add pre-commit workflow (#11973)

* add pre-commit workflow

* run 'pre-commit run --all-files'

* setup python version

gen_ocr_train_val_test.py

@@ -17,8 +17,16 @@ def isCreateOrDeleteFolder(path, flag):
     return flagAbsPath
 
 
-def splitTrainVal(root, abs_train_root_path, abs_val_root_path, abs_test_root_path, train_txt, val_txt, test_txt, flag):
-
+def splitTrainVal(
+    root,
+    abs_train_root_path,
+    abs_val_root_path,
+    abs_test_root_path,
+    train_txt,
+    val_txt,
+    test_txt,
+    flag,
+):
     data_abs_path = os.path.abspath(root)
     label_file_name = args.detLabelFileName if flag == "det" else args.recLabelFileName
     label_file_path = os.path.join(data_abs_path, label_file_name)
@@ -29,13 +37,15 @@ def splitTrainVal(root, abs_train_root_path, abs_val_root_path, abs_test_root_pa
         label_record_len = len(label_file_content)
 
         for index, label_record_info in enumerate(label_file_content):
-            image_relative_path, image_label = label_record_info.split('\t')
+            image_relative_path, image_label = label_record_info.split("\t")
             image_name = os.path.basename(image_relative_path)
 
             if flag == "det":
                 image_path = os.path.join(data_abs_path, image_name)
             elif flag == "rec":
-                image_path = os.path.join(data_abs_path, args.recImageDirName, image_name)
+                image_path = os.path.join(
+                    data_abs_path, args.recImageDirName, image_name
+                )
 
             train_val_test_ratio = args.trainValTestRatio.split(":")
             train_ratio = eval(train_val_test_ratio[0]) / 10
@@ -77,27 +87,46 @@ def genDetRecTrainVal(args):
     removeFile(os.path.join(args.recRootPath, "val.txt"))
     removeFile(os.path.join(args.recRootPath, "test.txt"))
 
-    detTrainTxt = open(os.path.join(args.detRootPath, "train.txt"), "a", encoding="UTF-8")
+    detTrainTxt = open(
+        os.path.join(args.detRootPath, "train.txt"), "a", encoding="UTF-8"
+    )
     detValTxt = open(os.path.join(args.detRootPath, "val.txt"), "a", encoding="UTF-8")
     detTestTxt = open(os.path.join(args.detRootPath, "test.txt"), "a", encoding="UTF-8")
-    recTrainTxt = open(os.path.join(args.recRootPath, "train.txt"), "a", encoding="UTF-8")
+    recTrainTxt = open(
+        os.path.join(args.recRootPath, "train.txt"), "a", encoding="UTF-8"
+    )
     recValTxt = open(os.path.join(args.recRootPath, "val.txt"), "a", encoding="UTF-8")
     recTestTxt = open(os.path.join(args.recRootPath, "test.txt"), "a", encoding="UTF-8")
 
-    splitTrainVal(args.datasetRootPath, detAbsTrainRootPath, detAbsValRootPath, detAbsTestRootPath, detTrainTxt, detValTxt,
-                  detTestTxt, "det")
+    splitTrainVal(
+        args.datasetRootPath,
+        detAbsTrainRootPath,
+        detAbsValRootPath,
+        detAbsTestRootPath,
+        detTrainTxt,
+        detValTxt,
+        detTestTxt,
+        "det",
+    )
 
     for root, dirs, files in os.walk(args.datasetRootPath):
         for dir in dirs:
-            if dir == 'crop_img':
-                splitTrainVal(root, recAbsTrainRootPath, recAbsValRootPath, recAbsTestRootPath, recTrainTxt, recValTxt,
-                              recTestTxt, "rec")
+            if dir == "crop_img":
+                splitTrainVal(
+                    root,
+                    recAbsTrainRootPath,
+                    recAbsValRootPath,
+                    recAbsTestRootPath,
+                    recTrainTxt,
+                    recValTxt,
+                    recTestTxt,
+                    "rec",
+                )
             else:
                 continue
         break
 
 
-
 if __name__ == "__main__":
     # 功能描述：分别划分检测和识别的训练集、验证集、测试集
     # 说明：可以根据自己的路径和需求调整参数，图像数据往往多人合作分批标注，每一批图像数据放在一个文件夹内用PPOCRLabel进行标注，
@@ -107,40 +136,43 @@ def genDetRecTrainVal(args):
         "--trainValTestRatio",
         type=str,
         default="6:2:2",
-        help="ratio of trainset:valset:testset")
+        help="ratio of trainset:valset:testset",
+    )
     parser.add_argument(
         "--datasetRootPath",
         type=str,
         default="../train_data/",
-        help="path to the dataset marked by ppocrlabel, E.g, dataset folder named 1,2,3..."
+        help="path to the dataset marked by ppocrlabel, E.g, dataset folder named 1,2,3...",
     )
     parser.add_argument(
         "--detRootPath",
         type=str,
         default="../train_data/det",
-        help="the path where the divided detection dataset is placed")
+        help="the path where the divided detection dataset is placed",
+    )
     parser.add_argument(
         "--recRootPath",
         type=str,
         default="../train_data/rec",
-        help="the path where the divided recognition dataset is placed"
+        help="the path where the divided recognition dataset is placed",
     )
     parser.add_argument(
         "--detLabelFileName",
         type=str,
         default="Label.txt",
-        help="the name of the detection annotation file")
+        help="the name of the detection annotation file",
+    )
     parser.add_argument(
         "--recLabelFileName",
         type=str,
         default="rec_gt.txt",
-        help="the name of the recognition annotation file"
+        help="the name of the recognition annotation file",
     )
     parser.add_argument(
         "--recImageDirName",
         type=str,
         default="crop_img",
-        help="the name of the folder where the cropped recognition dataset is located"
+        help="the name of the folder where the cropped recognition dataset is located",
     )
     args = parser.parse_args()
-    genDetRecTrainVal(args)
+    genDetRecTrainVal(args)

