| Title: | Geometric Morphometric Methods and Virtual Anthropology Tools |
|---|---|
| Description: | Tools for geometric morphometric analysis. The package includes tools of virtual anthropology to align two not articulated parts belonging to the same specimen, to build virtual cavities as endocast (Profico et al, 2021 <doi:10.1002/ajpa.24340>). |
| Authors: | Antonio Profico, Costantino Buzi, Silvia Castiglione, Marina Melchionna, Paolo Piras, Pasquale Raia, Alessio Veneziano |
| Maintainer: | Antonio Profico <[email protected]> |
| License: | GPL-2 |
| Version: | 2.0.5 |
| Built: | 2025-02-10 04:14:29 UTC |
| Source: | https://github.com/cran/Arothron |
Tools for geometric morphometric analysis. The package includes tools of virtual anthropology to align two not articulated parts belonging to the same specimen, to build virtual cavities as endocast (Profico et al, 2021 <doi:10.1002/ajpa.24340>).
Antonio Profico, Costantino Buzi, Silvia Castiglione, Marina Melchionna, Paolo Piras, Pasquale Raia, Alessio Veneziano
2D image of the Altamura man fossil
data(Altapic)data(Altapic)
Antonio Profico
Find the closest matches between a reference (2D or 3D matrix) and a target matrix (2D/3D) or mesh returning row indices and distances
aro.clo.points(target, reference)aro.clo.points(target, reference)
target |
kxm matrix or object of class "mesh3d" |
reference |
numeric: a kxm matrix (coordinates) |
position numeric: a vector of the row indices
distances numeric: a vector of the coordinates distances
Antonio Profico, Costantino Buzi, Marina Melchionna, Paolo Piras, Pasquale Raia, Alessio Veneziano
#load an example: mesh, and L set data(yoda_sur) data(yoda_set) sur<-yoda_sur set<-yoda_set ver_pos<-aro.clo.points(target=sur,reference=set)#load an example: mesh, and L set data(yoda_sur) data(yoda_set) sur<-yoda_sur set<-yoda_set ver_pos<-aro.clo.points(target=sur,reference=set)
converts an array in a list storing each element of the third dimension of the array (specimen) as element of the list
arraytolist(array)arraytolist(array)
array |
a kx3xn array with landmark coordinates |
a list containing the landmark configurations stored as separated elements
Antonio Profico, Costantino Buzi, Marina Melchionna, Paolo Piras, Pasquale Raia, Alessio Veneziano
This function calculates the barycenter of a matrix or a 3D mesh
bary.mesh(mesh)bary.mesh(mesh)
mesh |
matrix mesh vertex |
barycenter numeric: x,y,z coordinates of the barycenter of the mesh
Antonio Profico, Costantino Buzi, Marina Melchionna, Paolo Piras, Pasquale Raia, Alessio Veneziano
#load an example: mesh, and L set data(SCP1.mesh) sur<-SCP1.mesh bary<-bary.mesh(mesh=sur)#load an example: mesh, and L set data(SCP1.mesh) sur<-SCP1.mesh bary<-bary.mesh(mesh=sur)
This function applyes the Digital Alignment Tool (DTA) on a disarticulated model using a reference landmark configuration
compare_check.set(RM_set_1, RM_set_2, DM_set_1, DM_set_2, DM_mesh_1, DM_mesh_2)compare_check.set(RM_set_1, RM_set_2, DM_set_1, DM_set_2, DM_mesh_1, DM_mesh_2)
RM_set_1 |
matrix: 3D landmark set of the first module acquired on the reference model |
RM_set_2 |
matrix: 3D landmark set of the second module acquired on the reference model |
DM_set_1 |
matrix: 3D landmark set of the first module acquired on the disarticulated model |
DM_set_2 |
matrix: 3D landmark set of the second module acquired on the disarticulated model |
DM_mesh_1 |
mesh3d: mesh of the disarticulated model (first module) |
DM_mesh_2 |
mesh3d: mesh of the disarticulated model (second module) |
SF1 numeric: scale factor used to scale the reference set (first module)
SF2 numeric: scale factor used to scale the reference set (second module)
RM_set_1_sc matrix: scaled 3D reference set (first module)
RM_set_2_sc matrix: scaled 3D reference set (second module)
AM_model list: output of the Morpho::rotmesh.onto function
dist_from_mesh numeric: mesh distance between the aligned model and the scaled reference set
eucl_dist_1 numeric: euclidean distance between the landmark configuration of the disarticulated and reference model (first module)
eucl_dist_2 numeric: euclidean distance between the landmark configuration of the disarticulated and reference model (second module)
procr_dist numeric: procrustes distance between the landmark configuration of the aligned and reference model
procr_dist_1 numeric: procrustes distance between the landmark configuration of the disarticulated and reference model (first module)
procr_dist_2 numeric: procrustes distance between the landmark configuration of the disarticulated and reference model (second module)
eucl_dist numeric: euclidean distance between the landmark configuration of the aligned and reference model
single_l_1 numeric: euclidean distance between the landmark configuration of the disarticulated and reference model (first module)
single_l_2 numeric: euclidean distance between the landmark configuration of the disarticulated and reference model (second module)
Antonio Profico, Costantino Buzi, Marina Melchionna, Paolo Piras, Pasquale Raia, Alessio Veneziano
CScorreffect Plot showing the correlation in the shape space between original and combined dataset omitting or including the normalization factors calculated with Arothron and MLECScorrection
CScorreffect( array1, array2, nPCs = c(1:3), from = 0.02, to = 0.9, length.out = 100 )CScorreffect( array1, array2, nPCs = c(1:3), from = 0.02, to = 0.9, length.out = 100 )
array1 |
array: first set of landmark configuration |
array2 |
array: second set of landmark configuration |
nPCs |
numeric vector: specify which PC scores will be selected in the correlation test |
from |
numeric: the lower interval of the normalization factor distribution |
to |
numeric: the lower interval of the normalization factor distribution |
