CheckBox class skimage.viewer.widgets.CheckBox(name, value=False, alignment='center', ptype='kwarg', callback=None) [source] Bases: skimage.viewer.widgets.core.BaseWidget CheckBox widget Parameters: name : str Name of CheckBox parameter. If this parameter is passed as a keyword argument, it must match the name of that keyword argument (spaces are replaced with underscores). In addition, this name is displayed as the name of the CheckBox. value: {False, True}, optional Initial state of th

gray2rgb skimage.color.gray2rgb(image, alpha=None) [source] Create an RGB representation of a gray-level image. Parameters: image : array_like Input image of shape (M, N [, P]). alpha : bool, optional Ensure that the output image has an alpha layer. If None, alpha layers are passed through but not created. Returns: rgb : ndarray RGB image of shape (M, N, [, P], 3). Raises: ValueError If the input is not a 2- or 3-dimensional image.

RectangleTool class skimage.viewer.canvastools.RectangleTool(manager, on_move=None, on_release=None, on_enter=None, maxdist=10, rect_props=None) [source] Bases: skimage.viewer.canvastools.base.CanvasToolBase, matplotlib.widgets.RectangleSelector Widget for selecting a rectangular region in a plot. After making the desired selection, press “Enter” to accept the selection and call the on_enter callback function. Parameters: manager : Viewer or PlotPlugin. Skimage viewer or plot plugin object

img_as_bool skimage.util.img_as_bool(image, force_copy=False) [source] Convert an image to boolean format. Parameters: image : ndarray Input image. force_copy : bool Force a copy of the data, irrespective of its current dtype. Returns: out : ndarray of bool (bool_) Output image. Notes The upper half of the input dtype’s positive range is True, and the lower half is False. All negative values (if present) are False.

wiener skimage.filters.wiener(data, impulse_response=None, filter_params={}, K=0.25, predefined_filter=None) [source] Minimum Mean Square Error (Wiener) inverse filter. Parameters: data : (M,N) ndarray Input data. K : float or (M,N) ndarray Ratio between power spectrum of noise and undegraded image. impulse_response : callable f(r, c, **filter_params) Impulse response of the filter. See LPIFilter2D.__init__. filter_params : dict Additional keyword parameters to the impulse_response f

adjust_sigmoid skimage.exposure.adjust_sigmoid(image, cutoff=0.5, gain=10, inv=False) [source] Performs Sigmoid Correction on the input image. Also known as Contrast Adjustment. This function transforms the input image pixelwise according to the equation O = 1/(1 + exp*(gain*(cutoff - I))) after scaling each pixel to the range 0 to 1. Parameters: image : ndarray Input image. cutoff : float Cutoff of the sigmoid function that shifts the characteristic curve in horizontal direction. Defaul

PiecewiseAffineTransform class skimage.transform.PiecewiseAffineTransform [source] Bases: skimage.transform._geometric.GeometricTransform 2D piecewise affine transformation. Control points are used to define the mapping. The transform is based on a Delaunay triangulation of the points to form a mesh. Each triangle is used to find a local affine transform. Attributes affines (list of AffineTransform objects) Affine transformations for each triangle in the mesh. inverse_affines (list of Affine

resize skimage.transform.resize(image, output_shape, order=1, mode='constant', cval=0, clip=True, preserve_range=False) [source] Resize image to match a certain size. Performs interpolation to up-size or down-size images. For down-sampling N-dimensional images by applying the arithmetic sum or mean, see skimage.measure.local_sum and skimage.transform.downscale_local_mean, respectively. Parameters: image : ndarray Input image. output_shape : tuple or ndarray Size of the generated output i

threshold_li skimage.filters.threshold_li(image) [source] Return threshold value based on adaptation of Li’s Minimum Cross Entropy method. Parameters: image : array Input image. Returns: threshold : float Upper threshold value. All pixels intensities more than this value are assumed to be foreground. References [R202] Li C.H. and Lee C.K. (1993) “Minimum Cross Entropy Thresholding” Pattern Recognition, 26(4): 617-625 [R203] Li C.H. and Tam P.K.S. (1998) “An Iterative Algorithm for Mi

adjust_gamma skimage.exposure.adjust_gamma(image, gamma=1, gain=1) [source] Performs Gamma Correction on the input image. Also known as Power Law Transform. This function transforms the input image pixelwise according to the equation O = I**gamma after scaling each pixel to the range 0 to 1. Parameters: image : ndarray Input image. gamma : float Non negative real number. Default value is 1. gain : float The constant multiplier. Default value is 1. Returns: out : ndarray Gamma corre