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corner.py
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corner.py
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import numpy as np
from scipy import ndimage as ndi
from scipy import stats
from ..util import img_as_float, pad
from ..feature import peak_local_max
from ..feature.util import _prepare_grayscale_input_2D
from ..feature.corner_cy import _corner_fast
from ._hessian_det_appx import _hessian_matrix_det
from ..transform import integral_image
from .._shared.utils import safe_as_int
def _compute_derivatives(image, mode='constant', cval=0):
"""Compute derivatives in x and y direction using the Sobel operator.
Parameters
----------
image : ndarray
Input image.
mode : {'constant', 'reflect', 'wrap', 'nearest', 'mirror'}, optional
How to handle values outside the image borders.
cval : float, optional
Used in conjunction with mode 'constant', the value outside
the image boundaries.
Returns
-------
imx : ndarray
Derivative in x-direction.
imy : ndarray
Derivative in y-direction.
"""
imy = ndi.sobel(image, axis=0, mode=mode, cval=cval)
imx = ndi.sobel(image, axis=1, mode=mode, cval=cval)
return imx, imy
def structure_tensor(image, sigma=1, mode='constant', cval=0):
"""Compute structure tensor using sum of squared differences.
The structure tensor A is defined as::
A = [Axx Axy]
[Axy Ayy]
which is approximated by the weighted sum of squared differences in a local
window around each pixel in the image.
Parameters
----------
image : ndarray
Input image.
sigma : float
Standard deviation used for the Gaussian kernel, which is used as a
weighting function for the local summation of squared differences.
mode : {'constant', 'reflect', 'wrap', 'nearest', 'mirror'}, optional
How to handle values outside the image borders.
cval : float, optional
Used in conjunction with mode 'constant', the value outside
the image boundaries.
Returns
-------
Axx : ndarray
Element of the structure tensor for each pixel in the input image.
Axy : ndarray
Element of the structure tensor for each pixel in the input image.
Ayy : ndarray
Element of the structure tensor for each pixel in the input image.
Examples
--------
>>> from skimage.feature import structure_tensor
>>> square = np.zeros((5, 5))
>>> square[2, 2] = 1
>>> Axx, Axy, Ayy = structure_tensor(square, sigma=0.1)
>>> Axx
array([[ 0., 0., 0., 0., 0.],
[ 0., 1., 0., 1., 0.],
[ 0., 4., 0., 4., 0.],
[ 0., 1., 0., 1., 0.],
[ 0., 0., 0., 0., 0.]])
"""
image = _prepare_grayscale_input_2D(image)
imx, imy = _compute_derivatives(image, mode=mode, cval=cval)
# structure tensore
Axx = ndi.gaussian_filter(imx * imx, sigma, mode=mode, cval=cval)
Axy = ndi.gaussian_filter(imx * imy, sigma, mode=mode, cval=cval)
Ayy = ndi.gaussian_filter(imy * imy, sigma, mode=mode, cval=cval)
return Axx, Axy, Ayy
def hessian_matrix(image, sigma=1, mode='constant', cval=0):
"""Compute Hessian matrix.
The Hessian matrix is defined as::
H = [Hxx Hxy]
[Hxy Hyy]
which is computed by convolving the image with the second derivatives
of the Gaussian kernel in the respective x- and y-directions.
Parameters
----------
image : ndarray
Input image.
sigma : float
Standard deviation used for the Gaussian kernel, which is used as
weighting function for the auto-correlation matrix.
mode : {'constant', 'reflect', 'wrap', 'nearest', 'mirror'}, optional
How to handle values outside the image borders.
cval : float, optional
Used in conjunction with mode 'constant', the value outside
the image boundaries.
Returns
-------
Hxx : ndarray
Element of the Hessian matrix for each pixel in the input image.
Hxy : ndarray
Element of the Hessian matrix for each pixel in the input image.
Hyy : ndarray
Element of the Hessian matrix for each pixel in the input image.
Examples
--------
>>> from skimage.feature import hessian_matrix
>>> square = np.zeros((5, 5))
>>> square[2, 2] = -1.0 / 1591.54943092
>>> Hxx, Hxy, Hyy = hessian_matrix(square, sigma=0.1)
>>> Hxx
array([[ 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0.],
[ 0., 0., 1., 0., 0.],
[ 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0.]])
