sum_percentile skimage.filters.rank.sum_percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=0, p1=1) [source] Return the local sum of pixels. Only greyvalues between percentiles [p0, p1] are considered in the filter. Note that the sum may overflow depending on the data type of the input array. 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 inp

API Reference skimageSubpackages Utility Functions dtype_limits img_as_bool img_as_float img_as_int img_as_ubyte img_as_uint test Module: colorcombine_stains convert_colorspace deltaE_cie76 deltaE_ciede2000 deltaE_ciede94 deltaE_cmc gray2rgb guess_spatial_dimensions hed2rgb hsv2rgb lab2lch lab2rgb lab2xyz label2rgb lch2lab luv2rgb luv2xyz rgb2gray rgb2grey rgb2hed rgb2hsv rgb2lab rgb2luv rgb2rgbcie rgb2xyz rgbcie2rgb separate_stains xyz2lab xyz2luv xyz2rgb Module: dataastronaut binary_blob

CircleModel class skimage.measure.CircleModel [source] Bases: skimage.measure.fit.BaseModel Total least squares estimator for 2D circles. The functional model of the circle is: r**2 = (x - xc)**2 + (y - yc)**2 This estimator minimizes the squared distances from all points to the circle: min{ sum((r - sqrt((x_i - xc)**2 + (y_i - yc)**2))**2) } A minimum number of 3 points is required to solve for the parameters. Attributes params (tuple) Circle model parameters in the following order xc, yc

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.

imread skimage.external.tifffile.imread(files, **kwargs) [source] Return image data from TIFF file(s) as numpy array. The first image series is returned if no arguments are provided. Parameters: files : str or list File name, glob pattern, or list of file names. key : int, slice, or sequence of page indices Defines which pages to return as array. series : int Defines which series of pages in file to return as array. multifile : bool If True (default), OME-TIFF data may include pages

hough_circle skimage.transform.hough_circle(image, radius, normalize=True, full_output=False) [source] Perform a circular Hough transform. Parameters: image : (M, N) ndarray Input image with nonzero values representing edges. radius : scalar or sequence of scalars Radii at which to compute the Hough transform. Floats are converted to integers. normalize : boolean, optional (default True) Normalize the accumulator with the number of pixels used to draw the radius. full_output : boolean

PaintTool class skimage.viewer.canvastools.PaintTool(manager, overlay_shape, radius=5, alpha=0.3, on_move=None, on_release=None, on_enter=None, rect_props=None) [source] Bases: skimage.viewer.canvastools.base.CanvasToolBase Widget for painting on top of a plot. Parameters: manager : Viewer or PlotPlugin. Skimage viewer or plot plugin object. overlay_shape : shape tuple 2D shape tuple used to initialize overlay image. alpha : float (between [0, 1]) Opacity of overlay on_move : function

roberts_pos_diag skimage.filters.roberts_pos_diag(image, mask=None) [source] 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 pixels surrounding masked regions are also masked to prevent masked regions from affecti

draw_rag skimage.future.graph.draw_rag(labels, rag, img, border_color=None, node_color='#ffff00', edge_color='#00ff00', colormap=None, thresh=inf, desaturate=False, in_place=True) [source] Draw a Region Adjacency Graph on an image. Given a labelled image and its corresponding RAG, draw the nodes and edges of the RAG on the image with the specified colors. Nodes are marked by the centroids of the corresponding regions. Parameters: labels : ndarray, shape (M, N) The labelled image. rag : RA

prewitt_v skimage.filters.prewitt_v(image, mask=None) [source] Find the vertical edges of an image using the Prewitt transform. 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 affecting the result. Returns: output : 2-D array The Prewitt edge map. Notes We use the following kernel: 1 0 -1 1 0 -1 1