length.out |
numeric: number of values ranged between from and to |
PCscores PCscores matrix of the combined dataset applying the normalization factor calculated by using the maximum likelihood estimation
PCs PCs matrix of the combined dataset applying the normalization factor calculated by using the maximum likelihood estimation
corr mean correlation between original and combined dataset
CSratios normalization factor calculated by using the maximum likelihood estimation
Antonio Profico, Costantino Buzi, Marina Melchionna, Paolo Piras, Pasquale Raia, Alessio Veneziano
## Not run: # Femora case study data(femsets) all_pois<-matrix(1:(200*61),nrow=61,ncol=200,byrow = FALSE) set_ext_100<-femsets[all_pois[,1:100],,] set_int_100<-femsets[all_pois[,101:200],,] set_int_50<-set_int_100[c(matrix(1:6100,ncol=61)[seq(1,100,2),]),,] set_int_20<-set_int_100[c(matrix(1:6100,ncol=61)[seq(1,100,5),]),,] set.seed(123) sel<-sample(1:100,10) set_int_10r<-set_int_100[c(matrix(1:6100,ncol=61)[sel,]),,] CScorreffect(set_ext_100,set_int_50,nPCs=1:3) CScorreffect(set_ext_100,set_int_20,nPCs=1:3) CScorreffect(set_ext_100,set_int_10r,nPCs=1:3) ## End(Not run)## Not run: # Femora case study data(femsets) all_pois<-matrix(1:(200*61),nrow=61,ncol=200,byrow = FALSE) set_ext_100<-femsets[all_pois[,1:100],,] set_int_100<-femsets[all_pois[,101:200],,] set_int_50<-set_int_100[c(matrix(1:6100,ncol=61)[seq(1,100,2),]),,] set_int_20<-set_int_100[c(matrix(1:6100,ncol=61)[seq(1,100,5),]),,] set.seed(123) sel<-sample(1:100,10) set_int_10r<-set_int_100[c(matrix(1:6100,ncol=61)[sel,]),,] CScorreffect(set_ext_100,set_int_50,nPCs=1:3) CScorreffect(set_ext_100,set_int_20,nPCs=1:3) CScorreffect(set_ext_100,set_int_10r,nPCs=1:3) ## End(Not run)
This function computes the order of points on a open 3D curve and finds intermediate points
dec.curve(mat_input, mag, plot = TRUE)dec.curve(mat_input, mag, plot = TRUE)
mat_input |
numeric: a kx3 matrix |
mag |
numeric: how many times will be divided by the number of initial points |
plot |
logical: if TRUE will be plotted the starting and final point matrices |
matt numeric: a kx3 matrix with points coordinates
Antonio Profico, Costantino Buzi, Marina Melchionna, Paolo Piras, Pasquale Raia, Alessio Veneziano
## Not run: ## Create and plot a 3D curve require(compositions) require(rgl) curve_3D<-cbind(1:10,seq(1,5,length=10),rnorm(10,sd = 0.2)) plot3D(curve_3D,bbox=FALSE) close3d() ## Create and plot the new 3D curve (with intermediate points) dec_curve_3D<-dec.curve(curve_3D, 2, plot = TRUE) ## End(Not run)## Not run: ## Create and plot a 3D curve require(compositions) require(rgl) curve_3D<-cbind(1:10,seq(1,5,length=10),rnorm(10,sd = 0.2)) plot3D(curve_3D,bbox=FALSE) close3d() ## Create and plot the new 3D curve (with intermediate points) dec_curve_3D<-dec.curve(curve_3D, 2, plot = TRUE) ## End(Not run)
3D mesh of the first part of the Homo sapiens disarticulated model
data(DM_base_sur)data(DM_base_sur)
Antonio Profico, Costantino Buzi, Marina Melchionna, Paolo Piras, Pasquale Raia, Alessio Veneziano
3D mesh of the second part of the Homo sapiens disarticulated model
data(DM_face_sur)data(DM_face_sur)
Antonio Profico, Costantino Buzi, Marina Melchionna, Paolo Piras, Pasquale Raia, Alessio Veneziano
Landmark configurations of the two part of the disarticulated model
data(DM_set)data(DM_set)
Antonio Profico, Costantino Buzi, Marina Melchionna, Paolo Piras, Pasquale Raia, Alessio Veneziano
This function applyes the Digital Alignment Tool (DTA) on a disarticulated model using a reference sample
dta( RM_sample, mod_1, mod_2, pairs_1, pairs_2, DM_mesh_1, DM_mesh_2, DM_set_1, DM_set_2, method = c("euclidean") )dta( RM_sample, mod_1, mod_2, pairs_1, pairs_2, DM_mesh_1, DM_mesh_2, DM_set_1, DM_set_2, method = c("euclidean") )
RM_sample |
3D array: 3D landmark configurations of the reference sample |
mod_1 |
numeric vector: vector containing the position of which landmarks belong to the first module |
mod_2 |
numeric vector: vector containing the position of which landmarks belong to the second module |
pairs_1 |
matrix: a X x 2 matrix containing the indices of right and left landmarks of the first module |
pairs_2 |
matrix: a X x 2 matrix containing the indices of right and left landmarks of the second module |
DM_mesh_1 |
mesh3d: mesh of the disarticulated model (first module) |
DM_mesh_2 |
mesh3d: mesh of the disarticulated model (second module) |
DM_set_1 |
matrix: 3D landmark set of the first module acquired on the disarticulated model |
DM_set_2 |
matrix: 3D landmark set of the second module acquired on the disarticulated model |
method |
character: specify method to be used to individuate the best DTA ("euclidean" or "procrustes") |
AM_mesh mesh3d: mesh of the aligned model
AM_set matrix: landmark configuration of the aligned model
AM_id character: name of the item of the reference sample resulted as best DTA
AM_SF_1 numeric: scale factor used to scale the reference set (first module)
AM_SF_2 numeric: scale factor used to scale the reference set (second module)
distance numeric: distance between the landmark configuration of the aligned and the reference model
tot_proc numeric vector: procrustes distances between aligned and reference models (all DTAs)
tot_eucl numeric vector: euclidean distances between aligned and reference models (all DTAs)
setarray 3D array: landmark configurations of the disarticulated model aligned on each item of the reference sample
Antonio Profico, Costantino Buzi, Marina Melchionna, Paolo Piras, Pasquale Raia, Alessio Veneziano
Profico, A., Buzi, C., Davis, C., Melchionna, M., Veneziano, A., Raia, P., & Manzi, G. (2019). A new tool for digital alignment in Virtual Anthropology. The Anatomical Record, 302(7), 1104-1115.
