tusimple 格式数据集制作与标注小结
最近在看 LaneNet ，然后参考网上的博客记录一下自己制作个人 tusimple 格式数据集的过程。
1.前期准备
工具： labelme 3.6.12 + windows10
(1) 先创建一个虚拟环境，控制台指令：
conda create -n yourenvname python==3.6.0
(2) 激活环境并安装，控制台指令：(推荐用国内源)
conda activate yourenvname
pip install labelme==3.6.12
2.进行标注
(1) 控制台进入软件：
直接控制台环境下输入 labelme ；
(2) 利用点(Point)进行标注(亲测可用)：
控制台进入labelme, Edit → \rightarrow →Create Point , 即可在图像上进行标注，双击生成label并输入；
(3) 利用线(LineStrip)进行标注(亲测可用)：
控制台进入labelme, Edit → \rightarrow →Create LineStrip , 即可在图像上进行标注，双击生成label并输入；
最后得到一系列 .json 文件
PS：笔者暂时只做到利用 LaneNet 可以进行正常的训练，不知道是否有好的效果。
; 3.数据格式转化
(1) 将 json 文件转化成 dataset
import argparse
import json
import os
import os.path as osp
import warnings
import PIL.Image
import yaml
from labelme import utils
import base64
def main():
warnings.warn("This script is aimed to demonstrate how to convert the\n"
"JSON file to a single image dataset, and not to handle\n"
"multiple JSON files to generate a real-use dataset.")
parser = argparse.ArgumentParser()
parser.add_argument('--json_file', default='D:\\Study\\Video_Frame\\Line\\')
parser.add_argument('--out', default=None)
args = parser.parse_args()
json_file = args.json_file
if args.out is None:
out_dir = osp.basename(json_file).replace('.', '_')
out_dir = osp.join(osp.dirname(json_file), out_dir)
else:
out_dir = args.out
if not osp.exists(out_dir):
os.mkdir(out_dir)
count = os.listdir(json_file)
for i in range(0, len(count)):
path = os.path.join(json_file, count[i])
if os.path.isfile(path):
data = json.load(open(path))
if data['imageData']:
imageData = data['imageData']
else:
imagePath = os.path.join(os.path.dirname(path), data['imagePath'])
with open(imagePath, 'rb') as f:
imageData = f.read()
imageData = base64.b64encode(imageData).decode('utf-8')
img = utils.img_b64_to_arr(imageData)
label_name_to_value = {'_background_': 0}
for shape in data['shapes']:
label_name = shape['label']
if label_name in label_name_to_value:
label_value = label_name_to_value[label_name]
else:
label_value = len(label_name_to_value)
label_name_to_value[label_name] = label_value
label_values, label_names = [], []
for ln, lv in sorted(label_name_to_value.items(), key=lambda x: x[1]):
label_values.append(lv)
label_names.append(ln)
assert label_values == list(range(len(label_values)))
lbl = utils.shapes_to_label(img.shape, data['shapes'], label_name_to_value)
captions = ['{}: {}'.format(lv, ln)
for ln, lv in label_name_to_value.items()]
lbl_viz = utils.draw_label(lbl, img, captions)
out_dir = osp.basename(count[i]).replace('.', '_')
out_dir = osp.join(osp.dirname(count[i]), out_dir)
if not osp.exists(out_dir):
os.mkdir(out_dir)
PIL.Image.fromarray(img).save(osp.join(out_dir, 'img.png'))
utils.lblsave(osp.join(out_dir, 'label.png'), lbl)
PIL.Image.fromarray(lbl_viz).save(osp.join(out_dir, 'label_viz.png'))
with open(osp.join(out_dir, 'label_names.txt'), 'w') as f:
for lbl_name in label_names:
f.write(lbl_name + '\n')
warnings.warn('info.yaml is being replaced by label_names.txt')
info = dict(label_names=label_names)
with open(osp.join(out_dir, 'info.yaml'), 'w') as f:
yaml.safe_dump(info, f, default_flow_style=False)
print('Saved to: %s' % out_dir)
if __name__ == '__main__':
main()
以Create Strip 标注的数据集生成结果如下，用Create Point 步骤完全一样。
(2) dataset 转 tusimple 数据集格式
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")
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_rows
return None
if __name__ == "__main__":
print('--------------开始执行----------------')
with open("./train.txt", 'w+') as file:
for images_dir in os.listdir("./images_line"):
dir_name = os.path.join("./images_line", 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, "./", json_dir.split('\\')[-1])
file.write(train_rows)
生成结果如下：
以 gt_binary_image 为例：
通过上面的操作就可以得到 tusimple 数据集格式的个人数据集，就可以放到 LaneNet 中去训练啦，可以参考我的另一篇博客：LaneNet调试记录
参考博客：
tusimple车道线检测 处理自己的数据集 用自己的数据集训练模型
本博客仅供学习交流，如有侵权行为，请联系我删除，谢谢。
[En]
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Original: https://blog.csdn.net/yyq_163/article/details/119882892
Author: 原点哈哈哈
Title: 制作自己的 tusimple 格式数据集
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