mirror of
https://github.com/wassname/scikit-image.git
synced 2026-07-03 11:27:45 +08:00
Johannes Schönberger
a6532a8dae
* Fix cval bug in interpolation which was ignored * Remove fast_homography as standalone function and automatically include functionality in warp * Fix bug in warp_coords for graylevel images * move warp functions to warp file
674 lines
20 KiB
Python
674 lines
20 KiB
Python
import math
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import numpy as np
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class GeometricTransform(object):
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"""Perform geometric transformations on a set of coordinates.
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"""
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def __call__(self, coords):
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"""Apply forward transformation.
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Parameters
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----------
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coords : (N, 2) array
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Source coordinates.
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Returns
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-------
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coords : (N, 2) array
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Transformed coordinates.
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"""
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raise NotImplementedError()
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def inverse(self, coords):
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"""Apply inverse transformation.
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Parameters
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----------
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coords : (N, 2) array
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Source coordinates.
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Returns
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-------
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coords : (N, 2) array
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Transformed coordinates.
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"""
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raise NotImplementedError()
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def __add__(self, other):
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"""Combine this transformation with another.
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"""
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raise NotImplementedError()
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class ProjectiveTransform(GeometricTransform):
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"""Matrix transformation.
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Apply a projective transformation (homography) on coordinates.
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For each homogeneous coordinate :math:`\mathbf{x} = [x, y, 1]^T`, its
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target position is calculated by multiplying with the given matrix,
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:math:`H`, to give :math:`H \mathbf{x}`::
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[[a0 a1 a2]
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[b0 b1 b2]
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[c0 c1 1 ]].
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E.g., to rotate by theta degrees clockwise, the matrix should be::
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[[cos(theta) -sin(theta) 0]
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[sin(theta) cos(theta) 0]
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[0 0 1]]
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or, to translate x by 10 and y by 20::
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[[1 0 10]
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[0 1 20]
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[0 0 1 ]].
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Parameters
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----------
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matrix : (3, 3) array, optional
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Homogeneous transformation matrix.
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"""
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coeffs = range(8)
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def __init__(self, matrix=None):
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if matrix is None:
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# default to an identity transform
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matrix = np.eye(3)
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if matrix.shape != (3, 3):
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raise ValueError("invalid shape of transformation matrix")
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self._matrix = matrix
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@property
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def _inv_matrix(self):
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return np.linalg.inv(self._matrix)
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def _apply_mat(self, coords, matrix):
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coords = np.array(coords, copy=False, ndmin=2)
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x, y = np.transpose(coords)
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src = np.vstack((x, y, np.ones_like(x)))
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dst = np.dot(src.transpose(), matrix.transpose())
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# rescale to homogeneous coordinates
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dst[:, 0] /= dst[:, 2]
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dst[:, 1] /= dst[:, 2]
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return dst[:, :2]
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def __call__(self, coords):
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return self._apply_mat(coords, self._matrix)
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def inverse(self, coords):
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return self._apply_mat(coords, self._inv_matrix)
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def estimate(self, src, dst):
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"""Set the transformation matrix with the explicit transformation
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parameters.
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You can determine the over-, well- and under-determined parameters
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with the total least-squares method.
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Number of source and destination coordinates must match.
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The transformation is defined as::
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X = (a0*x + a1*y + a2) / (c0*x + c1*y + 1)
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Y = (b0*x + b1*y + b2) / (c0*x + c1*y + 1)
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These equations can be transformed to the following form::
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0 = a0*x + a1*y + a2 - c0*x*X - c1*y*X - X
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0 = b0*x + b1*y + b2 - c0*x*Y - c1*y*Y - Y
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which exist for each set of corresponding points, so we have a set of
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N * 2 equations. The coefficients appear linearly so we can write
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A x = 0, where::
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A = [[x y 1 0 0 0 -x*X -y*X -X]
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[0 0 0 x y 1 -x*Y -y*Y -Y]
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...
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...
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]
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x.T = [a0 a1 a2 b0 b1 b2 c0 c1 c3]
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In case of total least-squares the solution of this homogeneous system
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of equations is the right singular vector of A which corresponds to the
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smallest singular value normed by the coefficient c3.
