tusimple车道线检测标注自己的数据集

一、打开labelme工具进行数据标注

在这里插入图片描述

二、数据格式转换

2.1 标签转换

运行Anaconda Prompt，进入json标签文件所在目录，逐个转换，分布运行

labelme_json_to_dataset 0000.json
labelme_json_to_dataset 0001.json
labelme_json_to_dataset 0002.json
labelme_json_to_dataset 0003.json
labelme_json_to_dataset 0004.json
labelme_json_to_dataset 0005.json

结束后，会得到这些文件夹
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看看里面是什么内容。。

2.2 生成二值图标签和train.py

创建一个data_json_train空文件夹
在data_json_train下，创建images、clips、gt_image、gt_binary_image、gt_instance_image 共5个文件夹
在images下创建data空文件夹
在data下创建annotations空文件夹
将2.1节的生成的文件复制到annotations文件夹下
在data_json_train下，创建data.py，代码如下：

# data.py
import cv2
from skimage import measure, color
from skimage.measure import regionprops
import numpy as np
import os
import copy# 优化的地方：应该用文件的名字来命名新的文件，而不是简单的进行 +1 操作就进行对图片文件的命名
# BUG 已解决
def skimageFilter(gray):binary_warped = copy.copy(gray)binary_warped[binary_warped > 0.1] = 255gray = (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, graydef 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")origin_img = cv2.resize(origin_img, (1280, 720))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_rowsreturn Noneif __name__ == "__main__":print('--------------开始执行----------------')#count = 1with open("./train.txt", 'w+') as file:for images_dir in os.listdir("./images"):dir_name = os.path.join("./images", images_dir + "/annotations")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, "./", str(count).zfill(4))# 利用 json 文件夹的名字来对生成的图片进行命名，比较好对应train_rows = moveImageTodir(json_dir, "./", json_dir.split('\\')[-1])file.write(train_rows)#count += 1