denoise_tv_chambolle skimage.restoration.denoise_tv_chambolle(im, weight=0.1, eps=0.0002, n_iter_max=200, multichannel=False) [source] Perform total-variation denoising on n-dimensional images. Parameters: im : ndarray of ints, uints or floats Input data to be denoised. im can be of any numeric type, but it is cast into an ndarray of floats for the computation of the denoised image. weight : float, optional Denoising weight. The greater weight, the more denoising (at the expense of fidel

ProjectiveTransform class skimage.transform.ProjectiveTransform(matrix=None) [source] Bases: skimage.transform._geometric.GeometricTransform Matrix transformation. Apply a projective transformation (homography) on coordinates. For each homogeneous coordinate , its target position is calculated by multiplying with the given matrix, , to give : [[a0 a1 a2] [b0 b1 b2] [c0 c1 1 ]]. E.g., to rotate by theta degrees clockwise, the matrix should be: [[cos(theta) -sin(theta) 0] [sin(theta) cos(

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

MCP_Connect class skimage.graph.MCP_Connect Bases: skimage.graph._mcp.MCP Connect source points using the distance-weighted minimum cost function. A front is grown from each seed point simultaneously, while the origin of the front is tracked as well. When two fronts meet, create_connection() is called. This method must be overloaded to deal with the found edges in a way that is appropriate for the application. __init__() x.__init__(...) initializes x; see help(type(x)) for signature cre

convert_colorspace skimage.color.convert_colorspace(arr, fromspace, tospace) [source] Convert an image array to a new color space. Parameters: arr : array_like The image to convert. fromspace : str The color space to convert from. Valid color space strings are ['RGB', 'HSV', 'RGB CIE', 'XYZ']. Value may also be specified as lower case. tospace : str The color space to convert to. Valid color space strings are ['RGB', 'HSV', 'RGB CIE', 'XYZ']. Value may also be specified as lower case.

median skimage.filters.median(image, selem, out=None, mask=None, shift_x=False, shift_y=False) [source] Return local median 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 (defau

ImageViewer class skimage.viewer.ImageViewer(image, useblit=True) [source] Bases: PyQt4.QtGui.QMainWindow Viewer for displaying images. This viewer is a simple container object that holds a Matplotlib axes for showing images. ImageViewer doesn’t subclass the Matplotlib axes (or figure) because of the high probability of name collisions. Subclasses and plugins will likely extend the update_image method to add custom overlays or filter the displayed image. Parameters: image : array Image bei

warp skimage.transform.warp(image, inverse_map=None, map_args={}, output_shape=None, order=1, mode='constant', cval=0.0, clip=True, preserve_range=False) [source] Warp an image according to a given coordinate transformation. Parameters: image : ndarray Input image. inverse_map : transformation object, callable cr = f(cr, **kwargs), or ndarray Inverse coordinate map, which transforms coordinates in the output images into their corresponding coordinates in the input image. There are a numb

marching_cubes skimage.measure.marching_cubes(volume, level, spacing=(1.0, 1.0, 1.0), gradient_direction='descent') [source] Marching cubes algorithm to find iso-valued surfaces in 3d volumetric data Parameters: volume : (M, N, P) array of doubles Input data volume to find isosurfaces. Will be cast to np.float64. level : float Contour value to search for isosurfaces in volume. spacing : length-3 tuple of floats Voxel spacing in spatial dimensions corresponding to numpy array indexing d

unsupervised_wiener skimage.restoration.unsupervised_wiener(image, psf, reg=None, user_params=None, is_real=True, clip=True) [source] Unsupervised Wiener-Hunt deconvolution. Return the deconvolution with a Wiener-Hunt approach, where the hyperparameters are automatically estimated. The algorithm is a stochastic iterative process (Gibbs sampler) described in the reference below. See also wiener function. Parameters: image : (M, N) ndarray The input degraded image. psf : ndarray The impuls