9iSSKJr SSKrSSKJr SSKJr SrSr Sr \"S S 5SS j5r \"S S 5SS j5r \"S S 5SS j5r g))reduceN)polygon)requirec[R"U[S9nUVVs/sHupEUPM nnnUVVs/sHupEUPM nnn[XgU5upX#X4'U$s snnfs snnf)N)dtype)npzerosintr) verticesshapelabelmaskxyprpcrrccs b/var/www/html/land-doc-ocr/venv/lib/python3.13/site-packages/skimage/future/manual_segmentation.py_mask_from_verticesr sb 88E %D !!B ! !!B ! RU #FBL K " !s AA! matplotlibz>=3.3cSSKJn SSKJn SSKJn U"USS9nU"U/SUS9nUR U5nUR5 U$)Nr)Polygon)PatchCollectionT)closed)match_originalalpha)matplotlib.patchesrmatplotlib.collectionsrmatplotlib.pyplotpyplotadd_collectiondraw) axrr rrpltrppolygon_objects r _draw_polygonr+sF*6#ht,G $eDA&&q)NHHJ c ^^^^ ^ ^ ^ ^ ^^SSKnSSKJm /m /m /m/m TRS;a [ S5eT R 5um mT R SS9 TRTSS9 TR5 U U U 4S jnT R/S Q5mURRTS 5nURU5 UUU U U U UU4S jnT RRS U5 T RSS9 U4Sj[!T SS95nU(a["R$"U5$['["R(U["R*"STR,SS55$)ayReturn a label image based on polygon selections made with the mouse. Parameters ---------- image : (M, N[, 3]) array Grayscale or RGB image. alpha : float, optional Transparency value for polygons drawn over the image. return_all : bool, optional If True, an array containing each separate polygon drawn is returned. (The polygons may overlap.) If False (default), latter polygons "overwrite" earlier ones where they overlap. Returns ------- labels : array of int, shape ([Q, ]M, N) The segmented regions. If mode is `'separate'`, the leading dimension of the array corresponds to the number of regions that the user drew. Notes ----- Use left click to select the vertices of the polygon and right click to confirm the selection once all vertices are selected. Examples -------- >>> from skimage import data, future >>> import matplotlib.pyplot as plt # doctest: +SKIP >>> camera = data.camera() >>> mask = future.manual_polygon_segmentation(camera) # doctest: +SKIP >>> fig, ax = plt.subplots() # doctest: +SKIP >>> ax.imshow(mask) # doctest: +SKIP >>> plt.show() # doctest: +SKIP rNrr.Only 2D grayscale or RGB images are supported.皙?bottomgraycmapc>T(aKTR5 TR5nUR5 TRR5 ggNpopremovecanvas draw_idleargskwargs last_polyfiglist_of_vertex_listspolygons_drawns r_undo*manual_polygon_segmentation.._undoXA  $ $ &&**,I     JJ " r,g333333?g?333333?rH⟲ct>URbURT LagTR5(agUR[:XanT R UR UR /5 T (a T R5nUR5 [TT TS- S9nT R U5 gUR[:XanT (dgTR T SS5 [TT TS9nT R U5 T R5nUR5 T SS2 TR5 gg)Ngffffff?r ) inaxesget_navigate_modebutton LEFT_CLICKappendxdataydatar9r:r+ RIGHT_CLICKr&) eventpolyrr* preview_polyr r'rBr(rCpreview_polygon_drawn temp_listundo_poss r_extend_polygon4manual_polygon_segmentation.._extend_polygones << 5<<8#;    ! !  <<: %   ekk5;;7 8$,002 $B %#+GG ! ( ( 1 \\[ ( ! ' ' ! 5*2yFN  ! !. 11446L    !! HHJ!)r,button_press_eventTblockc3\># UH!up[UTRSSU5v M# g7fNrrr.0irimages r .manual_polygon_segmentation..0CKA Hekk"1oq99C),rstartr)rr#r$ndim ValueErrorsubplotssubplots_adjustimshow set_axis_offadd_axeswidgetsButton on_clickedr; mpl_connectshow enumerater stackrmaximum broadcast_tor)rer return_allrrD undo_buttonrZlabelsr'rArBr(rCrWrXrYs`` @@@@@@@@rmanual_polygon_segmentationr!s4L#NI zzIJJllnGCs#IIe&I!OO#||67H$$++Hh?K5!##JJJ/AHH4H$%9CFxxbjj&"//!U[[!_*MNNr,c ^^^ ^ ^ ^ ^ SSKnSSKJm /m /m TRS;a [ S5eT R 5um m T R SS9 T RTSS9 T R5 U U U 4S jnT R/S Q5nURRUS 5nURU5 UU U U U 4S jnURRT U5 T RS S9 U4Sj[T SS95nU(a[ R""U5$[%[ R&U[ R("STR*SS55$)a;Return a label image based on freeform selections made with the mouse. Parameters ---------- image : (M, N[, 3]) array Grayscale or RGB image. alpha : float, optional Transparency value for polygons drawn over the image. return_all : bool, optional If True, an array containing each separate polygon drawn is returned. (The polygons may overlap.) If False (default), latter polygons "overwrite" earlier ones where they overlap. Returns ------- labels : array of int, shape ([Q, ]M, N) The segmented regions. If mode is `'separate'`, the leading dimension of the array corresponds to the number of regions that the user drew. Notes ----- Press and hold the left mouse button to draw around each object. Examples -------- >>> from skimage import data, future >>> import matplotlib.pyplot as plt # doctest: +SKIP >>> camera = data.camera() >>> mask = future.manual_lasso_segmentation(camera) # doctest: +SKIP >>> fig, ax = plt.subplots() # doctest: +SKIP >>> ax.imshow(mask) # doctest: +SKIP >>> plt.show() # doctest: +SKIP rNr.r/r0r1r3r4c>T(aKTR5 TR5nUR5 TRR5 ggr7r8r=s rrD(manual_lasso_segmentation.._undorFr,rGrIc>[U5S:agTRU5 [TUTS9nTRU5 TR5 g)NrrK)lenrPr+r&)rr*r r'rBr(rCs r_on_lasso_selection6manual_lasso_segmentation.._on_lasso_selectionsE x=1  ##H-&r85An-  r,Tr]c3\># UH!up[UTRSSU5v M# g7fr`rarbs rrf,manual_lasso_segmentation..rhrirrjr)rr#r$rlrmrnrorprqrrrsrtru LassoSelectorrwrxr ryrrzr{r)rer r|rrDrYr}rr~r'rArBr(rCs`` @@@@@rmanual_lasso_segmentationrs)J#N zzIJJllnGCs#IIe&I!OO#||67H$$++Hh?K5!$$R)<=HH4H$%9CFxxbjj&"//!U[[!_*MNNr,)皙?)rF) functoolsrnumpyr r&r_shared.version_requirementsrrOrSrr+rrr,rrs|2   w    wsO sOl wRO ROr,