Initial commit with project files

3_Signalorientierte_Bildverarbeitung/ü2/l_a.py

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