * C).astype('int64') + preds.argmax(1), minlength=C + **2).reshape([C, C]).astype('float32') + diag = np.diag(hist) + recalls = diag / hist.sum(1).clip(min=1) + precisions = diag / hist.sum(0).clip(min=1) + f1 = 2 * recalls * precisions / (recalls + precisions).clip(min=1e-8) + return f1[classes] + + def combine_results(self, results): + node = np.concatenate(self.node, 0) + gts = np.concatenate(self.gt, 0) + results = self.compute_f1_score(node, gts) + data = {'hmean': results.mean()} + return data + + def get_metric(self): + + metrics = self.combine_results(self.results) + self.reset() + return metrics + + def reset(self): + self.results = [] # clear results + self.node = [] + self.gt = [] diff --git a/toolbox/PaddleOCR/ppocr/metrics/rec_metric.py b/toolbox/PaddleOCR/ppocr/metrics/rec_metric.py new file mode 100644 index 0000000000000000000000000000000000000000..d54e9d5cf01c0c1494834eabc4e936313b9a11fb --- /dev/null +++ b/toolbox/PaddleOCR/ppocr/metrics/rec_metric.py @@ -0,0 +1,78 @@ +# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve. +# Copyright (c) 2024, Shanghai Iluvatar CoreX Semiconductor Co., Ltd. +# All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import Levenshtein +import string + + +class RecMetric(object): + def __init__(self, + main_indicator='acc', + is_filter=False, + ignore_space=True, + **kwargs): + self.main_indicator = main_indicator + self.is_filter = is_filter + self.ignore_space = ignore_space + self.eps = 1e-5 + self.reset() + + def _normalize_text(self, text): + text = ''.join( + filter(lambda x: x in (string.digits + string.ascii_letters), text)) + return text.lower() + + def __call__(self, pred_label, *args, **kwargs): + preds, labels = pred_label + correct_num = 0 + all_num = 0 + norm_edit_dis = 0.0 + for (pred, pred_conf), (target, _) in zip(preds, labels): + if self.ignore_space: + pred = pred.replace(" ", "") + target = target.replace(" ", "") + if self.is_filter: + pred = self._normalize_text(pred) + target = self._normalize_text(target) + norm_edit_dis += Levenshtein.distance(pred, target) / max( + len(pred), len(target), 1) + if pred == target: + correct_num += 1 + all_num += 1 + self.correct_num += correct_num + self.all_num += all_num + self.norm_edit_dis += norm_edit_dis + return { + 'acc': correct_num / (all_num + self.eps), + 'norm_edit_dis': 1 - norm_edit_dis / (all_num + self.eps) + } + + def get_metric(self): + """ + return metrics { + 'acc': 0, + 'norm_edit_dis': 0, + } + """ + acc = 1.0 * self.correct_num / (self.all_num + self.eps) + norm_edit_dis = 1 - self.norm_edit_dis / (self.all_num + self.eps) + self.reset() + return {'acc': acc, 'norm_edit_dis': norm_edit_dis} + + def reset(self): + self.correct_num = 0 + self.all_num = 0 + self.norm_edit_dis = 0 diff --git a/toolbox/PaddleOCR/ppocr/modeling/architectures/__init__.py b/toolbox/PaddleOCR/ppocr/modeling/architectures/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..0bbdd0f8cfa3e6767e1138c3eb9caef2cbb2fed7 --- /dev/null +++ b/toolbox/PaddleOCR/ppocr/modeling/architectures/__init__.py @@ -0,0 +1,34 @@ +# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. +# Copyright (c) 2024, Shanghai Iluvatar CoreX Semiconductor Co., Ltd. +# All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import copy +import importlib + +from .base_model import BaseModel +from .distillation_model import DistillationModel + +__all__ = ['build_model'] + + +def build_model(config): + config = copy.deepcopy(config) + if not "name" in config: + arch = BaseModel(config) + else: + name = config.pop("name") + mod = importlib.import_module(__name__) + arch = getattr(mod, name)(config) + return arch diff --git a/toolbox/PaddleOCR/ppocr/modeling/backbones/kie_unet_sdmgr.py b/toolbox/PaddleOCR/ppocr/modeling/backbones/kie_unet_sdmgr.py new file mode 100644 index 0000000000000000000000000000000000000000..48f84f4cdea6e19437177b678dc6bb567a3b6734 --- /dev/null +++ b/toolbox/PaddleOCR/ppocr/modeling/backbones/kie_unet_sdmgr.py @@ -0,0 +1,188 @@ +# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve. +# Copyright (c) 2024, Shanghai Iluvatar CoreX Semiconductor Co., Ltd. +# All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import paddle +from paddle import nn +import numpy as np +import cv2 + +__all__ = ["Kie_backbone"] + + +class Encoder(nn.Layer): + def __init__(self, num_channels, num_filters): + super(Encoder, self).__init__() + self.conv1 = nn.Conv2D( + num_channels, + num_filters, + kernel_size=3, + stride=1, + padding=1, + bias_attr=False) + self.bn1 = nn.BatchNorm(num_filters, act='relu') + + self.conv2 = nn.Conv2D( + num_filters, + num_filters, + kernel_size=3, + stride=1, + padding=1, + bias_attr=False) + self.bn2 = nn.BatchNorm(num_filters, act='relu') + + self.pool = nn.MaxPool2D(kernel_size=3, stride=2, padding=1) + + def forward(self, inputs): + x = self.conv1(inputs) + x = self.bn1(x) + x = self.conv2(x) + x = self.bn2(x) + x_pooled = self.pool(x) + return x, x_pooled + + +class Decoder(nn.Layer): + def __init__(self, num_channels, num_filters): + super(Decoder, self).__init__() + + self.conv1 = nn.Conv2D( + num_channels, + num_filters, + kernel_size=3, + stride=1, + padding=1, + bias_attr=False) + self.bn1 = nn.BatchNorm(num_filters, act='relu') + + self.conv2 = nn.Conv2D( + num_filters, + num_filters, + kernel_size=3, + stride=1, + padding=1, + bias_attr=False) + self.bn2 = nn.BatchNorm(num_filters, act='relu') + + self.conv0 = nn.Conv2D( + num_channels, + num_filters, + kernel_size=1, + stride=1, + padding=0, + bias_attr=False) + self.bn0 = nn.BatchNorm(num_filters, act='relu') + + def forward(self, inputs_prev, inputs): + x = self.conv0(inputs) + x = self.bn0(x) + x = paddle.nn.functional.interpolate( + x, scale_factor=2, mode='bilinear', align_corners=False) + x = paddle.concat([inputs_prev, x], axis=1) + x = self.conv1(x) + x = self.bn1(x) + x = self.conv2(x) + x = self.bn2(x) + return x + + +class UNet(nn.Layer): + def __init__(self): + super(UNet, self).__init__() + self.down1 = Encoder(num_channels=3, num_filters=16) + self.down2 = Encoder(num_channels=16, num_filters=32) + self.down3 = Encoder(num_channels=32, num_filters=64) + self.down4 = Encoder(num_channels=64, num_filters=128) + self.down5 = Encoder(num_channels=128, num_filters=256) + + self.up1 = Decoder(32, 16) + self.up2 = Decoder(64, 32) + self.up3 = Decoder(128, 64) + self.up4 = Decoder(256, 128) + self.out_channels = 16 + + def forward(self, inputs): + x1, _ = self.down1(inputs) + _, x2 = self.down2(x1) + _, x3 = self.down3(x2) + _, x4 = self.down4(x3) + _, x5 = self.down5(x4) + + x = self.up4(x4, x5) + x = self.up3(x3, x) + x = self.up2(x2, x) + x = self.up1(x1, x) + return x + + +class Kie_backbone(nn.Layer): + def __init__(self, in_channels, **kwargs): + super(Kie_backbone, self).__init__() + self.out_channels = 16 + self.img_feat = UNet() + self.maxpool = nn.MaxPool2D(kernel_size=7) + + def bbox2roi(self, bbox_list): + rois_list = [] + rois_num = [] + for img_id, bboxes in enumerate(bbox_list): + rois_num.append(bboxes.shape[0]) + rois_list.append(bboxes) + rois = paddle.concat(rois_list, 0) + rois_num = paddle.to_tensor(rois_num, dtype='int32') + return rois, rois_num + + def pre_process(self, img, relations, texts, gt_bboxes, tag, img_size): + img, relations, texts, gt_bboxes, tag, img_size = img.numpy( + ), relations.numpy(), texts.numpy(), gt_bboxes.numpy(), tag.numpy( + ).tolist(), img_size.numpy() + temp_relations, temp_texts, temp_gt_bboxes = [], [], [] + h, w = int(np.max(img_size[:, 0])), int(np.max(img_size[:, 1])) + img = paddle.to_tensor(img[:, :, :h, :w]) + batch = len(tag) + for i in range(batch): + num, recoder_len = tag[i][0], tag[i][1] + temp_relations.append( + paddle.to_tensor( + relations[i, :num, :num, :], dtype='float32')) + temp_texts.append( + paddle.to_tensor( + texts[i, :num, :recoder_len], dtype='float32')) + temp_gt_bboxes.append( + paddle.to_tensor( + gt_bboxes[i, :num, ...], dtype='float32')) + return img, temp_relations, temp_texts, temp_gt_bboxes + + def forward(self, inputs): + img = inputs[0] + relations, texts, gt_bboxes, tag, img_size = inputs[1], inputs[ + 2], inputs[3], inputs[5], inputs[-1] + img, relations, texts, gt_bboxes = self.pre_process( + img, relations, texts, gt_bboxes, tag, img_size) + x = self.img_feat(img) + boxes, rois_num = self.bbox2roi(gt_bboxes) + feats = paddle.fluid.layers.roi_align( + x, + boxes, + spatial_scale=1.0, + pooled_height=7, + pooled_width=7, + rois_num=rois_num) + feats = self.maxpool(feats).squeeze(-1).squeeze(-1) + return [relations, texts, feats] diff --git a/toolbox/PaddleOCR/ppocr/modeling/backbones/rec_resnet_fpn.py b/toolbox/PaddleOCR/ppocr/modeling/backbones/rec_resnet_fpn.py new file mode 100644 index 0000000000000000000000000000000000000000..0132a2708cab987e7144d9b3750001fe98cf394f --- /dev/null +++ b/toolbox/PaddleOCR/ppocr/modeling/backbones/rec_resnet_fpn.py @@ -0,0 +1,309 @@ +#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve. +# Copyright (c) 2024, Shanghai Iluvatar CoreX Semiconductor Co., Ltd. +# All Rights Reserved. +# +#Licensed under the Apache License, Version 2.0 (the "License"); +#you may not use this file except in compliance with the License. +#You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +#Unless required by applicable law or agreed to in writing, software +#distributed under the License is distributed on an "AS IS" BASIS, +#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +#See the License for the specific language governing permissions and +#limitations under the License. + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from paddle import nn, ParamAttr +from paddle.nn import functional as F +import paddle.fluid as fluid +import paddle +import numpy as np + +__all__ = ["ResNetFPN"] + + +class ResNetFPN(nn.Layer): + def __init__(self, in_channels=1, layers=50, **kwargs): + super(ResNetFPN, self).__init__() + supported_layers = { + 18: { + 'depth': [2, 2, 2, 2], + 'block_class': BasicBlock + }, + 34: { + 'depth': [3, 4, 6, 3], + 'block_class': BasicBlock + }, + 50: { + 'depth': [3, 4, 6, 3], + 'block_class': BottleneckBlock + }, + 101: { + 'depth': [3, 4, 23, 3], + 'block_class': BottleneckBlock + }, + 152: { + 'depth': [3, 8, 36, 3], + 'block_class': BottleneckBlock + } + } + stride_list = [(2, 2), (2, 2), (1, 1), (1, 1)] + num_filters = [64, 128, 256, 512] + self.depth = supported_layers[layers]['depth'] + self.F = [] + self.conv = ConvBNLayer( + in_channels=in_channels, + out_channels=64, + kernel_size=7, + stride=2, + act="relu", + name="conv1") + self.block_list = [] + in_ch = 64 + if layers >= 50: + for block in range(len(self.depth)): + for i in range(self.depth[block]): + if layers in [101, 152] and block == 2: + if i == 0: + conv_name = "res" + str(block + 2) + "a" + else: + conv_name = "res" + str(block + 2) + "b" + str(i) + else: + conv_name = "res" + str(block + 2) + chr(97 + i) + block_list = self.add_sublayer( + "bottleneckBlock_{}_{}".format(block, i), + BottleneckBlock( + in_channels=in_ch, + out_channels=num_filters[block], + stride=stride_list[block] if i == 0 else 1, + name=conv_name)) + in_ch = num_filters[block] * 4 + self.block_list.append(block_list) + self.F.append(block_list) + else: + for block in range(len(self.depth)): + for i in range(self.depth[block]): + conv_name = "res" + str(block + 2) + chr(97 + i) + if i == 0 and block != 0: + stride = (2, 1) + else: + stride = (1, 1) + basic_block = self.add_sublayer( + conv_name, + BasicBlock( + in_channels=in_ch, + out_channels=num_filters[block], + stride=stride_list[block] if i == 0 else 1, + is_first=block == i == 0, + name=conv_name)) + in_ch = basic_block.out_channels + self.block_list.append(basic_block) + out_ch_list = [in_ch // 4, in_ch // 2, in_ch] + self.base_block = [] + self.conv_trans = [] + self.bn_block = [] + for i in [-2, -3]: + in_channels = out_ch_list[i + 1] + out_ch_list[i] + + self.base_block.append( + self.add_sublayer( + "F_{}_base_block_0".format(i), + nn.Conv2D( + in_channels=in_channels, + out_channels=out_ch_list[i], + kernel_size=1, + weight_attr=ParamAttr(trainable=True), + bias_attr=ParamAttr(trainable=True)))) + self.base_block.append( + self.add_sublayer( + "F_{}_base_block_1".format(i), + nn.Conv2D( + in_channels=out_ch_list[i], + out_channels=out_ch_list[i], + kernel_size=3, + padding=1, + weight_attr=ParamAttr(trainable=True), + bias_attr=ParamAttr(trainable=True)))) + self.base_block.append( + self.add_sublayer( + "F_{}_base_block_2".format(i), + nn.BatchNorm( + num_channels=out_ch_list[i], + act="relu", + param_attr=ParamAttr(trainable=True), + bias_attr=ParamAttr(trainable=True)))) + self.base_block.append( + self.add_sublayer( + "F_{}_base_block_3".format(i), + nn.Conv2D( + in_channels=out_ch_list[i], + out_channels=512, + kernel_size=1, + bias_attr=ParamAttr(trainable=True), + weight_attr=ParamAttr(trainable=True)))) + self.out_channels = 512 + + def __call__(self, x): + x = self.conv(x) + fpn_list = [] + F = [] + for i in range(len(self.depth)): + fpn_list.append(np.sum(self.depth[:i + 1])) + + for i, block in enumerate(self.block_list): + x = block(x) + for number in fpn_list: + if i + 1 == number: + F.append(x) + base = F[-1] + + j = 0 + for i, block in enumerate(self.base_block): + if i % 3 == 0 and i < 6: + j = j + 1 + b, c, w, h = F[-j - 1].shape + if [w, h] == list(base.shape[2:]): + base = base + else: + base = self.conv_trans[j - 1](base) + base = self.bn_block[j - 1](base) + base = paddle.concat([base, F[-j - 1]], axis=1) + base = block(base) + return base + + +class ConvBNLayer(nn.Layer): + def __init__(self, + in_channels, + out_channels, + kernel_size, + stride=1, + groups=1, + act=None, + name=None): + super(ConvBNLayer, self).__init__() + self.conv = nn.Conv2D( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=2 if stride == (1, 1) else kernel_size, + dilation=2 if stride == (1, 1) else 1, + stride=stride, + padding=(kernel_size - 1) // 2, + groups=groups, + weight_attr=ParamAttr(name=name + '.conv2d.output.1.w_0'), + bias_attr=False, ) + + if name == "conv1": + bn_name = "bn_" + name + else: + bn_name = "bn" + name[3:] + self.bn = nn.BatchNorm( + num_channels=out_channels, + act=act, + param_attr=ParamAttr(name=name + '.output.1.w_0'), + bias_attr=ParamAttr(name=name + '.output.1.b_0'), + moving_mean_name=bn_name + "_mean", + moving_variance_name=bn_name + "_variance") + + def __call__(self, x): + x = self.conv(x) + x = self.bn(x) + return x + + +class ShortCut(nn.Layer): + def __init__(self, in_channels, out_channels, stride, name, is_first=False): + super(ShortCut, self).__init__() + self.use_conv = True + + if in_channels != out_channels or stride != 1 or is_first == True: + if stride == (1, 1): + self.conv = ConvBNLayer( + in_channels, out_channels, 1, 1, name=name) + else: # stride==(2,2) + self.conv = ConvBNLayer( + in_channels, out_channels, 1, stride, name=name) + else: + self.use_conv = False + + def forward(self, x): + if self.use_conv: + x = self.conv(x) + return x + + +class BottleneckBlock(nn.Layer): + def __init__(self, in_channels, out_channels, stride, name): + super(BottleneckBlock, self).__init__() + self.conv0 = ConvBNLayer( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=1, + act='relu', + name=name + "_branch2a") + self.conv1 = ConvBNLayer( + in_channels=out_channels, + out_channels=out_channels, + kernel_size=3, + stride=stride, + act='relu', + name=name + "_branch2b") + + self.conv2 = ConvBNLayer( + in_channels=out_channels, + out_channels=out_channels * 4, + kernel_size=1, + act=None, + name=name + "_branch2c") + + self.short = ShortCut( + in_channels=in_channels, + out_channels=out_channels * 4, + stride=stride, + is_first=False, + name=name + "_branch1") + self.out_channels = out_channels * 4 + + def forward(self, x): + y = self.conv0(x) + y = self.conv1(y) + y = self.conv2(y) + y = y + self.short(x) + y = F.relu(y) + return y + + +class BasicBlock(nn.Layer): + def __init__(self, in_channels, out_channels, stride, name, is_first): + super(BasicBlock, self).__init__() + self.conv0 = ConvBNLayer( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=3, + act='relu', + stride=stride, + name=name + "_branch2a") + self.conv1 = ConvBNLayer( + in_channels=out_channels, + out_channels=out_channels, + kernel_size=3, + act=None, + name=name + "_branch2b") + self.short = ShortCut( + in_channels=in_channels, + out_channels=out_channels, + stride=stride, + is_first=is_first, + name=name + "_branch1") + self.out_channels = out_channels + + def forward(self, x): + y = self.conv0(x) + y = self.conv1(y) + y = y + self.short(x) + return F.relu(y) diff --git a/toolbox/PaddleOCR/ppocr/modeling/heads/rec_sar_head.py b/toolbox/PaddleOCR/ppocr/modeling/heads/rec_sar_head.py new file mode 100644 index 0000000000000000000000000000000000000000..16bc4fac37037696d39169f379157365c02c8756 --- /dev/null +++ b/toolbox/PaddleOCR/ppocr/modeling/heads/rec_sar_head.py @@ -0,0 +1,412 @@ +# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve. +# Copyright (c) 2024, Shanghai Iluvatar CoreX Semiconductor Co., Ltd. +# All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +This code is refer from: +https://github.com/open-mmlab/mmocr/blob/main/mmocr/models/textrecog/encoders/sar_encoder.py +https://github.com/open-mmlab/mmocr/blob/main/mmocr/models/textrecog/decoders/sar_decoder.py +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import math +import paddle +from paddle import ParamAttr +import paddle.nn as nn +import paddle.nn.functional as F + + +class SAREncoder(nn.Layer): + """ + Args: + enc_bi_rnn (bool): If True, use bidirectional RNN in encoder. + enc_drop_rnn (float): Dropout probability of RNN layer in encoder. + enc_gru (bool): If True, use GRU, else LSTM in encoder. + d_model (int): Dim of channels from backbone. + d_enc (int): Dim of encoder RNN layer. + mask (bool): If True, mask padding in RNN sequence. + """ + + def __init__(self, + enc_bi_rnn=False, + enc_drop_rnn=0.1, + enc_gru=False, + d_model=512, + d_enc=512, + mask=True, + **kwargs): + super().__init__() + assert isinstance(enc_bi_rnn, bool) + assert isinstance(enc_drop_rnn, (int, float)) + assert 0 <= enc_drop_rnn < 1.0 + assert isinstance(enc_gru, bool) + assert isinstance(d_model, int) + assert isinstance(d_enc, int) + assert isinstance(mask, bool) + + self.enc_bi_rnn = enc_bi_rnn + self.enc_drop_rnn = enc_drop_rnn + self.mask = mask + + # LSTM Encoder + if enc_bi_rnn: + direction = 'bidirectional' + else: + direction = 'forward' + kwargs = dict( + input_size=d_model, + hidden_size=d_enc, + num_layers=2, + time_major=False, + dropout=enc_drop_rnn, + direction=direction) + if enc_gru: + self.rnn_encoder = nn.GRU(**kwargs) + else: + self.rnn_encoder = nn.LSTM(**kwargs) + + # global feature transformation + encoder_rnn_out_size = d_enc * (int(enc_bi_rnn) + 1) + self.linear = nn.Linear(encoder_rnn_out_size, encoder_rnn_out_size) + + def forward(self, feat, img_metas=None): + if img_metas is not None: + assert len(img_metas[0]) == feat.shape[0] + + valid_ratios = None + if img_metas is not None and self.mask: + valid_ratios = img_metas[-1] + + h_feat = feat.shape[2] # bsz c h w + feat_v = F.max_pool2d( + feat, kernel_size=(h_feat, 1), stride=1, padding=0) + feat_v = feat_v.squeeze(2) # bsz * C * W + feat_v = paddle.transpose(feat_v, perm=[0, 2, 1]) # bsz * W * C + holistic_feat = self.rnn_encoder(feat_v)[0] # bsz * T * C + + if valid_ratios is not None: + valid_hf = [] + T = holistic_feat.shape[1] + for i in range(len(valid_ratios)): + valid_step = min(T, math.ceil(T * valid_ratios[i])) - 1 + valid_hf.append(holistic_feat[i, valid_step, :]) + valid_hf = paddle.stack(valid_hf, axis=0) + else: + valid_hf = holistic_feat[:, -1, :] # bsz * C + holistic_feat = self.linear(valid_hf) # bsz * C + + return holistic_feat + + +class BaseDecoder(nn.Layer): + def __init__(self, **kwargs): + super().__init__() + + def forward_train(self, feat, out_enc, targets, img_metas): + raise NotImplementedError + + def forward_test(self, feat, out_enc, img_metas): + raise NotImplementedError + + def forward(self, + feat, + out_enc, + label=None, + img_metas=None, + train_mode=True): + self.train_mode = train_mode + + if train_mode: + return self.forward_train(feat, out_enc, label, img_metas) + return self.forward_test(feat, out_enc, img_metas) + + +class ParallelSARDecoder(BaseDecoder): + """ + Args: + out_channels (int): Output class number. + enc_bi_rnn (bool): If True, use bidirectional RNN in encoder. + dec_bi_rnn (bool): If True, use bidirectional RNN in decoder. + dec_drop_rnn (float): Dropout of RNN layer in decoder. + dec_gru (bool): If True, use GRU, else LSTM in decoder. + d_model (int): Dim of channels from backbone. + d_enc (int): Dim of encoder RNN layer. + d_k (int): Dim of channels of attention module. + pred_dropout (float): Dropout probability of prediction layer. + max_seq_len (int): Maximum sequence length for decoding. + mask (bool): If True, mask padding in feature map. + start_idx (int): Index of start token. + padding_idx (int): Index of padding token. + pred_concat (bool): If True, concat glimpse feature from + attention with holistic feature and hidden state. + """ + + def __init__( + self, + out_channels, # 90 + unknown + start + padding + enc_bi_rnn=False, + dec_bi_rnn=False, + dec_drop_rnn=0.0, + dec_gru=False, + d_model=512, + d_enc=512, + d_k=64, + pred_dropout=0.1, + max_text_length=30, + mask=True, + pred_concat=True, + **kwargs): + super().