mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-30 18:16:09 +08:00
Move image transform functions to _geometric file
This commit is contained in:
Johannes Schönberger
@@ -2,7 +2,7 @@ from .hough_transform import *
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from .radon_transform import *
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from .finite_radon_transform import *
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from .integral import *
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from ._geometric import (estimate_transform,
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from ._geometric import (warp, warp_coords, estimate_transform,
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SimilarityTransform, AffineTransform,
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ProjectiveTransform, PolynomialTransform)
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from ._warps import warp, warp_coords, rotate, swirl, homography
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from ._warps import rotate, swirl, homography
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@@ -1,5 +1,8 @@
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import math
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import numpy as np
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from scipy import ndimage
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from skimage.util import img_as_float
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from ._warps_cy import _warp_fast
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class GeometricTransform(object):
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@@ -584,6 +587,11 @@ TRANSFORMS = {
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'projective': ProjectiveTransform,
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'polynomial': PolynomialTransform,
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}
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HOMOGRAPHY_TRANSFORMS = (
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SimilarityTransform,
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AffineTransform,
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ProjectiveTransform
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)
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def estimate_transform(ttype, src, dst, **kwargs):
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@@ -671,3 +679,201 @@ def matrix_transform(coords, matrix):
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"""
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return ProjectiveTransform(matrix)(coords)
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def _stackcopy(a, b):
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"""Copy b into each color layer of a, such that::
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a[:,:,0] = a[:,:,1] = ... = b
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Parameters
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----------
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a : (M, N) or (M, N, P) ndarray
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Target array.
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b : (M, N)
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Source array.
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Notes
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-----
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Color images are stored as an ``(M, N, 3)`` or ``(M, N, 4)`` arrays.
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"""
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if a.ndim == 3:
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a[:] = b[:, :, np.newaxis]
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else:
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a[:] = b
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def warp_coords(coord_map, shape, dtype=np.float64):
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"""Build the source coordinates for the output pixels of an image warp.
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Parameters
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----------
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coord_map : callable like GeometricTransform.inverse
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Return input coordinates for given output coordinates.
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shape : tuple
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Shape of output image ``(rows, cols[, bands])``.
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dtype : np.dtype or string
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dtype for return value (sane choices: float32 or float64).
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Returns
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-------
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coords : (ndim, rows, cols[, bands]) array of dtype `dtype`
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Coordinates for `scipy.ndimage.map_coordinates`, that will yield
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an image of shape (orows, ocols, bands) by drawing from source
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points according to the `coord_transform_fn`.
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Notes
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-----
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This is a lower-level routine that produces the source coordinates used by
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`warp()`.
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It is provided separately from `warp` to give additional flexibility to
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users who would like, for example, to re-use a particular coordinate
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mapping, to use specific dtypes at various points along the the
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image-warping process, or to implement different post-processing logic
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than `warp` performs after the call to `ndimage.map_coordinates`.
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Examples
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--------
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Produce a coordinate map that Shifts an image to the right:
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>>> from skimage import data
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>>> from scipy.ndimage import map_coordinates
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>>>
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>>> def shift_right(xy):
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... xy[:, 0] -= 10
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... return xy
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>>>
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>>> image = data.lena().astype(np.float32)
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>>> coords = warp_coords(shift_right, image.shape)
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>>> warped_image = map_coordinates(image, coords)
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"""
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rows, cols = shape[0], shape[1]
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coords_shape = [len(shape), rows, cols]
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if len(shape) == 3:
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coords_shape.append(shape[2])
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coords = np.empty(coords_shape, dtype=dtype)
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# Reshape grid coordinates into a (P, 2) array of (x, y) pairs
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tf_coords = np.indices((cols, rows), dtype=dtype).reshape(2, -1).T
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# Map each (x, y) pair to the source image according to
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# the user-provided mapping
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tf_coords = coord_map(tf_coords)
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# Reshape back to a (2, M, N) coordinate grid
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tf_coords = tf_coords.T.reshape((-1, cols, rows)).swapaxes(1, 2)
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# Place the y-coordinate mapping
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_stackcopy(coords[1, ...], tf_coords[0, ...])
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# Place the x-coordinate mapping
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_stackcopy(coords[0, ...], tf_coords[1, ...])
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if len(shape) == 3:
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coords[2, ...] = range(shape[2])
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return coords
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def warp(image, inverse_map=None, map_args={}, output_shape=None, order=1,
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mode='constant', cval=0., reverse_map=None):
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"""Warp an image according to a given coordinate transformation.
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Parameters
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----------
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image : 2-D array
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Input image.
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inverse_map : transformation object, callable xy = f(xy, **kwargs)
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Inverse coordinate map. A function that transforms a (N, 2) array of
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``(x, y)`` coordinates in the *output image* into their corresponding
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coordinates in the *source image* (e.g. a transformation object or its
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inverse).
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map_args : dict, optional
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Keyword arguments passed to `inverse_map`.