## Load and plot the disarticulated model of the Homo sapiens case study library(compositions) library(rgl) data(DM_base_sur) data(DM_face_sur) open3d() wire3d(DM_base_sur,col="white") wire3d(DM_face_sur,col="white") ## Load the landmark configurations associated to the DM data(DM_set) ## Load the reference sample data(RMs_sets) ## Define the landmarks belonging to the first and second module mod_1<-c(1:17) #cranial base mod_2<-c(18:32) #facial complex ## Define the paired landmarks for each module (optional symmetrization process) pairs_1<-cbind(c(4,6,8,10,12,14,16),c(5,7,9,11,13,15,17)) pairs_2<-cbind(c(23,25,27,29,31),c(24,26,28,30,32)) ## Run DTA ex.dta<-dta(RM_sample=RMs_sets, mod_1=mod_1, mod_2=mod_2, pairs_1=pairs_1, pairs_2=pairs_2, DM_mesh_1=DM_base_sur,DM_mesh_2=DM_face_sur, DM_set_1= DM_set[mod_1,], DM_set_2=DM_set[mod_2,]) ## Print the name of the best RM ex.dta$AM_id ## Save the mesh and the landmark set of the AM AM_mesh<-ex.dta$AM_mesh AM_set<-ex.dta$AM_set ## Plot the aligned 3D model library(compositions) library(rgl) open3d() wire3d(AM_mesh,col="white") plot3D(AM_set,bbox=FALSE,add=TRUE)## Load and plot the disarticulated model of the Homo sapiens case study library(compositions) library(rgl) data(DM_base_sur) data(DM_face_sur) open3d() wire3d(DM_base_sur,col="white") wire3d(DM_face_sur,col="white") ## Load the landmark configurations associated to the DM data(DM_set) ## Load the reference sample data(RMs_sets) ## Define the landmarks belonging to the first and second module mod_1<-c(1:17) #cranial base mod_2<-c(18:32) #facial complex ## Define the paired landmarks for each module (optional symmetrization process) pairs_1<-cbind(c(4,6,8,10,12,14,16),c(5,7,9,11,13,15,17)) pairs_2<-cbind(c(23,25,27,29,31),c(24,26,28,30,32)) ## Run DTA ex.dta<-dta(RM_sample=RMs_sets, mod_1=mod_1, mod_2=mod_2, pairs_1=pairs_1, pairs_2=pairs_2, DM_mesh_1=DM_base_sur,DM_mesh_2=DM_face_sur, DM_set_1= DM_set[mod_1,], DM_set_2=DM_set[mod_2,]) ## Print the name of the best RM ex.dta$AM_id ## Save the mesh and the landmark set of the AM AM_mesh<-ex.dta$AM_mesh AM_set<-ex.dta$AM_set ## Plot the aligned 3D model library(compositions) library(rgl) open3d() wire3d(AM_mesh,col="white") plot3D(AM_set,bbox=FALSE,add=TRUE)
POVs defined inside the endocranial cavity
data(endo_set)data(endo_set)
Antonio Profico, Costantino Buzi, Marina Melchionna, Paolo Piras, Pasquale Raia, Alessio Veneziano
Build endocast from a skull 3D mesh
endomaker( mesh = NULL, path_in = NULL, param1_endo = 1, npovs = 50, volume = TRUE, alpha_vol = 100, nVoxels = 1e+05, decmesh = 20000, alpha_ext = 30, ncells = 50000, npovs_calse = 50, param1_calse = 2, param1_ast = 1.3, decendo = 20000, scalendo = 0.5, alpha_end = 100, mpovdist = 10, plot = FALSE, colmesh = "orange", save = FALSE, outpath = tempdir(), num.cores = NULL )endomaker( mesh = NULL, path_in = NULL, param1_endo = 1, npovs = 50, volume = TRUE, alpha_vol = 100, nVoxels = 1e+05, decmesh = 20000, alpha_ext = 30, ncells = 50000, npovs_calse = 50, param1_calse = 2, param1_ast = 1.3, decendo = 20000, scalendo = 0.5, alpha_end = 100, mpovdist = 10, plot = FALSE, colmesh = "orange", save = FALSE, outpath = tempdir(), num.cores = NULL )
mesh |
mesh3d: 3D model of the skull |
path_in |
character: path of the skull where is stored |
param1_endo |
numeric: parameter for spherical flipping |
npovs |
numeric: number of Points of View used in the endocast construction |
volume |
logical: if TRUE the calculation of the volume (expressed in cc) through concave is returned |
alpha_vol |
numeric: alpha shape for volume calculation |
nVoxels |
numeric: number of voxels for estimation endocranial volume |
decmesh |
numeric: decmesh |
alpha_ext |
numeric: alpha shape for construction external cranial mesh |
ncells |
numeric: approximative number of cell for 3D grid construction |
npovs_calse |
numeric: number of Points of View for construction of skull shell |
param1_calse |
numeric: parameter for calse (construction shell) |
param1_ast |
numeric: parameter for ast3d (construction row endocast) |
decendo |
numeric: desired number of triangles (row endocast) |
scalendo |
numeric: scale factor row endocast (for definition of POVs) |
alpha_end |
numeric: alpha shape value for concave hull (row endocast) |
mpovdist |
numeric: mean value between POVs and mesh |
plot |
logical: if TRUE the endocast is plotted |
colmesh |
character: color of the mesh to be plotted |
save |
logical: if TRUE the mesh of the endocast is saved |
outpath |
character: path where save the endocast |
num.cores |
numeric: numbers of cores to be used in parallel elaboration |
endocast mesh3d: mesh of the endocast
volume numeric: volume of the endocast expressed in cc
Antonio Profico, Costantino Buzi, Marina Melchionna, Paolo Piras, Pasquale Raia, Alessio Veneziano
Profico, A., Buzi, C., Melchionna, M., Veneziano, A., & Raia, P. (2020). Endomaker, a new algorithm for fully automatic extraction of cranial endocasts and the calculation of their volumes. American Journal of Physical Anthropology.
## Not run: library(rgl) data(human_skull) sapendo<-endomaker(human_skull,param1_endo = 1.0,decmesh = 20000, num.cores=NULL) open3d() wire3d(sapendo$endocast,col="violet") ecv<-sapendo$volume ## End(Not run)## Not run: library(rgl) data(human_skull) sapendo<-endomaker(human_skull,param1_endo = 1.0,decmesh = 20000, num.cores=NULL) open3d() wire3d(sapendo$endocast,col="violet") ecv<-sapendo$volume ## End(Not run)
Build library of endocasts from skull 3D meshes
endomaker_dir( dir_path, param1_endo = 1.5, npovs = 50, volume = TRUE, alpha_vol = 50, nVoxels = 1e+05, decmesh = 20000, alpha_ext = 30, ncells = 50000, npovs_calse = 50, param1_calse = 3, param1_ast = 1.3, decendo = 20000, scalendo = 0.5, alpha_end = 100, mpovdist = 10, plotall = FALSE, colmesh = "orange", save = FALSE, outpath = tempdir(), num.cores = NULL )endomaker_dir( dir_path, param1_endo = 1.5, npovs = 50, volume = TRUE, alpha_vol = 50, nVoxels = 1e+05, decmesh = 20000, alpha_ext = 30, ncells = 50000, npovs_calse = 50, param1_calse = 3, param1_ast = 1.3, decendo = 20000, scalendo = 0.5, alpha_end = 100, mpovdist = 10, plotall = FALSE, colmesh = "orange", save = FALSE, outpath = tempdir(), num.cores = NULL )
dir_path |
character: path of the folder where the skull meshes are stored |
param1_endo |
numeric vector: parameter for spherical flipping |
npovs |
numeric: number of Points of View used in the endocast construction |
volume |
logical: if TRUE the volume of the endocast (ECV) is estimated |
alpha_vol |
numeric: alpha shape for volume calculation |
nVoxels |
numeric: number of voxels for estimation endocranial volume |
decmesh |
numeric: decmesh |
alpha_ext |
numeric: alpha shape for construction external cranial mesh |
ncells |
numeric: approximative number of cell for 3D grid construction |
npovs_calse |
numeric: number of Points of View for construction of skull shell |
param1_calse |
numeric: parameter for calse (construction shell) |
param1_ast |
numeric: parameter for ast3d (construction row endocast) |
decendo |
numeric: desired number of triangles (row endocast) |
scalendo |
numeric: scale factor row endocast (for definition of POVs) |
alpha_end |
numeric: alpha shape value for concave hull (row endocast) |
mpovdist |
numeric vector: mean value between POVs and mesh |
plotall |
logical: if TRUE the endocasts are plotted |
colmesh |
character: color of the mesh to be plotted |
save |
logical: if TRUE the mesh of the endocast is saved |
outpath |
character: path where save the endocast |
num.cores |
numeric: number of cores to be used in parallel elaboration |
endocasts mesh3d: list of meshes of the extracted endocasts
volumes numeric: volumes of the endocasts expressed in cc
Antonio Profico, Costantino Buzi, Marina Melchionna, Paolo Piras, Pasquale Raia, Alessio Veneziano
Profico, A., Buzi, C., Melchionna, M., Veneziano, A., & Raia, P. (2020). Endomaker, a new algorithm for fully automatic extraction of cranial endocasts and the calculation of their volumes. American Journal of Physical Anthropology.
