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https://github.com/wassname/scikit-image.git
synced 2026-07-23 13:10:18 +08:00
Clip to min and max range of input image, add missing clip parameter to other functions
This commit is contained in:
Johannes Schönberger
@@ -994,6 +994,24 @@ def warp_coords(coord_map, shape, dtype=np.float64):
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return coords
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def _clip_warp_output(input_image, output_image, clip, mode, order, cval):
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"""Clip output image to range of values of input image, considering the
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parameters of a call to warp.
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"""
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if clip and order != 0:
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min_val = input_image.min()
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max_val = input_image.max()
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clipped = np.clip(output_image, min_val, max_val)
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if mode == 'constant' and not (min_val <= cval <= max_val):
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clipped[output_image == cval] = cval
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return clipped
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return output_image
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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., clip=True):
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"""Warp an image according to a given coordinate transformation.
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@@ -1055,17 +1073,17 @@ def warp(image, inverse_map=None, map_args={}, output_shape=None, order=1,
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Used in conjunction with mode 'constant', the value outside
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the image boundaries.
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clip : bool, optional
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Whether to clip the output to the float range of ``[0, 1]``, or
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``[-1, 1]`` for input images with negative values. This is enabled by
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default, since higher order interpolation may produce values outside
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the given input range.
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Whether to clip the output to the range of values of the input image.
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This is enabled by default, since higher order interpolation may
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produce values outside the given input range.
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Notes
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-----
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In case of a `SimilarityTransform`, `AffineTransform` and
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`ProjectiveTransform` and `order` in [0, 3] this function uses the
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underlying transformation matrix to warp the image with a much faster
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routine.
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- The input image is converted to a `double` image.
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- In case of a `SimilarityTransform`, `AffineTransform` and
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`ProjectiveTransform` and `order` in [0, 3] this function uses the
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underlying transformation matrix to warp the image with a much faster
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routine.
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Examples
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--------
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@@ -1124,7 +1142,7 @@ def warp(image, inverse_map=None, map_args={}, output_shape=None, order=1,
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"""
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image = img_as_float(image)
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image = image.astype(np.double)
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input_shape = np.array(image.shape)
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if output_shape is None:
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@@ -1219,21 +1237,7 @@ def warp(image, inverse_map=None, map_args={}, output_shape=None, order=1,
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out = ndimage.map_coordinates(image, coords, prefilter=prefilter,
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mode=mode, order=order, cval=cval)
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if clip:
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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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if np.min(image) < 0:
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min_val = -1
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else:
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min_val = 0
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max_val = 1
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clipped = np.clip(out, min_val, max_val)
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if mode == 'constant' and not (0 <= cval <= 1):
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clipped[out == cval] = cval
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out = clipped
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out = _clip_warp_output(image, out, clip, mode, order, cval)
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return out
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+35
-16
@@ -2,11 +2,11 @@ import numpy as np
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from scipy import ndimage
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from skimage.transform._geometric import (warp, SimilarityTransform,
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AffineTransform)
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AffineTransform, _clip_warp_output)
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from skimage.measure import block_reduce
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def resize(image, output_shape, order=1, mode='constant', cval=0.):
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def resize(image, output_shape, order=1, mode='constant', cval=0, clip=True):
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"""Resize image to match a certain size.
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Performs interpolation to up-size or down-size images. For down-sampling
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@@ -40,6 +40,10 @@ def resize(image, output_shape, order=1, mode='constant', cval=0.):
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cval : float, optional
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Used in conjunction with mode 'constant', the value outside
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the image boundaries.
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clip : bool, optional
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Whether to clip the output to the range of values of the input image.
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This is enabled by default, since higher order interpolation may
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produce values outside the given input range.
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Examples
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--------
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@@ -71,8 +75,10 @@ def resize(image, output_shape, order=1, mode='constant', cval=0.):
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coord_map = np.array([map_rows, map_cols, map_dims])
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out = ndimage.map_coordinates(image, coord_map, order=order, mode=mode,
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cval=cval)
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out = ndimage.map_coordinates(image, coord_map, order=order,
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mode=mode, cval=cval)
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out = _clip_warp_output(image, out, clip, mode, order, cval)
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else: # 2-dimensional interpolation
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@@ -87,12 +93,12 @@ def resize(image, output_shape, order=1, mode='constant', cval=0.):
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tform.estimate(src_corners, dst_corners)
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out = warp(image, tform, output_shape=output_shape, order=order,
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mode=mode, cval=cval)
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mode=mode, cval=cval, clip=clip)
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return out
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def rescale(image, scale, order=1, mode='constant', cval=0.):
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def rescale(image, scale, order=1, mode='constant', cval=0, clip=True):
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"""Scale image by a certain factor.