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In case of the affine transformation the coefficients c0 and c1 are 0.
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Thus the system of equations is::
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A = [[x y 1 0 0 0 -X]
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[0 0 0 x y 1 -Y]
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...
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...
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]
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x.T = [a0 a1 a2 b0 b1 b2 c3]
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Parameters
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----------
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src : (N, 2) array
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Source coordinates.
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dst : (N, 2) array
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Destination coordinates.
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"""
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xs = src[:, 0]
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ys = src[:, 1]
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xd = dst[:, 0]
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yd = dst[:, 1]
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rows = src.shape[0]
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# params: a0, a1, a2, b0, b1, b2, c0, c1
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A = np.zeros((rows * 2, 9))
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A[:rows, 0] = xs
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A[:rows, 1] = ys
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A[:rows, 2] = 1
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A[:rows, 6] = - xd * xs
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A[:rows, 7] = - xd * ys
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A[rows:, 3] = xs
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A[rows:, 4] = ys
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A[rows:, 5] = 1
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A[rows:, 6] = - yd * xs
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A[rows:, 7] = - yd * ys
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A[:rows, 8] = xd
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A[rows:, 8] = yd
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# Select relevant columns, depending on params
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A = A[:, self.coeffs + [8]]
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_, _, V = np.linalg.svd(A)
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H = np.zeros((3, 3))
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# solution is right singular vector that corresponds to smallest
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# singular value
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H.flat[self.coeffs + [8]] = - V[-1, :-1] / V[-1, -1]
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H[2, 2] = 1
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self._matrix = H
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def __add__(self, other):
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"""Combine this transformation with another.
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"""
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if isinstance(other, ProjectiveTransform):
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# combination of the same types result in a transformation of this
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# type again, otherwise use general projective transformation
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if type(self) == type(other):
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tform = self.__class__
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else:
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tform = ProjectiveTransform
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return tform(other._matrix.dot(self._matrix))
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else:
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raise TypeError("Cannot combine transformations of differing "
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"types.")
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class AffineTransform(ProjectiveTransform):
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"""2D affine transformation of the form::
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X = a0*x + a1*y + a2 =
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= sx*x*cos(rotation) - sy*y*sin(rotation + shear) + a2
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Y = b0*x + b1*y + b2 =
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= sx*x*sin(rotation) + sy*y*cos(rotation + shear) + b2
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where ``sx`` and ``sy`` are zoom factors in the x and y directions,
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and the homogeneous transformation matrix is::
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[[a0 a1 a2]
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[b0 b1 b2]
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[0 0 1]]
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Parameters
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----------
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matrix : (3, 3) array, optional
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Homogeneous transformation matrix.
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scale : (sx, sy) as array, list or tuple, optional
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Scale factors.
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rotation : float, optional
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Rotation angle in counter-clockwise direction as radians.
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shear : float, optional
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Shear angle in counter-clockwise direction as radians.
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translation : (tx, ty) as array, list or tuple, optional
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Translation parameters.
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"""
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coeffs = range(6)
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def __init__(self, matrix=None, scale=None, rotation=None, shear=None,
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translation=None):
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params = any(param is not None
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for param in (scale, rotation, shear, translation))
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if params and matrix is not None:
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raise ValueError("You cannot specify the transformation matrix and "
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"the implicit parameters at the same time.")
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elif matrix is not None:
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if matrix.shape != (3, 3):
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raise ValueError("Invalid shape of transformation matrix.")
