structure_tensor_eigvals skimage.feature.structure_tensor_eigvals(Axx, Axy, Ayy) [source] Compute Eigen values of structure tensor. Parameters: Axx : ndarray Element of the structure tensor for each pixel in the input image. Axy : ndarray Element of the structure tensor for each pixel in the input image. Ayy : ndarray Element of the structure tensor for each pixel in the input image. Returns: l1 : ndarray Larger eigen value for each input matrix. l2 : ndarray Smaller eigen value

RAG class skimage.future.graph.RAG(label_image=None, connectivity=1, data=None, **attr) [source] Bases: networkx.classes.graph.Graph The Region Adjacency Graph (RAG) of an image, subclasses networx.Graph Parameters: label_image : array of int An initial segmentation, with each region labeled as a different integer. Every unique value in label_image will correspond to a node in the graph. connectivity : int in {1, ..., label_image.ndim}, optional The connectivity between pixels in label_i

plugin_order skimage.io.plugin_order() [source] Return the currently preferred plugin order. Returns: p : dict Dictionary of preferred plugin order, with function name as key and plugins (in order of preference) as value.

threshold_isodata skimage.filters.threshold_isodata(image, nbins=256, return_all=False) [source] Return threshold value(s) based on ISODATA method. Histogram-based threshold, known as Ridler-Calvard method or inter-means. Threshold values returned satisfy the following equality: threshold = (image[image <= threshold].mean() + image[image > threshold].mean()) / 2.0 That is, returned thresholds are intensities that separate the image into two groups of pixels, where the threshold inten

checkerboard skimage.data.checkerboard() [source] Checkerboard image. Checkerboards are often used in image calibration, since the corner-points are easy to locate. Because of the many parallel edges, they also visualise distortions particularly well.

ImageViewer class skimage.viewer.viewers.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 I

gabor skimage.filters.gabor(image, frequency, theta=0, bandwidth=1, sigma_x=None, sigma_y=None, n_stds=3, offset=0, mode='reflect', cval=0) [source] 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

mesh_surface_area skimage.measure.mesh_surface_area(verts, faces) [source] Compute surface area, given vertices & triangular faces Parameters: 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 coordinates as provided in verts Returns: area : float Surface area of mesh. Units now [coordinate units] ** 2. See also skimage.measure.marching_cubes, ski

ProjectiveTransform class skimage.transform.ProjectiveTransform(matrix=None) [source] Bases: skimage.transform._geometric.GeometricTransform Matrix transformation. Apply a projective transformation (homography) on coordinates. For each homogeneous coordinate , its target position is calculated by multiplying with the given matrix, , to give : [[a0 a1 a2] [b0 b1 b2] [c0 c1 1 ]]. E.g., to rotate by theta degrees clockwise, the matrix should be: [[cos(theta) -sin(theta) 0] [sin(theta) cos(

greycomatrix skimage.feature.greycomatrix(image, distances, angles, levels=256, symmetric=False, normed=False) [source] Calculate the grey-level co-occurrence matrix. A grey level co-occurrence matrix is a histogram of co-occurring greyscale values at a given offset over an image. Parameters: image : array_like of uint8 Integer typed input image. The image will be cast to uint8, so the maximum value must be less than 256. distances : array_like List of pixel pair distance offsets. angle