前言

進行HLS過濾工作

說明

HLS S-Channel Threshold

# Define a function that thresholds the S-channel of HLS
# Use exclusive lower bound (>) and inclusive upper (<=)
def hls_sthresh(img, thresh=(125, 255)):
    # 1) Convert to HLS color space
    hls = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
    # 2) Apply a threshold to the S channel
    binary_output = np.zeros_like(hls[:,:,2])
    binary_output[(hls[:,:,2] > thresh[0]) & (hls[:,:,2] <= thresh[1])] = 1
    # 3) Return a binary image of threshold result
    return binary_output
print('...')

Visualize HLS S-Channel threshold

def update(min_thresh, max_thresh):
    exampleImg_SThresh = hls_sthresh(exampleImg_unwarp, (min_thresh, max_thresh))
    # Visualize hls s-channel threshold
    f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))
    f.subplots_adjust(hspace = .2, wspace=.05)
    ax1.imshow(exampleImg_unwarp)
    ax1.set_title('Unwarped Image', fontsize=30)
    ax2.imshow(exampleImg_SThresh, cmap='gray')
    ax2.set_title('HLS S-Channel', fontsize=30)

interact(update,
         min_thresh=(0,255), 
         max_thresh=(0,255))

print('...')

成果