l0jNdZddlZddlZddlmZddlmZmZmZm Z ddl m Z ddl m Z ddlmZdd lmZdd Zd Zd Zdd Z ddZdZdZddZddZGddeZdS)z@ points.py ------------- Functions dealing with (n, d) points. N)float64)cachinggroupingtransformationsutil)tol)plane_transform) Geometry3D) VertexColorctj|t}||}n||z }tj||jtj|z }|S)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 Q/home/wildlama/miniconda3/envs/lam/lib/python3.11/site-packages/trimesh/points.pypoint_plane_distancers[$]6 1 1 1F  \ !|QS))BINN<,H,HHI ctj|\}}}tjtj||}|S)a 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 )rrsvdrunitizer)r_USVaxiss r major_axisr%/s;y}}V$$HB1 <q! % %D Krctj|t}|jdks |jdksJ|jdkr@|d}||dddfz }tj|j|}n>|d}||dddddfz }tjd||}tj |dd }||fS) a 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$Nrz pnd, pnm->pdm).) rrrndimmeanrreinsumrr)rCxMNs r plane_fitr2Cs&]6 1 1 1F ;!  v{a/// / {a KKQK   QtQQQwZ  F13NN KKQK   Qqqq$z] " Ioq! , , aG$A a4Krc `|.|d|d|d g}tj||}ntj||g\}}tjdtjtj||z t jkrtdtj||}tj||}||z }tj tj||tj||}|| dddS)a4 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*) rcrossrr absrr zero ValueErrorarctan2argsort)roriginnormalstartaxis0axis1vectorsangless r radial_sortrAms4 }F1Iq z2'' fe_55  6!bfRVFE22333 4 4sx ? ?BCC C''''voG Zw..w0F0F G GF &..""44R4( ))rFTc`tjtj|tjkrt d|t ||}tj||}|ddddt|z f}|r#tj |}||fS|S)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() rallr5r r6 NameErrorr rtransform_pointsintrinv)rrr transformreturn_transform return_planar transformed polygon_to_3Ds rproject_to_planerMs6 vbf\""SX-..31222#L,?? !269EEKaaaa#m*<*<&K* i00 M)) rcjddlm}||}||d}tj|t |}||d}||dtjt |zz}tj t |t }d ||<tj r9|||} t | |dksJ|||fS) a 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.spatialrP query_pairsrbincountravellenargmaxarangeonesboolr strict) rradiusrPtreepairscountcolumnhighestmasktests r remove_closerfs!&&%%%%% 76??D   V  ; ;E K V = = =E 5\ a ( (FkkmmFQ3v;;)?)?%??@G 73v;;d + + +DDM z2wvd|$$4##F++,,1111 $< rc (ddlm}ddlm}t j|t }|d}d||tj k<||z }|||fi|\}}||z} || } | |dd} | | fS)a Find k centroids that attempt to minimize the k- means problem: https://en.wikipedia.org/wiki/Metric_k-center Parameters ---------- points: (n, d) float Points in space k : int Number of centroids to compute **kwargs : dict Passed directly to scipy.cluster.vq.kmeans Returns ---------- centroids : (k, d) float Points in some space labels: (n) int Indexes for which points belong to which centroid r)kmeansrOrr)r)k) scipy.cluster.vqrhrTrPrrrstdr r6query) rrikwargsrhrP points_stdwhitenedcentroids_whitened _distortion centroidsr_labelss rk_meansrts*('''''%%%%%% ]6 1 1 1F##J()JzCH$% "H&,fXq&C&CF&C&C# "Z/I 79  D ZZ!Z $ $Q 'F f rc~tj|t}t|jdkrt dt |}tjt|t}d||<tj t|tj dz }||d<tj t|dz t}tj t|tj }tj|jd}tt|dz D]k}|||}tj |||z dz|} | } ||| } d|| <| ||dz<| | ||<l|dz}||fS)a 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?)rrrrXshaper7rFr[r\zerosint64rZrangerargmin) rr< unvisited traversalr index_masksum_onesicurrentdist min_index successors rtsprs:]6 1 1 1F 6<A9::: JJEF 4000IIeVBH5559IIaLVq888I3v;;bh777J wv|A''H3v;;? # #''1& vvi(72q8(CCKKMM y))4 $ )$ !a%I ! #I i rcddlm}ddlm}t j|t }t|jdkrtd|jddkr;| }| d d }|j |j n8|jddkr|j |j ntd |j|r|dSdS) 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.mplot3drrrrrXrvr7figure add_subplotscatterrshow)rrpltrfigaxs r plot_pointsrks$#####++++++ ]6 1 1 1F 6<A3444 |A!jjll __ST_ 2 2 FH aA   VXS[[A--:?&&z488 8!#vS!P!P!P :rc|jjS)z Stored per- point color Returns ---------- colors : (len(self.vertices), 4) np.uint8 Per- point RGBA color rrrs rrzPointCloud.colorsYs{((rc||j_dSrr)rrs rrzPointCloud.colorses$( !!!rclddlm}||jtj}|S)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 rrO)rTrPrviewrrQ)rrPr_s rkdtreezPointCloud.kdtreeis< *)))))wt}))"*5566 rcBddlm}||jS)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 rrzPointCloud.convex_hullzs, !!$-000rc$ddlm}||S)z A scene containing just the PointCloud Returns ---------- scene : trimesh.Scene Scene object containing this PointCloud r)Scene) scene.scener)rrs rscenezPointCloud.scenes$ '&&&&&uT{{rc D|jdi|dS)zH Open a viewer window displaying the current PointCloud N)rr)rrms rrzPointCloud.shows+  ##F#####rc &ddlm}||f||d|S)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)rrrrmrs rexportzPointCloud.exports5 100000{4R(iRR6RRRrc 8ddlm}||j||jfi|S)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_pointsrmrs rrlzPointCloud.querys7 100000   dkTTVTTTrct|jt|jcxkrdkrnnd}nt|jdkrgdgt|jzn|j}t|jdkrgdgt|jzn|j}tj||f}t tj|j|jf|S)Nr)rrr)rr)rXrrrvstackr)rotherr other_colors self_colorss r__add__zPointCloud.__add__s u|  DK 0 0 5 5 5 5A 5 5 5 5 5FF u|$$))  3u~#6#666\ t{##q((  3t}#5#555[  Y \:;;FY u~>??    r)NN)__name__ __module__ __qualname____doc__rrrpropertyrvrrrrrrrrrrsetterrrcache_decoratorrrrrrrlrrrrrrs0 ;;;;8:::::: # #X # ' 'X '> % % %     . . .    P PX P + +X + * *X * Q QX Q_ Q Q_ Q ) )X ) ]))])    1 1 1   $$$ SSSS$UUU      rrr)NFT)r)T)rrnumpyrrrrrrr constantsr geometryr parentr visual.colorr rr%r2rArMrfrtrrrrrrrs 666666666666%%%%%%%%%%%%6('''T'*'*'*'*\ ''''T000f###LN N N N b@E E E E E E E E E E r