mirror of
https://github.com/wassname/scikit-image.git
synced 2026-09-09 11:33:41 +08:00
Add dense DAISY feature description.
This commit is contained in:
Anders Boesen Lindbo Larsen
@@ -0,0 +1,167 @@
|
||||
import numpy as np
|
||||
import scipy as sp
|
||||
from numpy.linalg import norm
|
||||
from scipy import sqrt, pi, arctan2, cos, sin, exp
|
||||
from scipy.ndimage import gaussian_filter
|
||||
from scipy.special import iv
|
||||
|
||||
def daisy(img, step=4, radius=15, rings=3, histograms=8, orientations=8,
|
||||
normalization='l1', sigmas=None, ring_radii=None):
|
||||
'''Extract DAISY feature descriptors densely for the given image.
|
||||
|
||||
DAISY is a feature descriptor similar to SIFT formulated in a way
|
||||
that allows for fast dense extraction. Typically, this is practical
|
||||
for bag-of-features image representations.
|
||||
|
||||
The implementation follows Tola et al. [1] but deviate on the
|
||||
following points:
|
||||
* Histogram bin contribution are smoothed with a circular
|
||||
Gaussian window over the tonal range (the angular range).
|
||||
* The sigma values of the spatial Gaussian smoothing in this code
|
||||
do not match the sigma values in the original code by Tola et
|
||||
al. [2]. In their code, spatial smoothing is applied to both the
|
||||
input image and the center histogram. However, this smoothing is
|
||||
not documented in [1] and, therefore, it is omitted.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
img : (M, N) array
|
||||
Input image (greyscale).
|
||||
step : int, optional
|
||||
Distance between descriptor sampling points.
|
||||
radius : int, optional
|
||||
Radius (in pixels) of the outermost ring.
|
||||
rings : int, optional
|
||||
Number of rings.
|
||||
histograms : int, optional
|
||||
Number of histograms sampled per ring.
|
||||
orientations : int, optional
|
||||
Number of orientations (bins) per histogram.
|
||||
normalization : {'l1', 'l2', 'daisy', 'off'}, optional
|
||||
How to normalize the descriptors:
|
||||
* 'l1': L1-normalization of each descriptor.
|
||||
* 'l2': L2-normalization of each descriptor.
|
||||
* 'daisy': L2-normalization of individual histograms.
|
||||
* 'off': Disable normalization.
|
||||
sigmas : 1D array of float, optional
|
||||
Standard deviation of spatial Gaussian smoothing for the center
|
||||
histogram and for each ring of histograms. The array of sigmas
|
||||
should be sorted from the center and out. I.e. the first sigma
|
||||
value specifies the spatial smoothing of the center histogram
|
||||
and the last sigma value specifies the spatial smoothing of the
|
||||
outermost ring.
|
||||
Specifying sigmas overrides the following parameter:
|
||||
rings = len(sigmas)-1
|
||||
ring_radii : 1D array of int, optional
|
||||
Radius (in pixels) for each ring.
|
||||
Specifying ring_radii overrides the following two parameters:
|
||||
rings = len(ring_radii)
|
||||
radius = ring_radii[-1]
|
||||
If both sigmas and ring_radii are given, they must satisfy
|
||||
len(ring_radii) == len(sigmas)+1
|
||||
since no radius is needed for the center histogram.
|
||||
|
||||
Returns
|
||||
-------
|
||||
descs : array
|
||||
Grid of DAISY descriptors for the given image as an array
|
||||
dimensionality (P, Q, R) where
|
||||
P = ceil((M-radius*2)/step)
|
||||
Q = ceil((N-radius*2)/step)
|
||||
R = (rings*histograms + 1)*orientations
|
||||
|
||||
References
|
||||
----------
|
||||
[1] Tola et al. "Daisy: An efficient dense descriptor applied to
|
||||
wide-baseline stereo." Pattern Analysis and Machine Intelligence,
|
||||
IEEE Transactions on 32.5 (2010): 815-830.
|
||||
[2] http://cvlab.epfl.ch/alumni/tola/daisy.html
|
||||
'''
|
||||
|
||||
# Validate image format.
|
||||
if img.ndim > 2:
|
||||
raise ValueError('Only grey-level images are supported.')
|
||||
if img.dtype.kind == 'u':
|
||||
img = img.astype(float)
|
||||
img = img/255.