__init__() + + self.num_classes = out_channels + self.enc_bi_rnn = enc_bi_rnn + self.d_k = d_k + self.start_idx = out_channels - 2 + self.padding_idx = out_channels - 1 + self.max_seq_len = max_text_length + self.mask = mask + self.pred_concat = pred_concat + + encoder_rnn_out_size = d_enc * (int(enc_bi_rnn) + 1) + decoder_rnn_out_size = encoder_rnn_out_size * (int(dec_bi_rnn) + 1) + + # 2D attention layer + self.conv1x1_1 = nn.Linear(decoder_rnn_out_size, d_k) + self.conv3x3_1 = nn.Conv2D( + d_model, d_k, kernel_size=3, stride=1, padding=1) + self.conv1x1_2 = nn.Linear(d_k, 1) + + # Decoder RNN layer + if dec_bi_rnn: + direction = 'bidirectional' + else: + direction = 'forward' + + kwargs = dict( + input_size=encoder_rnn_out_size, + hidden_size=encoder_rnn_out_size, + num_layers=2, + time_major=False, + dropout=dec_drop_rnn, + direction=direction) + if dec_gru: + self.rnn_decoder = nn.GRU(**kwargs) + else: + self.rnn_decoder = nn.LSTM(**kwargs) + + # Decoder input embedding + self.embedding = nn.Embedding( + self.num_classes, + encoder_rnn_out_size, + padding_idx=self.padding_idx) + + # Prediction layer + self.pred_dropout = nn.Dropout(pred_dropout) + pred_num_classes = self.num_classes - 1 + if pred_concat: + fc_in_channel = decoder_rnn_out_size + d_model + encoder_rnn_out_size + else: + fc_in_channel = d_model + self.prediction = nn.Linear(fc_in_channel, pred_num_classes) + + def _2d_attention(self, + decoder_input, + feat, + holistic_feat, + valid_ratios=None): + + y = self.rnn_decoder(decoder_input)[0] + # y: bsz * (seq_len + 1) * hidden_size + + attn_query = self.conv1x1_1(y) # bsz * (seq_len + 1) * attn_size + bsz, seq_len, attn_size = attn_query.shape + attn_query = paddle.unsqueeze(attn_query, axis=[3, 4]) + # (bsz, seq_len + 1, attn_size, 1, 1) + + attn_key = self.conv3x3_1(feat) + # bsz * attn_size * h * w + attn_key = attn_key.unsqueeze(1) + # bsz * 1 * attn_size * h * w + + attn_weight = paddle.tanh(paddle.add(attn_key, attn_query)) + + # bsz * (seq_len + 1) * attn_size * h * w + attn_weight = paddle.transpose(attn_weight, perm=[0, 1, 3, 4, 2]) + # bsz * (seq_len + 1) * h * w * attn_size + attn_weight = self.conv1x1_2(attn_weight) + # bsz * (seq_len + 1) * h * w * 1 + bsz, T, h, w, c = attn_weight.shape + assert c == 1 + + if valid_ratios is not None: + # cal mask of attention weight + for i in range(len(valid_ratios)): + valid_width = min(w, math.ceil(w * valid_ratios[i])) + if valid_width < w: + attn_weight[i, :, :, valid_width:, :] = float('-inf') + + attn_weight = paddle.reshape(attn_weight, [bsz, T, -1]) + attn_weight = F.softmax(attn_weight, axis=-1) + + attn_weight = paddle.reshape(attn_weight, [bsz, T, h, w, c]) + attn_weight = paddle.transpose(attn_weight, perm=[0, 1, 4, 2, 3]) + # attn_weight: bsz * T * c * h * w + # feat: bsz * c * h * w + attn_feat = paddle.sum(paddle.multiply(feat.unsqueeze(1), attn_weight), + (3, 4), + keepdim=False) + # bsz * (seq_len + 1) * C + + # Linear transformation + if self.pred_concat: + hf_c = holistic_feat.shape[-1] + holistic_feat = paddle.expand( + holistic_feat, shape=[bsz, seq_len, hf_c]) + y = self.prediction(paddle.concat((y, attn_feat, holistic_feat), 2)) + else: + y = self.prediction(attn_feat) + # bsz * (seq_len + 1) * num_classes + if self.train_mode: + y = self.pred_dropout(y) + + return y + + def forward_train(self, feat, out_enc, label, img_metas): + ''' + img_metas: [label, valid_ratio] + ''' + if img_metas is not None: + assert len(img_metas[0]) == feat.shape[0] + + valid_ratios = None + if img_metas is not None and self.mask: + valid_ratios = img_metas[-1] + + lab_embedding = self.embedding(label) + # bsz * seq_len * emb_dim + out_enc = out_enc.unsqueeze(1) + # bsz * 1 * emb_dim + in_dec = paddle.concat((out_enc, lab_embedding), axis=1) + # bsz * (seq_len + 1) * C + out_dec = self._2d_attention( + in_dec, feat, out_enc, valid_ratios=valid_ratios) + # bsz * (seq_len + 1) * num_classes + + return out_dec[:, 1:, :] # bsz * seq_len * num_classes + + def forward_test(self, feat, out_enc, img_metas): + if img_metas is not None: + assert len(img_metas[0]) == feat.shape[0] + + valid_ratios = None + if img_metas is not None and self.mask: + valid_ratios = img_metas[-1] + + seq_len = self.max_seq_len + bsz = feat.shape[0] + start_token = paddle.full( + (bsz, ), fill_value=self.start_idx, dtype='int64') + # bsz + start_token = self.embedding(start_token) + # bsz * emb_dim + emb_dim = start_token.shape[1] + start_token = start_token.unsqueeze(1) + start_token = paddle.expand(start_token, shape=[bsz, seq_len, emb_dim]) + # bsz * seq_len * emb_dim + out_enc = out_enc.unsqueeze(1) + # bsz * 1 * emb_dim + decoder_input = paddle.concat((out_enc, start_token), axis=1) + # bsz * (seq_len + 1) * emb_dim + + outputs = [] + for i in range(1, seq_len + 1): + decoder_output = self._2d_attention( + decoder_input, feat, out_enc, valid_ratios=valid_ratios) + char_output = decoder_output[:, i, :] # bsz * num_classes + char_output = F.softmax(char_output, -1) + outputs.append(char_output) + max_idx = paddle.argmax(char_output, axis=1, keepdim=False) + char_embedding = self.embedding(max_idx) # bsz * emb_dim + if i < seq_len: + decoder_input[:, i + 1, :] = char_embedding + + outputs = paddle.stack(outputs, 1) # bsz * seq_len * num_classes + + return outputs + + +class SARHead(nn.Layer): + def __init__(self, + in_channels, + out_channels, + enc_dim=512, + max_text_length=30, + enc_bi_rnn=False, + enc_drop_rnn=0.1, + enc_gru=False, + dec_bi_rnn=False, + dec_drop_rnn=0.0, + dec_gru=False, + d_k=512, + pred_dropout=0.1, + pred_concat=True, + **kwargs): + super(SARHead, self).__init__() + + # encoder module + self.encoder = SAREncoder( + enc_bi_rnn=enc_bi_rnn, + enc_drop_rnn=enc_drop_rnn, + enc_gru=enc_gru, + d_model=in_channels, + d_enc=enc_dim) + + # decoder module + self.decoder = ParallelSARDecoder( + out_channels=out_channels, + enc_bi_rnn=enc_bi_rnn, + dec_bi_rnn=dec_bi_rnn, + dec_drop_rnn=dec_drop_rnn, + dec_gru=dec_gru, + d_model=in_channels, + d_enc=enc_dim, + d_k=d_k, + pred_dropout=pred_dropout, + max_text_length=max_text_length, + pred_concat=pred_concat) + + def forward(self, feat, targets=None): + ''' + img_metas: [label, valid_ratio] + ''' + holistic_feat = self.encoder(feat, targets) # bsz c + + if self.training: + label = targets[0] # label + label = paddle.to_tensor(label, dtype='int64') + final_out = self.decoder( + feat, holistic_feat, label, img_metas=targets) + else: + final_out = self.decoder( + feat, + holistic_feat, + label=None, + img_metas=targets, + train_mode=False) + # (bsz, seq_len, num_classes) + + return final_out diff --git a/toolbox/PaddleOCR/ppocr/modeling/heads/rec_srn_head.py b/toolbox/PaddleOCR/ppocr/modeling/heads/rec_srn_head.py new file mode 100644 index 0000000000000000000000000000000000000000..b8c2e8cebdd9710c099eee6d11c91c36758e6ee9 --- /dev/null +++ b/toolbox/PaddleOCR/ppocr/modeling/heads/rec_srn_head.py @@ -0,0 +1,282 @@ +# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve. +# Copyright (c) 2024, Shanghai Iluvatar CoreX Semiconductor Co., Ltd. +# All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import math +import paddle +from paddle import nn, ParamAttr +from paddle.nn import functional as F +import paddle.fluid as fluid +import numpy as np +from .self_attention import WrapEncoderForFeature +from .self_attention import WrapEncoder +from paddle.static import Program +from ppocr.modeling.backbones.rec_resnet_fpn import ResNetFPN +import paddle.fluid.framework as framework + +from collections import OrderedDict +gradient_clip = 10 + + +class PVAM(nn.Layer): + def __init__(self, in_channels, char_num, max_text_length, num_heads, + num_encoder_tus, hidden_dims): + super(PVAM, self).__init__() + self.char_num = char_num + self.max_length = max_text_length + self.num_heads = num_heads + self.num_encoder_TUs = num_encoder_tus + self.hidden_dims = hidden_dims + # Transformer encoder + t = 256 + c = 512 + self.wrap_encoder_for_feature = WrapEncoderForFeature( + src_vocab_size=1, + max_length=t, + n_layer=self.num_encoder_TUs, + n_head=self.num_heads, + d_key=int(self.hidden_dims / self.num_heads), + d_value=int(self.hidden_dims / self.num_heads), + d_model=self.hidden_dims, + d_inner_hid=self.hidden_dims, + prepostprocess_dropout=0.1, + attention_dropout=0.1, + relu_dropout=0.1, + preprocess_cmd="n", + postprocess_cmd="da", + weight_sharing=True) + + # PVAM + self.flatten0 = paddle.nn.Flatten(start_axis=0, stop_axis=1) + self.fc0 = paddle.nn.Linear( + in_features=in_channels, + out_features=in_channels, ) + self.emb = paddle.nn.Embedding( + num_embeddings=self.max_length, embedding_dim=in_channels) + self.flatten1 = paddle.nn.Flatten(start_axis=0, stop_axis=2) + self.fc1 = paddle.nn.Linear( + in_features=in_channels, out_features=1, bias_attr=False) + + def forward(self, inputs, encoder_word_pos, gsrm_word_pos): + b, c, h, w = inputs.shape + conv_features = paddle.reshape(inputs, shape=[-1, c, h * w]) + conv_features = paddle.transpose(conv_features, perm=[0, 2, 1]) + # transformer encoder + b, t, c = conv_features.shape + + enc_inputs = [conv_features, encoder_word_pos, None] + word_features = self.wrap_encoder_for_feature(enc_inputs) + + # pvam + b, t, c = word_features.shape + word_features = self.fc0(word_features) + word_features_ = paddle.reshape(word_features, [-1, 1, t, c]) + word_features_ = paddle.tile(word_features_, [1, self.max_length, 1, 1]) + word_pos_feature = self.emb(gsrm_word_pos) + word_pos_feature_ = paddle.reshape(word_pos_feature, + [-1, self.max_length, 1, c]) + word_pos_feature_ = paddle.tile(word_pos_feature_, [1, 1, t, 1]) + y = word_pos_feature_ + word_features_ + y = F.tanh(y) + attention_weight = self.fc1(y) + attention_weight = paddle.reshape( + attention_weight, shape=[-1, self.max_length, t]) + attention_weight = F.softmax(attention_weight, axis=-1) + pvam_features = paddle.matmul(attention_weight, + word_features) #[b, max_length, c] + return pvam_features + + +class GSRM(nn.Layer): + def __init__(self, in_channels, char_num, max_text_length, num_heads, + num_encoder_tus, num_decoder_tus, hidden_dims): + super(GSRM, self).__init__() + self.char_num = char_num + self.max_length = max_text_length + self.num_heads = num_heads + self.num_encoder_TUs = num_encoder_tus + self.num_decoder_TUs = num_decoder_tus + self.hidden_dims = hidden_dims + + self.fc0 = paddle.nn.Linear( + in_features=in_channels, out_features=self.char_num) + self.wrap_encoder0 = WrapEncoder( + src_vocab_size=self.char_num + 1, + max_length=self.max_length, + n_layer=self.num_decoder_TUs, + n_head=self.num_heads, + d_key=int(self.hidden_dims / self.num_heads), + d_value=int(self.hidden_dims / self.num_heads), + d_model=self.hidden_dims, + d_inner_hid=self.hidden_dims, + prepostprocess_dropout=0.1, + attention_dropout=0.1, + relu_dropout=0.1, + preprocess_cmd="n", + postprocess_cmd="da", + weight_sharing=True) + + self.wrap_encoder1 = WrapEncoder( + src_vocab_size=self.char_num + 1, + max_length=self.max_length, + n_layer=self.num_decoder_TUs, + n_head=self.num_heads, + d_key=int(self.hidden_dims / self.num_heads), + d_value=int(self.hidden_dims / self.num_heads), + d_model=self.hidden_dims, + d_inner_hid=self.hidden_dims, + prepostprocess_dropout=0.1, + attention_dropout=0.1, + relu_dropout=0.1, + preprocess_cmd="n", + postprocess_cmd="da", + weight_sharing=True) + + self.mul = lambda x: paddle.matmul(x=x, + y=self.wrap_encoder0.prepare_decoder.emb0.weight, + transpose_y=True) + + def forward(self, inputs, gsrm_word_pos, gsrm_slf_attn_bias1, + gsrm_slf_attn_bias2): + # ===== GSRM Visual-to-semantic embedding block ===== + b, t, c = inputs.shape + pvam_features = paddle.reshape(inputs, [-1, c]) + word_out = self.fc0(pvam_features) + word_ids = paddle.argmax(F.softmax(word_out), axis=1) + word_ids = paddle.reshape(x=word_ids, shape=[-1, t, 1]) + + #===== GSRM Semantic reasoning block ===== + """ + This module is achieved through bi-transformers, + ngram_feature1 is the froward one, ngram_fetaure2 is the backward one + """ + pad_idx = self.char_num + + word1 = paddle.cast(word_ids, "float32") + word1 = F.pad(word1, [1, 0], value=1.0 * pad_idx, data_format="NLC") + word1 = paddle.cast(word1, "int64") + word1 = word1[:, :-1, :] + word2 = word_ids + + enc_inputs_1 = [word1, gsrm_word_pos, gsrm_slf_attn_bias1] + enc_inputs_2 = [word2, gsrm_word_pos, gsrm_slf_attn_bias2] + + gsrm_feature1 = self.wrap_encoder0(enc_inputs_1) + gsrm_feature2 = self.wrap_encoder1(enc_inputs_2) + + gsrm_feature2 = F.pad(gsrm_feature2, [0, 1], + value=0., + data_format="NLC") + gsrm_feature2 = gsrm_feature2[:, 1:, ] + gsrm_features = gsrm_feature1 + gsrm_feature2 + + gsrm_out = self.mul(gsrm_features) + + b, t, c = gsrm_out.shape + gsrm_out = paddle.reshape(gsrm_out, [-1, c]) + + return gsrm_features, word_out, gsrm_out + + +class VSFD(nn.Layer): + def __init__(self, in_channels=512, pvam_ch=512, char_num=38): + super(VSFD, self).__init__() + self.char_num = char_num + self.fc0 = paddle.nn.Linear( + in_features=in_channels * 2, out_features=pvam_ch) + self.fc1 = paddle.nn.Linear( + in_features=pvam_ch, out_features=self.char_num) + + def forward(self, pvam_feature, gsrm_feature): + b, t, c1 = pvam_feature.shape + b, t, c2 = gsrm_feature.shape + combine_feature_ = paddle.concat([pvam_feature, gsrm_feature], axis=2) + img_comb_feature_ = paddle.reshape( + combine_feature_, shape=[-1, c1 + c2]) + img_comb_feature_map = self.fc0(img_comb_feature_) + img_comb_feature_map = F.sigmoid(img_comb_feature_map) + img_comb_feature_map = paddle.reshape( + img_comb_feature_map, shape=[-1, t, c1]) + combine_feature = img_comb_feature_map * pvam_feature + ( + 1.0 - img_comb_feature_map) * gsrm_feature + img_comb_feature = paddle.reshape(combine_feature, shape=[-1, c1]) + + out = self.fc1(img_comb_feature) + return out + + +class SRNHead(nn.Layer): + def __init__(self, in_channels, out_channels, max_text_length, num_heads, + num_encoder_TUs, num_decoder_TUs, hidden_dims, **kwargs): + super(SRNHead, self).__init__() + self.char_num = out_channels + self.max_length = max_text_length + self.num_heads = num_heads + self.num_encoder_TUs = num_encoder_TUs + self.num_decoder_TUs = num_decoder_TUs + self.hidden_dims = hidden_dims + + self.pvam = PVAM( + in_channels=in_channels, + char_num=self.char_num, + max_text_length=self.max_length, + num_heads=self.num_heads, + num_encoder_tus=self.num_encoder_TUs, + hidden_dims=self.hidden_dims) + + self.gsrm = GSRM( + in_channels=in_channels, + char_num=self.char_num, + max_text_length=self.max_length, + num_heads=self.num_heads, + num_encoder_tus=self.num_encoder_TUs, + num_decoder_tus=self.num_decoder_TUs, + hidden_dims=self.hidden_dims) + self.vsfd = VSFD(in_channels=in_channels, char_num=self.char_num) + + self.gsrm.wrap_encoder1.prepare_decoder.emb0 = self.gsrm.wrap_encoder0.prepare_decoder.emb0 + + def forward(self, inputs, targets=None): + others = targets[-4:] + encoder_word_pos = others[0] + gsrm_word_pos = others[1] + gsrm_slf_attn_bias1 = others[2] + gsrm_slf_attn_bias2 = others[3] + + pvam_feature = self.pvam(inputs, encoder_word_pos, gsrm_word_pos) + + gsrm_feature, word_out, gsrm_out = self.gsrm( + pvam_feature, gsrm_word_pos, gsrm_slf_attn_bias1, + gsrm_slf_attn_bias2) + + final_out = self.vsfd(pvam_feature, gsrm_feature) + if not self.training: + final_out = F.softmax(final_out, axis=1) + + _, decoded_out = paddle.topk(final_out, k=1) + + predicts = OrderedDict([ + ('predict', final_out), + ('pvam_feature', pvam_feature), + ('decoded_out', decoded_out), + ('word_out', word_out), + ('gsrm_out', gsrm_out), + ]) + + return predicts diff --git a/toolbox/PaddleOCR/ppocr/modeling/heads/self_attention.py b/toolbox/PaddleOCR/ppocr/modeling/heads/self_attention.py new file mode 100644 index 0000000000000000000000000000000000000000..4ab17825719fb2572c1501b75e2e1565814daa64 --- /dev/null +++ b/toolbox/PaddleOCR/ppocr/modeling/heads/self_attention.py @@ -0,0 +1,408 @@ +# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve. +# Copyright (c) 2024, Shanghai Iluvatar CoreX Semiconductor Co., Ltd. +# All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import math + +import paddle +from paddle import ParamAttr, nn +from paddle import nn, ParamAttr +from paddle.nn import functional as F +import paddle.fluid as fluid +import numpy as np +gradient_clip = 10 + + +class WrapEncoderForFeature(nn.Layer): + def __init__(self, + src_vocab_size, + max_length, + n_layer, + n_head, + d_key, + d_value, + d_model, + d_inner_hid, + prepostprocess_dropout, + attention_dropout, + relu_dropout, + preprocess_cmd, + postprocess_cmd, + weight_sharing, + bos_idx=0): + super(WrapEncoderForFeature, self).__init__() + + self.prepare_encoder = PrepareEncoder( + src_vocab_size, + d_model, + max_length, + prepostprocess_dropout, + bos_idx=bos_idx, + word_emb_param_name="src_word_emb_table") + self.encoder = Encoder(n_layer, n_head, d_key, d_value, d_model, + d_inner_hid, prepostprocess_dropout, + attention_dropout, relu_dropout, preprocess_cmd, + postprocess_cmd) + + def forward(self, enc_inputs): + conv_features, src_pos, src_slf_attn_bias = enc_inputs + enc_input = self.prepare_encoder(conv_features, src_pos) + enc_output = self.encoder(enc_input, src_slf_attn_bias) + return enc_output + + +class WrapEncoder(nn.Layer): + """ + embedder + encoder + """ + + def __init__(self, + src_vocab_size, + max_length, + n_layer, + n_head, + d_key, + d_value, + d_model, + d_inner_hid, + prepostprocess_dropout, + attention_dropout, + relu_dropout, + preprocess_cmd, + postprocess_cmd, + weight_sharing, + bos_idx=0): + super(WrapEncoder, self).__init__() + + self.prepare_decoder = PrepareDecoder( + src_vocab_size, + d_model, + max_length, + prepostprocess_dropout, + bos_idx=bos_idx) + self.encoder = Encoder(n_layer, n_head, d_key, d_value, d_model, + d_inner_hid, prepostprocess_dropout, + attention_dropout, relu_dropout, preprocess_cmd, + postprocess_cmd) + + def forward(self, enc_inputs): + src_word, src_pos, src_slf_attn_bias = enc_inputs + enc_input = self.prepare_decoder(src_word, src_pos) + enc_output = self.encoder(enc_input, src_slf_attn_bias) + return enc_output + + +class Encoder(nn.Layer): + """ + encoder + """ + + def __init__(self, + n_layer, + n_head, + d_key, + d_value, + d_model, + d_inner_hid, + prepostprocess_dropout, + attention_dropout, + relu_dropout, + preprocess_cmd="n", + postprocess_cmd="da"): + + super(Encoder, self).__init__() + + self.encoder_layers = list() + for i in range(n_layer): + self.encoder_layers.append( + self.add_sublayer( + "layer_%d" % i, + EncoderLayer(n_head, d_key, d_value, d_model, d_inner_hid, + prepostprocess_dropout, attention_dropout, + relu_dropout, preprocess_cmd, + postprocess_cmd))) + self.processer = PrePostProcessLayer(preprocess_cmd, d_model, + prepostprocess_dropout) + + def forward(self, enc_input, attn_bias): + for encoder_layer in self.encoder_layers: + enc_output = encoder_layer(enc_input, attn_bias) + enc_input = enc_output + enc_output = self.processer(enc_output) + return enc_output + + +class EncoderLayer(nn.Layer): + """ + EncoderLayer + """ + + def __init__(self, + n_head, + d_key, + d_value, + d_model, + d_inner_hid, + prepostprocess_dropout, + attention_dropout, + relu_dropout, + preprocess_cmd="n", + postprocess_cmd="da"): + + super(EncoderLayer, self).__init__() + self.preprocesser1 = PrePostProcessLayer(preprocess_cmd, d_model, + prepostprocess_dropout) + self.self_attn = MultiHeadAttention(d_key, d_value, d_model, n_head, + attention_dropout) + self.postprocesser1 = PrePostProcessLayer(postprocess_cmd, d_model, + prepostprocess_dropout) + + self.preprocesser2 = PrePostProcessLayer(preprocess_cmd, d_model, + prepostprocess_dropout) + self.ffn = FFN(d_inner_hid, d_model, relu_dropout) + self.postprocesser2 = PrePostProcessLayer(postprocess_cmd, d_model, + prepostprocess_dropout) + + def forward(self, enc_input, attn_bias): + attn_output = self.self_attn( + self.preprocesser1(enc_input), None, None, attn_bias) + attn_output = self.postprocesser1(attn_output, enc_input) + ffn_output = self.ffn(self.preprocesser2(attn_output)) + ffn_output = self.postprocesser2(ffn_output, attn_output) + return ffn_output + + +class MultiHeadAttention(nn.Layer): + """ + Multi-Head Attention + """ + + def __init__(self, d_key, d_value, d_model, n_head=1, dropout_rate=0.): + super(MultiHeadAttention, self).__init__() + self.n_head = n_head + self.d_key = d_key + self.d_value = d_value + self.d_model = d_model + self.dropout_rate = dropout_rate + self.q_fc = paddle.nn.Linear( + in_features=d_model, out_features=d_key * n_head, bias_attr=False) + self.k_fc = paddle.nn.Linear( + in_features=d_model, out_features=d_key * n_head, bias_attr=False) + self.v_fc = paddle.nn.Linear( + in_features=d_model, out_features=d_value * n_head, bias_attr=False) + self.proj_fc = paddle.nn.Linear( + in_features=d_value * n_head, out_features=d_model, bias_attr=False) + + def _prepare_qkv(self, queries, keys, values, cache=None): + if keys is None: # self-attention + keys, values = queries, queries + static_kv = False + else: # cross-attention + static_kv = True + + q = self.q_fc(queries) + q = paddle.reshape(x=q, shape=[0, 0, self.n_head, self.d_key]) + q = paddle.transpose(x=q, perm=[0, 2, 1, 3]) + + if cache is not None and static_kv and "static_k" in cache: + # for encoder-decoder attention in inference and has cached + k = cache["static_k"] + v = cache["static_v"] + else: + k = self.k_fc(keys) + v = self.v_fc(values) + k = paddle.reshape(x=k, shape=[0, 0, self.n_head, self.d_key]) + k = paddle.transpose(x=k, perm=[0, 2, 1, 3]) + v = paddle.reshape(x=v, shape=[0, 0, self.n_head, self.d_value]) + v = paddle.transpose(x=v, perm=[0, 2, 1, 3]) + + if cache is not None: + if static_kv and not "static_k" in cache: + # for encoder-decoder attention in inference and has not cached + cache["static_k"], cache["static_v"] = k, v + elif not static_kv: + # for decoder self-attention in inference + cache_k, cache_v = cache["k"], cache["v"] + k = paddle.concat([cache_k, k], axis=2) + v = paddle.concat([cache_v, v], axis=2) + cache["k"], cache["v"] = k, v + + return q, k, v + + def forward(self, queries, keys, values, attn_bias, cache=None): + # compute q ,k ,v + keys = queries if keys is None else keys + values = keys if values is None else values + q, k, v = self._prepare_qkv(queries, keys, values, cache) + + # scale dot product attention + product = paddle.matmul(x=q, y=k, transpose_y=True) + product = product * self.d_model**-0.5 + if attn_bias is not None: + product += attn_bias + weights = F.softmax(product) + if self.dropout_rate: + weights = F.dropout( + weights, p=self.dropout_rate, mode="downscale_in_infer") + out = paddle.matmul(weights, v) + + # combine heads + out = paddle.transpose(out, perm=[0, 2, 1, 3]) + out = paddle.reshape(x=out, shape=[0, 0, out.shape[2] * out.shape[3]]) + + # project to output + out = self.proj_fc(out) + + return out + + +class PrePostProcessLayer(nn.Layer): + """ + PrePostProcessLayer + """ + + def __init__(self, process_cmd, d_model, dropout_rate): + super(PrePostProcessLayer, self).__init__() + self.process_cmd = process_cmd + self.functors = [] + for cmd in self.process_cmd: + if cmd == "a": # add residual connection + self.functors.append(lambda x, y: x + y if y is not None else x) + elif cmd == "n": # add layer normalization + self.functors.append( + self.add_sublayer( + "layer_norm_%d" % len(self.sublayers()), + paddle.nn.LayerNorm( + normalized_shape=d_model, + weight_attr=fluid.ParamAttr( + initializer=fluid.initializer.Constant(1.)), + bias_attr=fluid.ParamAttr( + initializer=fluid.initializer.Constant(0.))))) + elif cmd == "d": # add dropout + self.functors.append(lambda x: F.dropout( + x, p=dropout_rate, mode="downscale_in_infer") + if dropout_rate else x) + + def forward(self, x, residual=None): + for i, cmd in enumerate(self.process_cmd): + if cmd == "a": + x = self.functors[i](x, residual) + else: + x = self.functors[i](x) + return x + + +class PrepareEncoder(nn.Layer): + def __init__(self, + src_vocab_size, + src_emb_dim, + src_max_len, + dropout_rate=0, + bos_idx=0, + word_emb_param_name=None, + pos_enc_param_name=None): + super(PrepareEncoder, self).