Canny邊緣檢測(cè)+霍夫變換

顏色閾值+圖像掩模的方法雖然簡(jiǎn)單，但是只能應(yīng)對(duì)一些固定顏色車道線的場(chǎng)景。圖像像素受光照影響將是一個(gè)極其常見(jiàn)的問(wèn)題。

canny邊緣檢測(cè)+霍夫變換是另外一種簡(jiǎn)單提取車道線的方法。首先依靠canny提取到原圖像的邊緣信息，再依靠霍夫變換提取滿足要求的直線

import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import cv2




# Read in and grayscale the image
image = mpimg.imread('test.jpg')
gray = cv2.cvtColor(image,cv2.COLOR_RGB2GRAY)


# Define a kernel size and apply Gaussian smoothing
kernel_size = 5
blur_gray = cv2.GaussianBlur(gray,(kernel_size, kernel_size),0)


# Define our parameters for Canny and apply
low_threshold = 50
high_threshold = 150
edges = 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 mask
imshape = image.shape
vertices = 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 region
cv2.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 on
rho = 1 # distance resolution in pixels of the Hough grid
theta = np.pi/180 # angular resolution in radians of the Hough grid
threshold = 1     # minimum number of votes (intersections in Hough grid cell)
min_line_length = 5 #minimum number of pixels making up a line
max_line_gap = 1    # maximum gap in pixels between connectable line segments
line_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 segments
lines = 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 image
for 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 image
color_edges = np.dstack((edges, edges, edges))


# Draw the lines on the edge image
lines_edges = cv2.addWeighted(color_edges, 0.8, line_image, 1, 0)
plt.imshow(lines_edges)
plt.show()

canny邊緣后，進(jìn)行霍夫直線檢測(cè)的結(jié)果

在此基礎(chǔ)上，增加一個(gè)四邊形的圖像掩模的結(jié)果

四邊形的設(shè)定，寫(xiě)在了代碼中，只是進(jìn)行了注釋

總結(jié)：

以上兩種方法只適合簡(jiǎn)單的demo，顯然并不能識(shí)別具備一定曲率的車道線，也無(wú)法適應(yīng)光照不同的情況。