Merge pull request #7782 from Evezerest/release2.6

[CP] new changes in PPOCRLabel

PPOCRLabel/PPOCRLabel.py

@@ -2449,13 +2449,6 @@ class MainWindow(QMainWindow):
             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()
@@ -2478,28 +2471,19 @@ class MainWindow(QMainWindow):
                         labeldict[file] = eval(label)
                     else:
                         labeldict[file] = []
+        
+        # read table recognition output
+        TableRec_excel_dir = os.path.join(
+            self.lastOpenDir, 'tableRec_excel_output')
 
-        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
+        # save txt
+        fid = open(
+            "{}/gt.txt".format(self.lastOpenDir), "w", encoding='utf-8')
         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')
+            csv_path = os.path.join(
+                TableRec_excel_dir, filename + '.xlsx')
             if not os.path.exists(csv_path):
                 continue
 
@@ -2518,28 +2502,31 @@ class MainWindow(QMainWindow):
             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}, 'cells': 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)
+                cells.append({
+                    'tokens': tokens, 
+                    'bbox': anno['points']
+                    })
+
+            # 构造标注信息
+            html = {
+                'structure': {
+                    'tokens': token_list
+                    }, 
+                'cells': cells
+                }
+            d = {
+                'filename': os.path.basename(image_path), 
+                'html': html
+                }
+            # 重构HTML
+            d['gt'] = rebuild_html_from_ppstructure_label(d)
+            fid.write('{}\n'.format(
+                json.dumps(
+                    d, ensure_ascii=False)))
+                    
+        # convert to PP-Structure label format
+        fid.close()
+        msg = 'JSON sucessfully saved in {}/gt.txt'.format(self.lastOpenDir)
         QMessageBox.information(self, "Information", msg)
 
     def autolcm(self):
@@ -2728,6 +2715,9 @@ class MainWindow(QMainWindow):
 
             self._update_shape_color(shape)
             self.keyDialog.addLabelHistory(key_text)
+            
+        # save changed shape
+        self.setDirty()
 
     def undoShapeEdit(self):
         self.canvas.restoreShape()

PPOCRLabel/README.md

@@ -105,9 +105,9 @@ python PPOCRLabel.py --kie True # [KIE mode] for [detection + recognition + keyw
 #### 1.2.3 Build and Install the Whl Package Locally
 Compile and install a new whl package, where 1.0.2 is the version number, you can specify the new version in 'setup.py'.
 ```bash
-cd PaddleOCR/PPOCRLabel
+cd ./PPOCRLabel
 python3 setup.py bdist_wheel
-pip3 install dist/PPOCRLabel-1.0.2-py2.py3-none-any.whl
+pip3 install dist/PPOCRLabel-2.1.2-py2.py3-none-any.whl
 ```
 
 

PPOCRLabel/README_ch.md

@@ -104,9 +104,9 @@ python PPOCRLabel.py --lang ch
 编译与安装新的whl包，其中1.0.2为版本号，可在 `setup.py` 中指定新版本。
 
 ```bash
-cd PaddleOCR/PPOCRLabel
+cd ./PPOCRLabel
 python3 setup.py bdist_wheel 
-pip3 install dist/PPOCRLabel-1.0.2-py2.py3-none-any.whl -i https://mirror.baidu.com/pypi/simple
+pip3 install dist/PPOCRLabel-2.1.2-py2.py3-none-any.whl -i https://mirror.baidu.com/pypi/simple
 ```
 
 

PPOCRLabel/libs/canvas.py

@@ -611,8 +611,8 @@ class Canvas(QWidget):
 
         if self.drawing() and not self.prevPoint.isNull() and not self.outOfPixmap(self.prevPoint):
             p.setPen(QColor(0, 0, 0))
-            p.drawLine(self.prevPoint.x(), 0, self.prevPoint.x(), self.pixmap.height())
-            p.drawLine(0, self.prevPoint.y(), self.pixmap.width(), self.prevPoint.y())
+            p.drawLine(int(self.prevPoint.x()), 0, int(self.prevPoint.x()), self.pixmap.height())
+            p.drawLine(0, int(self.prevPoint.y()), self.pixmap.width(), int(self.prevPoint.y()))
 
         self.setAutoFillBackground(True)
         if self.verified:
@@ -909,4 +909,4 @@ class Canvas(QWidget):
     def updateShapeIndex(self):
         for i in range(len(self.shapes)):
             self.shapes[i].idx = i
-        self.update()
+        self.update()
\ No newline at end of file

