将语义分割的数据集标注好后如下所示：
整理好图片和标签文后需要按照比例划分为训练集，验证集，测试集。
具体划分代码见下：
import glob
import os.path
import argparse
import warnings
import numpy as npdef parse_args():parser = argparse.ArgumentParser(description='A tool for proportionally randomizing dataset to produce file lists.')parser.add_argument('dataset_root', help='the dataset root path', type=str) # 数据集根目录路径parser.add_argument('images_dir_name', help='the directory name of images', type=str) # 图片所在的文件路径parser.add_argument('labels_dir_name', help='the directory name of labels', type=str) # 标签所在的文件路径parser.add_argument('--split', help='', nargs=3, type=float, default=[0.7, 0.3, 0]) # 此代码为默认比例7 ：3 :1 可以自定义修改比例parser.add_argument('--separator',dest='separator',help='file list separator',default=" ",type=str)parser.add_argument('--format',help='data format of images and labels, e.g. jpg, tif or png.',type=str,nargs=2,default=['jpg', 'png'])parser.add_argument('--postfix',help='postfix of images or labels',type=str,nargs=2,default=['', ''])return parser.parse_args()def get_files(path, format, postfix):pattern = '*%s.%s' % (postfix, format)search_files = os.path.join(path, pattern)search_files2 = os.path.join(path, "*", pattern) # 包含子目录search_files3 = os.path.join(path, "*", "*", pattern) # 包含三级目录filenames = glob.glob(search_files)filenames2 = glob.glob(search_files2)filenames3 = glob.glob(search_files3)filenames = filenames + filenames2 + filenames3return sorted(filenames)def generate_list(args):separator = args.separatordataset_root = args.dataset_rootif abs(sum(args.split) - 1.0) > 1e-8:raise ValueError("The sum of input params `--split` should be 1")image_dir = os.path.join(dataset_root, args.images_dir_name)label_dir = os.path.join(dataset_root, args.labels_dir_name)image_files = get_files(image_dir, args.format[0], args.postfix[0])label_files = get_files(label_dir, args.format[1], args.postfix[1])if not image_files:warnings.warn("No files in {}".format(image_dir))if not label_files:warnings.warn("No files in {}".format(label_dir))num_images = len(image_files)num_label = len(label_files)if num_images != num_label:raise Exception("Number of images = {}, number of labels = {}.""The number of images is not equal to number of labels, ""Please check your dataset!".format(num_images, num_label))image_files = np.array(image_files)label_files = np.array(label_files)state = np.random.get_state()np.random.shuffle(image_files)np.random.set_state(state)np.random.shuffle(label_files)start = 0num_split = len(args.split)dataset_name = ['train', 'val', 'test']for i in range(num_split):dataset_split = dataset_name[i]print("Creating {}.txt...".format(dataset_split))if args.split[i] > 1.0 or args.split[i] < 0:raise ValueError("{} dataset percentage should be 0~1.".format(dataset_split))file_list = os.path.join(dataset_root, dataset_split + '.txt')with open(file_list, "w") as f:num = round(args.split[i] * num_images)end = start + numif i == num_split - 1:end = num_imagesfor item in range(start, end):left = image_files[item].replace(dataset_root, '')if left[0] == os.path.sep:left = left.lstrip(os.path.sep)try:right = label_files[item].replace(dataset_root, '')if right[0] == os.path.sep:right = right.lstrip(os.path.sep)line = left + separator + right + '\n'except:line = left + '\n'f.write(line)print(line)start = endif __name__ == '__main__':args = parse_args()generate_list(args)
将上面代码保存到一个英文路径下，并命名为：split_dataset_list.py，通过cmd进入到保存此.py文件的终端界面，运行以下命令：
python split_dataset_list.py <dataset_root> <images_dir_name> <labels_dir_name> ${FLAGS}
上面命令的参数解释:
dataset_root: 数据集根目录
images_dir_name: 原始图像目录名
labels_dir_name: 标注图像目录名
此代码为默认比例7 ：3 :1 可以自定义修改比例
！！！！！注意：导入路径时一定要用绝对路径才行！！！！！
具体样纸如下：
到数据集根目录下查看划分后的结果，如下：
以上就是paddleseg数据集自定义比例划分为测试集test.txt，训练集train.txt，验证集val.txt的详细划分过程，谢谢！
