1-Ph$rdZddlZddlmZddlmZddlm Z ddl m Z m Z ddl mZd Z dd d ddZdS)z canny.py - Canny Edge detector Reference: Canny, J., A Computational Approach To Edge Detection, IEEE Trans. Pattern Analysis and Machine Intelligence, 8:679-714, 1986 N) dtype_limits)gaussian)_supported_float_typecheck_nD) _nonmaximum_suppression_bilinearct|||d}|dkp|du}t|j}|l|rtj|j|}|}tj|jt } d| ddddf<d| ddddf<d| ddddf<d| ddddf<ng|t d }tj|}||||<tj d d } tj || d } |rd$.>&u{33J |  :75;j999D gek666  BQBE BCCF AAArrE AAArssF{{4e{,,}U++ "4[ T  )!Q / /(qqAAA  T[[%[88 L LO L Lhz""& '  l>>o>>N %*$ ; &&?Fr)r r c(tj|jtjs$tj|jtjrt dt |dt|dd}|d}n&|rd|cxkrd ksnt d n||z}|d }n&|rd|cxkrd ksnt d n||z}||krt d t|||||\} } tj | d } tj | d } | | z} | | | zz } tj | | |r tj | d|zd|zg\}}t| | | | |}|dk}tjdt}tj||\}}|dkr|S|||kz}tj||}tj|dzft}d||<||}|S)a Edge filter an image using the Canny algorithm. Parameters ---------- image : 2D array Grayscale input image to detect edges on; can be of any dtype. sigma : float, optional Standard deviation of the Gaussian filter. low_threshold : float, optional Lower bound for hysteresis thresholding (linking edges). If None, low_threshold is set to 10% of dtype's max. high_threshold : float, optional 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]. mode : str, {'reflect', 'constant', 'nearest', 'mirror', 'wrap'} The ``mode`` parameter determines how the array borders are handled during Gaussian filtering, where ``cval`` is the value when mode is equal to 'constant'. cval : float, optional Value to fill past edges of input if `mode` is 'constant'. Returns ------- output : 2D array (image) The binary edge map. See also -------- skimage.filters.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 :DOI:`10.1109/TPAMI.1986.4767851` .. [2] William Green's Canny tutorial https://en.wikipedia.org/wiki/Canny_edge_detector Examples -------- >>> from skimage import feature >>> rng = np.random.default_rng() >>> # Generate noisy image of a square >>> im = np.zeros((256, 256)) >>> im[64:-64, 64:-64] = 1 >>> im += 0.2 * rng.random(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) z'64-bit integer images are not supportedrF) clip_negativerNg?r.r-z,Quantile thresholds must be between 0 and 1.g?z1low_threshold should be lower then high_threshold)axisr)outgY@)r3T)r issubdtyperint64uint64 ValueErrorrrr+rsobelsqrt percentiler rrlabeluniquezeros)r r low_thresholdhigh_thresholdr! use_quantilesr r dtype_maxsmoothedr&jsobelisobel magnitude low_maskedlow_maskstrellabelscount high_mask nonzero_sums good_label output_masks r*cannyrOgsD }U["(++Dr}U[")/T/TDBCCC UAU%888;I #}++++++++KLL L, "  $~,,,,,,,,KLL L- )# %%LMMM(tUD$GGHkYxa ( ( (F Yxa ( ( (FI & IGI9%%%% (*  -u~/EF) ) % ~ 2 ; JA~H GFD ! !EIh..MFE zzJ.89I9VI.//L519,--J#J|V$K r,)r-NNNF)__doc__numpyr scipy.ndimagendimager util.dtyper_shared.filtersr _shared.utilsrr _canny_cyr r+rOr,r*rYs%%%%%%&&&&&&;;;;;;;;777777S'S'S'p   _  _______r,