This function exports a list of 3D landmark set in separate files (format landmarkAscii)
export_amira(lista, path)export_amira(lista, path)
lista |
list containing 3D landmark sets |
path |
character: path of the folder where saving the Amira landmark sets |
Antonio Profico, Costantino Buzi, Marina Melchionna, Paolo Piras, Pasquale Raia, Alessio Veneziano
x<-c(1:20) y<-seq(1,3,length=20) z<-rnorm(20,0.01) vertices<-cbind(x,y,z) set<-list(vertices) example<-export_amira(set,path=tempdir())x<-c(1:20) y<-seq(1,3,length=20) z<-rnorm(20,0.01) vertices<-cbind(x,y,z) set<-list(vertices) example<-export_amira(set,path=tempdir())
Convert and save a 3D matrix into a AmiraMesh ASCII Lineset (.am) object
export_amira.path( vertices, filename, Lines = c(1:(dim(vertices)[1] - 1) - 1, -1), path )export_amira.path( vertices, filename, Lines = c(1:(dim(vertices)[1] - 1) - 1, -1), path )
vertices |
numeric: a kx3 matrix |
filename |
character: name of the requested output |
Lines |
numeric: sequence of the vertices that defines the line |
path |
character: folder path |
Antonio Profico, Costantino Buzi, Marina Melchionna, Paolo Piras, Pasquale Raia, Alessio Veneziano
x<-c(1:20) y<-seq(1,3,length=20) z<-rnorm(20,0.01) vertices<-cbind(x,y,z) export_amira.path(vertices=vertices,filename="example_line",path=tempdir())x<-c(1:20) y<-seq(1,3,length=20) z<-rnorm(20,0.01) vertices<-cbind(x,y,z) export_amira.path(vertices=vertices,filename="example_line",path=tempdir())
This function finds the vertices visible from a set of points of view
ext.int.mesh( mesh, views = 20, dist.sphere = 3, param1 = 2.5, param2 = 10, default = TRUE, import_pov, matrix_pov, expand = 1, scale.factor, method = "ast3d", start.points = 250, num.cores = NULL )ext.int.mesh( mesh, views = 20, dist.sphere = 3, param1 = 2.5, param2 = 10, default = TRUE, import_pov, matrix_pov, expand = 1, scale.factor, method = "ast3d", start.points = 250, num.cores = NULL )
mesh |
object of class mesh3d |
views |
numeric: number of points of view |
dist.sphere |
numeric: scale factor. This parameter the distance betweem the barycenter of the mesh and the radius of the sphere used to define set of points of view |
param1 |
numeric: first parameter for spherical flipping (usually ranged from 0.5 to 5, try!) |
param2 |
numeric second paramter for spherical flipping (don't change it!) |
default |
logical: if TRUE the points of views are defined automatically, if FALSE define the matrix_pov |
import_pov |
logical: if NULL an interactive 3D plot for the definition of the points of view is returned |
matrix_pov |
matrix: external set of points of view |
expand |
numeric: scale factor for the grid for the interactive 3D plot |
scale.factor |
numeric: scale factor for sphere inscribed into the mesh |
method |
character: select "a" or "c" |
start.points |
numeric: number of POVs available |
num.cores |
numeric: number of cores |
position numeric: a vector with vertex number nearest the landmark set
Antonio Profico, Costantino Buzi, Marina Melchionna, Paolo Piras, Pasquale Raia, Alessio Veneziano
Profico, A., Schlager, S., Valoriani, V., Buzi, C., Melchionna, M., Veneziano, A., ... & Manzi, G. (2018). Reproducing the internal and external anatomy of fossil bones: Two new automatic digital tools. American Journal of Physical Anthropology, 166(4), 979-986.
This function returns a 3D mesh with colours based on the vertices visibile from each point of view
ext.mesh.rai(scans, mesh)ext.mesh.rai(scans, mesh)
scans |
an ext.int.mesh |
mesh |
matrix mesh vertex (the same of the ext.int.mesh object) |
Antonio Profico, Costantino Buzi, Marina Melchionna, Paolo Piras, Pasquale Raia, Alessio Veneziano
3D semilandmark configurations of 21 human femora
data(femsets)data(femsets)
Antonio Profico, Costantino Buzi, Marina Melchionna, Paolo Piras, Pasquale Raia, Alessio Veneziano
This function creates a grid for an interactive way to define the set of the points of view
grid_pov(mesh, expand = 1)grid_pov(mesh, expand = 1)
mesh |
object of class mesh3d |
expand |
numeric: scale factor for the grid for the interactive 3D plot |
matrice matrix: matrix with the x,y,z coordinates of the points of view
Antonio Profico, Costantino Buzi, Marina Melchionna, Paolo Piras, Pasquale Raia, Alessio Veneziano
3D mesh of a human skull
data(human_skull)data(human_skull)
Antonio Profico, Costantino Buzi, Marina Melchionna, Paolo Piras, Pasquale Raia, Alessio Veneziano
Create palettes from an image
image2palettes( array, resize = 4, unique = FALSE, scale = F, k = 3, lcols = 7, plsaxis = 1, cex = 5, cext = 0.5 )image2palettes( array, resize = 4, unique = FALSE, scale = F, k = 3, lcols = 7, plsaxis = 1, cex = 5, cext = 0.5 )
array |
array: rgb array |
resize |
numeric: desidered resize factor |
unique |
logical: if TRUE each color is counted once |
scale |
logical: if TRUE (color) variables are scaled |
k |
numeric: desidered number of clusters (i.e., number of palettes) |
lcols |
numeric: length of the color vector of each palette |
plsaxis |
numeric: desidered PLS axis |
cex |
numeric: size of colored squares |
cext |
numeric: size of color names |
paletteslist list: color palettes arranged in a list
Antonio Profico
## Not run: require(jpeg) require(Morpho) data("Altapic") image2palettes(Altapic,resize=1,unique=T,scale=T,k=3,lcols=5,plsaxis=1,cext=0.5) ## End(Not run)## Not run: require(jpeg) require(Morpho) data("Altapic") image2palettes(Altapic,resize=1,unique=T,scale=T,k=3,lcols=5,plsaxis=1,cext=0.5) ## End(Not run)
3D mesh of a decidous Neanderthal tooth
data(krd1_tooth)data(krd1_tooth)
Antonio Profico, Costantino Buzi, Marina Melchionna, Paolo Piras, Pasquale Raia, Alessio Veneziano
This function converts the .frm files, from Evan Toolbox, stored in a folder into the format landmarkAscii
landmark_frm2amira(path_folder_frm, path_amira_folder)landmark_frm2amira(path_folder_frm, path_amira_folder)
path_folder_frm |
character: path of the folder where the .frm files are stored |
path_amira_folder |