"""
image = _prepare_grayscale_input_2D(image)
# Window extent which covers > 99% of the normal distribution.
window_ext = max(1, np.ceil(3 * sigma))
ky, kx = np.mgrid[-window_ext:window_ext + 1, -window_ext:window_ext + 1]
# Second derivative Gaussian kernels.
gaussian_exp = np.exp(-(kx ** 2 + ky ** 2) / (2 * sigma ** 2))
kernel_xx = 1 / (2 * np.pi * sigma ** 4) * (kx ** 2 / sigma ** 2 - 1)
kernel_xx *= gaussian_exp
kernel_xy = 1 / (2 * np.pi * sigma ** 6) * (kx * ky)
kernel_xy *= gaussian_exp
kernel_yy = kernel_xx.transpose()
# Remove small kernel values.
eps = np.finfo(kernel_xx.dtype).eps
kernel_xx[np.abs(kernel_xx) < eps * np.abs(kernel_xx).max()] = 0
kernel_xy[np.abs(kernel_xy) < eps * np.abs(kernel_xy).max()] = 0
kernel_yy[np.abs(kernel_yy) < eps * np.abs(kernel_yy).max()] = 0
Hxx = ndi.convolve(image, kernel_xx, mode=mode, cval=cval)
Hxy = ndi.convolve(image, kernel_xy, mode=mode, cval=cval)
Hyy = ndi.convolve(image, kernel_yy, mode=mode, cval=cval)
return Hxx, Hxy, Hyy
def hessian_matrix_det(image, sigma):
"""Computes the approximate Hessian Determinant over an image.
This method uses box filters over integral images to compute the
approximate Hessian Determinant as described in [1]_.
Parameters
----------
image : array
The image over which to compute Hessian Determinant.
sigma : float
Standard deviation used for the Gaussian kernel, used for the Hessian
matrix.
Returns
-------
out : array
The array of the Determinant of Hessians.
References
----------
.. [1] Herbert Bay, Andreas Ess, Tinne Tuytelaars, Luc Van Gool,
"SURF: Speeded Up Robust Features"
ftp://ftp.vision.ee.ethz.ch/publications/articles/eth_biwi_00517.pdf
Notes
-----
The running time of this method only depends on size of the image. It is
independent of `sigma` as one would expect. The downside is that the
result for `sigma` less than `3` is not accurate, i.e., not similar to
the result obtained if someone computed the Hessian and took it's
determinant.
"""
image = img_as_float(image)
image = integral_image(image)
return np.array(_hessian_matrix_det(image, sigma))
def _image_orthogonal_matrix22_eigvals(M00, M01, M11):
l1 = (M00 + M11) / 2 + np.sqrt(4 * M01 ** 2 + (M00 - M11) ** 2) / 2
l2 = (M00 + M11) / 2 - np.sqrt(4 * M01 ** 2 + (M00 - M11) ** 2) / 2
return l1, l2
def structure_tensor_eigvals(Axx, Axy, Ayy):
"""Compute Eigen values of structure tensor.
Parameters
----------
Axx : ndarray
Element of the structure tensor for each pixel in the input image.
Axy : ndarray
Element of the structure tensor for each pixel in the input image.
Ayy : ndarray
Element of the structure tensor for each pixel in the input image.
Returns
-------
l1 : ndarray
Larger eigen value for each input matrix.
l2 : ndarray
Smaller eigen value for each input matrix.
Examples
--------
>>> from skimage.feature import structure_tensor, structure_tensor_eigvals
>>> square = np.zeros((5, 5))
>>> square[2, 2] = 1
>>> Axx, Axy, Ayy = structure_tensor(square, sigma=0.1)
>>> structure_tensor_eigvals(Axx, Axy, Ayy)[0]
array([[ 0., 0., 0., 0., 0.],
[ 0., 2., 4., 2., 0.],
[ 0., 4., 0., 4., 0.],
[ 0., 2., 4., 2., 0.],
[ 0., 0., 0., 0., 0.]])
"""
return _image_orthogonal_matrix22_eigvals(Axx, Axy, Ayy)
def hessian_matrix_eigvals(Hxx, Hxy, Hyy):
"""Compute Eigen values of Hessian matrix.
Parameters
----------
Hxx : ndarray
Element of the Hessian matrix for each pixel in the input image.
