warn skimage.viewer.warn(message, category=None, stacklevel=2) [source] A version of warnings.warn with a default stacklevel of 2.

imread skimage.io.imread(fname, as_grey=False, plugin=None, flatten=None, **plugin_args) [source] Load an image from file. Parameters: fname : string Image file name, e.g. test.jpg or URL. as_grey : bool If True, convert color images to grey-scale (32-bit floats). Images that are already in grey-scale format are not converted. plugin : str Name of plugin to use. By default, the different plugins are tried (starting with the Python Imaging Library) until a suitable candidate is found. I

points_in_poly skimage.measure.points_in_poly() Test whether points lie inside a polygon. Parameters: points : (N, 2) array Input points, (x, y). verts : (M, 2) array Vertices of the polygon, sorted either clockwise or anti-clockwise. The first point may (but does not need to be) duplicated. Returns: mask : (N,) array of bool True if corresponding point is inside the polygon. See also grid_points_in_poly

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.

estimate_transform skimage.transform.estimate_transform(ttype, src, dst, **kwargs) [source] Estimate 2D geometric transformation parameters. You can determine the over-, well- and under-determined parameters with the total least-squares method. Number of source and destination coordinates must match. Parameters: ttype : {‘similarity’, ‘affine’, ‘piecewise-affine’, ‘projective’, ‘polynomial’} Type of transform. kwargs : array or int Function parameters (src, dst, n, angle): NAME / TTYPE

LinearColormap class skimage.viewer.utils.LinearColormap(name, segmented_data, **kwargs) [source] Bases: matplotlib.colors.LinearSegmentedColormap LinearSegmentedColormap in which color varies smoothly. This class is a simplification of LinearSegmentedColormap, which doesn’t support jumps in color intensities. Parameters: name : str Name of colormap. segmented_data : dict Dictionary of ‘red’, ‘green’, ‘blue’, and (optionally) ‘alpha’ values. Each color key contains a list of x, y tuples.

skeletonize_3d skimage.morphology.skeletonize_3d(img) [source] Compute the skeleton of a binary image. Thinning is used to reduce each connected component in a binary image to a single-pixel wide skeleton. Parameters: img : ndarray, 2D or 3D A binary image containing the objects to be skeletonized. Zeros represent background, nonzero values are foreground. Returns: skeleton : ndarray The thinned image. See also skeletonize, medial_axis Notes The method of [Lee94] uses an octree dat

hubble_deep_field skimage.data.hubble_deep_field() [source] Hubble eXtreme Deep Field. This photograph contains the Hubble Telescope’s farthest ever view of the universe. It can be useful as an example for multi-scale detection. Notes This image was downloaded from HubbleSite. The image was captured by NASA and may be freely used in the public domain.

lch2lab skimage.color.lch2lab(lch) [source] CIE-LCH to CIE-LAB color space conversion. LCH is the cylindrical representation of the LAB (Cartesian) colorspace Parameters: lch : array_like The N-D image in CIE-LCH format. The last (N+1-th) dimension must have at least 3 elements, corresponding to the L, a, and b color channels. Subsequent elements are copied. Returns: out : ndarray The image in LAB format, with same shape as input lch. Raises: ValueError If lch does not have at leas

richardson_lucy skimage.restoration.richardson_lucy(image, psf, iterations=50, clip=True) [source] Richardson-Lucy deconvolution. Parameters: image : ndarray Input degraded image (can be N dimensional). psf : ndarray The point spread function. iterations : int Number of iterations. This parameter plays the role of regularisation. clip : boolean, optional True by default. If true, pixel value of the result above 1 or under -1 are thresholded for skimage pipeline compatibility. Retur