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181 lines
6.4 KiB
Python
181 lines
6.4 KiB
Python
import numpy as np
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from scipy.ndimage.filters import maximum_filter, minimum_filter
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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 .censure_cy import _censure_dob_loop, _slanted_integral_image, _censure_octagon_loop
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def _get_filtered_image(image, no_of_scales, mode):
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# TODO : Implement the STAR mode
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if mode == 'DoB':
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scales = np.zeros((image.shape[0], image.shape[1], no_of_scales))
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for i in range(no_of_scales):
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n = i + 1
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inner_wt = (1.0 / (2 * n + 1)**2)
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outer_wt = (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, inner_wt, outer_wt)
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scales[:, :, i] = filtered_image
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return scales
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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), (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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scales = np.zeros((image.shape[0], image.shape[1], no_of_scales))
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integral_img = integral_image(image)
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integral_img1 = _slanted_integral_image_modes(image, 1)
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print '8'
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integral_img2 = _slanted_integral_image_modes(image, 2)
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print '9'
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integral_img3 = _slanted_integral_image_modes(image, 3)
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print '10'
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integral_img4 = _slanted_integral_image_modes(image, 4)
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print '11'
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for k in range(no_of_scales):
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n = k + 1
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filtered_image = np.zeros(image.shape)
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mo = outer_shape[n - 1][0]
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no = outer_shape[n - 1][1]
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mi = inner_shape[n - 1][0]
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ni = inner_shape[n - 1][1]
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outer_pixels = (mo + 2 * no)**2 - 2 * no * (no + 1)
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inner_pixels = (mi + 2 * ni)**2 - 2 * ni * (ni + 1)
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outer_wt = 1.0 / (outer_pixels - inner_pixels)
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inner_wt = 1.0 / inner_pixels
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_censure_octagon_loop(image, integral_img, integral_img1, integral_img2, integral_img3, integral_img4, filtered_image, outer_wt, inner_wt, mo, no, mi, ni)
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scales[:, :, k] = filtered_image
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return scales
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def _slanted_integral_image_modes(img, mode=1):
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if mode == 1:
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image = np.copy(img)
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mode1 = np.zeros((image.shape[0] + 1, image.shape[1]))
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_slanted_integral_image(image, mode1)
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print '7'
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return mode1[1:, :]
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elif mode == 2:
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image = np.copy(img)
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image = np.fliplr(image)
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image = np.flipud(image)
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mode2 = np.zeros((image.shape[0] + 1, image.shape[1]))
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_slanted_integral_image(image, mode2)
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print '7'
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mode2 = mode2[1:, :]
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mode2 = np.fliplr(mode2)
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mode2 = np.flipud(mode2)
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return mode2
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elif mode == 3:
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image = np.copy(img)
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image = np.flipud(image)
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image = image.T
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mode3 = np.zeros((image.shape[0] + 1, image.shape[1]))
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_slanted_integral_image(image, mode3)
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print '7'
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mode3 = mode3[1:, :]
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mode3 = np.flipud(mode3.T)
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return mode3
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else:
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image = np.copy(img)
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image = np.fliplr(image)
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image = image.T
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mode4 = np.zeros((image.shape[0] + 1, image.shape[1]))
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_slanted_integral_image(image, mode4)
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print '7'
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mode4 = mode4[1:, :]
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mode4 = np.fliplr(mode4.T)
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return mode4
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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, no_of_scales=7, mode='DoB', threshold=0.03, 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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no_of_scales : positive integer
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Number of scales to extract keypoints from. Default is 7.
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mode : 'DoB'
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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'. Default is 'DoB'.
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threshold :
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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. Default
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is 0.03.
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rpc_threshold :
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Threshold for rejecting interest points which have ratio of principal
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curvatures greater than this value. Default is 10.
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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 = np.zeros((image.shape[0], image.shape[1], no_of_scales))
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scales = _get_filtered_image(image, no_of_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).astype(int) * scales
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maximas = (maximum_filter(scales, (3, 3, 3)) == scales).astype(int) * scales
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# Suppressing minimas and maximas weaker than threshold
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minimas[np.abs(minimas) < threshold] = 0
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maximas[np.abs(maximas) < threshold] = 0
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response = maximas + minimas
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for i in range(1, no_of_scales - 1):
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response[:, :, i] = _suppress_line(response[:, :, i], (1 + i / 3.0), rpc_threshold)
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# Returning keypoints with its scale
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keypoints = np.transpose(np.nonzero(response[:, :, 1:no_of_scales])) + [0, 0, 1]
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return keypoints
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