制作自己的图像分割数据集（VOC格式）

1.默认标注好了所有数据，将标注好的json转成VOC分割数据集格式

from __future__ import print_function

import argparse
import glob
import os
import os.path as osp
import sys

import imgviz
import numpy as np

import labelme


def main():
    parser = argparse.ArgumentParser(
        formatter_class=argparse.ArgumentDefaultsHelpFormatter
    )
    parser.add_argument("--input_dir",default="label", help="input annotated directory")
    parser.add_argument("--output_dir",default="data_dataset_voc", help="output dataset directory")
    parser.add_argument("--labels",default="label.txt", help="labels file")
    parser.add_argument(
        "--noviz", help="no visualization", action="store_true"
    )
    args = parser.parse_args()

    if osp.exists(args.output_dir):
        print("Output directory already exists:", args.output_dir)
        sys.exit(1)
    os.makedirs(args.output_dir)
    os.makedirs(osp.join(args.output_dir, "JPEGImages"))
    os.makedirs(osp.join(args.output_dir, "SegmentationClass"))
    os.makedirs(osp.join(args.output_dir, "SegmentationClassPNG"))
    if not args.noviz:
        os.makedirs(
            osp.join(args.output_dir, "SegmentationClassVisualization")
        )
    print("Creating dataset:", args.output_dir)

    class_names = []
    class_name_to_id = {
    
    }
    for i, line in enumerate(open(args.labels).readlines()):
        class_id = i - 1  # starts with -1
        class_name = line.strip()
        class_name_to_id[class_name] = class_id
        if class_id == -1:
            assert class_name == "__ignore__"
            continue
        elif class_id == 0:
            assert class_name == "_background_"
        class_names.append(class_name)
    class_names = tuple(class_names)
    print("class_names:", class_names)
    out_class_names_file = osp.join(args.output_dir, "class_names.txt")
    with open(out_class_names_file, "w") as f:
        f.writelines("\n".join(class_names))
    print("Saved class_names:", out_class_names_file)

    for filename in glob.glob(osp.join(args.input_dir, "*.json")):
        print("Generating dataset from:", filename)

        label_file = labelme.LabelFile(filename=filename)

        base = osp.splitext(osp.basename(filename))[0]
        out_img_file = osp.join(args.output_dir, "JPEGImages", base + ".jpg")
        out_lbl_file = osp.join(
            args.output_dir, "SegmentationClass", base + ".npy"
        )
        out_png_file = osp.join(
            args.output_dir, "SegmentationClassPNG", base + ".png"
        )
        if not args.noviz:
            out_viz_file = osp.join(
                args.output_dir,
                "SegmentationClassVisualization",
                base + ".jpg",
            )

        with open(out_img_file, "wb") as f:
            f.write(label_file.imageData)
        img = labelme.utils.img_data_to_arr(label_file.imageData)

        lbl, _ = labelme.utils.shapes_to_label(
            img_shape=img.shape,
            shapes=label_file.shapes,
            label_name_to_value=class_name_to_id,
        )
        labelme.utils.lblsave(out_png_file, lbl)

        np.save(out_lbl_file, lbl)

        if not args.noviz:
            viz = imgviz.label2rgb(
                label=lbl,
                #img改成image，labelme接口的问题不然会报错
                #img=imgviz.rgb2gray(img),
                image=imgviz.rgb2gray(img),
                font_size=15,
                label_names=class_names,
                loc="rb",
            )
            imgviz.io.imsave(out_viz_file, viz)


if __name__ == "__main__":
    main()

label.txt里放
__ignore__
_background_
class1
class2
...

转换成功后有以下几个文件

JPEGImages里放的是原图，SegmentationClass文件下是npy格的分割数据，SegmentationClassPNG分割需要的mask，SegmentationClassVisualization图像标签叠加的图像，主要看标注有没有出现错误。

2.划分训练验证测试集

from sklearn.model_selection import train_test_split
import os

imagedir = 'JPEGImages路径'
outdir = 'data_dataset_voc'

images = []
for file in os.listdir(imagedir):
    filename = file.split('.')[0]
    images.append(filename)

train, test = train_test_split(images, train_size=0.8, random_state=0)
val, test = train_test_split(test, train_size=0.1 / 0.2, random_state=0)

with open(outdir + "train.txt", 'w') as f:
    f.write('\n'.join(train))

with open(outdir + "val.txt", 'w') as f:
    f.write('\n'.join(val))

with open(outdir + "test.txt", 'w') as f:
    f.write('\n'.join(test))