ó  ‰\c@sˆdZddlZddljZddlmZmZmZm Z ddl m Z ddl m Z d„Zdddded „ZdS( s¾ canny.py - Canny Edge detector Reference: Canny, J., A Computational Approach To Edge Detection, IEEE Trans. Pattern Analysis and Machine Intelligence, 8:679-714, 1986 Originally part of CellProfiler, code licensed under both GPL and BSD licenses. Website: http://www.cellprofiler.org Copyright (c) 2003-2009 Massachusetts Institute of Technology Copyright (c) 2009-2011 Broad Institute All rights reserved. Original author: Lee Kamentsky iÿÿÿÿN(tgaussian_filtertgenerate_binary_structuretbinary_erosiontlabeli(t dtype_limits(t assert_nDcCse||jtƒƒ}tj|j|jƒ}||||<||ƒ}||tjtƒj}|S(sSmooth an image with a linear function, ignoring masked pixels Parameters ---------- image : array Image you want to smooth. function : callable A function that does image smoothing. mask : array Mask with 1's for significant pixels, 0's for masked pixels. Notes ------ This function calculates the fractional contribution of masked pixels by applying the function to the mask (which gets you the fraction of the pixel data that's due to significant points). We then mask the image and apply the function. The resulting values will be lower by the bleed-over fraction, so you can recalibrate by dividing by the function on the mask to recover the effect of smoothing from just the significant pixels. 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Parameters ----------- image : 2D array Grayscale input image to detect edges on; can be of any dtype. sigma : float Standard deviation of the Gaussian filter. low_threshold : float Lower bound for hysteresis thresholding (linking edges). If None, low_threshold is set to 10% of dtype's max. high_threshold : float Upper bound for hysteresis thresholding (linking edges). If None, high_threshold is set to 20% of dtype's max. mask : array, dtype=bool, optional Mask to limit the application of Canny to a certain area. use_quantiles : bool, optional If True then treat low_threshold and high_threshold as quantiles of the edge magnitude image, rather than absolute edge magnitude values. If True then the thresholds must be in the range [0, 1]. Returns ------- output : 2D array (image) The binary edge map. See also -------- skimage.sobel Notes ----- The steps of the algorithm are as follows: * Smooth the image using a Gaussian with ``sigma`` width. * Apply the horizontal and vertical Sobel operators to get the gradients within the image. The edge strength is the norm of the gradient. * Thin potential edges to 1-pixel wide curves. First, find the normal to the edge at each point. This is done by looking at the signs and the relative magnitude of the X-Sobel and Y-Sobel to sort the points into 4 categories: horizontal, vertical, diagonal and antidiagonal. Then look in the normal and reverse directions to see if the values in either of those directions are greater than the point in question. Use interpolation to get a mix of points instead of picking the one that's the closest to the normal. * Perform a hysteresis thresholding: first label all points above the high threshold as edges. Then recursively label any point above the low threshold that is 8-connected to a labeled point as an edge. References ----------- .. [1] Canny, J., A Computational Approach To Edge Detection, IEEE Trans. Pattern Analysis and Machine Intelligence, 8:679-714, 1986 .. [2] William Green's Canny tutorial http://dasl.unlv.edu/daslDrexel/alumni/bGreen/www.pages.drexel.edu/_weg22/can_tut.html Examples -------- >>> from skimage import feature >>> # Generate noisy image of a square >>> im = np.zeros((256, 256)) >>> im[64:-64, 64:-64] = 1 >>> im += 0.2 * np.random.rand(*im.shape) >>> # First trial with the Canny filter, with the default smoothing >>> edges1 = feature.canny(im) >>> # Increase the smoothing for better results >>> edges2 = feature.canny(im, sigma=3) igš™™™™™¹?t clip_negativeigš™™™™™É?R cst|ˆddƒS(Ntmodetconstant(R(tx(tsigma(s5lib/python2.7/site-packages/skimage/feature/_canny.pytfsmooth§staxisit border_valueNiÿÿÿÿgð?s%Quantile thresholds must not be > 1.0gs%Quantile thresholds must not be < 0.0gY@itcopytndmin(ii(RtNoneRtFalseRtonesR tboolRtnditsobeltabsthypotRRR t ValueErrort percentileRtarraytsumtarangetint32(#RRt low_thresholdthigh_thresholdRt use_quantilesRtsmoothedtjsobeltisobelt abs_isobelt abs_jsobelt magnitudetst eroded_maskt local_maximatpts_plust pts_minustptstc1tc2tmtwtc_plustc_minustc1atc2at high_masktlow_masktstreltlabelstcounttsumst good_labelt output_mask((Rs5lib/python2.7/site-packages/skimage/feature/_canny.pytcanny5s´g        66 66    66 66    66 66    66 66  (t__doc__tnumpyRt scipy.ndimagetndimageR$RRRRtRt _shared.utilsRRR R!RM(((s5lib/python2.7/site-packages/skimage/feature/_canny.pyt s "