def load_obj(obj):
with open(obj, 'rb') as f:
model = binvox.read_as_3d_array(f)
model = model.data#.astype(np.uint8)*255
#print model[210:220,210:220,128]
#print model.sum()
#model2 = np.zeros((N,N,Nz))
#model = cloadobj.resize(np.array(model).astype(np.float32),N,Nz)
#print model[210:220,210:220,128]
#print model.sum()
#model2 = ndimage.filters.gaussian_filter(model2,sigma =0.05)
#r = 20
#r2 = 12
#r3 = 8
# c = scipy.ndimage.binary_closing(model2,structure=np.ones((r,r,r))).astype(np.uint8)
#d = scipy.ndimage.binary_dilation(model2,structure=struct(r2)).astype(np.uint8)
#e = scipy.ndimage.binary_erosion(d,structure=struct(r3)).astype(np.uint8)
return model
def __init__(self, filename):
with open(filename, 'rb') as f:
model = binvox.read_as_3d_array(f)
self.data = model.data.astype(np.uint8)
self.points = []
self.dim = model.dims[0]
self.indices = np.where(self.data == 1)
total = self.data.sum()
translate = (self.dim - 1) * 0.5
for i in range(total):
x = self.indices[0][i] - translate
y = self.indices[1][i] - translate
z = self.indices[2][i] - translate
point = [x, y, z]
self.points.append(point)
def read_binvox(fname):
with open(fname, 'rb') as f:
model = binvox.read_as_3d_array(f)
data = vox2tanh(model.data.astype(np.float32))
return data
def read_binvox(path):
with open(path + ".binvox", 'rb') as f:
m = binvox.read_as_3d_array(f).data
return m
def read_binvox(filename):
with open(filename, 'rb') as f:
model = binvox.read_as_3d_array(f)
data = model.data.astype(np.float32)
return np.expand_dims(data, -1)
# -*- coding: utf-8 -*-
"""
Created on Wed May 22 14:45:36 2019
@author: Sooram Kang
"""
import os
import numpy as np
import binvox
X = []
examples_dir = 'C:\\Users\\CHANG\\PycharmProjects\\3dCNN\\DCGAN\\modelnet\\bed\\bed_binvox\\'
for example in os.listdir(examples_dir):
if 'binvox' in example:
with open(os.path.join(examples_dir, example), 'rb') as file:
data = np.int32(binvox.read_as_3d_array(file).data)
# padded_data = np.pad(data, 3, 'constant')
X.append(data)
np.savez_compressed('modelnet10_bed.npz', X_train=X)
def read_binvox(fname):
with open(fname, 'rb') as f:
model = binvox.read_as_3d_array(f)
model = (model.data.astype(np.float32) - 0.5) / 0.5
return model
def read_binvox(fn):
with open(fn, 'rb') as f:
model = binvox.read_as_3d_array(f)
return model.data.astype(np.float32)