几个不错的博客:
【Lane】 Ultra-Fast-Lane-Detection 复现 by 摇曳的树
车道线检测模型笔记 by 小驴淘米666
ultra fast lane detection数据集制作 by 小王在线秃头
(1)使用labelme进行车道线标注
labelme打开需要标注的图片文件夹./datasets/imgs,选择Creat LineStrip进行车道线标注,并将标注文件存至./datasets/jsons
(2)labelme_json_to_dataset批量进行标注文件转换
在datasets目录下运行 python labelme_json_to_dataset.py ./jsons
import argparse
import json
import os
import os.path as osp
import warnings
import PIL.Image
import yaml
from labelme import utils
import base64
#批量转换代码
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')
parser.add_argument('-o', '--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])
print(path)
if os.path.isfile(path):
data = json.load(open(path, encoding='UTF8'))
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 must be dense
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'))
#PIL.Image.fromarray(lbl).save(osp.join(out_dir, 'label.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()
生成的每个文件夹中包含下列5个文件
将文件统一移至./datasets/annotations
(3)生成训练文件train_data
运行./datasets/gen_train_gt.py
import cv2
from skimage import measure, color
from skimage.measure import regionprops
import numpy as np
import os
import copy
from PIL import Image
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"
train_rows = image_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)
print(targetPath + "/" + image_name)
# img = cv2.imread(path + '/label.png')
# img = cv2.resize(img, (1280, 720))
# gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) #将bgr格式的图片转换成灰度图片
# binary_warped, instance = skimageFilter(gray)
# cv2.imwrite(targetPath + "/" + binary_name, binary_warped)
# print(targetPath + "/" + binary_name)
# cv2.imwrite(targetPath + "/" + instance_name, instance)
ins = Image.open(path + '/label.png')
ins = ins.resize((1280,720))
ins.save(targetPath + "/" + instance_name)
print("success create data name is : ", train_rows)
return train_rows
return None
if __name__ == "__main__":
count = 1
with open(r"/media/ai/D/Teamwork/wushuli/LaneDet/Ultra-Fast-Lane-Detection-master/datasets/train_data/train_gt.txt", 'w+') as file:
dir_name = r"/media/ai/D/Teamwork/wushuli/LaneDet/Ultra-Fast-Lane-Detection-master/datasets/annotations"
for annotations_dir in os.listdir(dir_name):
json_dir = os.path.join(dir_name, annotations_dir)
# print(json_dir)
target_path = r"/media/ai/D/Teamwork/wushuli/LaneDet/Ultra-Fast-Lane-Detection-master/datasets/train_data"
if os.path.isdir(json_dir):
train_rows = moveImageTodir(json_dir,target_path, str(count).zfill(4))
file.write(train_rows)
count += 1
整体目录结构
(4)进行训练
修改配置文件参数configs/tusimple.py
data_root:训练数据集train_data路径 log_path:训练记录和模型存储位置
运行 python train.py configs/tusimple.py进行训练