0-Phr0ddlZddlZddlZddlmZddlmZddl m Z ddl m Z ddd Z d d d Zd ddddZdZd ddddZd dddZdS)N)ndimage)_supported_float_type)mean_squared_error) img_as_floatF multichannelc|s|jn |jdz }tj|d|j}d||jdz<||z}|rVtj |}t|j dD]&}tj |d|f|d|d|f<'ntj ||d}|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_filterinterpis _/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/skimage/restoration/j_invariant.py_interpolate_imager" s %1D%**ej1nK/ Q??FFu{SSK12K (A-.;??$$$KAu%%u{2'' U UA \%Q-8TTTF36NN Ue[x@@@ M)stridectj|ft|z}tfd|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]] c3:K|]}t|dVdS)N)slice).0pr%s r! z'_generate_grid_slice..Us/88Aq$''888888r#)r unravel_indexlentuple)roffsetr%phasesmasks ` r!_generate_grid_slicer2*sLT fvi#e**&< = =F 8888888 8 8D Kr#)r%masksdenoiser_kwargsc ttj}|d|i}|dddu}t |}t j}|5|sjn jdz  z} fdt| D}|D]3} } || | | <|| fi|| || <4|S)a Apply a J-invariant version of a denoising function. Parameters ---------- image : ndarray (M[, N[, ...]][, C]) of ints, uints or floats Input data to be denoised. `image` can be of any numeric type, but it is cast into a ndarray of floats (using `img_as_float`) for the computation of the denoised image. 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`. Notes ----- A denoising function is J-invariant if the prediction it makes for each pixel does not depend on the value of that pixel in the original image. The prediction for each pixel may instead use all the relevant information contained in the rest of the image, which is typically quite significant. Any function can be converted into a J-invariant one using a simple masking procedure, as described in [1]. The pixel-wise error of a J-invariant denoiser is uncorrelated to the noise, so long as the noise in each pixel is independent. Consequently, the average difference between the denoised image and the oisy image, the *self-supervised loss*, is the same as the difference between the denoised image and the original clean image, the *ground-truth loss* (up to a constant). This means that the best J-invariant denoiser for a given image can be found using the noisy data alone, by selecting the denoiser minimizing the self- supervised loss. References ---------- .. [1] J. Batson & L. Royer. Noise2Self: Blind Denoising by Self-Supervision, International Conference on Machine Learning, p. 524-533 (2019). Examples -------- >>> import skimage >>> from skimage.restoration import denoise_invariant, denoise_tv_chambolle >>> image = skimage.util.img_as_float(skimage.data.chelsea()) >>> noisy = skimage.util.random_noise(image, var=0.2 ** 2) >>> denoised = denoise_invariant(noisy, denoise_function=denoise_tv_chambolle) F)copyN channel_axisrr c3VK|]#}tjd|V$dS)Nr/r%)r2r)r)idxrrr%s r!r+z$denoise_invariant..sQ   !\k\!:3v V V V      r#) rrrrgetr"rrrrr7) rdenoise_functionr%r4r5 float_dtyper routputn_masksr1 input_imagers ` ` @r!denoise_invariantrBZs4x   E( 44K LL5L 1 1E"&&~t<>> from skimage import color, data >>> from skimage.restoration import denoise_tv_chambolle >>> 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 = {'weight': np.arange(0.01, 0.3, 0.02)} >>> denoising_function = calibrate_denoiser(noisy, denoise_tv_chambolle, ... denoise_parameters=parameters) >>> denoised_img = denoising_function(img) )denoise_parametersr%rM)r=r%r5)_calibrate_denoiser_searchrargmin functoolspartialrB) rr=rPr%rMrNparameters_testedlossesr;best_parametersbest_denoise_functions r!calibrate_denoiserrYs^!; -) !!!v )F  C',O%-)' %$'8&&AAA$$r#)r%rMct|}tt|}g}|D]}|dddu}|s$t ||||} t || } nj|s|jn |jdz } || z} t|jd| | dz|} t ||| g|}t || || } | | ||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`. r8N)r%r5r rr:)r4r5) rlistrLr<rBrrr2rappend)rr=rPr%rMrUrVr5r denoisedlossrr@r1masked_denoiseds r!rQrQ/s7:   E/0BCCDD F,&**>4@@L  J('H&eX66DD,8L%**ej1nKk)G' L[L)'Q,vD0'vO&eDk?43HIID d f $$r#)rErSnumpyrscipyrr _shared.utilsrmetricsrutilrr"r2rBrLrYrQr#r!rfs9 111111((((((/4<34-----b()dUUUUUp'''6 d%d%d%d%d%P<=t7%7%7%7%7%7%7%r#