41 lines
1.4 KiB
Python
41 lines
1.4 KiB
Python
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import numpy as np
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import numpy.typing as npt
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from matplotlib import pyplot as plt
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import random
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import cv2
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import uz_framework.image as uz_image
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#################################################################
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# EXCERCISE 1: Exercise 1: Global approach to image description #
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#################################################################
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def ex1():
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one_a()
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def one_a():
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"""
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Firstly, you will implement the function myhist3 that computes a 3-D histogram
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from a three channel image. The images you will use are RGB, but the function
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should also work on other color spaces. The resulting histogram is stored in a 3-D
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matrix. The size of the resulting histogram is determined by the parameter n_bins.
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The bin range calculation is exactly the same as in the previous assignment, except
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now you will get one index for each image channel. Iterate through the image pixels
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and increment the appropriate histogram cells. You can create an empty 3-D numpy
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array with H = np.zeros((n_bins,n_bins,n_bins)). Take care that you normalize
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the resulting histogram.
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"""
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test_image = uz_image.imread('./data/images/museum.jpg', uz_image.ImageType.float64)
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uz_image.get_image_bins_ND(test_image, 10)
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plt.imshow(test_image)
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plt.show()
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# ######## #
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# SOLUTION #
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# ######## #
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def main():
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ex1()
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if __name__ == '__main__':
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main()
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