9i$tSrSSKrSSKJr SSKJr SSKJ r SSK J r J r SSK Jr S rSS S S .S jjrg)z canny.py - Canny Edge detector Reference: Canny, J., A Computational Approach To Edge Detection, IEEE Trans. Pattern Analysis and Machine Intelligence, 8:679-714, 1986 N) dtype_limits)gaussian)_supported_float_typecheck_nD) _nonmaximum_suppression_bilinearc[X#USS9nUS:H=(d USLn[UR5nUcxU(a[R"UR US9nUn[R"UR [ S9n SU SS2SS24'SU SS2SS24'SU SS2SS24'SU SS2SS24'O\UR[ SS 9n[R"U5nXX'[R"S S 5n [R"XSS 9n U(a;[URUSS 940UD6[R"U5R-n [U40UD6n U(aU W -n X4$) aZGenerate a smoothed image and an eroded mask. The image is smoothed using a gaussian filter ignoring masked pixels and the mask is eroded. Parameters ---------- image : array Image to be smoothed. mask : array Mask with 1's for significant pixels, 0's for masked pixels. sigma : scalar or sequence of scalars Standard deviation for Gaussian kernel. The standard deviations of the Gaussian filter are given for each axis as a sequence, or as a single number, in which case it is equal for all axes. mode : str, {'reflect', 'constant', 'nearest', 'mirror', 'wrap'} The ``mode`` parameter determines how the array borders are handled, 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 ------- smoothed_image : ndarray The smoothed array eroded_mask : ndarray The eroded mask. 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. F)sigmamodecvalpreserve_rangeconstantN)dtyperr)copyr) border_value)dictrrnponesshapeboolastype zeros_likendigenerate_binary_structurebinary_erosionrfinfoeps) imagemaskr r r gaussian_kwargscompute_bleedover float_type masked_image eroded_masks bleed_oversmoothed_images V/var/www/html/land-doc-ocr/venv/lib/python3.13/site-packages/skimage/feature/_canny.py _preprocessr+seRUSO *>d$.>&u{{3J | 775;;j9D ggekk6  BQBE BCF ArrE ArsF{{4e{,}}U+ "[   ) )!Q /((qA  T[[%[8 LO Lhhz"&& '  l>o>N *$  &&r)r r c[R"UR[R5(d4[R"UR[R5(a [ S5e[ US5 [USS9SnUcSnO&U(aSUs=::aS::d O [ S 5eOX(-nUcS nO&U(aSUs=::aS::d O [ S 5eOX8-nX2:a [ S 5e[XXU5up[R"U SS 9n [R"U S S 9n X-n XU -- n [R"XS9 U(a![R"U SU-SU-/5up#[XXU5nUS :n[R"S[5n[R "UU5unnUS :XaU$XU:-n[R""UU5n[R$"US-4[5nSUU'UUnU$)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_negativerg?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[["((++r}}U[["))/T/TBCC UAU%8;I }++KL L, "  ~,,KL L- #%LMM(U$GHYYxa (F YYxa (FI & IGGI%(*  -u~/EF) % 2 JA~H GGFD !EIIh.MFE z.89I99VI./L519,-J#J|V$K r,)r0NNNF)__doc__numpyr scipy.ndimagendimager util.dtyper_shared.filtersr _shared.utilsrr _canny_cyr r+rOr,r*rYsJ%&;7S'p   _  _r,