Initial commit with project files

3_Signalorientierte_Bildverarbeitung/ü6/l_a.py

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+import cv2
+import numpy as np
+
+normal = cv2.imread("../../data/lena.png", cv2.IMREAD_GRAYSCALE)
+
+
+def valid_rows(N):
+    n = np.log2(N)
+    n = np.ceil(n)
+    N = np.power(2, n)
+    return N
+
+
+def create_H(m, N):
+    """
+    m: Columns
+    n: Rows
+    """
+    H = np.zeros((int(N), int(m)))
+    for i in range(1, int(N) + 1):
+        for j in range(1, int(m) + 1):
+            if i <= (N/2):
+                if j == (2*i -1) or j == 2*i:
+                    H[i-1,j-1] = 1 / np.sqrt(2)
+            else:
+                #print(j, i)
+                if j == (2 * (i - N/2) - 1):
+                    H[i-1,j-1] = 1 / np.sqrt(2)
+                elif j == (2 * (i - N/2)):
+                    H[i-1,j-1] = -1 / np.sqrt(2)
+    return H
+
+''' Reshape to valid resolution '''
+N, m = normal.shape
+new_N = valid_rows(N)
+print("Original Resolution:", m, "x", N)
+print("New Resolution:", m, "x", new_N)
+if N != new_N:
+    _ = np.zeros((new_N, m))
+    _[:N, :m] = normal
+    normal = _
+
+''' Get Haar-Matrix '''
+H = create_H(m, new_N)
+print(H[0:5, 0:5])
+print(H[256:261, 0:5])
+
+''' Filter with HxI'''
+haar = np.matmul(H, normal)
+
+''' Invert Filter '''
+haar_inv = np.matmul(np.transpose(H), haar)
+
+''' Show images '''
+cv2.imshow("normal", normal)
+cv2.imshow("haar", haar / 255)
+cv2.imshow("haar_inv", haar_inv / 255)
+print("Difference:", np.sum(np.abs(normal-haar_inv)))
+
+cv2.waitKey()