from ..filters.edges import (sobel, hsobel, vsobel, sobel_h, sobel_v, scharr, hscharr, vscharr, scharr_h, scharr_v, prewitt, hprewitt, vprewitt, prewitt_h, prewitt_v, roberts, roberts_positive_diagonal, roberts_negative_diagonal, roberts_pos_diag, roberts_neg_diag) from ..filters._rank_order import rank_order from ..filters._gabor import gabor_kernel, gabor_filter from ..filters.thresholding import (threshold_adaptive, threshold_otsu, threshold_yen, threshold_isodata) from ..filters import rank from ..filters.rank import median from skimage._shared.utils import deprecated from skimage import restoration denoise_bilateral = deprecated('skimage.restoration.denoise_bilateral')\ (restoration.denoise_bilateral) denoise_tv_bregman = deprecated('skimage.restoration.denoise_tv_bregman')\ (restoration.denoise_tv_bregman) denoise_tv_chambolle = deprecated('skimage.restoration.denoise_tv_chambolle')\ (restoration.denoise_tv_chambolle) # Backward compatibility v<0.11 @deprecated('skimage.feature.canny') def canny(*args, **kwargs): # Hack to avoid circular import from skimage.feature._canny import canny as canny_ return canny_(*args, **kwargs) __all__ = ['inverse', 'wiener',
>>> x, y, z = np.ogrid[0:40, 0:40, 0:40] >>> 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) """ im_type = im.dtype if not im_type.kind == 'f': im = img_as_float(im) if im.ndim == 2: out = _denoise_tv_chambolle_2d(im, weight, eps, n_iter_max) elif im.ndim == 3: if multichannel: out = np.zeros_like(im) for c in range(im.shape[2]): out[..., c] = _denoise_tv_chambolle_2d(im[..., c], weight, eps, n_iter_max) else: out = _denoise_tv_chambolle_3d(im, weight, eps, n_iter_max) else: raise ValueError('only 2-d and 3-d images may be denoised with this ' 'function') return out tv_denoise = deprecated('skimage.filter.denoise_tv_chambolle')\ (denoise_tv_chambolle)
from .lpi_filter import inverse, wiener, LPIFilter2D from ._gaussian import gaussian_filter from ._canny import canny from .edges import (sobel, hsobel, vsobel, scharr, hscharr, vscharr, prewitt, hprewitt, vprewitt, roberts, roberts_positive_diagonal, roberts_negative_diagonal) from ._rank_order import rank_order from ._gabor import gabor_kernel, gabor_filter from .thresholding import (threshold_adaptive, threshold_otsu, threshold_yen, threshold_isodata) from . import rank from skimage._shared.utils import deprecated from skimage import restoration denoise_bilateral = deprecated('skimage.restoration.denoise_bilateral')\ (restoration.denoise_bilateral) denoise_tv_bregman = deprecated('skimage.restoration.denoise_tv_bregman')\ (restoration.denoise_tv_bregman) denoise_tv_chambolle = deprecated('skimage.restoration.denoise_tv_chambolle')\ (restoration.denoise_tv_chambolle) __all__ = [ 'inverse', 'wiener', 'LPIFilter2D', 'gaussian_filter', 'canny', 'sobel', 'hsobel', 'vsobel', 'scharr', 'hscharr', 'vscharr', 'prewitt', 'hprewitt', 'vprewitt', 'roberts', 'roberts_positive_diagonal', 'roberts_negative_diagonal', 'denoise_tv_chambolle', 'denoise_bilateral', 'denoise_tv_bregman', 'rank_order', 'gabor_kernel', 'gabor_filter', 'threshold_adaptive', 'threshold_otsu', 'threshold_yen', 'threshold_isodata', 'rank' ]