|
||||
|
||||
# Validate parameters.
|
||||
if sigmas != None and ring_radii != None \
|
||||
and len(sigmas)-1 != len(ring_radii):
|
||||
raise ValueError('len(sigmas)-1 != len(ring_radii)')
|
||||
if ring_radii != None:
|
||||
rings = len(ring_radii)
|
||||
radius = ring_radii[-1]
|
||||
if sigmas != None:
|
||||
rings = len(sigmas)-1
|
||||
if sigmas == None:
|
||||
sigmas = [radius*(i+1)/float(2*rings) for i in range(rings)]
|
||||
if ring_radii == None:
|
||||
ring_radii = [radius*(i+1)/float(rings) for i in range(rings)]
|
||||
if normalization not in ['l1', 'l2', 'daisy', 'off']:
|
||||
raise ValueError('Invalid normalization method.')
|
||||
|
||||
# Compute image derivatives.
|
||||
dx = np.zeros(img.shape)
|
||||
dy = np.zeros(img.shape)
|
||||
dx[:, :-1] = np.diff(img, n=1, axis=1)
|
||||
dy[:-1, :] = np.diff(img, n=1, axis=0)
|
||||
|
||||
# Compute gradient orientation and magnitude and their contribution
|
||||
# to the histograms.
|
||||
grad_mag = sqrt(dx**2 + dy**2)
|
||||
grad_ori = arctan2(dy, dx)
|
||||
hist_sigma = pi/orientations
|
||||
kappa = 1./hist_sigma;
|
||||
bessel = iv(0, kappa)
|
||||
hist = np.empty((orientations,) + img.shape, dtype=float)
|
||||
for i in range(orientations):
|
||||
mu = 2*i*pi/orientations-pi
|
||||
# Weigh bin contribution by the circular normal distribution
|
||||
hist[i,:,:] = exp(kappa*cos(grad_ori-mu))/(2*pi*bessel)
|
||||
# Weigh bin contribution by the gradient magnitude
|
||||
hist[i,:,:] = np.multiply(hist[i,:,:], grad_mag)
|
||||
|
||||
# Smooth orientation histograms for the center and all rings.
|
||||
sigmas = [sigmas[0]] + sigmas
|
||||
hist_smooth = np.empty((rings+1,)+hist.shape, dtype=float)
|
||||
for i in range(rings+1):
|
||||
for j in range(orientations):
|
||||
hist_smooth[i,j,:,:] = \
|
||||
gaussian_filter(hist[j,:,:], sigma=sigmas[i])
|
||||
|
||||
# Assemble descriptor grid.
|
||||
theta = [2*pi*j/histograms for j in range(histograms)]
|
||||
desc_dims = (rings*histograms + 1)*orientations
|
||||
descs = np.empty((desc_dims, img.shape[0]-2*radius,
|
||||
img.shape[1]-2*radius))
|
||||
descs[:orientations,:,:] = \
|
||||
hist_smooth[0,:,radius:-radius,radius:-radius]
|
||||
idx = orientations
|
||||
for i in range(rings):
|
||||
for j in range(histograms):
|
||||
y_min = radius + int(round(ring_radii[i]*sin(theta[j])))
|
||||
y_max = descs.shape[1] + y_min
|
||||
x_min = radius + int(round(ring_radii[i]*cos(theta[j])))
|
||||
x_max = descs.shape[2] + x_min
|
||||
descs[idx:idx+orientations,:,:] = \
|
||||
hist_smooth[i+1,:,y_min:y_max,x_min:x_max]
|
||||
idx += orientations
|
||||
descs = descs[:,::step,::step]
|
||||
descs = descs.swapaxes(0,1).swapaxes(1,2)
|
||||
|
||||
# Normalize descriptors.
|
||||
if normalization != 'off':
|
||||
descs += 1e-10
|
||||
if normalization == 'l1':
|
||||
descs /= np.sum(descs, axis=2)[:,:,np.newaxis]
|
||||
elif normalization == 'l2':
|
||||
descs /= sqrt(np.sum(descs**2, axis=2))[:,:,np.newaxis]
|
||||
elif normalization == 'daisy':
|
||||
for i in range(0, desc_dims, orientations):
|
||||
norms = sqrt(np.sum(descs[:,:,i:i+orientations]**2,
|
||||
axis=2))
|
||||
descs[:,:,i:i+orientations] /= norms[:,:,np.newaxis]
|
||||
|
||||
return descs
|
||||
|
||||
Reference in New Issue
Block a user