self.normalize)).float(),
'kp_2d':
torch.tensor(kps).float(),
'image_name':
self.images[index],
'data_set':
'lsp'
}
if __name__ == '__main__':
lsp = LspLoader(
data_set_path='/home/ubuntu/anaconda3/envs/hmr/pytorch_HMR/data/lsp/',
use_crop=True,
scale_range=[1.05, 1.2],
use_flip=True,
flip_prob=1.0)
l = lsp.__len__()
data_loader = DataLoader(lsp, batch_size=10, shuffle=True)
for _ in range(l):
r = lsp.__getitem__(_)
image = r['image'].cpu().numpy().astype(np.uint8)
kps = r['kp_2d'].cpu().numpy()
kps[:, :2] = (kps[:, :2] + 1) * args.crop_size / 2.0
base_name = os.path.basename(r['image_name'])
draw_lsp_14kp__bone(image, kps)
cv2.imshow(
base_name,
cv2.resize(image, (512, 512), interpolation=cv2.INTER_CUBIC))
cv2.waitKey(0)
self.normalize)).float(),
'kp_2d':
torch.from_numpy(kps).float(),
'kp_3d':
torch.from_numpy(kp_3d).float(),
'theta':
torch.zeros(85).float(),
'image_name':
self.images[index],
'w_smpl':
0.0,
'w_3d':
1.0,
'data_set':
'mpi inf 3dhp'
}
if __name__ == '__main__':
mpi = mpi_inf_3dhp_dataloader('E:/HMR/data/mpii_inf_3dhp', True,
[1.1, 2.0], False, 5)
l = len(mpi)
for _ in range(l):
r = mpi.__getitem__(_)
base_name = os.path.basename(r['image_name'])
draw_lsp_14kp__bone(r['image'], r['kp_2d'])
cv2.imshow(
base_name,
cv2.resize(r['image'], (512, 512), interpolation=cv2.INTER_CUBIC))
cv2.waitKey(0)