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

A crash course on NumPy for images Images manipulated by scikit-image are simply NumPy arrays. Hence, a large fraction of operations on images will just consist in using NumPy: >>> from skimage import data >>> camera = data.camera() >>> type(camera) <type 'numpy.ndarray'> Retrieving the geometry of the image and the number of pixels: >>> camera.shape (512, 512) >>> camera.size 262144 Retrieving statistical information about gray values: >&

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

active_contour skimage.segmentation.active_contour(image, snake, alpha=0.01, beta=0.1, w_line=0, w_edge=1, gamma=0.01, bc='periodic', max_px_move=1.0, max_iterations=2500, convergence=0.1) [source] Active contour model. Active contours by fitting snakes to features of images. Supports single and multichannel 2D images. Snakes can be periodic (for segmentation) or have fixed and/or free ends. Parameters: image : (N, M) or (N, M, 3) ndarray Input image. snake : (N, 2) ndarray Initialisatio

denoise_nl_means skimage.restoration.denoise_nl_means(image, patch_size=7, patch_distance=11, h=0.1, multichannel=True, fast_mode=True) [source] 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, optional Maxima

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

gabor_filter skimage.filters.gabor_filter(*args, **kwargs) [source] Deprecated function. Use skimage.filters.gabor instead. Return real and imaginary responses to Gabor filter. The real and imaginary parts of the Gabor filter kernel are applied to the image and the response is returned as a pair of arrays. Gabor filter is a linear filter with a Gaussian kernel which is modulated by a sinusoidal plane wave. Frequency and orientation representations of the Gabor filter are similar to those of

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

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

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