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

luv2xyz skimage.color.luv2xyz(luv, illuminant='D65', observer='2') [source] CIE-Luv to XYZ color space conversion. Parameters: luv : (M, N, [P,] 3) array_like The 3 or 4 dimensional image in CIE-Luv format. Final dimension denotes channels. illuminant : {“A”, “D50”, “D55”, “D65”, “D75”, “E”}, optional The name of the illuminant (the function is NOT case sensitive). observer : {“2”, “10”}, optional The aperture angle of the observer. Returns: out : (M, N, [P,] 3) ndarray The image i

deltaE_cie76 skimage.color.deltaE_cie76(lab1, lab2) [source] Euclidean distance between two points in Lab color space Parameters: lab1 : array_like reference color (Lab colorspace) lab2 : array_like comparison color (Lab colorspace) Returns: dE : array_like distance between colors lab1 and lab2 References [R32] http://en.wikipedia.org/wiki/Color_difference [R33] A. R. Robertson, “The CIE 1976 color-difference formulae,” Color Res. Appl. 2, 7-11 (1977).

structural_similarity skimage.measure.structural_similarity(*args, **kwargs) [source] Deprecated function. Use compare_ssim instead.

Module: restoration Image restoration module.

view_as_windows skimage.util.view_as_windows(arr_in, window_shape, step=1) [source] Rolling window view of the input n-dimensional array. Windows are overlapping views of the input array, with adjacent windows shifted by a single row or column (or an index of a higher dimension). Parameters: arr_in : ndarray N-d input array. window_shape : integer or tuple of length arr_in.ndim Defines the shape of the elementary n-dimensional orthotope (better know as hyperrectangle [R383]) of the rolli

TiffWriter class skimage.external.tifffile.TiffWriter(filename, bigtiff=False, byteorder=None, software='tifffile.py') [source] Bases: object Write image data to TIFF file. TiffWriter instances must be closed using the close method, which is automatically called when using the ‘with’ statement. Examples >>> data = numpy.random.rand(2, 5, 3, 301, 219) >>> with TiffWriter('temp.tif', bigtiff=True) as tif: ... for i in range(data.shape[0]): ... tif.save(data[i], co

start_qtapp skimage.viewer.utils.start_qtapp(app=None) [source] Start Qt mainloop

ORB class skimage.feature.ORB(downscale=1.2, n_scales=8, n_keypoints=500, fast_n=9, fast_threshold=0.08, harris_k=0.04) [source] Bases: skimage.feature.util.FeatureDetector, skimage.feature.util.DescriptorExtractor Oriented FAST and rotated BRIEF feature detector and binary descriptor extractor. Parameters: n_keypoints : int, optional Number of keypoints to be returned. The function will return the best n_keypoints according to the Harris corner response if more than n_keypoints are detect

route_through_array skimage.graph.route_through_array(array, start, end, fully_connected=True, geometric=True) [source] Simple example of how to use the MCP and MCP_Geometric classes. See the MCP and MCP_Geometric class documentation for explanation of the path-finding algorithm. Parameters: array : ndarray Array of costs. start : iterable n-d index into array defining the starting point end : iterable n-d index into array defining the end point fully_connected : bool (optional) If T