find_contours skimage.measure.find_contours(array, level, fully_connected='low', positive_orientation='low') [source] Find iso-valued contours in a 2D array for a given level value. Uses the “marching squares” method to compute a the iso-valued contours of the input 2D array for a particular level value. Array values are linearly interpolated to provide better precision for the output contours. Parameters: array : 2D ndarray of double Input data in which to find contours. level : float V

find_boundaries skimage.segmentation.find_boundaries(label_img, connectivity=1, mode='thick', background=0) [source] Return bool array where boundaries between labeled regions are True. Parameters: label_img : array of int or bool An array in which different regions are labeled with either different integers or boolean values. connectivity: int in {1, ..., `label_img.ndim`}, optional A pixel is considered a boundary pixel if any of its neighbors has a different label. connectivity contro

find_available_plugins skimage.io.find_available_plugins(loaded=False) [source] List available plugins. Parameters: loaded : bool If True, show only those plugins currently loaded. By default, all plugins are shown. Returns: p : dict Dictionary with plugin names as keys and exposed functions as values.

FigureCanvas class skimage.viewer.utils.FigureCanvas(figure, **kwargs) [source] Bases: matplotlib.backends.backend_qt4agg.FigureCanvasQTAgg Canvas for displaying images. __init__(figure, **kwargs) [source] resizeEvent(event) [source]

figimage skimage.viewer.utils.figimage(image, scale=1, dpi=None, **kwargs) [source] Return figure and axes with figure tightly surrounding image. Unlike pyplot.figimage, this actually plots onto an axes object, which fills the figure. Plotting the image onto an axes allows for subsequent overlays of axes artists. Parameters: image : array image to plot scale : float If scale is 1, the figure and axes have the same dimension as the image. Smaller values of scale will shrink the figure. d

felzenszwalb skimage.segmentation.felzenszwalb(image, scale=1, sigma=0.8, min_size=20) [source] Computes Felsenszwalb’s efficient graph based image segmentation. Produces an oversegmentation of a multichannel (i.e. RGB) image using a fast, minimum spanning tree based clustering on the image grid. The parameter scale sets an observation level. Higher scale means less and larger segments. sigma is the diameter of a Gaussian kernel, used for smoothing the image prior to segmentation. The number

Module: novice

estimate_transform skimage.transform.estimate_transform(ttype, src, dst, **kwargs) [source] Estimate 2D geometric transformation parameters. You can determine the over-, well- and under-determined parameters with the total least-squares method. Number of source and destination coordinates must match. Parameters: ttype : {‘similarity’, ‘affine’, ‘piecewise-affine’, ‘projective’, ‘polynomial’} Type of transform. kwargs : array or int Function parameters (src, dst, n, angle): NAME / TTYPE

erosion skimage.morphology.erosion(image, selem=None, *args, **kwargs) [source] Return greyscale morphological erosion of an image. Morphological erosion sets a pixel at (i,j) to the minimum over all pixels in the neighborhood centered at (i,j). Erosion shrinks bright regions and enlarges dark regions. Parameters: image : ndarray Image array. selem : ndarray, optional The neighborhood expressed as an array of 1’s and 0’s. If None, use cross-shaped structuring element (connectivity=1). o

equalize_hist skimage.exposure.equalize_hist(image, nbins=256, mask=None) [source] Return image after histogram equalization. Parameters: image : array Image array. nbins : int, optional Number of bins for image histogram. Note: this argument is ignored for integer images, for which each integer is its own bin. mask: ndarray of bools or 0s and 1s, optional Array of same shape as image. Only points at which mask == True are used for the equalization, which is applied to the whole image.