Hxy : ndarray
Element of the Hessian matrix for each pixel in the input image.
Hyy : ndarray
Element of the Hessian matrix for each pixel in the input image.
Returns
-------
l1 : ndarray
Larger eigen value for each input matrix.
l2 : ndarray
Smaller eigen value for each input matrix.
Examples
--------
>>> from skimage.feature import hessian_matrix, hessian_matrix_eigvals
>>> square = np.zeros((5, 5))
>>> square[2, 2] = -1 / 1591.54943092
>>> Hxx, Hxy, Hyy = hessian_matrix(square, sigma=0.1)
>>> hessian_matrix_eigvals(Hxx, Hxy, Hyy)[0]
array([[ 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0.],
[ 0., 0., 1., 0., 0.],
[ 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0.]])
"""
return _image_orthogonal_matrix22_eigvals(Hxx, Hxy, Hyy)
def corner_kitchen_rosenfeld(image, mode='constant', cval=0):
"""Compute Kitchen and Rosenfeld corner measure response image.
The corner measure is calculated as follows::
(imxx * imy**2 + imyy * imx**2 - 2 * imxy * imx * imy)
/ (imx**2 + imy**2)
Where imx and imy are the first and imxx, imxy, imyy the second
derivatives.
Parameters
----------
image : ndarray
Input image.
mode : {'constant', 'reflect', 'wrap', 'nearest', 'mirror'}, optional
How to handle values outside the image borders.
cval : float, optional
Used in conjunction with mode 'constant', the value outside
the image boundaries.
Returns
-------
response : ndarray
Kitchen and Rosenfeld response image.
"""
imx, imy = _compute_derivatives(image, mode=mode, cval=cval)
imxx, imxy = _compute_derivatives(imx, mode=mode, cval=cval)
imyx, imyy = _compute_derivatives(imy, mode=mode, cval=cval)
numerator = (imxx * imy ** 2 + imyy * imx ** 2 - 2 * imxy * imx * imy)
denominator = (imx ** 2 + imy ** 2)
response = np.zeros_like(image, dtype=np.double)
mask = denominator != 0
response[mask] = numerator[mask] / denominator[mask]
return response
def corner_harris(image, method='k', k=0.05, eps=1e-6, sigma=1):
"""Compute Harris corner measure response image.
This corner detector uses information from the auto-correlation matrix A::
A = [(imx**2) (imx*imy)] = [Axx Axy]
[(imx*imy) (imy**2)] [Axy Ayy]
Where imx and imy are first derivatives, averaged with a gaussian filter.
The corner measure is then defined as::
det(A) - k * trace(A)**2
or::
2 * det(A) / (trace(A) + eps)
Parameters
----------
image : ndarray
Input image.
method : {'k', 'eps'}, optional
Method to compute the response image from the auto-correlation matrix.
k : float, optional
Sensitivity factor to separate corners from edges, typically in range
`[0, 0.2]`. Small values of k result in detection of sharp corners.
eps : float, optional
Normalisation factor (Noble's corner measure).
sigma : float, optional
Standard deviation used for the Gaussian kernel, which is used as
weighting function for the auto-correlation matrix.
Returns
-------
response : ndarray
Harris response image.
References
----------
.. [1] http://kiwi.cs.dal.ca/~dparks/CornerDetection/harris.htm
.. [2] http://en.wikipedia.org/wiki/Corner_detection
Examples
--------
>>> from skimage.feature import corner_harris, corner_peaks
>>> square = np.zeros([10, 10])
>>> square[2:8, 2:8] = 1
>>> square.astype(int)
array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
[0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
[0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
[0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
[0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
[0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])
>>> corner_peaks(corner_harris(square), min_distance=1)
array([[2, 2],
[2, 7],
[7, 2],
[7, 7]])
"""
Axx, Axy, Ayy = structure_tensor(image, sigma)
# determinant
detA = Axx * Ayy - Axy ** 2
# trace
traceA = Axx + Ayy
if method == 'k':
response = detA - k * traceA ** 2
else:
response = 2 * detA / (traceA + eps)
return response
def corner_shi_tomasi(image, sigma=1):
"""Compute Shi-Tomasi (Kanade-Tomasi) corner measure response image.
