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Changing no_of_scales to n_scales

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Ankit Agrawal
2013-07-31 12:59:35 +05:30
parent 6c1424732b
commit ec7ea0007c
1 changed files with 10 additions and 10 deletions
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skimage/feature/censure.py
+10 -10
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@@ -8,11 +8,11 @@ from ..util import img_as_float
from .censure_cy import _censure_dob_loop, _slanted_integral_image, _censure_octagon_loop
def _get_filtered_image(image, no_of_scales, mode):
def _get_filtered_image(image, n_scales, mode):
# TODO : Implement the STAR mode
if mode == 'DoB':
scales = np.zeros((image.shape[0], image.shape[1], no_of_scales))
for i in range(no_of_scales):
scales = np.zeros((image.shape[0], image.shape[1], n_scales))
for i in range(n_scales):
n = i + 1
inner_wt = (1.0 / (2 * n + 1)**2)
outer_wt = (1.0 / (12 * n**2 + 4 * n))
@@ -25,7 +25,7 @@ def _get_filtered_image(image, no_of_scales, mode):
# TODO : Decide the shapes of Octagon filters for scales > 7
outer_shape = [(5, 2), (5, 3), (7, 3), (9, 4), (9, 7), (13, 7), (15, 10)]
inner_shape = [(3, 0), (3, 1), (3, 2), (5, 2), (5, 3), (5, 4), (5, 5)]
scales = np.zeros((image.shape[0], image.shape[1], no_of_scales))
scales = np.zeros((image.shape[0], image.shape[1], n_scales))
integral_img = integral_image(image)
integral_img1 = _slanted_integral_image_modes(image, 1)
integral_img2 = _slanted_integral_image_modes(image, 2)
@@ -34,7 +34,7 @@ def _get_filtered_image(image, no_of_scales, mode):
integral_img3 = np.ascontiguousarray(integral_img3)
integral_img4 = _slanted_integral_image_modes(image, 4)
integral_img4 = np.ascontiguousarray(integral_img4)
for k in range(no_of_scales):
for k in range(n_scales):
n = k + 1
filtered_image = np.zeros(image.shape)
mo = outer_shape[n - 1][0]
@@ -146,7 +146,7 @@ def _suppress_line(response, sigma, rpc_threshold):
return response
def censure_keypoints(image, no_of_scales=7, mode='DoB', threshold=0.03, rpc_threshold=10):
def censure_keypoints(image, n_scales=7, mode='DoB', threshold=0.03, rpc_threshold=10):
"""
Extracts Censure keypoints along with the corresponding scale using
either Difference of Boxes, Octagon or STAR bilevel filter.
@@ -156,7 +156,7 @@ def censure_keypoints(image, no_of_scales=7, mode='DoB', threshold=0.03, rpc_thr
image : 2D ndarray
Input image.
no_of_scales : positive integer
n_scales : positive integer
Number of scales to extract keypoints from. The keypoints will be
extracted from all the scales except the first and the last.
@@ -199,7 +199,7 @@ def censure_keypoints(image, no_of_scales=7, mode='DoB', threshold=0.03, rpc_thr
image = np.ascontiguousarray(image)
# Generating all the scales
scales = _get_filtered_image(image, no_of_scales, mode)
scales = _get_filtered_image(image, n_scales, mode)
# Suppressing points that are neither minima or maxima in their 3 x 3 x 3
# neighbourhood to zero
@@ -211,12 +211,12 @@ def censure_keypoints(image, no_of_scales=7, mode='DoB', threshold=0.03, rpc_thr
maximas[np.abs(maximas) < threshold] = 0
response = maximas + minimas
for i in range(1, no_of_scales - 1):
for i in range(1, n_scales - 1):
# sigma = (window_size - 1) / 6.0
# window_size = 7 + 2 * i
# Hence sigma = 1 + i / 3.0
response[:, :, i] = _suppress_line(response[:, :, i], (1 + i / 3.0), rpc_threshold)
# Returning keypoints with its scale
keypoints = np.transpose(np.nonzero(response[:, :, 1:no_of_scales - 1])) + [0, 0, 2]
keypoints = np.transpose(np.nonzero(response[:, :, 1:n_scales - 1])) + [0, 0, 2]
return keypoints