text stringlengths 1 93.6k |
|---|
total_time += time.time() - start
|
print(total_time / len(data_loader))
|
def run_evaluate():
|
from lib.datasets import make_data_loader
|
from lib.evaluators import make_evaluator
|
import tqdm
|
import torch
|
from lib.networks import make_network
|
from lib.utils import net_utils
|
import time
|
network = make_network(cfg).cuda()
|
net_utils.load_network(network,
|
cfg.trained_model_dir,
|
resume=cfg.resume,
|
epoch=cfg.test.epoch)
|
network.eval()
|
data_loader = make_data_loader(cfg, is_train=False)
|
evaluator = make_evaluator(cfg)
|
net_time = []
|
for batch in tqdm.tqdm(data_loader):
|
for k in batch:
|
if k != 'meta':
|
if k == 'rendering_video_meta':
|
for i in range(len(batch[k])):
|
for v in batch[k][i]:
|
batch[k][i][v] = batch[k][i][v].cuda()
|
else:
|
batch[k] = batch[k].cuda()
|
if cfg.save_video:
|
with torch.no_grad():
|
network(batch)
|
else:
|
with torch.no_grad():
|
torch.cuda.synchronize()
|
start_time = time.time()
|
output = network(batch)
|
torch.cuda.synchronize()
|
end_time = time.time()
|
net_time.append(end_time - start_time)
|
evaluator.evaluate(output, batch)
|
if not cfg.save_video:
|
evaluator.summarize()
|
if len(net_time) > 1:
|
# print('net_time: ', np.mean(net_time[1:]))
|
print('FPS: ', 1./np.mean(net_time[1:]))
|
else:
|
# print('net_time: ', np.mean(net_time))
|
print('FPS: ', 1./np.mean(net_time))
|
if cfg.save_ply:
|
dataset_name = cfg.train_dataset_module.split('.')[-2]
|
ply_dir = os.path.join(cfg.result_dir, 'pointclouds', dataset_name)
|
for item in os.listdir(ply_dir):
|
data_dir = os.path.join(ply_dir, item)
|
img_dir = os.path.join(data_dir, 'images')
|
depth_dir = os.path.join(data_dir, 'depth')
|
cam_dir = os.path.join(data_dir, 'cam')
|
img_ls = glob.glob(os.path.join(img_dir, '*.png'))
|
img_name = [os.path.basename(im).split('.')[0] for im in img_ls]
|
# for the final point cloud
|
vertexs = []
|
vertex_colors = []
|
for name in img_name:
|
ref_name = name
|
ref_intrinsics, ref_extrinsics = read_camera_parameters(os.path.join(cam_dir, ref_name+'.txt'))
|
ref_img = read_img(os.path.join(img_dir, ref_name+'.png'))
|
ref_depth_est = read_pfm(os.path.join(depth_dir, ref_name+'.pfm'))[0]
|
height, width = ref_depth_est.shape[:2]
|
x, y = np.meshgrid(np.arange(0, width), np.arange(0, height))
|
x, y = x.reshape(-1), y.reshape(-1)
|
depth = ref_depth_est.reshape(-1)
|
color = ref_img.reshape(-1,3)
|
xyz_ref = np.matmul(np.linalg.inv(ref_intrinsics),
|
np.vstack((x, y, np.ones_like(x))) * depth)
|
xyz_world = np.matmul(np.linalg.inv(ref_extrinsics),
|
np.vstack((xyz_ref, np.ones_like(x))))[:3]
|
vertexs.append(xyz_world.transpose((1, 0)))
|
vertex_colors.append((color * 255).astype(np.uint8))
|
vertexs = np.concatenate(vertexs, axis=0)
|
vertex_colors = np.concatenate(vertex_colors, axis=0)
|
scene = os.path.basename(data_dir)
|
ply_path = os.path.join(data_dir, f'{scene}.ply')
|
print(f'saving {ply_path}')
|
storePly(ply_path, vertexs, vertex_colors)
|
## point cloud --> mesh
|
import open3d as o3d
|
import trimesh
|
pcd = o3d.io.read_point_cloud(ply_path)
|
pcd.estimate_normals()
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.