init_qtapp skimage.viewer.utils.init_qtapp() [source] Initialize QAppliction. The QApplication needs to be initialized before creating any QWidgets

correct_mesh_orientation skimage.measure.correct_mesh_orientation(volume, verts, faces, spacing=(1.0, 1.0, 1.0), gradient_direction='descent') [source] Correct orientations of mesh faces. Parameters: volume : (M, N, P) array of doubles Input data volume to find isosurfaces. Will be cast to np.float64. verts : (V, 3) array of floats Array containing (x, y, z) coordinates for V unique mesh vertices. faces : (F, 3) array of ints List of length-3 lists of integers, referencing vertex coord

Module: novice

lena skimage.data.lena(*args, **kwargs) [source] Deprecated function. Use skimage.data.astronaut instead. Colour “Lena” image. This image has been removed from scikit-image due to copyright concerns. The standard, yet sometimes controversial Lena test image was scanned from the November 1972 edition of Playboy magazine. From an image processing perspective, this image is useful because it contains smooth, textured, shaded as well as detail areas.

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

nl_means_denoising skimage.restoration.nl_means_denoising(*args, **kwargs) [source] Deprecated function. Use skimage.restoration.denoise_nl_means instead. Perform non-local means denoising on 2-D or 3-D grayscale images, and 2-D RGB images. Parameters: image : 2D or 3D ndarray Input image to be denoised, which can be 2D or 3D, and grayscale or RGB (for 2D images only, see multichannel parameter). patch_size : int, optional Size of patches used for denoising. patch_distance : int, option

test skimage.test(doctest=False, verbose=False) [source] Run all unit tests.

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.

wiener skimage.restoration.wiener(image, psf, balance, reg=None, is_real=True, clip=True) [source] Wiener-Hunt deconvolution Return the deconvolution with a Wiener-Hunt approach (i.e. with Fourier diagonalisation). Parameters: image : (M, N) ndarray Input degraded image psf : ndarray Point Spread Function. This is assumed to be the impulse response (input image space) if the data-type is real, or the transfer function (Fourier space) if the data-type is complex. There is no constraints o

BaseWidget class skimage.viewer.widgets.BaseWidget(name, ptype=None, callback=None) [source] Bases: PyQt4.QtGui.QWidget __init__(name, ptype=None, callback=None) [source] plugin = 'Widget is not attached to a Plugin.' val