mirror of
https://github.com/wassname/scikit-image.git
synced 2026-07-12 23:29:44 +08:00
Ankit Agrawal
212 lines
7.5 KiB
Python
212 lines
7.5 KiB
Python
import numpy as np
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from scipy.ndimage.filters import maximum_filter, minimum_filter, convolve
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from ..transform import integral_image
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from ..feature.corner import _compute_auto_correlation
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from ..util import img_as_float
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from ..morphology import convex_hull_image
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from .censure_cy import _censure_dob_loop
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def _get_filtered_image(image, n_scales, mode):
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# TODO : Implement the STAR mode
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scales = np.zeros((image.shape[0], image.shape[1], n_scales),
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dtype=np.double)
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if mode == 'DoB':
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for i in range(n_scales):
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n = i + 1
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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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# 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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integral_img = integral_image(image)
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filtered_image = np.zeros(image.shape)
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_censure_dob_loop(image, n, integral_img, filtered_image,
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inner_weight, outer_weight)
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scales[:, :, i] = filtered_image
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# NOTE : For the Octagon shaped filter, we implemented and evaluated the
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# slanted integral image based image filtering but the performance was
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# more or less equal to image filtering using
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# scipy.ndimage.filters.convolve(). Hence we have decided to use the
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# later for a much cleaner implementation.
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elif mode == 'Octagon':
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# TODO : Decide the shapes of Octagon filters for scales > 7
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outer_shape = [(5, 2), (5, 3), (7, 3), (9, 4), (9, 7), (13, 7),
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(15, 10)]
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inner_shape = [(3, 0), (3, 1), (3, 2), (5, 2), (5, 3), (5, 4), (5, 5)]
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#
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for i in range(n_scales):
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scales[:, :, i] = convolve(image,
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_octagon_filter(outer_shape[i][0],
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outer_shape[i][1], inner_shape[i][0],
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inner_shape[i][1]))
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else:
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shape = [1, 2, 3, 4, 6, 8, 11, 12, 16, 22, 23, 32, 45, 46, 64, 90,
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128]
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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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for i in range(n_scales):
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scales[:, :, i] = convolve(image,
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_star_filter(shape[filter_shape[i][0]],
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shape[filter_shape[i][1]]))
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return scales
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# TODO : Import from selem after getting #669 merged.
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def _oct(m, n):
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f = np.zeros((m + 2*n, m + 2*n))
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f[0, n] = 1
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f[n, 0] = 1
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f[0, m + n -1] = 1
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f[m + n - 1, 0] = 1
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f[-1, n] = 1
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f[n, -1] = 1
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f[-1, m + n - 1] = 1
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f[m + n - 1, -1] = 1
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return convex_hull_image(f).astype(int)
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def _octagon_filter(mo, no, mi, ni):
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outer = (mo + 2 * no)**2 - 2 * no * (no + 1)
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inner = (mi + 2 * ni)**2 - 2 * ni * (ni + 1)
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outer_weight = 1.0 / (outer - inner)
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inner_weight = 1.0 / inner
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c = ((mo + 2 * no) - (mi + 2 * ni)) / 2
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outer_oct = _oct(mo, no)
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inner_oct = np.zeros((mo + 2 * no, mo + 2 * no))
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inner_oct[c: -c, c: -c] = _oct(mi, ni)
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bfilter = (outer_weight * outer_oct -
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(outer_weight + inner_weight) * inner_oct)
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return bfilter
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def _star(a):
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if a == 1:
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bfilter = np.zeros((3, 3))
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bfilter[:] = 1
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return bfilter
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m = 2 * a + 1
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n = a / 2
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selem_square = np.zeros((m + 2 * n, m + 2 * n), dtype=np.uint8)
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selem_square[n: m + n, n: m + n] = 1
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selem_triangle = np.zeros((m + 2 * n, m + 2 * n), dtype=np.uint8)
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selem_triangle[(m + 2 * n - 1) / 2, 0] = 1
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selem_triangle[(m + 1) / 2, n - 1] = 1
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selem_triangle[(m + 4 * n - 3) / 2, n - 1] = 1
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selem_triangle = convex_hull_image(selem_triangle).astype(int)
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selem_triangle += (selem_triangle[:, ::-1] + selem_triangle.T +
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selem_triangle.T[::-1, :])
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return selem_square + selem_triangle
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def _star_filter(m, n):
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c = m + m / 2 - n - n / 2
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outer_star = _star(m)
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inner_star = np.zeros((outer_star.shape))
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inner_star[c: -c, c: -c] = _star(n)
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outer_weight = 1.0 / (np.sum(outer_star - inner_star))
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inner_weight = 1.0 / np.sum(inner_star)
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bfilter = (outer_weight * outer_star -
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(outer_weight + inner_weight) * inner_star)
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return bfilter
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def _suppress_line(response, sigma, rpc_threshold):
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Axx, Axy, Ayy = _compute_auto_correlation(response, sigma)
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detA = Axx * Ayy - Axy**2
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traceA = Axx + Ayy
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# ratio of principal curvatures
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rpc = traceA**2 / (detA + 0.001)
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response[rpc > rpc_threshold] = 0
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return response
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def censure_keypoints(image, n_scales=7, mode='DoB', nms_threshold=0.03,
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rpc_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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Parameters
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----------
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image : 2D ndarray
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Input image.
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n_scales : positive integer
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Number of scales to extract keypoints from. The keypoints will be
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extracted from all the scales except the first and the last.
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mode : ('DoB', 'Octagon', 'STAR')
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Type of bilevel filter used to get the scales of input image. Possible
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values are 'DoB', 'Octagon' and 'STAR'.
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nms_threshold : float
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Threshold value used to suppress maximas and minimas with a weak
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magnitude response obtained after Non-Maximal Suppression.
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rpc_threshold : float
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Threshold for rejecting interest points which have ratio of principal
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curvatures greater than this value.
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Returns
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-------
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keypoints : (N, 3) array
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Location of extracted keypoints along with the corresponding scale.
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References
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----------
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.. [1] Motilal Agrawal, Kurt Konolige and Morten Rufus Blas
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"CenSurE: Center Surround Extremas for Realtime Feature
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Detection and Matching",
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http://link.springer.com/content/pdf/10.1007%2F978-3-540-88693-8_8.pdf
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.. [2] Adam Schmidt, Marek Kraft, Michal Fularz and Zuzanna Domagala
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"Comparative Assessment of Point Feature Detectors and
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Descriptors in the Context of Robot Navigation"
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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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image = np.squeeze(image)
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if image.ndim != 2:
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raise ValueError("Only 2-D gray-scale images supported.")
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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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scales = _get_filtered_image(image, n_scales, 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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minimas = (minimum_filter(scales, (3, 3, 3)) == scales) * scales
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maximas = (maximum_filter(scales, (3, 3, 3)) == scales) * scales
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# Suppressing minimas and maximas weaker than nms_threshold
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minimas[np.abs(minimas) < nms_threshold] = 0
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maximas[np.abs(maximas) < nms_threshold] = 0
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response = maximas + minimas
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for i in range(1, n_scales - 1):
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# sigma = (window_size - 1) / 6.0
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# window_size = 7 + 2 * i
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# Hence sigma = 1 + i / 3.0
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response[:, :, i] = _suppress_line(response[:, :, i], (1 + i / 3.0),
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rpc_threshold)
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# Returning keypoints with its scale
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keypoints = (np.transpose(np.nonzero(response[:, :, 1:n_scales - 1]))
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+ [0, 0, 2])
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return keypoints
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