+ Ai^RIHt^RIt^RIHt^RIHt^t^t Rt ]!RR4R Rl4t ]!RR4R R l4t ]!RR4R R l4t R#) )reduceN)polygon)requirec\P!V\R7pVUUu.uFwrEVNK pppVUUu.uFwrEVNK ppp\WgV4wrW#W3&V#uuppiuuppi))dtype)npzerosintr) verticesshapelabelmaskxyprpcrrccs &&& `/var/www/html/photoedit/myenv/lib/python3.14/site-packages/skimage/future/manual_segmentation.py_mask_from_verticesr sb 88E %D !!B ! !!B ! RU #FBL K " !s A A" matplotlibz>=3.3c^RIHp^RIHp^RIHpV!VRR7pV!V.RVR7pVP V4pVP4V#)r)Polygon)PatchCollectionNT)closed)match_originalalpha)matplotlib.patchesrmatplotlib.collectionsrmatplotlib.pyplotpyplotadd_collectiondraw) axr rrrpltrppolygon_objects &&& r _draw_polygonr(sF*6#ht,G $eDA&&q)NHHJ c aaaa a a a a aa^RIp^RIHo .o .o .o.o SPR9d \ R4hS P 4wo oS P RR7SPSRR7SP4V V V 3RlpS P.RO4oVPPSR4pVPV4VVV V V V VV3R lpS PPR V4S PR R 7V3R l\!S ^R74pV'd\"P$!V4#\'\"P(V\"P*!^SP,R,44#)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 N.Only 2D grayscale or RGB images are supported.皙?bottomgraycmapc<S'dMSP4SP4pVP4SPP4R#R#Npopremovecanvas draw_idleargskwargs last_polyfiglist_of_vertex_listspolygons_drawns*, r_undo*manual_polygon_segmentation.._undoXA  $ $ &&**,I     JJ " r)⟲c<VPeVPS JdR#SP4'dR#VP\8XdxS P VP VP .4S 'd!S P4pVP4\SS SR, R7pS P V4R#VP\8XdwS 'gR#SP S R,4\SS SR7pS P V4S P4pVP4S RSP4R#R#)Ngffffff?r:NNN) inaxesget_navigate_modebutton LEFT_CLICKappendxdataydatar5r6r( RIGHT_CLICKr#) eventpolyrr' preview_polyrr$r>r%r?preview_polygon_drawn temp_listundo_poss & r_extend_polygon4manual_polygon_segmentation.._extend_polygones << 5<<8#;    ! !  <<: %   ekk5;;7 8$,002 $B %#+GG ! ( ( 1 \\[ ( ! ' ' ! 5*2yFN  ! !. 11446L    !! HHJ!)r)button_press_eventTblockc3d<"TF%wr\VSPR,V4xK' R#5iNNNrr .0ir images& r .manual_polygon_segmentation...CKA Hekk"oq99C-0startr[r\g333333?g?333333?rk)rr r!ndim ValueErrorsubplotssubplots_adjustimshow set_axis_offadd_axeswidgetsButton on_clickedr7 mpl_connectshow enumeraterstackrmaximum broadcast_tor )rar return_allrr@ undo_buttonrTlabelsr$r=r>r%r?rQrRrSsff& @@@@@@@@rmanual_polygon_segmentationr!s2L#NI zzIJJllnGCs#IIe&I!OO#||67H$$++Hh?K5!##JJJ/AHH4H$%9CFxxbjj&"//!U[[_*MNNr)c aaa a a a a ^RIp^RIHo .o .o SPR9d \ R4hS P 4wo o S P RR7S PSRR7S P4V V V 3RlpS P.RO4pVPPVR4pVPV4VV V V V 3R lpVPPS V4S PR R 7V3R l\S ^R 74pV'd\ P"!V4#\%\ P&V\ P(!^SP*R,44#)a;Return a label image based on freeform selections made with the mouse. Parameters ---------- image : (M, N[, 3]) array Grayscale or RGB image. alpha : float, optional Transparency value for polygons drawn over the image. return_all : bool, optional If True, an array containing each separate polygon drawn is returned. (The polygons may overlap.) If False (default), latter polygons "overwrite" earlier ones where they overlap. Returns ------- labels : array of int, shape ([Q, ]M, N) The segmented regions. If mode is `'separate'`, the leading dimension of the array corresponds to the number of regions that the user drew. Notes ----- Press and hold the left mouse button to draw around each object. Examples -------- >>> from skimage import data, future >>> import matplotlib.pyplot as plt # doctest: +SKIP >>> camera = data.camera() >>> mask = future.manual_lasso_segmentation(camera) # doctest: +SKIP >>> fig, ax = plt.subplots() # doctest: +SKIP >>> ax.imshow(mask) # doctest: +SKIP >>> plt.show() # doctest: +SKIP Nr+r,r-r/r0c<S'dMSP4SP4pVP4SPP4R#R#r3r4r9s*, rr@(manual_lasso_segmentation.._undorBr)rCc<\V4^8dR#SPV4\SVSR7pSPV4SP4R#)riNrE)lenrJr(r#)r r'rr$r>r%r?s& r_on_lasso_selection6manual_lasso_segmentation.._on_lasso_selectionsE x=1  ##H-&r85An-  r)TrWc3d<"TF%wr\VSPR,V4xK' R#5irZr]r^s& rrb,manual_lasso_segmentation..rdrerfr[rhrj)rr r!rlrmrnrorprqrrrsrtru LassoSelectorrwrxrryrrzr{r )rarr|rr@rSr}rr~r$r=r>r%r?sff& @@@@@rmanual_lasso_segmentationrs'J#N zzIJJllnGCs#IIe&I!OO#||67H$$++Hh?K5!$$R)<=HH4H$%9CFxxbjj&"//!U[[_*MNNr))皙?)rF) functoolsrnumpyrr#r_shared.version_requirementsrrIrMrr(rrr)rrs|2   w    wsO sOl wRO ROr)