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self._matrix = matrix
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elif params:
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if scale is None:
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scale = (1, 1)
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if rotation is None:
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rotation = 0
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if shear is None:
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shear = 0
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if translation is None:
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translation = (0, 0)
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sx, sy = scale
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self._matrix = np.array([
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[sx * math.cos(rotation), - sy * math.sin(rotation + shear), 0],
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[sx * math.sin(rotation), sy * math.cos(rotation + shear), 0],
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[ 0, 0, 1]
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])
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self._matrix[0:2, 2] = translation
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else:
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# default to an identity transform
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self._matrix = np.eye(3)
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@property
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def scale(self):
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sx = math.sqrt(self._matrix[0, 0] ** 2 + self._matrix[1, 0] ** 2)
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sy = math.sqrt(self._matrix[0, 1] ** 2 + self._matrix[1, 1] ** 2)
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return sx, sy
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@property
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def rotation(self):
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return math.atan2(self._matrix[1, 0], self._matrix[0, 0])
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@property
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def shear(self):
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beta = math.atan2(- self._matrix[0, 1], self._matrix[1, 1])
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return beta - self.rotation
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@property
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def translation(self):
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return self._matrix[0:2, 2]
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class SimilarityTransform(ProjectiveTransform):
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"""2D similarity transformation of the form::
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X = a0*x - b0*y + a1 =
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= m*x*cos(rotation) + m*y*sin(rotation) + a1
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Y = b0*x + a0*y + b1 =
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= m*x*sin(rotation) + m*y*cos(rotation) + b1
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where ``m`` is a zoom factor and the homogeneous transformation matrix is::
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[[a0 b0 a1]
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[b0 a0 b1]
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[0 0 1]]
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Parameters
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----------
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matrix : (3, 3) array, optional
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Homogeneous transformation matrix.
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scale : float, optional
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Scale factor.
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rotation : float, optional
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Rotation angle in counter-clockwise direction as radians.
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translation : (tx, ty) as array, list or tuple, optional
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x, y translation parameters.
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"""
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def __init__(self, matrix=None, scale=None, rotation=None,
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translation=None):
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params = any(param is not None
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for param in (scale, rotation, translation))
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if params and matrix is not None:
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raise ValueError("You cannot specify the transformation matrix and "
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"the implicit parameters at the same time.")
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elif matrix is not None:
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if matrix.shape != (3, 3):
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raise ValueError("Invalid shape of transformation matrix.")
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self._matrix = matrix
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elif params:
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if scale is None:
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scale = 1
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if rotation is None:
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rotation = 0
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if translation is None:
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translation = (0, 0)
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self._matrix = np.array([
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[math.cos(rotation), - math.sin(rotation), 0],
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[math.sin(rotation), math.cos(rotation), 0],
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[ 0, 0, 1]
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])
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self._matrix *= scale
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self._matrix[0:2, 2] = translation
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else:
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# default to an identity transform
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self._matrix = np.eye(3)
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def estimate(self, src, dst):
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"""Set the transformation matrix with the explicit parameters.
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You can determine the over-, well- and under-determined parameters
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with the total least-squares method.
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Number of source and destination coordinates must match.
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The transformation is defined as::
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X = a0*x - b0*y + a1
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Y = b0*x + a0*y + b1
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These equations can be transformed to the following form::
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0 = a0*x - b0*y + a1 - X
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0 = b0*x + a0*y + b1 - Y
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which exist for each set of corresponding points, so we have a set of
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N * 2 equations. The coefficients appear linearly so we can write
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A x = 0, where::
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A = [[x 1 -y 0 -X]
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[y 0 x 1 -Y]
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...
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...
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]
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x.T = [a0 a1 b0 b1 c3]
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In case of total least-squares the solution of this homogeneous system
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of equations is the right singular vector of A which corresponds to the
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smallest singular value normed by the coefficient c3.
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Parameters
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----------
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src : (N, 2) array
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Source coordinates.
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dst : (N, 2) array
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Destination coordinates.
