Ultra Fast Structure-aware Deep Lane Detection的训练实战
1、模型介绍
2、基于CULane数据集格式的训练
2.1、video to img
import glob
import os
import cv2
# --------视频转图像-----------------------------------------
def video2image(video_path, save_path, index):
if not os.path.exists(save_path): os.makedirs(save_path)
cap = cv2.VideoCapture(video_path)
img_num = 0
while 1:
print(index)
ret, frame = cap.read()
if img_num >2000:break
if ret == False:break
save_name = os.path.join(save_path, str(index) + ".jpg")
cv2.imwrite(save_name, frame)
index += 1
img_num +=1
cap.release()
return index
if __name__ == "__main__":
video_path = r"Ultra-Fast-Lane-Detection\dataset\video"
video_list = os.listdir(video_path)
print(video_list)
index = 0
for i in range(len(video_list)):
video_load = os.path.join(video_path, video_list[i])
save_path = os.path.join(r"Ultra-Fast-Lane-Detection\dataset\img", video_list[i].split(".")[0])
print(save_path)
index = video2image(video_load,save_path,index)
2.2 Labelme标注
2.3 json转label图片
单张图像的代码位于：anaconda2\envs\labelme\Lib\site-packages\labelme\cli\json_to_dataset.py;
批量使用：
import os
import glob
os.chdir("B:\Anaconda\envs\Lane_Detection\Lib\site-packages\labelme\cli")
path1 = r"conda activate your env"
os.system(path1)
json_file = r'your json file' # 文件路径
json_list = glob.glob("%s/*.json" % (json_file))
for i in json_list:
path11 = r"python json_to_dataset.py "
path22 = path11 + i
os.system(path22)
结果如图所示：
2.4 基于json生成点坐标的文本文件
import os
import json
import numpy as np
dir_json = r'your json path' # json存储的文件目录
dir_txt = r'your txt save path' # txt存储目录
if not os.path.exists(dir_txt):
os.makedirs(dir_txt)
list_json = os.listdir(dir_json)
def json2txt(path_json, path_txt): # 可修改生成格式
with open(path_json, 'r') as path_json:
jsonx = json.load(path_json)
with open(path_txt, 'w+') as ftxt:
for shape in jsonx['shapes']:
label = str(shape['label']) + ' '
xy = np.array(shape['points'])
strxy = ''
for m, n in xy:
m = int(m)
n = int(n)
# print('m:',m)
# print('n：',n)
strxy += str(m) + ' ' + str(n) + ' '
label = strxy
ftxt.writelines(label + "\n")
for cnt, json_name in enumerate(list_json):
path_json = os.path.join(dir_json, json_name)
print(path_json)
path_txt = os.path.join(dir_txt, json_name.replace('.json', '.lines.txt'))
print(path_txt)
json2txt(path_json, path_txt)
结果如图所示：
2.5 train.txt\test.txt\val.txt制作
import os
folder_path = "img/val_img/" # 文件夹路径
txt_file_path = "list/val.txt" # 输出txt文件路径
with open(txt_file_path, "w") as txt_file:
for file_name in os.listdir(folder_path):
if file_name.endswith(".jpg"):
file_path = "/"+folder_path+file_name
txt_file.write(file_path + "\n") # 将文件名写入txt文件，每个文件名占一行
效果如图所示：
2.5 label_Seg文件夹的制作
将2.3步骤生成的文件夹的里的label.png重命名并保存到其它文件夹
import os
import glob
import shutil
save_path = r"D:\pythonFiles\Lane_Detection\2-Ultra-Fast-Lane-Detection\dataset\CULane\laneseg"
seg_img_path = r"D:\pythonFiles\Lane_Detection\2-Ultra-Fast-Lane-Detection\dataset\CULane\laneseg\all"
img_list = glob.glob("%s/*/*.png" % (seg_img_path))
for img_path in img_list:
if img_path.split("\\")[-1] == "label.png":
num = img_path.split("\\")[-2].split("_")[0]
new_name = num + ".png"
save_name = os.path.join(save_path, new_name)
shutil.copy(img_path, save_name)
效果如图所示：
2.6 制作train_gt.txt\val_gt.txt
import os
folder_path = "img/train_img/" # 文件夹路径
txt_file_path = "list/train_gt.txt" # 输出txt文件路径
seg_path = "laneseg/train_img/"
with open(txt_file_path, "w") as txt_file:
for file_name in os.listdir(folder_path):
if file_name.endswith(".jpg"):
file_path = "/"+folder_path+file_name
print(file_path)
txt_file.write(file_path + " ") # 将文件名写入txt文件
file_gt_path = "/"+seg_path+file_name.split(".")[0]+".png"
print(file_gt_path)
txt_file.write(file_gt_path + " ")
#
txt_file.write("1 1 1 1\n")
效果如图所示
2.7 最终数据集结构
1 CULane
1.1 img
1.2 laneseg
1.3 list
3 开始训练
3.1 configs/culane.py
3.2 开始训练
python train.py configs/culane.py
3.3 测试
把之前生成的test.txt复制到CULane数据集的一级目录下
python test.py configs/culane.py --test_model weights/ep048.pth --test_work_dir ./tmp
