+ Ai~^RIt^RIHt^RIHtHt!RR4t!RR4tRR R RR R R R/Rlt Rt Rt Rt R#)N) img_as_float)_supported_float_typecheck_nDc,a]tRt^ toRtRtRtVtR#)FeatureDetectorc<\P!.4VnR#N)nparray keypoints_selfs&R/var/www/html/photoedit/myenv/lib/python3.14/site-packages/skimage/feature/util.py__init__FeatureDetector.__init__ s((2,c \4h)zUDetect keypoints in image. Parameters ---------- image : 2D array Input image. NotImplementedError)rimages&&rdetectFeatureDetector.detects "##r)r N)__name__ __module__ __qualname____firstlineno__rr__static_attributes____classdictcell__ __classdict__s@rrr s' $ $rrc,a]tRt^toRtRtRtVtR#)DescriptorExtractorc<\P!.4VnR#r )r r descriptors_r s&rrDescriptorExtractor.__init__sHHRLrc \4h)zExtract feature descriptors in image for given keypoints. Parameters ---------- image : 2D array Input image. keypoints : (N, 2) array Keypoint locations as ``(row, col)``. r)rr keypointss&&&rextractDescriptorExtractor.extracts "##r)r$N)rrrrrr(rrrs@rr"r"s) $ $rr"keypoints_colork matches_color only_matchesF alignment horizontalc$ \V4p\V4p\VP4p \VP4p VP^,VP^,8dVP^,V ^&M?VP^,VP^,8dVP^,V ^&VP^,VP^,8dVP^,V ^&M?VP^,VP^,8dVP^,V ^&WP8wdP\P!WP R7p W RVP^,1RVP^,13&T pWP8wdP\P!WP R7p WRVP^,1RVP^,13&T p\P !VP4pV R8Xd \P!W.^R7p^V^&M7V R8Xd \P!W.^R7p^V^&MRV R2p\V4hV'gcVPVR,VR,RVR 7VPVR,V^,,VR,V^,,RVR 7TPVR R 7VP^VP^,V^,,VP^,V^,,^34VP^,p^R I H pVfK\PP^R 7p\!V4Uu.uFpVP^4NK ppM[V!V4'd\!V4Uu.uFpVNK ppM2\#VR4'd\%V4V8XdTpM Rp\V4h\'V4FkwppVwppVP)VV^3,VV^3,V^,,3VV^3,VV^3,V^,,3RVV,R7Km R#uupiuupi)aPlot matched features between two images. .. versionadded:: 0.23 Parameters ---------- image0 : (N, M [, 3]) array First image. image1 : (N, M [, 3]) array Second image. keypoints0 : (K1, 2) array First keypoint coordinates as ``(row, col)``. keypoints1 : (K2, 2) array Second keypoint coordinates as ``(row, col)``. matches : (Q, 2) array Indices of corresponding matches in first and second sets of descriptors, where `matches[:, 0]` (resp. `matches[:, 1]`) contains the indices in the first (resp. second) set of descriptors. ax : matplotlib.axes.Axes The Axes object where the images and their matched features are drawn. keypoints_color : matplotlib color, optional Color for keypoint locations. matches_color : matplotlib color or sequence thereof, optional Single color or sequence of colors for each line defined by `matches`, which connect keypoint matches. See [1]_ for an overview of supported color formats. By default, colors are picked randomly. only_matches : bool, optional Set to True to plot matches only and not the keypoint locations. alignment : {'horizontal', 'vertical'}, optional Whether to show the two images side by side (`'horizontal'`), or one above the other (`'vertical'`). References ---------- .. [1] https://matplotlib.org/stable/users/explain/colors/colors.html#specifying-colors Notes ----- To make a sequence of colors passed to `matches_color` work for any number of `matches`, you can wrap that sequence in :func:`itertools.cycle`. )dtypeNr/)axisverticalzV`plot_matched_features` accepts either 'horizontal' or 'vertical' for alignment, but 'z~' was given. See https://scikit-image.org/docs/dev/api/skimage.feature.html#skimage.feature.plot_matched_features for details.none) facecolors edgecolorsgray)cmap) is_color_like)seed__len__zb`matches_color` needs to be a single color or a sequence of length equal to the number of matches.-)colorNNNr?r)rlistshaper zerosr1r concatenate ValueErrorscatterimshowr2matplotlib.colorsr9random default_rngrangehasattrlen enumerateplot)image0image1 keypoints0 keypoints1matchesaxr*r,r-r. new_shape0 new_shape1 new_image0 new_image1offsetrmesgnumber_of_matchesr9rng_colors error_messageimatchidx0idx1s&&$$$$$$$$ rplot_matched_featuresrf,sl& !F & !Ffll#Jfll#J ||Aa( Q 1 a6<<? * Q 1  ||Aa( Q 1 a6<<? * Q 1 \\!XXj = ;A$V\\!_$&7 Q&778\\!XXj = ;A$V\\!_$&7 Q&778 XXfll #FL /a8q j /a8q (k*    t  t &  t vay ( t vay (&  IIe&I!GGQ Q&)+V\\!_vay-H! LM a(/ii###+)./@)AB)AA#**Q-)AB } % %)./@)AB)AA-)AB  * *s=/AEV/V F ''g&5 d  a *T1W"5q "A B a *T1W"5q "A B )  'CBs R R c\P!V4p\V^4\V4p\ VP 4pVP VRR7#F)copy)r squeezerrrr1astyper float_dtypes& r_prepare_grayscale_input_2DrosE JJu E UA  E' 4K << %< 00rc\P!V4p\V\^^44\ V4p\ VP 4pVPVRR7#rh)r rkrrLrrr1rlrms& r_prepare_grayscale_input_nDrqsK JJu E UE!QK  E' 4K << %< 00rcV^,pV^,pV^, VR,8VR,W2, ^,8,V^, VR,8,VR,WB, ^,8,pV#)aMask coordinates that are within certain distance from the image border. Parameters ---------- image_shape : (2,) array_like Shape of the image as ``(rows, cols)``. keypoints : (N, 2) array Keypoint coordinates as ``(rows, cols)``. distance : int Image border distance. Returns ------- mask : (N,) bool array Mask indicating if pixels are within the image (``True``) or in the border region of the image (``False``). rAr>) image_shaper'distancerowscolsmasks&&& r_mask_border_keypointsrys{( q>D q>D Q,)D/ ) T?do1 2 4 qLIdO + - T?do1 2 4  Kr) numpyr utilr _shared.utilsrrrr"rfrorqryrsrrr}sf $ $ $$$K K K K K \11r