PolynomialTransform class skimage.transform.PolynomialTransform(params=None) [source] Bases: skimage.transform._geometric.GeometricTransform 2D transformation of the form: ..:math: X = sum[j=0:order]( sum[i=0:j]( a_ji * x**(j - i) * y**i )) Y = sum[j=0:order]( sum[i=0:j]( b_ji * x**(j - i) * y**i )) Parameters: params : (2, N) array, optional Polynomial coefficients where N * 2 = (order + 1) * (order + 2). So, a_ji is defined in params[0, :] and b_ji in params[1, :]. Attributes params (

polygon_perimeter skimage.draw.polygon_perimeter(cr, cc, shape=None, clip=False) [source] Generate polygon perimeter coordinates. Parameters: cr : (N,) ndarray Row (Y) coordinates of vertices of polygon. cc : (N,) ndarray Column (X) coordinates of vertices of polygon. shape : tuple, optional Image shape which is used to determine maximum extents of output pixel coordinates. This is useful for polygons which exceed the image size. By default the full extents of the polygon are used. cl

polygon skimage.draw.polygon() Generate coordinates of pixels within polygon. Parameters: y : (N,) ndarray Y-coordinates of vertices of polygon. x : (N,) ndarray X-coordinates of vertices of polygon. shape : tuple, optional Image shape which is used to determine the maximum extent of output pixel coordinates. This is useful for polygons which exceed the image size. By default the full extent of the polygon are used. Returns: rr, cc : ndarray of int Pixel coordinates of polygon. May

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

plugin_order skimage.io.plugin_order() [source] Return the currently preferred plugin order. Returns: p : dict Dictionary of preferred plugin order, with function name as key and plugins (in order of preference) as value.

plugin_info skimage.io.plugin_info(plugin) [source] Return plugin meta-data. Parameters: plugin : str Name of plugin. Returns: m : dict Meta data as specified in plugin .ini.

plot_matches skimage.feature.plot_matches(ax, image1, image2, keypoints1, keypoints2, matches, keypoints_color='k', matches_color=None, only_matches=False) [source] Plot matched features. Parameters: ax : matplotlib.axes.Axes Matches and image are drawn in this ax. image1 : (N, M [, 3]) array First grayscale or color image. image2 : (N, M [, 3]) array Second grayscale or color image. keypoints1 : (K1, 2) array First keypoint coordinates as (row, col). keypoints2 : (K2, 2) array Sec

PiecewiseAffineTransform class skimage.transform.PiecewiseAffineTransform [source] Bases: skimage.transform._geometric.GeometricTransform 2D piecewise affine transformation. Control points are used to define the mapping. The transform is based on a Delaunay triangulation of the points to form a mesh. Each triangle is used to find a local affine transform. Attributes affines (list of AffineTransform objects) Affine transformations for each triangle in the mesh. inverse_affines (list of Affine

Picture class skimage.novice.Picture(path=None, array=None, xy_array=None) [source] Bases: object A 2-D picture made up of pixels. Examples Load an image from a file: >>> from skimage import novice >>> from skimage import data >>> picture = novice.open(data.data_dir + '/chelsea.png') Load an image from a URL (the URL must start with http(s):// or ftp(s)://): >>> picture = novice.open('http://scikit-image.org/_static/img/logo.png') Create a blank 100 pixe

perimeter skimage.measure.perimeter(image, neighbourhood=4) [source] Calculate total perimeter of all objects in binary image. Parameters: image : array Binary image. neighbourhood : 4 or 8, optional Neighborhood connectivity for border pixel determination. Returns: perimeter : float Total perimeter of all objects in binary image. References [R282] K. Benkrid, D. Crookes. Design and FPGA Implementation of a Perimeter Estimator. The Queen’s University of Belfast. http://www.cs.qub.a