42 lines
1.3 KiB
Python
42 lines
1.3 KiB
Python
import numpy as np
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import cv2
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def get_motion_psf(kernel_size, motion_angle, motion_dis):
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""" "Point Spread Function" um Bewegung zu simulieren """
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psf = np.zeros(kernel_size) # point spread function
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x_center = (kernel_size[0] - 1) / 2
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y_center = (kernel_size[1] - 1) / 2
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sin_val = np.sin(motion_angle * np.pi / 180)
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cos_val = np.cos(motion_angle * np.pi / 180)
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for i in range(motion_dis):
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x_offset = round(sin_val * i)
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y_offset = round(cos_val * i)
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psf[int(x_center - x_offset), int(y_center + y_offset)] = 1
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return psf / psf.sum()
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''' Bild laden und in den Frequenzraum transformieren '''
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img = cv2.imread("../../data/eth_blurred.png", cv2.IMREAD_GRAYSCALE)
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IMG = np.fft.fft2(img)
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''' Erstellen des Filterkernels und Transformation in den Frequenzraum '''
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rows, cols = img.shape
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h = get_motion_psf(kernel_size=(rows, cols), motion_angle=11, motion_dis=31)
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h = np.fft.fftshift(h) # Muss gemacht werden, da der "Motion Blur Vector" mittig zentriert ist
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H = np.fft.fft2(h)
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H[np.abs(H) < 0.01] = 0.01 # Begrenzen der Degradationsfunktion um numerische Probleme zu verhindern
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''' Inverses Filter anwenden '''
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IMG_FILTERED = IMG / H
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img_filtered = np.fft.ifft2(IMG_FILTERED).clip(0, 255).astype(np.uint8)
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''' Ergebnis anzeigen '''
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cv2.imshow("Original", img)
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cv2.imshow("Inverses Filter", img_filtered)
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cv2.waitKey()
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