def process(info_file, save_path, total_scenes_index, total_scenes_count):
# gt depth data loader
width, height = 640, 480
transform = transforms.Compose([
transforms.ResizeImage((width,height)),
transforms.ToTensor(),
])
dataset = SceneDataset(info_file, transform, frame_types=['depth'])
dataloader = torch.utils.data.DataLoader(dataset, batch_size=None,
batch_sampler=None, num_workers=2)
scene = dataset.info['scene']
# get info about tsdf
file_tsdf_pred = os.path.join(save_path, '%s.npz'%scene)
temp = TSDF.load(file_tsdf_pred)
voxel_size = int(temp.voxel_size*100)
# re-fuse to remove hole filling since filled holes are penalized in
# mesh metrics
vol_dim = list(temp.tsdf_vol.shape)
origin = temp.origin
tsdf_fusion = TSDFFusion(vol_dim, float(voxel_size)/100, origin, color=False)
device = tsdf_fusion.device
# mesh renderer
renderer = Renderer()
mesh_file = os.path.join(save_path, '%s.ply'%scene)
mesh = trimesh.load(mesh_file, process=False)
mesh_opengl = renderer.mesh_opengl(mesh)
for i, d in enumerate(dataloader):
if i%25==0:
print(total_scenes_index, total_scenes_count,scene, i, len(dataloader))
depth_trgt = d['depth'].numpy()
_, depth_pred = renderer(height, width, d['intrinsics'], d['pose'], mesh_opengl)
temp = eval_depth(depth_pred, depth_trgt)
if i==0:
metrics_depth = temp
else:
metrics_depth = {key:value+temp[key]
for key, value in metrics_depth.items()}
# # play video visualizations of depth
# viz1 = (np.clip((depth_trgt-.5)/5,0,1)*255).astype(np.uint8)
# viz2 = (np.clip((depth_pred-.5)/5,0,1)*255).astype(np.uint8)
# viz1 = cv2.applyColorMap(viz1, cv2.COLORMAP_JET)
# viz2 = cv2.applyColorMap(viz2, cv2.COLORMAP_JET)
# viz1[depth_trgt==0]=0
# viz2[depth_pred==0]=0
# viz = np.hstack((viz1,viz2))
# cv2.imshow('test', viz)
# cv2.waitKey(1)
tsdf_fusion.integrate((d['intrinsics'] @ d['pose'].inverse()[:3,:]).to(device),
torch.as_tensor(depth_pred).to(device))
metrics_depth = {key:value/len(dataloader)
for key, value in metrics_depth.items()}
# save trimed mesh
file_mesh_trim = os.path.join(save_path, '%s_trim.ply'%scene)
tsdf_fusion.get_tsdf().get_mesh().export(file_mesh_trim)
# eval tsdf
file_tsdf_trgt = dataset.info['file_name_vol_%02d'%voxel_size]
metrics_tsdf = eval_tsdf(file_tsdf_pred, file_tsdf_trgt)
# eval trimed mesh
file_mesh_trgt = dataset.info['file_name_mesh_gt']
metrics_mesh = eval_mesh(file_mesh_trim, file_mesh_trgt)
# transfer labels from pred mesh to gt mesh using nearest neighbors
file_attributes = os.path.join(save_path, '%s_attributes.npz'%scene)
if os.path.exists(file_attributes):
mesh.vertex_attributes = np.load(file_attributes)
print(mesh.vertex_attributes)
mesh_trgt = trimesh.load(file_mesh_trgt, process=False)
mesh_transfer = project_to_mesh(mesh, mesh_trgt, 'semseg')
semseg = mesh_transfer.vertex_attributes['semseg']
# save as txt for benchmark evaluation
np.savetxt(os.path.join(save_path, '%s.txt'%scene), semseg, fmt='%d')
mesh_transfer.export(os.path.join(save_path, '%s_transfer.ply'%scene))
# TODO: semseg val evaluation
metrics = {**metrics_depth, **metrics_mesh, **metrics_tsdf}
print(metrics)
rslt_file = os.path.join(save_path, '%s_metrics.json'%scene)
json.dump(metrics, open(rslt_file, 'w'))
return scene, metrics
def eval_scene(info_file, pathout):
""" Evaluates COLMAP inference compared to ground truth
Args:
info_file: path to info_json file for the scene
pathout: path where intermediate and final results are stored
"""
info = load_info_json(info_file)
dataset = info['dataset']
scene = info['scene']
frames = info['frames']
fnames = os.listdir(
os.path.join(pathout, dataset, scene, 'stereo', 'depth_maps'))
frames = [
frame for frame in frames
if os.path.split(frame['file_name_image'])[1] +
'.geometric.bin' in fnames
]
# 2d depth metrics
for i, frame in enumerate(frames):
if i % 25 == 0:
print(scene, i, len(fnames))
fname_trgt = frame['file_name_depth']
fname_pred = os.path.join(
pathout, dataset, scene, 'stereo', 'depth_maps',
os.path.split(frame['file_name_image'])[1] + '.geometric.bin')
depth_trgt = imageio.imread(fname_trgt).astype('float32') / 1000
depth_pred = read_array(fname_pred)
depth_pred[
depth_pred >
5] = 0 # ignore depth beyond 5 meters as it is probably wrong
depth_pred = resize(depth_pred, depth_trgt.shape)
temp = eval_depth(depth_pred, depth_trgt)
if i == 0:
metrics_depth = temp
else:
metrics_depth = {
key: value + temp[key]
for key, value in metrics_depth.items()
}
metrics_depth = {
key: value / len(frames)
for key, value in metrics_depth.items()
}
# 3d point metrics
fname_pred = os.path.join(pathout, dataset, scene, 'fused.ply')
fname_trgt = info['file_name_mesh_gt']
metrics_mesh = eval_mesh(fname_pred, fname_trgt)
metrics = {**metrics_depth, **metrics_mesh}
print(metrics)
rslt_file = os.path.join(pathout, dataset, scene, 'metrics.json')
json.dump(metrics, open(rslt_file, 'w'))
return metrics