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output_shape : tuple (rows, cols)
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Shape of the output image generated.
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order : int
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Order of splines used in interpolation. See
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`scipy.ndimage.map_coordinates` for detail.
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mode : string
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How to handle values outside the image borders. See
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`scipy.ndimage.map_coordinates` for detail.
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cval : float
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Used in conjunction with mode 'constant', the value outside
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the image boundaries.
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Examples
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--------
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Shift an image to the right:
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>>> from skimage import data
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>>> image = data.camera()
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>>>
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>>> def shift_right(xy):
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... xy[:, 0] -= 10
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... return xy
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>>>
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>>> warp(image, shift_right)
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"""
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# Backward API compatibility
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if reverse_map is not None:
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inverse_map = reverse_map
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if image.ndim < 2:
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raise ValueError("Input must have more than 1 dimension.")
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orig_ndim = image.ndim
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image = np.atleast_3d(img_as_float(image))
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ishape = np.array(image.shape)
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bands = ishape[2]
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# use fast Cython version for specific parameters
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fast_modes = ('constant', 'reflect', 'wrap')
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if order in (0, 1) and mode in fast_modes and not map_args:
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matrix = None
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if isinstance(inverse_map, HOMOGRAPHY_TRANSFORMS):
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matrix = inverse_map._matrix
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elif inverse_map.__name__ == 'inverse' \
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and inverse_map.im_class in HOMOGRAPHY_TRANSFORMS:
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matrix = np.linalg.inv(inverse_map.im_self._matrix)
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if matrix is not None:
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# transform all bands
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dims = []
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for dim in range(image.shape[2]):
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dims.append(_warp_fast(image[..., dim], matrix,
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output_shape=output_shape,
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order=order, mode=mode, cval=cval))
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out = np.dstack(dims)
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if orig_ndim == 2:
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out = out[..., 0]
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return out
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if output_shape is None:
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output_shape = ishape
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rows, cols = output_shape[:2]
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def coord_map(*args):
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return inverse_map(*args, **map_args)
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coords = warp_coords(coord_map, (rows, cols, bands))
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# Prefilter not necessary for order 1 interpolation
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prefilter = order > 1
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mapped = ndimage.map_coordinates(image, coords, prefilter=prefilter,
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mode=mode, order=order, cval=cval)
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# The spline filters sometimes return results outside [0, 1],
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# so clip to ensure valid data
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clipped = np.clip(mapped, 0, 1)
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if mode == 'constant' and not (0 <= cval <= 1):
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clipped[mapped == cval] = cval
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# Remove singleton dim introduced by atleast_3d
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return clipped.squeeze()
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+1
-210
@@ -1,214 +1,5 @@
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import numpy as np
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from scipy import ndimage
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from skimage.util import img_as_float
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from ._geometric import (SimilarityTransform, AffineTransform,
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ProjectiveTransform)
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from ._warps_cy import _warp_fast
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HOMOGRAPHY_TRANSFORMS = (
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SimilarityTransform,
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AffineTransform,
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ProjectiveTransform
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)
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def _stackcopy(a, b):
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"""Copy b into each color layer of a, such that::
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a[:,:,0] = a[:,:,1] = ... = b
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Parameters
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----------
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a : (M, N) or (M, N, P) ndarray
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Target array.
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b : (M, N)
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Source array.
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Notes
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-----
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Color images are stored as an ``(M, N, 3)`` or ``(M, N, 4)`` arrays.
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"""
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if a.ndim == 3:
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a[:] = b[:, :, np.newaxis]
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else:
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a[:] = b
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def warp_coords(coord_map, shape, dtype=np.float64):
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"""Build the source coordinates for the output pixels of an image warp.
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Parameters
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----------
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coord_map : callable like GeometricTransform.inverse
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Return input coordinates for given output coordinates.
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shape : tuple
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Shape of output image ``(rows, cols[, bands])``.
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dtype : np.dtype or string
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dtype for return value (sane choices: float32 or float64).
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Returns
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-------
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coords : (ndim, rows, cols[, bands]) array of dtype `dtype`
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Coordinates for `scipy.ndimage.map_coordinates`, that will yield
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an image of shape (orows, ocols, bands) by drawing from source
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points according to the `coord_transform_fn`.
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Notes
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-----
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This is a lower-level routine that produces the source coordinates used by
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`warp()`.
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It is provided separately from `warp` to give additional flexibility to
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users who would like, for example, to re-use a particular coordinate
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mapping, to use specific dtypes at various points along the the
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image-warping process, or to implement different post-processing logic
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than `warp` performs after the call to `ndimage.map_coordinates`.
