Initial commit with project files

3_Signalorientierte_Bildverarbeitung/ü4/l_a.py

@@ -0,0 +1,46 @@
+import cv2
+import numpy as np
+
+raster = cv2.imread("../../data/lena_raster.png", cv2.IMREAD_GRAYSCALE)
+normal = cv2.imread("../../data/lena.png", cv2.IMREAD_GRAYSCALE)
+
+''' FFT '''
+RASTER = np.fft.fftshift(np.fft.fft2(raster))
+RASTER_magnitude = np.abs(RASTER)
+RASTER_angle = np.angle(RASTER)
+
+NORMAL = np.fft.fftshift(np.fft.fft2(normal))
+NORMAL_magnitude = np.abs(NORMAL)
+NORMAL_angle = np.angle(NORMAL)
+
+''' Bild anzeigen '''
+cv2.imshow("raster", raster)
+cv2.imshow("normal", normal)
+cv2.imshow("RASTER_magnitude", 255 * RASTER_magnitude / np.max(RASTER_magnitude))
+cv2.imshow("NORMAL_magnitude", 255 * NORMAL_magnitude / np.max(NORMAL_magnitude))
+
+''' Maskieren '''
+centroids = [
+    [85, 85],
+    [255, 85],
+    [425, 85],
+    [85, 255],
+    [255, 425],
+    [425, 255],
+    [85, 425],
+    [425, 425]
+]
+
+s = 10
+for x, y in centroids:
+    RASTER[np.maximum(0, y - s):y + s, np.maximum(0, x - s):x + s] = 0
+FILTERED_magnitude = np.abs(RASTER)
+
+''' Inverse fft '''
+filtered = np.fft.ifft2(np.fft.ifftshift(RASTER)).astype(np.uint8)
+
+''' Lösung anzeigen '''
+cv2.imshow("FILTERED_magnitude", 255 * FILTERED_magnitude / np.max(FILTERED_magnitude))
+cv2.imshow("filtered", filtered)
+
+cv2.waitKey()