crop skimage.util.crop(ar, crop_width, copy=False, order='K') [source] Crop array ar by crop_width along each dimension. Parameters: ar : array-like of rank N Input array. crop_width : {sequence, int} Number of values to remove from the edges of each axis. ((before_1, after_1), ... (before_N, after_N)) specifies unique crop widths at the start and end of each axis. ((before, after),) specifies a fixed start and end crop for every axis. (n,) or n for integer n is a shortcut for before = a

Module: viewer.canvastools skimage.viewer.canvastools.LineTool(manager) Widget for line selection in a plot. skimage.viewer.canvastools.PaintTool(...[, ...]) Widget for painting on top of a plot. skimage.viewer.canvastools.RectangleTool(manager) Widget for selecting a rectangular region in a plot. skimage.viewer.canvastools.ThickLineTool(manager) Widget for line selection in a plot.

watershed skimage.morphology.watershed(image, markers, connectivity=None, offset=None, mask=None) [source] Return a matrix labeled using the watershed segmentation algorithm Parameters: image: ndarray (2-D, 3-D, ...) of integers Data array where the lowest value points are labeled first. markers: ndarray of the same shape as `image` An array marking the basins with the values to be assigned in the label matrix. Zero means not a marker. This array should be of an integer type. connectivi

TiffWriter class skimage.external.tifffile.TiffWriter(filename, bigtiff=False, byteorder=None, software='tifffile.py') [source] Bases: object Write image data to TIFF file. TiffWriter instances must be closed using the close method, which is automatically called when using the ‘with’ statement. Examples >>> data = numpy.random.rand(2, 5, 3, 301, 219) >>> with TiffWriter('temp.tif', bigtiff=True) as tif: ... for i in range(data.shape[0]): ... tif.save(data[i], co

view_as_windows skimage.util.view_as_windows(arr_in, window_shape, step=1) [source] Rolling window view of the input n-dimensional array. Windows are overlapping views of the input array, with adjacent windows shifted by a single row or column (or an index of a higher dimension). Parameters: arr_in : ndarray N-d input array. window_shape : integer or tuple of length arr_in.ndim Defines the shape of the elementary n-dimensional orthotope (better know as hyperrectangle [R383]) of the rolli

structural_similarity skimage.measure.structural_similarity(*args, **kwargs) [source] Deprecated function. Use compare_ssim instead.

luv2xyz skimage.color.luv2xyz(luv, illuminant='D65', observer='2') [source] CIE-Luv to XYZ color space conversion. Parameters: luv : (M, N, [P,] 3) array_like The 3 or 4 dimensional image in CIE-Luv format. Final dimension denotes channels. illuminant : {“A”, “D50”, “D55”, “D65”, “D75”, “E”}, optional The name of the illuminant (the function is NOT case sensitive). observer : {“2”, “10”}, optional The aperture angle of the observer. Returns: out : (M, N, [P,] 3) ndarray The image i

lab2rgb skimage.color.lab2rgb(lab) [source] Lab to RGB color space conversion. Parameters: lab : array_like The image in Lab format, in a 3-D array of shape (.., .., 3). Returns: out : ndarray The image in RGB format, in a 3-D array of shape (.., .., 3). Raises: ValueError If lab is not a 3-D array of shape (.., .., 3). Notes This function uses lab2xyz and xyz2rgb.

gradient_percentile skimage.filters.rank.gradient_percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=0, p1=1) [source] Return local gradient of an image (i.e. local maximum - local minimum). Only greyvalues between percentiles [p0, p1] are considered in the filter. 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

hsv2rgb skimage.color.hsv2rgb(hsv) [source] HSV to RGB color space conversion. Parameters: hsv : array_like The image in HSV format, in a 3-D array of shape (.., .., 3). Returns: out : ndarray The image in RGB format, in a 3-D array of shape (.., .., 3). Raises: ValueError If hsv is not a 3-D array of shape (.., .., 3). Notes The conversion assumes an input data range of [0, 1] for all color components. Conversion between RGB and HSV color spaces results in some loss of precision