55 lines
1.5 KiB
Python
55 lines
1.5 KiB
Python
import cv2 as cv
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import matplotlib.pyplot as plt
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import numpy as np
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def global_linear_transmation(im, c=0, d=255):
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img = im.copy()
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maxV = img.max()
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minV = img.min()
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if maxV == minV:
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return np.uint8(img)
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for i in range(img.shape[0]):
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for j in range(img.shape[1]):
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img[i, j] = ((d - c) / (maxV - minV)) * (img[i, j] - minV) + c
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return np.uint8(img)
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def histogram_equalization(im):
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return np.uint8(cv.equalizeHist(im))
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if __name__ == "__main__":
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im = cv.imread(r"img\iris.jpg", cv.IMREAD_GRAYSCALE)
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im1 = global_linear_transmation(im, 0, 150)
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im2 = global_linear_transmation(im, 100)
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im3 = global_linear_transmation(im, 50, 150)
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im4 = histogram_equalization(im)
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plt.figure()
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plt.subplot(241)
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plt.imshow(im1, cmap="gray")
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plt.title("darker")
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plt.axis("off")
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plt.subplot(242)
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plt.imshow(im2, cmap="gray")
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plt.title("brighter")
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plt.axis("off")
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plt.subplot(243)
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plt.imshow(im3, cmap="gray")
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plt.title("lower contrast")
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plt.axis("off")
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plt.subplot(244)
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plt.imshow(im4, cmap="gray")
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plt.title("equalized")
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plt.axis("off")
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plt.subplot(245)
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plt.hist(im1.flatten(), 256, [0, 256]) # type: ignore
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plt.subplot(246)
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plt.hist(im2.flatten(), 256, [0, 256]) # type: ignore
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plt.subplot(247)
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plt.hist(im3.flatten(), 256, [0, 256]) # type: ignore
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plt.subplot(248)
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plt.hist(im4.flatten(), 256, [0, 256]) # type: ignore
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plt.tight_layout()
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plt.show()
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