Generative Adversarial Networks Projects

Loading and visualizing a 3D image

The 3D ShapeNets dataset contains Computer-aided design (CAD) models of different object categories, which are in the .mat file format. We will convert these .mat files to NumPy ndarrays. We will also visualize a 3D image to get a visual understanding of the dataset.

Execute the following code to load a 3D image from a .mat file:

1. Use the loadmat() function from scipy to retrieve the voxels. The code is as follows:

import scipy.io as iovoxels = io.loadmat("path to .mat file")['instance']

2. The shape of the loaded 3D image is 30x30x30. Our network requires images of shape 64x64x64. We will use NumPy's pad() method to increase the size of the 3D image to 32x32x32:

import numpy as npvoxels = np.pad(voxels, ...