python中用Canny边缘检测和霍夫变实现车道线检测方法

Canny边缘检测+霍夫变换

颜色阈值+图像掩模的方法虽然简单，但是只能应对一些固定颜色车道线的场景。图像像素受光照影响将是一个极其常见的问题。

canny边缘检测+霍夫变换是另外一种简单提取车道线的方法。首先依靠canny提取到原图像的边缘信息，再依靠霍夫变换提取满足要求的直线

importmatplotlib.pyplot as pltimportmatplotlib.image as mpimgimportnumpy as npimportcv2# Readin and grayscale the imageimage= mpimg.imread('test.jpg')gray= cv2.cvtColor(image,cv2.COLOR_RGB2GRAY)# Define a kernel size and apply Gaussian smoothingkernel_size=5blur_gray= cv2.GaussianBlur(gray,(kernel_size, kernel_size),0)# Define our parametersforCanny and applylow_threshold=50high_threshold=150edges= cv2.Canny(blur_gray, low_threshold, high_threshold)# Next we'll create a masked edges image using cv2.fillPoly()mask= np.zeros_like(edges)ignore_mask_color=255# Thistime we are defining a four sided polygon to maskimshape= image.shapevertices= np.array([[(0,imshape[0]),(0,0), (imshape[1],0), (imshape[1],imshape[0])]], dtype=np.int32) #allimage# vertices = np.array([[(0,imshape[0]),(554, 460), (700, 446), (imshape[1],imshape[0])]], dtype=np.int32) # defining a quadrilateral regioncv2.fillPoly(mask, vertices, ignore_mask_color)masked_edges= cv2.bitwise_and(edges, mask)# Define the Hough transform parameters# Make a blank the same size as our image to draw onrho=1# distance resolution inpixels of the Hough gridtheta= np.pi/180# angular resolution in radians of the Hough gridthreshold=1# minimum number of votes (intersections in Hough grid cell)min_line_length=5#minimum number of pixels making up a linemax_line_gap=1#maximum gap in pixels between connectable line segmentsline_image= np.copy(image)*0# creating a blank to draw lineson# Run Hough on edge detected image# Output "lines" is an array containing endpoints of detected line segmentslines= cv2.HoughLinesP(masked_edges, rho, theta, threshold, np.array([]),min_line_length, max_line_gap)# Iterate over the output "lines" and draw lines on a blank imageforline in lines:forx1,y1,x2,y2in line:cv2.line(line_image,(x1,y1),(x2,y2),(255,0,0),10)# Create a "color" binary image to combine with line imagecolor_edges= np.dstack((edges, edges, edges))# Draw the lines on the edge imagelines_edges= cv2.addWeighted(color_edges,0.8, line_image,1,0)plt.imshow(lines_edges)plt.show()

canny边缘后，进行霍夫直线检测的结果

在此基础上，增加一个四边形的图像掩模的结果

四边形的设定，写在了代码中，只是进行了注释

总结：

以上两种方法只适合简单的demo，显然并不能识别具备一定曲率的车道线，也无法适应光照不同的情况。