mirror of
https://github.com/wassname/scikit-image.git
synced 2026-08-12 12:30:16 +08:00
Refactor image warps
* 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
This commit is contained in:
Johannes Schönberger
@@ -1,30 +1,5 @@
|
||||
import math
|
||||
import numpy as np
|
||||
from scipy import ndimage
|
||||
from skimage.util import img_as_float
|
||||
|
||||
|
||||
def _stackcopy(a, b):
|
||||
"""Copy b into each color layer of a, such that::
|
||||
|
||||
a[:,:,0] = a[:,:,1] = ... = b
|
||||
|
||||
Parameters
|
||||
----------
|
||||
a : (M, N) or (M, N, P) ndarray
|
||||
Target array.
|
||||
b : (M, N)
|
||||
Source array.
|
||||
|
||||
Notes
|
||||
-----
|
||||
Color images are stored as an ``(M, N, 3)`` or ``(M, N, 4)`` arrays.
|
||||
|
||||
"""
|
||||
if a.ndim == 3:
|
||||
a[:] = b[:, :, np.newaxis]
|
||||
else:
|
||||
a[:] = b
|
||||
|
||||
|
||||
class GeometricTransform(object):
|
||||
@@ -603,7 +578,7 @@ class PolynomialTransform(GeometricTransform):
|
||||
'then apply the forward transformation.')
|
||||
|
||||
|
||||
TRANSFORMATIONS = {
|
||||
TRANSFORMS = {
|
||||
'similarity': SimilarityTransform,
|
||||
'affine': AffineTransform,
|
||||
'projective': ProjectiveTransform,
|
||||
@@ -669,11 +644,11 @@ def estimate_transform(ttype, src, dst, **kwargs):
|
||||
|
||||
"""
|
||||
ttype = ttype.lower()
|
||||
if ttype not in TRANSFORMATIONS:
|
||||
if ttype not in TRANSFORMS:
|
||||
raise ValueError('the transformation type \'%s\' is not'
|
||||
'implemented' % ttype)
|
||||
|
||||
tform = TRANSFORMATIONS[ttype]()
|
||||
tform = TRANSFORMS[ttype]()
|
||||
tform.estimate(src, dst, **kwargs)
|
||||
|
||||
return tform
|
||||
@@ -696,158 +671,3 @@ def matrix_transform(coords, matrix):
|
||||
|
||||
"""
|
||||
return ProjectiveTransform(matrix)(coords)
|
||||
|
||||
|
||||
def warp_coords(orows, ocols, bands, coord_transform_fn,
|
||||
dtype=np.float64):
|
||||
"""Build the source coordinates for the output pixels of an image warp.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
orows : int
|
||||
Number of output rows.
|
||||
ocols : int
|
||||
Number of output columns.
|
||||
bands : int
|
||||
Number of color bands (aka channels).
|
||||
coord_transform_fn : callable like GeometricTransform.inverse
|
||||
Return input coordinates for given output coordinates.
|
||||
dtype : np.dtype or string
|
||||
dtype for return value (sane choices: float32 or float64)
|
||||
|
||||
Returns
|
||||
-------
|
||||
coords : (3, orows, ocols, bands) array of dtype `dtype`
|
||||
Coordinates for `scipy.ndimage.map_coordinates`, that will yield
|
||||
an image of shape (orows, ocols, bands) by drawing from source
|
||||
points according to the `coord_transform_fn`.
|
||||
|
||||
Notes
|
||||
-----
|
||||
This is a lower-level routine that produces the source coordinates used by
|
||||
`warp()`.
|
||||
|
||||
It is provided separately from `warp` to give additional flexibility to
|
||||
users who would like, for example, to re-use a particular coordinate
|
||||
mapping, to use specific dtypes at various points along the the
|
||||
image-warping process, or to implement different post-processing logic
|
||||
than `warp` performs after the call to `ndimage.map_coordinates`.