This corner detector uses information from the auto-correlation matrix A::
A = [(imx**2) (imx*imy)] = [Axx Axy]
[(imx*imy) (imy**2)] [Axy Ayy]
Where imx and imy are first derivatives, averaged with a gaussian filter.
The corner measure is then defined as the smaller eigenvalue of A::
((Axx + Ayy) - sqrt((Axx - Ayy)**2 + 4 * Axy**2)) / 2
Parameters
----------
image : ndarray
Input image.
sigma : float, optional
Standard deviation used for the Gaussian kernel, which is used as
weighting function for the auto-correlation matrix.
Returns
-------
response : ndarray
Shi-Tomasi response image.
References
----------
.. [1] http://kiwi.cs.dal.ca/~dparks/CornerDetection/harris.htm
.. [2] http://en.wikipedia.org/wiki/Corner_detection
Examples
--------
>>> from skimage.feature import corner_shi_tomasi, corner_peaks
>>> square = np.zeros([10, 10])
>>> square[2:8, 2:8] = 1
>>> square.astype(int)
array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
[0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
[0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
[0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
[0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
[0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])
>>> corner_peaks(corner_shi_tomasi(square), min_distance=1)
array([[2, 2],
[2, 7],
[7, 2],
[7, 7]])
"""
Axx, Axy, Ayy = structure_tensor(image, sigma)
# minimum eigenvalue of A
response = ((Axx + Ayy) - np.sqrt((Axx - Ayy) ** 2 + 4 * Axy ** 2)) / 2
return response
def corner_foerstner(image, sigma=1):
"""Compute Foerstner corner measure response image.
This corner detector uses information from the auto-correlation matrix A::
A = [(imx**2) (imx*imy)] = [Axx Axy]
[(imx*imy) (imy**2)] [Axy Ayy]
Where imx and imy are first derivatives, averaged with a gaussian filter.
The corner measure is then defined as::
w = det(A) / trace(A) (size of error ellipse)
q = 4 * det(A) / trace(A)**2 (roundness of error ellipse)
Parameters
----------
image : ndarray
Input image.
sigma : float, optional
Standard deviation used for the Gaussian kernel, which is used as
weighting function for the auto-correlation matrix.
Returns
-------
w : ndarray
Error ellipse sizes.
q : ndarray
Roundness of error ellipse.
References
----------
.. [1] http://www.ipb.uni-bonn.de/uploads/tx_ikgpublication/foerstner87.fast.pdf
.. [2] http://en.wikipedia.org/wiki/Corner_detection
Examples
--------
>>> from skimage.feature import corner_foerstner, corner_peaks
>>> square = np.zeros([10, 10])
>>> square[2:8, 2:8] = 1
>>> square.astype(int)
array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
[0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
[0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
[0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
[0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
[0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])
>>> w, q = corner_foerstner(square)
>>> accuracy_thresh = 0.5
>>> roundness_thresh = 0.3
>>> foerstner = (q > roundness_thresh) * (w > accuracy_thresh) * w
>>> corner_peaks(foerstner, min_distance=1)
array([[2, 2],
[2, 7],
[7, 2],
[7, 7]])
"""
Axx, Axy, Ayy = structure_tensor(image, sigma)
# determinant
detA = Axx * Ayy - Axy ** 2
# trace
traceA = Axx + Ayy
w = np.zeros_like(image, dtype=np.double)
q = np.zeros_like(image, dtype=np.double)
mask = traceA != 0
w[mask] = detA[mask] / traceA[mask]
q[mask] = 4 * detA[mask] / traceA[mask] ** 2
return w, q
def corner_fast(image, n=12, threshold=0.15):
"""Extract FAST corners for a given image.
Parameters
----------
image : 2D ndarray
Input image.
n : int
Minimum number of consecutive pixels out of 16 pixels on the circle
that should all be either brighter or darker w.r.t testpixel.
A point c on the circle is darker w.r.t test pixel p if
`Ic < Ip - threshold` and brighter if `Ic > Ip + threshold`. Also
stands for the n in `FAST-n` corner detector.
threshold : float
Threshold used in deciding whether the pixels on the circle are
brighter, darker or similar w.r.t. the test pixel. Decrease the
threshold when more corners are desired and vice-versa.
Returns
-------
response : ndarray
FAST corner response image.
