forked from 170010011/fr
223 lines
9.7 KiB
Python
223 lines
9.7 KiB
Python
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import numpy as np
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from numpy import sqrt, pi, arctan2, cos, sin, exp
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from scipy.ndimage import gaussian_filter
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from .. import img_as_float, draw
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from ..color import gray2rgb
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from .._shared.utils import check_nD
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def daisy(image, step=4, radius=15, rings=3, histograms=8, orientations=8,
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normalization='l1', sigmas=None, ring_radii=None, visualize=False):
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'''Extract DAISY feature descriptors densely for the given image.
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DAISY is a feature descriptor similar to SIFT formulated in a way that
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allows for fast dense extraction. Typically, this is practical for
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bag-of-features image representations.
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The implementation follows Tola et al. [1]_ but deviate on the following
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points:
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* Histogram bin contribution are smoothed with a circular Gaussian
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window over the tonal range (the angular range).
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* The sigma values of the spatial Gaussian smoothing in this code do not
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match the sigma values in the original code by Tola et al. [2]_. In
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their code, spatial smoothing is applied to both the input image and
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the center histogram. However, this smoothing is not documented in [1]_
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and, therefore, it is omitted.
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Parameters
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----------
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image : (M, N) array
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Input image (grayscale).
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step : int, optional
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Distance between descriptor sampling points.
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radius : int, optional
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Radius (in pixels) of the outermost ring.
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rings : int, optional
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Number of rings.
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histograms : int, optional
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Number of histograms sampled per ring.
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orientations : int, optional
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Number of orientations (bins) per histogram.
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normalization : [ 'l1' | 'l2' | 'daisy' | 'off' ], optional
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How to normalize the descriptors
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* 'l1': L1-normalization of each descriptor.
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* 'l2': L2-normalization of each descriptor.
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* 'daisy': L2-normalization of individual histograms.
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* 'off': Disable normalization.
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sigmas : 1D array of float, optional
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Standard deviation of spatial Gaussian smoothing for the center
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histogram and for each ring of histograms. The array of sigmas should
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be sorted from the center and out. I.e. the first sigma value defines
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the spatial smoothing of the center histogram and the last sigma value
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defines the spatial smoothing of the outermost ring. Specifying sigmas
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overrides the following parameter.
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``rings = len(sigmas) - 1``
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ring_radii : 1D array of int, optional
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Radius (in pixels) for each ring. Specifying ring_radii overrides the
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following two parameters.
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``rings = len(ring_radii)``
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``radius = ring_radii[-1]``
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If both sigmas and ring_radii are given, they must satisfy the
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following predicate since no radius is needed for the center
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histogram.
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``len(ring_radii) == len(sigmas) + 1``
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visualize : bool, optional
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Generate a visualization of the DAISY descriptors
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Returns
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-------
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descs : array
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Grid of DAISY descriptors for the given image as an array
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dimensionality (P, Q, R) where
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``P = ceil((M - radius*2) / step)``
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``Q = ceil((N - radius*2) / step)``
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``R = (rings * histograms + 1) * orientations``
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descs_img : (M, N, 3) array (only if visualize==True)
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Visualization of the DAISY descriptors.
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References
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----------
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.. [1] Tola et al. "Daisy: An efficient dense descriptor applied to wide-
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baseline stereo." Pattern Analysis and Machine Intelligence, IEEE
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Transactions on 32.5 (2010): 815-830.
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.. [2] http://cvlab.epfl.ch/software/daisy
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'''
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check_nD(image, 2, 'img')
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image = img_as_float(image)
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# Validate parameters.
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if sigmas is not None and ring_radii is not None \
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and len(sigmas) - 1 != len(ring_radii):
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raise ValueError('`len(sigmas)-1 != len(ring_radii)`')
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if ring_radii is not None:
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rings = len(ring_radii)
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radius = ring_radii[-1]
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if sigmas is not None:
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rings = len(sigmas) - 1
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if sigmas is None:
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sigmas = [radius * (i + 1) / float(2 * rings) for i in range(rings)]
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if ring_radii is None:
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ring_radii = [radius * (i + 1) / float(rings) for i in range(rings)]
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if normalization not in ['l1', 'l2', 'daisy', 'off']:
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raise ValueError('Invalid normalization method.')
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# Compute image derivatives.
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dx = np.zeros(image.shape)
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dy = np.zeros(image.shape)
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dx[:, :-1] = np.diff(image, n=1, axis=1)
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dy[:-1, :] = np.diff(image, n=1, axis=0)
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# Compute gradient orientation and magnitude and their contribution
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# to the histograms.
