MikTSrSSKrSSKJr SSKrSSKJr SSKJrJ r J r J r SSK J r SSKJr SS KJr SS KJr SS KJrJr SS KJr SS jrSrSrSSjrSSjrSrSrSSjr SSjr!"SS\5r"g)z@ points.py ------------- Functions dealing with (n, d) points. N)sha256)float64)cachinggroupingtransformationsutil)tol)plane_transform)points_inertia) Geometry3D) ArrayLikeNDArray) VertexColorc[R"U[S9nUcUnOX- n[R"XR5[R R U5- nU$)a( The minimum perpendicular distance of a point to a plane. Parameters ----------- points : (n, 3) float Points in space plane_normal : (3,) float Unit normal vector plane_origin : (3,) float Plane origin in space Returns ------------ distances : (n,) float Distance from point to plane dtype)np asanyarrayrdotTlinalgnorm)points plane_normal plane_originw distancess h/var/www/eduai.edurigo.com/storigo/production/storigo_env/lib/python3.13/site-packages/trimesh/points.pypoint_plane_distancer sQ$]]6 1F   !|SS)BIINN<,HHI c[RRU5upn[R"[R "X#55nU$)z Returns an approximate vector representing the major axis of the passed points. Parameters ------------- points : (n, dimension) float Points in space Returns ------------- axis : (dimension,) float Vector along approximate major axis )rrsvdr unitizer)r_USVaxiss r major_axisr)2s5yy}}V$HB1 <<q %D Kr!c[R"U[S9nURS:XdURS:XdeURS:Xa<UR SS9nXSSS24- n[R "UR U5nO2UR SS9nXSS2SSS24- nURSS5U-n[RRU5SSnX4$) aH Fit a plane to points using SVD. Parameters --------- points : (n, 3) float or (p, n, 3,) float 3D points in space Second option allows to simultaneously compute p centroids and normals Returns --------- C : (3,) float or (p, 3,) float Point on the plane N : (3,) float or (p, 3,) float Unit normal vector of plane rrr(Nr).) rrrndimmeanrrswapaxesrr#)rCxMNs r plane_fitr6Fs&]]6 1F ;;! v{{a// / {{a KKQK  tQwZ  FF133N KKQK  q$z] " JJq! q  aG$A 4Kr!c PUc&USUSUS*/n[R"X$5nO[R"X#/5up#[R"S[R"[R "X#55- 5[ R:a [S5e[R"X25n[R"XB5nX- n[R"[R "Xd5[R "Xe55nXR5SSS2$)a Sorts a set of points radially (by angle) around an axis specified by origin and normal vector. Parameters -------------- points : (n, 3) float Points in space origin : (3,) float Origin to sort around normal : (3,) float Vector to sort around start : (3,) float Vector to specify start position in counter-clockwise order viewing in direction of normal, MUST not be parallel with normal Returns -------------- ordered : (n, 3) float Same as input points but reordered Nrr+rz#start must not parallel with normalr.) rcrossr r$absrr zero ValueErrorarctan2argsort)roriginnormalstartaxis0axis1vectorsangless r radial_sortrEps4 }F1Iq z2' f_5  66!bffRVVF233 4sxx ?BC C''oG ZZw.w0F GF .."4R4( ))r!cd[R"[R"U5[R:5(a [ S5eUc [ X!5n[R"X5nUSS2SS[U5- 24nU(a"[RRU5nXg4$U$)a Project (n, 3) points onto a plane. Parameters ----------- points : (n, 3) float Points in space. plane_normal : (3,) float Unit normal vector of plane plane_origin : (3,) Origin point of plane transform : None or (4, 4) float Homogeneous transform, if specified, normal+origin are overridden return_transform : bool Returns the (4, 4) matrix used or not return_planar : bool Return (n, 2) points rather than (n, 3) points zNormal must be nonzero!Nrr,) rallr9r r: NameErrorr rtransform_pointsintrinv)rrr transformreturn_transform