September 2019
Intermediate to advanced
420 pages
10h 29m
English
book
Machine Learning for OpenCV 4 - Second Edition
by Aditya Sharma, Michael Beyeler (USD), Vishwesh Ravi Shrimali, Michael Beyeler
Content preview from Machine Learning for OpenCV 4 - Second Edition
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Reducing the color palette using k-means
By referring to the following steps, you will be able to project a color image into a reduced color palette using k-means clustering:
- Now, let's reduce the 16 million colors to a mere 16 by indicating k-means to cluster all of the 16 million color variations into 16 distinct clusters. We will use the previously mentioned procedure, but now define 16 as the number of clusters:
In [9]: criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER,... 10, 1.0)... flags = cv2.KMEANS_RANDOM_CENTERS... img_data = img_data.astype(np.float32)... compactness, labels, centers = cv2.kmeans(img_data,... 16, None, criteria,... 10, flags)
- The 16 different colors of your reduced color palette correspond to the ...
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