python实现简单的车道线检测，本文章将介绍两种简单的方法

1.颜色阈值+区域掩模

2.canny边缘检测+霍夫变换

这两种方法都能实现简单的车道线检测demo，注意仅仅是demo

下面的图片是用到的测试图片

1.颜色阈值+ 区域掩模

我们可以仅仅通过设置一些RGB通道阈值，来提取车道线。

以下的代码设置了RGB通道阈值为220，大于220的像素将设置为黑色，这样可以将测试图片中的车道线提取出来

效果如下

我们发现符合阈值的像素既包括了车道线，也包含了其他非车道线部分。

显然，一个成熟的自动驾驶感知算法，是不可能使用这种方法的。仅仅依靠颜色，既不科学也不鲁棒。

有一种改进思路是利用图像掩模的方法

假设拍摄图像的前置摄像头安装在汽车上的固定位置，这样车道线将始终出现在图像的相同区域中。我们将设置了一个区域，认为车道线处于该区域内。

我们设置了一个三角形的区域，原则上你可以使用其他形状

python代码如下

import matplotlib.pyplot as pltimport matplotlib.image as mpimgimport numpy as np
# Read in the imageimage = mpimg.imread('test.jpg')
# Grab the x and y sizes and make two copies of the image# With one copy we'll extract only the pixels that meet our selection,# then we'll paint those pixels red in the original image to see our selection# overlaid on the original.ysize = image.shape[0]xsize = image.shape[1]color_select= np.copy(image)line_image = np.copy(image)
# Define our color criteriared_threshold = 220green_threshold = 220blue_threshold = 220rgb_threshold = [red_threshold, green_threshold, blue_threshold]
# Define a triangle region of interest (Note: if you run this code,left_bottom = [0, ysize-1]right_bottom = [xsize-1, ysize-1]apex = [650, 400]
fit_left = np.polyfit((left_bottom[0], apex[0]), (left_bottom[1], apex[1]), 1)fit_right = np.polyfit((right_bottom[0], apex[0]), (right_bottom[1], apex[1]), 1)fit_bottom = np.polyfit((left_bottom[0], right_bottom[0]), (left_bottom[1], right_bottom[1]), 1)
# Mask pixels below the thresholdcolor_thresholds = (image[:,:,0] < rgb_threshold[0]) | \                    (image[:,:,1] < rgb_threshold[1]) | \                    (image[:,:,2] < rgb_threshold[2])
# Find the region inside the linesXX, YY = np.meshgrid(np.arange(0, xsize), np.arange(0, ysize))region_thresholds = (YY > (XX*fit_left[0] + fit_left[1])) & \                    (YY > (XX*fit_right[0] + fit_right[1])) & \                    (YY < (XX*fit_bottom[0] + fit_bottom[1]))# Mask color selectioncolor_select[color_thresholds] = [0,0,0]# Find where image is both colored right and in the regionline_image[~color_thresholds & region_thresholds] = [255,0,0]
# Display our two output imagesplt.imshow(color_select)plt.imshow(line_image)
# uncomment if plot does not displayplt.show()

2.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

import matplotlib.pyplot as pltimport matplotlib.image as mpimgimport numpy as npimport cv2

# Read in 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 parameters for Canny 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
# This time 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)  # all image# 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 in pixels 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 lines on
# 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 imagefor line in lines:    for x1,y1,x2,y2 in 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。

之后会介绍更多的识别车道线方法。

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