Add gabor filter

digital_image_processing/filters/gabor_filter.py

@@ -0,0 +1,56 @@
+# Implementation of the Gaborfilter
+# https://en.wikipedia.org/wiki/Gabor_filter
+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