Add gabor filter

digital_image_processing/filters/gabor_filter.py

@@ -0,0 +1,55 @@
+# Implementation of the Gaborfilter
+import numpy as np
+from cv2 import imread, cvtColor, COLOR_BGR2GRAY, filter2D, CV_8UC3, imshow, waitKey
+
+
+def gabor_filter_kernel(ksize, sigma, theta, lambd, gamma, psi):
+    # prepare kernel
+    gabor = np.zeros((ksize, ksize), dtype=np.float32)
+
+    # each value
+    for y in range(ksize):
+        for x in range(ksize):
+            # distance from center
+            px = x - ksize // 2
+            py = y - ksize // 2
+
+            # get kernel x
+            _x = np.cos(theta) * px + np.sin(theta) * py
+
+            # get kernel y
+            _y = -np.sin(theta) * px + np.cos(theta) * py
+
+            # fill kernel
+            gabor[y, x] = np.exp(
+                -(_x ** 2 + gamma ** 2 * _y ** 2) / (2 * sigma ** 2)
+            ) * np.cos(2 * np.pi * _x / lambd + psi)
+
+    return gabor
+
+
+if __name__ == "__main__":
+    # read original image
+    img = imread("../image_data/lena.jpg")
+    # turn image in gray scale value
+    gray = cvtColor(img, COLOR_BGR2GRAY)
+
+    ksize = 10
+    sigma = 8
+    lambd = 10
+    gamma = 0
+    psi = 0
+
+    # Apply multiple Kernel to detect edges
+    out = np.zeros(gray.shape[:2])
+    for theta in [0, 30, 60, 90, 120, 150]:
+        kernel_10 = gabor_filter_kernel(ksize, sigma, theta, lambd, gamma, psi)
+        out += filter2D(gray, CV_8UC3, kernel_10)
+    out = out / out.max() * 255
+    out = out.astype(np.uint8)
+
+    imshow("original", gray)
+    imshow("gabor filter with 20x20 mask and 6 directions", out)
+
+    waitKey(0)
+Mozartuss/Python