动手学无人驾驶（7）：车道线检测

1.Method介绍

概率 P i , j , : P_{i, j,:} Pi,j,: 是 ( w + 1 ) (w+1) (w+1) 维的向量。

2.自采数据集车道线检测

下载完源码后，创建一个lane_detection.py，首先导入需要的库：

import torch
import scipy.special
import os, cv2
from PIL import Image
from model.model import parsingNet
import numpy as np
import torchvision.transforms as transforms
from data.constant import tusimple_row_anchor

net = parsingNet(pretrained = False, backbone=18,cls_dim = (101,56,4),
                    use_aux=False) # we dont need auxiliary segmentation in testing

test_model = tusimple_18.pth
state_dict = torch.load(test_model, map_location=cpu)[model]

compatible_state_dict = {
          
   }
for k, v in state_dict.items():
	if module. in k:
	    compatible_state_dict[k[7:]] = v
	else:
	    compatible_state_dict[k] = v

net.load_state_dict(compatible_state_dict, strict=False)
net.eval()

img_w, img_h = 1280, 560
size = (img_w,img_h)
cls_num_per_lane = 56
row_anchor = tusimple_row_anchor

img_transforms = transforms.Compose([
	transforms.Resize((288, 800)),
	transforms.ToTensor(),
	transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
])

下一步就是车道线检测与可视化：

with torch.no_grad():
 	out = net(img)
      
col_sample = np.linspace(0, 800-1, 100)
col_sample_w = col_sample[1] - col_sample[0]

out_j = out[0].data.cpu().numpy()
out_j = out_j[:, ::-1, :]                               
prob = scipy.special.softmax(out_j[:-1, :, :], axis=0)  
idx = np.arange(100) + 1
idx = idx.reshape(-1, 1, 1)
loc = np.sum(prob * idx, axis=0)
out_j = np.argmax(out_j, axis=0)
loc[out_j == 100] = 0
out_j = loc                                             

for i in range(out_j.shape[1]):
	if np.sum(out_j[:, i] != 0) > 2:
		for k in range(out_j.shape[0]):
			if out_j[k, i] > 0:
				ppp = (int(out_j[k,i] * col_sample_w * img_w / 800) - 1, int(img_h *   
				      (row_anchor[cls_num_per_lane-1-k]/288))-1)
				cv2.circle(vis,ppp,4,(0,0,255),-1)
Image.fromarray(vis).show()

最后检测结果如下：