o h@sddlZddlmZddlmZddlmZmZm Z gdZ ddZ d d Z d d d dZ ddddZddZddddZdS)N)entropy) dtype_range)_supported_float_typecheck_shape_equalitywarn)mean_squared_errornormalized_root_msepeak_signal_noise_rationormalized_mutual_informationcCs4t|j|jf}tj||d}tj||d}||fS)zK Promote im1, im2 to nearest appropriate floating point precision. dtype)rr npasarray)image0image1 float_typerY/var/www/html/scripts/venv/lib/python3.10/site-packages/skimage/metrics/simple_metrics.py _as_floatssrcCs0t||t||\}}tj||dtjdS)a Compute the mean-squared error between two images. Parameters ---------- image0, image1 : ndarray Images. Any dimensionality, must have same shape. Returns ------- mse : float The mean-squared error (MSE) metric. Notes ----- .. versionchanged:: 0.16 This function was renamed from ``skimage.measure.compare_mse`` to ``skimage.metrics.mean_squared_error``. rr )rrrmeanfloat64)rrrrrrs r euclidean) normalizationcCst||t||\}}|}|dkr"ttj||tjd}n|dkr/||}n |dkr8|}nt dtt |||S)a0 Compute the normalized root mean-squared error (NRMSE) between two images. Parameters ---------- image_true : ndarray Ground-truth image, same shape as im_test. image_test : ndarray Test image. normalization : {'euclidean', 'min-max', 'mean'}, optional Controls the normalization method to use in the denominator of the NRMSE. There is no standard method of normalization across the literature [1]_. The methods available here are as follows: - 'euclidean' : normalize by the averaged Euclidean norm of ``im_true``:: NRMSE = RMSE * sqrt(N) / || im_true || where || . || denotes the Frobenius norm and ``N = im_true.size``. This result is equivalent to:: NRMSE = || im_true - im_test || / || im_true ||. - 'min-max' : normalize by the intensity range of ``im_true``. - 'mean' : normalize by the mean of ``im_true`` Returns ------- nrmse : float The NRMSE metric. Notes ----- .. versionchanged:: 0.16 This function was renamed from ``skimage.measure.compare_nrmse`` to ``skimage.metrics.normalized_root_mse``. References ---------- .. [1] https://en.wikipedia.org/wiki/Root-mean-square_deviation rr zmin-maxrzUnsupported norm_type) rrlowerrsqrtrrmaxmin ValueErrorr) image_true image_testrdenomrrrr 2s - r ) data_rangecCst|||dur=|j|jkrtdt|jj\}}t|t|}}||ks.||kr2td|dkr9|}n||}t ||\}}t ||}dt |d|S)a Compute the peak signal to noise ratio (PSNR) for an image. Parameters ---------- image_true : ndarray Ground-truth image, same shape as im_test. image_test : ndarray Test image. data_range : int, 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. Returns ------- psnr : float The PSNR metric. Notes ----- .. versionchanged:: 0.16 This function was renamed from ``skimage.measure.compare_psnr`` to ``skimage.metrics.peak_signal_noise_ratio``. References ---------- .. [1] https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio NzFInputs have mismatched dtype. Setting data_range based on image_true.zuimage_true has intensity values outside the range expected for its data type. Please manually specify the data_range.r r) rr rrtyperrrrrrlog10)rr r"dmindmaxtrue_mintrue_maxerrrrrr os   r cCsZtddt||jDstd|d|jdddt||jD}tj||dd d S) abPad an array with trailing zeros to a given target shape. Parameters ---------- arr : ndarray The input array. shape : tuple The target shape. Returns ------- padded : ndarray The padded array. Examples -------- >>> _pad_to(np.ones((1, 1), dtype=int), (1, 3)) array([[1, 0, 0]]) css|] \}}||kVqdS)Nr.0sirrr sz_pad_to..z Target shape z# cannot be smaller than inputshape z along any axis.cSsg|] \}}d||fqS)rrr+rrr sz_pad_to..constantr) pad_widthmodeconstant_values)allzipshaperrpad)arrr7paddingrrr_pad_tos   r;d)binsc Cs|j|jkrtd|jd|jd|j|jkr,t|j|j}t||}t||}n||}}tjt|dt|dg|dd\}}ttj |dd}ttj |d d} tt|d} || | S) aCompute the normalized mutual information (NMI). The normalized mutual information of :math:`A` and :math:`B` is given by:: .. math:: Y(A, B) = \frac{H(A) + H(B)}{H(A, B)} where :math:`H(X) := - \sum_{x \in X}{x \log x}` is the entropy. It was proposed to be useful in registering images by Colin Studholme and colleagues [1]_. It ranges from 1 (perfectly uncorrelated image values) to 2 (perfectly correlated image values, whether positively or negatively). Parameters ---------- image0, image1 : ndarray Images to be compared. The two input images must have the same number of dimensions. bins : int or sequence of int, optional The number of bins along each axis of the joint histogram. Returns ------- nmi : float The normalized mutual information between the two arrays, computed at the granularity given by ``bins``. Higher NMI implies more similar input images. Raises ------ ValueError If the images don't have the same number of dimensions. Notes ----- If the two input images are not the same shape, the smaller image is padded with zeros. References ---------- .. [1] C. Studholme, D.L.G. Hill, & D.J. Hawkes (1999). An overlap invariant entropy measure of 3D medical image alignment. Pattern Recognition 32(1):71-86 :DOI:`10.1016/S0031-3203(98)00091-0` z6NMI requires images of same number of dimensions. Got zD for `image0` and zD for `image1`.T)r=densityr)axis) ndimrr7rmaximumr; histogramddreshapersum) rrr= max_shapepadded0padded1hist bin_edgesH0H1H01rrrr s( /      r )numpyr scipy.statsr util.dtyper _shared.utilsrrr__all__rrr r r;r rrrrs   =7