References
----------
.. [1] Edward Rosten and Tom Drummond
"Machine Learning for high-speed corner detection",
http://www.edwardrosten.com/work/rosten_2006_machine.pdf
.. [2] Wikipedia, "Features from accelerated segment test",
https://en.wikipedia.org/wiki/Features_from_accelerated_segment_test
Examples
--------
>>> from skimage.feature import corner_fast, corner_peaks
>>> square = np.zeros((12, 12))
>>> square[3:9, 3:9] = 1
>>> square.astype(int)
array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0],
[0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0],
[0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0],
[0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0],
[0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0],
[0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])
>>> corner_peaks(corner_fast(square, 9), min_distance=1)
array([[3, 3],
[3, 8],
[8, 3],
[8, 8]])
"""
image = _prepare_grayscale_input_2D(image)
image = np.ascontiguousarray(image)
response = _corner_fast(image, n, threshold)
return response
def corner_subpix(image, corners, window_size=11, alpha=0.99):
"""Determine subpixel position of corners.
A statistical test decides whether the corner is defined as the
intersection of two edges or a single peak. Depending on the classification
result, the subpixel corner location is determined based on the local
covariance of the grey-values. If the significance level for either
statistical test is not sufficient, the corner cannot be classified, and
the output subpixel position is set to NaN.
Parameters
----------
image : ndarray
Input image.
corners : (N, 2) ndarray
Corner coordinates `(row, col)`.
window_size : int, optional
Search window size for subpixel estimation.
alpha : float, optional
Significance level for corner classification.
Returns
-------
positions : (N, 2) ndarray
Subpixel corner positions. NaN for "not classified" corners.
References
----------
.. [1] http://www.ipb.uni-bonn.de/uploads/tx_ikgpublication/\
foerstner87.fast.pdf
.. [2] http://en.wikipedia.org/wiki/Corner_detection
Examples
--------
>>> from skimage.feature import corner_harris, corner_peaks, corner_subpix
>>> img = np.zeros((10, 10))
>>> img[:5, :5] = 1
>>> img[5:, 5:] = 1
>>> img.astype(int)
array([[1, 1, 1, 1, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 1, 1, 1, 1],
[0, 0, 0, 0, 0, 1, 1, 1, 1, 1],
[0, 0, 0, 0, 0, 1, 1, 1, 1, 1],
[0, 0, 0, 0, 0, 1, 1, 1, 1, 1],
[0, 0, 0, 0, 0, 1, 1, 1, 1, 1]])
>>> coords = corner_peaks(corner_harris(img), min_distance=2)
>>> coords_subpix = corner_subpix(img, coords, window_size=7)
>>> coords_subpix
array([[ 4.5, 4.5]])
"""
# window extent in one direction
wext = (window_size - 1) // 2
image = pad(image, pad_width=wext, mode='constant', constant_values=0)
# add pad width, make sure to not modify the input values in-place
corners = safe_as_int(corners + wext)
# normal equation arrays
N_dot = np.zeros((2, 2), dtype=np.double)
N_edge = np.zeros((2, 2), dtype=np.double)
b_dot = np.zeros((2, ), dtype=np.double)
b_edge = np.zeros((2, ), dtype=np.double)
# critical statistical test values
redundancy = window_size ** 2 - 2
t_crit_dot = stats.f.isf(1 - alpha, redundancy, redundancy)
t_crit_edge = stats.f.isf(alpha, redundancy, redundancy)
# coordinates of pixels within window
y, x = np.mgrid[- wext:wext + 1, - wext:wext + 1]
corners_subpix = np.zeros_like(corners, dtype=np.double)
for i, (y0, x0) in enumerate(corners):
# crop window around corner + border for sobel operator
miny = y0 - wext - 1
maxy = y0 + wext + 2
minx = x0 - wext - 1
maxx = x0 + wext + 2
