[;j"ddlZddlmZddlmZddlmZddlm Z m Z m Z gdZ dZ ed Zed d d Zdd dZdZdddZy)N)entropy) dispatchable) dtype_range)_supported_float_typecheck_shape_equalitywarn)mean_squared_errornormalized_root_msepeak_signal_noise_rationormalized_mutual_informationct|j|jf}tj||}tj||}||fS)zK Promote im1, im2 to nearest appropriate floating point precision. dtype)rrnpasarray)image0image1 float_types gG:\00. PROJECTS\API\Inventory\templateJSON\kerjaOCR\Lib\site-packages\skimage/metrics/simple_metrics.py _as_floatsrsE' fll'CDJ ZZj 1F ZZj 1F 6>ct||t||\}}tj||z dztjS)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)rrs rr r s<,(/NFF 77FVO) <>#33 & !011 77%j*= > FFr) data_rangect||||j|jk7r tdt|jj\}}t j |t j|}}||kDs||kr td|dk\r|}n||z }t||\}}t||}t|}dt j|dz|z zS)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 zFInputs 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) rrr rtyperr"r!r#rr floatlog10)r$r%r'dmindmaxtrue_mintrue_maxerrs rr r ss>Z0   z// /  !!1!1!6!67 dVVJ/ 1C( d?hoM  q=JJ' J?J Z 4Cz"J *a-3./ //rctdt||jDstd|d|jdt||jDcgc] \}}d||z f}}}t j ||ddScc}}w)abPad an array with trailing zeros to a given target shape. Parameters ---------- arr : ndarray The input array. shape : tuple The target shape. Returns ------- padded : ndarray The padded array. Examples -------- >>> _pad_to(np.ones((1, 1), dtype=int), (1, 3)) array([[1, 0, 0]]) c3,K|] \}}||k\yw)N).0sis r z_pad_to..s8"7$!QqAv"7sz Target shape z# cannot be smaller than inputshape z along any axis.rconstant) pad_widthmodeconstant_values)allzipshaper#rpad)arrr?r6r7paddings r_pad_torCs( 8#eSYY"78 8E7#YYK/ 1  '*%&;<&;da1q5z&;G< 66#z1 MM=sB d)binsc|j|jk7r&td|jd|jd|j|jk7rCtj|j|j}t ||}t ||}n||}}tj tj|dtj|dg|d\}}ttj|d}ttj|d } ttj|d} || z| z S) a:Compute 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}{p(x) \log p(x)}` is the entropy, :math:`X` is the set of image values, and :math:`p(x)` is the probability of occurrence of value :math:`x \in X`. 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)rEdensityr)axis) ndimr#r?rmaximumrC histogramddreshapersum) rrrE max_shapepadded0padded1hist bin_edgesH0H1H01s rr r sb{{fkk!;;-2{{m? ,  ||v||#JJv||V\\: &),&),!6nn GR "**Wb"9: OD) 1% &B 1% &B "**T2& 'C Gs?r)numpyr scipy.statsrutil._backendsr util.dtyper _shared.utilsrrr __all__rr r r rCr r4rrr^so)$MM ==4AL:G:GzCG80vN:;>Hr