__init__() + self.src_emb_dim = src_emb_dim + self.src_max_len = src_max_len + self.emb = paddle.nn.Embedding( + num_embeddings=self.src_max_len, embedding_dim=self.src_emb_dim) + self.dropout_rate = dropout_rate + + def forward(self, src_word, src_pos): + src_word_emb = src_word + src_word_emb = fluid.layers.cast(src_word_emb, 'float32') + src_word_emb = paddle.scale(x=src_word_emb, scale=self.src_emb_dim**0.5) + src_pos = paddle.squeeze(src_pos, axis=-1) + src_pos_enc = self.emb(src_pos) + src_pos_enc.stop_gradient = True + enc_input = src_word_emb + src_pos_enc + if self.dropout_rate: + out = F.dropout( + x=enc_input, p=self.dropout_rate, mode="downscale_in_infer") + else: + out = enc_input + return out + + +class PrepareDecoder(nn.Layer): + def __init__(self, + src_vocab_size, + src_emb_dim, + src_max_len, + dropout_rate=0, + bos_idx=0, + word_emb_param_name=None, + pos_enc_param_name=None): + super(PrepareDecoder, self).__init__() + self.src_emb_dim = src_emb_dim + """ + self.emb0 = Embedding(num_embeddings=src_vocab_size, + embedding_dim=src_emb_dim) + """ + self.emb0 = paddle.nn.Embedding( + num_embeddings=src_vocab_size, + embedding_dim=self.src_emb_dim, + padding_idx=bos_idx, + weight_attr=paddle.ParamAttr( + name=word_emb_param_name, + initializer=nn.initializer.Normal(0., src_emb_dim**-0.5))) + self.emb1 = paddle.nn.Embedding( + num_embeddings=src_max_len, + embedding_dim=self.src_emb_dim, + weight_attr=paddle.ParamAttr(name=pos_enc_param_name)) + self.dropout_rate = dropout_rate + + def forward(self, src_word, src_pos): + src_word = fluid.layers.cast(src_word, 'int64') + src_word = paddle.squeeze(src_word, axis=-1) + src_word_emb = self.emb0(src_word) + src_word_emb = paddle.scale(x=src_word_emb, scale=self.src_emb_dim**0.5) + src_pos = paddle.squeeze(src_pos, axis=-1) + src_pos_enc = self.emb1(src_pos) + src_pos_enc.stop_gradient = True + enc_input = src_word_emb + src_pos_enc + if self.dropout_rate: + out = F.dropout( + x=enc_input, p=self.dropout_rate, mode="downscale_in_infer") + else: + out = enc_input + return out + + +class FFN(nn.Layer): + """ + Feed-Forward Network + """ + + def __init__(self, d_inner_hid, d_model, dropout_rate): + super(FFN, self).__init__() + self.dropout_rate = dropout_rate + self.fc1 = paddle.nn.Linear( + in_features=d_model, out_features=d_inner_hid) + self.fc2 = paddle.nn.Linear( + in_features=d_inner_hid, out_features=d_model) + + def forward(self, x): + hidden = self.fc1(x) + hidden = F.relu(hidden) + if self.dropout_rate: + hidden = F.dropout( + hidden, p=self.dropout_rate, mode="downscale_in_infer") + out = self.fc2(hidden) + return out diff --git a/toolbox/PaddleOCR/ppocr/postprocess/db_postprocess.py b/toolbox/PaddleOCR/ppocr/postprocess/db_postprocess.py new file mode 100644 index 0000000000000000000000000000000000000000..e2d4896a96e16ae45e484bf7dec10806ddd15b27 --- /dev/null +++ b/toolbox/PaddleOCR/ppocr/postprocess/db_postprocess.py @@ -0,0 +1,222 @@ +# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. +# Copyright (c) 2024, Shanghai Iluvatar CoreX Semiconductor Co., Ltd. +# All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +This code is refered from: +https://github.com/WenmuZhou/DBNet.pytorch/blob/master/post_processing/seg_detector_representer.py +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np +import cv2 +import paddle +from shapely.geometry import Polygon +import pyclipper + + +class DBPostProcess(object): + """ + The post process for Differentiable Binarization (DB). + """ + + def __init__(self, + thresh=0.3, + box_thresh=0.7, + max_candidates=1000, + unclip_ratio=2.0, + use_dilation=False, + score_mode="fast", + **kwargs): + self.thresh = thresh + self.box_thresh = box_thresh + self.max_candidates = max_candidates + self.unclip_ratio = unclip_ratio + self.min_size = 3 + self.score_mode = score_mode + assert score_mode in [ + "slow", "fast" + ], "Score mode must be in [slow, fast] but got: {}".format(score_mode) + + self.dilation_kernel = None if not use_dilation else np.array( + [[1, 1], [1, 1]]) + + def boxes_from_bitmap(self, pred, _bitmap, dest_width, dest_height): + ''' + _bitmap: single map with shape (1, H, W), + whose values are binarized as {0, 1} + ''' + + bitmap = _bitmap + height, width = bitmap.shape + + outs = cv2.findContours((bitmap * 255).astype(np.uint8), cv2.RETR_LIST, + cv2.CHAIN_APPROX_SIMPLE) + if len(outs) == 3: + img, contours, _ = outs[0], outs[1], outs[2] + elif len(outs) == 2: + contours, _ = outs[0], outs[1] + + num_contours = min(len(contours), self.max_candidates) + + boxes = [] + scores = [] + for index in range(num_contours): + contour = contours[index] + points, sside = self.get_mini_boxes(contour) + if sside < self.min_size: + continue + points = np.array(points) + if self.score_mode == "fast": + score = self.box_score_fast(pred, points.reshape(-1, 2)) + else: + score = self.box_score_slow(pred, contour) + if self.box_thresh > score: + continue + + box = self.unclip(points).reshape(-1, 1, 2) + box, sside = self.get_mini_boxes(box) + if sside < self.min_size + 2: + continue + box = np.array(box) + + box[:, 0] = np.clip( + np.round(box[:, 0] / width * dest_width), 0, dest_width) + box[:, 1] = np.clip( + np.round(box[:, 1] / height * dest_height), 0, dest_height) + boxes.append(box.astype(np.int16)) + scores.append(score) + return np.array(boxes, dtype=np.int16), scores + + def unclip(self, box): + unclip_ratio = self.unclip_ratio + poly = Polygon(box) + distance = poly.area * unclip_ratio / poly.length + offset = pyclipper.PyclipperOffset() + offset.AddPath(box, pyclipper.JT_ROUND, pyclipper.ET_CLOSEDPOLYGON) + expanded = np.array(offset.Execute(distance)) + return expanded + + def get_mini_boxes(self, contour): + bounding_box = cv2.minAreaRect(contour) + points = sorted(list(cv2.boxPoints(bounding_box)), key=lambda x: x[0]) + + index_1, index_2, index_3, index_4 = 0, 1, 2, 3 + if points[1][1] > points[0][1]: + index_1 = 0 + index_4 = 1 + else: + index_1 = 1 + index_4 = 0 + if points[3][1] > points[2][1]: + index_2 = 2 + index_3 = 3 + else: + index_2 = 3 + index_3 = 2 + + box = [ + points[index_1], points[index_2], points[index_3], points[index_4] + ] + return box, min(bounding_box[1]) + + def box_score_fast(self, bitmap, _box): + ''' + box_score_fast: use bbox mean score as the mean score + ''' + h, w = bitmap.shape[:2] + box = _box.copy() + xmin = np.clip(np.floor(box[:, 0].min()).astype(np.int), 0, w - 1) + xmax = np.clip(np.ceil(box[:, 0].max()).astype(np.int), 0, w - 1) + ymin = np.clip(np.floor(box[:, 1].min()).astype(np.int), 0, h - 1) + ymax = np.clip(np.ceil(box[:, 1].max()).astype(np.int), 0, h - 1) + + mask = np.zeros((ymax - ymin + 1, xmax - xmin + 1), dtype=np.uint8) + box[:, 0] = box[:, 0] - xmin + box[:, 1] = box[:, 1] - ymin + cv2.fillPoly(mask, box.reshape(1, -1, 2).astype(np.int32), 1) + return cv2.mean(bitmap[ymin:ymax + 1, xmin:xmax + 1], mask)[0] + + def box_score_slow(self, bitmap, contour): + ''' + box_score_slow: use polyon mean score as the mean score + ''' + h, w = bitmap.shape[:2] + contour = contour.copy() + contour = np.reshape(contour, (-1, 2)) + + xmin = np.clip(np.min(contour[:, 0]), 0, w - 1) + xmax = np.clip(np.max(contour[:, 0]), 0, w - 1) + ymin = np.clip(np.min(contour[:, 1]), 0, h - 1) + ymax = np.clip(np.max(contour[:, 1]), 0, h - 1) + + mask = np.zeros((ymax - ymin + 1, xmax - xmin + 1), dtype=np.uint8) + + contour[:, 0] = contour[:, 0] - xmin + contour[:, 1] = contour[:, 1] - ymin + + cv2.fillPoly(mask, contour.reshape(1, -1, 2).astype(np.int32), 1) + return cv2.mean(bitmap[ymin:ymax + 1, xmin:xmax + 1], mask)[0] + + def __call__(self, outs_dict, shape_list): + pred = outs_dict['maps'] + if isinstance(pred, paddle.Tensor): + pred = pred.numpy() + pred = pred[:, 0, :, :] + segmentation = pred > self.thresh + + boxes_batch = [] + for batch_index in range(pred.shape[0]): + src_h, src_w, ratio_h, ratio_w = shape_list[batch_index] + if self.dilation_kernel is not None: + mask = cv2.dilate( + np.array(segmentation[batch_index]).astype(np.uint8), + self.dilation_kernel) + else: + mask = segmentation[batch_index] + boxes, scores = self.boxes_from_bitmap(pred[batch_index], mask, + src_w, src_h) + + boxes_batch.append({'points': boxes}) + return boxes_batch + + +class DistillationDBPostProcess(object): + def __init__(self, + model_name=["student"], + key=None, + thresh=0.3, + box_thresh=0.6, + max_candidates=1000, + unclip_ratio=1.5, + use_dilation=False, + score_mode="fast", + **kwargs): + self.model_name = model_name + self.key = key + self.post_process = DBPostProcess( + thresh=thresh, + box_thresh=box_thresh, + max_candidates=max_candidates, + unclip_ratio=unclip_ratio, + use_dilation=use_dilation, + score_mode=score_mode) + + def __call__(self, predicts, shape_list): + results = {} + for k in self.model_name: + results[k] = self.post_process(predicts[k], shape_list=shape_list) + return results diff --git a/toolbox/PaddleOCR/ppocr/utils/save_load.py b/toolbox/PaddleOCR/ppocr/utils/save_load.py new file mode 100644 index 0000000000000000000000000000000000000000..a3c0a1345bf03916b70fb0fd42c5d45e696cbccf --- /dev/null +++ b/toolbox/PaddleOCR/ppocr/utils/save_load.py @@ -0,0 +1,187 @@ +# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve. +# Copyright (c) 2024, Shanghai Iluvatar CoreX Semiconductor Co., Ltd. +# All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import errno +import os +import pickle +import six + +import paddle + +from ppocr.utils.logging import get_logger + +__all__ = ['load_model'] + + +def _mkdir_if_not_exist(path, logger): + """ + mkdir if not exists, ignore the exception when multiprocess mkdir together + """ + if not os.path.exists(path): + try: + os.makedirs(path) + except OSError as e: + if e.errno == errno.EEXIST and os.path.isdir(path): + logger.warning( + 'be happy if some process has already created {}'.format( + path)) + else: + raise OSError('Failed to mkdir {}'.format(path)) + + +def load_model(config, model, optimizer=None, model_type='det'): + """ + load model from checkpoint or pretrained_model + """ + logger = get_logger() + global_config = config['Global'] + checkpoints = global_config.get('checkpoints') + pretrained_model = global_config.get('pretrained_model') + best_model_dict = {} + + if model_type == 'vqa': + checkpoints = config['Architecture']['Backbone']['checkpoints'] + # load vqa method metric + if checkpoints: + if os.path.exists(os.path.join(checkpoints, 'metric.states')): + with open(os.path.join(checkpoints, 'metric.states'), + 'rb') as f: + states_dict = pickle.load(f) if six.PY2 else pickle.load( + f, encoding='latin1') + best_model_dict = states_dict.get('best_model_dict', {}) + if 'epoch' in states_dict: + best_model_dict['start_epoch'] = states_dict['epoch'] + 1 + logger.info("resume from {}".format(checkpoints)) + + if optimizer is not None: + if checkpoints[-1] in ['/', '\\']: + checkpoints = checkpoints[:-1] + if os.path.exists(checkpoints + '.pdopt'): + optim_dict = paddle.load(checkpoints + '.pdopt') + optimizer.set_state_dict(optim_dict) + else: + logger.warning( + "{}.pdopt is not exists, params of optimizer is not loaded". + format(checkpoints)) + return best_model_dict + + if checkpoints: + if checkpoints.endswith('.pdparams'): + checkpoints = checkpoints.replace('.pdparams', '') + assert os.path.exists(checkpoints + ".pdparams"), \ + "The {}.pdparams does not exists!".format(checkpoints) + + # load params from trained model + params = paddle.load(checkpoints + '.pdparams') + state_dict = model.state_dict() + new_state_dict = {} + for key, value in state_dict.items(): + if key not in params: + logger.warning("{} not in loaded params {} !".format( + key, params.keys())) + continue + pre_value = params[key] + if list(value.shape) == list(pre_value.shape): + new_state_dict[key] = pre_value + else: + logger.warning( + "The shape of model params {} {} not matched with loaded params shape {} !". + format(key, value.shape, pre_value.shape)) + model.set_state_dict(new_state_dict) + + if optimizer is not None: + if os.path.exists(checkpoints + '.pdopt'): + optim_dict = paddle.load(checkpoints + '.pdopt') + optimizer.set_state_dict(optim_dict) + else: + logger.warning( + "{}.pdopt is not exists, params of optimizer is not loaded". + format(checkpoints)) + + if os.path.exists(checkpoints + '.states'): + with open(checkpoints + '.states', 'rb') as f: + states_dict = pickle.load(f) if six.PY2 else pickle.load( + f, encoding='latin1') + best_model_dict = states_dict.get('best_model_dict', {}) + if 'epoch' in states_dict: + best_model_dict['start_epoch'] = states_dict['epoch'] + 1 + logger.info("resume from {}".format(checkpoints)) + elif pretrained_model: + load_pretrained_params(model, pretrained_model) + else: + logger.info('train from scratch') + return best_model_dict + + +def load_pretrained_params(model, path): + logger = get_logger() + if path.endswith('.pdparams'): + path = path.replace('.pdparams', '') + assert os.path.exists(path + ".pdparams"), \ + "The {}.pdparams does not exists!".format(path) + + params = paddle.load(path + '.pdparams') + state_dict = model.state_dict() + new_state_dict = {} + for k1 in params.keys(): + if k1 not in state_dict.keys(): + logger.warning("The pretrained params {} not in model".format(k1)) + else: + if list(state_dict[k1].shape) == list(params[k1].shape): + new_state_dict[k1] = params[k1] + else: + logger.warning( + "The shape of model params {} {} not matched with loaded params {} {} !". + format(k1, state_dict[k1].shape, k1, params[k1].shape)) + model.set_state_dict(new_state_dict) + logger.info("load pretrain successful from {}".format(path)) + return model + + +def save_model(model, + optimizer, + model_path, + logger, + config, + is_best=False, + prefix='ppocr', + **kwargs): + """ + save model to the target path + """ + _mkdir_if_not_exist(model_path, logger) + model_prefix = os.path.join(model_path, prefix) + paddle.save(optimizer.state_dict(), model_prefix + '.pdopt') + if config['Architecture']["model_type"] != 'vqa': + paddle.save(model.state_dict(), model_prefix + '.pdparams') + metric_prefix = model_prefix + else: + if config['Global']['distributed']: + model._layers.backbone.model.save_pretrained(model_prefix) + else: + model.backbone.model.save_pretrained(model_prefix) + metric_prefix = os.path.join(model_prefix, 'metric') + # save metric and config + if is_best: + with open(metric_prefix + '.states', 'wb') as f: + pickle.dump(kwargs, f, protocol=2) + logger.info('save best model is to {}'.format(model_prefix)) + else: + logger.info("save model in {}".format(model_prefix)) diff --git a/toolbox/PaddleOCR/ppstructure/table/table_metric/table_metric.py b/toolbox/PaddleOCR/ppstructure/table/table_metric/table_metric.py new file mode 100644 index 0000000000000000000000000000000000000000..a7216f3671639478828dab73d59b90487fed5983 --- /dev/null +++ b/toolbox/PaddleOCR/ppstructure/table/table_metric/table_metric.py @@ -0,0 +1,250 @@ +# Copyright 2020 IBM +# Author: peter.zhong@au1.ibm.com +# +# Copyright (c) 2024, Shanghai Iluvatar CoreX Semiconductor Co., Ltd. +# All Rights Reserved. +# +# This is free software; you can redistribute it and/or modify +# it under the terms of the Apache 2.0 License. +# +# This software is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# Apache 2.0 License for more details. + +import distance +from apted import APTED, Config +from apted.helpers import Tree +from lxml import etree, html +from collections import deque +from .parallel import parallel_process +from tqdm import tqdm + + +class TableTree(Tree): + def __init__(self, tag, colspan=None, rowspan=None, content=None, *children): + self.tag = tag + self.colspan = colspan + self.rowspan = rowspan + self.content = content + self.children = list(children) + + def bracket(self): + """Show tree using brackets notation""" + if self.tag == 'td': + result = '"tag": %s, "colspan": %d, "rowspan": %d, "text": %s' % \ + (self.tag, self.colspan, self.rowspan, self.content) + else: + result = '"tag": %s' % self.tag + for child in self.children: + result += child.bracket() + return "{{{}}}".format(result) + + +class CustomConfig(Config): + @staticmethod + def maximum(*sequences): + """Get maximum possible value + """ + return max(map(len, sequences)) + + def normalized_distance(self, *sequences): + """Get distance from 0 to 1 + """ + return float(distance.levenshtein(*sequences)) / self.maximum(*sequences) + + def rename(self, node1, node2): + """Compares attributes of trees""" + #print(node1.tag) + if (node1.tag != node2.tag) or (node1.colspan != node2.colspan) or (node1.rowspan != node2.rowspan): + return 1. + if node1.tag == 'td': + if node1.content or node2.content: + #print(node1.content, ) + return self.normalized_distance(node1.content, node2.content) + return 0. + + + +class CustomConfig_del_short(Config): + @staticmethod + def maximum(*sequences): + """Get maximum possible value + """ + return max(map(len, sequences)) + + def normalized_distance(self, *sequences): + """Get distance from 0 to 1 + """ + return float(distance.levenshtein(*sequences)) / self.maximum(*sequences) + + def rename(self, node1, node2): + """Compares attributes of trees""" + if (node1.tag != node2.tag) or (node1.colspan != node2.colspan) or (node1.rowspan != node2.rowspan): + return 1. + if node1.tag == 'td': + if node1.content or node2.content: + #print('before') + #print(node1.content, node2.content) + #print('after') + node1_content = node1.content + node2_content = node2.content + if len(node1_content) < 3: + node1_content = ['####'] + if len(node2_content) < 3: + node2_content = ['####'] + return self.normalized_distance(node1_content, node2_content) + return 0. + +class CustomConfig_del_block(Config): + @staticmethod + def maximum(*sequences): + """Get maximum possible value + """ + return max(map(len, sequences)) + + def normalized_distance(self, *sequences): + """Get distance from 0 to 1 + """ + return float(distance.levenshtein(*sequences)) / self.maximum(*sequences) + + def rename(self, node1, node2): + """Compares attributes of trees""" + if (node1.tag != node2.tag) or (node1.colspan != node2.colspan) or (node1.rowspan != node2.rowspan): + return 1. + if node1.tag == 'td': + if node1.content or node2.content: + + node1_content = node1.content + node2_content = node2.content + while ' ' in node1_content: + print(node1_content.index(' ')) + node1_content.pop(node1_content.index(' ')) + while ' ' in node2_content: + print(node2_content.index(' ')) + node2_content.pop(node2_content.index(' ')) + return self.normalized_distance(node1_content, node2_content) + return 0. + +class TEDS(object): + ''' Tree Edit Distance basead Similarity + ''' + + def __init__(self, structure_only=False, n_jobs=1, ignore_nodes=None): + assert isinstance(n_jobs, int) and ( + n_jobs >= 1), 'n_jobs must be an integer greather than 1' + self.structure_only = structure_only + self.n_jobs = n_jobs + self.ignore_nodes = ignore_nodes + self.__tokens__ = [] + + def tokenize(self, node): + ''' Tokenizes table cells + ''' + self.__tokens__.append('<%s>' % node.tag) + if node.text is not None: + self.__tokens__ += list(node.text) + for n in node.getchildren(): + self.tokenize(n) + if node.tag != 'unk': + self.__tokens__.append('' % node.tag) + if node.tag != 'td' and node.tail is not None: + self.__tokens__ += list(node.tail) + + def load_html_tree(self, node, parent=None): + ''' Converts HTML tree to the format required by apted + ''' + global __tokens__ + if node.tag == 'td': + if self.structure_only: + cell = [] + else: + self.__tokens__ = [] + self.tokenize(node) + cell = self.__tokens__[1:-1].copy() + new_node = TableTree(node.tag, + int(node.attrib.get('colspan', '1')), + int(node.attrib.get('rowspan', '1')), + cell, *deque()) + else: + new_node = TableTree(node.tag, None, None, None, *deque()) + if parent is not None: + parent.children.append(new_node) + if node.tag != 'td': + for n in node.getchildren(): + self.load_html_tree(n, new_node) + if parent is None: + return new_node + + def evaluate(self, pred, true): + ''' Computes TEDS score between the prediction and the ground truth of a + given sample + ''' + if (not pred) or (not true): + return 0.0 + parser = html.HTMLParser(remove_comments=True, encoding='utf-8') + pred = html.fromstring(pred, parser=parser) + true = html.fromstring(true, parser=parser) + if pred.xpath('body/table') and true.xpath('body/table'): + pred = pred.xpath('body/table')[0] + true = true.xpath('body/table')[0] + if self.ignore_nodes: + etree.strip_tags(pred, *self.ignore_nodes) + etree.strip_tags(true, *self.ignore_nodes) + n_nodes_pred = len(pred.xpath(".//*")) + n_nodes_true = len(true.xpath(".//*")) + n_nodes = max(n_nodes_pred, n_nodes_true) + tree_pred = self.load_html_tree(pred) + tree_true = self.load_html_tree(true) + distance = APTED(tree_pred, tree_true, + CustomConfig()).compute_edit_distance() + return 1.0 - (float(distance) / n_nodes) + else: + return 0.0 + + def batch_evaluate(self, pred_json, true_json): + ''' Computes TEDS score between the prediction and the ground truth of + a batch of samples + @params pred_json: {'FILENAME': 'HTML CODE', ...} + @params true_json: {'FILENAME': {'html': 'HTML CODE'}, ...} + @output: {'FILENAME': 'TEDS SCORE', ...