71 lines
2.3 KiB
Python
71 lines
2.3 KiB
Python
import os
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import sys
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os.environ["CUDA_VISIBLE_DEVICES"] = '1'
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import math
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import time
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import torch
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import torchvision as tv
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import numpy as np
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import scipy.io as sio
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import plotly.express as ex
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from torch.utils.data import Dataset
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from render import Render, DepthRenderer
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class Dataset(Dataset):
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def __init__(self, folder):
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self.folder = folder
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self.transforms = tv.transforms.ToTensor()
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self.frames = os.listdir(os.path.join(folder, 'mesh_posed'))
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self.render = DepthRenderer()
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self.body_faces = np.load('custom_data/smpl_faces.npy')
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def __len__(self):
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return len(self.frames)
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def __getitem__(self, idx):
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path = os.path.join(self.folder, 'mesh_posed', self.frames[idx])
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mesh_posed = np.load(path)['mesh_posed']
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id = self.frames[idx].split('_')[0]
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faces = np.load(os.path.join(self.folder, 'faces', id + '.npy'))
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mask = np.load(os.path.join(self.folder, 'mesh_mask', id + '.npy'))
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body = np.load(os.path.join(self.folder, 'mesh_body_posed', self.frames[idx][:-1] + 'y'))
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info_folder = '/home/workspace/data/dataset/' + ('train' if self.folder.split('/')[-1] == 'train' else 'validation') + '/' + id + '/info.mat'
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info = sio.loadmat(info_folder)
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combined = torch.Tensor(np.concatenate((body, mesh_posed[mask, :]))).to('cuda')
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combined_faces = torch.Tensor(np.concatenate((self.body_faces, faces + 14475))).to('cuda')
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start = time.perf_counter()
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# depth_image = self.render.render(mesh_posed[mask, :], faces, info['camLoc'][0])[:,:,0]
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# depth_image = self.render.render(combined, combined_faces, info['camLoc'][0])[:,:,0]
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depth_image = self.render(combined, combined_faces, info['camLoc'][0])
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t = time.perf_counter() - start
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print(t)
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# image = self.transforms(image)
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return { 'mesh_posed': mesh_posed, 'depth_image': depth_image, 'mask': mask }
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if __name__ == '__main__':
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from torch.utils.data import DataLoader
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device = "cuda" if torch.cuda.is_available() else "cpu"
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dataset = Dataset('/home/workspace/local-data/train')
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train_dataloader = DataLoader(dataset, batch_size=4, drop_last=True, shuffle=True)
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for batch in train_dataloader:
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batch
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pass
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pass
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