o :ήck+@sddlZddlZddlZddlmZddlmZddl m Z ddl m Z ddd d Z d d d dZddddddZddZddddddZdddddZdS)N)ndimage)_supported_float_type)mean_squared_error) img_as_floatF multichannelcCs|s|jn|jd}t|d|j}d||jd<||}|rFt |}t |j dD]}tj |d|f|dd|d|f<q1|Stj ||dd}|S)aReplacing each pixel in ``image`` with the average of its neighbors. Parameters ---------- image : ndarray Input data to be interpolated. multichannel : bool, optional Whether the last axis of the image is to be interpreted as multiple channels or another spatial dimension. Returns ------- interp : ndarray Interpolated version of `image`. rr.mirror)mode) ndimndigenerate_binary_structureastypedtyperavelsizesumnp zeros_likerangeshapeconvolve)imager spatialdims conv_filterinterpirF/tmp/pip-target-vg8gfxp4/lib/python/skimage/restoration/j_invariant.py_interpolate_image s  r!stridecs0t|ft|}tfdd|D}|S)a'Generate slices of uniformly-spaced points in an array. Parameters ---------- shape : tuple of int Shape of the mask. offset : int The offset of the grid of ones. Iterating over ``offset`` will cover the entire array. It should be between 0 and ``stride ** ndim``, not inclusive, where ``ndim = len(shape)``. stride : int, optional The spacing between ones, used in each dimension. Returns ------- mask : ndarray The mask. Examples -------- >>> shape = (4, 4) >>> array = np.zeros(shape, dtype=int) >>> grid_slice = _generate_grid_slice(shape, offset=0, stride=2) >>> array[grid_slice] = 1 >>> print(array) [[1 0 1 0] [0 0 0 0] [1 0 1 0] [0 0 0 0]] Changing the offset moves the location of the 1s: >>> array = np.zeros(shape, dtype=int) >>> grid_slice = _generate_grid_slice(shape, offset=3, stride=2) >>> array[grid_slice] = 1 >>> print(array) [[0 0 0 0] [0 1 0 1] [0 0 0 0] [0 1 0 1]] c3s|] }t|dVqdS)N)slice).0pr#rr Wsz'_generate_grid_slice..)r unravel_indexlentuple)roffsetr$phasesmaskrr#r _generate_grid_slice,s*r/)r$masksdenoiser_kwargsc sttj}j|dd|duri}d|vr|d}n|dddu}t|d}t}|durQ|s;jnjd} fdd t | D}|D]} } || | | <|| fi|| || <qS|S) aApply a J-invariant version of `denoise_function`. Parameters ---------- image : ndarray Input data to be denoised (converted using `img_as_float`). denoise_function : function Original denoising function. stride : int, optional Stride used in masking procedure that converts `denoise_function` to J-invariance. masks : list of ndarray, optional Set of masks to use for computing J-invariant output. If `None`, a full set of masks covering the image will be used. denoiser_kwargs: Keyword arguments passed to `denoise_function`. Returns ------- output : ndarray Denoised image, of same shape as `image`. F)copyNr channel_axisrr c3s(|]}tjd|dVqdS)Nr,r$)r/r)r&idxrrr$rr r(s z%_invariant_denoise..) rrrrgetr!rrr rr3) rdenoise_functionr$r1r2 float_dtyperroutputn_masksr. input_imagerr7r _invariant_denoise\s*     r>ccs2|}tj|D] }tt||Vq dS)aUtility function to convert parameter ranges to parameter combinations. Converts a dict of lists into a list of dicts whose values consist of the cartesian product of the values in the original dict. Parameters ---------- dictionary : dict of lists Dictionary of lists to be multiplied. Yields ------ selections : dicts of values Dicts containing individual combinations of the values in the input dict. N)keys itertoolsproductvaluesdictzip) dictionaryr?elementrrr _product_from_dicts rGT)r$approximate_loss extra_outputc CsNt|||||d\}}t|}||} tjt||| d} |r%| ||ffS| S)a Calibrate a denoising function and return optimal J-invariant version. The returned function is partially evaluated with optimal parameter values set for denoising the input image. Parameters ---------- image : ndarray Input data to be denoised (converted using `img_as_float`). denoise_function : function Denoising function to be calibrated. denoise_parameters : dict of list Ranges of parameters for `denoise_function` to be calibrated over. stride : int, optional Stride used in masking procedure that converts `denoise_function` to J-invariance. approximate_loss : bool, optional Whether to approximate the self-supervised loss used to evaluate the denoiser by only computing it on one masked version of the image. If False, the runtime will be a factor of `stride**image.ndim` longer. extra_output : bool, optional If True, return parameters and losses in addition to the calibrated denoising function Returns ------- best_denoise_function : function The optimal J-invariant version of `denoise_function`. If `extra_output` is True, the following tuple is also returned: (parameters_tested, losses) : tuple (list of dict, list of int) List of parameters tested for `denoise_function`, as a dictionary of kwargs Self-supervised loss for each set of parameters in `parameters_tested`. Notes ----- The calibration procedure uses a self-supervised mean-square-error loss to evaluate the performance of J-invariant versions of `denoise_function`. The minimizer of the self-supervised loss is also the minimizer of the ground-truth loss (i.e., the true MSE error) [1]. The returned function can be used on the original noisy image, or other images with similar characteristics. Increasing the stride increases the performance of `best_denoise_function` at the expense of increasing its runtime. It has no effect on the runtime of the calibration. References ---------- .. [1] J. Batson & L. Royer. Noise2Self: Blind Denoising by Self-Supervision, International Conference on Machine Learning, p. 524-533 (2019). Examples -------- >>> from skimage import color, data >>> from skimage.restoration import denoise_wavelet >>> import numpy as np >>> img = color.rgb2gray(data.astronaut()[:50, :50]) >>> rng = np.random.default_rng() >>> noisy = img + 0.5 * img.std() * rng.standard_normal(img.shape) >>> parameters = {'sigma': np.arange(0.1, 0.4, 0.02)} >>> denoising_function = calibrate_denoiser(noisy, denoise_wavelet, ... denoise_parameters=parameters) >>> denoised_img = denoising_function(img) )denoise_parametersr$rH)r9r$r2)_calibrate_denoiser_searchrargmin functoolspartialr>) rr9rJr$rHrIparameters_testedlossesr6best_parametersbest_denoise_functionrrr calibrate_denoisers"J  rS)r$rHcCst|}tt|}g}|D]V}d|vr|d}n|dddu}|s1t||||d} t|| } n.|s6|jn|jd} || } t|jd| | d|d} t||| g|d}t|| || } | | q||fS) a Return a parameter search history with losses for a denoise function. Parameters ---------- image : ndarray Input data to be denoised (converted using `img_as_float`). denoise_function : function Denoising function to be calibrated. denoise_parameters : dict of list Ranges of parameters for `denoise_function` to be calibrated over. stride : int, optional Stride used in masking procedure that converts `denoise_function` to J-invariance. approximate_loss : bool, optional Whether to approximate the self-supervised loss used to evaluate the denoiser by only computing it on one masked version of the image. If False, the runtime will be a factor of `stride**image.ndim` longer. Returns ------- parameters_tested : list of dict List of parameters tested for `denoise_function`, as a dictionary of kwargs. losses : list of int Self-supervised loss for each set of parameters in `parameters_tested`. rr4N)r$r2r rr5)r1r2) rlistrGr8r>rr r/rappend)rr9rJr$rHrOrPr2rdenoisedlossrr<r.masked_denoisedrrr rK s8    rK)r@rMnumpyrscipyrr _shared.utilsrmetricsrutilrr!r/r>rGrSrKrrrr s$    0 8 b