First quick implementation of BRIEF Feature descriptor

skimage/feature/_brief.py

@@ -0,0 +1,63 @@
+# TODO Normal sampling from image patch of size 49 x 49
+# TODO Tests, example, doc
+
+import numpy as np
+from skimage.color import rgb2gray
+from scipy.ndimage.filters import gaussian_filter
+
+KERNEL_SIZE = (9, 9)
+PATCH_SIZE = (49, 49)
+
+
+def _remove_border_keypoints(image, keypoints, dist):
+
+	width = image.shape[0]
+	height = image.shape[1]
+	for i, j in keypoints:
+		if i > width - dist[0] or i < dist[0] or j < dist[1] or j > height - dist[0]:
+			keypoints.remove((i, j))
+	return keypoints
+
+
+def brief(image, keypoints, descriptor_size=32, mode='uniform'):
+
+	if descriptor_size not in (16, 32, 64):
+		raise ValueError('Descriptor size should be either 16, 32 or 64 bytes')
+
+	if np.squeeze(image).ndim == 3:
+		image = rgb2gray(image)
+
+	keypoints = _remove_border_keypoints(image, keypoints, (PATCH_SIZE[0] / 2, PATCH_SIZE[1] / 2))
+
+	descriptor = np.zeros((len(keypoints), descriptor_size * 8), dtype=int)
+
+	image = gaussian_filter(image, 2)
+
+	if mode == 'uniform':
+		np.random.seed(1)
+		first = np.random.randint(-24, 25, (descriptor_size * 8, 2))
+		np.random.seed(2)
+		second = np.random.randint(-24, 25, (descriptor_size * 8, 2))
+	else:
+		#TODO mode='normal'
+		pass
+
+	for i in range(len(keypoints)):
+		set_1 = first + keypoints[i]
+		set_2 = second + keypoints[i]
+
+		for j in range(descriptor_size * 8):
+			if image[set_1[j, 0]][set_1[j, 1]] < image[set_2[j, 0]][set_2[j, 0]]:
+				descriptor[i][j] = 1
+			else:
+				descriptor[i][j] = 0
+
+	return descriptor
+
+def hamming_distance(descriptor_1, descriptor_2):
+
+	distance = np.zeros((len(descriptor_1), len(descriptor_2)), dtype=int)
+	for i in range(len(descriptor_1)):
+		for j in range(len(descriptor_2)):
+			distance[i, j] = sum(np.bitwise_xor(descriptor_1[i][:], descriptor_2[j][:]))
+	return distance / descriptor_1.shape[1]