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Reverting back to changes made by Johannes
This commit is contained in:
Ankit Agrawal
+30
-30
@@ -4,7 +4,7 @@ from scipy.ndimage.filters import maximum_filter, minimum_filter, convolve
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from skimage.transform import integral_image
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from skimage.feature.corner import _compute_auto_correlation
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from skimage.util import img_as_float
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from skimage.morphology import convex_hull_image, octagon, star
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from skimage.morphology import octagon, star
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from skimage.feature.util import _mask_border_keypoints
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from skimage.feature.censure_cy import _censure_dob_loop
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@@ -20,18 +20,17 @@ STAR_FILTER_SHAPE = [(1, 0), (3, 1), (4, 2), (5, 3), (7, 4), (8, 5),
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(9, 6), (11, 8), (13, 10), (14, 11), (15, 12), (16, 14)]
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def _get_filtered_image(image, min_scale, max_scale, mode):
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def _filter_image(image, min_scale, max_scale, mode):
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scales = np.zeros((image.shape[0], image.shape[1],
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max_scale - min_scale + 1), dtype=np.double)
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response = np.zeros((image.shape[0], image.shape[1],
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max_scale - min_scale + 1), dtype=np.double)
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if mode == 'dob':
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# make scales[:, :, i] contiguous memory block
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item_size = scales.itemsize
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scales.strides = (item_size * scales.shape[0],
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item_size,
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item_size * scales.shape[0] * scales.shape[1])
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# make response[:, :, i] contiguous memory block
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item_size = response.itemsize
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response.strides = (item_size * response.shape[0], item_size,
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item_size * response.shape[0] * response.shape[1])
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integral_img = integral_image(image)
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@@ -39,12 +38,12 @@ def _get_filtered_image(image, min_scale, max_scale, mode):
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n = min_scale + i
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# Constant multipliers for the outer region and the inner region
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# of the bilevel filters with the constraint of keeping the
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# of the bi-level filters with the constraint of keeping the
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# DC bias 0.
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inner_weight = (1.0 / (2 * n + 1)**2)
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outer_weight = (1.0 / (12 * n**2 + 4 * n))
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_censure_dob_loop(n, integral_img, scales[:, :, i],
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_censure_dob_loop(n, integral_img, response[:, :, i],
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inner_weight, outer_weight)
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# NOTE : For the Octagon shaped filter, we implemented and evaluated the
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@@ -58,17 +57,17 @@ def _get_filtered_image(image, min_scale, max_scale, mode):
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for i in range(max_scale - min_scale + 1):
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mo, no = OCTAGON_OUTER_SHAPE[min_scale + i - 1]
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mi, ni = OCTAGON_INNER_SHAPE[min_scale + i - 1]
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scales[:, :, i] = convolve(image,
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_octagon_filter_kernel(mo, no, mi, ni))
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response[:, :, i] = convolve(image,
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_octagon_filter_kernel(mo, no, mi, ni))
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elif mode == 'star':
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for i in range(max_scale - min_scale + 1):
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m = STAR_SHAPE[STAR_FILTER_SHAPE[min_scale + i - 1][0]]
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n = STAR_SHAPE[STAR_FILTER_SHAPE[min_scale + i - 1][1]]
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scales[:, :, i] = convolve(image,
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_star_filter_kernel(m, n))
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response[:, :, i] = convolve(image, _star_filter_kernel(m, n))
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return scales
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return response
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def _octagon_filter_kernel(mo, no, mi, ni):
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@@ -106,24 +105,24 @@ def _suppress_lines(feature_mask, image, sigma, line_threshold):
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def keypoints_censure(image, min_scale=1, max_scale=7, mode='DoB',
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non_max_threshold=0.15, line_threshold=10):
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"""
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Extracts Censure keypoints along with the corresponding scale using
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either Difference of Boxes, Octagon or STAR bilevel filter.
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Extracts CenSurE keypoints along with the corresponding scale using
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either Difference of Boxes, Octagon or STAR bi-level filter.
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Parameters
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----------
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image : 2D ndarray
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Input image.
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min_scale : positive integer
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min_scale : int
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Minimum scale to extract keypoints from.
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max_scale : positive integer
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max_scale : int
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Maximum scale to extract keypoints from. The keypoints will be
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extracted from all the scales except the first and the last i.e.
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from the scales in the range [min_scale + 1, max_scale - 1].
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mode : ('DoB', 'Octagon', 'STAR')
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Type of bilevel filter used to get the scales of the input image.
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mode : {'DoB', 'Octagon', 'STAR'}
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Type of bi-level filter used to get the scales of the input image.
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Possible values are 'DoB', 'Octagon' and 'STAR'. The three modes
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represent the shape of the bilevel filters i.e. box(square), octagon
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and star respectively. For instance, a bilevel octagon filter consists
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represent the shape of the bi-level filters i.e. box(square), octagon
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and star respectively. For instance, a bi-level octagon filter consists
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of a smaller inner octagon and a larger outer octagon with the filter
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weights being uniformly negative in both the inner octagon while
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uniformly positive in the difference region. Use STAR and Octagon for
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@@ -138,7 +137,7 @@ def keypoints_censure(image, min_scale=1, max_scale=7, mode='DoB',
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Returns
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-------
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keypoints : (N, 2) array
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Location of the extracted keypoints in the (row, col) format.
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Location of the extracted keypoints in the ``(row, col)`` format.
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scales : (N, 1) array
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The corresponding scale of the N extracted keypoints.
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@@ -155,8 +154,9 @@ def keypoints_censure(image, min_scale=1, max_scale=7, mode='DoB',
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http://www.jamris.org/01_2013/saveas.php?QUEST=JAMRIS_No01_2013_P_11-20.pdf
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"""
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# (1) First we generate the required scales on the input grayscale image
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# using a bilevel filter and stack them up in `filter_response`.
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# using a bi-level filter and stack them up in `filter_response`.
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# (2) We then perform Non-Maximal suppression in 3 x 3 x 3 window on the
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# filter_response to suppress points that are neither minima or maxima in
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# 3 x 3 x 3 neighbourhood. We obtain a boolean ndarray `feature_mask`
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@@ -175,15 +175,15 @@ def keypoints_censure(image, min_scale=1, max_scale=7, mode='DoB',
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if mode not in ('dob', 'octagon', 'star'):
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raise ValueError('Mode must be one of "DoB", "Octagon", "STAR".')
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if max_scale - min_scale < 2:
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raise ValueError('The number of scales should be greater than or'
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'equal to 3.')
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if min_scale < 1 or max_scale < 1 or max_scale - min_scale < 2:
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raise ValueError('The scales must be >= 1 and the number of scales '
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'should be >= 3.')
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image = img_as_float(image)
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image = np.ascontiguousarray(image)
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# Generating all the scales
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filter_response = _get_filtered_image(image, min_scale, max_scale, mode)
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filter_response = _filter_image(image, min_scale, max_scale, mode)
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# Suppressing points that are neither minima or maxima in their 3 x 3 x 3
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# neighbourhood to zero
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