pip install labelme

__ignore__
_background_
road

cd D:\Dataset\road_dataset

labelme --labels label.txt

#!/usr/bin/env python

from __future__ import print_function

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

import imgviz
import numpy as np

import labelme


def main():
    parser = argparse.ArgumentParser(
        formatter_class=argparse.ArgumentDefaultsHelpFormatter
    )
    parser.add_argument("--input_dir", default="D:/Dataset/road_dataset/annotations", help="input annotated directory")
    parser.add_argument("--output_dir", default="D:/Dataset/road_dataset", help="output dataset directory")
    parser.add_argument("--labels", default="D:/Dataset/road_dataset/label.txt", help="labels file")
    args = parser.parse_args()
    args.noviz = False

    if not osp.exists(args.output_dir):
        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(
                lbl,
                imgviz.rgb2gray(img),
                font_size=15,
                label_names=class_names,
                loc="rb",
            )
            imgviz.io.imsave(out_viz_file, viz)


if __name__ == "__main__":
    main()

import json
import cv2
import numpy as np
from tqdm import tqdm
import os

fill_color = (255, 255, 255)
root_dir = 'D:/Dataset/road_dataset'


def visualize_one(label_name):
    with open(root_dir + '/annotations' + '/' + label_name + '.json', 'r') as obj:
        dict = json.load(obj)
    img = cv2.imread(root_dir + '/images' + '/' + label_name + '.jpg')
    for label in dict['shapes']:
        points = np.array(label['points'], dtype=np.int32)
        black_img = np.zeros(img.shape)
        cv2.polylines(black_img, [points], isClosed=True, color=fill_color, thickness=1)
        cv2.fillPoly(black_img, [points], color=fill_color)

    cv2.imwrite(root_dir + '/labels' + '/' + label_name + '.jpg', black_img)


if __name__ == '__main__':
    os.mkdir(root_dir + '/labels')
    for i in tqdm(os.listdir(os.path.join(root_dir, "annotations"))):
        label_name = i[:-5]
        visualize_one(label_name)