a6dm*ddlZddlZddlZddlmZddlmZddl 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 ?lib/python3.11/site-packages/skimage/restoration/j_invariant.py_interpolate_imager" s %1D%**ej1nK/ Q??FFu{SSK12K (A-.;??$$$KAu%%u{2'' 9 9A \%Q-/7999F36NN 9e[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..Ws/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)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)copyN channel_axisrr c3VK|]#}tjd|V$dS)Nr/r%)r2r)r)idxrrr%s r!r+z%_invariant_denoise..s[,,&ek,;,&?-0AAA,,,,,,r#) rrrrgetr"rrrrr7) rdenoise_functionr%r4r5 float_dtyper routputn_masksr1 input_imagers ` ` @r!_invariant_denoiserB\s30   E( 44K LL5L 1 1E"&&~t<>> 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%rM)r=r%r5)_calibrate_denoiser_searchrargmin functoolspartialrB) rr=rPr%rMrNparameters_testedlossesr;best_parametersbest_denoise_functions r!calibrate_denoiserrYsT!; -) !!!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!rQrQs>8   E/0BCCDD F,&**>4@@L  J)' /H &eX66DD,8L%**ej1nK +G' L[L(A/6!|FLLLD1'f /O &eDk?43HIID d f $$r#)rErSnumpyrscipyrr _shared.utilsrmetricsrutilrr"r2rBrLrYrQr#r!rfs4 111111((((((/4>34-----`;<!400000f'''. $$)^%^%^%^%^%D'($9%9%9%9%9%9%9%r#