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fix and improve comments, doc strings, variable names for consistency reasons

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This commit is contained in:
Johannes Schönberger
2012-08-09 07:51:58 +02:00
parent 54452550f1
commit e5ed1882d3
2 changed files with 48 additions and 53 deletions
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skimage/transform/_geometric.py
+47 -52
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@@ -18,7 +18,7 @@ def _stackcopy(a, b):
Notes Notes
----- -----
Color images are stored as an ``MxNx3`` or ``MxNx4`` arrays. Color images are stored as an ``(M, N, 3)`` or ``(M, N, 4)`` arrays.
""" """
if a.ndim == 3: if a.ndim == 3:
@@ -36,12 +36,12 @@ class GeometricTransform(object):
Parameters Parameters
---------- ----------
coords : Nx2 array coords : (N, 2) array
source coordinates source coordinates
Returns Returns
------- -------
coords : Nx2 array coords : (N, 2) array
transformed coordinates transformed coordinates
""" """
@@ -52,12 +52,12 @@ class GeometricTransform(object):
Parameters Parameters
---------- ----------
coords : Nx2 array coords : (N, 2) array
source coordinates source coordinates
Returns Returns
------- -------
coords : Nx2 array coords : (N, 2) array
transformed coordinates transformed coordinates
""" """
@@ -78,17 +78,13 @@ class ProjectiveTransform(GeometricTransform):
For each homogeneous coordinate :math:`\mathbf{x} = [x, y, 1]^T`, its For each homogeneous coordinate :math:`\mathbf{x} = [x, y, 1]^T`, its
target position is calculated by multiplying with the given matrix, target position is calculated by multiplying with the given matrix,
:math:`H`, to give :math:`H \mathbf{x}`. E.g., to rotate by theta degrees :math:`H`, to give :math:`H \mathbf{x}`. E.g., to rotate by theta degrees
clockwise, the matrix should be clockwise, the matrix should be::
::
[[cos(theta) -sin(theta) 0] [[cos(theta) -sin(theta) 0]
[sin(theta) cos(theta) 0] [sin(theta) cos(theta) 0]
[0 0 1]] [0 0 1]]
or, to translate x by 10 and y by 20, or, to translate x by 10 and y by 20::
::
[[1 0 10] [[1 0 10]
[0 1 20] [0 1 20]
@@ -96,12 +92,12 @@ class ProjectiveTransform(GeometricTransform):
Parameters Parameters
---------- ----------
matrix : 3x3 array, optional matrix : (3, 3) array, optional
Homogeneous transformation matrix. Homogeneous transformation matrix.
""" """
_coefs = range(8) coeffs = range(8)
def __init__(self, matrix=None): def __init__(self, matrix=None):
self._matrix = matrix self._matrix = matrix
@@ -136,13 +132,13 @@ class ProjectiveTransform(GeometricTransform):
You can determine the over-, well- and under-determined parameters You can determine the over-, well- and under-determined parameters
with the total least-squares method. with the total least-squares method.
Number of source must match number of destination coordinates. Number of source and destination coordinates must match.
Parameters Parameters
---------- ----------
src : Nx2 array src : (N, 2) array
source coordinates source coordinates
dst : Nx2 array dst : (N, 2) array
destination coordinates destination coordinates
""" """
@@ -152,7 +148,7 @@ class ProjectiveTransform(GeometricTransform):
yd = dst[:, 1] yd = dst[:, 1]
rows = src.shape[0] rows = src.shape[0]
#: params: a0, a1, a2, b0, b1, b2, c0, c1 # params: a0, a1, a2, b0, b1, b2, c0, c1
A = np.zeros((rows * 2, 9)) A = np.zeros((rows * 2, 9))
A[:rows, 0] = xs A[:rows, 0] = xs
A[:rows, 1] = ys A[:rows, 1] = ys
@@ -167,15 +163,15 @@ class ProjectiveTransform(GeometricTransform):
A[:rows, 8] = xd A[:rows, 8] = xd
A[rows:, 8] = yd A[rows:, 8] = yd
# Select relevant columns, depending on coeffs # Select relevant columns, depending on params
A = A[:, self._coefs + [8]] A = A[:, self.coeffs + [8]]
_, _, V = np.linalg.svd(A) _, _, V = np.linalg.svd(A)
H = np.zeros((3, 3)) H = np.zeros((3, 3))
# solution is right singular vector that corresponds to smallest # solution is right singular vector that corresponds to smallest
# singular value and normed by c3 # singular value and normed by c3
H.flat[self._coefs + [8]] = - V[-1, :-1] / V[-1, -1] H.flat[self.coeffs + [8]] = - V[-1, :-1] / V[-1, -1]
H[2, 2] = 1 H[2, 2] = 1
self._matrix = H self._matrix = H
@@ -216,12 +212,12 @@ class AffineTransform(ProjectiveTransform):
Parameters Parameters
---------- ----------
matrix : 3x3 array, optional matrix : (3, 3) array, optional
Homogeneous transformation matrix. Homogeneous transformation matrix.
