def stltrdog(stlpath, resolution):
mesh, bounding_box = read_and_reshape_stl(stlpath, resolution)
voxels, bounding_box = voxelize(mesh, bounding_box)
resdog = voxels.transpose((2, 1, 0))
return resdog
pass
def voxelize(self):
resolution = int(self.resolution)
mesh, self.bounding_box = read_and_reshape_stl(self.model_fileName,
resolution)
process_number = int(multiprocessing.cpu_count()) * 2
self.voxels = voxelize(mesh, self.bounding_box, process_number)
def voxelize(self):
resolution = int(self.spinBox.value())
mesh, self.bounding_box = read_and_reshape_stl(self.model_fileName,
resolution)
process_number = int(self.spinBox_2.value())
self.voxels = voxelize(mesh, self.bounding_box, process_number)
self.pushButton_8.clicked.connect(self.generate_final_model)
self.horizontalSlider.sliderReleased.connect(self.update_graph)
self.label_6.setText('{}'.format(self.bounding_box[0]))
self.label_7.setText('{}'.format(self.bounding_box[1]))
self.label_8.setText('{}'.format(self.bounding_box[2]))
self.horizontalSlider.setRange(0, self.bounding_box[2] - 1)
self.horizontalSlider.setValue(int(np.floor(self.bounding_box[2] / 2)))
self.update_graph()
self.i0 = np.array([])
self.i1 = np.array([])
self.i2 = np.array([])
self.o1 = np.array([])
self.o2 = np.array([])
def run(self):
mesh, bounding_box = read_and_reshape_stl(self.input_filename, self.resolution)
self.voxels, bounding_box = voxelize(mesh, bounding_box)
self.voxelize_finished.emit('finished')
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Feb 11 08:55:42 2019
@author: bene
"""
import numpy as np
import matplotlib.pyplot as plt
from mesh_vox import read_and_reshape_stl, voxelize
# path to the stl file
input_path = '/Users/bene/Dropbox/Dokumente/Promotion/PROJECTS/BOSTON/MUSCAT/PYTHON/muScat/Data/NEURON/neuron.stl'
# number of voxels used to represent the largest dimension of the 3D model
resolution = 32
# read and rescale
mesh, bounding_box = read_and_reshape_stl(input_path, resolution)
# create voxel array
voxels, bounding_box = voxelize(mesh, bounding_box)
print(voxels)
np.save(
'/Users/bene/Dropbox/Dokumente/Promotion/PROJECTS/BOSTON/MUSCAT/PYTHON/muScat/Data/NEURON/myneuron_32_32_70.npy',
voxels[0:70, 35:32 + 35, 35:32 + 35])