character: path folder to store the landmarkAscii configurations |
Antonio Profico, Costantino Buzi, Marina Melchionna, Paolo Piras, Pasquale Raia, Alessio Veneziano
listtoarray convert a list into an array
listtoarray(mylist)listtoarray(mylist)
mylist |
a list |
a kx3xn array with landmark coordinates
Antonio Profico, Costantino Buzi, Marina Melchionna, Paolo Piras, Pasquale Raia, Alessio Veneziano
localmeshdiff Calculate and Visualize local differences between two meshes
localmeshdiff( mesh1, mesh2, ploton = 1, diffarea = ((area_shape1 - area_shape2)/area_shape2) * 100, paltot = rainbow(200), from = NULL, to = NULL, n.int = 200, out.rem = TRUE, fact = 1.5, visual = 1, scale01 = TRUE, colwire = "pink" )localmeshdiff( mesh1, mesh2, ploton = 1, diffarea = ((area_shape1 - area_shape2)/area_shape2) * 100, paltot = rainbow(200), from = NULL, to = NULL, n.int = 200, out.rem = TRUE, fact = 1.5, visual = 1, scale01 = TRUE, colwire = "pink" )
mesh1 |
reference mesh: object of class "mesh3d" |
mesh2 |
target mesh: object of class "mesh3d" |
ploton |
numeric: define which mesh will be used to visualize local differences |
diffarea |
formula: define how calculating differences in area. area_shape1 refers to mesh1, area_shape2 refers to mesh2 |
paltot |
character vector: specify the colors which are used to create a color palette |
from |
numeric: minimum distance to be colorised |
to |
numeric: maximum distance to be colorised |
n.int |
numeric: determines break points for color palette |
out.rem |
logical: if TRUE outliers will be removed |
fact |
numeric: factor k of the interquartile range |
visual |
numeric: if equals to 1 the mesh is plotted without a wireframe, if set on 2 a wireframe is added |
scale01 |
logical: if TRUE the vector of distances is scaled from 0 to 1 |
colwire |
character: color of the wireframe |
vect numeric vector containing local differeces in area between the reference and the target mesh
Antonio Profico, Costantino Buzi, Silvia Castiglione, Marina Melchionna, Paolo Piras, Pasquale Raia, Alessio Veneziano
Melchionna, M., Profico, A., Castiglione, S., Sansalone, G., Serio, C., Mondanaro, A., ... & Manzi, G. (2020). From smart apes to human brain boxes. A uniquely derived brain shape in late hominins clade. Frontiers in Earth Science, 8, 273.
## Not run: library(Arothron) library(rgl) data("primendoR") neaset<-primendoR$sets[,,11] sapset<-primendoR$sets[,,14] #defining a mesh for the neanderthal right hemisphere neasur<-list("vb"=t(cbind(neaset,1)),"it"=primendoR$sur$it) class(neasur)<-"mesh3d" #defining a mesh for the modern human right hemisphere sapsur<-list("vb"=t(cbind(sapset,1)),"it"=primendoR$sur$it) class(neasur)<-"mesh3d" layout3d(t(c(1,2)),sharedMouse = TRUE) localmeshdiff(sapsur,neasur,1,scale01 = TRUE, paltot=c("darkred","red","orange","white","lightblue","blue","darkblue")) next3d() localmeshdiff(neasur,sapsur,1,scale01 = TRUE, paltot=c("darkred","red","orange","white","lightblue","blue","darkblue")) ## End(Not run)## Not run: library(Arothron) library(rgl) data("primendoR") neaset<-primendoR$sets[,,11] sapset<-primendoR$sets[,,14] #defining a mesh for the neanderthal right hemisphere neasur<-list("vb"=t(cbind(neaset,1)),"it"=primendoR$sur$it) class(neasur)<-"mesh3d" #defining a mesh for the modern human right hemisphere sapsur<-list("vb"=t(cbind(sapset,1)),"it"=primendoR$sur$it) class(neasur)<-"mesh3d" layout3d(t(c(1,2)),sharedMouse = TRUE) localmeshdiff(sapsur,neasur,1,scale01 = TRUE, paltot=c("darkred","red","orange","white","lightblue","blue","darkblue")) next3d() localmeshdiff(neasur,sapsur,1,scale01 = TRUE, paltot=c("darkred","red","orange","white","lightblue","blue","darkblue")) ## End(Not run)
List containing five 2D-landmark configurations acquired along five different anatomical views
data(Lset2D_list)data(Lset2D_list)
Antonio Profico, Costantino Buzi, Marina Melchionna, Paolo Piras, Pasquale Raia, Alessio Veneziano
Array containing a cranial 3D-landmark configuration acquired on a Primate sample
data(Lset3D_array)data(Lset3D_array)
Antonio Profico, Costantino Buzi, Marina Melchionna, Paolo Piras, Pasquale Raia, Alessio Veneziano
3D mesh of a human malleus
data(malleus_bone)data(malleus_bone)
Antonio Profico, Costantino Buzi, Marina Melchionna, Paolo Piras, Pasquale Raia, Alessio Veneziano
Landmark configurations of the manual alignments
data(MAs_sets)data(MAs_sets)
Antonio Profico, Costantino Buzi, Marina Melchionna, Paolo Piras, Pasquale Raia, Alessio Veneziano
MLECScorrection Maximum Likelihood Estimation of the normalization factor to be applied to optimize the correlation between two landmark configurations to be combined by using twodviews and
MLECScorrection(array1, array2, scale = TRUE, nPCs = 1:5)MLECScorrection(array1, array2, scale = TRUE, nPCs = 1:5)
array1 |
array: first set of landmark configuration |
array2 |
array: second set of landmark configuration |
scale |
logical: if FALSE the analysis is performed in the shape space, if TRUE the analysis is performed in the size and shape space (gpa without scaling) |
nPCs |
numeric vector: specify which PC scores will be selected in the correlation test |
PCscores PCscores matrix of the combined dataset applying the normalization factor calculated by using the maximum likelihood estimation
PCs PCs matrix of the combined dataset applying the normalization factor calculated by using the maximum likelihood estimation
corr mean correlation between original and combined dataset
CSratios normalization factor calculated by using the maximum likelihood estimation
Antonio Profico, Costantino Buzi, Marina Melchionna, Paolo Piras, Pasquale Raia, Alessio Veneziano
This function adds noise to a mesh
noise.mesh(mesh, noise = 0.025, seed = 123)noise.mesh(mesh, noise = 0.025, seed = 123)
mesh |
triangular mesh stored as object of class "mesh3d" |
noise |
sd deviation to define vertex noise |
seed |
seed for random number generator |
mesh_n a 3D model of class "mesh3d" with noise