""" """
_coefs = range(6) coeffs = range(6)
def compose_implicit(self, scale=None, rotation=None, shear=None, def compose_implicit(self, scale=None, rotation=None, shear=None,
translation=None): translation=None):
@@ -294,25 +290,24 @@ class SimilarityTransform(ProjectiveTransform):
Parameters Parameters
---------- ----------
matrix : 3x3 array, optional matrix : (3, 3) array, optional
Homogeneous transformation matrix. Homogeneous transformation matrix.
""" """
def estimate(self, src, dst): def estimate(self, src, dst):
"""Set the transformation matrix with the explicit transformation """Set the transformation matrix with the explicit parameters.
parameters.
You can determine the over-, well- and under-determined parameters You can determine the over-, well- and under-determined parameters
with the total least-squares method. with the total least-squares method.
Number of source must match number of destination coordinates. Number of source and destination coordinates must match.
Parameters Parameters
---------- ----------
src : Nx2 array src : (N, 2) array
source coordinates source coordinates
dst : Nx2 array dst : (N, 2) array
destination coordinates destination coordinates
""" """
@@ -322,7 +317,7 @@ class SimilarityTransform(ProjectiveTransform):
yd = dst[:, 1] yd = dst[:, 1]
rows = src.shape[0] rows = src.shape[0]
#: params: a0, a1, b0, b1 # params: a0, a1, b0, b1
A = np.zeros((rows * 2, 5)) A = np.zeros((rows * 2, 5))
A[:rows, 0] = xs A[:rows, 0] = xs
A[:rows, 2] = - ys A[:rows, 2] = - ys
@@ -398,14 +393,14 @@ class PolynomialTransform(GeometricTransform):
Parameters Parameters
---------- ----------
coeffs : 2xN array, optional params : (2, N) array, optional
Polynomial coefficients where `N * 2 = (order + 1) * (order + 2)`. So, Polynomial coefficients where `N * 2 = (order + 1) * (order + 2)`. So,
a_ji is defined in `coeffs[0, :]` and b_ji in `coeffs[1, :]`. a_ji is defined in `params[0, :]` and b_ji in `params[1, :]`.
""" """
def __init__(self, coeffs=None): def __init__(self, params=None):
self._coeffs = coeffs self._params = params
def estimate(self, src, dst, order): def estimate(self, src, dst, order):
"""Set the transformation matrix with the explicit transformation """Set the transformation matrix with the explicit transformation
@@ -414,13 +409,13 @@ class PolynomialTransform(GeometricTransform):
You can determine the over-, well- and under-determined parameters You can determine the over-, well- and under-determined parameters
with the total least-squares method. with the total least-squares method.
Number of source must match number of destination coordinates. Number of source and destination coordinates must match.
