I would like to improve this code in order to return a list
of "solutions" at the end:
[bcoords[0, 1, 2, 3], R[0, 1, 2, 3], G[0, 1, 2, 3], B[0, 1, 2, 3]]
code:
import csv
import numpy as np
import scipy.spatial
points = np.array([(float(X), float(Y), float(Z))
for R, G, B, X, Y, Z in csv.reader(open('XYZcolorlist_D65.csv'))])
# load XYZ coordinates of 'points' in a np.array
tri = scipy.spatial.Delaunay(points)
# do the triangulation
indices = tri.simplices
# indices of vertices
vert = points[tri.simplices]
# the vertices for each tetrahedron
targets = np.array([(float(X), float(Y), float(Z))
for X, Y, Z in csv.reader(open('targets.csv'))])
# load the XYZ target values in a np.array
tetrahedra = tri.find_simplex(targets)
# find which tetrahedron each point belong to
X = tri.transform[tetrahedra,:3]
Y = targets - tri.transform[tetrahedra,3]
b = np.einsum('ijk,ik->ij', X, Y)
bcoords = np.c_[b, 1 - b.sum(axis=1)]
# find the barycentric coordinates of each point
print bcoords
_
The code loads two .csv
files in two np.array
and use the module scipy.spatial.Delaunay
to find the barycentric coordinates of a point
in a tetrahedron
.
XYZcolorlist.csv
is a cloud of points R, G, B, X, Y, Z
and targets.csv
is a set of targets X, Y, Z
XYZcolorlist.csv:
255,63,127,35.5344302104,21.380721966,20.3661095969
255,95,127,40.2074945517,26.5282949405,22.7094284437
255,127,127,43.6647438365,32.3482625492,23.6181801523
255,159,127,47.1225628354,39.1780944388,22.9366615044
255,223,159,61.7379149646,62.8387601708,32.3936200864
...
targets.csv:
49.72,5,8.64
50.06,5,8.64
50.4,5,8.64
50.74,5,8.64
51.08,5,8.64
51.42,5,8.64
51.76,5,8.64
...
For each point of targets.csv
, I want to get:
the 4
vertices
containing thepoint
the 4
float(R), float(G), float(B)
associated with each vertex:the 4
barycentric coordinates
associated with eachpoint
and I want to do it fast, using numpy
The code gives all this, except for the 4 R, G, B
's
alternatively, I could load the entire file's data with this code:
points = np.array([(float(R), float(G), float(B), float(X), float(Y), float(Z))
for R, G, B, X, Y, Z in csv.reader(open('XYZcolorlist_D65.csv'))])
# load R,G,B,X,Y,Z coordinates of 'points' in a np.array
How can I return the list:
[bcoords[0, 1, 2, 3], R[0, 1, 2, 3], G[0, 1, 2, 3], B[0, 1, 2, 3]]
?
Is it possible to build a dict[]
?
Thanks