ó  ‰\c@sIddlZddlZddlmZddlmZddlm Z m Z ddl m Z m Z ddlZddljZddlZddddd d dd „Zd d ed„Zdddd„Zddded„Zd„Zd„Zdd„Zdddddd„Zddddeedd„Zeed„ZdS(iÿÿÿÿN(tceili(t img_as_float(t_denoise_bilateralt_denoise_tv_bregman(tskimage_deprecationtwarnii'tconstantic CsZ|d krtdƒt}n|rÞ|jdkrm|jdkrRtdƒ‚qÛtdj|jƒƒ‚q|jdd kr|jddkr¼d}t|j|j|jdƒƒqÛd}t|j|jƒƒqn*|jdkrtd j|jƒƒ‚n|d kr>td dtt d|ƒƒd ƒ}nt |||||||ƒS(s¼ Denoise image using bilateral filter. This is an edge-preserving, denoising filter. It averages pixels based on their spatial closeness and radiometric similarity [1]_. Spatial closeness is measured by the Gaussian function of the Euclidean distance between two pixels and a certain standard deviation (`sigma_spatial`). Radiometric similarity is measured by the Gaussian function of the Euclidean distance between two color values and a certain standard deviation (`sigma_color`). Parameters ---------- image : ndarray, shape (M, N[, 3]) Input image, 2D grayscale or RGB. win_size : int Window size for filtering. If win_size is not specified, it is calculated as ``max(5, 2 * ceil(3 * sigma_spatial) + 1)``. sigma_color : float Standard deviation for grayvalue/color distance (radiometric similarity). A larger value results in averaging of pixels with larger radiometric differences. Note, that the image will be converted using the `img_as_float` function and thus the standard deviation is in respect to the range ``[0, 1]``. If the value is ``None`` the standard deviation of the ``image`` will be used. sigma_spatial : float Standard deviation for range distance. A larger value results in averaging of pixels with larger spatial differences. bins : int Number of discrete values for Gaussian weights of color filtering. A larger value results in improved accuracy. mode : {'constant', 'edge', 'symmetric', 'reflect', 'wrap'} How to handle values outside the image borders. See `numpy.pad` for detail. cval : string Used in conjunction with mode 'constant', the value outside the image boundaries. multichannel : bool Whether the last axis of the image is to be interpreted as multiple channels or another spatial dimension. Returns ------- denoised : ndarray Denoised image. References ---------- .. [1] http://users.soe.ucsc.edu/~manduchi/Papers/ICCV98.pdf Examples -------- >>> from skimage import data, img_as_float >>> astro = img_as_float(data.astronaut()) >>> astro = astro[220:300, 220:320] >>> noisy = astro + 0.6 * astro.std() * np.random.random(astro.shape) >>> noisy = np.clip(noisy, 0, 1) >>> denoised = denoise_bilateral(noisy, sigma_color=0.05, sigma_spatial=15) s=denoise_bilateral will default to multichannel=False in v0.15iis“Use ``multichannel=False`` for 2D grayscale images. The last axis of the input image must be multiple color channels not another spatial dimension.s¥Bilateral filter is only implemented for 2D grayscale images (image.ndim == 2) and 2D multichannel (image.ndim == 3) images, but the input image has {0} dimensions. isÂThe last axis of the input image is interpreted as channels. Input image with shape {0} has {1} channels in last axis. ``denoise_bilateral`` is implemented for 2D grayscale and color images onlys?Input image must be grayscale, RGB, or RGBA; but has shape {0}.s¢Bilateral