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"""
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xs = src[:, 0]
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ys = src[:, 1]
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xd = dst[:, 0]
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yd = dst[:, 1]
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rows = src.shape[0]
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# params: a0, a1, b0, b1
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A = np.zeros((rows * 2, 5))
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A[:rows, 0] = xs
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A[:rows, 2] = - ys
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A[:rows, 1] = 1
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A[rows:, 2] = xs
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A[rows:, 0] = ys
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A[rows:, 3] = 1
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A[:rows, 4] = xd
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A[rows:, 4] = yd
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_, _, V = np.linalg.svd(A)
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# solution is right singular vector that corresponds to smallest
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# singular value
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a0, a1, b0, b1 = - V[-1, :-1] / V[-1, -1]
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self._matrix = np.array([[a0, -b0, a1],
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[b0, a0, b1],
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[ 0, 0, 1]])
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@property
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def scale(self):
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if math.cos(self.rotation) == 0:
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# sin(self.rotation) == 1
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scale = self._matrix[0, 1]
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else:
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scale = self._matrix[0, 0] / math.cos(self.rotation)
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return scale
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@property
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def rotation(self):
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return math.atan2(self._matrix[1, 0], self._matrix[1, 1])
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@property
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def translation(self):
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return self._matrix[0:2, 2]
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class PolynomialTransform(GeometricTransform):
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"""2D transformation of the form::
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X = sum[j=0:order]( sum[i=0:j]( a_ji * x**(j - i) * y**i ))
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Y = sum[j=0:order]( sum[i=0:j]( b_ji * x**(j - i) * y**i ))
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Parameters
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----------
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params : (2, N) array, optional
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Polynomial coefficients where `N * 2 = (order + 1) * (order + 2)`. So,
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a_ji is defined in `params[0, :]` and b_ji in `params[1, :]`.
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"""
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def __init__(self, params=None):
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if params is None:
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# default to transformation which preserves original coordinates
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params = np.array([[0, 1, 0], [0, 0, 1]])
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if params.shape[0] != 2:
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raise ValueError("invalid shape of transformation parameters")
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self._params = params
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def estimate(self, src, dst, order):
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"""Set the transformation matrix with the explicit transformation
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parameters.
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You can determine the over-, well- and under-determined parameters
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with the total least-squares method.
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Number of source and destination coordinates must match.
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The transformation is defined as::
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X = sum[j=0:order]( sum[i=0:j]( a_ji * x**(j - i) * y**i ))
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Y = sum[j=0:order]( sum[i=0:j]( b_ji * x**(j - i) * y**i ))
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These equations can be transformed to the following form::
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0 = sum[j=0:order]( sum[i=0:j]( a_ji * x**(j - i) * y**i )) - X
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0 = sum[j=0:order]( sum[i=0:j]( b_ji * x**(j - i) * y**i )) - Y
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which exist for each set of corresponding points, so we have a set of
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N * 2 equations. The coefficients appear linearly so we can write
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A x = 0, where::
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A = [[1 x y x**2 x*y y**2 ... 0 ... 0 -X]
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[0 ... 0 1 x y x**2 x*y y**2 -Y]
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...
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...
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]
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x.T = [a00 a10 a11 a20 a21 a22 ... ann
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b00 b10 b11 b20 b21 b22 ... bnn c3]
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In case of total least-squares the solution of this homogeneous system
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of equations is the right singular vector of A which corresponds to the
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smallest singular value normed by the coefficient c3.
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Parameters
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----------
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src : (N, 2) array
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Source coordinates.
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dst : (N, 2) array
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Destination coordinates.
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order : int
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Polynomial order (number of coefficients is order + 1).
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"""
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xs = src[:, 0]
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ys = src[:, 1]
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xd = dst[:, 0]
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yd = dst[:, 1]
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rows = src.shape[0]
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# number of unknown polynomial coefficients
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u = (order + 1) * (order + 2)
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A = np.zeros((rows * 2, u + 1))
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pidx = 0
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for j in range(order + 1):
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for i in range(j + 1):
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A[:rows, pidx] = xs ** (j - i) * ys ** i
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A[rows:, pidx + u / 2] = xs ** (j - i) * ys ** i
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pidx += 1
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A[:rows, -1] = xd
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A[rows:, -1] = yd
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_, _, V = np.linalg.svd(A)
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# solution is right singular vector that corresponds to smallest
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# singular value
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params = - V[-1, :-1] / V[-1, -1]
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self._params = params.reshape((2, u / 2))
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def __call__(self, coords):
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"""Apply forward transformation.
