def visualize(self, data):
r''' Visualizes the current output data of the model.
The point clouds for respective input data is plotted.
Args:
data (tensor): input data
'''
device = self.device
points_gt = data.get('pointcloud').to(device)
inputs = data.get('inputs').to(device)
with torch.no_grad():
points_out = self.model(inputs)
points_out = points_out.cpu().numpy()
points_gt = points_gt.cpu().numpy()
batch_size = inputs.size(0)
for i in trange(batch_size):
input_img_path = os.path.join(self.vis_dir, '%03d_in.png' % i)
vis.visualize_data(inputs[i].cpu(), self.input_type,
input_img_path)
out_file = os.path.join(self.vis_dir, '%03d.png' % i)
out_file_gt = os.path.join(self.vis_dir, '%03d_gt.png' % i)
vis.visualize_pointcloud(points_out[i], out_file=out_file)
vis.visualize_pointcloud(points_gt[i], out_file=out_file_gt)
def visualize(self, data):
r''' Visualises the GT point cloud and predicted vertices (as a point cloud).
Arguments:
data (tensor): input data
'''
points_gt = data.get('pointcloud').to(self.device)
img = data.get('inputs').to(self.device)
camera_args = common.get_camera_args(data,
'pointcloud.loc',
'pointcloud.scale',
device=self.device)
world_mat, camera_mat = camera_args['Rt'], camera_args['K']
if not os.path.isdir(self.vis_dir):
os.mkdir(self.vis_dir)
with torch.no_grad():
outputs1, outputs2 = self.model(img, camera_mat)
pred_vertices_1, pred_vertices_2, pred_vertices_3 = outputs1
points_out = common.transform_points_back(pred_vertices_3, world_mat)
points_out = points_out.cpu().numpy()
input_img_path = os.path.join(self.vis_dir, 'input.png')
save_image(img.cpu(), input_img_path, nrow=4)
points_gt = points_gt.cpu().numpy()
batch_size = img.size(0)
for i in range(batch_size):
out_file = os.path.join(self.vis_dir, '%03d.png' % i)
out_file_gt = os.path.join(self.vis_dir, '%03d_gt.png' % i)
vis.visualize_pointcloud(points_out[i], out_file=out_file)
vis.visualize_pointcloud(points_gt[i], out_file=out_file_gt)
def visualize(self, data, it=0, vis_type='mesh'):
''' Visualized the data.
Args:
data (dict): data dictionary
it (int): training iteration
vis_type (string): visualization type
'''
if self.multi_gpu:
print(
"Sorry, visualizations currently not implemented when using \
multi GPU training.")
return 0
device = self.device
inputs = data.get('inputs', torch.empty(1, 0)).to(device)
batch_size = inputs.shape[0]
c = self.model.encode_inputs(inputs)
if vis_type == 'voxel':
shape = (32, 32, 32)
p = make_3d_grid([-0.5] * 3, [0.5] * 3, shape).to(device)
p = p.unsqueeze(0).repeat(batch_size, 1, 1)
with torch.no_grad():
p_r = self.model.decode(p, c=c).probs
voxels_out = (p_r >= self.threshold).cpu().numpy()
voxels_out = voxels_out.reshape(batch_size, 32, 32, 32)
for i in range(batch_size):
out_file = os.path.join(self.vis_dir, '%03d.png' % i)
vis.visualize_voxels(voxels_out[i], out_file)
elif vis_type == 'pointcloud':
p = torch.rand(batch_size, 60000, 3).to(device) - 0.5
with torch.no_grad():
occ = self.model.decode(p, c=c).probs
mask = occ > self.threshold
for i in range(batch_size):
pi = p[i][mask[i]].cpu()
out_file = os.path.join(self.vis_dir, '%03d.png' % i)
vis.visualize_pointcloud(pi, out_file=out_file)
elif vis_type == 'mesh':
try:
mesh_list = self.generator.generate_meshes(
data, return_stats=False)
for i, mesh in tqdm(enumerate(mesh_list)):
if self.overwrite_visualization:
ending = ''
else:
ending = '_%010d' % it
mesh_out_file = os.path.join(
self.vis_dir, '%03d%s.ply' % (i, ending))
mesh.export(mesh_out_file)
except Exception as e:
print("Exception occurred during visualization: ", e)
else:
print('The visualization type %s is not valid!' % vis_type)
def test():
model_class = '02691156'
model_id = '10155655850468db78d106ce0a280f87'
if depth_pred:
depth_foldername = 'depth_pred'
else:
depth_foldername = 'depth'
depth_folder = os.path.join(DEPTH_ROOT, model_class, model_id,
depth_foldername)
mask_folder = os.path.join(MASK_ROOT, model_class, model_id, 'mask')
output_folder = os.path.join(TEST_ROOT)
if not os.path.exists(output_folder):
os.mkdir(output_folder)
depth_range_file = os.path.join(depth_folder, 'depth_range.txt')
worker = DepthToPCNp()
with open(depth_range_file, 'r') as f:
for i in range(N_VIEWS):
depth_range = f.readline().split(' ')
depth_min = float(depth_range[0])
depth_max = float(depth_range[1])
depth_unit = float(depth_range[2])
depth_file = os.path.join(depth_folder, '%.2d_depth.png' % i)
mask_file = os.path.join(mask_folder, '%.2d_mask.png' % i)
depth_img = Image.open(depth_file).convert('L')
depth_img.save(os.path.join(output_folder, '%.2d_depth.png' % i))
mask_img = Image.open(mask_file)
pts = worker.work(depth_img,
mask_img,
depth_min,
depth_max,
n=N,
unit=depth_unit)
output_file = os.path.join(output_folder,
'%.2d_pointcloud.npz' % i)
np.savez(output_file, pointcloud=pts)
output_file = os.path.join(output_folder, '%.2d_pc.png' % i)
visualize_pointcloud(pts, out_file=output_file, show=(i % 4 == 0))
def visualize(self, data):
''' Performs a visualization step for the data.