filter is not implemented for grayscale images of 3 or more dimensions, but input image has {0} dimension. Use ``multichannel=True`` for 2-D RGB images.iiN(ii( tNoneRtTruetndimt ValueErrortformattshapetmaxtintRR( timagetwin_sizet sigma_colort sigma_spatialtbinstmodetcvalt multichanneltmsg((s;lib/python2.7/site-packages/skimage/restoration/_denoise.pytdenoise_bilateral s,@    #  *idgü©ñÒMbP?cCst|||||ƒS(sÂPerform total-variation denoising using split-Bregman optimization. Total-variation denoising (also know as total-variation regularization) tries to find an image with less total-variation under the constraint of being similar to the input image, which is controlled by the regularization parameter ([1]_, [2]_, [3]_, [4]_). Parameters ---------- image : ndarray Input data to be denoised (converted using img_as_float`). weight : float Denoising weight. The smaller the `weight`, the more denoising (at the expense of less similarity to the `input`). The regularization parameter `lambda` is chosen as `2 * weight`. eps : float, optional Relative difference of the value of the cost function that determines the stop criterion. The algorithm stops when:: SUM((u(n) - u(n-1))**2) < eps max_iter : int, optional Maximal number of iterations used for the optimization. isotropic : boolean, optional Switch between isotropic and anisotropic TV denoising. Returns ------- u : ndarray Denoised image. References ---------- .. [1] http://en.wikipedia.org/wiki/Total_variation_denoising .. [2] Tom Goldstein and Stanley Osher, "The Split Bregman Method For L1 Regularized Problems", ftp://ftp.math.ucla.edu/pub/camreport/cam08-29.pdf .. [3] Pascal Getreuer, "Rudin–Osher–Fatemi Total Variation Denoising using Split Bregman" in Image Processing On Line on 2012–05–19, http://www.ipol.im/pub/art/2012/g-tvd/article_lr.pdf .. [4] http://www.math.ucsb.edu/~cgarcia/UGProjects/BregmanAlgorithms_JacquelineBush.pdf (R(Rtweighttmax_itertepst isotropic((s;lib/python2.7/site-packages/skimage/restoration/_denoise.pytdenoise_tv_bregmanxs,gš™™™™™¹?g-Cëâ6*?iÈcCsÀ|j}tj|jf|jd|jƒ}tj|ƒ}tj|ƒ}d}xg||kr»|dkrC|jdƒ }td ƒg|} td ƒg|d} xŒt |ƒD]~} tdd ƒ| | >> from skimage import color, data >>> img = color.rgb2gray(data.astronaut())[:50, :50] >>> img += 0.5 * img.std() * np.random.randn(*img.shape) >>> denoised_img = denoise_tv_chambolle(img, weight=60) 3D example on synthetic data: >>> x, y, z = np.ogrid[0:20, 0:20, 0:20] >>> mask = (x - 22)**2 + (y - 20)**2 + (z - 17)**2 < 8**2 >>> mask = mask.astype(np.float) >>> mask += 0.2*np.random.randn(*mask.shape) >>> res = denoise_tv_chambolle(mask, weight=100) tfiÿÿÿÿ.(RtkindRR R"R%R R<(RRRR-Rtim_typeR5tc((s;lib/python2.7/site-packages/skimage/restoration/_denoise.pytdenoise_tv_chambolleùsH cCsLtj||ƒ}tj|jƒj}|tjt|||ƒƒ}|S(s:BayesShrink threshold for a zero-mean details coeff array.(R tmeantfinfoRRR(R (tdetailstvartdvarRtthresh((s;lib/python2.7/site-packages/skimage/restoration/_denoise.pyt _bayes_threshOs cCs!