Antonio Profico, Costantino Buzi, Marina Melchionna, Paolo Piras, Pasquale Raia, Alessio Veneziano
#load mesh library(compositions) library(rgl) data("SCP1.mesh") mesh<-SCP1.mesh #add noise noised<-noise.mesh(mesh,noise=0.05) #plot original and mesh with noise added open3d() shade3d(mesh,col=3) shade3d(noised,col=2,add=TRUE)#load mesh library(compositions) library(rgl) data("SCP1.mesh") mesh<-SCP1.mesh #add noise noised<-noise.mesh(mesh,noise=0.05) #plot original and mesh with noise added open3d() shade3d(mesh,col=3) shade3d(noised,col=2,add=TRUE)
This function separates a 3D mesh subjected to the ext.int.mesh into two 3D models: the visible mesh and the not visible one
out.inn.mesh(scans, mesh, plot = TRUE)out.inn.mesh(scans, mesh, plot = TRUE)
scans |
an ext.int.mesh |
mesh |
matrix mesh vertex (the same of the ext.int.mesh object) |
plot |
logical: if TRUE the wireframe of the mesh with the visible vertices is plotted |
Antonio Profico, Costantino Buzi, Marina Melchionna, Paolo Piras, Pasquale Raia, Alessio Veneziano
## Not run: #CA-LSE tool on Neanderthal tooth #load a mesh data(krd1_tooth) library(rgl) library(Rvcg) library(compositions) ca_lse_krd1<-ext.int.mesh(mesh= krd1_tooth, views=50, param1=3, default=TRUE, import_pov = NULL,expand=1, scale.factor=1,num.cores = NULL) vis_inv_krd1<-out.inn.mesh(ca_lse_krd1, krd1_tooth, plot=TRUE) inv_mesh<-vcgIsolated(vis_inv_krd1$invisible) open3d() shade3d(inv_mesh,col=2) open3d() shade3d(vis_inv_krd1$visible, col=3) #CA-LSE tool on human malleus #load a mesh data(malleus_bone) ca_lse_malleus<-ext.int.mesh(mesh= malleus_bone, views=50, param1=3, default=TRUE, import_pov = NULL, expand=1, scale.factor=1) vis_inv_malleus<-out.inn.mesh(ca_lse_malleus, malleus_bone, plot=TRUE) inv_mesh<- vis_inv_malleus$invisible inv_mesh<-ca_lse_malleus$invisible #AST-3D tool #load a mesh data(human_skull) data(endo_set) ast3d_endocast<-ext.int.mesh(mesh=human_skull, views=50, param1=0.6, default=FALSE, import_pov = TRUE,expand=1, matrix_pov =endo_set, scale.factor=1,num.cores = NULL) vis_inv_endo<-out.inn.mesh(ast3d_endocast,human_skull,plot=TRUE) vis_mesh<-vcgIsolated(vis_inv_endo$visible) open3d() shade3d(vis_mesh,col=3) open3d() shade3d(vis_inv_endo$invisible, col=2) ## End(Not run)## Not run: #CA-LSE tool on Neanderthal tooth #load a mesh data(krd1_tooth) library(rgl) library(Rvcg) library(compositions) ca_lse_krd1<-ext.int.mesh(mesh= krd1_tooth, views=50, param1=3, default=TRUE, import_pov = NULL,expand=1, scale.factor=1,num.cores = NULL) vis_inv_krd1<-out.inn.mesh(ca_lse_krd1, krd1_tooth, plot=TRUE) inv_mesh<-vcgIsolated(vis_inv_krd1$invisible) open3d() shade3d(inv_mesh,col=2) open3d() shade3d(vis_inv_krd1$visible, col=3) #CA-LSE tool on human malleus #load a mesh data(malleus_bone) ca_lse_malleus<-ext.int.mesh(mesh= malleus_bone, views=50, param1=3, default=TRUE, import_pov = NULL, expand=1, scale.factor=1) vis_inv_malleus<-out.inn.mesh(ca_lse_malleus, malleus_bone, plot=TRUE) inv_mesh<- vis_inv_malleus$invisible inv_mesh<-ca_lse_malleus$invisible #AST-3D tool #load a mesh data(human_skull) data(endo_set) ast3d_endocast<-ext.int.mesh(mesh=human_skull, views=50, param1=0.6, default=FALSE, import_pov = TRUE,expand=1, matrix_pov =endo_set, scale.factor=1,num.cores = NULL) vis_inv_endo<-out.inn.mesh(ast3d_endocast,human_skull,plot=TRUE) vis_mesh<-vcgIsolated(vis_inv_endo$visible) open3d() shade3d(vis_mesh,col=3) open3d() shade3d(vis_inv_endo$invisible, col=2) ## End(Not run)
This function converts the .frm files, from Evan Toolbox, stored in a folder into the format landmarkAscii (semilandmark patches)
patches_frm2amira(path_folder_frm, path_amira_folder)patches_frm2amira(path_folder_frm, path_amira_folder)
path_folder_frm |
character: path of the folder where the .frm files are stored |
path_amira_folder |
character: path folder to store the landmarkAscii configurations |
Antonio Profico, Costantino Buzi, Marina Melchionna, Paolo Piras, Pasquale Raia, Alessio Veneziano
PCscoresCorr Perform a correlation test between two matrices of PCscores
PCscoresCorr(matrix1, matrix2, nPCs = 1:5)PCscoresCorr(matrix1, matrix2, nPCs = 1:5)
matrix1 |
matrix: first set of PC scores |
matrix2 |
matrix: second set of PC scores |
nPCs |
numeric vector: specify which PC scores will be selected in the correlation test |
corr the correlation values associated to each pair of PC scores
p.values p-values associated to the correlation test
Antonio Profico, Costantino Buzi, Marina Melchionna, Paolo Piras, Pasquale Raia, Alessio Veneziano
Create palettes from an image
permutangle( mat, var, group1, group2, scale = FALSE, iter = 100, cex1 = range01(var[group1] + 1), cex2 = range01(var[group2] + 1), cex3 = 0.7, cex4 = 1.2, labels = c("stgr1", "stgr2", "endgr1", "endgr2"), pch1 = 19, pch2 = 19, pch3 = 19, col1 = "red", col2 = "blue" )permutangle( mat, var, group1, group2, scale = FALSE, iter = 100, cex1 = range01(var[group1] + 1), cex2 = range01(var[group2] + 1), cex3 = 0.7, cex4 = 1.2, labels = c("stgr1", "stgr2", "endgr1", "endgr2"), pch1 = 19, pch2 = 19, pch3 = 19, col1 = "red", col2 = "blue" )
mat |
array: rgb array |
var |
numeric: desidered resize factor |
group1 |
logical: if TRUE each color is counted once |
group2 |
logical: if TRUE (color) variables are scaled |
scale |
numeric: desidered number of clusters (i.e., number of palettes) |
iter |
numeric: length of the color vector of each palette |
cex1 |
numeric: desidered PLS axis |
cex2 |
numeric: size of colored squares |
cex3 |
numeric: size of color names |
cex4 |
numeric: size of color names |
labels |
numeric: size of color names |
pch1 |
numeric: size of color names |
pch2 |
numeric: size of color names |
pch3 |
numeric: size of color names |
col1 |
numeric: size of color names |
col2 |
numeric: size of color names |
angle list: color palettes arranged in a list
permangles list: color palettes arranged in a list
angle list: color palettes arranged in a list
iterangles list: color palettes arranged in a list