libs/__init__.py

@@ -1,2 +1,2 @@
-__version_info__ = ('1', '0', '0')
-__version__ = '.'.join(__version_info__)
+__version_info__ = ("1", "0", "0")
+__version__ = ".".join(__version_info__)

libs/autoDialog.py

@@ -29,40 +29,53 @@ def __init__(self, ocr, mImgList, mainThread, model):
         self.mImgList = mImgList
         self.mainThread = mainThread
         self.model = model
-        self.setStackSize(1024*1024)
+        self.setStackSize(1024 * 1024)
 
     def run(self):
         try:
             findex = 0
             for Imgpath in self.mImgList:
                 if self.handle == 0:
                     self.listValue.emit(Imgpath)
-                    if self.model == 'paddle':
-                        h, w, _ = cv2.imdecode(np.fromfile(Imgpath, dtype=np.uint8), 1).shape
+                    if self.model == "paddle":
+                        h, w, _ = cv2.imdecode(
+                            np.fromfile(Imgpath, dtype=np.uint8), 1
+                        ).shape
                         if h > 32 and w > 32:
-                            self.result_dic = self.ocr.ocr(Imgpath, cls=True, det=True)[0]
+                            self.result_dic = self.ocr.ocr(Imgpath, cls=True, det=True)[
+                                0
+                            ]
                         else:
-                            print('The size of', Imgpath, 'is too small to be recognised')
+                            print(
+                                "The size of", Imgpath, "is too small to be recognised"
+                            )
                             self.result_dic = None
 
                     # 结果保存
                     if self.result_dic is None or len(self.result_dic) == 0:
-                        print('Can not recognise file', Imgpath)
+                        print("Can not recognise file", Imgpath)
                         pass
                     else:
-                        strs = ''
+                        strs = ""
                         for res in self.result_dic:
                             chars = res[1][0]
                             cond = res[1][1]
                             posi = res[0]
-                            strs += "Transcription: " + chars + " Probability: " + str(cond) + \
-                                    " Location: " + json.dumps(posi) +'\n'
+                            strs += (
+                                "Transcription: "
+                                + chars
+                                + " Probability: "
+                                + str(cond)
+                                + " Location: "
+                                + json.dumps(posi)
+                                + "\n"
+                            )
                         # Sending large amounts of data repeatedly through pyqtSignal may affect the program efficiency
                         self.listValue.emit(strs)
                         self.mainThread.result_dic = self.result_dic
                         self.mainThread.filePath = Imgpath
                         # 保存
-                        self.mainThread.saveFile(mode='Auto')
+                        self.mainThread.saveFile(mode="Auto")
                     findex += 1
                     self.progressBarValue.emit(findex)
                 else:
@@ -75,8 +88,9 @@ def run(self):
 
 
 class AutoDialog(QDialog):
-
-    def __init__(self, text="Enter object label", parent=None, ocr=None, mImgList=None, lenbar=0):
+    def __init__(
+        self, text="Enter object label", parent=None, ocr=None, mImgList=None, lenbar=0
+    ):
         super(AutoDialog, self).__init__(parent)
         self.setFixedWidth(1000)
         self.parent = parent
@@ -89,13 +103,13 @@ def __init__(self, text="Enter object label", parent=None, ocr=None, mImgList=No
 
         layout = QVBoxLayout()
         layout.addWidget(self.pb)
-        self.model = 'paddle'
+        self.model = "paddle"
         self.listWidget = QListWidget(self)
         layout.addWidget(self.listWidget)
 
         self.buttonBox = bb = BB(BB.Ok | BB.Cancel, Qt.Horizontal, self)
-        bb.button(BB.Ok).setIcon(newIcon('done'))
-        bb.button(BB.Cancel).setIcon(newIcon('undo'))
+        bb.button(BB.Ok).setIcon(newIcon("done"))
+        bb.button(BB.Cancel).setIcon(newIcon("undo"))
         bb.accepted.connect(self.validate)
         bb.rejected.connect(self.reject)
         layout.addWidget(bb)
@@ -107,7 +121,7 @@ def __init__(self, text="Enter object label", parent=None, ocr=None, mImgList=No
 
         # self.setWindowFlags(Qt.WindowCloseButtonHint)
 
-        self.thread_1 = Worker(self.ocr, self.mImgList, self.parent, 'paddle')
+        self.thread_1 = Worker(self.ocr, self.mImgList, self.parent, "paddle")
         self.thread_1.progressBarValue.connect(self.handleProgressBarSingal)
         self.thread_1.listValue.connect(self.handleListWidgetSingal)
         self.thread_1.endsignal.connect(self.handleEndsignalSignal)
@@ -117,8 +131,14 @@ def handleProgressBarSingal(self, i):
         self.pb.setValue(i)
 
         # calculate time left of auto labeling
-        avg_time = (time.time() - self.time_start) / i  # Use average time to prevent time fluctuations
-        time_left = str(datetime.timedelta(seconds=avg_time * (self.lender - i))).split(".")[0]  # Remove microseconds
+        avg_time = (
+            time.time() - self.time_start
+        ) / i  # Use average time to prevent time fluctuations
+        time_left = str(datetime.timedelta(seconds=avg_time * (self.lender - i))).split(
+            "."
+        )[
+            0
+        ]  # Remove microseconds
         self.setWindowTitle("PPOCRLabel  --  " + f"Time Left: {time_left}")  # show
 
     def handleListWidgetSingal(self, i):