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Examples
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--------
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Produce a coordinate map that Shifts an image to the right:
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>>> from skimage import data
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>>> from scipy.ndimage import map_coordinates
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>>>
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>>> def shift_right(xy):
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... xy[:, 0] -= 10
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... return xy
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>>>
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>>> image = data.lena().astype(np.float32)
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>>> coords = warp_coords(shift_right, image.shape)
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>>> warped_image = map_coordinates(image, coords)
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"""
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rows, cols = shape[0], shape[1]
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coords_shape = [len(shape), rows, cols]
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if len(shape) == 3:
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coords_shape.append(shape[2])
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coords = np.empty(coords_shape, dtype=dtype)
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# Reshape grid coordinates into a (P, 2) array of (x, y) pairs
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tf_coords = np.indices((cols, rows), dtype=dtype).reshape(2, -1).T
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# Map each (x, y) pair to the source image according to
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# the user-provided mapping
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tf_coords = coord_map(tf_coords)
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# Reshape back to a (2, M, N) coordinate grid
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tf_coords = tf_coords.T.reshape((-1, cols, rows)).swapaxes(1, 2)
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# Place the y-coordinate mapping
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_stackcopy(coords[1, ...], tf_coords[0, ...])
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# Place the x-coordinate mapping
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_stackcopy(coords[0, ...], tf_coords[1, ...])
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if len(shape) == 3:
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coords[2, ...] = range(shape[2])
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return coords
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def warp(image, inverse_map=None, map_args={}, output_shape=None, order=1,
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mode='constant', cval=0., reverse_map=None):
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"""Warp an image according to a given coordinate transformation.
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Parameters
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----------
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image : 2-D array
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Input image.
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inverse_map : transformation object, callable xy = f(xy, **kwargs)
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Inverse coordinate map. A function that transforms a (N, 2) array of
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``(x, y)`` coordinates in the *output image* into their corresponding
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coordinates in the *source image* (e.g. a transformation object or its
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inverse).
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map_args : dict, optional
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Keyword arguments passed to `inverse_map`.
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output_shape : tuple (rows, cols)
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Shape of the output image generated.
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order : int
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Order of splines used in interpolation. See
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`scipy.ndimage.map_coordinates` for detail.
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mode : string
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How to handle values outside the image borders. See
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`scipy.ndimage.map_coordinates` for detail.
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cval : float
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Used in conjunction with mode 'constant', the value outside
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the image boundaries.
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Examples
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--------
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Shift an image to the right:
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>>> from skimage import data
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>>> image = data.camera()
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>>>
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>>> def shift_right(xy):
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... xy[:, 0] -= 10
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... return xy
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>>>
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>>> warp(image, shift_right)
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"""
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# Backward API compatibility
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if reverse_map is not None:
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inverse_map = reverse_map
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if image.ndim < 2:
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raise ValueError("Input must have more than 1 dimension.")
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orig_ndim = image.ndim
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image = np.atleast_3d(img_as_float(image))
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ishape = np.array(image.shape)
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bands = ishape[2]
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# use fast Cython version for specific parameters
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fast_modes = ('constant', 'reflect', 'wrap')
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if order in (0, 1) and mode in fast_modes and not map_args:
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matrix = None
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if isinstance(inverse_map, HOMOGRAPHY_TRANSFORMS):
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matrix = inverse_map._matrix
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elif inverse_map.__name__ == 'inverse' \
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and inverse_map.im_class in HOMOGRAPHY_TRANSFORMS:
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matrix = np.linalg.inv(inverse_map.im_self._matrix)
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if matrix is not None:
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# transform all bands
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dims = []
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for dim in range(image.shape[2]):
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dims.append(_warp_fast(image[..., dim], matrix,
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output_shape=output_shape,
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order=order, mode=mode, cval=cval))
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out = np.dstack(dims)
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if orig_ndim == 2:
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out = out[..., 0]
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return out
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if output_shape is None:
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output_shape = ishape
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rows, cols = output_shape[:2]
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def coord_map(*args):
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return inverse_map(*args, **map_args)
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coords = warp_coords(coord_map, (rows, cols, bands))
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# Prefilter not necessary for order 1 interpolation
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prefilter = order > 1
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mapped = ndimage.map_coordinates(image, coords, prefilter=prefilter,
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mode=mode, order=order, cval=cval)
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# The spline filters sometimes return results outside [0, 1],
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# so clip to ensure valid data
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clipped = np.clip(mapped, 0, 1)
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if mode == 'constant' and not (0 <= cval <= 1):
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clipped[mapped == cval] = cval
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# Remove singleton dim introduced by atleast_3d
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return clipped.squeeze()
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from ._geometric import warp, SimilarityTransform
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def rotate(image, angle, resize=False, order=1, mode='constant', cval=0.):
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@@ -1,10 +1,9 @@
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import numpy as np
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from numpy.testing import assert_equal, assert_array_almost_equal
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from skimage.transform._warps import _stackcopy
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from skimage.transform import (estimate_transform, SimilarityTransform,
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AffineTransform, ProjectiveTransform,
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PolynomialTransform)
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from skimage.transform._geometric import _stackcopy
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from skimage.transform import (estimate_transform,
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SimilarityTransform, AffineTransform,
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ProjectiveTransform, PolynomialTransform)
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SRC = np.array([
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