121 lines
4.9 KiB
Python
121 lines
4.9 KiB
Python
# coding:utf8
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import os
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import shutil
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import random
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import argparse
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# 删除划分的训练集和验证集文件夹，重新创建一个空的文件夹
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def isCreateOrDeleteFolder(path, flag):
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flagPath = os.path.join(path, flag)
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if os.path.exists(flagPath):
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shutil.rmtree(flagPath)
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os.makedirs(flagPath)
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flagAbsPath = os.path.abspath(flagPath)
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return flagAbsPath
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def splitTrainVal(root, dir, absTrainRootPath, absValRootPath, trainTxt, valTxt, flag):
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# 按照指定的比例划分训练集和验证集
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labelPath = os.path.join(root, dir)
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labelAbsPath = os.path.abspath(labelPath)
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if flag == "det":
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labelFilePath = os.path.join(labelAbsPath, args.detLabelFileName)
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elif flag == "rec":
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labelFilePath = os.path.join(labelAbsPath, args.recLabelFileName)
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labelFileRead = open(labelFilePath, "r", encoding="UTF-8")
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labelFileContent = labelFileRead.readlines()
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random.shuffle(labelFileContent)
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labelRecordLen = len(labelFileContent)
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for index, labelRecordInfo in enumerate(labelFileContent):
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imageRelativePath = labelRecordInfo.split('\t')[0]
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imageLabel = labelRecordInfo.split('\t')[1]
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imageName = os.path.basename(imageRelativePath)
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if flag == "det":
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imagePath = os.path.join(labelAbsPath, imageName)
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elif flag == "rec":
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imagePath = os.path.join(labelAbsPath, "{}\\{}".format(args.recImageDirName, imageName))
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# 小于划分比例trainValRatio时，数据集划分到训练集，否则测试集
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if index / labelRecordLen < args.trainValRatio:
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imageCopyPath = os.path.join(absTrainRootPath, imageName)
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shutil.copy(imagePath, imageCopyPath)
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trainTxt.write("{}\t{}".format(imageCopyPath, imageLabel))
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else:
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imageCopyPath = os.path.join(absValRootPath, imageName)
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shutil.copy(imagePath, imageCopyPath)
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valTxt.write("{}\t{}".format(imageCopyPath, imageLabel))
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# 删掉存在的文件
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def removeFile(path):
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if os.path.exists(path):
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os.remove(path)
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def genDetRecTrainVal(args):
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detAbsTrainRootPath = isCreateOrDeleteFolder(args.detRootPath, "train")
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detAbsValRootPath = isCreateOrDeleteFolder(args.detRootPath, "val")
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recAbsTrainRootPath = isCreateOrDeleteFolder(args.recRootPath, "train")
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recAbsValRootPath = isCreateOrDeleteFolder(args.recRootPath, "val")
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removeFile(os.path.join(args.detRootPath, "train.txt"))
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removeFile(os.path.join(args.detRootPath, "val.txt"))
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removeFile(os.path.join(args.recRootPath, "train.txt"))
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removeFile(os.path.join(args.recRootPath, "val.txt"))
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detTrainTxt = open(os.path.join(args.detRootPath, "train.txt"), "a", encoding="UTF-8")
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detValTxt = open(os.path.join(args.detRootPath, "val.txt"), "a", encoding="UTF-8")
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recTrainTxt = open(os.path.join(args.recRootPath, "train.txt"), "a", encoding="UTF-8")
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recValTxt = open(os.path.join(args.recRootPath, "val.txt"), "a", encoding="UTF-8")
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for root, dirs, files in os.walk(args.labelRootPath):
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for dir in dirs:
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splitTrainVal(root, dir, detAbsTrainRootPath, detAbsValRootPath, detTrainTxt, detValTxt, "det")
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splitTrainVal(root, dir, recAbsTrainRootPath, recAbsValRootPath, recTrainTxt, recValTxt, "rec")
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break
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if __name__ == "__main__":
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# 功能描述：分别划分检测和识别的训练集和验证集
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# 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，
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# 如此会有多个标注好的图像文件夹汇总并划分训练集和验证集的需求
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--trainValRatio",
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type=float,
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default=0.8,
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help="ratio of training set to validation set")
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parser.add_argument(
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"--labelRootPath",
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type=str,
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default="./train_data/label",
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help="path to the dataset marked by ppocrlabel, E.g, dataset folder named 1,2,3..."
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)
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parser.add_argument(
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"--detRootPath",
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type=str,
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default="./train_data/det/demPanel",
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help="the path where the divided detection dataset is placed")
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parser.add_argument(
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"--recRootPath",
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type=str,
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default="./train_data/rec/demPanel",
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help="the path where the divided recognition dataset is placed"
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)
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parser.add_argument(
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"--detLabelFileName",
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type=str,
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default="Label.txt",
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help="the name of the detection annotation file")
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parser.add_argument(
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"--recLabelFileName",
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type=str,
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default="rec_gt.txt",
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help="the name of the recognition annotation file"
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)
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parser.add_argument(
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"--recImageDirName",
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type=str,
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default="crop_img",
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help="the name of the folder where the cropped recognition dataset is located"
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)
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args = parser.parse_args()
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genDetRecTrainVal(args)
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