} + ''' + samples = true_json.keys() + if self.n_jobs == 1: + scores = [self.evaluate(pred_json.get( + filename, ''), true_json[filename]['html']) for filename in tqdm(samples)] + else: + inputs = [{'pred': pred_json.get( + filename, ''), 'true': true_json[filename]['html']} for filename in samples] + scores = parallel_process( + inputs, self.evaluate, use_kwargs=True, n_jobs=self.n_jobs, front_num=1) + scores = dict(zip(samples, scores)) + return scores + + def batch_evaluate_html(self, pred_htmls, true_htmls): + ''' Computes TEDS score between the prediction and the ground truth of + a batch of samples + ''' + if self.n_jobs == 1: + scores = [self.evaluate(pred_html, true_html) for ( + pred_html, true_html) in zip(pred_htmls, true_htmls)] + else: + inputs = [{"pred": pred_html, "true": true_html} for( + pred_html, true_html) in zip(pred_htmls, true_htmls)] + + scores = parallel_process( + inputs, self.evaluate, use_kwargs=True, n_jobs=self.n_jobs, front_num=1) + return scores + + +if __name__ == '__main__': + import json + import pprint + with open('sample_pred.json') as fp: + pred_json = json.load(fp) + with open('sample_gt.json') as fp: + true_json = json.load(fp) + teds = TEDS(n_jobs=4) + scores = teds.batch_evaluate(pred_json, true_json) + pp = pprint.PrettyPrinter() + pp.pprint(scores) diff --git a/toolbox/PaddleOCR/ppstructure/vqa/tools/eval_with_label_end2end.py b/toolbox/PaddleOCR/ppstructure/vqa/tools/eval_with_label_end2end.py new file mode 100644 index 0000000000000000000000000000000000000000..7e6a0eefd6aaa8055b2ef43061cbb205cdf00f74 --- /dev/null +++ b/toolbox/PaddleOCR/ppstructure/vqa/tools/eval_with_label_end2end.py @@ -0,0 +1,261 @@ +# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +# Copyright (c) 2024, Shanghai Iluvatar CoreX Semiconductor Co., Ltd. +# All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import re +import sys +import shapely +from shapely.geometry import Polygon +import numpy as np +from collections import defaultdict +import operator +import Levenshtein +import argparse +import json +import copy + + +def parse_ser_results_fp(fp, fp_type="gt", ignore_background=True): + # img/zh_val_0.jpg { + # "height": 3508, + # "width": 2480, + # "ocr_info": [ + # {"text": "Maribyrnong", "label": "other", "bbox": [1958, 144, 2184, 198]}, + # {"text": "CITYCOUNCIL", "label": "other", "bbox": [2052, 183, 2171, 214]}, + # ] + assert fp_type in ["gt", "pred"] + key = "label" if fp_type == "gt" else "pred" + res_dict = dict() + with open(fp, "r", encoding='utf-8') as fin: + lines = fin.readlines() + + for _, line in enumerate(lines): + img_path, info = line.strip().split("\t") + # get key + image_name = os.path.basename(img_path) + res_dict[image_name] = [] + # get infos + json_info = json.loads(info) + for single_ocr_info in json_info["ocr_info"]: + label = single_ocr_info[key].upper() + if label in ["O", "OTHERS", "OTHER"]: + label = "O" + if ignore_background and label == "O": + continue + single_ocr_info["label"] = label + res_dict[image_name].append(copy.deepcopy(single_ocr_info)) + return res_dict + + +def polygon_from_str(polygon_points): + """ + Create a shapely polygon object from gt or dt line. + """ + polygon_points = np.array(polygon_points).reshape(4, 2) + polygon = Polygon(polygon_points).convex_hull + return polygon + + +def polygon_iou(poly1, poly2): + """ + Intersection over union between two shapely polygons. + """ + if not poly1.intersects( + poly2): # this test is fast and can accelerate calculation + iou = 0 + else: + try: + inter_area = poly1.intersection(poly2).area + union_area = poly1.area + poly2.area - inter_area + iou = float(inter_area) / union_area + except shapely.geos.TopologicalError: + # except Exception as e: + # print(e) + print('shapely.geos.TopologicalError occurred, iou set to 0') + iou = 0 + return iou + + +def ed(args, str1, str2): + if args.ignore_space: + str1 = str1.replace(" ", "") + str2 = str2.replace(" ", "") + if args.ignore_case: + str1 = str1.lower() + str2 = str2.lower() + return Levenshtein.distance(str1, str2) + + +def convert_bbox_to_polygon(bbox): + """ + bbox : [x1, y1, x2, y2] + output: [[x1, y1], [x2, y2], [x3, y3], [x4, y4]] + """ + xmin, ymin, xmax, ymax = bbox + poly = [[xmin, ymin], [xmax, ymin], [xmax, ymax], [xmin, ymax]] + return poly + + +def eval_e2e(args): + # gt + gt_results = parse_ser_results_fp(args.gt_json_path, "gt", + args.ignore_background) + # pred + dt_results = parse_ser_results_fp(args.pred_json_path, "pred", + args.ignore_background) + iou_thresh = args.iou_thres + num_gt_chars = 0 + gt_count = 0 + dt_count = 0 + hit = 0 + ed_sum = 0 + + for img_name in dt_results: + gt_info = gt_results[img_name] + gt_count += len(gt_info) + + dt_info = dt_results[img_name] + dt_count += len(dt_info) + + dt_match = [False] * len(dt_info) + gt_match = [False] * len(gt_info) + + all_ious = defaultdict(tuple) + # gt: {text, label, bbox or poly} + for index_gt, gt in enumerate(gt_info): + if "poly" not in gt: + gt["poly"] = convert_bbox_to_polygon(gt["bbox"]) + gt_poly = polygon_from_str(gt["poly"]) + for index_dt, dt in enumerate(dt_info): + if "poly" not in dt: + dt["poly"] = convert_bbox_to_polygon(dt["bbox"]) + dt_poly = polygon_from_str(dt["poly"]) + iou = polygon_iou(dt_poly, gt_poly) + if iou >= iou_thresh: + all_ious[(index_gt, index_dt)] = iou + sorted_ious = sorted( + all_ious.items(), key=operator.itemgetter(1), reverse=True) + sorted_gt_dt_pairs = [item[0] for item in sorted_ious] + + # matched gt and dt + for gt_dt_pair in sorted_gt_dt_pairs: + index_gt, index_dt = gt_dt_pair + if gt_match[index_gt] == False and dt_match[index_dt] == False: + gt_match[index_gt] = True + dt_match[index_dt] = True + # ocr rec results + gt_text = gt_info[index_gt]["text"] + dt_text = dt_info[index_dt]["text"] + + # ser results + gt_label = gt_info[index_gt]["label"] + dt_label = dt_info[index_dt]["pred"] + + if True: # ignore_masks[index_gt] == '0': + ed_sum += ed(args, gt_text, dt_text) + num_gt_chars += len(gt_text) + if gt_text == dt_text: + if args.ignore_ser_prediction or gt_label == dt_label: + hit += 1 + +# unmatched dt + for tindex, dt_match_flag in enumerate(dt_match): + if dt_match_flag == False: + dt_text = dt_info[tindex]["text"] + gt_text = "" + ed_sum += ed(args, dt_text, gt_text) + +# unmatched gt + for tindex, gt_match_flag in enumerate(gt_match): + if gt_match_flag == False: + dt_text = "" + gt_text = gt_info[tindex]["text"] + ed_sum += ed(args, gt_text, dt_text) + num_gt_chars += len(gt_text) + + eps = 1e-9 + print("config: ", args) + print('hit, dt_count, gt_count', hit, dt_count, gt_count) + precision = hit / (dt_count + eps) + recall = hit / (gt_count + eps) + fmeasure = 2.0 * precision * recall / (precision + recall + eps) + avg_edit_dist_img = ed_sum / len(gt_results) + avg_edit_dist_field = ed_sum / (gt_count + eps) + character_acc = 1 - ed_sum / (num_gt_chars + eps) + + print('character_acc: %.2f' % (character_acc * 100) + "%") + print('avg_edit_dist_field: %.2f' % (avg_edit_dist_field)) + print('avg_edit_dist_img: %.2f' % (avg_edit_dist_img)) + print('precision: %.2f' % (precision * 100) + "%") + print('recall: %.2f' % (recall * 100) + "%") + print('fmeasure: %.2f' % (fmeasure * 100) + "%") + + return + + +def parse_args(): + """ + """ + + def str2bool(v): + return v.lower() in ("true", "t", "1") + + parser = argparse.ArgumentParser() + ## Required parameters + parser.add_argument( + "--gt_json_path", + default=None, + type=str, + required=True, ) + parser.add_argument( + "--pred_json_path", + default=None, + type=str, + required=True, ) + + parser.add_argument("--iou_thres", default=0.5, type=float) + + parser.add_argument( + "--ignore_case", + default=False, + type=str2bool, + help="whether to do lower case for the strs") + + parser.add_argument( + "--ignore_space", + default=True, + type=str2bool, + help="whether to ignore space") + + parser.add_argument( + "--ignore_background", + default=True, + type=str2bool, + help="whether to ignore other label") + + parser.add_argument( + "--ignore_ser_prediction", + default=False, + type=str2bool, + help="whether to ignore ocr pred results") + + args = parser.parse_args() + return args + + +if __name__ == '__main__': + args = parse_args() + eval_e2e(args) diff --git a/toolbox/PaddleOCR/requirements.txt b/toolbox/PaddleOCR/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..d7314c0e40ecd1466642cc03d3bc2db5e9b86d70 --- /dev/null +++ b/toolbox/PaddleOCR/requirements.txt @@ -0,0 +1,29 @@ +# Copyright (c) 2024, Shanghai Iluvatar CoreX Semiconductor Co., Ltd. +# All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); you may +# not use this file except in compliance with the License. You may obtain +# a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the +# License for the specific language governing permissions and limitations +# under the License. + +shapely +scikit-image +imgaug==0.4.0 +pyclipper +lmdb +tqdm +numpy +visualdl +python-Levenshtein +cython +lxml +premailer +openpyxl +attrdict diff --git a/toolbox/PaddleOCR/setup.py b/toolbox/PaddleOCR/setup.py new file mode 100644 index 0000000000000000000000000000000000000000..4b1614f3d873c9612680d0b6bb8c05c2c3b16831 --- /dev/null +++ b/toolbox/PaddleOCR/setup.py @@ -0,0 +1,59 @@ +# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. +# Copyright (c) 2024, Shanghai Iluvatar CoreX Semiconductor Co., Ltd. +# All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from setuptools import setup +from io import open +#from paddleocr import VERSION + +with open('requirements.txt', encoding="utf-8-sig") as f: + requirements = f.readlines() + requirements.append('tqdm') + + +def readme(): + with open('doc/doc_en/whl_en.md', encoding="utf-8-sig") as f: + README = f.read() + return README + +VERSION = '2.5.0.1' +setup( + name='paddleocr', + packages=['paddleocr'], + package_dir={'paddleocr': ''}, + include_package_data=True, + entry_points={"console_scripts": ["paddleocr= paddleocr.paddleocr:main"]}, + version=VERSION, + install_requires=requirements, + license='Apache License 2.0', + description='Awesome OCR toolkits based on PaddlePaddle (8.6M ultra-lightweight pre-trained model, support training and deployment among server, mobile, embeded and IoT devices', + long_description=readme(), + long_description_content_type='text/markdown', + url='https://github.com/PaddlePaddle/PaddleOCR', + download_url='https://github.com/PaddlePaddle/PaddleOCR.git', + keywords=[ + 'ocr textdetection textrecognition paddleocr crnn east star-net rosetta ocrlite db chineseocr chinesetextdetection chinesetextrecognition' + ], + classifiers=[ + 'Intended Audience :: Developers', 'Operating System :: OS Independent', + 'Natural Language :: Chinese (Simplified)', + 'Programming Language :: Python :: 3', + 'Programming Language :: Python :: 3.2', + 'Programming Language :: Python :: 3.3', + 'Programming Language :: Python :: 3.4', + 'Programming Language :: Python :: 3.5', + 'Programming Language :: Python :: 3.6', + 'Programming Language :: Python :: 3.7', 'Topic :: Utilities' + ], ) diff --git a/toolbox/PaddleOCR/test_tipc/test_train_inference_python.sh b/toolbox/PaddleOCR/test_tipc/test_train_inference_python.sh new file mode 100644 index 0000000000000000000000000000000000000000..75376e84c71034c51b34476124c984d010b67e4c --- /dev/null +++ b/toolbox/PaddleOCR/test_tipc/test_train_inference_python.sh @@ -0,0 +1,393 @@ +#!/bin/bash +# Copyright (c) 2024, Shanghai Iluvatar CoreX Semiconductor Co., Ltd. +# All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); you may +# not use this file except in compliance with the License. You may obtain +# a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the +# License for the specific language governing permissions and limitations +# under the License. + +source test_tipc/common_func.sh + +FILENAME=$1 +# MODE be one of ['lite_train_lite_infer' 'lite_train_whole_infer' 'whole_train_whole_infer', 'whole_infer', 'klquant_whole_infer'] +MODE=$2 + +dataline=$(awk 'NR==1, NR==51{print}' $FILENAME) + +# parser params +IFS=$'\n' +lines=(${dataline}) + +# The training params +model_name=$(func_parser_value "${lines[1]}") +python=$(func_parser_value "${lines[2]}") +gpu_list=$(func_parser_value "${lines[3]}") +train_use_gpu_key=$(func_parser_key "${lines[4]}") +train_use_gpu_value=$(func_parser_value "${lines[4]}") +autocast_list=$(func_parser_value "${lines[5]}") +autocast_key=$(func_parser_key "${lines[5]}") +epoch_key=$(func_parser_key "${lines[6]}") +epoch_num=$(func_parser_params "${lines[6]}" "${MODE}") +save_model_key=$(func_parser_key "${lines[7]}") +train_batch_key=$(func_parser_key "${lines[8]}") +train_batch_value=$(func_parser_params "${lines[8]}" "${MODE}") +pretrain_model_key=$(func_parser_key "${lines[9]}") +pretrain_model_value=$(func_parser_value "${lines[9]}") +train_model_name=$(func_parser_value "${lines[10]}") +train_infer_img_dir=$(func_parser_value "${lines[11]}") +train_param_key1=$(func_parser_key "${lines[12]}") +train_param_value1=$(func_parser_value "${lines[12]}") + +trainer_list=$(func_parser_value "${lines[14]}") +trainer_norm=$(func_parser_key "${lines[15]}") +norm_trainer=$(func_parser_value "${lines[15]}") +pact_key=$(func_parser_key "${lines[16]}") +pact_trainer=$(func_parser_value "${lines[16]}") +fpgm_key=$(func_parser_key "${lines[17]}") +fpgm_trainer=$(func_parser_value "${lines[17]}") +distill_key=$(func_parser_key "${lines[18]}") +distill_trainer=$(func_parser_value "${lines[18]}") +trainer_key1=$(func_parser_key "${lines[19]}") +trainer_value1=$(func_parser_value "${lines[19]}") +trainer_key2=$(func_parser_key "${lines[20]}") +trainer_value2=$(func_parser_value "${lines[20]}") + +eval_py=$(func_parser_value "${lines[23]}") +eval_key1=$(func_parser_key "${lines[24]}") +eval_value1=$(func_parser_value "${lines[24]}") + +save_infer_key=$(func_parser_key "${lines[27]}") +export_weight=$(func_parser_key "${lines[28]}") +norm_export=$(func_parser_value "${lines[29]}") +pact_export=$(func_parser_value "${lines[30]}") +fpgm_export=$(func_parser_value "${lines[31]}") +distill_export=$(func_parser_value "${lines[32]}") +export_key1=$(func_parser_key "${lines[33]}") +export_value1=$(func_parser_value "${lines[33]}") +export_key2=$(func_parser_key "${lines[34]}") +export_value2=$(func_parser_value "${lines[34]}") +inference_dir=$(func_parser_value "${lines[35]}") + +# parser inference model +infer_model_dir_list=$(func_parser_value "${lines[36]}") +infer_export_list=$(func_parser_value "${lines[37]}") +infer_is_quant=$(func_parser_value "${lines[38]}") +# parser inference +inference_py=$(func_parser_value "${lines[39]}") +use_gpu_key=$(func_parser_key "${lines[40]}") +use_gpu_list=$(func_parser_value "${lines[40]}") +use_mkldnn_key=$(func_parser_key "${lines[41]}") +use_mkldnn_list=$(func_parser_value "${lines[41]}") +cpu_threads_key=$(func_parser_key "${lines[42]}") +cpu_threads_list=$(func_parser_value "${lines[42]}") +batch_size_key=$(func_parser_key "${lines[43]}") +batch_size_list=$(func_parser_value "${lines[43]}") +use_trt_key=$(func_parser_key "${lines[44]}") +use_trt_list=$(func_parser_value "${lines[44]}") +precision_key=$(func_parser_key "${lines[45]}") +precision_list=$(func_parser_value "${lines[45]}") +infer_model_key=$(func_parser_key "${lines[46]}") +image_dir_key=$(func_parser_key "${lines[47]}") +infer_img_dir=$(func_parser_value "${lines[47]}") +save_log_key=$(func_parser_key "${lines[48]}") +benchmark_key=$(func_parser_key "${lines[49]}") +benchmark_value=$(func_parser_value "${lines[49]}") +infer_key1=$(func_parser_key "${lines[50]}") +infer_value1=$(func_parser_value "${lines[50]}") + +# parser klquant_infer +if [ ${MODE} = "klquant_whole_infer" ]; then + dataline=$(awk 'NR==1, NR==17{print}' $FILENAME) + lines=(${dataline}) + model_name=$(func_parser_value "${lines[1]}") + python=$(func_parser_value "${lines[2]}") + export_weight=$(func_parser_key "${lines[3]}") + save_infer_key=$(func_parser_key "${lines[4]}") + # parser inference model + infer_model_dir_list=$(func_parser_value "${lines[5]}") + infer_export_list=$(func_parser_value "${lines[6]}") + infer_is_quant=$(func_parser_value "${lines[7]}") + # parser inference + inference_py=$(func_parser_value "${lines[8]}") + use_gpu_key=$(func_parser_key "${lines[9]}") + use_gpu_list=$(func_parser_value "${lines[9]}") + use_mkldnn_key=$(func_parser_key "${lines[10]}") + use_mkldnn_list=$(func_parser_value "${lines[10]}") + cpu_threads_key=$(func_parser_key "${lines[11]}") + cpu_threads_list=$(func_parser_value "${lines[11]}") + batch_size_key=$(func_parser_key "${lines[12]}") + batch_size_list=$(func_parser_value "${lines[12]}") + use_trt_key=$(func_parser_key "${lines[13]}") + use_trt_list=$(func_parser_value "${lines[13]}") + precision_key=$(func_parser_key "${lines[14]}") + precision_list=$(func_parser_value "${lines[14]}") + infer_model_key=$(func_parser_key "${lines[15]}") + image_dir_key=$(func_parser_key "${lines[16]}") + infer_img_dir=$(func_parser_value "${lines[16]}") + save_log_key=$(func_parser_key "${lines[17]}") + save_log_value=$(func_parser_value "${lines[17]}") + benchmark_key=$(func_parser_key "${lines[18]}") + benchmark_value=$(func_parser_value "${lines[18]}") + infer_key1=$(func_parser_key "${lines[19]}") + infer_value1=$(func_parser_value "${lines[19]}") +fi + +LOG_PATH="./test_tipc/output/${model_name}" +mkdir -p ${LOG_PATH} +status_log="${LOG_PATH}/results_python.log" + + +function func_inference(){ + IFS='|' + _python=$1 + _script=$2 + _model_dir=$3 + _log_path=$4 + _img_dir=$5 + _flag_quant=$6 + # inference + for use_gpu in ${use_gpu_list[*]}; do + if [ ${use_gpu} = "False" ] || [ ${use_gpu} = "cpu" ]; then + for use_mkldnn in ${use_mkldnn_list[*]}; do + if [ ${use_mkldnn} = "False" ] && [ ${_flag_quant} = "True" ]; then + continue + fi + for threads in ${cpu_threads_list[*]}; do + for batch_size in ${batch_size_list[*]}; do + for precision in ${precision_list[*]}; do + if [ ${use_mkldnn} = "False" ] && [ ${precision} = "fp16" ]; then + continue + fi # skip when enable fp16 but disable mkldnn + if [ ${_flag_quant} = "True" ] && [ ${precision} != "int8" ]; then + continue + fi # skip when quant model inference but precision is not int8 + set_precision=$(func_set_params "${precision_key}" "${precision}") + + _save_log_path="${_log_path}/python_infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_precision_${precision}_batchsize_${batch_size}.log" + set_infer_data=$(func_set_params "${image_dir_key}" "${_img_dir}") + set_benchmark=$(func_set_params "${benchmark_key}" "${benchmark_value}") + set_batchsize=$(func_set_params "${batch_size_key}" "${batch_size}") + set_mkldnn=$(func_set_params "${use_mkldnn_key}" "${use_mkldnn}") + set_cpu_threads=$(func_set_params "${cpu_threads_key}" "${threads}") + set_model_dir=$(func_set_params "${infer_model_key}" "${_model_dir}") + set_infer_params0=$(func_set_params "${save_log_key}" "${save_log_value}") + set_infer_params1=$(func_set_params "${infer_key1}" "${infer_value1}") + command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${set_mkldnn} ${set_cpu_threads} ${set_model_dir} ${set_batchsize} ${set_infer_params0} ${set_infer_data} ${set_benchmark} ${set_precision} ${set_infer_params1} > ${_save_log_path} 2>&1 " + eval $command + last_status=${PIPESTATUS[0]} + eval "cat ${_save_log_path}" + status_check $last_status "${command}" "${status_log}" + done + done + done + done + elif [ ${use_gpu} = "True" ] || [ ${use_gpu} = "gpu" ]; then + for use_trt in ${use_trt_list[*]}; do + for precision in ${precision_list[*]}; do + if [[ ${_flag_quant} = "False" ]] && [[ ${precision} =~ "int8" ]]; then + continue + fi + if [[ ${precision} =~ "fp16" || ${precision} =~ "int8" ]] && [ ${use_trt} = "False" ]; then + continue + fi + if [[ ${use_trt} = "False" && ${precision} =~ "int8" ]] && [ ${_flag_quant} = "True" ]; then + continue + fi + for batch_size in ${batch_size_list[*]}; do + _save_log_path="${_log_path}/python_infer_gpu_usetrt_${use_trt}_precision_${precision}_batchsize_${batch_size}.log" + set_infer_data=$(func_set_params "${image_dir_key}" "${_img_dir}") + set_benchmark=$(func_set_params "${benchmark_key}" "${benchmark_value}") + set_batchsize=$(func_set_params "${batch_size_key}" "${batch_size}") + set_tensorrt=$(func_set_params "${use_trt_key}" "${use_trt}") + set_precision=$(func_set_params "${precision_key}" "${precision}") + set_model_dir=$(func_set_params "${infer_model_key}" "${_model_dir}") + set_infer_params0=$(func_set_params "${save_log_key}" "${save_log_value}") + set_infer_params1=$(func_set_params "${infer_key1}" "${infer_value1}") + command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${set_tensorrt} ${set_precision} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} ${set_infer_params1} ${set_infer_params0} > ${_save_log_path} 2>&1 " + eval $command + last_status=${PIPESTATUS[0]} + eval "cat ${_save_log_path}" + status_check $last_status "${command}" "${status_log}" + + done + done + done + else + echo "Does not support hardware other than CPU and GPU Currently!" + fi + done +} + +if [ ${MODE} = "whole_infer" ] || [ ${MODE} = "klquant_whole_infer" ]; then + GPUID=$3 + if [ ${#GPUID} -le 0 ];then + env=" " + else + env="export CUDA_VISIBLE_DEVICES=${GPUID}" + fi + # set CUDA_VISIBLE_DEVICES + eval $env + export Count=0 + IFS="|" + infer_run_exports=(${infer_export_list}) + infer_quant_flag=(${infer_is_quant}) + for infer_model in ${infer_model_dir_list[*]}; do + # run export + if [ ${infer_run_exports[Count]} != "null" ];then + if [ ${MODE} = "klquant_whole_infer" ]; then + save_infer_dir="${infer_model}_klquant" + fi + if [ ${MODE} = "whole_infer" ]; then + save_infer_dir="${infer_model}" + fi + set_export_weight=$(func_set_params "${export_weight}" "${infer_model}") + set_save_infer_key=$(func_set_params "${save_infer_key}" "${save_infer_dir}") + export_cmd="${python} ${infer_run_exports[Count]} ${set_export_weight} ${set_save_infer_key}" + echo ${infer_run_exports[Count]} + echo $export_cmd + eval $export_cmd + status_export=$? + status_check $status_export "${export_cmd}" "${status_log}" + else + save_infer_dir=${infer_model} + fi + #run inference + is_quant=${infer_quant_flag[Count]} + if [ ${MODE} = "klquant_whole_infer" ]; then + is_quant="True" + fi + func_inference "${python}" "${inference_py}" "${save_infer_dir}" "${LOG_PATH}" "${infer_img_dir}" ${is_quant} + Count=$(($Count + 1)) + done +else + IFS="|" + export Count=0 + USE_GPU_KEY=(${train_use_gpu_value}) + for gpu in ${gpu_list[*]}; do + train_use_gpu=${USE_GPU_KEY[Count]} + Count=$(($Count + 1)) + ips="" + if [ ${gpu} = "-1" ];then + env="" + elif [ ${#gpu} -le 1 ];then + env="export CUDA_VISIBLE_DEVICES=${gpu}" + elif [ ${#gpu} -le 15 ];then + IFS="," + array=(${gpu}) + env="export CUDA_VISIBLE_DEVICES=${array[0]}" + IFS="|" + else + IFS=";" + array=(${gpu}) + ips=${array[0]} + gpu=${array[1]} + IFS="|" + env=" " + fi + for autocast in ${autocast_list[*]}; do + if [ ${autocast} = "amp" ]; then + set_amp_config="Global.use_amp=True Global.scale_loss=1024.0 Global.use_dynamic_loss_scaling=True" + else + set_amp_config=" " + fi + for trainer in ${trainer_list[*]}; do + flag_quant=False + if [ ${trainer} = ${pact_key} ]; then + run_train=${pact_trainer} + run_export=${pact_export} + flag_quant=True + elif [ ${trainer} = "${fpgm_key}" ]; then + run_train=${fpgm_trainer} + run_export=${fpgm_export} + elif [ ${trainer} = "${distill_key}" ]; then + run_train=${distill_trainer} + run_export=${distill_export} + elif [ ${trainer} = ${trainer_key1} ]; then + run_train=${trainer_value1} + run_export=${export_value1} + elif [[ ${trainer} = ${trainer_key2} ]]; then + run_train=${trainer_value2} + run_export=${export_value2} + else + run_train=${norm_trainer} + run_export=${norm_export} + fi + + if [ ${run_train} = "null" ]; then + continue + fi + set_autocast=$(func_set_params "${autocast_key}" "${autocast}") + set_epoch=$(func_set_params "${epoch_key}" "${epoch_num}") + set_pretrain=$(func_set_params "${pretrain_model_key}" "${pretrain_model_value}") + set_batchsize=$(func_set_params "${train_batch_key}" "${train_batch_value}") + set_train_params1=$(func_set_params "${train_param_key1}" "${train_param_value1}") + set_use_gpu=$(func_set_params "${train_use_gpu_key}" "${train_use_gpu}") + if [ ${#ips} -le 26 ];then + save_log="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}" + nodes=1 + else + IFS="," + ips_array=(${ips}) + IFS="|" + nodes=${#ips_array[@]} + save_log="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}_nodes_${nodes}" + fi + + + set_save_model=$(func_set_params "${save_model_key}" "${save_log}") + if [ ${#gpu} -le 2 ];then # train with cpu or single gpu + cmd="${python} ${run_train} ${set_use_gpu} ${set_save_model} ${set_epoch} ${set_pretrain} ${set_autocast} ${set_batchsize} ${set_train_params1} ${set_amp_config} " + elif [ ${#ips} -le 26 ];then # train with multi-gpu + cmd="${python} -m paddle.distributed.launch --gpus=${gpu} ${run_train} ${set_use_gpu} ${set_save_model} ${set_epoch} ${set_pretrain} ${set_autocast} ${set_batchsize} ${set_train_params1} ${set_amp_config}" + else # train with multi-machine + cmd="${python} -m paddle.distributed.launch --ips=${ips} --gpus=${gpu} ${run_train} ${set_use_gpu} ${set_save_model} ${set_pretrain} ${set_epoch} ${set_autocast} ${set_batchsize} ${set_train_params1} ${set_amp_config}" + fi + # run train + eval $cmd + status_check $? "${cmd}" "${status_log}" + + set_eval_pretrain=$(func_set_params "${pretrain_model_key}" "${save_log}/${train_model_name}") + + # run eval + if [ ${eval_py} != "null" ]; then + eval ${env} + set_eval_params1=$(func_set_params "${eval_key1}" "${eval_value1}") + eval_cmd="${python} ${eval_py} ${set_eval_pretrain} ${set_use_gpu} ${set_eval_params1}" + eval $eval_cmd + status_check $? "${eval_cmd}" "${status_log}" + fi + # run export model + if [ ${run_export} != "null" ]; then + # run export model + save_infer_path="${save_log}" + set_export_weight=$(func_set_params "${export_weight}" "${save_log}/${train_model_name}") + set_save_infer_key=$(func_set_params "${save_infer_key}" "${save_infer_path}") + export_cmd="${python} ${run_export} ${set_export_weight} ${set_save_infer_key}" + eval $export_cmd + status_check $? "${export_cmd}" "${status_log}" + + #run inference + eval $env + save_infer_path="${save_log}" + if [[ ${inference_dir} != "null" ]] && [[ ${inference_dir} != '##' ]]; then + infer_model_dir="${save_infer_path}/${inference_dir}" + else + infer_model_dir=${save_infer_path} + fi + func_inference "${python}" "${inference_py}" "${infer_model_dir}" "${LOG_PATH}" "${train_infer_img_dir}" "${flag_quant}" + + eval "unset CUDA_VISIBLE_DEVICES" + fi + done # done with: for trainer in ${trainer_list[*]}; do + done # done with: for autocast in ${autocast_list[*]}; do + done # done with: for gpu in ${gpu_list[*]}; do +fi # end if [ ${MODE} = "infer" ]; then + diff --git a/toolbox/PaddleOCR/tools/export_model.py b/toolbox/PaddleOCR/tools/export_model.py new file mode 100644 index 0000000000000000000000000000000000000000..92217c7f35cd532bf6af4ad94d4246bff4ffc2c6 --- /dev/null +++ b/toolbox/PaddleOCR/tools/export_model.py @@ -0,0 +1,174 @@ +# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. +# Copyright (c) 2024, Shanghai Iluvatar CoreX Semiconductor Co., Ltd. +# All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import sys + +__dir__ = os.path.dirname(os.path.abspath(__file__)) +sys.path.append(__dir__) +sys.path.append(os.path.abspath(os.path.join(__dir__, ".."))) + +import argparse + +import paddle +from paddle.jit import to_static + +from ppocr.modeling.architectures import build_model +from ppocr.postprocess import build_post_process +from ppocr.utils.save_load import load_model +from ppocr.utils.logging import get_logger +from tools.program import load_config, merge_config, ArgsParser + + +def export_single_model(model, arch_config, save_path, logger, quanter=None): + if arch_config["algorithm"] == "SRN": + max_text_length = arch_config["Head"]["max_text_length"] + other_shape = [ + paddle.static.InputSpec( + shape=[None, 1, 64, 256], dtype="float32"), [ + paddle.static.InputSpec( + shape=[None, 256, 1], + dtype="int64"), paddle.static.InputSpec( + shape=[None, max_text_length, 1], dtype="int64"), + paddle.static.InputSpec( + shape=[None, 8, max_text_length, max_text_length], + dtype="int64"), paddle.static.InputSpec( + shape=[None, 8, max_text_length, max_text_length], + dtype="int64") + ] + ] + model = to_static(model, input_spec=other_shape) + elif arch_config["algorithm"] == "SAR": + other_shape = [ + paddle.static.InputSpec( + shape=[None, 3, 48, 160], dtype="float32"), + ] + model = to_static(model, input_spec=other_shape) + elif arch_config["algorithm"] == "SVTR": + if arch_config["Head"]["name"] == 'MultiHead': + other_shape = [ + paddle.static.InputSpec( + shape=[None, 3, 48, -1], dtype="float32"), + ] + else: + other_shape = [ + paddle.static.InputSpec( + shape=[None, 3, 64, 256], dtype="float32"), + ] + model = to_static(model, input_spec=other_shape) + elif arch_config["algorithm"] == "PREN": + other_shape = [ + paddle.static.InputSpec( + shape=[None, 3, 64, 512], dtype="float32"), + ] + model = to_static(model, input_spec=other_shape) + else: + infer_shape = [3, -1, -1] + if arch_config["model_type"] == "rec": + infer_shape = [3, 32, -1] # for rec model, H must be 32 + if "Transform" in arch_config and arch_config[ + "Transform"] is not None and arch_config["Transform"][ + "name"] == "TPS": + logger.info( + "When there is tps in the network, variable length input is not supported, and the input size needs to be the same as during training" + ) + infer_shape[-1] = 100 + if arch_config["algorithm"] == "NRTR": + infer_shape = [1, 32, 100] + elif arch_config["model_type"] == "table": + infer_shape = [3, 488, 488] + model = to_static( + model, + input_spec=[ + paddle.static.InputSpec( + shape=[None] + infer_shape, dtype="float32") + ]) + + if quanter is None: + paddle.jit.save(model, save_path) + else: + quanter.save_quantized_model(model, save_path) + logger.info("inference model is saved to {}".format(save_path)) + return + + +def main(): + FLAGS = ArgsParser().parse_args() + config = load_config(FLAGS.config) + config = merge_config(config, FLAGS.opt) + logger = get_logger() + # build post process + + post_process_class = build_post_process(config["PostProcess"], + config["Global"]) + + # build model + # for rec algorithm + if hasattr(post_process_class, "character"): + char_num = len(getattr(post_process_class, "character")) + if config["Architecture"]["algorithm"] in ["Distillation", + ]: # distillation model + for key in config["Architecture"]["Models"]: + if config["Architecture"]["Models"][key]["Head"][ + "name"] == 'MultiHead': # multi head + out_channels_list = {} + if config['PostProcess'][ + 'name'] == 'DistillationSARLabelDecode': + char_num = char_num - 2 + out_channels_list['CTCLabelDecode'] = char_num + out_channels_list['SARLabelDecode'] = char_num + 2 + config['Architecture']['Models'][key]['Head'][ + 'out_channels_list'] = out_channels_list + else: + config["Architecture"]["Models"][key]["Head"][ + "out_channels"] = char_num + # just one final tensor needs to exported for inference + config["Architecture"]["Models"][key][ + "return_all_feats"] = False + elif config['Architecture']['Head'][ + 'name'] == 'MultiHead': # multi head + out_channels_list = {} + char_num = len(getattr(post_process_class, 'character')) + if config['PostProcess']['name'] == 'SARLabelDecode': + char_num = char_num - 2 + out_channels_list['CTCLabelDecode'] = char_num + out_channels_list['SARLabelDecode'] = char_num + 2 + config['Architecture']['Head'][ + 'out_channels_list'] = out_channels_list + else: # base rec model + config["Architecture"]["Head"]["out_channels"] = char_num + + model = build_model(config["Architecture"]) + load_model(config, model) + model.eval() + + save_path = config["Global"]["save_inference_dir"] + + arch_config = config["Architecture"] + + if arch_config["algorithm"] in ["Distillation", ]: # distillation model + archs = list(arch_config["Models"].values()) + for idx, name in enumerate(model.model_name_list): + sub_model_save_path = os.path.join(save_path, name, "inference") + export_single_model(model.model_list[idx], archs[idx], + sub_model_save_path, logger) + else: + save_path = os.path.join(save_path, "inference") + export_single_model(model, arch_config, save_path, logger) + + +if __name__ == "__main__": + main() diff --git a/toolbox/PaddleOCR/tools/infer/predict_det.py b/toolbox/PaddleOCR/tools/infer/predict_det.py new file mode 100644 index 0000000000000000000000000000000000000000..566fefdf3197dbc7d0b3b68492b2baf5531c9f23 --- /dev/null +++ b/toolbox/PaddleOCR/tools/infer/predict_det.py @@ -0,0 +1,305 @@ +# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. +# Copyright (c) 2024, Shanghai Iluvatar CoreX Semiconductor Co., Ltd. +# All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import sys + +__dir__ = os.path.dirname(os.path.abspath(__file__)) +sys.path.append(__dir__) +sys.path.insert(0, os.path.abspath(os.path.join(__dir__, '../..'))) + +os.environ["FLAGS_allocator_strategy"] = 'auto_growth' + +import cv2 +import numpy as np +import time +import sys + +import tools.infer.utility as utility +from ppocr.utils.logging import get_logger +from ppocr.utils.utility import get_image_file_list, check_and_read_gif +from ppocr.data import create_operators, transform +from ppocr.postprocess import build_post_process +import json +logger = get_logger() + + +class TextDetector(object): + def __init__(self, args): + self.args = args + self.det_algorithm = args.det_algorithm + self.use_onnx = args.use_onnx + pre_process_list = [{ + 'DetResizeForTest': { + 'limit_side_len': args.det_limit_side_len, + 'limit_type': args.det_limit_type, + } + }, { + 'NormalizeImage': { + 'std': [0.229, 0.224, 0.225], + 'mean': [0.485, 0.456, 0.406], + 'scale': '1./255.', + 'order': 'hwc' + } + }, { + 'ToCHWImage': None + }, { + 'KeepKeys': { + 'keep_keys': ['image', 'shape'] + } + }] + postprocess_params = {} + if self.det_algorithm == "DB": + postprocess_params['name'] = 'DBPostProcess' + postprocess_params["thresh"] = args.det_db_thresh + postprocess_params["box_thresh"] = args.det_db_box_thresh + postprocess_params["max_candidates"] = 1000 + postprocess_params["unclip_ratio"] = args.det_db_unclip_ratio + postprocess_params["use_dilation"] = args.use_dilation + postprocess_params["score_mode"] = args.det_db_score_mode + elif self.det_algorithm == "EAST": + postprocess_params['name'] = 'EASTPostProcess' + postprocess_params["score_thresh"] = args.det_east_score_thresh + postprocess_params["cover_thresh"] = args.det_east_cover_thresh + postprocess_params["nms_thresh"] = args.det_east_nms_thresh + elif self.det_algorithm == "SAST": + pre_process_list[0] = { + 'DetResizeForTest': { + 'resize_long': args.det_limit_side_len + } + } + postprocess_params['name'] = 'SASTPostProcess' + postprocess_params["score_thresh"] = args.det_sast_score_thresh + postprocess_params["nms_thresh"] = args.det_sast_nms_thresh + self.det_sast_polygon = args.det_sast_polygon + if self.det_sast_polygon: + postprocess_params["sample_pts_num"] = 6 + postprocess_params["expand_scale"] = 1.2 + postprocess_params["shrink_ratio_of_width"] = 0.2 + else: + postprocess_params["sample_pts_num"] = 2 + postprocess_params["expand_scale"] = 1.0 + postprocess_params["shrink_ratio_of_width"] = 0.3 + elif self.det_algorithm == "PSE": + postprocess_params['name'] = 'PSEPostProcess' + postprocess_params["thresh"] = args.det_pse_thresh + postprocess_params["box_thresh"] = args.det_pse_box_thresh + postprocess_params["min_area"] = args.det_pse_min_area + postprocess_params["box_type"] = args.det_pse_box_type + postprocess_params["scale"] = args.det_pse_scale + self.det_pse_box_type = args.det_pse_box_type + elif self.det_algorithm == "FCE": + pre_process_list[0] = { + 'DetResizeForTest': { + 'rescale_img': [1080, 736] + } + } + postprocess_params['name'] = 'FCEPostProcess' + postprocess_params["scales"] = args.scales + postprocess_params["alpha"] = args.alpha + postprocess_params["beta"] = args.beta + postprocess_params["fourier_degree"] = args.fourier_degree + postprocess_params["box_type"] = args.det_fce_box_type + else: + logger.info("unknown det_algorithm:{}".format(self.det_algorithm)) + sys.exit(0) + + self.preprocess_op = create_operators(pre_process_list) + self.postprocess_op = build_post_process(postprocess_params) + self.predictor, self.input_tensor, self.output_tensors, self.config = utility.create_predictor( + args, 'det', logger) + + if self.use_onnx: + img_h, img_w = self.input_tensor.shape[2:] + if img_h is not None and img_w is not None and img_h > 0 and img_w > 0: + pre_process_list[0] = { + 'DetResizeForTest': { + 'image_shape': [img_h, img_w] + } + } + self.preprocess_op = create_operators(pre_process_list) + + if args.benchmark: + import auto_log + pid = os.getpid() + gpu_id = utility.get_infer_gpuid() + self.autolog = auto_log.AutoLogger( + model_name="det", + model_precision=args.precision, + batch_size=1, + data_shape="dynamic", + save_path=None, + inference_config=self.config, + pids=pid, + process_name=None, + gpu_ids=gpu_id if args.use_gpu else None, + time_keys=[ + 'preprocess_time', 'inference_time', 'postprocess_time' + ], + warmup=2, + logger=logger) + + def order_points_clockwise(self, pts): + rect = np.zeros((4, 2), dtype="float32") + s = pts.sum(axis=1) + rect[0] = pts[np.argmin(s)] + rect[2] = pts[np.argmax(s)] + diff = np.diff(pts, axis=1) + rect[1] = pts[np.argmin(diff)] + rect[3] = pts[np.argmax(diff)] + return rect + + def clip_det_res(self, points, img_height, img_width): + for pno in range(points.shape[0]): + points[pno, 0] = int(min(max(points[pno, 0], 0), img_width - 1)) + points[pno, 1] = int(min(max(points[pno, 1], 0), img_height - 1)) + return points + + def filter_tag_det_res(self, dt_boxes, image_shape): + img_height, img_width = image_shape[0:2] + dt_boxes_new = [] + for box in dt_boxes: + box = self.order_points_clockwise(box) + box = self.clip_det_res(box, img_height, img_width) + rect_width = int(np.linalg.norm(box[0] - box[1])) + rect_height = int(np.linalg.norm(box[0] - box[3])) + if rect_width <= 3 or rect_height <= 3: + continue + dt_boxes_new.append(box) + dt_boxes = np.array(dt_boxes_new) + return dt_boxes + + def filter_tag_det_res_only_clip(self, dt_boxes, image_shape): + img_height, img_width = image_shape[0:2] + dt_boxes_new = [] + for box in dt_boxes: + box = self.clip_det_res(box, img_height, img_width) + dt_boxes_new.append(box) + dt_boxes = np.array(dt_boxes_new) + return dt_boxes + + def __call__(self, img): + ori_im = img.copy() + data = {'image': img} + + st = time.time() + + if self.args.benchmark: + self.autolog.times.start() + + data = transform(data, self.preprocess_op) + img, shape_list = data + if img is None: + return None, 0 + img = np.expand_dims(img, axis=0) + shape_list = np.expand_dims(shape_list, axis=0) + img = img.copy() + + if self.args.benchmark: + self.autolog.times.stamp() + if self.use_onnx: + input_dict = {} + input_dict[self.input_tensor.name] = img + outputs = self.predictor.run(self.output_tensors, input_dict) + else: + self.input_tensor.copy_from_cpu(img) + self.predictor.run() + outputs = [] + for output_tensor in self.output_tensors: + output = output_tensor.copy_to_cpu() + outputs.append(output) + if self.args.benchmark: + self.autolog.times.stamp() + + preds = {} + if self.det_algorithm == "EAST": + preds['f_geo'] = outputs[0] + preds['f_score'] = outputs[1] + elif self.det_algorithm == 'SAST': + preds['f_border'] = outputs[0] + preds['f_score'] = outputs[1] + preds['f_tco'] = outputs[2] + preds['f_tvo'] = outputs[3] + elif self.det_algorithm in ['DB', 'PSE']: + preds['maps'] = outputs[0] + elif self.det_algorithm == 'FCE': + for i, output in enumerate(outputs): + preds['level_{}'.format(i)] = output + else: + raise NotImplementedError + + #self.predictor.try_shrink_memory() + post_result = self.postprocess_op(preds, shape_list) + dt_boxes = post_result[0]['points'] + if (self.det_algorithm == "SAST" and self.det_sast_polygon) or ( + self.det_algorithm in ["PSE", "FCE"] and + self.postprocess_op.box_type == 'poly'): + dt_boxes = self.filter_tag_det_res_only_clip(dt_boxes, ori_im.shape) + else: + dt_boxes = self.filter_tag_det_res(dt_boxes, ori_im.shape) + + if self.args.benchmark: + self.autolog.times.end(stamp=True) + et = time.time() + return dt_boxes, et - st + + +if __name__ == "__main__": + args = utility.parse_args() + image_file_list = get_image_file_list(args.image_dir) + text_detector = TextDetector(args) + count = 0 + total_time = 0 + draw_img_save = "./inference_results" + + if args.warmup: + img = np.random.uniform(0, 255, [640, 640, 3]).astype(np.uint8) + for i in range(2): + res = text_detector(img) + + if not os.path.exists(draw_img_save): + os.makedirs(draw_img_save) + save_results = [] + for image_file in image_file_list: + img, flag = check_and_read_gif(image_file) + if not flag: + img = cv2.imread(image_file) + if img is None: + logger.info("error in loading image:{}".format(image_file)) + continue + st = time.time() + dt_boxes, _ = text_detector(img) + elapse = time.time() - st + if count > 0: + total_time += elapse + count += 1 + save_pred = os.path.basename(image_file) + "\t" + str( + json.dumps([x.tolist() for x in dt_boxes])) + "\n" + save_results.append(save_pred) + logger.info(save_pred) + logger.info("The predict time of {}: {}".format(image_file, elapse)) + src_im = utility.draw_text_det_res(dt_boxes, image_file) + img_name_pure = os.path.split(image_file)[-1] + img_path = os.path.join(draw_img_save, + "det_res_{}".format(img_name_pure)) + cv2.imwrite(img_path, src_im) + logger.info("The visualized image saved in {}".format(img_path)) + + with open(os.path.join(draw_img_save, "det_results.txt"), 'w') as f: + f.writelines(save_results) + f.close() + if args.benchmark: + text_detector.autolog.report() diff --git a/toolbox/PaddleOCR/tools/infer/predict_rec.py b/toolbox/PaddleOCR/tools/infer/predict_rec.py new file mode 100644 index 0000000000000000000000000000000000000000..9c5be3d9e6708b5bc448de28fdc08b4121a28144 --- /dev/null +++ b/toolbox/PaddleOCR/tools/infer/predict_rec.py @@ -0,0 +1,441 @@ +# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. +# Copyright (c) 2024, Shanghai Iluvatar CoreX Semiconductor Co., Ltd. +# All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import sys +from PIL import Image +__dir__ = os.path.dirname(os.path.abspath(__file__)) +sys.path.append(__dir__) +sys.path.insert(0, os.path.abspath(os.path.join(__dir__, '../..'))) + +os.environ["FLAGS_allocator_strategy"] = 'auto_growth' + +import cv2 +import numpy as np +import math +import time +import traceback +import paddle + +import tools.infer.utility as utility +from ppocr.postprocess import build_post_process +from ppocr.utils.logging import get_logger +from ppocr.utils.utility import get_image_file_list, check_and_read_gif + +logger = get_logger() + + +class TextRecognizer(object): + def __init__(self, args): + self.rec_image_shape = [int(v) for v in args.rec_image_shape.split(",")] + self.rec_batch_num = args.rec_batch_num + self.rec_algorithm = args.rec_algorithm + postprocess_params = { + 'name': 'CTCLabelDecode', + "character_dict_path": args.rec_char_dict_path, + "use_space_char": args.use_space_char + } + if self.rec_algorithm == "SRN": + postprocess_params = { + 'name': 'SRNLabelDecode', + "character_dict_path": args.rec_char_dict_path, + "use_space_char": args.use_space_char + } + elif self.rec_algorithm == "RARE": + postprocess_params = { + 'name': 'AttnLabelDecode', + "character_dict_path": args.rec_char_dict_path, + "use_space_char": args.use_space_char + } + elif self.rec_algorithm == 'NRTR': + postprocess_params = { + 'name': 'NRTRLabelDecode', + "character_dict_path": args.rec_char_dict_path, + "use_space_char": args.use_space_char + } + elif self.rec_algorithm == "SAR": + postprocess_params = { + 'name': 'SARLabelDecode', + "character_dict_path": args.rec_char_dict_path, + "use_space_char": args.use_space_char + } + self.postprocess_op = build_post_process(postprocess_params) + self.predictor, self.input_tensor, self.output_tensors, self.config = \ + utility.create_predictor(args, 'rec', logger) + self.benchmark = args.benchmark + self.use_onnx = args.use_onnx + if args.benchmark: + import auto_log + pid = os.getpid() + gpu_id = utility.get_infer_gpuid() + self.autolog = auto_log.AutoLogger( + model_name="rec", + model_precision=args.precision, + batch_size=args.rec_batch_num, + data_shape="dynamic", + save_path=None, #args.save_log_path, + inference_config=self.config, + pids=pid, + process_name=None, + gpu_ids=gpu_id if args.use_gpu else None, + time_keys=[ + 'preprocess_time', 'inference_time', 'postprocess_time' + ], + warmup=0, + logger=logger) + + def resize_norm_img(self, img, max_wh_ratio): + imgC, imgH, imgW = self.rec_image_shape + if self.rec_algorithm == 'NRTR': + img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) + # return padding_im + image_pil = Image.fromarray(np.uint8(img)) + img = image_pil.resize([100, 32], Image.ANTIALIAS) + img = np.array(img) + norm_img = np.expand_dims(img, -1) + norm_img = norm_img.transpose((2, 0, 1)) + return norm_img.astype(np.float32) / 128. - 1. + + assert imgC == img.shape[2] + imgW = int((imgH * max_wh_ratio)) + if self.use_onnx: + w = self.input_tensor.shape[3:][0] + if w is not None and w > 0: + imgW = w + + h, w = img.shape[:2] + ratio = w / float(h) + if math.ceil(imgH * ratio) > imgW: + resized_w = imgW + else: + resized_w = int(math.ceil(imgH * ratio)) + if self.rec_algorithm == 'RARE': + if resized_w > self.rec_image_shape[2]: + resized_w = self.rec_image_shape[2] + imgW = self.rec_image_shape[2] + resized_image = cv2.resize(img, (resized_w, imgH)) + resized_image = resized_image.astype('float32') + resized_image = resized_image.transpose((2, 0, 1)) / 255 + resized_image -= 0.5 + resized_image /= 0.5 + padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32) + padding_im[:, :, 0:resized_w] = resized_image + return padding_im + + def resize_norm_img_svtr(self, img, image_shape): + + imgC, imgH, imgW = image_shape + resized_image = cv2.resize( + img, (imgW, imgH), interpolation=cv2.INTER_LINEAR) + resized_image = resized_image.astype('float32') + resized_image = resized_image.transpose((2, 0, 1)) / 255 + resized_image -= 0.5 + resized_image /= 0.5 + return resized_image + + def resize_norm_img_srn(self, img, image_shape): + imgC, imgH, imgW = image_shape + + img_black = np.zeros((imgH, imgW)) + im_hei = img.shape[0] + im_wid = img.shape[1] + + if im_wid <= im_hei * 1: + img_new = cv2.resize(img, (imgH * 1, imgH)) + elif im_wid <= im_hei * 2: + img_new = cv2.resize(img, (imgH * 2, imgH)) + elif im_wid <= im_hei * 3: + img_new = cv2.resize(img, (imgH * 3, imgH)) + else: + img_new = cv2.resize(img, (imgW, imgH)) + + img_np = np.asarray(img_new) + img_np = cv2.cvtColor(img_np, cv2.COLOR_BGR2GRAY) + img_black[:, 0:img_np.shape[1]] = img_np + img_black = img_black[:, :, np.newaxis] + + row, col, c = img_black.shape + c = 1 + + return np.reshape(img_black, (c, row, col)).astype(np.float32) + + def srn_other_inputs(self, image_shape, num_heads, max_text_length): + + imgC, imgH, imgW = image_shape + feature_dim = int((imgH / 8) * (imgW / 8)) + + encoder_word_pos = np.array(range(0, feature_dim)).reshape( + (feature_dim, 1)).astype('int64') + gsrm_word_pos = np.array(range(0, max_text_length)).reshape( + (max_text_length, 1)).astype('int64') + + gsrm_attn_bias_data = np.ones((1, max_text_length, max_text_length)) + gsrm_slf_attn_bias1 = np.triu(gsrm_attn_bias_data, 1).reshape( + [-1, 1, max_text_length, max_text_length]) + gsrm_slf_attn_bias1 = np.tile( + gsrm_slf_attn_bias1, + [1, num_heads, 1, 1]).astype('float32') * [-1e9] + + gsrm_slf_attn_bias2 = np.tril(gsrm_attn_bias_data, -1).reshape( + [-1, 