local_binary_pattern skimage.feature.local_binary_pattern(image, P, R, method='default') [source] Gray scale and rotation invariant LBP (Local Binary Patterns). LBP is an invariant descriptor that can be used for texture classification. Parameters: image : (N, M) array Graylevel image. P : int Number of circularly symmetric neighbour set points (quantization of the angular space). R : float Radius of circle (spatial resolution of the operator). method : {‘default’, ‘ror’, ‘uniform’, ‘

load_surf skimage.io.load_surf(f) [source] Read SIFT or SURF features from a file. Parameters: f : string or open file Input file generated by the feature detectors from http://people.cs.ubc.ca/~lowe/keypoints/ or http://www.vision.ee.ethz.ch/~surf/ Returns: data : record array with fields row: int row position of feature column: int column position of feature scale: float feature scale orientation: float feature orientation data: array feature values

load_sift skimage.io.load_sift(f) [source] Read SIFT or SURF features from a file. Parameters: f : string or open file Input file generated by the feature detectors from http://people.cs.ubc.ca/~lowe/keypoints/ or http://www.vision.ee.ethz.ch/~surf/ Returns: data : record array with fields row: int row position of feature column: int column position of feature scale: float feature scale orientation: float feature orientation data: array feature values

load skimage.data.load(f) [source] Load an image file located in the data directory. Parameters: f : string File name. Returns: img : ndarray Image loaded from skimage.data_dir.

LineModelND class skimage.measure.LineModelND [source] Bases: skimage.measure.fit.BaseModel Total least squares estimator for N-dimensional lines. Lines are defined by a point (origin) and a unit vector (direction) according to the following vector equation: X = origin + lambda * direction Attributes params (tuple) Line model parameters in the following order origin, direction. __init__() [source] estimate(data) [source] Estimate line model from data. Parameters: data : (N, dim) arra

LineTool class skimage.viewer.canvastools.LineTool(manager, on_move=None, on_release=None, on_enter=None, maxdist=10, line_props=None, handle_props=None, **kwargs) [source] Bases: skimage.viewer.canvastools.base.CanvasToolBase Widget for line selection in a plot. Parameters: manager : Viewer or PlotPlugin. Skimage viewer or plot plugin object. on_move : function Function called whenever a control handle is moved. This function must accept the end points of line as the only argument. on_

LineModel class skimage.measure.LineModel [source] Bases: skimage.measure.fit.BaseModel Total least squares estimator for 2D lines. Lines are parameterized using polar coordinates as functional model: dist = x * cos(theta) + y * sin(theta) This parameterization is able to model vertical lines in contrast to the standard line model y = a*x + b. This estimator minimizes the squared distances from all points to the line: min{ sum((dist - x_i * cos(theta) + y_i * sin(theta))**2) } A minimum nu

LinearColormap class skimage.viewer.utils.LinearColormap(name, segmented_data, **kwargs) [source] Bases: matplotlib.colors.LinearSegmentedColormap LinearSegmentedColormap in which color varies smoothly. This class is a simplification of LinearSegmentedColormap, which doesn’t support jumps in color intensities. Parameters: name : str Name of colormap. segmented_data : dict Dictionary of ‘red’, ‘green’, ‘blue’, and (optionally) ‘alpha’ values. Each color key contains a list of x, y tuples.

line skimage.draw.line() Generate line pixel coordinates. Parameters: y0, x0 : int Starting position (row, column). y1, x1 : int End position (row, column). Returns: rr, cc : (N,) ndarray of int Indices of pixels that belong to the line. May be used to directly index into an array, e.g. img[rr, cc] = 1. See also line_aa Anti-aliased line generator Examples >>> from skimage.draw import line >>> img = np.zeros((10, 10), dtype=np.uint8) >>> rr, cc = lin

line_aa skimage.draw.line_aa() Generate anti-aliased line pixel coordinates. Parameters: y0, x0 : int Starting position (row, column). y1, x1 : int End position (row, column). Returns: rr, cc, val : (N,) ndarray (int, int, float) Indices of pixels (rr, cc) and intensity values (val). img[rr, cc] = val. References [R72] A Rasterizing Algorithm for Drawing Curves, A. Zingl, 2012 http://members.chello.at/easyfilter/Bresenham.pdf Examples >>> from skimage.draw import line_aa