Args:
data (dict): data dictionary
'''
device = self.device
encoder_inputs, raw_data = compose_inputs(
data,
mode='train',
device=self.device,
input_type=self.input_type,
depth_pointcloud_transfer=self.depth_pointcloud_transfer,
)
world_mat = None
if (self.model.encoder_world_mat is not None) \
or self.gt_pointcloud_transfer in ('view', 'view_scale_model'):
if 'world_mat' in raw_data:
world_mat = raw_data['world_mat']
else:
world_mat = get_world_mat(data, device=device)
gt_pc = compose_pointcloud(data,
device,
self.gt_pointcloud_transfer,
world_mat=world_mat)
batch_size = gt_pc.size(0)
self.model.eval()
with torch.no_grad():
if self.model.encoder_world_mat is not None:
out = self.model(encoder_inputs, world_mat=world_mat)
else:
out = self.model(encoder_inputs)
if isinstance(out, tuple):
out, _ = out
for i in trange(batch_size):
pc = gt_pc[i].cpu()
vis.visualize_pointcloud(pc,
out_file=os.path.join(
self.vis_dir, '%03d_gt_pc.png' % i))
pc = out[i].cpu()
vis.visualize_pointcloud(pc,
out_file=os.path.join(
self.vis_dir, '%03d_pr_pc.png' % i))
pc = encoder_inputs[i].cpu()
vis.visualize_pointcloud(pc,
out_file=os.path.join(
self.vis_dir,
'%03d_input_half_pc.png' % i))
pointcloud_out_file = os.path.join(
pointcloud_dir, '%s.ply' % i)
export_pointcloud(pointcloud, pointcloud_out_file)
out_file_dict['pointcloud'] = pointcloud_out_file
if cfg['generation']['copy_input']:
# Save inputs
if input_type == 'img':
for i in range(len(data['inputs'])):
inputs_path = os.path.join(in_dir, '%s.jpg' % i)
inputs = data['inputs'][i].squeeze(0).cpu()
visualize_data(inputs, 'img', inputs_path)
out_file_dict['in'] = inputs_path
out_file = os.path.join(gt_dir, 'gt.png')
pt = data['pointcloud'].squeeze(0).cpu().numpy()
vis.visualize_pointcloud(pt, out_file=out_file)
elif input_type == 'voxels':
inputs_path = os.path.join(in_dir, '%s.off' % modelname)
inputs = data['inputs'].squeeze(0).cpu()
voxel_mesh = VoxelGrid(inputs).to_mesh()
voxel_mesh.export(inputs_path)
out_file_dict['in'] = inputs_path
elif input_type == 'pointcloud':
inputs_path = os.path.join(in_dir, '%s.ply' % modelname)
inputs = data['inputs'].squeeze(0).cpu().numpy()
export_pointcloud(inputs, inputs_path, False)
out_file_dict['in'] = inputs_path
# Copy to visualization directory for first vis_n_output samples
c_it = model_counter[category_id]
if c_it < vis_n_outputs:
def visualize(self, data):
''' Performs a visualization step for the data.
Args:
data (dict): data dictionary
'''
device = self.device
batch_size = data['points'].size(0)
shape = (32, 32, 32)
p = make_3d_grid([-0.5] * 3, [0.5] * 3, shape).to(device)
p = p.expand(batch_size, *p.size())
encoder_inputs, raw_data = compose_inputs(
data,
mode='val',
device=self.device,
input_type=self.input_type,
use_gt_depth_map=self.use_gt_depth_map,
depth_map_mix=self.depth_map_mix,
with_img=self.with_img,
depth_pointcloud_transfer=self.depth_pointcloud_transfer,
local=self.local)
kwargs = {}
with torch.no_grad():
p_r = self.model.forward_halfway(p,
encoder_inputs,
sample=self.eval_sample,
**kwargs)
occ_hat = p_r.probs.view(batch_size, *shape)
voxels_out = (occ_hat >= self.threshold).cpu().numpy()
# visualize
if self.local:
encoder_inputs = encoder_inputs[None]
if self.input_type == 'depth_pred':
gt_mask = raw_data['mask']
if self.with_img:
encoder_inputs = encoder_inputs['depth']
for i in trange(batch_size):
if self.use_gt_depth_map:
input_img_path = os.path.join(self.vis_dir,
'%03d_in_gt.png' % i)
else:
input_img_path = os.path.join(self.vis_dir,
'%03d_in_pr.png' % i)
depth_map = encoder_inputs[i].cpu()
depth_map = depth_to_L(depth_map, gt_mask[i].cpu())
vis.visualize_data(depth_map, 'img', input_img_path)
vis.visualize_voxels(
voxels_out[i], os.path.join(self.vis_dir, '%03d.png' % i))
elif self.input_type == 'depth_pointcloud':
for i in trange(batch_size):
input_pointcloud_file = os.path.join(
self.vis_dir, '%03d_depth_pointcloud.png' % i)
pc = encoder_inputs[i].cpu()
if self.depth_pointcloud_transfer in ('view',
'view_scale_model'):
vis.visualize_pointcloud(pc,
out_file=input_pointcloud_file,
elev=15,
azim=180)
else:
vis.visualize_pointcloud(pc,
out_file=input_pointcloud_file)
vis.visualize_voxels(
voxels_out[i], os.path.join(self.vis_dir, '%03d.png' % i))