p-value list: color palettes arranged in a list
PCA_angle list: color palettes arranged in a list
PCA_interangles list: color palettes arranged in a list
PCA_p-value list: color palettes arranged in a list
Antonio Profico
## Not run: require(shapes) require(Morpho) data("gorf.dat") data("gorm.dat") Array<-bindArr(gorf.dat,gorm.dat,along=3) CS<-apply(Array,3,cSize) Sex<-c(rep("F",dim(gorf.dat)[3]),rep("M",dim(gorm.dat)[3])) #Shape and size space AllTrajFB<-permutangle(procSym(Array,scale=FALSE,CSinit = FALSE)$PCscores, var=CS,group1=which(Sex=="F"),group2=which(Sex=="M"),scale=FALSE,iter=50) hist(AllTrajFB$iterangles,breaks = 100,xlim=c(0,90)) abline(v=AllTrajFB$angle,lwd=2,col="red") hist(AllTrajFB$PCA_iterangles,breaks = 100,xlim=c(0,90)) abline(v=AllTrajFB$PCA_angle,lwd=2,col="red") #Shape space AllTrajFB<-permutangle(procSym(Array)$PCscores, var=CS,group1=which(Sex=="F"),group2=which(Sex=="M"),scale=FALSE,iter=50) hist(AllTrajFB$iterangles,breaks = 100,xlim=c(0,90)) abline(v=AllTrajFB$angle,lwd=2,col="red") hist(AllTrajFB$PCA_iterangles,breaks = 100,xlim=c(0,90)) abline(v=AllTrajFB$PCA_angle,lwd=2,col="red") ## End(Not run)## Not run: require(shapes) require(Morpho) data("gorf.dat") data("gorm.dat") Array<-bindArr(gorf.dat,gorm.dat,along=3) CS<-apply(Array,3,cSize) Sex<-c(rep("F",dim(gorf.dat)[3]),rep("M",dim(gorm.dat)[3])) #Shape and size space AllTrajFB<-permutangle(procSym(Array,scale=FALSE,CSinit = FALSE)$PCscores, var=CS,group1=which(Sex=="F"),group2=which(Sex=="M"),scale=FALSE,iter=50) hist(AllTrajFB$iterangles,breaks = 100,xlim=c(0,90)) abline(v=AllTrajFB$angle,lwd=2,col="red") hist(AllTrajFB$PCA_iterangles,breaks = 100,xlim=c(0,90)) abline(v=AllTrajFB$PCA_angle,lwd=2,col="red") #Shape space AllTrajFB<-permutangle(procSym(Array)$PCscores, var=CS,group1=which(Sex=="F"),group2=which(Sex=="M"),scale=FALSE,iter=50) hist(AllTrajFB$iterangles,breaks = 100,xlim=c(0,90)) abline(v=AllTrajFB$angle,lwd=2,col="red") hist(AllTrajFB$PCA_iterangles,breaks = 100,xlim=c(0,90)) abline(v=AllTrajFB$PCA_angle,lwd=2,col="red") ## End(Not run)
Internal function to define the points of view
pov_selecter(mesh, grid, start.points = 250, method = "ast3d")pov_selecter(mesh, grid, start.points = 250, method = "ast3d")
mesh |
object of class mesh3d |
grid |
matrix: a 3D grid |
start.points |
numeric: number of center to be found |
method |
character: select "a" or "c" for respectively AST-3D and CA-LSE method |
selection numeric: positioning vector of the selected points of the grid
Antonio Profico, Costantino Buzi, Marina Melchionna, Paolo Piras, Pasquale Raia, Alessio Veneziano
right brain hemisphere of 19 primate species
data(primendoR)data(primendoR)
Antonio Profico, Costantino Buzi, Marina Melchionna, Paolo Piras, Pasquale Raia, Alessio Veneziano
This function reads and stores in an array the coordinated allocated in a folder in separate files (format landmarkAscii)
read.amira.dir(path.dir, nland)read.amira.dir(path.dir, nland)
path.dir |
character: path of the folder |
nland |
numeric: number of landmark sampled in Amira |
array.set numeric: a kx3xn array with landmark coordinates
Antonio Profico, Costantino Buzi, Marina Melchionna, Paolo Piras, Pasquale Raia, Alessio Veneziano
This function converts a landmarkAscii file set in a kx3x1 array
read.amira.set(name.file, nland)read.amira.set(name.file, nland)
name.file |
character: path of a landmarkAscii file |
nland |
numeric: number of landmark sampled in Amira, if is set on "auto" it will be automatically recognized |
array.set numeric: a kx3x1 array with landmark coordinates
Antonio Profico, Costantino Buzi, Marina Melchionna, Paolo Piras, Pasquale Raia, Alessio Veneziano
This function extracts and orders the coordinate matrix from a surface path file from Amira
read.path.amira(path.name)read.path.amira(path.name)
path.name |
character: path of surface path .ascii extension file |
data numeric: a kxd matrix with xyz coordinates
Antonio Profico, Costantino Buzi, Marina Melchionna, Paolo Piras, Pasquale Raia, Alessio Veneziano
This function repeats copies of a matrix
repmat(X, m, n)repmat(X, m, n)
X |
numeric: a matrix |
m |
numeric: number of times to repeat the X matrix in row and column dimension |
n |
numeric: repetition factor for each dimesion |
matrice: repeated matrix
Antonio Profico, Costantino Buzi, Marina Melchionna, Paolo Piras, Pasquale Raia, Alessio Veneziano
Array containing the landmark coordinates of the reference sample for Digital Alignment Tool example
data(RMs_sets)data(RMs_sets)
Antonio Profico, Costantino Buzi, Marina Melchionna, Paolo Piras, Pasquale Raia, Alessio Veneziano
Mesh of the Saccopastore 1 Neanderthal skull
data(SCP1.mesh)data(SCP1.mesh)
Antonio Profico, Costantino Buzi, Marina Melchionna, Paolo Piras, Pasquale Raia, Alessio Veneziano
POVs sampled inside the maxillary sinus cavity
data(sinus_set)data(sinus_set)
Antonio Profico, Costantino Buzi, Marina Melchionna, Paolo Piras, Pasquale Raia, Alessio Veneziano
Landmark configuration associated to the starting model
data(SM_set)data(SM_set)
Antonio Profico, Costantino Buzi, Marina Melchionna, Paolo Piras, Pasquale Raia, Alessio Veneziano
Internal spherical flippping function
spherical.flipping(C, mesh, param1, param2)spherical.flipping(C, mesh, param1, param2)
C |
numeric: coordinates of the point of view |
mesh |
object of class mesh3d |
param1 |
numeric: first parameter for spherical flipping (usually ranged from 0.1 to 3, try!) |
param2 |
numeric second paramter for spherical flipping (don't change it!) |
Antonio Profico, Costantino Buzi, Marina Melchionna, Paolo Piras, Pasquale Raia, Alessio Veneziano
Profico, A., Schlager, S., Valoriani, V., Buzi, C., Melchionna, M., Veneziano, A., ... & Manzi, G. (2018). Reproducing the internal and external anatomy of fossil bones: Two new automatic digital tools. American Journal of Physical Anthropology, 166(4), 979-986.#' @export
Katz, S., Tal, A., & Basri, R. (2007). Direct visibility of point sets. In ACM SIGGRAPH 2007 papers (pp. 24-es).
