Add wahab filter to filters in image processing

Author: rishavk99

Image_Processing/src/Filters/src/wahab filter/ppf1.png

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+#!/usr/bin/env python3
+
+import matplotlib.pyplot as plt
+import numpy as np
+from optparse import OptionParser
+import scipy.misc as misc
+import scipy.ndimage as ndimage
+from skimage import draw
+
+import utils
+from utils import showImage
+
+parser = OptionParser(usage="%prog [options] sourceimage [destinationimage]")
+
+parser.add_option("-i", dest="images", default=0, action="count",
+        help="Show intermediate images.")
+
+parser.add_option("-d", "--dry-run", dest="dryrun", default=False, action="store_true",
+        help="Do not save the result.")
+
+parser.add_option("-b", "--no-binarization", dest="binarize", default=True, action="store_false",
+        help="Use this option to disable the final binarization step")
+
+(options, args) = parser.parse_args()
+
+if len(args) == 0 or len(args) > 2:
+    parser.print_help()
+    exit(1)
+
+sourceImage = args[0]
+if len(args) == 1:
+    destinationImage = args[0]
+else:
+    destinationImage = args[1]
+
+def wahabKernel(size, angle):
+    y = int(np.sin(angle) * size)
+    x = int(np.cos(angle) * size)
+
+    kernel = np.zeros((np.abs(y) + 1, np.abs(x) + 1))
+
+    if y < 0:
+        rr, cc = draw.line(0, 0, y, x)
+    else:
+        rr, cc = draw.line(-y, 0, 0, x)
+
+    kernel[rr, cc] = 1.0
+    return kernel
+
+
+def wahabFilter(image, orientations, w=8):
+    result = np.empty(image.shape)
+
+    height, width = image.shape
+    for y in range(0, height - w, w):
+        for x in range(0, width - w, w):
+            orientation = orientations[y+w//2, x+w//2]
+            kernel = wahabKernel(16, orientation)
+            result[y:y+w, x:x+w] = utils.convolve(image, kernel, (y, x), (w, w))
+            result[y:y+w, x:x+w] /= np.sum(kernel)
+
+    return result
+
+
+if __name__ == '__main__':
+    np.set_printoptions(
+            threshold=np.inf,
+            precision=4,
+            suppress=True)
+
+    print("Reading image")
+    image = ndimage.imread(sourceImage, mode="L").astype("float64")
+    if options.images > 0:
+        utils.showImage(image, "original", vmax=255.0)
+
+    print("Normalizing")
+    image = utils.normalize(image)
+    if options.images > 1:
+        utils.showImage(image, "normalized")
+
+    print("Finding mask")
+    mask = utils.findMask(image)
+    if options.images > 1:
+        utils.showImage(mask, "mask")
+
+    print("Applying local normalization")
+    image = np.where(mask == 1.0, utils.localNormalize(image), image)
+    if options.images > 1:
+        utils.showImage(image, "locally normalized")
+
+    print("Estimating orientations")
+    orientations = np.where(mask == 1.0, utils.estimateOrientations(image, interpolate=False), -1.0)
+    if options.images > 0:
+        utils.showOrientations(image, orientations, "orientations", 8)
+
+    print("Filtering")
+    image = np.where(mask == 1.0, wahabFilter(image, orientations), 1.0)
+    if options.images > 0:
+        utils.showImage(image, "filtered")
+
+    if options.binarize:
+        print("Binarizing")
+        image = np.where(mask == 1.0, utils.binarize(image), 1.0)
+        if options.images > 0:
+            utils.showImage(image, "binarized")
+
+    if options.images > 0:
+        plt.show()
+
+    if not options.dryrun:
+        misc.imsave(destinationImage, image)