1, max_text_length, max_text_length]) + gsrm_slf_attn_bias2 = np.tile( + gsrm_slf_attn_bias2, + [1, num_heads, 1, 1]).astype('float32') * [-1e9] + + encoder_word_pos = encoder_word_pos[np.newaxis, :] + gsrm_word_pos = gsrm_word_pos[np.newaxis, :] + + return [ + encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1, + gsrm_slf_attn_bias2 + ] + + def process_image_srn(self, img, image_shape, num_heads, max_text_length): + norm_img = self.resize_norm_img_srn(img, image_shape) + norm_img = norm_img[np.newaxis, :] + + [encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1, gsrm_slf_attn_bias2] = \ + self.srn_other_inputs(image_shape, num_heads, max_text_length) + + gsrm_slf_attn_bias1 = gsrm_slf_attn_bias1.astype(np.float32) + gsrm_slf_attn_bias2 = gsrm_slf_attn_bias2.astype(np.float32) + encoder_word_pos = encoder_word_pos.astype(np.int64) + gsrm_word_pos = gsrm_word_pos.astype(np.int64) + + return (norm_img, encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1, + gsrm_slf_attn_bias2) + + def resize_norm_img_sar(self, img, image_shape, + width_downsample_ratio=0.25): + imgC, imgH, imgW_min, imgW_max = image_shape + h = img.shape[0] + w = img.shape[1] + valid_ratio = 1.0 + # make sure new_width is an integral multiple of width_divisor. + width_divisor = int(1 / width_downsample_ratio) + # resize + ratio = w / float(h) + resize_w = math.ceil(imgH * ratio) + if resize_w % width_divisor != 0: + resize_w = round(resize_w / width_divisor) * width_divisor + if imgW_min is not None: + resize_w = max(imgW_min, resize_w) + if imgW_max is not None: + valid_ratio = min(1.0, 1.0 * resize_w / imgW_max) + resize_w = min(imgW_max, resize_w) + resized_image = cv2.resize(img, (resize_w, imgH)) + resized_image = resized_image.astype('float32') + # norm + if image_shape[0] == 1: + resized_image = resized_image / 255 + resized_image = resized_image[np.newaxis, :] + else: + resized_image = resized_image.transpose((2, 0, 1)) / 255 + resized_image -= 0.5 + resized_image /= 0.5 + resize_shape = resized_image.shape + padding_im = -1.0 * np.ones((imgC, imgH, imgW_max), dtype=np.float32) + padding_im[:, :, 0:resize_w] = resized_image + pad_shape = padding_im.shape + + return padding_im, resize_shape, pad_shape, valid_ratio + + def __call__(self, img_list): + img_num = len(img_list) + # Calculate the aspect ratio of all text bars + width_list = [] + for img in img_list: + width_list.append(img.shape[1] / float(img.shape[0])) + # Sorting can speed up the recognition process + indices = np.argsort(np.array(width_list)) + rec_res = [['', 0.0]] * img_num + batch_num = self.rec_batch_num + st = time.time() + if self.benchmark: + self.autolog.times.start() + for beg_img_no in range(0, img_num, batch_num): + end_img_no = min(img_num, beg_img_no + batch_num) + norm_img_batch = [] + imgC, imgH, imgW = self.rec_image_shape + max_wh_ratio = imgW / imgH + # max_wh_ratio = 0 + for ino in range(beg_img_no, end_img_no): + h, w = img_list[indices[ino]].shape[0:2] + wh_ratio = w * 1.0 / h + max_wh_ratio = max(max_wh_ratio, wh_ratio) + for ino in range(beg_img_no, end_img_no): + + if self.rec_algorithm == "SAR": + norm_img, _, _, valid_ratio = self.resize_norm_img_sar( + img_list[indices[ino]], self.rec_image_shape) + norm_img = norm_img[np.newaxis, :] + valid_ratio = np.expand_dims(valid_ratio, axis=0) + valid_ratios = [] + valid_ratios.append(valid_ratio) + norm_img_batch.append(norm_img) + elif self.rec_algorithm == "SRN": + norm_img = self.process_image_srn( + img_list[indices[ino]], self.rec_image_shape, 8, 25) + encoder_word_pos_list = [] + gsrm_word_pos_list = [] + gsrm_slf_attn_bias1_list = [] + gsrm_slf_attn_bias2_list = [] + encoder_word_pos_list.append(norm_img[1]) + gsrm_word_pos_list.append(norm_img[2]) + gsrm_slf_attn_bias1_list.append(norm_img[3]) + gsrm_slf_attn_bias2_list.append(norm_img[4]) + norm_img_batch.append(norm_img[0]) + elif self.rec_algorithm == "SVTR": + norm_img = self.resize_norm_img_svtr( + img_list[indices[ino]], self.rec_image_shape) + norm_img = norm_img[np.newaxis, :] + norm_img_batch.append(norm_img) + else: + norm_img = self.resize_norm_img(img_list[indices[ino]], + max_wh_ratio) + norm_img = norm_img[np.newaxis, :] + norm_img_batch.append(norm_img) + norm_img_batch = np.concatenate(norm_img_batch) + norm_img_batch = norm_img_batch.copy() + if self.benchmark: + self.autolog.times.stamp() + + if self.rec_algorithm == "SRN": + encoder_word_pos_list = np.concatenate(encoder_word_pos_list) + gsrm_word_pos_list = np.concatenate(gsrm_word_pos_list) + gsrm_slf_attn_bias1_list = np.concatenate( + gsrm_slf_attn_bias1_list) + gsrm_slf_attn_bias2_list = np.concatenate( + gsrm_slf_attn_bias2_list) + + inputs = [ + norm_img_batch, + encoder_word_pos_list, + gsrm_word_pos_list, + gsrm_slf_attn_bias1_list, + gsrm_slf_attn_bias2_list, + ] + if self.use_onnx: + input_dict = {} + input_dict[self.input_tensor.name] = norm_img_batch + outputs = self.predictor.run(self.output_tensors, + input_dict) + preds = {"predict": outputs[2]} + else: + input_names = self.predictor.get_input_names() + for i in range(len(input_names)): + input_tensor = self.predictor.get_input_handle( + input_names[i]) + input_tensor.copy_from_cpu(inputs[i]) + self.predictor.run() + outputs = [] + for output_tensor in self.output_tensors: + output = output_tensor.copy_to_cpu() + outputs.append(output) + if self.benchmark: + self.autolog.times.stamp() + preds = {"predict": outputs[2]} + elif self.rec_algorithm == "SAR": + valid_ratios = np.concatenate(valid_ratios) + inputs = [ + norm_img_batch, + valid_ratios, + ] + if self.use_onnx: + input_dict = {} + input_dict[self.input_tensor.name] = norm_img_batch + outputs = self.predictor.run(self.output_tensors, + input_dict) + preds = outputs[0] + else: + input_names = self.predictor.get_input_names() + for i in range(len(input_names)): + input_tensor = self.predictor.get_input_handle( + input_names[i]) + input_tensor.copy_from_cpu(inputs[i]) + self.predictor.run() + outputs = [] + for output_tensor in self.output_tensors: + output = output_tensor.copy_to_cpu() + outputs.append(output) + if self.benchmark: + self.autolog.times.stamp() + preds = outputs[0] + else: + if self.use_onnx: + input_dict = {} + input_dict[self.input_tensor.name] = norm_img_batch + outputs = self.predictor.run(self.output_tensors, + input_dict) + preds = outputs[0] + else: + self.input_tensor.copy_from_cpu(norm_img_batch) + self.predictor.run() + outputs = [] + for output_tensor in self.output_tensors: + output = output_tensor.copy_to_cpu() + outputs.append(output) + if self.benchmark: + self.autolog.times.stamp() + if len(outputs) != 1: + preds = outputs + else: + preds = outputs[0] + rec_result = self.postprocess_op(preds) + for rno in range(len(rec_result)): + rec_res[indices[beg_img_no + rno]] = rec_result[rno] + if self.benchmark: + self.autolog.times.end(stamp=True) + return rec_res, time.time() - st + + +def main(args): + image_file_list = get_image_file_list(args.image_dir) + text_recognizer = TextRecognizer(args) + valid_image_file_list = [] + img_list = [] + + # warmup 2 times + if args.warmup: + img = np.random.uniform(0, 255, [32, 320, 3]).astype(np.uint8) + for i in range(2): + res = text_recognizer([img] * int(args.rec_batch_num)) + + for image_file in image_file_list: + img, flag = check_and_read_gif(image_file) + if not flag: + img = cv2.imread(image_file) + if img is None: + logger.info("error in loading image:{}".format(image_file)) + continue + valid_image_file_list.append(image_file) + img_list.append(img) + try: + rec_res, _ = text_recognizer(img_list) + + except Exception as E: + logger.info(traceback.format_exc()) + logger.info(E) + exit() + for ino in range(len(img_list)): + logger.info("Predicts of {}:{}".format(valid_image_file_list[ino], + rec_res[ino])) + if args.benchmark: + text_recognizer.autolog.report() + + +if __name__ == "__main__": + main(utility.parse_args()) diff --git a/toolbox/PaddleOCR/tools/infer/predict_system.py b/toolbox/PaddleOCR/tools/infer/predict_system.py new file mode 100644 index 0000000000000000000000000000000000000000..d893c4409125e9b2669efd7ecf95269df0e3e675 --- /dev/null +++ b/toolbox/PaddleOCR/tools/infer/predict_system.py @@ -0,0 +1,220 @@ +# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. +# Copyright (c) 2024, Shanghai Iluvatar CoreX Semiconductor Co., Ltd. +# All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import sys +import subprocess + +__dir__ = os.path.dirname(os.path.abspath(__file__)) +sys.path.append(__dir__) +sys.path.insert(0, os.path.abspath(os.path.join(__dir__, '../..'))) + +os.environ["FLAGS_allocator_strategy"] = 'auto_growth' + +import cv2 +import copy +import numpy as np +import json +import time +import logging +from PIL import Image +import tools.infer.utility as utility +import tools.infer.predict_rec as predict_rec +import tools.infer.predict_det as predict_det +import tools.infer.predict_cls as predict_cls +from ppocr.utils.utility import get_image_file_list, check_and_read_gif +from ppocr.utils.logging import get_logger +from tools.infer.utility import draw_ocr_box_txt, get_rotate_crop_image +logger = get_logger() + + +class TextSystem(object): + def __init__(self, args): + if not args.show_log: + logger.setLevel(logging.INFO) + + self.text_detector = predict_det.TextDetector(args) + self.text_recognizer = predict_rec.TextRecognizer(args) + self.use_angle_cls = args.use_angle_cls + self.drop_score = args.drop_score + if self.use_angle_cls: + self.text_classifier = predict_cls.TextClassifier(args) + + self.args = args + self.crop_image_res_index = 0 + + def draw_crop_rec_res(self, output_dir, img_crop_list, rec_res): + os.makedirs(output_dir, exist_ok=True) + bbox_num = len(img_crop_list) + for bno in range(bbox_num): + cv2.imwrite( + os.path.join(output_dir, + f"mg_crop_{bno+self.crop_image_res_index}.jpg"), + img_crop_list[bno]) + logger.debug(f"{bno}, {rec_res[bno]}") + self.crop_image_res_index += bbox_num + + def __call__(self, img, cls=True): + ori_im = img.copy() + dt_boxes, elapse = self.text_detector(img) + + logger.debug("dt_boxes num : {}, elapse : {}".format( + len(dt_boxes), elapse)) + if dt_boxes is None: + return None, None + img_crop_list = [] + + dt_boxes = sorted_boxes(dt_boxes) + + for bno in range(len(dt_boxes)): + tmp_box = copy.deepcopy(dt_boxes[bno]) + img_crop = get_rotate_crop_image(ori_im, tmp_box) + img_crop_list.append(img_crop) + if self.use_angle_cls and cls: + img_crop_list, angle_list, elapse = self.text_classifier( + img_crop_list) + logger.debug("cls num : {}, elapse : {}".format( + len(img_crop_list), elapse)) + + rec_res, elapse = self.text_recognizer(img_crop_list) + logger.debug("rec_res num : {}, elapse : {}".format( + len(rec_res), elapse)) + if self.args.save_crop_res: + self.draw_crop_rec_res(self.args.crop_res_save_dir, img_crop_list, + rec_res) + filter_boxes, filter_rec_res = [], [] + for box, rec_result in zip(dt_boxes, rec_res): + text, score = rec_result + if score >= self.drop_score: + filter_boxes.append(box) + filter_rec_res.append(rec_result) + return filter_boxes, filter_rec_res + + +def sorted_boxes(dt_boxes): + """ + Sort text boxes in order from top to bottom, left to right + args: + dt_boxes(array):detected text boxes with shape [4, 2] + return: + sorted boxes(array) with shape [4, 2] + """ + num_boxes = dt_boxes.shape[0] + sorted_boxes = sorted(dt_boxes, key=lambda x: (x[0][1], x[0][0])) + _boxes = list(sorted_boxes) + + for i in range(num_boxes - 1): + if abs(_boxes[i + 1][0][1] - _boxes[i][0][1]) < 10 and \ + (_boxes[i + 1][0][0] < _boxes[i][0][0]): + tmp = _boxes[i] + _boxes[i] = _boxes[i + 1] + _boxes[i + 1] = tmp + return _boxes + + +def main(args): + image_file_list = get_image_file_list(args.image_dir) + image_file_list = image_file_list[args.process_id::args.total_process_num] + text_sys = TextSystem(args) + is_visualize = True + font_path = args.vis_font_path + drop_score = args.drop_score + draw_img_save_dir = args.draw_img_save_dir + os.makedirs(draw_img_save_dir, exist_ok=True) + save_results = [] + + # warm up 10 times + if args.warmup: + img = np.random.uniform(0, 255, [640, 640, 3]).astype(np.uint8) + for i in range(10): + res = text_sys(img) + + total_time = 0 + cpu_mem, gpu_mem, gpu_util = 0, 0, 0 + _st = time.time() + count = 0 + for idx, image_file in enumerate(image_file_list): + + img, flag = check_and_read_gif(image_file) + if not flag: + img = cv2.imread(image_file) + if img is None: + logger.debug("error in loading image:{}".format(image_file)) + continue + starttime = time.time() + dt_boxes, rec_res = text_sys(img) + elapse = time.time() - starttime + total_time += elapse + + logger.debug( + str(idx) + " Predict time of %s: %.3fs" % (image_file, elapse)) + for text, score in rec_res: + logger.debug("{}, {:.3f}".format(text, score)) + + res = [{ + "transcription": rec_res[idx][0], + "points": np.array(dt_boxes[idx]).astype(np.int32).tolist(), + } for idx in range(len(dt_boxes))] + save_pred = os.path.basename(image_file) + "\t" + json.dumps( + res, ensure_ascii=False) + "\n" + save_results.append(save_pred) + + if is_visualize: + image = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) + boxes = dt_boxes + txts = [rec_res[i][0] for i in range(len(rec_res))] + scores = [rec_res[i][1] for i in range(len(rec_res))] + + draw_img = draw_ocr_box_txt( + image, + boxes, + txts, + scores, + drop_score=drop_score, + font_path=font_path) + if flag: + image_file = image_file[:-3] + "png" + cv2.imwrite( + os.path.join(draw_img_save_dir, os.path.basename(image_file)), + draw_img[:, :, ::-1]) + logger.debug("The visualized image saved in {}".format( + os.path.join(draw_img_save_dir, os.path.basename(image_file)))) + + logger.info("The predict total time is {}".format(time.time() - _st)) + if args.benchmark: + text_sys.text_detector.autolog.report() + text_sys.text_recognizer.autolog.report() + + with open(os.path.join(draw_img_save_dir, "system_results.txt"), 'w', encoding='utf-8') as f: + f.writelines(save_results) + + +if __name__ == "__main__": + args = utility.parse_args() + if args.use_mp: + p_list = [] + total_process_num = args.total_process_num + for process_id in range(total_process_num): + cmd = [sys.executable, "-u"] + sys.argv + [ + "--process_id={}".format(process_id), + "--use_mp={}".format(False) + ] + p = subprocess.Popen(cmd, stdout=sys.stdout, stderr=sys.stdout) + p_list.append(p) + for p in p_list: + p.wait() + else: + main(args) diff --git a/toolbox/PaddleOCR/tools/infer/utility.py b/toolbox/PaddleOCR/tools/infer/utility.py new file mode 100644 index 0000000000000000000000000000000000000000..05187d06916b127bde8a6731879eb2d719eba38a --- /dev/null +++ b/toolbox/PaddleOCR/tools/infer/utility.py @@ -0,0 +1,647 @@ +# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. +# Copyright (c) 2024, Shanghai Iluvatar CoreX Semiconductor Co., Ltd. +# All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import argparse +import os +import sys +import platform +import cv2 +import numpy as np +import paddle +from PIL import Image, ImageDraw, ImageFont +import math +from paddle import inference +import time +from ppocr.utils.logging import get_logger + + +def str2bool(v): + return v.lower() in ("true", "t", "1") + + +def init_args(): + parser = argparse.ArgumentParser() + # params for prediction engine + parser.add_argument("--use_gpu", type=str2bool, default=True) + parser.add_argument("--ir_optim", type=str2bool, default=True) + parser.add_argument("--use_tensorrt", type=str2bool, default=False) + parser.add_argument("--min_subgraph_size", type=int, default=15) + parser.add_argument("--precision", type=str, default="fp32") + parser.add_argument("--gpu_mem", type=int, default=500) + + # params for text detector + parser.add_argument("--image_dir", type=str) + parser.add_argument("--det_algorithm", type=str, default='DB') + parser.add_argument("--det_model_dir", type=str) + parser.add_argument("--det_limit_side_len", type=float, default=960) + parser.add_argument("--det_limit_type", type=str, default='max') + + # DB parmas + parser.add_argument("--det_db_thresh", type=float, default=0.3) + parser.add_argument("--det_db_box_thresh", type=float, default=0.6) + parser.add_argument("--det_db_unclip_ratio", type=float, default=1.5) + parser.add_argument("--max_batch_size", type=int, default=10) + parser.add_argument("--use_dilation", type=str2bool, default=False) + parser.add_argument("--det_db_score_mode", type=str, default="fast") + # EAST parmas + parser.add_argument("--det_east_score_thresh", type=float, default=0.8) + parser.add_argument("--det_east_cover_thresh", type=float, default=0.1) + parser.add_argument("--det_east_nms_thresh", type=float, default=0.2) + + # SAST parmas + parser.add_argument("--det_sast_score_thresh", type=float, default=0.5) + parser.add_argument("--det_sast_nms_thresh", type=float, default=0.2) + parser.add_argument("--det_sast_polygon", type=str2bool, default=False) + + # PSE parmas + parser.add_argument("--det_pse_thresh", type=float, default=0) + parser.add_argument("--det_pse_box_thresh", type=float, default=0.85) + parser.add_argument("--det_pse_min_area", type=float, default=16) + parser.add_argument("--det_pse_box_type", type=str, default='quad') + parser.add_argument("--det_pse_scale", type=int, default=1) + + # FCE parmas + parser.add_argument("--scales", type=list, default=[8, 16, 32]) + parser.add_argument("--alpha", type=float, default=1.0) + parser.add_argument("--beta", type=float, default=1.0) + parser.add_argument("--fourier_degree", type=int, default=5) + parser.add_argument("--det_fce_box_type", type=str, default='poly') + + # params for text recognizer + parser.add_argument("--rec_algorithm", type=str, default='CRNN') + parser.add_argument("--rec_model_dir", type=str) + parser.add_argument("--rec_image_shape", type=str, default="3, 32, 320") + parser.add_argument("--rec_batch_num", type=int, default=6) + parser.add_argument("--max_text_length", type=int, default=25) + parser.add_argument( + "--rec_char_dict_path", + type=str, + default="./ppocr/utils/ppocr_keys_v1.txt") + parser.add_argument("--use_space_char", type=str2bool, default=True) + parser.add_argument( + "--vis_font_path", type=str, default="./doc/fonts/simfang.ttf") + parser.add_argument("--drop_score", type=float, default=0.5) + + # params for e2e + parser.add_argument("--e2e_algorithm", type=str, default='PGNet') + parser.add_argument("--e2e_model_dir", type=str) + parser.add_argument("--e2e_limit_side_len", type=float, default=768) + parser.add_argument("--e2e_limit_type", type=str, default='max') + + # PGNet parmas + parser.add_argument("--e2e_pgnet_score_thresh", type=float, default=0.5) + parser.add_argument( + "--e2e_char_dict_path", type=str, default="./ppocr/utils/ic15_dict.txt") + parser.add_argument("--e2e_pgnet_valid_set", type=str, default='totaltext') + parser.add_argument("--e2e_pgnet_mode", type=str, default='fast') + + # params for text classifier + parser.add_argument("--use_angle_cls", type=str2bool, default=False) + parser.add_argument("--cls_model_dir", type=str) + parser.add_argument("--cls_image_shape", type=str, default="3, 48, 192") + parser.add_argument("--label_list", type=list, default=['0', '180']) + parser.add_argument("--cls_batch_num", type=int, default=6) + parser.add_argument("--cls_thresh", type=float, default=0.9) + + parser.add_argument("--enable_mkldnn", type=str2bool, default=False) + parser.add_argument("--cpu_threads", type=int, default=10) + parser.add_argument("--use_pdserving", type=str2bool, default=False) + parser.add_argument("--warmup", type=str2bool, default=False) + + # + parser.add_argument( + "--draw_img_save_dir", type=str, default="./inference_results") + parser.add_argument("--save_crop_res", type=str2bool, default=False) + parser.add_argument("--crop_res_save_dir", type=str, default="./output") + + # multi-process + parser.add_argument("--use_mp", type=str2bool, default=False) + parser.add_argument("--total_process_num", type=int, default=1) + parser.add_argument("--process_id", type=int, default=0) + + parser.add_argument("--benchmark", type=str2bool, default=False) + parser.add_argument("--save_log_path", type=str, default="./log_output/") + + parser.add_argument("--show_log", type=str2bool, default=True) + parser.add_argument("--use_onnx", type=str2bool, default=False) + return parser + + +def parse_args(): + parser = init_args() + return parser.parse_args() + + +def create_predictor(args, mode, logger): + if mode == "det": + model_dir = args.det_model_dir + elif mode == 'cls': + model_dir = args.cls_model_dir + elif mode == 'rec': + model_dir = args.rec_model_dir + elif mode == 'table': + model_dir = args.table_model_dir + else: + model_dir = args.e2e_model_dir + + if model_dir is None: + logger.info("not find {} model file path {}".format(mode, model_dir)) + sys.exit(0) + if args.use_onnx: + import onnxruntime as ort + model_file_path = model_dir + if not os.path.exists(model_file_path): + raise ValueError("not find model file path {}".format( + model_file_path)) + sess = ort.InferenceSession(model_file_path) + return sess, sess.get_inputs()[0], None, None + + else: + model_file_path = model_dir + "/inference.pdmodel" + params_file_path = model_dir + "/inference.pdiparams" + if not os.path.exists(model_file_path): + raise ValueError("not find model file path {}".format( + model_file_path)) + if not os.path.exists(params_file_path): + raise ValueError("not find params file path {}".format( + params_file_path)) + + config = inference.Config(model_file_path, params_file_path) + + if hasattr(args, 'precision'): + if args.precision == "fp16" and args.use_tensorrt: + precision = inference.PrecisionType.Half + elif args.precision == "int8": + precision = inference.PrecisionType.Int8 + else: + precision = inference.PrecisionType.Float32 + else: + precision = inference.PrecisionType.Float32 + + if args.use_gpu: + gpu_id = get_infer_gpuid() + if gpu_id is None: + logger.warning( + "GPU is not found in current device by nvidia-smi. Please check your device or ignore it if run on jetson." + ) + config.enable_use_gpu(args.gpu_mem, 0) + if args.use_tensorrt: + config.enable_tensorrt_engine( + workspace_size=1 << 30, + precision_mode=precision, + max_batch_size=args.max_batch_size, + min_subgraph_size=args.min_subgraph_size) + # skip the minmum trt subgraph + use_dynamic_shape = True + if mode == "det": + min_input_shape = { + "x": [1, 3, 50, 50], + "conv2d_92.tmp_0": [1, 120, 20, 20], + "conv2d_91.tmp_0": [1, 24, 10, 10], + "conv2d_59.tmp_0": [1, 96, 20, 20], + "nearest_interp_v2_1.tmp_0": [1, 256, 10, 10], + "nearest_interp_v2_2.tmp_0": [1, 256, 20, 20], + "conv2d_124.tmp_0": [1, 256, 20, 20], + "nearest_interp_v2_3.tmp_0": [1, 64, 20, 20], + "nearest_interp_v2_4.tmp_0": [1, 64, 20, 20], + "nearest_interp_v2_5.tmp_0": [1, 64, 20, 20], + "elementwise_add_7": [1, 56, 2, 2], + "nearest_interp_v2_0.tmp_0": [1, 256, 2, 2] + } + max_input_shape = { + "x": [1, 3, 1536, 1536], + "conv2d_92.tmp_0": [1, 