return_planar transformed polygon_to_3Ds rproject_to_planerQs6 vvbff\"SXX-..122#L? !226EKaa#m*<&K i0 )) r!cSSKJn U"U5nURUSS9n[R"UR 5[ U5S9nXTRSS9nUR 5US[R"[ U55--n[R"[ U5[S 9nS X'[R(a*U"X5n [ U RU55S:XdeXU4$) am Given an (n, m) array of points return a subset of points where no point is closer than radius. Parameters ------------ points : (n, dimension) float Points in space radius : float Minimum radius between result points Returns ------------ culled : (m, dimension) float Points in space mask : (n,) bool Which points from the original points were returned rcKDTreendarray) output_type) minlengthrr-r+rF) scipy.spatialrT query_pairsrbincountravellenargmaxarangeonesboolr strict) rradiusrTtreepairscountcolumnhighestmasktests r remove_closerjs&& 6?D   V  ;E KK V =E \ a (FkkmFQ3v;)?%??@G 773v;d +DDM zzv|$4##F+,111 < r!c SSKJn SSKJn [R "U[ S9nURSS9nSXU[R:'X- nU"Xa40UD6upxXu-n U"U 5n U RUSS9Sn X4$)a Find k centroids that attempt to minimize the k- means problem: https://en.wikipedia.org/wiki/Metric_k-center Parameters ---------- points: (n, d) float Points in space k : int Number of centroids to compute **kwargs : dict Passed directly to scipy.cluster.vq.kmeans Returns ---------- centroids : (k, d) float Points in some space labels: (n) int Indexes for which points belong to which centroid r)kmeansrSrr-r)k) scipy.cluster.vqrlrXrTrrrstdr r:query) rrmkwargsrlrT points_stdwhitenedcentroids_whitened _distortion centroidsrclabelss rk_meansrxs*(% ]]6 1F#J()JCHH$%"H&,X&CF&C#"/I 9 D ZZ!Z $Q 'F  r!c[R"U[S9n[UR5S:wa [ S5e[ U5n[R"[U5[S9nSX!'[R"[U5[RS9S- nXS'[R"[U5S- [S9n[R"[U5[RS9n[R"URS5n[[U5S- 5HQnXUn[R"XU- S-U5n U R5n XRU n SX+'XUS-'XXG'MS US-nX44$)a9 Find an ordering of points where each is visited and the next point is the closest in euclidean distance, and if there are multiple points with equal distance go to an arbitrary one. Assumes every point is visitable from every other point, i.e. the travelling salesman problem on a fully connected graph. It is not a MINIMUM traversal; rather it is a "not totally goofy traversal, quickly." On random points this traversal is often ~20x shorter than random ordering, and executes on 1000 points in around 29ms on a 2014 i7. Parameters --------------- points : (n, dimension) float ND points in space start : int The index of points we should start at Returns --------------- traversal : (n,) int Ordered traversal visiting every point distances : (n - 1,) float The euclidean distance between points in traversal rr+zpoints must be (n, dimension)!Frrg?)rrrr\shaper;rJr_r`zerosint64r^rangerargmin) rr@ unvisited traversalr index_masksum_onesicurrentdist min_index successors rtsprsX:]]6 1F 6<<A9:: JEF 40IIVBHH59IaLVq8I3v;bhh7J wwv||A'H3v;? #1& vvv(72q8(CKKM ))4 $ $!a% %$*#I  r!cSSKJn SSKJn [R "U[ S9n[UR5S:wa [S5eURSS:Xa:UR5nURS S S 9nUR"UR6 OEURSS:XaUR"UR6 O[S UR35eU(aUR5 gg) z Plot an (n, 3) list of points using matplotlib Parameters ------------- points : (n, 3) float Points in space show : bool If False, will not show until plt.show() is called rN)Axes3Drr+zPoints must be (n, 2|3)!rr,o3d) projectionzpoints not 2D/3D: )matplotlib.pyplotpyplotmpl_toolkits.mplot3drrrrr\rzr;figure