roberts skimage.filters.roberts(image, mask=None) [source] Find the edge magnitude using Roberts’ cross operator. Parameters: image : 2-D array Image to process. mask : 2-D array, optional An optional mask to limit the application to a certain area. Note that pixels surrounding masked regions are also masked to prevent masked regions from affecting the result. Returns: output : 2-D array The Roberts’ Cross edge map. See also sobel, scharr, prewitt, feature.canny Examples >>

rag_boundary skimage.future.graph.rag_boundary(labels, edge_map, connectivity=2) [source] Comouter RAG based on region boundaries Given an image’s initial segmentation and its edge map this method constructs the corresponding Region Adjacency Graph (RAG). Each node in the RAG represents a set of pixels within the image with the same label in labels. The weight between two adjacent regions is the average value in edge_map along their boundary. labels : ndarray The labelled image. edge_map

User Guide Getting started A crash course on NumPy for images NumPy indexing Color images Coordinate conventions Notes on array order A note on time Image data types and what they meanInput types Output types Image processing pipeline Rescaling intensity values Note about negative values References Image adjustment: transforming image contentColor manipulation Contrast and exposure I/O Plugin Infrastructure TutorialsImage Segmentation How to parallelize loops Getting help on using skimag

I/O Plugin Infrastructure A plugin consists of two files, the source and the descriptor .ini. Let’s say we’d like to provide a plugin for imshow using matplotlib. We’ll call our plugin mpl: skimage/io/_plugins/mpl.py skimage/io/_plugins/mpl.ini The name of the .py and .ini files must correspond. Inside the .ini file, we give the plugin meta-data: [mpl] <-- name of the plugin, may be anything description = Matplotlib image I/O plugin provides = imshow <-- a comma-separated list, one or mo

threshold skimage.filters.rank.threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False) [source] Local threshold of an image. The resulting binary mask is True if the greyvalue of the center pixel is greater than the local mean. 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 that de

API Reference skimageSubpackages Utility Functions dtype_limits img_as_bool img_as_float img_as_int img_as_ubyte img_as_uint test Module: colorcombine_stains convert_colorspace deltaE_cie76 deltaE_ciede2000 deltaE_ciede94 deltaE_cmc gray2rgb guess_spatial_dimensions hed2rgb hsv2rgb lab2lch lab2rgb lab2xyz label2rgb lch2lab luv2rgb luv2xyz rgb2gray rgb2grey rgb2hed rgb2hsv rgb2lab rgb2luv rgb2rgbcie rgb2xyz rgbcie2rgb separate_stains xyz2lab xyz2luv xyz2rgb Module: dataastronaut binary_blob

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

maximum skimage.filters.rank.maximum(image, selem, out=None, mask=None, shift_x=False, shift_y=False) [source] Return local maximum of an 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 that defines (>0) area of the image included in the local neighborhood. If None, the complete image is use

plugin_info skimage.io.plugin_info(plugin) [source] Return plugin meta-data. Parameters: plugin : str Name of plugin. Returns: m : dict Meta data as specified in plugin .ini.

CENSURE class skimage.feature.CENSURE(min_scale=1, max_scale=7, mode='DoB', non_max_threshold=0.15, line_threshold=10) [source] Bases: skimage.feature.util.FeatureDetector CENSURE keypoint detector. min_scale : int, optional Minimum scale to extract keypoints from. max_scale : int, optional Maximum scale to extract keypoints from. The keypoints will be extracted from all the scales except the first and the last i.e. from the scales in the range [min_scale + 1, max_scale - 1]. The filter