This function centers a mesh on the barycenter coordinates
trasf.mesh(mesh, barycenter)trasf.mesh(mesh, barycenter)
mesh |
a 3D mesh of class "mesh3d" |
barycenter |
numeric: coordinates of the center |
mesh a 3D mesh of class "mesh3d"
Antonio Profico, Costantino Buzi, Marina Melchionna, Paolo Piras, Pasquale Raia, Alessio Veneziano
twodvarshape Calculates the shape variation associated to a value of PC scores associated to a specific combined landmark configuration or view
twodvarshape(twodviews_ob, scores, PC, view)twodvarshape(twodviews_ob, scores, PC, view)
twodviews_ob |
object from twodviews() |
scores |
numeric: the values of the PC scores for which the visualization is called |
PC |
PC chosen |
view |
numeric: which landmark configuration will be used to build the shape variation |
mat matrix of coordinates associated to the called shape variation
Antonio Profico, Costantino Buzi, Marina Melchionna, Paolo Piras, Pasquale Raia, Alessio Veneziano
Profico, A., Piras, P., Buzi, C., Del Bove, A., Melchionna, M., Senczuk, G., ... & Manzi, G. (2019). Seeing the wood through the trees. Combining shape information from different landmark configurations. Hystrix, 157-165.
library(Arothron) #load the 2D primate dataset data("Lset2D_list") #combine the 2D datasets and PCA combin2D<-twodviews(Lset2D_list,scale=TRUE,vector=c(1:5)) #calculate the shape variation associated to the negative extreme value of PC1 min_PC1<-twodvarshape(combin2D,min(combin2D$PCscores[,1]),1,5) plot(min_PC1,asp=1) #calculate the shape variation associated to the positive extreme value of PC1 max_PC1<-twodvarshape(combin2D,max(combin2D$PCscores[,1]),1,5) plot(max_PC1,asp=1)library(Arothron) #load the 2D primate dataset data("Lset2D_list") #combine the 2D datasets and PCA combin2D<-twodviews(Lset2D_list,scale=TRUE,vector=c(1:5)) #calculate the shape variation associated to the negative extreme value of PC1 min_PC1<-twodvarshape(combin2D,min(combin2D$PCscores[,1]),1,5) plot(min_PC1,asp=1) #calculate the shape variation associated to the positive extreme value of PC1 max_PC1<-twodvarshape(combin2D,max(combin2D$PCscores[,1]),1,5) plot(max_PC1,asp=1)
twodviews Combine and calculate the PCscores matrix from a list of different landmark configurations to be combined
twodviews(twodlist, scale = TRUE, vector = NULL)twodviews(twodlist, scale = TRUE, vector = NULL)
twodlist |
a list containing the landmark configurations of each anatomical view stored as separated lists |
scale |
logical: TRUE for shape-space, FALSE for form-space |
vector |
numeric vector: defines which views are to be used |
PCscores PC scores
PCs Pricipal Components (eigenvector matrix)
Variance table of the explained variance by the PCs
size vector containing the Centroid Size of each configuration
mshapes a list containing the mean shape of each landmark configuration
dims number of landmarks of each configuration
dimm dimension (2D or 3D) of each combined landmark configuration
twodlist the list used as input
Antonio Profico, Costantino Buzi, Marina Melchionna, Paolo Piras, Pasquale Raia, Alessio Veneziano
Profico, A., Piras, P., Buzi, C., Del Bove, A., Melchionna, M., Senczuk, G., ... & Manzi, G. (2019). Seeing the wood through the trees. Combining shape information from different landmark configurations. Hystrix, 157-165.
library(Morpho) #load the 2D primate dataset data("Lset2D_list") length(Lset2D_list) #combine the 2D datasets and PCA combin2D<-twodviews(Lset2D_list,scale=TRUE,vector=c(1:5)) combin2D$size #plot of the first two Principal Components plot(combin2D$PCscores) text(combin2D$PCscores,labels=rownames(combin2D$PCscores)) #load the 3D primate dataset data("Lset3D_array") #GPA and PCA GPA_3D<-procSym(Lset3D_array) #plot of the first two Principal Components plot(GPA_3D$PCscores) text(GPA_3D$PCscores,labels=rownames(GPA_3D$PCscores))library(Morpho) #load the 2D primate dataset data("Lset2D_list") length(Lset2D_list) #combine the 2D datasets and PCA combin2D<-twodviews(Lset2D_list,scale=TRUE,vector=c(1:5)) combin2D$size #plot of the first two Principal Components plot(combin2D$PCscores) text(combin2D$PCscores,labels=rownames(combin2D$PCscores)) #load the 3D primate dataset data("Lset3D_array") #GPA and PCA GPA_3D<-procSym(Lset3D_array) #plot of the first two Principal Components plot(GPA_3D$PCscores) text(GPA_3D$PCscores,labels=rownames(GPA_3D$PCscores))
Calculate the volume of a mesh by using a voxel-based method
volendo(mesh, alpha_vol = 100, ncells = 1e+05)volendo(mesh, alpha_vol = 100, ncells = 1e+05)
mesh |
object of class mesh3d |
alpha_vol |
numeric: alpha shape for construction external concave hull |
ncells |
numeric: approximative number of cell for 3D grid construction |
vol numeric: volume of the mesh expressed in cc
Antonio Profico, Costantino Buzi, Marina Melchionna, Paolo Piras, Pasquale Raia, Alessio Veneziano
## Not run: #load the human skull library(rgl) data(human_skull) sapendo<-endomaker(human_skull,param1_endo = 1.0,vol=FALSE, num.cores=NULL) volsap<-volendo(sapendo$endocast) ## End(Not run)## Not run: #load the human skull library(rgl) data(human_skull) sapendo<-endomaker(human_skull,param1_endo = 1.0,vol=FALSE, num.cores=NULL) volsap<-volendo(sapendo$endocast) ## End(Not run)
Landmark set on Yoda
data(yoda_set)data(yoda_set)
Antonio Profico, Costantino Buzi, Marina Melchionna, Paolo Piras, Pasquale Raia, Alessio Veneziano
Mesh of Yoda
data(yoda_sur)data(yoda_sur)
Antonio Profico, Costantino Buzi, Marina Melchionna, Paolo Piras, Pasquale Raia, Alessio Veneziano