sum_bilateral skimage.filters.rank.sum_bilateral(image, selem, out=None, mask=None, shift_x=False, shift_y=False, s0=10, s1=10) [source] 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 structuring element (selem). Radiometric similarity is defined by the greylevel interva

mean skimage.filters.rank.mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False) [source] Return local mean of an image. Parameters: image : 2-D array (uint8, uint16) Input image. selem : 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 (defaul

imsave skimage.external.tifffile.imsave(filename, data, **kwargs) [source] Write image data to TIFF file. Refer to the TiffWriter class and member functions for documentation. Parameters: filename : str Name of file to write. data : array_like Input image. The last dimensions are assumed to be image depth, height, width, and samples. kwargs : dict Parameters ‘byteorder’, ‘bigtiff’, and ‘software’ are passed to the TiffWriter class. Parameters ‘photometric’, ‘planarconfig’, ‘resolution’

img_as_ubyte skimage.img_as_ubyte(image, force_copy=False) [source] Convert an image to 8-bit unsigned integer format. Parameters: image : ndarray Input image. force_copy : bool Force a copy of the data, irrespective of its current dtype. Returns: out : ndarray of ubyte (uint8) Output image. Notes Negative input values will be clipped. Positive values are scaled between 0 and 255.

roberts_positive_diagonal skimage.filters.roberts_positive_diagonal(*args, **kwargs) [source] Deprecated function. Use skimage.filters.roberts_pos_diag instead. Find the cross edges of an image using Roberts’ cross operator. The kernel is applied to the input image to produce separate measurements of the gradient component one orientation. Parameters: image : 2-D array Image to process. mask : 2-D array, optional An optional mask to limit the application to a certain area. Note that pixe

Subpackages color Color space conversion. data Test images and example data. draw Drawing primitives (lines, text, etc.) that operate on NumPy arrays. exposure Image intensity adjustment, e.g., histogram equalization, etc. feature Feature detection and extraction, e.g., texture analysis corners, etc. filters Sharpening, edge finding, rank filters, thresholding, etc. graph Graph-theoretic operations, e.g., shortest paths. io Reading, saving, and displaying images and video. measure Measurement o

roberts_neg_diag skimage.filters.roberts_neg_diag(image, mask=None) [source] Find the cross edges of an image using the Roberts’ Cross operator. The kernel is applied to the input image to produce separate measurements of the gradient component one orientation. Parameters: image : 2-D array Image to process. mask : 2-D array, optional An optional mask to limit the application to a certain area. Note that pixels surrounding masked regions are also masked to prevent masked regions from aff

img_as_int skimage.img_as_int(image, force_copy=False) [source] Convert an image to 16-bit signed integer format. Parameters: image : ndarray Input image. force_copy : bool Force a copy of the data, irrespective of its current dtype. Returns: out : ndarray of uint16 Output image. Notes The values are scaled between -32768 and 32767. If the input data-type is positive-only (e.g., uint8), then the output image will still only have positive values.

ClearColormap class skimage.viewer.utils.ClearColormap(rgb, max_alpha=1, name='clear_color') [source] Bases: skimage.viewer.utils.core.LinearColormap Color map that varies linearly from alpha = 0 to 1 __init__(rgb, max_alpha=1, name='clear_color') [source]

equalize_hist skimage.exposure.equalize_hist(image, nbins=256, mask=None) [source] Return image after histogram equalization. Parameters: image : array Image array. nbins : int, optional Number of bins for image histogram. Note: this argument is ignored for integer images, for which each integer is its own bin. mask: ndarray of bools or 0s and 1s, optional Array of same shape as image. Only points at which mask == True are used for the equalization, which is applied to the whole image.