|tjdtj|jƒƒS(s3 Universal threshold used by the VisuShrink method i(R R(tlogR+(timgtsigma((s;lib/python2.7/site-packages/skimage/restoration/_denoise.pyt_universal_threshXstGaussiancCsi|tj|ƒ}|jƒdkrYtjjjdƒ}tjtj|ƒƒ|}n t dƒ‚|S(sjCalculate the robust median estimator of the noise standard deviation. Parameters ---------- detail_coeffs : ndarray The detail coefficients corresponding to the discrete wavelet transform of an image. distribution : str The underlying noise distribution. Returns ------- sigma : float The estimated noise standard deviation (see section 4.2 of [1]_). References ---------- .. [1] D. L. Donoho and I. M. Johnstone. "Ideal spatial adaptation by wavelet shrinkage." Biometrika 81.3 (1994): 425-455. DOI:10.1093/biomet/81.3.425 tgaussiangè?s5Only Gaussian noise estimation is currently supported( R tnonzerotlowertscipytstatsR8tppftmedianR,R (t detail_coeffst distributiontdenomRK((s;lib/python2.7/site-packages/skimage/restoration/_denoise.pyt_sigma_est_dwt]s  tsoftcsStj|ƒ}td„|jDƒƒ}|dkr„|j}tjg|jD]} tj| |ƒ^qMƒ}t |ddƒ}ntj |d|d|ƒ} | d} |dkrß| dd|j } t | dd ƒ}n|dk r ˆdk r t d j|ƒƒnˆdkr¯|d ‰|dkr>td ƒ‚q¯|d kryg| D]‰‡‡fd†ˆDƒ^qQ‰q¯|dkr—t||ƒ‰q¯tdj|ƒƒ‚ntjˆƒrðg| D]"‰‡‡‡fd†ˆDƒ^qÅ} n>gtˆ| ƒD](\‰‰‡‡‡fd†ˆDƒ^q} | dg| }tj||ƒ|S(sQPerform wavelet thresholding. Parameters ---------- image : ndarray (2d or 3d) of ints, uints or floats Input data to be denoised. `image` can be of any numeric type, but it is cast into an ndarray of floats for the computation of the denoised image. wavelet : string The type of wavelet to perform. Can be any of the options pywt.wavelist outputs. For example, this may be any of ``{db1, db2, db3, db4, haar}``. method : {'BayesShrink', 'VisuShrink'}, optional Thresholding method to be used. The currently supported methods are "BayesShrink" [1]_ and "VisuShrink" [2]_. If it is set to None, a user-specified ``threshold`` must be supplied instead. threshold : float, optional The thresholding value to apply during wavelet coefficient thresholding. The default value (None) uses the selected ``method`` to estimate appropriate threshold(s) for noise removal. sigma : float, optional The standard deviation of the noise. The noise is estimated when sigma is None (the default) by the method in [2]_. mode : {'soft', 'hard'}, optional An optional argument to choose the type of denoising performed. It noted that choosing soft thresholding given additive noise finds the best approximation of the original image. wavelet_levels : int or None, optional The number of wavelet decomposition levels to use. The default is three less than the maximum number of possible decomposition levels (see Notes below). Returns ------- out : ndarray Denoised image. References ---------- .. [1] Chang, S. Grace, Bin Yu, and Martin Vetterli. "Adaptive wavelet thresholding for image denoising and compression." Image Processing, IEEE Transactions on 9.9 (2000): 1532-1546. DOI: 10.1109/83.862633 .. [2] D. L. Donoho and I. M. Johnstone. "Ideal spatial adaptation by wavelet shrinkage." Biometrika 81.3 (1994): 425-455. DOI: 10.1093/biomet/81.3.425 css|]}t|ƒVqdS(N(R$(t.0ts((s;lib/python2.7/site-packages/skimage/restoration/_denoise.pys ¶siitwavelettleveliÿÿÿÿR0RVRMsOThresholding method {} selected. The user-specified threshold will be ignored.is0If method is None, a threshold must be provided.t BayesShrinkcs&i|]}tˆ|ˆƒ|“qS((RH(RZtkey(R]RE(s;lib/python2.7/site-packages/skimage/restoration/_denoise.pys Ös t VisuShrinksUnrecognized method: {}cs2i|](}tjˆ|dˆdˆƒ|“qS(tvalueR(tpywtt threshold(RZR_(R]RRc(s;lib/python2.7/site-packages/skimage/restoration/_denoise.pys às cs6i|],}tjˆ|dˆ|dˆƒ|“qS(RaR(RbRc(RZR_(R]RRG(s;lib/python2.7/site-packages/skimage/restoration/_denoise.pys æs iN(RbtWaveletR&R Rtdec_lenR tmint dwt_max_levelR twavedecnR RXRR R RLtisscalartziptwaverecn(RR\tmethodRcRKRtwavelet_levelstoriginal_extenttdlenR[tcoeffstdcoeffsRUtdenoised_detailtdenoised_coeffs((R]RRGRcREs;lib/python2.7/site-packages/skimage/restoration/_denoise.pyt_wavelet_threshold€sB2  +         , /;tdb1R^c Cs;|dkr$tdj|ƒƒ‚nt|ƒ}|rnt|tjƒsT|dkrn|g|jd}qnn|rã|rqtj |ƒ}xÓt dƒD]Å} |d| fj ƒ|d| fj ƒ} } |d| f| } | | | :} t | d|d|d || d |d |ƒ|d| f<|d| f| | |d| f<|d| fc| 7>> from skimage import color, data >>> img = img_as_float(data.astronaut()) >>> img = color.rgb2gray(img) >>> img += 0.1 * np.random.randn(*img.shape) >>> img = np.clip(img, 0, 1) >>> denoised_img = denoise_wavelet(img, sigma=0.1) R^R`sVInvalid method: {}. The currently supported methods are "BayesShrink" and "VisuShrink"iÿÿÿÿi.R\RlRKRRmii(R^R`N(iÿÿÿÿi(ii(R R Rt isinstancetnumberstNumberRR tcolort rgb2ycbcrR%RfR tdenoise_wavelett ycbcr2rgbR t empty_likeRttclip(RRKR\RRmRt convert2ycbcrRlR5R1RfR tchannelR@t clip_range((s;lib/python2.7/site-packages/skimage/restoration/_denoise.pyR{îsDX  -"  c CsÐ|rd|jd}gt|ƒD]"}t|d|fdtƒ^q }|r`tj|ƒ}n|S|jddkršd}t|j|jdƒƒntj |ddƒ}|d|j }t |d d ƒS( sË Robust wavelet-based estimator of the (Gaussian) noise standard deviation. Parameters ---------- image : ndarray Image for which to estimate the noise standard deviation. average_sigmas : bool, optional If true, average the channel estimates of `sigma`. Otherwise return a list of sigmas corresponding to each channel. multichannel : bool Estimate sigma separately for each channel. Returns ------- sigma : float or list Estimated noise standard deviation(s). If `multichannel` is True and `average_sigmas` is False, a separate noise estimate for each channel is returned. Otherwise, the average of the individual channel estimates is returned. Notes ----- This function assumes the noise follows a Gaussian distribution. The estimation algorithm is based on the median absolute deviation of the wavelet detail coefficients as described in section 4.2 of [1]_. References ---------- .. [1] D. L. Donoho and I. M. Johnstone. "Ideal spatial adaptation by wavelet shrinkage." Biometrika 81.3 (1994): 425-455. DOI:10.1093/biomet/81.3.425 Examples -------- >>> import skimage.data >>> from skimage import img_as_float >>> img = img_as_float(skimage.data.camera()) >>> sigma = 0.1 >>> img = img + sigma * np.random.standard_normal(img.shape) >>> sigma_hat = estimate_sigma(img, multichannel=False) iÿÿÿÿ.Ris–image is size {0} on the last axis, but multichannel is False. If this is a color image, please set multichannel to True for proper noise estimation.R\tdb2R0RVRM( R R%testimate_sigmatFalseR RBRR RbtdwtnR RX( Rtaverage_sigmasRt nchannelsR@tsigmasRRpRU((s;lib/python2.7/site-packages/skimage/restoration/_denoise.pyRƒrs+ 2(t scipy.statsRQtnumpyR tmathRtRtrestoration._denoise_cyRRt _shared.utilsRRRbt skimage.colorRyRwRRRRR<R„RARHRLRXRtR{Rƒ(((s;lib/python2.7/site-packages/skimage/restoration/_denoise.pyts.     j/R  U  #m ‚