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Performs interpolation to upscale or down-scale images. For down-sampling
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@@ -124,6 +130,10 @@ def rescale(image, scale, order=1, mode='constant', cval=0.):
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cval : float, optional
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Used in conjunction with mode 'constant', the value outside
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the image boundaries.
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clip : bool, optional
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Whether to clip the output to the range of values of the input image.
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This is enabled by default, since higher order interpolation may
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produce values outside the given input range.
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Examples
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--------
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@@ -147,11 +157,12 @@ def rescale(image, scale, order=1, mode='constant', cval=0.):
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cols = np.round(col_scale * orig_cols)
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output_shape = (rows, cols)
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return resize(image, output_shape, order=order, mode=mode, cval=cval)
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return resize(image, output_shape, order=order, mode=mode, cval=cval,
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clip=clip)
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def rotate(image, angle, resize=False, order=1, mode='constant', cval=0.,
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center=None):
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def rotate(image, angle, resize=False, center=None, order=1, mode='constant',
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cval=0, clip=True):
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"""Rotate image by a certain angle around its center.
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Parameters
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@@ -164,6 +175,9 @@ def rotate(image, angle, resize=False, order=1, mode='constant', cval=0.,
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Determine whether the shape of the output image will be automatically
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calculated, so the complete rotated image exactly fits. Default is
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False.
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center : iterable of length 2
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The rotation center. If ``center=None``, the image is rotated around
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its center, i.e. ``center=(rows / 2 - 0.5, cols / 2 - 0.5)``.
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Returns
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-------
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@@ -181,9 +195,10 @@ def rotate(image, angle, resize=False, order=1, mode='constant', cval=0.,
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cval : float, optional
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Used in conjunction with mode 'constant', the value outside
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the image boundaries.
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center : iterable of length 2
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The rotation center. If ``center=None``, the image is rotated around
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its center, i.e. ``center=(rows / 2 - 0.5, cols / 2 - 0.5)``.
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clip : bool, optional
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Whether to clip the output to the range of values of the input image.
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This is enabled by default, since higher order interpolation may
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produce values outside the given input range.
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Examples
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--------
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@@ -230,10 +245,10 @@ def rotate(image, angle, resize=False, order=1, mode='constant', cval=0.,
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tform = tform4 + tform
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return warp(image, tform, output_shape=output_shape, order=order,
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mode=mode, cval=cval)
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mode=mode, cval=cval, clip=clip)
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def downscale_local_mean(image, factors, cval=0):
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def downscale_local_mean(image, factors, cval=0, clip=True):
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"""Down-sample N-dimensional image by local averaging.
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The image is padded with `cval` if it is not perfectly divisible by the
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@@ -294,7 +309,7 @@ def _swirl_mapping(xy, center, rotation, strength, radius):
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def swirl(image, center=None, strength=1, radius=100, rotation=0,
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output_shape=None, order=1, mode='constant', cval=0):
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output_shape=None, order=1, mode='constant', cval=0, clip=True):
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"""Perform a swirl transformation.
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Parameters
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@@ -330,6 +345,10 @@ def swirl(image, center=None, strength=1, radius=100, rotation=0,
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cval : float, optional
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Used in conjunction with mode 'constant', the value outside
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the image boundaries.
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clip : bool, optional
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Whether to clip the output to the range of values of the input image.
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This is enabled by default, since higher order interpolation may
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produce values outside the given input range.
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"""
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@@ -343,4 +362,4 @@ def swirl(image, center=None, strength=1, radius=100, rotation=0,
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return warp(image, _swirl_mapping, map_args=warp_args,
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output_shape=output_shape,
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order=order, mode=mode, cval=cval)
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order=order, mode=mode, cval=cval, clip=clip)
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@@ -3,7 +3,6 @@
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#cython: nonecheck=False
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#cython: wraparound=False
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import numpy as np
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cimport numpy as cnp
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from skimage._shared.interpolation cimport (nearest_neighbour_interpolation,
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bilinear_interpolation,
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@@ -75,14 +75,15 @@ def test_warp_nd():
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def test_warp_clip():
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x = 2 * np.ones((5, 5), dtype=np.double)
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matrix = np.eye(3)
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x = np.zeros((5, 5), dtype=np.double)
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x[2, 2] = 1
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outx = warp(x, matrix, order=0, clip=False)
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assert_almost_equal(x, outx)
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outx = rescale(x, 3, order=3, clip=False)
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assert outx.min() < 0
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outx = warp(x, matrix, order=0, clip=True)
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assert_almost_equal(x / 2, outx)
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outx = rescale(x, 3, order=3, clip=True)
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assert_almost_equal(outx.min(), 0)
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assert_almost_equal(outx.max(), 1)
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def test_homography():
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