add feature lockedShapes

PPOCRLabel/PPOCRLabel.py

@@ -36,6 +36,7 @@ import numpy as np
 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("../ppstructure/vqa")
 sys.path.append("..")
 
 from paddleocr import PaddleOCR
@@ -126,7 +127,7 @@ class MainWindow(QMainWindow, WindowMixin):
         self.labelHist = []
         self.lastOpenDir = None
         self.result_dic = []
-
+        self.result_dic_locked = []
         self.changeFileFolder = False
         self.haveAutoReced = False
         self.labelFile = None
@@ -1058,6 +1059,7 @@ class MainWindow(QMainWindow, WindowMixin):
 
                 shape.addPoint(QPointF(x, y))
             shape.difficult = difficult
+            #shape.locked = False
             shape.close()
             s.append(shape)
 
@@ -1117,8 +1119,8 @@ class MainWindow(QMainWindow, WindowMixin):
         shapes = [] if mode == 'Auto' else \
             [format_shape(shape) for shape in self.canvas.shapes]
         # Can add differrent annotation formats here
-
-        for box in self.result_dic:
+        print("in save labels self.result_dic",self.result_dic)
+        for box in self.result_dic :
             trans_dic = {"label": box[1][0], "points": box[0], 'difficult': False}
             if trans_dic["label"] == "" and mode == 'Auto':
                 continue
@@ -1322,6 +1324,7 @@ class MainWindow(QMainWindow, WindowMixin):
         # 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)
@@ -1370,7 +1373,8 @@ class MainWindow(QMainWindow, WindowMixin):
             else:
                 self.dirty = False
                 self.actions.save.setEnabled(True)
-
+            if len(self.canvas.lockedShapes) != 0:
+                self.actions.save.setEnabled(True)
             self.canvas.setEnabled(True)
             self.adjustScale(initial=True)
             self.paintCanvas()
@@ -1397,11 +1401,16 @@ class MainWindow(QMainWindow, WindowMixin):
         #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:
-            shapes.append(("锁定框：待识别", [[s[0]*width,s[1]*height]for s in box['ratio']],DEFAULT_LOCK_COLOR, None, box['difficult']))
+            if self.canvas.isInTheSameImage:
+                shapes.append((box['transcription'], [[s[0]*width,s[1]*height]for s in box['ratio']],
+                DEFAULT_LOCK_COLOR, None, 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, None, box['difficult']))
         if imgidx in self.PPlabel.keys():
             for box in self.PPlabel[imgidx]:
-              #  print(box)
                 shapes.append((box['transcription'], box['points'], None, None, box['difficult']))
+ 
         self.loadLabels(shapes)
         self.canvas.verified = False
 
@@ -1660,8 +1669,37 @@ class MainWindow(QMainWindow, WindowMixin):
                 return fullFilePath
         return ''
     
+   
+    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':
+            if len(self.result_dic_locked) == 0:
+                img = cv2.imread(self.filePath)
+                width, height = self.image.width(), self.image.height()
+                for shape in self.canvas.lockedShapes:
+                    print(shape)
+                    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)
@@ -1676,13 +1714,13 @@ class MainWindow(QMainWindow, WindowMixin):
                     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)
 
         elif mode == 'Auto':
             if annotationFilePath and self.saveLabels(annotationFilePath, mode=mode):
-
                 self.setClean()
                 self.statusBar().showMessage('Saved to  %s' % annotationFilePath)
                 self.statusBar().show()
@@ -1746,7 +1784,9 @@ class MainWindow(QMainWindow, WindowMixin):
             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
@@ -1885,6 +1925,7 @@ class MainWindow(QMainWindow, WindowMixin):
         # 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]
@@ -1896,21 +1937,33 @@ class MainWindow(QMainWindow, WindowMixin):
                     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)
-                    print('result in reRec is ', result)
                         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('except:', e)           
 
-            if len(self.result_dic) > 0 and rec_flag > 0:
+            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:
                 QMessageBox.information(self, "Information", "The recognition result remains unchanged!")
@@ -2124,6 +2177,12 @@ class MainWindow(QMainWindow, WindowMixin):
         
     
     def lockSelectedShape(self):
+        """lock the selsected 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
@@ -2135,19 +2194,23 @@ class MainWindow(QMainWindow, WindowMixin):
         #lock
         if len(self.canvas.lockedShapes) == 0:
             for s in self.canvas.selectedShapes:
-                s.line_color=DEFAULT_LOCK_COLOR
-            shapes=[format_shape(shape) for shape 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_dic.append({"transcription": box['label'], "ratio": box['ratio'], 'difficult': box['difficult']})
             self.canvas.lockedShapes = trans_dic
-        #    print("self.canvas.lockedShapes：",self.canvas.lockedShapes)
+            self.actions.save.setEnabled(True)
+
         #unlock
         else:
             for s in self.canvas.shapes:
-                s.line_color=DEFAULT_LINE_COLOR
-            trans_dic = []
-            self.canvas.lockedShapes = trans_dic
+                s.line_color = DEFAULT_LINE_COLOR
+            self.canvas.lockedShapes = []
+            self.result_dic_locked = []
+            self.setDirty()
+            self.actions.save.setEnabled(True)
 
 
 def inverted(color):

