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Adding custom sampling seed, Normal sampling mode

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Ankit Agrawal
2013-06-29 12:09:56 +08:00
parent 566006bdaf
commit e931739502
1 changed files with 25 additions and 17 deletions
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skimage/feature/_brief.py
+25 -17
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@@ -5,45 +5,53 @@ 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:
keypoints_list = keypoints.tolist()
for i, j in keypoints_list:
if i > width - dist[0] or i < dist[0] or j < dist[1] or j > height - dist[0]:
keypoints.remove((i, j))
keypoints.remove([i, j])
keypoints = np.asarray(keypoints_list)
return keypoints
def brief(image, keypoints, descriptor_size=32, mode='uniform'):
def brief(image, keypoints, descriptor_size=256, mode='normal', patch_size=49, sample_seed=1):
if np.squeeze(image).ndim == 3:
image = rgb2gray(image)
keypoints = _remove_border_keypoints(image, keypoints, (PATCH_SIZE[0] / 2, PATCH_SIZE[1] / 2))
keypoints = np.round(keypoints)
descriptor = np.zeros((len(keypoints), descriptor_size * 8), dtype=int)
keypoints = _remove_border_keypoints(image, keypoints, (patch_size / 2, patch_size / 2))
descriptor = np.zeros((len(keypoints), descriptor_size), dtype=int)
# Gaussian Low pass filtering with variance 2 to alleviate noise sensitivity
image = gaussian_filter(image, 2)
if mode == 'uniform':
np.random.seed(1)
first = np.random.randint(-PATCH_SIZE / 2, (PATCH_SIZE / 2) + 1, (descriptor_size * 8, 2))
np.random.seed(2)
second = np.random.randint(-PATCH_SIZE / 2, (PATCH_SIZE / 2) + 1, (descriptor_size * 8, 2))
# Sampling pairs of decision pixels in patch_size x patch_size window
if mode == 'normal':
np.random.seed(sample_seed)
samples = np.round((patch_size / 5) * np.random.randn(descriptor_size * 8))
samples = samples[samples < (patch_size / 2)]
samples = samples[samples > - (patch_size - 1) / 2]
first = (samples[: descriptor_size * 2]).reshape(descriptor_size, 2)
second = (samples[descriptor_size * 2: descriptor_size * 4]).reshape(descriptor_size, 2)
else:
#TODO mode='normal'
pass
np.random.seed(sample_seed)
samples = np.random.randint(-patch_size / 2, (patch_size / 2) + 1, (descriptor_size * 2, 2))
first, second = np.split(samples, 2)
for i in range(len(keypoints)):
set_1 = first + keypoints[i]
set_2 = second + keypoints[i]
for j in range(descriptor_size * 8):
for j in range(descriptor_size):
if image[set_1[j, 0]][set_1[j, 1]] < image[set_2[j, 0]][set_2[j, 0]]:
descriptor[i][j] = 1
else: