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Fix censure example and fix some minor issues

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This commit is contained in:
Johannes Schönberger
2013-08-15 15:15:41 +05:30
committed by Ankit Agrawal
parent 3ee183fdf8
commit 7c4152b62b
2 changed files with 65 additions and 56 deletions
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doc/examples/plot_censure_keypoints.py
+36 -27
View File
@@ -1,12 +1,14 @@
"""
===========================
Censure Keypoints Detection
===========================
=========================
CenSurE Feature Detection
=========================
In this example, we detect and plot the Censure Keypoints at various scales
using Difference of Boxes, Octagon and Star shaped bi-level filters.
In this example we detect and plot the CenSurE (Center Surround Extrema)
features at various scales using Difference of Boxes, Octagon and Star shaped
bi-level filters.
"""
from skimage.feature import keypoints_censure
from skimage.data import lena
from skimage.color import rgb2gray
@@ -15,32 +17,39 @@ import matplotlib.pyplot as plt
# Initializing the parameters for Censure keypoints
img = lena()
gray_img = rgb2gray(img)
min_scale = 1
max_scale = 7
nms_threshold = 0.15
rpc_threshold = 10
min_scale = 2
max_scale = 6
non_max_threshold = 0.15
line_threshold = 10
f, ax = plt.subplots(nrows=(max_scale - min_scale - 1), ncols=3,
figsize=(6, 6))
plt.subplots_adjust(wspace=0.02, hspace=0.02, top=0.94,
bottom=0.02, left=0.06, right=0.98)
# Detecting Censure keypoints for the following filters
for mode in ['dob', 'octagon', 'star']:
for col, mode in enumerate(['dob', 'octagon', 'star']):
kp_censure, scale = keypoints_censure(gray_img, min_scale, max_scale,
mode, nms_threshold, rpc_threshold)
f, axarr = plt.subplots((max_scale - min_scale + 1) // 3, 3)
ax[0, col].set_title(mode.upper(), fontsize=12)
keypoints, scales = keypoints_censure(gray_img, min_scale, max_scale,
mode, non_max_threshold,
line_threshold)
# Plotting Censure features at all the scales
for i in range(max_scale - min_scale - 1):
keypoints = kp_censure[scale == i + min_scale + 1]
num = len(keypoints)
x = keypoints[:, 1]
y = keypoints[:, 0]
s = 5 * 2**(i + min_scale + 1)
axarr[i // 3, i - (i // 3) * 3].imshow(img)
axarr[i // 3, i - (i // 3) * 3].scatter(x, y, s, facecolors='none',
edgecolors='g')
axarr[i // 3, i - (i // 3) * 3].set_title(' %s %s-Censure features at '
'scale %d' % (num, mode, i +
min_scale + 1))
for row, scale in enumerate(range(min_scale + 1, max_scale)):
keypoints_i = keypoints[scales == scale]
num = len(keypoints_i)
x = keypoints_i[:, 1]
y = keypoints_i[:, 0]
s = 0.5 * 2 ** (scale + min_scale + 1)
ax[row, col].imshow(img)
ax[row, col].scatter(x, y, s, facecolors='none', edgecolors='b')
ax[row, col].set_xticks([])
ax[row, col].set_yticks([])
ax[row, col].axis((0, img.shape[1], img.shape[0], 0))
if col == 0:
ax[row, col].set_ylabel('Scale %d' % scale, fontsize=12)
plt.suptitle('NMS threshold = %f, RPC threshold = %d'
% (nms_threshold, rpc_threshold))
plt.show()
skimage/feature/censure.py
+29 -29
View File
@@ -19,18 +19,17 @@ STAR_FILTER_SHAPE = [(1, 0), (3, 1), (4, 2), (5, 3), (7, 4), (8, 5),
(9, 6), (11, 8), (13, 10), (14, 11), (15, 12), (16, 14)]
def _get_filtered_image(image, min_scale, max_scale, mode):
def _filter_image(image, min_scale, max_scale, mode):
scales = np.zeros((image.shape[0], image.shape[1],
max_scale - min_scale + 1), dtype=np.double)
response = np.zeros((image.shape[0], image.shape[1],
max_scale - min_scale + 1), dtype=np.double)
if mode == 'dob':
# make scales[:, :, i] contiguous memory block
item_size = scales.itemsize
scales.strides = (item_size * scales.shape[0],
item_size,
item_size * scales.shape[0] * scales.shape[1])
# make response[:, :, i] contiguous memory block
item_size = response.itemsize
response.strides = (item_size * response.shape[0], item_size,
item_size * response.shape[0] * response.shape[1])
integral_img = integral_image(image)
@@ -38,12 +37,12 @@ def _get_filtered_image(image, min_scale, max_scale, mode):
n = min_scale + i
# Constant multipliers for the outer region and the inner region
# of the bilevel filters with the constraint of keeping the
# of the bi-level filters with the constraint of keeping the
# DC bias 0.
inner_weight = (1.0 / (2 * n + 1)**2)
outer_weight = (1.0 / (12 * n**2 + 4 * n))
_censure_dob_loop(n, integral_img, scales[:, :, i],
_censure_dob_loop(n, integral_img, response[:, :, i],
inner_weight, outer_weight)
# NOTE : For the Octagon shaped filter, we implemented and evaluated the
@@ -57,17 +56,17 @@ def _get_filtered_image(image, min_scale, max_scale, mode):
for i in range(max_scale - min_scale + 1):
mo, no = OCTAGON_OUTER_SHAPE[min_scale + i - 1]
mi, ni = OCTAGON_INNER_SHAPE[min_scale + i - 1]
scales[:, :, i] = convolve(image,
_octagon_filter_kernel(mo, no, mi, ni))
response[:, :, i] = convolve(image,
_octagon_filter_kernel(mo, no, mi, ni))
elif mode == 'star':
for i in range(max_scale - min_scale + 1):
m = STAR_SHAPE[STAR_FILTER_SHAPE[min_scale + i - 1][0]]
n = STAR_SHAPE[STAR_FILTER_SHAPE[min_scale + i - 1][1]]
scales[:, :, i] = convolve(image,
_star_filter_kernel(m, n))
response[:, :, i] = convolve(image, _star_filter_kernel(m, n))
return scales
return response
# TODO : Import from selem after getting #669 merged.
@@ -138,24 +137,24 @@ def _suppress_lines(feature_mask, image, sigma, line_threshold):
def keypoints_censure(image, min_scale=1, max_scale=7, mode='DoB',
non_max_threshold=0.15, line_threshold=10):
"""
Extracts Censure keypoints along with the corresponding scale using
either Difference of Boxes, Octagon or STAR bilevel filter.
Extracts CenSurE keypoints along with the corresponding scale using
either Difference of Boxes, Octagon or STAR bi-level filter.
Parameters
----------
image : 2D ndarray
Input image.
min_scale : positive integer
min_scale : int
Minimum scale to extract keypoints from.
max_scale : positive integer
max_scale : int
Maximum scale to extract keypoints from. The keypoints will be
extracted from all the scales except the first and the last i.e.
from the scales in the range [min_scale + 1, max_scale - 1].
mode : ('DoB', 'Octagon', 'STAR')
Type of bilevel filter used to get the scales of the input image.
mode : {'DoB', 'Octagon', 'STAR'}
Type of bi-level filter used to get the scales of the input image.
Possible values are 'DoB', 'Octagon' and 'STAR'. The three modes
represent the shape of the bilevel filters i.e. box(square), octagon
and star respectively. For instance, a bilevel octagon filter consists
represent the shape of the bi-level filters i.e. box(square), octagon
and star respectively. For instance, a bi-level octagon filter consists
of a smaller inner octagon and a larger outer octagon with the filter
weights being uniformly negative in both the inner octagon while
uniformly positive in the difference region. Use STAR and Octagon for
@@ -170,7 +169,7 @@ def keypoints_censure(image, min_scale=1, max_scale=7, mode='DoB',
Returns
-------
keypoints : (N, 2) array
Location of the extracted keypoints in the (row, col) format.
Location of the extracted keypoints in the ``(row, col)`` format.
scales : (N, 1) array
The corresponding scale of the N extracted keypoints.
@@ -187,8 +186,9 @@ def keypoints_censure(image, min_scale=1, max_scale=7, mode='DoB',
http://www.jamris.org/01_2013/saveas.php?QUEST=JAMRIS_No01_2013_P_11-20.pdf
"""
# (1) First we generate the required scales on the input grayscale image
# using a bilevel filter and stack them up in `filter_response`.
# using a bi-level filter and stack them up in `filter_response`.
# (2) We then perform Non-Maximal suppression in 3 x 3 x 3 window on the
# filter_response to suppress points that are neither minima or maxima in
# 3 x 3 x 3 neighbourhood. We obtain a boolean ndarray `feature_mask`
@@ -207,15 +207,15 @@ def keypoints_censure(image, min_scale=1, max_scale=7, mode='DoB',
if mode not in ('dob', 'octagon', 'star'):
raise ValueError('Mode must be one of "DoB", "Octagon", "STAR".')
if max_scale - min_scale < 2:
raise ValueError('The number of scales should be greater than or'
'equal to 3.')
if min_scale < 1 or max_scale < 1 or max_scale - min_scale < 2:
raise ValueError('The scales must be >= 1 and the number of scales '
'should be >= 3.')
image = img_as_float(image)
image = np.ascontiguousarray(image)
# Generating all the scales
filter_response = _get_filtered_image(image, min_scale, max_scale, mode)
filter_response = _filter_image(image, min_scale, max_scale, mode)
# Suppressing points that are neither minima or maxima in their 3 x 3 x 3
# neighbourhood to zero