120, 400, 400], + "conv2d_91.tmp_0": [1, 24, 200, 200], + "conv2d_59.tmp_0": [1, 96, 400, 400], + "nearest_interp_v2_1.tmp_0": [1, 256, 200, 200], + "conv2d_124.tmp_0": [1, 256, 400, 400], + "nearest_interp_v2_2.tmp_0": [1, 256, 400, 400], + "nearest_interp_v2_3.tmp_0": [1, 64, 400, 400], + "nearest_interp_v2_4.tmp_0": [1, 64, 400, 400], + "nearest_interp_v2_5.tmp_0": [1, 64, 400, 400], + "elementwise_add_7": [1, 56, 400, 400], + "nearest_interp_v2_0.tmp_0": [1, 256, 400, 400] + } + opt_input_shape = { + "x": [1, 3, 640, 640], + "conv2d_92.tmp_0": [1, 120, 160, 160], + "conv2d_91.tmp_0": [1, 24, 80, 80], + "conv2d_59.tmp_0": [1, 96, 160, 160], + "nearest_interp_v2_1.tmp_0": [1, 256, 80, 80], + "nearest_interp_v2_2.tmp_0": [1, 256, 160, 160], + "conv2d_124.tmp_0": [1, 256, 160, 160], + "nearest_interp_v2_3.tmp_0": [1, 64, 160, 160], + "nearest_interp_v2_4.tmp_0": [1, 64, 160, 160], + "nearest_interp_v2_5.tmp_0": [1, 64, 160, 160], + "elementwise_add_7": [1, 56, 40, 40], + "nearest_interp_v2_0.tmp_0": [1, 256, 40, 40] + } + min_pact_shape = { + "nearest_interp_v2_26.tmp_0": [1, 256, 20, 20], + "nearest_interp_v2_27.tmp_0": [1, 64, 20, 20], + "nearest_interp_v2_28.tmp_0": [1, 64, 20, 20], + "nearest_interp_v2_29.tmp_0": [1, 64, 20, 20] + } + max_pact_shape = { + "nearest_interp_v2_26.tmp_0": [1, 256, 400, 400], + "nearest_interp_v2_27.tmp_0": [1, 64, 400, 400], + "nearest_interp_v2_28.tmp_0": [1, 64, 400, 400], + "nearest_interp_v2_29.tmp_0": [1, 64, 400, 400] + } + opt_pact_shape = { + "nearest_interp_v2_26.tmp_0": [1, 256, 160, 160], + "nearest_interp_v2_27.tmp_0": [1, 64, 160, 160], + "nearest_interp_v2_28.tmp_0": [1, 64, 160, 160], + "nearest_interp_v2_29.tmp_0": [1, 64, 160, 160] + } + min_input_shape.update(min_pact_shape) + max_input_shape.update(max_pact_shape) + opt_input_shape.update(opt_pact_shape) + elif mode == "rec": + if args.rec_algorithm != "CRNN": + use_dynamic_shape = False + imgH = int(args.rec_image_shape.split(',')[-2]) + min_input_shape = {"x": [1, 3, imgH, 10]} + max_input_shape = {"x": [args.rec_batch_num, 3, imgH, 1536]} + opt_input_shape = {"x": [args.rec_batch_num, 3, imgH, 320]} + elif mode == "cls": + min_input_shape = {"x": [1, 3, 48, 10]} + max_input_shape = {"x": [args.rec_batch_num, 3, 48, 1024]} + opt_input_shape = {"x": [args.rec_batch_num, 3, 48, 320]} + else: + use_dynamic_shape = False + if use_dynamic_shape: + config.set_trt_dynamic_shape_info( + min_input_shape, max_input_shape, opt_input_shape) + + else: + config.disable_gpu() + if hasattr(args, "cpu_threads"): + config.set_cpu_math_library_num_threads(args.cpu_threads) + else: + # default cpu threads as 10 + config.set_cpu_math_library_num_threads(10) + if args.enable_mkldnn: + # cache 10 different shapes for mkldnn to avoid memory leak + config.set_mkldnn_cache_capacity(10) + config.enable_mkldnn() + if args.precision == "fp16": + config.enable_mkldnn_bfloat16() + # enable memory optim + config.enable_memory_optim() + config.disable_glog_info() + config.delete_pass("conv_transpose_eltwiseadd_bn_fuse_pass") + config.delete_pass("matmul_transpose_reshape_fuse_pass") + if mode == 'table': + config.delete_pass("fc_fuse_pass") # not supported for table + config.switch_use_feed_fetch_ops(False) + config.switch_ir_optim(True) + + # create predictor + predictor = inference.create_predictor(config) + input_names = predictor.get_input_names() + for name in input_names: + input_tensor = predictor.get_input_handle(name) + output_tensors = get_output_tensors(args, mode, predictor) + return predictor, input_tensor, output_tensors, config + + +def get_output_tensors(args, mode, predictor): + output_names = predictor.get_output_names() + output_tensors = [] + if mode == "rec" and args.rec_algorithm == "CRNN": + output_name = 'softmax_0.tmp_0' + if output_name in output_names: + return [predictor.get_output_handle(output_name)] + else: + for output_name in output_names: + output_tensor = predictor.get_output_handle(output_name) + output_tensors.append(output_tensor) + else: + for output_name in output_names: + output_tensor = predictor.get_output_handle(output_name) + output_tensors.append(output_tensor) + return output_tensors + + +def get_infer_gpuid(): + sysstr = platform.system() + if sysstr == "Windows": + return 0 + + if not paddle.fluid.core.is_compiled_with_rocm(): + cmd = "env | grep CUDA_VISIBLE_DEVICES" + else: + cmd = "env | grep HIP_VISIBLE_DEVICES" + env_cuda = os.popen(cmd).readlines() + if len(env_cuda) == 0: + return 0 + else: + gpu_id = env_cuda[0].strip().split("=")[1] + return int(gpu_id[0]) + + +def draw_e2e_res(dt_boxes, strs, img_path): + src_im = cv2.imread(img_path) + for box, str in zip(dt_boxes, strs): + box = box.astype(np.int32).reshape((-1, 1, 2)) + cv2.polylines(src_im, [box], True, color=(255, 255, 0), thickness=2) + cv2.putText( + src_im, + str, + org=(int(box[0, 0, 0]), int(box[0, 0, 1])), + fontFace=cv2.FONT_HERSHEY_COMPLEX, + fontScale=0.7, + color=(0, 255, 0), + thickness=1) + return src_im + + +def draw_text_det_res(dt_boxes, img_path): + src_im = cv2.imread(img_path) + for box in dt_boxes: + box = np.array(box).astype(np.int32).reshape(-1, 2) + cv2.polylines(src_im, [box], True, color=(255, 255, 0), thickness=2) + return src_im + + +def resize_img(img, input_size=600): + """ + resize img and limit the longest side of the image to input_size + """ + img = np.array(img) + im_shape = img.shape + im_size_max = np.max(im_shape[0:2]) + im_scale = float(input_size) / float(im_size_max) + img = cv2.resize(img, None, None, fx=im_scale, fy=im_scale) + return img + + +def draw_ocr(image, + boxes, + txts=None, + scores=None, + drop_score=0.5, + font_path="./doc/fonts/simfang.ttf"): + """ + Visualize the results of OCR detection and recognition + args: + image(Image|array): RGB image + boxes(list): boxes with shape(N, 4, 2) + txts(list): the texts + scores(list): txxs corresponding scores + drop_score(float): only scores greater than drop_threshold will be visualized + font_path: the path of font which is used to draw text + return(array): + the visualized img + """ + if scores is None: + scores = [1] * len(boxes) + box_num = len(boxes) + for i in range(box_num): + if scores is not None and (scores[i] < drop_score or + math.isnan(scores[i])): + continue + box = np.reshape(np.array(boxes[i]), [-1, 1, 2]).astype(np.int64) + image = cv2.polylines(np.array(image), [box], True, (255, 0, 0), 2) + if txts is not None: + img = np.array(resize_img(image, input_size=600)) + txt_img = text_visual( + txts, + scores, + img_h=img.shape[0], + img_w=600, + threshold=drop_score, + font_path=font_path) + img = np.concatenate([np.array(img), np.array(txt_img)], axis=1) + return img + return image + + +def draw_ocr_box_txt(image, + boxes, + txts, + scores=None, + drop_score=0.5, + font_path="./doc/simfang.ttf"): + h, w = image.height, image.width + img_left = image.copy() + img_right = Image.new('RGB', (w, h), (255, 255, 255)) + + import random + + random.seed(0) + draw_left = ImageDraw.Draw(img_left) + draw_right = ImageDraw.Draw(img_right) + for idx, (box, txt) in enumerate(zip(boxes, txts)): + if scores is not None and scores[idx] < drop_score: + continue + color = (random.randint(0, 255), random.randint(0, 255), + random.randint(0, 255)) + draw_left.polygon(box, fill=color) + draw_right.polygon( + [ + box[0][0], box[0][1], box[1][0], box[1][1], box[2][0], + box[2][1], box[3][0], box[3][1] + ], + outline=color) + box_height = math.sqrt((box[0][0] - box[3][0])**2 + (box[0][1] - box[3][ + 1])**2) + box_width = math.sqrt((box[0][0] - box[1][0])**2 + (box[0][1] - box[1][ + 1])**2) + if box_height > 2 * box_width: + font_size = max(int(box_width * 0.9), 10) + font = ImageFont.truetype(font_path, font_size, encoding="utf-8") + cur_y = box[0][1] + for c in txt: + char_size = font.getsize(c) + draw_right.text( + (box[0][0] + 3, cur_y), c, fill=(0, 0, 0), font=font) + cur_y += char_size[1] + else: + font_size = max(int(box_height * 0.8), 10) + font = ImageFont.truetype(font_path, font_size, encoding="utf-8") + draw_right.text( + [box[0][0], box[0][1]], txt, fill=(0, 0, 0), font=font) + img_left = Image.blend(image, img_left, 0.5) + img_show = Image.new('RGB', (w * 2, h), (255, 255, 255)) + img_show.paste(img_left, (0, 0, w, h)) + img_show.paste(img_right, (w, 0, w * 2, h)) + return np.array(img_show) + + +def str_count(s): + """ + Count the number of Chinese characters, + a single English character and a single number + equal to half the length of Chinese characters. + args: + s(string): the input of string + return(int): + the number of Chinese characters + """ + import string + count_zh = count_pu = 0 + s_len = len(s) + en_dg_count = 0 + for c in s: + if c in string.ascii_letters or c.isdigit() or c.isspace(): + en_dg_count += 1 + elif c.isalpha(): + count_zh += 1 + else: + count_pu += 1 + return s_len - math.ceil(en_dg_count / 2) + + +def text_visual(texts, + scores, + img_h=400, + img_w=600, + threshold=0., + font_path="./doc/simfang.ttf"): + """ + create new blank img and draw txt on it + args: + texts(list): the text will be draw + scores(list|None): corresponding score of each txt + img_h(int): the height of blank img + img_w(int): the width of blank img + font_path: the path of font which is used to draw text + return(array): + """ + if scores is not None: + assert len(texts) == len( + scores), "The number of txts and corresponding scores must match" + + def create_blank_img(): + blank_img = np.ones(shape=[img_h, img_w], dtype=np.int8) * 255 + blank_img[:, img_w - 1:] = 0 + blank_img = Image.fromarray(blank_img).convert("RGB") + draw_txt = ImageDraw.Draw(blank_img) + return blank_img, draw_txt + + blank_img, draw_txt = create_blank_img() + + font_size = 20 + txt_color = (0, 0, 0) + font = ImageFont.truetype(font_path, font_size, encoding="utf-8") + + gap = font_size + 5 + txt_img_list = [] + count, index = 1, 0 + for idx, txt in enumerate(texts): + index += 1 + if scores[idx] < threshold or math.isnan(scores[idx]): + index -= 1 + continue + first_line = True + while str_count(txt) >= img_w // font_size - 4: + tmp = txt + txt = tmp[:img_w // font_size - 4] + if first_line: + new_txt = str(index) + ': ' + txt + first_line = False + else: + new_txt = ' ' + txt + draw_txt.text((0, gap * count), new_txt, txt_color, font=font) + txt = tmp[img_w // font_size - 4:] + if count >= img_h // gap - 1: + txt_img_list.append(np.array(blank_img)) + blank_img, draw_txt = create_blank_img() + count = 0 + count += 1 + if first_line: + new_txt = str(index) + ': ' + txt + ' ' + '%.3f' % (scores[idx]) + else: + new_txt = " " + txt + " " + '%.3f' % (scores[idx]) + draw_txt.text((0, gap * count), new_txt, txt_color, font=font) + # whether add new blank img or not + if count >= img_h // gap - 1 and idx + 1 < len(texts): + txt_img_list.append(np.array(blank_img)) + blank_img, draw_txt = create_blank_img() + count = 0 + count += 1 + txt_img_list.append(np.array(blank_img)) + if len(txt_img_list) == 1: + blank_img = np.array(txt_img_list[0]) + else: + blank_img = np.concatenate(txt_img_list, axis=1) + return np.array(blank_img) + + +def base64_to_cv2(b64str): + import base64 + data = base64.b64decode(b64str.encode('utf8')) + data = np.fromstring(data, np.uint8) + data = cv2.imdecode(data, cv2.IMREAD_COLOR) + return data + + +def draw_boxes(image, boxes, scores=None, drop_score=0.5): + if scores is None: + scores = [1] * len(boxes) + for (box, score) in zip(boxes, scores): + if score < drop_score: + continue + box = np.reshape(np.array(box), [-1, 1, 2]).astype(np.int64) + image = cv2.polylines(np.array(image), [box], True, (255, 0, 0), 2) + return image + + +def get_rotate_crop_image(img, points): + ''' + img_height, img_width = img.shape[0:2] + left = int(np.min(points[:, 0])) + right = int(np.max(points[:, 0])) + top = int(np.min(points[:, 1])) + bottom = int(np.max(points[:, 1])) + img_crop = img[top:bottom, left:right, :].copy() + points[:, 0] = points[:, 0] - left + points[:, 1] = points[:, 1] - top + ''' + assert len(points) == 4, "shape of points must be 4*2" + img_crop_width = int( + max( + np.linalg.norm(points[0] - points[1]), + np.linalg.norm(points[2] - points[3]))) + img_crop_height = int( + max( + np.linalg.norm(points[0] - points[3]), + np.linalg.norm(points[1] - points[2]))) + pts_std = np.float32([[0, 0], [img_crop_width, 0], + [img_crop_width, img_crop_height], + [0, img_crop_height]]) + M = cv2.getPerspectiveTransform(points, pts_std) + dst_img = cv2.warpPerspective( + img, + M, (img_crop_width, img_crop_height), + borderMode=cv2.BORDER_REPLICATE, + flags=cv2.INTER_CUBIC) + dst_img_height, dst_img_width = dst_img.shape[0:2] + if dst_img_height * 1.0 / dst_img_width >= 1.5: + dst_img = np.rot90(dst_img) + return dst_img + + +def check_gpu(use_gpu): + if use_gpu and not paddle.is_compiled_with_cuda(): + use_gpu = False + return use_gpu + + +if __name__ == '__main__': + pass diff --git a/toolbox/PaddleOCR/tools/infer_kie.py b/toolbox/PaddleOCR/tools/infer_kie.py new file mode 100644 index 0000000000000000000000000000000000000000..158e78760e1a47614130952f267b5ba79c203efe --- /dev/null +++ b/toolbox/PaddleOCR/tools/infer_kie.py @@ -0,0 +1,155 @@ +# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. +# Copyright (c) 2024, Shanghai Iluvatar CoreX Semiconductor Co., Ltd. +# All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np +import paddle.nn.functional as F + +import os +import sys + +__dir__ = os.path.dirname(os.path.abspath(__file__)) +sys.path.append(__dir__) +sys.path.insert(0, os.path.abspath(os.path.join(__dir__, '..'))) + +os.environ["FLAGS_allocator_strategy"] = 'auto_growth' + +import cv2 +import paddle + +from ppocr.data import create_operators, transform +from ppocr.modeling.architectures import build_model +from ppocr.utils.save_load import load_model +import tools.program as program +import time + + +def read_class_list(filepath): + dict = {} + with open(filepath, "r") as f: + lines = f.readlines() + for line in lines: + key, value = line.split(" ") + dict[key] = value.rstrip() + return dict + + +def draw_kie_result(batch, node, idx_to_cls, count): + img = batch[6].copy() + boxes = batch[7] + h, w = img.shape[:2] + pred_img = np.ones((h, w * 2, 3), dtype=np.uint8) * 255 + max_value, max_idx = paddle.max(node, -1), paddle.argmax(node, -1) + node_pred_label = max_idx.numpy().tolist() + node_pred_score = max_value.numpy().tolist() + + for i, box in enumerate(boxes): + if i >= len(node_pred_label): + break + new_box = [[box[0], box[1]], [box[2], box[1]], [box[2], box[3]], + [box[0], box[3]]] + Pts = np.array([new_box], np.int32) + cv2.polylines( + img, [Pts.reshape((-1, 1, 2))], + True, + color=(255, 255, 0), + thickness=1) + x_min = int(min([point[0] for point in new_box])) + y_min = int(min([point[1] for point in new_box])) + + pred_label = str(node_pred_label[i]) + if pred_label in idx_to_cls: + pred_label = idx_to_cls[pred_label] + pred_score = '{:.2f}'.format(node_pred_score[i]) + text = pred_label + '(' + pred_score + ')' + cv2.putText(pred_img, text, (x_min * 2, y_min), + cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 1) + vis_img = np.ones((h, w * 3, 3), dtype=np.uint8) * 255 + vis_img[:, :w] = img + vis_img[:, w:] = pred_img + save_kie_path = os.path.dirname(config['Global'][ + 'save_res_path']) + "/kie_results/" + if not os.path.exists(save_kie_path): + os.makedirs(save_kie_path) + save_path = os.path.join(save_kie_path, str(count) + ".png") + cv2.imwrite(save_path, vis_img) + logger.info("The Kie Image saved in {}".format(save_path)) + + +def main(): + global_config = config['Global'] + + # build model + model = build_model(config['Architecture']) + load_model(config, model) + + # create data ops + transforms = [] + for op in config['Eval']['dataset']['transforms']: + transforms.append(op) + + data_dir = config['Eval']['dataset']['data_dir'] + + ops = create_operators(transforms, global_config) + + save_res_path = config['Global']['save_res_path'] + class_path = config['Global']['class_path'] + idx_to_cls = read_class_list(class_path) + if not os.path.exists(os.path.dirname(save_res_path)): + os.makedirs(os.path.dirname(save_res_path)) + + model.eval() + + warmup_times = 0 + count_t = [] + with open(save_res_path, "wb") as fout: + with open(config['Global']['infer_img'], "rb") as f: + lines = f.readlines() + for index, data_line in enumerate(lines): + if index == 10: + warmup_t = time.time() + data_line = data_line.decode('utf-8') + substr = data_line.strip("\n").split("\t") + img_path, label = data_dir + "/" + substr[0], substr[1] + data = {'img_path': img_path, 'label': label} + with open(data['img_path'], 'rb') as f: + img = f.read() + data['image'] = img + st = time.time() + batch = transform(data, ops) + batch_pred = [0] * len(batch) + for i in range(len(batch)): + batch_pred[i] = paddle.to_tensor( + np.expand_dims( + batch[i], axis=0)) + st = time.time() + node, edge = model(batch_pred) + node = F.softmax(node, -1) + count_t.append(time.time() - st) + draw_kie_result(batch, node, idx_to_cls, index) + logger.info("success!") + logger.info("It took {} s for predict {} images.".format( + np.sum(count_t), len(count_t))) + ips = len(count_t[warmup_times:]) / np.sum(count_t[warmup_times:]) + logger.info("The ips is {} images/s".format(ips)) + + +if __name__ == '__main__': + config, device, logger, vdl_writer = program.preprocess() + main() diff --git a/toolbox/PaddleOCR/tools/infer_rec.py b/toolbox/PaddleOCR/tools/infer_rec.py new file mode 100644 index 0000000000000000000000000000000000000000..5e343a73fd8be47b30de8c5b0184b8eb92b08274 --- /dev/null +++ b/toolbox/PaddleOCR/tools/infer_rec.py @@ -0,0 +1,168 @@ +# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. +# Copyright (c) 2024, Shanghai Iluvatar CoreX Semiconductor Co., Ltd. +# All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +import os +import sys +import json + +__dir__ = os.path.dirname(os.path.abspath(__file__)) +sys.path.append(__dir__) +sys.path.insert(0, os.path.abspath(os.path.join(__dir__, '..'))) + +os.environ["FLAGS_allocator_strategy"] = 'auto_growth' + +import paddle + +from ppocr.data import create_operators, transform +from ppocr.modeling.architectures import build_model +from ppocr.postprocess import build_post_process +from ppocr.utils.save_load import load_model +from ppocr.utils.utility import get_image_file_list +import tools.program as program + + +def main(): + global_config = config['Global'] + + # build post process + post_process_class = build_post_process(config['PostProcess'], + global_config) + + # build model + if hasattr(post_process_class, 'character'): + char_num = len(getattr(post_process_class, 'character')) + if config['Architecture']["algorithm"] in ["Distillation", + ]: # distillation model + for key in config['Architecture']["Models"]: + if config['Architecture']['Models'][key]['Head'][ + 'name'] == 'MultiHead': # for multi head + out_channels_list = {} + if config['PostProcess'][ + 'name'] == 'DistillationSARLabelDecode': + char_num = char_num - 2 + out_channels_list['CTCLabelDecode'] = char_num + out_channels_list['SARLabelDecode'] = char_num + 2 + config['Architecture']['Models'][key]['Head'][ + 'out_channels_list'] = out_channels_list + else: + config['Architecture']["Models"][key]["Head"][ + 'out_channels'] = char_num + elif config['Architecture']['Head'][ + 'name'] == 'MultiHead': # for multi head loss + out_channels_list = {} + if config['PostProcess']['name'] == 'SARLabelDecode': + char_num = char_num - 2 + out_channels_list['CTCLabelDecode'] = char_num + out_channels_list['SARLabelDecode'] = char_num + 2 + config['Architecture']['Head'][ + 'out_channels_list'] = out_channels_list + else: # base rec model + config['Architecture']["Head"]['out_channels'] = char_num + + model = build_model(config['Architecture']) + + load_model(config, model) + + # create data ops + transforms = [] + for op in config['Eval']['dataset']['transforms']: + op_name = list(op)[0] + if 'Label' in op_name: + continue + elif op_name in ['RecResizeImg']: + op[op_name]['infer_mode'] = True + elif op_name == 'KeepKeys': + if config['Architecture']['algorithm'] == "SRN": + op[op_name]['keep_keys'] = [ + 'image', 'encoder_word_pos', 'gsrm_word_pos', + 'gsrm_slf_attn_bias1', 'gsrm_slf_attn_bias2' + ] + elif config['Architecture']['algorithm'] == "SAR": + op[op_name]['keep_keys'] = ['image', 'valid_ratio'] + else: + op[op_name]['keep_keys'] = ['image'] + transforms.append(op) + global_config['infer_mode'] = True + ops = create_operators(transforms, global_config) + + save_res_path = config['Global'].get('save_res_path', + "./output/rec/predicts_rec.txt") + if not os.path.exists(os.path.dirname(save_res_path)): + os.makedirs(os.path.dirname(save_res_path)) + + model.eval() + + with open(save_res_path, "w") as fout: + for file in get_image_file_list(config['Global']['infer_img']): + logger.info("infer_img: {}".format(file)) + with open(file, 'rb') as f: + img = f.read() + data = {'image': img} + batch = transform(data, ops) + if config['Architecture']['algorithm'] == "SRN": + encoder_word_pos_list = np.expand_dims(batch[1], axis=0) + gsrm_word_pos_list = np.expand_dims(batch[2], axis=0) + gsrm_slf_attn_bias1_list = np.expand_dims(batch[3], axis=0) + gsrm_slf_attn_bias2_list = np.expand_dims(batch[4], axis=0) + + others = [ + paddle.to_tensor(encoder_word_pos_list), + paddle.to_tensor(gsrm_word_pos_list), + paddle.to_tensor(gsrm_slf_attn_bias1_list), + paddle.to_tensor(gsrm_slf_attn_bias2_list) + ] + if config['Architecture']['algorithm'] == "SAR": + valid_ratio = np.expand_dims(batch[-1], axis=0) + img_metas = [paddle.to_tensor(valid_ratio)] + + images = np.expand_dims(batch[0], axis=0) + images = paddle.to_tensor(images) + if config['Architecture']['algorithm'] == "SRN": + preds = model(images, others) + elif config['Architecture']['algorithm'] == "SAR": + preds = model(images, img_metas) + else: + preds = model(images) + post_result = post_process_class(preds) + info = None + if isinstance(post_result, dict): + rec_info = dict() + for key in post_result: + if len(post_result[key][0]) >= 2: + rec_info[key] = { + "label": post_result[key][0][0], + "score": float(post_result[key][0][1]), + } + info = json.dumps(rec_info, ensure_ascii=False) + else: + if len(post_result[0]) >= 2: + info = post_result[0][0] + "\t" + str(post_result[0][1]) + + if info is not None: + logger.info("\t result: {}".format(info)) + fout.write(file + "\t" + info) + logger.info("success!") + + +if __name__ == '__main__': + config, device, logger, vdl_writer = program.preprocess() + main() diff --git a/toolbox/PaddleOCR/tools/program.py b/toolbox/PaddleOCR/tools/program.py new file mode 100644 index 0000000000000000000000000000000000000000..af5e24e8ce5da72a323efcf71ddd530c9e36b556 --- /dev/null +++ b/toolbox/PaddleOCR/tools/program.py @@ -0,0 +1,604 @@ +# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +# Copyright (c) 2024, Shanghai Iluvatar CoreX Semiconductor Co., Ltd. +# All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +import sys +import platform +import yaml +import time +import datetime +import paddle +import paddle.distributed as dist +from tqdm import tqdm +from argparse import ArgumentParser, RawDescriptionHelpFormatter + +from ppocr.utils.stats import TrainingStats +from ppocr.utils.save_load import save_model +from ppocr.utils.utility import print_dict, AverageMeter +from ppocr.utils.logging import get_logger +from ppocr.utils.loggers import VDLLogger, WandbLogger, Loggers +from ppocr.utils import profiler +from ppocr.data import build_dataloader + + +class ArgsParser(ArgumentParser): + def __init__(self): + super(ArgsParser, self).