Initial commit with project files

3_Signalorientierte_Bildverarbeitung/ü5/l_a.py

@@ -0,0 +1,42 @@
+import numpy as np
+import cv2
+
+''' Bild laden und in den Frequenzraum transformieren '''
+img = cv2.imread("../../data/car2.png", cv2.IMREAD_GRAYSCALE)
+img = img.astype(float)
+i_max, i_min = np.max(img), np.min(img)
+img = (img - i_min) / (i_max - i_min)
+
+cv2.imshow("Original", img )
+
+''' 1. Logarithmieren '''
+img_log = np.maximum(img, 1/255)  # log(0) ist illegal!
+img_log = np.log(img_log)
+
+''' 2. in den Frequenzbereich transformieren '''
+IMG = np.fft.fft2(img_log)
+
+''' 3. Niedrige mit H(k,l)Frequenzen unterdrücken '''
+gamma1, gamma2, gamma3 = 0.5, 0., 4
+H = np.zeros_like(img_log)
+for l in range(H.shape[0]):
+    for k in range(H.shape[1]):
+        if k < 1 and l < 1:
+            H[l, k] = 1
+        else:
+            H[l, k] = gamma1 - (gamma1 - gamma2) * np.exp(-(k*k + l*l) / (gamma3 * gamma3))
+
+IMG = IMG * H
+
+''' 4. Rücktransformation '''
+img_filtered = np.fft.ifft2(IMG)
+img_filtered = img_filtered.astype(float)
+
+''' 5. Umkehrfunktion der Logarithmierung '''
+img_filtered = np.exp(img_filtered)
+
+''' Ergebnis anzeigen '''
+i_max, i_min = np.max(img_filtered), np.min(img_filtered)
+img_filtered = (img_filtered - i_min) / (i_max - i_min)
+cv2.imshow("Homomorphe Filterung", img_filtered)
+cv2.waitKey()