bezier_curve skimage.draw.bezier_curve() Generate Bezier curve coordinates. Parameters: y0, x0 : int Coordinates of the first control point. y1, x1 : int Coordinates of the middle control point. y2, x2 : int Coordinates of the last control point. weight : double Middle control point weight, it describes the line tension. shape : tuple, optional Image shape which is used to determine the maximum extent of output pixel coordinates. This is useful for curves which exceed the image siz

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: >&

compare_psnr skimage.measure.compare_psnr(im_true, im_test, dynamic_range=None) [source] Compute the peak signal to noise ratio (PSNR) for an image. Parameters: im_true : ndarray Ground-truth image. im_test : ndarray Test image. dynamic_range : int The dynamic range of the input image (distance between minimum and maximum possible values). By default, this is estimated from the image data-type. Returns: psnr : float The PSNR metric. References [R260] https://en.wikipedia.org/wiki

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

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.

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

Module: future.graph skimage.future.graph.cut_normalized(labels, rag) Perform Normalized Graph cut on the Region Adjacency Graph. skimage.future.graph.cut_threshold(labels, ...) Combine regions separated by weight less than threshold. skimage.future.graph.draw_rag(labels, rag, img) Draw a Region Adjacency Graph on an image. skimage.future.graph.merge_hierarchical(...) Perform hierarchical merging of a RAG. skimage.future.graph.ncut(labels, rag[, ...]) Perform Normalized Graph cut on the Re

tophat skimage.filters.rank.tophat(image, selem, out=None, mask=None, shift_x=False, shift_y=False) [source] Local top-hat of an image. This filter computes the morphological opening of the image and then subtracts the result from the original 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 tha