from .generic import (autolevel, bottomhat, equalize, gradient, maximum, mean, subtract_mean, median, minimum, modal, enhance_contrast, pop, threshold, tophat, noise_filter, entropy, otsu, sum) from ._percentile import (autolevel_percentile, gradient_percentile, mean_percentile, subtract_mean_percentile, enhance_contrast_percentile, percentile, pop_percentile, sum_percentile, threshold_percentile) from .bilateral import mean_bilateral, pop_bilateral, sum_bilateral from skimage._shared.utils import deprecated percentile_autolevel = deprecated('autolevel_percentile')(autolevel_percentile) percentile_gradient = deprecated('gradient_percentile')(gradient_percentile) percentile_mean = deprecated('mean_percentile')(mean_percentile) bilateral_mean = deprecated('mean_bilateral')(mean_bilateral) meansubtraction = deprecated('subtract_mean')(subtract_mean) percentile_mean_subtraction = deprecated('subtract_mean_percentile')\ (subtract_mean_percentile) morph_contr_enh = deprecated('enhance_contrast')(enhance_contrast) percentile_morph_contr_enh = deprecated('enhance_contrast_percentile')\ (enhance_contrast_percentile) percentile_pop = deprecated('pop_percentile')(pop_percentile) bilateral_pop = deprecated('pop_bilateral')(pop_bilateral) percentile_threshold = deprecated('threshold_percentile')(threshold_percentile)
hprewitt, vprewitt, roberts, roberts_positive_diagonal, roberts_negative_diagonal, ) from ._rank_order import rank_order from ._gabor import gabor_kernel, gabor_filter from .thresholding import threshold_adaptive, threshold_otsu, threshold_yen, threshold_isodata from . import rank from skimage._shared.utils import deprecated from skimage import restoration denoise_bilateral = deprecated("skimage.restoration.denoise_bilateral")(restoration.denoise_bilateral) denoise_tv_bregman = deprecated("skimage.restoration.denoise_tv_bregman")(restoration.denoise_tv_bregman) denoise_tv_chambolle = deprecated("skimage.restoration.denoise_tv_chambolle")(restoration.denoise_tv_chambolle) __all__ = [ "inverse", "wiener", "LPIFilter2D", "gaussian_filter", "canny", "sobel", "hsobel", "vsobel", "scharr", "hscharr",
>>> x, y, z = np.ogrid[0:40, 0:40, 0:40] >>> 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(mask, weight=100) """ im_type = im.dtype if not im_type.kind == 'f': im = img_as_float(im) if im.ndim == 2: out = _denoise_tv_chambolle_2d(im, weight, eps, n_iter_max) elif im.ndim == 3: if multichannel: out = np.zeros_like(im) for c in range(im.shape[2]): out[..., c] = _denoise_tv_chambolle_2d(im[..., c], weight, eps, n_iter_max) else: out = _denoise_tv_chambolle_3d(im, weight, eps, n_iter_max) else: raise ValueError('only 2-d and 3-d images may be denoised with this ' 'function') return out tv_denoise = deprecated('skimage.filter.denoise_tv_chambolle')\ (denoise_tv_chambolle)
--------- >>> import scipy >>> # 2D example using lena >>> lena = scipy.lena() >>> import scipy >>> lena = scipy.lena().astype(np.float) >>> lena += 0.5 * lena.std()*np.random.randn(*lena.shape) >>> denoised_lena = denoise_tv(lena, weight=60) >>> # 3D example on synthetic data >>> x, y, z = np.ogrid[0:40, 0:40, 0:40] >>> 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_3d(mask, weight=100) """ im_type = im.dtype if not im_type.kind == 'f': im = img_as_float(im) if im.ndim == 2: out = _denoise_tv_2d(im, weight, eps, n_iter_max) elif im.ndim == 3: out = _denoise_tv_3d(im, weight, eps, n_iter_max) else: raise ValueError('only 2-d and 3-d images may be denoised with this ' 'function') return out tv_denoise = deprecated('skimage.filter.denoise_tv')(denoise_tv)