using for loop to install conda package
This commit is contained in:
275
.CondaPkg/env/Lib/site-packages/skimage/metrics/_structural_similarity.py
vendored
Normal file
275
.CondaPkg/env/Lib/site-packages/skimage/metrics/_structural_similarity.py
vendored
Normal file
@@ -0,0 +1,275 @@
|
||||
import functools
|
||||
|
||||
import numpy as np
|
||||
from scipy.ndimage import uniform_filter
|
||||
|
||||
from .._shared import utils
|
||||
from .._shared.filters import gaussian
|
||||
from .._shared.utils import _supported_float_type, check_shape_equality, warn
|
||||
from ..util.arraycrop import crop
|
||||
from ..util.dtype import dtype_range
|
||||
|
||||
__all__ = ['structural_similarity']
|
||||
|
||||
|
||||
def structural_similarity(im1, im2,
|
||||
*,
|
||||
win_size=None, gradient=False, data_range=None,
|
||||
channel_axis=None,
|
||||
gaussian_weights=False, full=False, **kwargs):
|
||||
"""
|
||||
Compute the mean structural similarity index between two images.
|
||||
Please pay attention to the `data_range` parameter with floating-point images.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
im1, im2 : ndarray
|
||||
Images. Any dimensionality with same shape.
|
||||
win_size : int or None, optional
|
||||
The side-length of the sliding window used in comparison. Must be an
|
||||
odd value. If `gaussian_weights` is True, this is ignored and the
|
||||
window size will depend on `sigma`.
|
||||
gradient : bool, optional
|
||||
If True, also return the gradient with respect to im2.
|
||||
data_range : float, optional
|
||||
The data range of the input image (distance between minimum and
|
||||
maximum possible values). By default, this is estimated from the image
|
||||
data type. This estimate may be wrong for floating-point image data.
|
||||
Therefore it is recommended to always pass this value explicitly
|
||||
(see note below).
|
||||
channel_axis : int or None, optional
|
||||
If None, the image is assumed to be a grayscale (single channel) image.
|
||||
Otherwise, this parameter indicates which axis of the array corresponds
|
||||
to channels.
|
||||
|
||||
.. versionadded:: 0.19
|
||||
``channel_axis`` was added in 0.19.
|
||||
gaussian_weights : bool, optional
|
||||
If True, each patch has its mean and variance spatially weighted by a
|
||||
normalized Gaussian kernel of width sigma=1.5.
|
||||
full : bool, optional
|
||||
If True, also return the full structural similarity image.
|
||||
|
||||
Other Parameters
|
||||
----------------
|
||||
use_sample_covariance : bool
|
||||
If True, normalize covariances by N-1 rather than, N where N is the
|
||||
number of pixels within the sliding window.
|
||||
K1 : float
|
||||
Algorithm parameter, K1 (small constant, see [1]_).
|
||||
K2 : float
|
||||
Algorithm parameter, K2 (small constant, see [1]_).
|
||||
sigma : float
|
||||
Standard deviation for the Gaussian when `gaussian_weights` is True.
|
||||
|
||||
Returns
|
||||
-------
|
||||
mssim : float
|
||||
The mean structural similarity index over the image.
|
||||
grad : ndarray
|
||||
The gradient of the structural similarity between im1 and im2 [2]_.
|
||||
This is only returned if `gradient` is set to True.
|
||||
S : ndarray
|
||||
The full SSIM image. This is only returned if `full` is set to True.
|
||||
|
||||
Notes
|
||||
-----
|
||||
If `data_range` is not specified, the range is automatically guessed
|
||||
based on the image data type. However for floating-point image data, this
|
||||
estimate yields a result double the value of the desired range, as the
|
||||
`dtype_range` in `skimage.util.dtype.py` has defined intervals from -1 to
|
||||
+1. This yields an estimate of 2, instead of 1, which is most often
|
||||
required when working with image data (as negative light intentsities are
|
||||
nonsensical). In case of working with YCbCr-like color data, note that
|
||||
these ranges are different per channel (Cb and Cr have double the range
|
||||
of Y), so one cannot calculate a channel-averaged SSIM with a single call
|
||||
to this function, as identical ranges are assumed for each channel.
|
||||
|
||||
To match the implementation of Wang et al. [1]_, set `gaussian_weights`
|
||||
to True, `sigma` to 1.5, `use_sample_covariance` to False, and
|
||||
specify the `data_range` argument.
