labelme标注车道线

1.原始数据集标注
在ubuntu环境下使用指令进行启动labelme

labelme

进入界面后选择图片，右键选择Create lineStrip选项进行线段标记

2. json数据转换
(1) 使用指令将json文件转换成标记数据

labelme_json_to_dataset ****.json

转换后会生成如下的文件：

(2) 批量处理需要编写相应的脚本文件：

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
 
 
if __name__ == '__main__':
        json_data = "/home/zhy/Documents/Perception/camera_data/lane_image/data/label/1" # 标注的Json文件目录
        for name in os.listdir(json_data):
                file_path = os.path.join(json_data, name)
                os.system(str("labelme_json_to_dataset " + file_path))
                print("success json to dataset: ", file_path)

3. 将转换后的数据进行归类处理
   (1) 按照tusimple数据集格式需要生成如下几个文件
   
   (2) 由于转换的数据不包含gt_binary_image和gt_instance_image的内容，需要自己编写脚本进行转换：

import sys

ros_path = '/opt/ros/kinetic/lib/python2.7/dist-packages'
if ros_path in sys.path:
    sys.path.remove(ros_path)

import copy
import os

import cv2
import numpy as np
from skimage import measure, color


def skimageFilter(gray):
    """

    :param gray:
    :return:
    """
    binary_warped = copy.copy(gray)
    binary_warped[binary_warped > 0.1] = 255

    gray = (np.dstack((gray, gray, gray)) * 255).astype('uint8')
    labels = measure.label(gray[:, :, 0], connectivity=1)
    dst = color.label2rgb(labels, bg_label=0, bg_color=(0, 0, 0))
    gray = cv2.cvtColor(np.uint8(dst * 255), cv2.COLOR_RGB2GRAY)
    return binary_warped, gray


def moveImageTodir(path, targetPath, name):
    """

    :param path:
    :param targetPath:
    :param name:
    :return:
    """
    if os.path.isdir(path):
        image_name = "gt_image/" + str(name) + ".png"
        binary_name = "gt_binary_image/" + str(name) + ".png"
        instance_name = "gt_instance_image/" + str(name) + ".png"

        train_rows = image_name + " " + binary_name + " " + instance_name + "\n"

        origin_img = cv2.imread(path + "/img.png")
        origin_img = cv2.resize(origin_img, (1920, 1200))
        cv2.imwrite(targetPath + "/" + image_name, origin_img)

        img = cv2.imread(path + '/label.png')
        img = cv2.resize(img, (1920, 1200))
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        binary_warped, instance = skimageFilter(gray)
        cv2.imwrite(targetPath + "/" + binary_name, binary_warped)
        cv2.imwrite(targetPath + "/" + instance_name, instance)
        print("success create data name is : ", train_rows)
        return train_rows
    return None


if __name__ == "__main__":

    count = 0
    with open("/home/zhy/Documents/Perception/camera_data/lane_image/labe/train.txt", 'w+') as file:

        for images_dir in os.listdir("/home/zhy/Documents/Perception/camera_data/lane_image/labe/1/"):
            dir_name = os.path.join("/home/zhy/Documents/Perception/camera_data/lane_image/labe/1", images_dir)
            # for annotations_dir in os.listdir(dir_name):
            #     json_dir = os.path.join(dir_name, annotations_dir)
            if os.path.isdir(dir_name):
                train_rows = moveImageTodir(dir_name, "/home/zhy/Documents/Perception/camera_data/lane_image/labe/",
                                            str(count).zfill(4))
                file.write(train_rows)
                count += 1