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import glob
import os
from multiprocessing import Process, Queue
from pathlib import Path
import cv2
import evo.main_ape as main_ape
import numpy as np
import torch
from evo.core import sync
from evo.core.metrics import PoseRelation
from evo.core.trajectory import PoseTrajectory3D
from evo.tools import file_interface
from dpvo.config import cfg
from dpvo.dpvo import DPVO
from dpvo.plot_utils import plot_trajectory
from dpvo.stream import image_stream
from dpvo.utils import Timer
SKIP = 0
def show_image(image, t=0):
image = image.permute(1, 2, 0).cpu().numpy()
cv2.imshow('image', image / 255.0)
cv2.waitKey(t)
@torch.no_grad()
def run(cfg, network, imagedir, calib, stride=1, viz=False, show_img=False):
slam = None
queue = Queue(maxsize=8)
reader = Process(target=image_stream, args=(queue, imagedir, calib, stride, 0))
reader.start()
while 1:
(t, image, intrinsics) = queue.get()
if t < 0: break
image = torch.from_numpy(image).permute(2,0,1).cuda()
intrinsics = torch.from_numpy(intrinsics).cuda()
if show_img:
show_image(image, 1)
if slam is None:
slam = DPVO(cfg, network, ht=image.shape[1], wd=image.shape[2], viz=viz)
with Timer("SLAM", enabled=False):
slam(t, image, intrinsics)
reader.join()
return slam.terminate()
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--network', type=str, default='dpvo.pth')
parser.add_argument('--config', default="config/default.yaml")
parser.add_argument('--stride', type=int, default=2)
parser.add_argument('--viz', action="store_true")
parser.add_argument('--show_img', action="store_true")
parser.add_argument('--trials', type=int, default=1)
parser.add_argument('--iclnuim_dir', default="datasets/ICL_NUIM", type=Path)
parser.add_argument('--backend_thresh', type=float, default=64.0)
parser.add_argument('--plot', action="store_true")
parser.add_argument('--opts', nargs='+', default=[])
parser.add_argument('--save_trajectory', action="store_true")
args = parser.parse_args()
cfg.merge_from_file(args.config)
cfg.BACKEND_THRESH = args.backend_thresh
cfg.merge_from_list(args.opts)
print("\nRunning with config...")
print(cfg, "\n")
torch.manual_seed(1234)
scenes = [
"living_room_traj0_loop",
"living_room_traj1_loop",
"living_room_traj2_loop",
"living_room_traj3_loop",
"office_room_traj0_loop",
"office_room_traj1_loop",
"office_room_traj2_loop",
"office_room_traj3_loop",
]
results = {}
for scene in scenes:
imagedir = args.iclnuim_dir / scene
if scene.startswith("living"):
groundtruth = args.iclnuim_dir / f"TrajectoryGT" / f"livingRoom{scene[-6]}.gt.freiburg"
elif scene.startswith("office"):
groundtruth = args.iclnuim_dir / f"TrajectoryGT" / f"traj{scene[-6]}.gt.freiburg"
traj_ref = file_interface.read_tum_trajectory_file(groundtruth)
scene_results = []
for i in range(args.trials):
traj_est, timestamps = run(cfg, args.network, imagedir, "calib/icl_nuim.txt", args.stride, args.viz, args.show_img)
images_list = sorted(glob.glob(os.path.join(imagedir, "*.png")))[::args.stride]
tstamps = np.arange(1, len(images_list)+1, args.stride, dtype=np.float64)
traj_est = PoseTrajectory3D(
positions_xyz=traj_est[:,:3],
orientations_quat_wxyz=traj_est[:, [6, 3, 4, 5]],
timestamps=tstamps)
traj_ref, traj_est = sync.associate_trajectories(traj_ref, traj_est)
result = main_ape.ape(traj_ref, traj_est, est_name='traj',
pose_relation=PoseRelation.translation_part, align=True, correct_scale=True)
ate_score = result.stats["rmse"]
if args.plot:
scene_name = scene.rstrip("_loop").title()
Path("trajectory_plots").mkdir(exist_ok=True)
plot_trajectory(traj_est, traj_ref, f"ICL_NUIM {scene_name} Trial #{i+1} (ATE: {ate_score:.03f})",
f"trajectory_plots/ICL_NUIM_{scene_name}_Trial{i+1:02d}.pdf", align=True, correct_scale=True)
if args.save_trajectory:
Path("saved_trajectories").mkdir(exist_ok=True)
file_interface.write_tum_trajectory_file(f"saved_trajectories/ICL_NUIM_{scene_name}_Trial{i+1:02d}.txt", traj_est)
scene_results.append(ate_score)
results[scene] = np.median(scene_results)
print(scene, sorted(scene_results))
xs = []
for scene in results:
print(scene, results[scene])
xs.append(results[scene])
print("AVG: ", np.mean(xs))
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