11. HLS Quiz

HLS Color Threshold

In this exercise, you can try applying a color threshold in the HLS color space. If you have any problems with the implementation, please refer to the previous video and instructor notes.

Your task here is to write a function that takes in an image and threshold values and returns a binary output from applying the threshold to the S-channel. Apply your thresholds as you did for gradients but this time use an exclusive ( > ) lower bound and an inclusive upper bound ( <= ).

You can download the test images in the quiz for local usage here .

HLS

Start Quiz: 

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

# Read in an image, you can also try test1.jpg or test4.jpg
image = mpimg.imread('test6.jpg') 

# TODO: Define a function that thresholds the S-channel of HLS
# Use exclusive lower bound (>) and inclusive upper (<=)
def hls_select(img, thresh=(0, 255)):
    # 1) Convert to HLS color space
    # 2) Apply a threshold to the S channel
    # 3) Return a binary image of threshold result
    binary_output = np.copy(img) # placeholder line
    return binary_output
    
# Optional TODO - tune the threshold to try to match the above image!    
hls_binary = hls_select(image, thresh=(0, 255))

# Plot the result
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(image)
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(hls_binary, cmap='gray')
ax2.set_title('Thresholded S', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
# Define a function that thresholds the S-channel of HLS
def hls_select(img, thresh=(0, 255)):
    hls = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
    s_channel = hls[:,:,2]
    binary_output = np.zeros_like(s_channel)
    binary_output[(s_channel > thresh[0]) & (s_channel <= thresh[1])] = 1
    return binary_output

hls_binary = hls_select(image, thresh=(90, 255))

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