lane_detection_module.py 文件源码

def HLS_sobel(img, s_thresh=(120, 255), sx_thresh=(20, 255),l_thresh=(40,255)):
    img = np.copy(img)

    # Convert to HLS color space and separate the V channel
    hls = cv2.cvtColor(img, cv2.COLOR_RGB2HLS).astype(np.float)
    #h_channel = hls[:,:,0]
    l_channel = hls[:,:,1]
    s_channel = hls[:,:,2]
    # Sobel x
    # sobelx = abs_sobel_thresh(img, orient='x', sobel_kernel=3, thresh=(0, 255))
    # l_channel_col=np.dstack((l_channel,l_channel, l_channel))
    sobelx = cv2.Sobel(l_channel, cv2.CV_64F, 1, 0) # Take the derivative in x
    abs_sobelx = np.absolute(sobelx) # Absolute x derivative to accentuate lines away from horizontal
    scaled_sobel = np.uint8(255*abs_sobelx/np.max(abs_sobelx))

    # Threshold x gradient
    sxbinary = np.zeros_like(scaled_sobel)
    sxbinary[(scaled_sobel >= sx_thresh[0]) & (scaled_sobel <= sx_thresh[1])] = 1

    # Threshold saturation channel
    s_binary = np.zeros_like(s_channel)
    s_binary[(s_channel >= s_thresh[0]) & (s_channel <= s_thresh[1])] = 1

    # Threshold lightness
    l_binary = np.zeros_like(l_channel)
    l_binary[(l_channel >= l_thresh[0]) & (l_channel <= l_thresh[1])] = 1

    channels = 255*np.dstack(( l_binary, sxbinary, s_binary)).astype('uint8')        
    binary = np.zeros_like(sxbinary)
    binary[((l_binary == 1) & (s_binary == 1) | (sxbinary==1))] = 1
    binary = 255*np.dstack((binary,binary,binary)).astype('uint8')            
    return  binary,channels