add_subplotscatterrshow)rrpltrfigaxs r plot_pointsrns$+ ]]6 1F 6<<A344 ||A!jjl __ST_ 2 FHH aA  VXX-fll^<==    r!c\rSrSrSrS#SjrSrSr\S5r \S5r S r S r \S \ \4S j5r\S \4S j5rSrSr\S5r\S5r\S5r\R0S \ \4Sj5r\S \ \4Sj5r\R6S\4Sj5r\S5r\R6S5r\S5r\R6S5r\R0S5r\R0S5r Sr!Sr"S#Sjr#S r$S!r%S"r&g)$ PointCloudizA Hold 3D points in an object which can be visualized in a scene. Nc $[R"5Ul[R"URR5Ul0UlUbUR RU5 XlSU;aUcUSn[X S9Ul g)z Load an array of points into a PointCloud object. Parameters ------------- vertices : (n, 3) float Points in space colors : (n, 4) uint8 or None RGBA colors for each point metadata : dict or None Metadata about points N vertex_colors)colorsobj) r DataStore_dataCache__hash___cachemetadataupdateverticesrvisual)selfrrrrqs r__init__PointCloud.__init__sx&&( mmDJJ$7$78    MM  *! f $O,F": r!c:URR"U0UD6$N)r __setitem__rargsrqs rrPointCloud.__setitem__}}(($9&99r!c:URR"U0UD6$r)r __getitem__rs rrPointCloud.__getitem__rr!c.URR$)z^ Get the shape of the pointcloud Returns ---------- shape : (2,) int Shape of vertex array )rrzrs rrzPointCloud.shapes}}"""r!c2[UR5S:H$)zg Are there any vertices defined or not. Returns ---------- empty : bool True if no vertices defined r)r\rrs ris_emptyPointCloud.is_emptys4==!Q&&r!cR[SS9n[R"URR5URl[R"UR 5Ul[R"UR 5UlURR5 U$)a6 Safely get a copy of the current point cloud. Copied objects will have emptied caches to avoid memory issues and so may be slow on initial operations until caches are regenerated. Current object will *not* have its cache cleared. Returns --------- copied : trimesh.PointCloud Copy of current point cloud N)r) rcopydeepcopyrdatarrrclear)rcopieds rrPointCloud.copyspT*!MM$**//:  dkk2 -- 6   r!c6URR5$)z\ Get a hash of the current vertices. Returns ---------- hash : str Hash of self.vertices )rrrs rhashPointCloud.hashszz""$$r!returnc6URR5$)z Return a simple array representing this PointCloud that can be used to identify identical arrays. Returns ---------- identifier : (9,) A flat array of data representing the cloud. )moment_inertiar[rs r identifierPointCloud.identifiers""((**r!c[URS-R5R[R 5R 55R5$)zN A hash of the PointCloud's identifier that can be used to detect duplicates. gj@)rrroundastyperr|tobytes hexdigestrs ridentifier_hashPointCloud.identifier_hashsE  __s " ) ) + 2 2288 < D D F )+ r!c[R"UR5upURUUlURb8[ UR5[ U5:XaURUUlggg)z6 Merge vertices closer than tol.merge (default: 1e-8) N)r unique_rowsrrr\)runiqueinverses rmerge_verticesPointCloud.merge_verticesse #..t}}= f-  ;; "s4;;'73w<'G++f-DK(H "r!cN[R"URUS9UlU$)z Apply a homogeneous transformation to the PointCloud object in- place. Parameters -------------- transform : (4, 4) float Homogeneous transformation to apply to PointCloud )matrix)rrIr)rrLs rapply_transformPointCloud.apply_transforms!(88yY  r!c[R"URRSS9URR SS9/5$)zr The axis aligned bounds of the PointCloud Returns ------------ bounds : (2, 3) float Minimum, Maximum verteex rr-)rarrayrminmaxrs rboundsPointCloud.bounds+s<xx***2DMM4E4E14E4MNOOr!c@[R"URSS9$)z{ The size of the axis aligned bounds Returns ------------ extents : (3,) float Edge length of axis aligned bounding box rr-)rptprrs rextentsPointCloud.extents7svvdkk**r!c4URRSS9$)z] The mean vertex position Returns ------------ centroid : (3,) float Mean vertex position rr-)rr0rs rcentroidPointCloud.centroidCs}}!!q!))r!c>[URURS9$)N)rweights)r rrrs rrPointCloud.moment_inertiaOsT]]DLLIIr!cURRS5nUcF[R"[ UR 5[R S9nX RS'U$U$)zp If each point has a specific weight assigned to it. Returns ----------- weights : (n,) A per-vertex weight. rr)rgetrr_r\rr)rrr_s rrPointCloud.weightsSsN**..