PPOCRLabel/libs/canvas.py

@@ -86,7 +86,11 @@ class Canvas(QWidget):
 
         #initialisation for panning
         self.pan_initial_pos = QPoint()
+
+        #lockedshapes related 
         self.lockedShapes = []
+        self.isInTheSameImage = False
+
     def setDrawingColor(self, qColor):
         self.drawingLineColor = qColor
         self.drawingRectColor = qColor

PPOCRLabel/libs/shape.py

@@ -58,7 +58,7 @@ class Shape(object):
         self.selected = False
         self.difficult = difficult
         self.paintLabel = paintLabel
-
+        self.locked = False
         self._highlightIndex = None
         self._highlightMode = self.NEAR_VERTEX
         self._highlightSettings = {

ppstructure/vqa/eval_re.py

@@ -24,7 +24,7 @@ import paddle
 from paddlenlp.transformers import LayoutXLMTokenizer, LayoutXLMModel, LayoutXLMForRelationExtraction
 
 from xfun import XFUNDataset
-from utils import parse_args, get_bio_label_maps, print_arguments
+from vaq_utils import parse_args, get_bio_label_maps, print_arguments
 from data_collator import DataCollator
 from metric import re_score
 

ppstructure/vqa/eval_ser.py

@@ -33,7 +33,7 @@ from paddlenlp.transformers import LayoutLMModel, LayoutLMTokenizer, LayoutLMFor
 
 from xfun import XFUNDataset
 from losses import SERLoss
-from utils import parse_args, get_bio_label_maps, print_arguments
+from vaq_utils import parse_args, get_bio_label_maps, print_arguments
 
 from ppocr.utils.logging import get_logger
 

ppstructure/vqa/infer_re.py

@@ -15,7 +15,7 @@ import paddle
 from paddlenlp.transformers import LayoutXLMTokenizer, LayoutXLMModel, LayoutXLMForRelationExtraction
 
 from xfun import XFUNDataset
-from utils import parse_args, get_bio_label_maps, draw_re_results
+from vaq_utils import parse_args, get_bio_label_maps, draw_re_results
 from data_collator import DataCollator
 
 from ppocr.utils.logging import get_logger

ppstructure/vqa/infer_ser.py

@@ -22,7 +22,7 @@ from copy import deepcopy
 import paddle
 
 # relative reference
-from utils import parse_args, get_image_file_list, draw_ser_results, get_bio_label_maps
+from vaq_utils import parse_args, get_image_file_list, draw_ser_results, get_bio_label_maps
 from paddlenlp.transformers import LayoutXLMModel, LayoutXLMTokenizer, LayoutXLMForTokenClassification
 from paddlenlp.transformers import LayoutLMModel, LayoutLMTokenizer, LayoutLMForTokenClassification
 

ppstructure/vqa/infer_ser_e2e.py

@@ -25,9 +25,9 @@ from paddlenlp.transformers import LayoutXLMModel, LayoutXLMTokenizer, LayoutXLM
 from paddlenlp.transformers import LayoutLMModel, LayoutLMTokenizer, LayoutLMForTokenClassification
 
 # relative reference
-from utils import parse_args, get_image_file_list, draw_ser_results, get_bio_label_maps
+from vaq_utils import parse_args, get_image_file_list, draw_ser_results, get_bio_label_maps
 
-from utils import pad_sentences, split_page, preprocess, postprocess, merge_preds_list_with_ocr_info
+from vaq_utils import pad_sentences, split_page, preprocess, postprocess, merge_preds_list_with_ocr_info
 
 MODELS = {
     'LayoutXLM':

ppstructure/vqa/infer_ser_re_e2e.py

@@ -24,7 +24,7 @@ import paddle
 from paddlenlp.transformers import LayoutXLMModel, LayoutXLMTokenizer, LayoutXLMForRelationExtraction
 
 # relative reference
-from utils import parse_args, get_image_file_list, draw_re_results
+from vaq_utils import parse_args, get_image_file_list, draw_re_results
 from infer_ser_e2e import SerPredictor
 
 

ppstructure/vqa/train_re.py

@@ -27,7 +27,7 @@ import paddle
 from paddlenlp.transformers import LayoutXLMTokenizer, LayoutXLMModel, LayoutXLMForRelationExtraction
 
 from xfun import XFUNDataset
-from utils import parse_args, get_bio_label_maps, print_arguments, set_seed
+from vaq_utils import parse_args, get_bio_label_maps, print_arguments, set_seed
 from data_collator import DataCollator
 from eval_re import evaluate
 

ppstructure/vqa/train_ser.py

@@ -32,7 +32,7 @@ from paddlenlp.transformers import LayoutXLMModel, LayoutXLMTokenizer, LayoutXLM
 from paddlenlp.transformers import LayoutLMModel, LayoutLMTokenizer, LayoutLMForTokenClassification
 
 from xfun import XFUNDataset
-from utils import parse_args, get_bio_label_maps, print_arguments, set_seed
+from vaq_utils import parse_args, get_bio_label_maps, print_arguments, set_seed
 from eval_ser import evaluate
 from losses import SERLoss
 from ppocr.utils.logging import get_logger