|
||||
|
||||
.. versionchanged:: 0.16
|
||||
This function was renamed from ``skimage.measure.compare_ssim`` to
|
||||
``skimage.metrics.structural_similarity``.
|
||||
|
||||
References
|
||||
----------
|
||||
.. [1] Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P.
|
||||
(2004). Image quality assessment: From error visibility to
|
||||
structural similarity. IEEE Transactions on Image Processing,
|
||||
13, 600-612.
|
||||
https://ece.uwaterloo.ca/~z70wang/publications/ssim.pdf,
|
||||
:DOI:`10.1109/TIP.2003.819861`
|
||||
|
||||
.. [2] Avanaki, A. N. (2009). Exact global histogram specification
|
||||
optimized for structural similarity. Optical Review, 16, 613-621.
|
||||
:arxiv:`0901.0065`
|
||||
:DOI:`10.1007/s10043-009-0119-z`
|
||||
|
||||
"""
|
||||
check_shape_equality(im1, im2)
|
||||
float_type = _supported_float_type(im1.dtype)
|
||||
|
||||
if channel_axis is not None:
|
||||
# loop over channels
|
||||
args = dict(win_size=win_size,
|
||||
gradient=gradient,
|
||||
data_range=data_range,
|
||||
channel_axis=None,
|
||||
gaussian_weights=gaussian_weights,
|
||||
full=full)
|
||||
args.update(kwargs)
|
||||
nch = im1.shape[channel_axis]
|
||||
mssim = np.empty(nch, dtype=float_type)
|
||||
|
||||
if gradient:
|
||||
G = np.empty(im1.shape, dtype=float_type)
|
||||
if full:
|
||||
S = np.empty(im1.shape, dtype=float_type)
|
||||
channel_axis = channel_axis % im1.ndim
|
||||
_at = functools.partial(utils.slice_at_axis, axis=channel_axis)
|
||||
for ch in range(nch):
|
||||
ch_result = structural_similarity(im1[_at(ch)],
|
||||
im2[_at(ch)], **args)
|
||||
if gradient and full:
|
||||
mssim[ch], G[_at(ch)], S[_at(ch)] = ch_result
|
||||
elif gradient:
|
||||
mssim[ch], G[_at(ch)] = ch_result
|
||||
elif full:
|
||||
mssim[ch], S[_at(ch)] = ch_result
|
||||
else:
|
||||
mssim[ch] = ch_result
|
||||
mssim = mssim.mean()
|
||||
if gradient and full:
|
||||
return mssim, G, S
|
||||
elif gradient:
|
||||
return mssim, G
|
||||
elif full:
|
||||
return mssim, S
|
||||
else:
|
||||
return mssim
|
||||
|
||||
K1 = kwargs.pop('K1', 0.01)
|
||||
K2 = kwargs.pop('K2', 0.03)
|
||||
sigma = kwargs.pop('sigma', 1.5)
|
||||
if K1 < 0:
|
||||
raise ValueError("K1 must be positive")
|
||||
if K2 < 0:
|
||||
raise ValueError("K2 must be positive")
|
||||
if sigma < 0:
|
||||
raise ValueError("sigma must be positive")
|
||||
use_sample_covariance = kwargs.pop('use_sample_covariance', True)
|
||||
|
||||
if gaussian_weights:
|
||||
# Set to give an 11-tap filter with the default sigma of 1.5 to match
|
||||
# Wang et. al. 2004.
|
||||
truncate = 3.5
|
||||
|
||||
if win_size is None:
|
||||
if gaussian_weights:
|
||||
# set win_size used by crop to match the filter size
|
||||
r = int(truncate * sigma + 0.5) # radius as in ndimage
|
||||
win_size = 2 * r + 1
|
||||
else:
|
||||
win_size = 7 # backwards compatibility
|
||||
|
||||
if np.any((np.asarray(im1.shape) - win_size) < 0):
|
||||
raise ValueError(
|
||||
'win_size exceeds image extent. '
|
||||
'Either ensure that your images are '
|
||||
'at least 7x7; or pass win_size explicitly '
|
||||
'in the function call, with an odd value '
|
||||
'less than or equal to the smaller side of your '
|
||||
'images. If your images are multichannel '
|
||||
'(with color channels), set channel_axis to '
|
||||
'the axis number corresponding to the channels.')
