Cherrypicking GH-10923 to PaddleOCR:dygraph

PPOCRLabel/gen_ocr_train_val_test.py

@@ -17,48 +17,43 @@ def isCreateOrDeleteFolder(path, flag):
     return flagAbsPath
 
 
-def splitTrainVal(root, absTrainRootPath, absValRootPath, absTestRootPath, trainTxt, valTxt, testTxt, flag):
-    # 按照指定的比例划分训练集、验证集、测试集
-    dataAbsPath = os.path.abspath(root)
-
-    if flag == "det":
-        labelFilePath = os.path.join(dataAbsPath, args.detLabelFileName)
-    elif flag == "rec":
-        labelFilePath = os.path.join(dataAbsPath, args.recLabelFileName)
-
-    labelFileRead = open(labelFilePath, "r", encoding="UTF-8")
-    labelFileContent = labelFileRead.readlines()
-    random.shuffle(labelFileContent)
-    labelRecordLen = len(labelFileContent)
-
-    for index, labelRecordInfo in enumerate(labelFileContent):
-        imageRelativePath = labelRecordInfo.split('\t')[0]
-        imageLabel = labelRecordInfo.split('\t')[1]
-        imageName = os.path.basename(imageRelativePath)
-
-        if flag == "det":
-            imagePath = os.path.join(dataAbsPath, imageName)
-        elif flag == "rec":
-            imagePath = os.path.join(dataAbsPath, "{}\\{}".format(args.recImageDirName, imageName))
-
-        # 按预设的比例划分训练集、验证集、测试集
-        trainValTestRatio = args.trainValTestRatio.split(":")
-        trainRatio = eval(trainValTestRatio[0]) / 10
-        valRatio = trainRatio + eval(trainValTestRatio[1]) / 10
-        curRatio = index / labelRecordLen
-
-        if curRatio < trainRatio:
-            imageCopyPath = os.path.join(absTrainRootPath, imageName)
-            shutil.copy(imagePath, imageCopyPath)
-            trainTxt.write("{}\t{}".format(imageCopyPath, imageLabel))
-        elif curRatio >= trainRatio and curRatio < valRatio:
-            imageCopyPath = os.path.join(absValRootPath, imageName)
-            shutil.copy(imagePath, imageCopyPath)
-            valTxt.write("{}\t{}".format(imageCopyPath, imageLabel))
-        else:
-            imageCopyPath = os.path.join(absTestRootPath, imageName)
-            shutil.copy(imagePath, imageCopyPath)
-            testTxt.write("{}\t{}".format(imageCopyPath, imageLabel))
+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)
+
+    with open(label_file_path, "r", encoding="UTF-8") as label_file:
+        label_file_content = label_file.readlines()
+        random.shuffle(label_file_content)
+        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_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)
+
+            train_val_test_ratio = args.trainValTestRatio.split(":")
+            train_ratio = eval(train_val_test_ratio[0]) / 10
+            val_ratio = train_ratio + eval(train_val_test_ratio[1]) / 10
+            cur_ratio = index / label_record_len
+
+            if cur_ratio < train_ratio:
+                image_copy_path = os.path.join(abs_train_root_path, image_name)
+                shutil.copy(image_path, image_copy_path)
+                train_txt.write("{}\t{}\n".format(image_copy_path, image_label))
+            elif cur_ratio >= train_ratio and cur_ratio < val_ratio:
+                image_copy_path = os.path.join(abs_val_root_path, image_name)
+                shutil.copy(image_path, image_copy_path)
+                val_txt.write("{}\t{}\n".format(image_copy_path, image_label))
+            else:
+                image_copy_path = os.path.join(abs_test_root_path, image_name)
+                shutil.copy(image_path, image_copy_path)
+                test_txt.write("{}\t{}\n".format(image_copy_path, image_label))
 
 
 # 删掉存在的文件
@@ -148,4 +143,4 @@ def genDetRecTrainVal(args):
         help="the name of the folder where the cropped recognition dataset is located"
     )
     args = parser.parse_args()
-    genDetRecTrainVal(args)
+    genDetRecTrainVal(args)