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Line Suppression of the response

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Ankit Agrawal
2013-08-15 14:59:51 +05:30
parent 781bbaf5bc
commit c40ab969af
2 changed files with 21 additions and 5 deletions
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skimage/feature/__init__.py
+3 -1
View File
@@ -9,6 +9,7 @@ from .corner_cy import corner_moravec
from .template import match_template
from ._brief import brief, match_keypoints_brief
from .util import pairwise_hamming_distance
from .censure import censure_keypoints
__all__ = ['daisy',
'hog',
@@ -26,4 +27,5 @@ __all__ = ['daisy',
'match_template',
'brief',
'pairwise_hamming_distance',
'match_keypoints_brief']
'match_keypoints_brief',
'censure_keypoints']
skimage/feature/censure.py
+18 -4
View File
@@ -1,6 +1,7 @@
import numpy as np
from scipy.ndimage.filters import maximum_filter, minimum_filter
from skimage.transform import integral_image
from skimage.feature import _compute_auto_correlation
import time
def _get_filtered_image(image, n, mode='DoB'):
@@ -13,15 +14,23 @@ def _get_filtered_image(image, n, mode='DoB'):
start = time.time()
for i in range(2 * n, image.shape[0] - 2 * n):
for j in range(2 * n, image.shape[1] - 2 * n):
inner = integral_img[i + n, j + n] + integral_img[i - n, j - n] - integral_img[i + n, j - n] - integral_img[i - n, j + n]
outer = integral_img[i + 2 * n, j + 2 * n] + integral_img[i - 2 * n, j - 2 * n] - integral_img[i + 2 * n, j - 2 * n] - integral_img[i - 2 * n, j + 2 * n]
inner = integral_img[i + n, j + n] + integral_img[i - n - 1, j - n - 1] - integral_img[i + n, j - n - 1] - integral_img[i - n - 1, j + n]
outer = integral_img[i + 2 * n, j + 2 * n] + integral_img[i - 2 * n - 1, j - 2 * n - 1] - integral_img[i + 2 * n, j - 2 * n - 1] - integral_img[i - 2 * n - 1, j + 2 * n]
filtered_image[i, j] = outer_wt * outer - (inner_wt + outer_wt) * inner
print time.time() - start
return filtered_image
def censure_keypoints(image, mode='DoB', threshold=0.1):
# TODO : Decide mode for convolve function
def _suppress_line(response, sigma):
Axx, Axy, Ayy = _compute_auto_correlation(response, sigma)
detA = Axx * Ayy - Axy**2
traceA = Axx + Ayy
# ratio of principal curvatures
rpc = traceA / detA
rpc[rpc > 10] = 0
return rpc
def censure_keypoints(image, mode='DoB', threshold=0.03):
# TODO : Decide number of scales. Image-size dependent?
image = np.squeeze(image)
if image.ndim != 2:
@@ -44,4 +53,9 @@ def censure_keypoints(image, mode='DoB', threshold=0.1):
minimas[np.abs(minimas) < threshold] = 0
maximas[np.abs(maximas) < threshold] = 0
response = maximas + np.abs(minimas)
response[:, :, 1] = _suppress_line(response[:, :, 1], 1.33)
response[:, :, 2] = _suppress_line(response[:, :, 2], 1.33)
response[:, :, 3] = _suppress_line(response[:, :, 3], 1.33)
response[:, :, 4] = _suppress_line(response[:, :, 4], 1.33)
response[:, :, 5] = _suppress_line(response[:, :, 5], 1.33)
return response