|
||||
|
||||
if not (win_size % 2 == 1):
|
||||
raise ValueError('Window size must be odd.')
|
||||
|
||||
if data_range is None:
|
||||
if (np.issubdtype(im1.dtype, np.floating) or
|
||||
np.issubdtype(im2.dtype, np.floating)):
|
||||
raise ValueError(
|
||||
'Since image dtype is floating point, you must specify '
|
||||
'the data_range parameter. Please read the documentation '
|
||||
'carefully (including the note). It is recommended that '
|
||||
'you always specify the data_range anyway.')
|
||||
if im1.dtype != im2.dtype:
|
||||
warn("Inputs have mismatched dtypes. Setting data_range based on im1.dtype.",
|
||||
stacklevel=2)
|
||||
dmin, dmax = dtype_range[im1.dtype.type]
|
||||
data_range = dmax - dmin
|
||||
if np.issubdtype(im1.dtype, np.integer) and (im1.dtype != np.uint8):
|
||||
warn("Setting data_range based on im1.dtype. " +
|
||||
("data_range = %.0f. " % data_range) +
|
||||
"Please specify data_range explicitly to avoid mistakes.", stacklevel=2)
|
||||
|
||||
ndim = im1.ndim
|
||||
|
||||
if gaussian_weights:
|
||||
filter_func = gaussian
|
||||
filter_args = {'sigma': sigma, 'truncate': truncate, 'mode': 'reflect'}
|
||||
else:
|
||||
filter_func = uniform_filter
|
||||
filter_args = {'size': win_size}
|
||||
|
||||
# ndimage filters need floating point data
|
||||
im1 = im1.astype(float_type, copy=False)
|
||||
im2 = im2.astype(float_type, copy=False)
|
||||
|
||||
NP = win_size ** ndim
|
||||
|
||||
# filter has already normalized by NP
|
||||
if use_sample_covariance:
|
||||
cov_norm = NP / (NP - 1) # sample covariance
|
||||
else:
|
||||
cov_norm = 1.0 # population covariance to match Wang et. al. 2004
|
||||
|
||||
# compute (weighted) means
|
||||
ux = filter_func(im1, **filter_args)
|
||||
uy = filter_func(im2, **filter_args)
|
||||
|
||||
# compute (weighted) variances and covariances
|
||||
uxx = filter_func(im1 * im1, **filter_args)
|
||||
uyy = filter_func(im2 * im2, **filter_args)
|
||||
uxy = filter_func(im1 * im2, **filter_args)
|
||||
vx = cov_norm * (uxx - ux * ux)
|
||||
vy = cov_norm * (uyy - uy * uy)
|
||||
vxy = cov_norm * (uxy - ux * uy)
|
||||
|
||||
R = data_range
|
||||
C1 = (K1 * R) ** 2
|
||||
C2 = (K2 * R) ** 2
|
||||
|
||||
A1, A2, B1, B2 = ((2 * ux * uy + C1,
|
||||
2 * vxy + C2,
|
||||
ux ** 2 + uy ** 2 + C1,
|
||||
vx + vy + C2))
|
||||
D = B1 * B2
|
||||
S = (A1 * A2) / D
|
||||
|
||||
# to avoid edge effects will ignore filter radius strip around edges
|
||||
pad = (win_size - 1) // 2
|
||||
|
||||
# compute (weighted) mean of ssim. Use float64 for accuracy.
|
||||
mssim = crop(S, pad).mean(dtype=np.float64)
|
||||
|
||||
if gradient:
|
||||
# The following is Eqs. 7-8 of Avanaki 2009.
|
||||
grad = filter_func(A1 / D, **filter_args) * im1
|
||||
grad += filter_func(-S / B2, **filter_args) * im2
|
||||
grad += filter_func((ux * (A2 - A1) - uy * (B2 - B1) * S) / D,
|
||||
**filter_args)
|
||||
grad *= (2 / im1.size)
|
||||
|
||||
if full:
|
||||
return mssim, grad, S
|
||||
else:
|
||||
return mssim, grad
|
||||
else:
|
||||
if full:
|
||||
return mssim, S
|
||||
else:
|
||||
return mssim
|
||||
Reference in New Issue
Block a user