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Parameters
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----------
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coords : (N, 2) array
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source coordinates
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|
|
Returns
|
|
-------
|
|
coords : (N, 2) array
|
|
Transformed coordinates.
|
|
|
|
"""
|
|
x = coords[:, 0]
|
|
y = coords[:, 1]
|
|
u = len(self._params.ravel())
|
|
# number of coefficients -> u = (order + 1) * (order + 2)
|
|
order = int((- 3 + math.sqrt(9 - 4 * (2 - u))) / 2)
|
|
dst = np.zeros(coords.shape)
|
|
|
|
pidx = 0
|
|
for j in range(order + 1):
|
|
for i in range(j + 1):
|
|
dst[:, 0] += self._params[0, pidx] * x ** (j - i) * y ** i
|
|
dst[:, 1] += self._params[1, pidx] * x ** (j - i) * y ** i
|
|
pidx += 1
|
|
|
|
return dst
|
|
|
|
def inverse(self, coords):
|
|
raise Exception(
|
|
'There is no explicit way to do the inverse polynomial '
|
|
'transformation. Instead, estimate the inverse transformation '
|
|
'parameters by exchanging source and destination coordinates,'
|
|
'then apply the forward transformation.')
|
|
|
|
|
|
TRANSFORMS = {
|
|
'similarity': SimilarityTransform,
|
|
'affine': AffineTransform,
|
|
'projective': ProjectiveTransform,
|
|
'polynomial': PolynomialTransform,
|
|
}
|
|
|
|
|
|
def estimate_transform(ttype, src, dst, **kwargs):
|
|
"""Estimate 2D geometric transformation parameters.
|
|
|
|
You can determine the over-, well- and under-determined parameters
|
|
with the total least-squares method.
|
|
|
|
Number of source and destination coordinates must match.
|
|
|
|
Parameters
|
|
----------
|
|
ttype : {'similarity', 'affine', 'projective', 'polynomial'}
|
|
Type of transform.
|
|
kwargs : array or int
|
|
Function parameters (src, dst, n, angle)::
|
|
|
|
NAME / TTYPE FUNCTION PARAMETERS
|
|
'similarity' `src, `dst`
|
|
'affine' `src, `dst`
|
|
'projective' `src, `dst`
|
|
'polynomial' `src, `dst`, `order` (polynomial order)
|
|
|
|
Also see examples below.
|
|
|
|
Returns
|
|
-------
|
|
tform : :class:`GeometricTransform`
|
|
Transform object containing the transformation parameters and providing
|
|
access to forward and inverse transformation functions.
|
|
|
|
Examples
|
|
--------
|
|
>>> import numpy as np
|
|
>>> from skimage import transform as tf
|
|
|
|
>>> # estimate transformation parameters
|
|
>>> src = np.array([0, 0, 10, 10]).reshape((2, 2))
|
|
>>> dst = np.array([12, 14, 1, -20]).reshape((2, 2))
|
|
|
|
>>> tform = tf.estimate_transform('similarity', src, dst)
|
|
|
|
>>> tform.inverse(tform(src)) # == src
|
|
|
|
>>> # warp image using the estimated transformation
|
|
>>> from skimage import data
|
|
>>> image = data.camera()
|
|
|
|
>>> warp(image, inverse_map=tform.inverse)
|
|
|
|
>>> # create transformation with explicit parameters
|
|
>>> tform2 = tf.SimilarityTransform()
|
|
>>> tform2.compose_implicit(scale=1.1, rotation=1, translation=(10, 20))
|
|
|
|
>>> # unite transformations, applied in order from left to right
|
|
>>> tform3 = tform + tform2
|
|
>>> tform3(src) # == tform2(tform(src))
|
|
|
|
"""
|
|
ttype = ttype.lower()
|
|
if ttype not in TRANSFORMS:
|
|
raise ValueError('the transformation type \'%s\' is not'
|
|
'implemented' % ttype)
|
|
|
|
tform = TRANSFORMS[ttype]()
|
|
tform.estimate(src, dst, **kwargs)
|
|
|
|
return tform
|
|
|
|
|
|
def matrix_transform(coords, matrix):
|
|
"""Apply 2D matrix transform.
|
|
|
|
Parameters
|
|
----------
|
|
coords : (N, 2) array
|
|
x, y coordinates to transform
|
|
matrix : (3, 3) array
|
|
Homogeneous transformation matrix.
|
|
|
|
Returns
|
|
-------
|
|
coords : (N, 2) array
|
|
Transformed coordinates.
|
|
|
|
"""
|
|
return ProjectiveTransform(matrix)(coords)
|