Parameters Parameters
---------- ----------
src : Nx2 array src : (N, 2) array
source coordinates source coordinates
dst : Nx2 array dst : (N, 2) array
destination coordinates destination coordinates
order : int order : int
polynomial order (number of coefficients is order + 1) polynomial order (number of coefficients is order + 1)
@@ -437,8 +432,8 @@ class PolynomialTransform(GeometricTransform):
A = np.zeros((rows * 2, u + 1)) A = np.zeros((rows * 2, u + 1))
pidx = 0 pidx = 0
for j in xrange(order + 1): for j in range(order + 1):
for i in xrange(j + 1): for i in range(j + 1):
A[:rows, pidx] = xs ** (j - i) * ys ** i A[:rows, pidx] = xs ** (j - i) * ys ** i
A[rows:, pidx + u / 2] = xs ** (j - i) * ys ** i A[rows:, pidx + u / 2] = xs ** (j - i) * ys ** i
pidx += 1 pidx += 1
@@ -450,36 +445,36 @@ class PolynomialTransform(GeometricTransform):
# solution is right singular vector that corresponds to smallest # solution is right singular vector that corresponds to smallest
# singular value and normed by c3 # singular value and normed by c3
coeffs = - V[-1, :-1] / V[-1, -1] params = - V[-1, :-1] / V[-1, -1]
self._coeffs = coeffs.reshape((2, u / 2)) self._params = params.reshape((2, u / 2))
def __call__(self, coords): def __call__(self, coords):
"""Apply forward transformation. """Apply forward transformation.
Parameters Parameters
---------- ----------
coords : Nx2 array coords : (N, 2) array
source coordinates source coordinates
Returns Returns
------- -------
coords : Nx2 array coords : (N, 2) array
transformed coordinates transformed coordinates
""" """
x = coords[:, 0] x = coords[:, 0]
y = coords[:, 1] y = coords[:, 1]
u = len(self._coeffs.ravel()) u = len(self._params.ravel())
# number of coefficients -> u = (order + 1) * (order + 2) # number of coefficients -> u = (order + 1) * (order + 2)
order = int((- 3 + math.sqrt(9 - 4 * (2 - u))) / 2) order = int((- 3 + math.sqrt(9 - 4 * (2 - u))) / 2)
dst = np.zeros(coords.shape) dst = np.zeros(coords.shape)
pidx = 0 pidx = 0
for j in xrange(order + 1): for j in range(order + 1):
for i in xrange(j + 1): for i in range(j + 1):
dst[:, 0] += self._coeffs[0, pidx] * x ** (j - i) * y ** i dst[:, 0] += self._params[0, pidx] * x ** (j - i) * y ** i
dst[:, 1] += self._coeffs[1, pidx] * x ** (j - i) * y ** i dst[:, 1] += self._params[1, pidx] * x ** (j - i) * y ** i
pidx += 1 pidx += 1
return dst return dst
@@ -506,7 +501,7 @@ def estimate_transform(ttype, src, dst, **kwargs):
You can determine the over-, well- and under-determined parameters You can determine the over-, well- and under-determined parameters
with the total least-squares method. with the total least-squares method.
Number of source must match number of destination coordinates. Number of source and destination coordinates must match.
Parameters Parameters
---------- ----------
@@ -573,14 +568,14 @@ def matrix_transform(coords, matrix):
Parameters Parameters
---------- ----------
coords : Nx2 array coords : (N, 2) array
x, y coordinates to transform x, y coordinates to transform
matrix : 3x3 array matrix : (3, 3) array
Homogeneous transformation matrix. Homogeneous transformation matrix.
Returns Returns
------- -------
coords : Nx2 array coords : (N, 2) array
transformed coordinates transformed coordinates
""" """
@@ -596,7 +591,7 @@ def warp(image, inverse_map=None, map_args={}, output_shape=None, order=1,
image : 2-D array image : 2-D array
Input image. Input image.
inverse_map : transformation object, callable xy = f(xy, **kwargs) inverse_map : transformation object, callable xy = f(xy, **kwargs)
Inverse coordinate map. A function that transforms a Px2 array of Inverse coordinate map. A function that transforms a (N, 2) array of
``(x, y)`` coordinates in the *output image* into their corresponding ``(x, y)`` coordinates in the *output image* into their corresponding
coordinates in the *source image*. In case of a transformation object coordinates in the *source image*. In case of a transformation object
its `inverse` method will be used as transformation function. Also see its `inverse` method will be used as transformation function. Also see
skimage/transform/_warps.py
+1 -1
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@@ -152,7 +152,7 @@ def homography(image, H, output_shape=None, order=1,
""" """
import warnings import warnings
warnings.warn('the homography function is deprecated; ' warnings.warn('the homography function is deprecated; '
'use the `warp` and `tform` function instead', 'use the `warp` and `ProjectiveTransform` class instead',
category=DeprecationWarning) category=DeprecationWarning)
tform = ProjectiveTransform(H) tform = ProjectiveTransform(H)