window = image[miny:maxy, minx:maxx]
winx, winy = _compute_derivatives(window, mode='constant', cval=0)
# compute gradient suares and remove border
winx_winx = (winx * winx)[1:-1, 1:-1]
winx_winy = (winx * winy)[1:-1, 1:-1]
winy_winy = (winy * winy)[1:-1, 1:-1]
# sum of squared differences (mean instead of gaussian filter)
Axx = np.sum(winx_winx)
Axy = np.sum(winx_winy)
Ayy = np.sum(winy_winy)
# sum of squared differences weighted with coordinates
# (mean instead of gaussian filter)
bxx_x = np.sum(winx_winx * x)
bxx_y = np.sum(winx_winx * y)
bxy_x = np.sum(winx_winy * x)
bxy_y = np.sum(winx_winy * y)
byy_x = np.sum(winy_winy * x)
byy_y = np.sum(winy_winy * y)
# normal equations for subpixel position
N_dot[0, 0] = Axx
N_dot[0, 1] = N_dot[1, 0] = - Axy
N_dot[1, 1] = Ayy
N_edge[0, 0] = Ayy
N_edge[0, 1] = N_edge[1, 0] = Axy
N_edge[1, 1] = Axx
b_dot[:] = bxx_y - bxy_x, byy_x - bxy_y
b_edge[:] = byy_y + bxy_x, bxx_x + bxy_y
# estimated positions
try:
est_dot = np.linalg.solve(N_dot, b_dot)
est_edge = np.linalg.solve(N_edge, b_edge)
except np.linalg.LinAlgError:
# if image is constant the system is singular
corners_subpix[i, :] = np.nan, np.nan
continue
# residuals
ry_dot = y - est_dot[0]
rx_dot = x - est_dot[1]
ry_edge = y - est_edge[0]
rx_edge = x - est_edge[1]
# squared residuals
rxx_dot = rx_dot * rx_dot
rxy_dot = rx_dot * ry_dot
ryy_dot = ry_dot * ry_dot
rxx_edge = rx_edge * rx_edge
rxy_edge = rx_edge * ry_edge
ryy_edge = ry_edge * ry_edge
# determine corner class (dot or edge)
# variance for different models
var_dot = np.sum(winx_winx * ryy_dot - 2 * winx_winy * rxy_dot
+ winy_winy * rxx_dot)
var_edge = np.sum(winy_winy * ryy_edge + 2 * winx_winy * rxy_edge
+ winx_winx * rxx_edge)
# test value (F-distributed)
if var_dot < np.spacing(1) and var_edge < np.spacing(1):
t = np.nan
elif var_dot == 0:
t = np.inf
else:
t = var_edge / var_dot
# 1 for edge, -1 for dot, 0 for "not classified"
corner_class = int(t < t_crit_edge) - int(t > t_crit_dot)
if corner_class == -1:
corners_subpix[i, :] = y0 + est_dot[0], x0 + est_dot[1]
elif corner_class == 0:
corners_subpix[i, :] = np.nan, np.nan
elif corner_class == 1:
corners_subpix[i, :] = y0 + est_edge[0], x0 + est_edge[1]
# subtract pad width
corners_subpix -= wext
return corners_subpix
def corner_peaks(image, min_distance=1, threshold_abs=None, threshold_rel=0.1,
exclude_border=True, indices=True, num_peaks=np.inf,
footprint=None, labels=None):
"""Find corners in corner measure response image.
This differs from `skimage.feature.peak_local_max` in that it suppresses
multiple connected peaks with the same accumulator value.
Parameters
----------
* : *
See :py:meth:`skimage.feature.peak_local_max`.
Examples
--------
>>> from skimage.feature import peak_local_max
>>> response = np.zeros((5, 5))
>>> response[2:4, 2:4] = 1
>>> response
array([[ 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0.],
[ 0., 0., 1., 1., 0.],
[ 0., 0., 1., 1., 0.],
[ 0., 0., 0., 0., 0.]])
>>> peak_local_max(response)
array([[2, 2],
[2, 3],
[3, 2],
[3, 3]])
>>> corner_peaks(response)
array([[2, 2]])
"""
peaks = peak_local_max(image, min_distance=min_distance,
threshold_abs=threshold_abs,
threshold_rel=threshold_rel,
exclude_border=exclude_border,
indices=False, num_peaks=num_peaks,
footprint=footprint, labels=labels)
if min_distance > 0:
coords = np.transpose(peaks.nonzero())
for r, c in coords:
if peaks[r, c]:
peaks[r - min_distance:r + min_distance + 1,
c - min_distance:c + min_distance + 1] = False
peaks[r, c] = True
if indices is True:
return np.transpose(peaks.nonzero())
else:
return peaks