__init__( + formatter_class=RawDescriptionHelpFormatter) + self.add_argument("-c", "--config", help="configuration file to use") + self.add_argument( + "-o", "--opt", nargs='+', help="set configuration options") + self.add_argument( + '-p', + '--profiler_options', + type=str, + default=None, + help='The option of profiler, which should be in format ' \ + '\"key1=value1;key2=value2;key3=value3\".' + ) + + def parse_args(self, argv=None): + args = super(ArgsParser, self).parse_args(argv) + assert args.config is not None, \ + "Please specify --config=configure_file_path." + args.opt = self._parse_opt(args.opt) + return args + + def _parse_opt(self, opts): + config = {} + if not opts: + return config + for s in opts: + s = s.strip() + k, v = s.split('=') + config[k] = yaml.load(v, Loader=yaml.Loader) + return config + + +def load_config(file_path): + """ + Load config from yml/yaml file. + Args: + file_path (str): Path of the config file to be loaded. + Returns: global config + """ + _, ext = os.path.splitext(file_path) + assert ext in ['.yml', '.yaml'], "only support yaml files for now" + config = yaml.load(open(file_path, 'rb'), Loader=yaml.Loader) + return config + + +def merge_config(config, opts): + """ + Merge config into global config. + Args: + config (dict): Config to be merged. + Returns: global config + """ + for key, value in opts.items(): + if "." not in key: + if isinstance(value, dict) and key in config: + config[key].update(value) + else: + config[key] = value + else: + sub_keys = key.split('.') + assert ( + sub_keys[0] in config + ), "the sub_keys can only be one of global_config: {}, but get: " \ + "{}, please check your running command".format( + config.keys(), sub_keys[0]) + cur = config[sub_keys[0]] + for idx, sub_key in enumerate(sub_keys[1:]): + if idx == len(sub_keys) - 2: + cur[sub_key] = value + else: + cur = cur[sub_key] + return config + + +def check_gpu(use_gpu): + """ + Log error and exit when set use_gpu=true in paddlepaddle + cpu version. + """ + err = "Config use_gpu cannot be set as true while you are " \ + "using paddlepaddle cpu version ! \nPlease try: \n" \ + "\t1. Install paddlepaddle-gpu to run model on GPU \n" \ + "\t2. Set use_gpu as false in config file to run " \ + "model on CPU" + + try: + if use_gpu and not paddle.is_compiled_with_cuda(): + print(err) + sys.exit(1) + except Exception as e: + pass + + +def check_xpu(use_xpu): + """ + Log error and exit when set use_xpu=true in paddlepaddle + cpu/gpu version. + """ + err = "Config use_xpu cannot be set as true while you are " \ + "using paddlepaddle cpu/gpu version ! \nPlease try: \n" \ + "\t1. Install paddlepaddle-xpu to run model on XPU \n" \ + "\t2. Set use_xpu as false in config file to run " \ + "model on CPU/GPU" + + try: + if use_xpu and not paddle.is_compiled_with_xpu(): + print(err) + sys.exit(1) + except Exception as e: + pass + + +def train(config, + train_dataloader, + valid_dataloader, + device, + model, + loss_class, + optimizer, + lr_scheduler, + post_process_class, + eval_class, + pre_best_model_dict, + logger, + log_writer=None, + scaler=None): + cal_metric_during_train = config['Global'].get('cal_metric_during_train', + False) + calc_epoch_interval = config['Global'].get('calc_epoch_interval', 1) + log_smooth_window = config['Global']['log_smooth_window'] + epoch_num = config['Global']['epoch_num'] + print_batch_step = config['Global']['print_batch_step'] + eval_batch_step = config['Global']['eval_batch_step'] + profiler_options = config['profiler_options'] + + global_step = 0 + if 'global_step' in pre_best_model_dict: + global_step = pre_best_model_dict['global_step'] + start_eval_step = 0 + if type(eval_batch_step) == list and len(eval_batch_step) >= 2: + start_eval_step = eval_batch_step[0] + eval_batch_step = eval_batch_step[1] + if len(valid_dataloader) == 0: + logger.info( + 'No Images in eval dataset, evaluation during training ' \ + 'will be disabled' + ) + start_eval_step = 1e111 + logger.info( + "During the training process, after the {}th iteration, " \ + "an evaluation is run every {} iterations". + format(start_eval_step, eval_batch_step)) + save_epoch_step = config['Global']['save_epoch_step'] + save_model_dir = config['Global']['save_model_dir'] + if not os.path.exists(save_model_dir): + os.makedirs(save_model_dir) + main_indicator = eval_class.main_indicator + best_model_dict = {main_indicator: 0} + best_model_dict.update(pre_best_model_dict) + train_stats = TrainingStats(log_smooth_window, ['lr']) + model_average = False + model.train() + + use_srn = config['Architecture']['algorithm'] == "SRN" + extra_input_models = ["SRN", "NRTR", "SAR", "SEED", "SVTR"] + extra_input = False + if config['Architecture']['algorithm'] == 'Distillation': + for key in config['Architecture']["Models"]: + extra_input = extra_input or config['Architecture']['Models'][key][ + 'algorithm'] in extra_input_models + else: + extra_input = config['Architecture']['algorithm'] in extra_input_models + try: + model_type = config['Architecture']['model_type'] + except: + model_type = None + + algorithm = config['Architecture']['algorithm'] + + start_epoch = best_model_dict[ + 'start_epoch'] if 'start_epoch' in best_model_dict else 1 + + total_samples = 0 + train_reader_cost = 0.0 + train_batch_cost = 0.0 + reader_start = time.time() + eta_meter = AverageMeter() + + max_iter = len(train_dataloader) - 1 if platform.system( + ) == "Windows" else len(train_dataloader) + + for epoch in range(start_epoch, epoch_num + 1): + if train_dataloader.dataset.need_reset: + train_dataloader = build_dataloader( + config, 'Train', device, logger, seed=epoch) + max_iter = len(train_dataloader) - 1 if platform.system( + ) == "Windows" else len(train_dataloader) + for idx, batch in enumerate(train_dataloader): + profiler.add_profiler_step(profiler_options) + train_reader_cost += time.time() - reader_start + if idx >= max_iter: + break + lr = optimizer.get_lr() + images = batch[0] + if use_srn: + model_average = True + + # use amp + if scaler: + with paddle.amp.auto_cast(): + if model_type == 'table' or extra_input: + preds = model(images, data=batch[1:]) + else: + preds = model(images) + else: + if model_type == 'table' or extra_input: + preds = model(images, data=batch[1:]) + elif model_type in ["kie", 'vqa']: + preds = model(batch) + else: + preds = model(images) + + loss = loss_class(preds, batch) + avg_loss = loss['loss'] + + if scaler: + scaled_avg_loss = scaler.scale(avg_loss) + scaled_avg_loss.backward() + scaler.minimize(optimizer, scaled_avg_loss) + else: + avg_loss.backward() + optimizer.step() + optimizer.clear_grad() + + if cal_metric_during_train and epoch % calc_epoch_interval == 0: # only rec and cls need + batch = [item.numpy() for item in batch] + if model_type in ['table', 'kie']: + eval_class(preds, batch) + else: + if config['Loss']['name'] in ['MultiLoss', 'MultiLoss_v2' + ]: # for multi head loss + post_result = post_process_class( + preds['ctc'], batch[1]) # for CTC head out + else: + post_result = post_process_class(preds, batch[1]) + eval_class(post_result, batch) + metric = eval_class.get_metric() + train_stats.update(metric) + + train_batch_time = time.time() - reader_start + train_batch_cost += train_batch_time + eta_meter.update(train_batch_time) + global_step += 1 + total_samples += len(images) + + if not isinstance(lr_scheduler, float): + lr_scheduler.step() + + # logger and visualdl + stats = {k: v.numpy().mean() for k, v in loss.items()} + stats['lr'] = lr + train_stats.update(stats) + + if log_writer is not None and dist.get_rank() == 0: + log_writer.log_metrics(metrics=train_stats.get(), prefix="TRAIN", step=global_step) + + if dist.get_rank() == 0 and ( + (global_step > 0 and global_step % print_batch_step == 0) or + (idx >= len(train_dataloader) - 1)): + logs = train_stats.log() + + eta_sec = ((epoch_num + 1 - epoch) * \ + len(train_dataloader) - idx - 1) * eta_meter.avg + eta_sec_format = str(datetime.timedelta(seconds=int(eta_sec))) + strs = 'epoch: [{}/{}], global_step: {}, {}, avg_reader_cost: ' \ + '{:.5f} s, avg_batch_cost: {:.5f} s, avg_samples: {}, ' \ + 'ips: {:.5f} samples/s, eta: {}'.format( + epoch, epoch_num, global_step, logs, + train_reader_cost / print_batch_step, + train_batch_cost / print_batch_step, + total_samples / print_batch_step, + total_samples / train_batch_cost, eta_sec_format) + logger.info(strs) + + total_samples = 0 + train_reader_cost = 0.0 + train_batch_cost = 0.0 + # eval + if global_step > start_eval_step and \ + (global_step - start_eval_step) % eval_batch_step == 0 \ + and dist.get_rank() == 0: + if model_average: + Model_Average = paddle.incubate.optimizer.ModelAverage( + 0.15, + parameters=model.parameters(), + min_average_window=10000, + max_average_window=15625) + Model_Average.apply() + cur_metric = eval( + model, + valid_dataloader, + post_process_class, + eval_class, + model_type, + extra_input=extra_input) + cur_metric_str = 'cur metric, {}'.format(', '.join( + ['{}: {}'.format(k, v) for k, v in cur_metric.items()])) + logger.info(cur_metric_str) + + # logger metric + if log_writer is not None: + log_writer.log_metrics(metrics=cur_metric, prefix="EVAL", step=global_step) + + if cur_metric[main_indicator] >= best_model_dict[ + main_indicator]: + best_model_dict.update(cur_metric) + best_model_dict['best_epoch'] = epoch + save_model( + model, + optimizer, + save_model_dir, + logger, + config, + is_best=True, + prefix='best_accuracy', + best_model_dict=best_model_dict, + epoch=epoch, + global_step=global_step) + best_str = 'best metric, {}'.format(', '.join([ + '{}: {}'.format(k, v) for k, v in best_model_dict.items() + ])) + logger.info(best_str) + # logger best metric + if log_writer is not None: + log_writer.log_metrics(metrics={ + "best_{}".format(main_indicator): best_model_dict[main_indicator] + }, prefix="EVAL", step=global_step) + + log_writer.log_model(is_best=True, prefix="best_accuracy", metadata=best_model_dict) + + reader_start = time.time() + if dist.get_rank() == 0: + save_model( + model, + optimizer, + save_model_dir, + logger, + config, + is_best=False, + prefix='latest', + best_model_dict=best_model_dict, + epoch=epoch, + global_step=global_step) + + if log_writer is not None: + log_writer.log_model(is_best=False, prefix="latest") + + if dist.get_rank() == 0 and epoch > 0 and epoch % save_epoch_step == 0: + save_model( + model, + optimizer, + save_model_dir, + logger, + config, + is_best=False, + prefix='iter_epoch_{}'.format(epoch), + best_model_dict=best_model_dict, + epoch=epoch, + global_step=global_step) + if log_writer is not None: + log_writer.log_model(is_best=False, prefix='iter_epoch_{}'.format(epoch)) + + best_str = 'best metric, {}'.format(', '.join( + ['{}: {}'.format(k, v) for k, v in best_model_dict.items()])) + logger.info(best_str) + if dist.get_rank() == 0 and log_writer is not None: + log_writer.close() + return + + +def eval(model, + valid_dataloader, + post_process_class, + eval_class, + model_type=None, + extra_input=False): + model.eval() + with paddle.no_grad(): + total_frame = 0.0 + total_time = 0.0 + pbar = tqdm( + total=len(valid_dataloader), + desc='eval model:', + position=0, + leave=True) + max_iter = len(valid_dataloader) - 1 if platform.system( + ) == "Windows" else len(valid_dataloader) + for idx, batch in enumerate(valid_dataloader): + if idx >= max_iter: + break + images = batch[0] + start = time.time() + if model_type == 'table' or extra_input: + preds = model(images, data=batch[1:]) + elif model_type in ["kie", 'vqa']: + preds = model(batch) + else: + preds = model(images) + + batch_numpy = [] + for item in batch: + if isinstance(item, paddle.Tensor): + batch_numpy.append(item.numpy()) + else: + batch_numpy.append(item) + # Obtain usable results from post-processing methods + total_time += time.time() - start + # Evaluate the results of the current batch + if model_type in ['table', 'kie']: + eval_class(preds, batch_numpy) + elif model_type in ['vqa']: + post_result = post_process_class(preds, batch_numpy) + eval_class(post_result, batch_numpy) + else: + post_result = post_process_class(preds, batch_numpy[1]) + eval_class(post_result, batch_numpy) + + pbar.update(1) + total_frame += len(images) + # Get final metric,eg. acc or hmean + metric = eval_class.get_metric() + + pbar.close() + model.train() + metric['fps'] = total_frame / total_time + return metric + + +def update_center(char_center, post_result, preds): + result, label = post_result + feats, logits = preds + logits = paddle.argmax(logits, axis=-1) + feats = feats.numpy() + logits = logits.numpy() + + for idx_sample in range(len(label)): + if result[idx_sample][0] == label[idx_sample][0]: + feat = feats[idx_sample] + logit = logits[idx_sample] + for idx_time in range(len(logit)): + index = logit[idx_time] + if index in char_center.keys(): + char_center[index][0] = ( + char_center[index][0] * char_center[index][1] + + feat[idx_time]) / (char_center[index][1] + 1) + char_center[index][1] += 1 + else: + char_center[index] = [feat[idx_time], 1] + return char_center + + +def get_center(model, eval_dataloader, post_process_class): + pbar = tqdm(total=len(eval_dataloader), desc='get center:') + max_iter = len(eval_dataloader) - 1 if platform.system( + ) == "Windows" else len(eval_dataloader) + char_center = dict() + for idx, batch in enumerate(eval_dataloader): + if idx >= max_iter: + break + images = batch[0] + start = time.time() + preds = model(images) + + batch = [item.numpy() for item in batch] + # Obtain usable results from post-processing methods + post_result = post_process_class(preds, batch[1]) + + #update char_center + char_center = update_center(char_center, post_result, preds) + pbar.update(1) + + pbar.close() + for key in char_center.keys(): + char_center[key] = char_center[key][0] + return char_center + + +def preprocess(is_train=False): + FLAGS = ArgsParser().parse_args() + profiler_options = FLAGS.profiler_options + config = load_config(FLAGS.config) + config = merge_config(config, FLAGS.opt) + profile_dic = {"profiler_options": FLAGS.profiler_options} + config = merge_config(config, profile_dic) + + if is_train: + # save_config + save_model_dir = config['Global']['save_model_dir'] + os.makedirs(save_model_dir, exist_ok=True) + with open(os.path.join(save_model_dir, 'config.yml'), 'w') as f: + yaml.dump( + dict(config), f, default_flow_style=False, sort_keys=False) + log_file = '{}/train.log'.format(save_model_dir) + else: + log_file = None + logger = get_logger(log_file=log_file) + + # check if set use_gpu=True in paddlepaddle cpu version + use_gpu = config['Global']['use_gpu'] + check_gpu(use_gpu) + + # check if set use_xpu=True in paddlepaddle cpu/gpu version + use_xpu = False + if 'use_xpu' in config['Global']: + use_xpu = config['Global']['use_xpu'] + check_xpu(use_xpu) + + alg = config['Architecture']['algorithm'] + assert alg in [ + 'EAST', 'DB', 'SAST', 'Rosetta', 'CRNN', 'STARNet', 'RARE', 'SRN', + 'CLS', 'PGNet', 'Distillation', 'NRTR', 'TableAttn', 'SAR', 'PSE', + 'SEED', 'SDMGR', 'LayoutXLM', 'LayoutLM', 'PREN', 'FCE', 'SVTR' + ] + + device = 'cpu' + if use_gpu: + device = 'gpu:{}'.format(dist.ParallelEnv().dev_id) + if use_xpu: + device = 'xpu' + device = paddle.set_device(device) + + config['Global']['distributed'] = dist.get_world_size() != 1 + + loggers = [] + + if 'use_visualdl' in config['Global'] and config['Global']['use_visualdl']: + save_model_dir = config['Global']['save_model_dir'] + vdl_writer_path = '{}/vdl/'.format(save_model_dir) + log_writer = VDLLogger(save_model_dir) + loggers.append(log_writer) + if ('use_wandb' in config['Global'] and config['Global']['use_wandb']) or 'wandb' in config: + save_dir = config['Global']['save_model_dir'] + wandb_writer_path = "{}/wandb".format(save_dir) + if "wandb" in config: + wandb_params = config['wandb'] + else: + wandb_params = dict() + wandb_params.update({'save_dir': save_model_dir}) + log_writer = WandbLogger(**wandb_params, config=config) + loggers.append(log_writer) + else: + log_writer = None + print_dict(config, logger) + + if loggers: + log_writer = Loggers(loggers) + else: + log_writer = None + + logger.info('train with paddle {} and device {}'.format(paddle.__version__, + device)) + return config, device, logger, log_writer diff --git a/toolbox/PaddleOCR/tools/train.py b/toolbox/PaddleOCR/tools/train.py new file mode 100644 index 0000000000000000000000000000000000000000..94abf6409313f31df933ef7d62c3da8ee63c5d95 --- /dev/null +++ b/toolbox/PaddleOCR/tools/train.py @@ -0,0 +1,191 @@ +# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. +# Copyright (c) 2024, Shanghai Iluvatar CoreX Semiconductor Co., Ltd. +# All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +import sys + +__dir__ = os.path.dirname(os.path.abspath(__file__)) +sys.path.append(__dir__) +sys.path.insert(0, os.path.abspath(os.path.join(__dir__, '..'))) + +import yaml +import paddle +import paddle.distributed as dist + +from ppocr.data import build_dataloader +from ppocr.modeling.architectures import build_model +from ppocr.losses import build_loss +from ppocr.optimizer import build_optimizer +from ppocr.postprocess import build_post_process +from ppocr.metrics import build_metric +from ppocr.utils.save_load import load_model +from ppocr.utils.utility import set_seed +import tools.program as program + +dist.get_world_size() + + +def main(config, device, logger, vdl_writer): + # init dist environment + if config['Global']['distributed']: + dist.init_parallel_env() + + global_config = config['Global'] + + # build dataloader + train_dataloader = build_dataloader(config, 'Train', device, logger) + if len(train_dataloader) == 0: + logger.error( + "No Images in train dataset, please ensure\n" + + "\t1. The images num in the train label_file_list should be larger than or equal with batch size.\n" + + + "\t2. The annotation file and path in the configuration file are provided normally." + ) + return + + if config['Eval']: + valid_dataloader = build_dataloader(config, 'Eval', device, logger) + else: + valid_dataloader = None + + # build post process + post_process_class = build_post_process(config['PostProcess'], + global_config) + + # build model + # for rec algorithm + if hasattr(post_process_class, 'character'): + char_num = len(getattr(post_process_class, 'character')) + if config['Architecture']["algorithm"] in ["Distillation", + ]: # distillation model + for key in config['Architecture']["Models"]: + if config['Architecture']['Models'][key]['Head'][ + 'name'] == 'MultiHead': # for multi head + if config['PostProcess'][ + 'name'] == 'DistillationSARLabelDecode': + char_num = char_num - 2 + # update SARLoss params + assert list(config['Loss']['loss_config_list'][-1].keys())[ + 0] == 'DistillationSARLoss' + config['Loss']['loss_config_list'][-1][ + 'DistillationSARLoss']['ignore_index'] = char_num + 1 + out_channels_list = {} + out_channels_list['CTCLabelDecode'] = char_num + out_channels_list['SARLabelDecode'] = char_num + 2 + config['Architecture']['Models'][key]['Head'][ + 'out_channels_list'] = out_channels_list + else: + config['Architecture']["Models"][key]["Head"][ + 'out_channels'] = char_num + elif config['Architecture']['Head'][ + 'name'] == 'MultiHead': # for multi head + if config['PostProcess']['name'] == 'SARLabelDecode': + char_num = char_num - 2 + # update SARLoss params + assert list(config['Loss']['loss_config_list'][1].keys())[ + 0] == 'SARLoss' + if config['Loss']['loss_config_list'][1]['SARLoss'] is None: + config['Loss']['loss_config_list'][1]['SARLoss'] = { + 'ignore_index': char_num + 1 + } + else: + config['Loss']['loss_config_list'][1]['SARLoss'][ + 'ignore_index'] = char_num + 1 + out_channels_list = {} + out_channels_list['CTCLabelDecode'] = char_num + out_channels_list['SARLabelDecode'] = char_num + 2 + config['Architecture']['Head'][ + 'out_channels_list'] = out_channels_list + else: # base rec model + config['Architecture']["Head"]['out_channels'] = char_num + + if config['PostProcess']['name'] == 'SARLabelDecode': # for SAR model + config['Loss']['ignore_index'] = char_num - 1 + + model = build_model(config['Architecture']) + if config['Global']['distributed']: + model = paddle.DataParallel(model) + + # build loss + loss_class = build_loss(config['Loss']) + + # build optim + optimizer, lr_scheduler = build_optimizer( + config['Optimizer'], + epochs=config['Global']['epoch_num'], + step_each_epoch=len(train_dataloader), + model=model) + + # build metric + eval_class = build_metric(config['Metric']) + # load pretrain model + pre_best_model_dict = load_model(config, model, optimizer, + config['Architecture']["model_type"]) + logger.info('train dataloader has {} iters'.format(len(train_dataloader))) + if valid_dataloader is not None: + logger.info('valid dataloader has {} iters'.format( + len(valid_dataloader))) + + use_amp = config["Global"].get("use_amp", False) + if use_amp: + AMP_RELATED_FLAGS_SETTING = { + 'FLAGS_cudnn_batchnorm_spatial_persistent': 1, + 'FLAGS_max_inplace_grad_add': 8, + } + paddle.fluid.set_flags(AMP_RELATED_FLAGS_SETTING) + scale_loss = config["Global"].get("scale_loss", 1.0) + use_dynamic_loss_scaling = config["Global"].get( + "use_dynamic_loss_scaling", False) + scaler = paddle.amp.GradScaler( + init_loss_scaling=scale_loss, + use_dynamic_loss_scaling=use_dynamic_loss_scaling) + else: + scaler = None + + # start train + program.train(config, train_dataloader, valid_dataloader, device, model, + loss_class, optimizer, lr_scheduler, post_process_class, + eval_class, pre_best_model_dict, logger, vdl_writer, scaler) + + +def test_reader(config, device, logger): + loader = build_dataloader(config, 'Train', device, logger) + import time + starttime = time.time() + count = 0 + try: + for data in loader(): + count += 1 + if count % 1 == 0: + batch_time = time.time() - starttime + starttime = time.time() + logger.info("reader: {}, {}, {}".format( + count, len(data[0]), batch_time)) + except Exception as e: + logger.info(e) + logger.info("finish reader: {}, Success!".format(count)) + + +if __name__ == '__main__': + config, device, logger, vdl_writer = program.preprocess(is_train=True) + seed = config['Global']['seed'] if 'seed' in config['Global'] else 1024 + set_seed(seed) + main(config, device, logger, vdl_writer) + # test_reader(config, device, logger)