PPOCRLabel/libs/dataPartitionDialog.py

-try:
-    from PyQt5.QtGui import *
-    from PyQt5.QtCore import *
-    from PyQt5.QtWidgets import *
-except ImportError:
-    from PyQt4.QtGui import *
-    from PyQt4.QtCore import *
-
-from libs.utils import newIcon
-
-import time
-import datetime
-import json
-import cv2
-import numpy as np
-
-
-BB = QDialogButtonBox
-
-class DataPartitionDialog(QDialog):
-    def __init__(self, parent=None):
-        super().__init__()
-        self.parnet = parent
-        self.title = 'DATA PARTITION'
-
-        self.train_ratio = 70
-        self.val_ratio = 15
-        self.test_ratio = 15
-        
-        self.initUI()
-
-    def initUI(self):
-        self.setWindowTitle(self.title)
-        self.setWindowModality(Qt.ApplicationModal)
-
-        self.flag_accept = True
-
-        if self.parnet.lang == 'ch':
-            msg = "导出JSON前请保存所有图像的标注且关闭EXCEL!"
-        else:
-            msg = "Please save all the annotations and close the EXCEL before exporting JSON!"
-
-        info_msg = QLabel(msg, self)
-        info_msg.setWordWrap(True)
-        info_msg.setStyleSheet("color: red")
-        info_msg.setFont(QFont('Arial', 12))
-
-        train_lbl = QLabel('Train split: ', self)
-        train_lbl.setFont(QFont('Arial', 15))
-        val_lbl = QLabel('Valid split: ', self)
-        val_lbl.setFont(QFont('Arial', 15))
-        test_lbl = QLabel('Test split: ', self)
-        test_lbl.setFont(QFont('Arial', 15))
-
-        self.train_input = QLineEdit(self)
-        self.train_input.setFont(QFont('Arial', 15))
-        self.val_input = QLineEdit(self)
-        self.val_input.setFont(QFont('Arial', 15))
-        self.test_input = QLineEdit(self)
-        self.test_input.setFont(QFont('Arial', 15))
-
-        self.train_input.setText(str(self.train_ratio))
-        self.val_input.setText(str(self.val_ratio))
-        self.test_input.setText(str(self.test_ratio))
-
-        validator = QIntValidator(0, 100)
-        self.train_input.setValidator(validator)
-        self.val_input.setValidator(validator)
-        self.test_input.setValidator(validator)
-
-        gridlayout = QGridLayout()
-        gridlayout.addWidget(info_msg, 0, 0, 1, 2)
-        gridlayout.addWidget(train_lbl, 1, 0)
-        gridlayout.addWidget(val_lbl, 2, 0)
-        gridlayout.addWidget(test_lbl, 3, 0)
-        gridlayout.addWidget(self.train_input, 1, 1)
-        gridlayout.addWidget(self.val_input, 2, 1)
-        gridlayout.addWidget(self.test_input, 3, 1)
-
-        bb = BB(BB.Ok | BB.Cancel, Qt.Horizontal, self)
-        bb.button(BB.Ok).setIcon(newIcon('done'))
-        bb.button(BB.Cancel).setIcon(newIcon('undo'))
-        bb.accepted.connect(self.validate)
-        bb.rejected.connect(self.cancel)
-        gridlayout.addWidget(bb, 4, 0, 1, 2)
-
-        self.setLayout(gridlayout)
-        
-        self.show()
-
-    def validate(self):
-        self.flag_accept = True
-        self.accept()
-
-    def cancel(self):
-        self.flag_accept = False
-        self.reject()
-    
-    def getStatus(self):
-        return self.flag_accept
-
-    def getDataPartition(self):
-        self.train_ratio = int(self.train_input.text())
-        self.val_ratio = int(self.val_input.text())
-        self.test_ratio = int(self.test_input.text())
-
-        return self.train_ratio, self.val_ratio, self.test_ratio
-
-    def closeEvent(self, event):
-        self.flag_accept = False
-        self.reject()
-
-

PPOCRLabel/libs/utils.py

@@ -176,18 +176,6 @@ def boxPad(box, imgShape, pad : int) -> np.array:
     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
@@ -232,6 +220,26 @@ def convert_token(html_list):
     return token_list
 
 
+def rebuild_html_from_ppstructure_label(label_info):
+        from html import escape
+        html_code = label_info['html']['structure']['tokens'].copy()
+        to_insert = [
+            i for i, tag in enumerate(html_code) if tag in ('<td>', '>')
+        ]
+        for i, cell in zip(to_insert[::-1], label_info['html']['cells'][::-1]):
+            if cell['tokens']:
+                cell = [
+                    escape(token) if len(token) == 1 else token
+                    for token in cell['tokens']
+                ]
+                cell = ''.join(cell)
+                html_code.insert(i + 1, cell)
+        html_code = ''.join(html_code)
+        html_code = '<html><body><table>{}</table></body></html>'.format(
+            html_code)
+        return html_code
+
+
 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" \

PPOCRLabel/resources/strings/strings-en.properties

@@ -113,4 +113,4 @@ keyDialogTip=Enter object label
 keyChange=Change Box Key
 TableRecognition=Table Recognition
 cellreRecognition=Cell Re-Recognition
-exportJSON=Export Excel Label(PubTabNet)
+exportJSON=Export Table Label

PPOCRLabel/resources/strings/strings-zh-CN.properties

@@ -113,4 +113,4 @@ 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
+exportJSON=导出表格标注
\ No newline at end of file

PPOCRLabel/setup.py

@@ -33,7 +33,7 @@ setup(
     package_dir={'PPOCRLabel': ''},
     include_package_data=True,
     entry_points={"console_scripts": ["PPOCRLabel= PPOCRLabel.PPOCRLabel:main"]},
-    version='2.1.1',
+    version='2.1.2',
     install_requires=requirements,
     license='Apache License 2.0',
     description='PPOCRLabel is a semi-automatic graphic annotation tool suitable for OCR field, with built-in PPOCR model to automatically detect and re-recognize data. It is written in python3 and pyqt5, supporting rectangular box annotation and four-point annotation modes. Annotations can be directly used for the training of PPOCR detection and recognition models',