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grad_mag = sqrt(dx ** 2 + dy ** 2)
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grad_ori = arctan2(dy, dx)
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orientation_kappa = orientations / pi
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orientation_angles = [2 * o * pi / orientations - pi
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for o in range(orientations)]
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hist = np.empty((orientations,) + image.shape, dtype=float)
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for i, o in enumerate(orientation_angles):
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# Weigh bin contribution by the circular normal distribution
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hist[i, :, :] = exp(orientation_kappa * cos(grad_ori - o))
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# Weigh bin contribution by the gradient magnitude
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hist[i, :, :] = np.multiply(hist[i, :, :], grad_mag)
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# Smooth orientation histograms for the center and all rings.
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sigmas = [sigmas[0]] + sigmas
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hist_smooth = np.empty((rings + 1,) + hist.shape, dtype=float)
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for i in range(rings + 1):
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for j in range(orientations):
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hist_smooth[i, j, :, :] = gaussian_filter(hist[j, :, :],
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sigma=sigmas[i])
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# Assemble descriptor grid.
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theta = [2 * pi * j / histograms for j in range(histograms)]
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desc_dims = (rings * histograms + 1) * orientations
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descs = np.empty((desc_dims, image.shape[0] - 2 * radius,
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image.shape[1] - 2 * radius))
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descs[:orientations, :, :] = hist_smooth[0, :, radius:-radius,
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radius:-radius]
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idx = orientations
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for i in range(rings):
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for j in range(histograms):
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y_min = radius + int(round(ring_radii[i] * sin(theta[j])))
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y_max = descs.shape[1] + y_min
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x_min = radius + int(round(ring_radii[i] * cos(theta[j])))
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x_max = descs.shape[2] + x_min
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descs[idx:idx + orientations, :, :] = hist_smooth[i + 1, :,
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y_min:y_max,
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x_min:x_max]
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idx += orientations
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descs = descs[:, ::step, ::step]
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descs = descs.swapaxes(0, 1).swapaxes(1, 2)
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# Normalize descriptors.
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if normalization != 'off':
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descs += 1e-10
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if normalization == 'l1':
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descs /= np.sum(descs, axis=2)[:, :, np.newaxis]
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elif normalization == 'l2':
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descs /= sqrt(np.sum(descs ** 2, axis=2))[:, :, np.newaxis]
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elif normalization == 'daisy':
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for i in range(0, desc_dims, orientations):
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norms = sqrt(np.sum(descs[:, :, i:i + orientations] ** 2,
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axis=2))
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descs[:, :, i:i + orientations] /= norms[:, :, np.newaxis]
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if visualize:
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descs_img = gray2rgb(image)
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for i in range(descs.shape[0]):
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for j in range(descs.shape[1]):
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# Draw center histogram sigma
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color = [1, 0, 0]
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desc_y = i * step + radius
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desc_x = j * step + radius
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rows, cols, val = draw.circle_perimeter_aa(desc_y, desc_x, int(sigmas[0]))
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draw.set_color(descs_img, (rows, cols), color, alpha=val)
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max_bin = np.max(descs[i, j, :])
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for o_num, o in enumerate(orientation_angles):
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# Draw center histogram bins
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bin_size = descs[i, j, o_num] / max_bin
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dy = sigmas[0] * bin_size * sin(o)
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dx = sigmas[0] * bin_size * cos(o)
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rows, cols, val = draw.line_aa(desc_y, desc_x, int(desc_y + dy),
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int(desc_x + dx))
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draw.set_color(descs_img, (rows, cols), color, alpha=val)
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for r_num, r in enumerate(ring_radii):
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color_offset = float(1 + r_num) / rings
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color = (1 - color_offset, 1, color_offset)
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for t_num, t in enumerate(theta):
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# Draw ring histogram sigmas
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hist_y = desc_y + int(round(r * sin(t)))
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hist_x = desc_x + int(round(r * cos(t)))
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rows, cols, val = draw.circle_perimeter_aa(hist_y, hist_x,
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int(sigmas[r_num + 1]))
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draw.set_color(descs_img, (rows, cols), color, alpha=val)
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for o_num, o in enumerate(orientation_angles):
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# Draw histogram bins
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bin_size = descs[i, j, orientations + r_num *
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histograms * orientations +
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t_num * orientations + o_num]
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bin_size /= max_bin
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dy = sigmas[r_num + 1] * bin_size * sin(o)
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dx = sigmas[r_num + 1] * bin_size * cos(o)
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rows, cols, val = draw.line_aa(hist_y, hist_x,
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int(hist_y + dy),
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int(hist_x + dx))
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draw.set_color(descs_img, (rows, cols), color, alpha=val)
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return descs, descs_img
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else:
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return descs
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