+ ?773t}}-RZZ@D$(JJy !Kr!valuesc[R"U[RS9nURURS4:wa [ S5eXR S'g)zw Assign a weight to each point for later computation. Parameters ----------- values : (n,) Weights for each vertex. rrzWeights must match vertices!rN)rrrrzr;rrrs rrresGvRZZ8 <S[A-::??&&z48 8!#vS!P :r!c.URR$)zo Stored per- point color Returns ---------- colors : (len(self.vertices), 4) np.uint8 Per- point RGBA color rrrs rrPointCloud.colorss{{(((r!c$XRlgrr)rrs rrrs $( !r!cpSSKJn U"URR[R 55nU$)z Return a scipy.spatial.cKDTree of the vertices of the mesh. Not cached as this lead to observed memory issues and segfaults. Returns --------- tree : scipy.spatial.cKDTree Contains mesh.vertices rrS)rXrTrviewrrU)rrTrcs rkdtreePointCloud.kdtrees) *t}}))"**56 r!cDSSKJn URUR5$)z A convex hull of every point. Returns ------------- convex_hull : trimesh.Trimesh A watertight mesh of the hull of the points r)convex)r convex_hullr)rrs rrPointCloud.convex_hulls !!$--00r!cSSKJn U"U5$)z| A scene containing just the PointCloud Returns ---------- scene : trimesh.Scene Scene object containing this PointCloud r)Scene) scene.scener)rrs rscenePointCloud.scenes 'T{r!c DUR5R"S0UD6 g)z8 Open a viewer window displaying the current PointCloud N)rr)rrqs rrPointCloud.shows #F#r!c $SSKJn U"U4XS.UD6$)a Export the current pointcloud to a file object. If file_obj is a filename, file will be written there. Supported formats are xyz Parameters ------------ file_obj: open writeable file object str, file name where to save the pointcloud None, if you would like this function to return the export blob file_type: str Which file type to export as. If file name is passed this is not required r) export_mesh)file_obj file_type)exchange.exportr )rr rrqr s rexportPointCloud.exports 14R(R6RRr!c JSSKJn U"URXR40UD6$)a Find the the closest points and associated attributes from this PointCloud. Parameters ------------ input_points : (n, 3) float Input query points kwargs : dict Arguments for proximity.query_from_points result : proximity.NearestQueryResult Result of the query. r)query_from_points) proximityrrr)r input_pointsrqrs rrpPointCloud.querys! 1  kkTVTTr!c [UR5[UR5s=:XaS:XaO OSnO[UR5S:Xa/SQ/[UR5-O URn[UR5S:Xa/SQ/[UR5-O URn[R"XC45n[ [R"URUR45US9$)Nr)rrr)rr)r\rrrvstackr)rotherr other_colors self_colorss r__add__PointCloud.__add__s u|| DKK 0 5A 5F u||$)  3u~~#66\\ t{{#q(  3t}}#55[[  YY :;FYY u~~>?  r!)rrrrrr)NN)'__name__ __module__ __qualname____firstlineno____doc__rrrpropertyrzrrrrrrstrrrrrrrrcache_decoratorrrsetterrrrrrrrrrpr__static_attributes__r r!rrrs ;8:: # # ' '> % +GG, + + .  P P + + * * J 0JJ)" ^^ 'i ' ' Q Q__ Q Q ) ) ]]))    1 1 $ S$U  r!rr)NFT)r)T)#r#rhashlibrnumpyrrrrrrr constantsr geometryr inertiar parentr typedrr visual.colorrr r)r6rErQrjrxrrrr r!rr1s~ 66%#%%6('T'*\ 'T0f#LN bBv v r!