lanenet 制作自己的跑道数据集（labelme）

最近做一个跑道线的检测，在此记录一下生成自己数据集的方法：

1.安装labelme 标注工具。 具体参考https://blog.youkuaiyun.com/qq_38451119/article/details/83036495 ， 不多赘述，安装完成后在控制器输入labelme打开标注工具。

2.选择自己图片所在文件夹，进行标注，标注方式可以在选中的图片上右击进行选择，我选择的是 Create LineStrip .

标注完成后会生成相应的json文件，如下图：

3.处理得到的json文件，会得到4个文件：

import os
def transfrom():
    json_data = "C:/Users/bcl/Desktop/json/cilp/"  #此处为你json文件所在的路径
    for name in os.listdir(json_data):
        file_path = os.path.join(json_data, name)
        os.system(str("json_to_data" + file_path))
        print("success", file_path)

4. 之后进行图片二值化的转变，分别生成对应的文件：

import cv2
from skimage import measure, color
from skimage.measure import regionprops
import numpy as np
import os
import copy


def skimageFilter(gray):
    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):
    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")
        cv2.imwrite(targetPath + "/" + image_name, origin_img)

        img = cv2.imread(path + '/label.png')
        img = cv2.resize(img, (1280, 720))
        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 = 1
    with open("C:/Users/bcl/Desktop/images/sun/train.txt", 'w+') as file:
        dir_name = os.path.join("C:/Users/bcl/Desktop/images/sun/annotations") 
         # 转化后json文件夹的位置
        for annotations_dir in os.listdir(dir_name):
            json_dir = os.path.join(dir_name, annotations_dir)
            if os.path.isdir(json_dir):
                train_rows = moveImageTodir(json_dir, "C:/Users/bcl/Desktop/images/sun", str(count).zfill(4))
                file.write(train_rows)
                count += 1

5.利用作者提供的  lanenet_data_feed_pipline.py  生成相应的tfrecords文件。