|
||||
|
||||
|
||||
Examples
|
||||
--------
|
||||
Produce a coordinate map that Shifts an image to the right:
|
||||
|
||||
>>> from skimage import data
|
||||
>>> from scipy.ndimage import map_coordinates
|
||||
>>>
|
||||
>>> def shift_right(xy):
|
||||
... xy[:, 0] -= 10
|
||||
... return xy
|
||||
>>>
|
||||
>>> coords = warp_coords(30, 30, 3, shift_right)
|
||||
>>> image = data.lena().astype(np.float32)
|
||||
>>> warped_image = map_coordinates(image, coords)
|
||||
|
||||
"""
|
||||
|
||||
coords = np.empty((3, orows, ocols, bands), dtype=dtype)
|
||||
|
||||
# Reshape grid coordinates into a (P, 2) array of (x, y) pairs
|
||||
tf_coords = np.indices((ocols, orows), dtype=dtype).reshape(2, -1).T
|
||||
|
||||
# Map each (x, y) pair to the source image according to
|
||||
# the user-provided mapping
|
||||
tf_coords = coord_transform_fn(tf_coords)
|
||||
|
||||
# Reshape back to a (2, M, N) coordinate grid
|
||||
tf_coords = tf_coords.T.reshape((-1, ocols, orows)).swapaxes(1, 2)
|
||||
|
||||
# Place the y-coordinate mapping
|
||||
_stackcopy(coords[1, ...], tf_coords[0, ...])
|
||||
|
||||
# Place the x-coordinate mapping
|
||||
_stackcopy(coords[0, ...], tf_coords[1, ...])
|
||||
|
||||
# colour-coordinate mapping
|
||||
coords[2, ...] = range(bands)
|
||||
|
||||
return coords
|
||||
|
||||
|
||||
def warp(image, inverse_map=None, map_args={}, output_shape=None, order=1,
|
||||
mode='constant', cval=0., reverse_map=None):
|
||||
"""Warp an image according to a given coordinate transformation.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
image : 2-D array
|
||||
Input image.
|
||||
inverse_map : transformation object, callable xy = f(xy, **kwargs)
|
||||
Inverse coordinate map. A function that transforms a (N, 2) array of
|
||||
``(x, y)`` coordinates in the *output image* into their corresponding
|
||||
coordinates in the *source image* (e.g. a transformation object or its
|
||||
inverse).
|
||||
map_args : dict, optional
|
||||
Keyword arguments passed to `inverse_map`.
|
||||
output_shape : tuple (rows, cols)
|
||||
Shape of the output image generated.
|
||||
order : int
|
||||
Order of splines used in interpolation. See
|
||||
`scipy.ndimage.map_coordinates` for detail.
|
||||
mode : string
|
||||
How to handle values outside the image borders. See
|
||||
`scipy.ndimage.map_coordinates` for detail.
|
||||
cval : float
|
||||
Used in conjunction with mode 'constant', the value outside
|
||||
the image boundaries.
|
||||
|
||||
Examples
|
||||
--------
|
||||
Shift an image to the right:
|
||||
|
||||
>>> from skimage import data
|
||||
>>> image = data.camera()
|
||||
>>>
|
||||
>>> def shift_right(xy):
|
||||
... xy[:, 0] -= 10
|
||||
... return xy
|
||||
>>>
|
||||
>>> warp(image, shift_right)
|
||||
|
||||
"""
|
||||
# Backward API compatibility
|
||||
if reverse_map is not None:
|
||||
inverse_map = reverse_map
|
||||
|
||||
if image.ndim < 2:
|
||||
raise ValueError("Input must have more than 1 dimension.")
|
||||
|
||||
image = np.atleast_3d(img_as_float(image))
|
||||
ishape = np.array(image.shape)
|
||||
bands = ishape[2]
|
||||
|
||||
if output_shape is None:
|
||||
output_shape = ishape
|
||||
|
||||
rows, cols = output_shape[:2]
|
||||
|
||||
def coord_transform_fn(*args):
|
||||
return inverse_map(*args, **map_args)
|
||||
|
||||
coords = warp_coords(rows, cols, bands, coord_transform_fn)
|
||||
|
||||
# Prefilter not necessary for order 1 interpolation
|
||||
prefilter = order > 1
|
||||
mapped = ndimage.map_coordinates(image, coords, prefilter=prefilter,
|
||||
mode=mode, order=order, cval=cval)
|
||||
|
||||
# The spline filters sometimes return results outside [0, 1],
|
||||
# so clip to ensure valid data
|
||||
clipped = np.clip(mapped, 0, 1)
|
||||
|
||||
if mode == 'constant' and not (0 <= cval <= 1):
|
||||
clipped[mapped == cval] = cval
|
||||
|
||||
# Remove singleton dim introduced by atleast_3d
|
||||
return clipped.squeeze()
|
||||
|
||||
Reference in New Issue
Block a user