"""Approximate bilateral rank filter for local (custom kernel) mean. The local histogram is computed using a sliding window similar to the method described in [1]_. The pixel neighborhood is defined by: * the given footprint (structuring element) * an interval [g-s0, g+s1] in graylevel around g the processed pixel graylevel The kernel is flat (i.e. each pixel belonging to the neighborhood contributes equally). Result image is 8-/16-bit or double with respect to the input image and the rank filter operation. References ---------- .. [1] Huang, T. ,Yang, G. ; Tang, G.. "A fast two-dimensional median filtering algorithm", IEEE Transactions on Acoustics, Speech and Signal Processing, Feb 1979. Volume: 27 , Issue: 1, Page(s): 13 - 18. """ from ..._shared.utils import check_nD, deprecate_kwarg from . import bilateral_cy from .generic import _preprocess_input __all__ = ['mean_bilateral', 'pop_bilateral', 'sum_bilateral'] def _apply(func, image, footprint, out, mask, shift_x, shift_y, s0, s1, out_dtype=None): check_nD(image, 2) image, footprint, out, mask, n_bins = _preprocess_input( image, footprint, out, mask, out_dtype ) func(image, footprint, shift_x=shift_x, shift_y=shift_y, mask=mask, out=out, n_bins=n_bins, s0=s0, s1=s1) return out.reshape(out.shape[:2]) @deprecate_kwarg(kwarg_mapping={'selem': 'footprint'}, removed_version="1.0", deprecated_version="0.19") def mean_bilateral(image, footprint, out=None, mask=None, shift_x=False, shift_y=False, s0=10, s1=10): """Apply a flat kernel bilateral filter. This is an edge-preserving and noise reducing denoising filter. It averages pixels based on their spatial closeness and radiometric similarity. Spatial closeness is measured by considering only the local pixel neighborhood given by a footprint (structuring element). Radiometric similarity is defined by the graylevel interval [g-s0, g+s1] where g is the current pixel graylevel. Only pixels belonging to the footprint and having a graylevel inside this interval are averaged. Parameters ---------- image : 2-D array (uint8, uint16) Input image. footprint : 2-D array The neighborhood expressed as a 2-D array of 1's and 0's. out : 2-D array (same dtype as input) If None, a new array is allocated. mask : ndarray Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). shift_x, shift_y : int Offset added to the footprint center point. Shift is bounded to the footprint sizes (center must be inside the given footprint). s0, s1 : int Define the [s0, s1] interval around the grayvalue of the center pixel to be considered for computing the value. Returns ------- out : 2-D array (same dtype as input image) Output image. See also -------- denoise_bilateral Examples -------- >>> import numpy as np >>> from skimage import data >>> from skimage.morphology import disk >>> from skimage.filters.rank import mean_bilateral >>> img = data.camera().astype(np.uint16) >>> bilat_img = mean_bilateral(img, disk(20), s0=10,s1=10) """ return _apply(bilateral_cy._mean, image, footprint, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, s0=s0, s1=s1) @deprecate_kwarg(kwarg_mapping={'selem': 'footprint'}, removed_version="1.0", deprecated_version="0.19") def pop_bilateral(image, footprint, out=None, mask=None, shift_x=False, shift_y=False, s0=10, s1=10): """Return the local number (population) of pixels. The number of pixels is defined as the number of pixels which are included in the footprint and the mask. Additionally pixels must have a graylevel inside the interval [g-s0, g+s1] where g is the grayvalue of the center pixel. Parameters ---------- image : 2-D array (uint8, uint16) Input image. footprint : 2-D array The neighborhood expressed as a 2-D array of 1's and 0's. out : 2-D array (same dtype as input) If None, a new array is allocated. mask : ndarray Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). shift_x, shift_y : int Offset added to the footprint center point. Shift is bounded to the footprint sizes (center must be inside the given footprint). s0, s1 : int Define the [s0, s1] interval around the grayvalue of the center pixel to be considered for computing the value. Returns ------- out : 2-D array (same dtype as input image) Output image. Examples -------- >>> import numpy as np >>> from skimage.morphology import square >>> import skimage.filters.rank as rank >>> img = 255 * np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) >>> rank.pop_bilateral(img, square(3), s0=10, s1=10) array([[3, 4, 3, 4, 3], [4, 4, 6, 4, 4], [3, 6, 9, 6, 3], [4, 4, 6, 4, 4], [3, 4, 3, 4, 3]], dtype=uint16) """ return _apply(bilateral_cy._pop, image, footprint, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, s0=s0, s1=s1) @deprecate_kwarg(kwarg_mapping={'selem': 'footprint'}, removed_version="1.0", deprecated_version="0.19") def sum_bilateral(image, footprint, out=None, mask=None, shift_x=False, shift_y=False, s0=10, s1=10): """Apply a flat kernel bilateral filter. This is an edge-preserving and noise reducing denoising filter. It averages pixels based on their spatial closeness and radiometric similarity. Spatial closeness is measured by considering only the local pixel neighborhood given by a footprint (structuring element). Radiometric similarity is defined by the graylevel interval [g-s0, g+s1] where g is the current pixel graylevel. Only pixels belonging to the footprint AND having a graylevel inside this interval are summed. Note that the sum may overflow depending on the data type of the input array. Parameters ---------- image : 2-D array (uint8, uint16) Input image. footprint : 2-D array The neighborhood expressed as a 2-D array of 1's and 0's. out : 2-D array (same dtype as input) If None, a new array is allocated. mask : ndarray Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). shift_x, shift_y : int Offset added to the footprint center point. Shift is bounded to the footprint sizes (center must be inside the given footprint). s0, s1 : int Define the [s0, s1] interval around the grayvalue of the center pixel to be considered for computing the value. Returns ------- out : 2-D array (same dtype as input image) Output image. See also -------- denoise_bilateral Examples -------- >>> import numpy as np >>> from skimage import data >>> from skimage.morphology import disk >>> from skimage.filters.rank import sum_bilateral >>> img = data.camera().astype(np.uint16) >>> bilat_img = sum_bilateral(img, disk(10), s0=10, s1=10) """ return _apply(bilateral_cy._sum, image, footprint, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, s0=s0, s1=s1)