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import sys
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, plot
from dpvo.config import cfg
from dpvo.dpvo import DPVO
from dpvo.plot_utils import plot_trajectory
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)
def tum_image_stream(queue, scene_dir, sequence, stride, skip=0):
""" image generator """
images_dir = scene_dir / "rgb"
fx, fy, cx, cy = 517.3, 516.5, 318.6, 255.3
K_l = np.array([fx, 0.0, cx, 0.0, fy, cy, 0.0, 0.0, 1.0]).reshape(3,3)
d_l = np.array([0.2624, -0.9531, -0.0054, 0.0026, 1.1633])
image_list = sorted(images_dir.glob("*.png"))[skip::stride]
for imfile in image_list:
image = cv2.imread(str(imfile))
image = cv2.undistort(image, K_l, d_l)
image = image.transpose(2,0,1)
intrinsics = np.asarray([fx, fy, cx, cy])
# crop image to remove distortion boundary
intrinsics[2] -= 16
intrinsics[3] -= 8
# intrinsics = intrinsics[None]
image = image[:, 8:-8, 16:-16]
queue.put((float(imfile.stem), image, intrinsics))
queue.put((-1, image, intrinsics))
@torch.no_grad()
def run(cfg, network, scene_dir, sequence, stride=1, viz=False, show_img=False):
slam = None
queue = Queue(maxsize=8)
reader = Process(target=tum_image_stream, args=(queue, scene_dir, sequence, stride, 0))
reader.start()
for step in range(sys.maxsize):
(t, images, intrinsics) = queue.get()
if t < 0: break
images = torch.as_tensor(images, device='cuda')
intrinsics = torch.as_tensor(intrinsics, dtype=torch.float, device='cuda')
if show_img:
show_image(images[0], 1)
if slam is None:
slam = DPVO(cfg, network, ht=images.shape[-2], wd=images.shape[-1], viz=viz)
intrinsics = intrinsics.cuda()
with Timer("SLAM", enabled=False):
slam(t, images, intrinsics)
reader.join()
poses, tstamps = slam.terminate()
np.save(f"poses_{sequence}.npy", poses)
np.save(f"tstamps_{sequence}.npy", tstamps)
return poses, tstamps
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=1)
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('--tumdir', type=Path, default="datasets/TUM_RGBD")
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)
tum_scenes = [
"rgbd_dataset_freiburg1_360",
"rgbd_dataset_freiburg1_desk",
"rgbd_dataset_freiburg1_desk2",
"rgbd_dataset_freiburg1_floor",
"rgbd_dataset_freiburg1_plant",
"rgbd_dataset_freiburg1_room",
"rgbd_dataset_freiburg1_rpy",
"rgbd_dataset_freiburg1_teddy",
"rgbd_dataset_freiburg1_xyz",
]
results = {}
for scene in tum_scenes:
scene_dir = args.tumdir / f"{scene}"
groundtruth = scene_dir / "groundtruth.txt"
traj_ref = file_interface.read_tum_trajectory_file(groundtruth)
scene_results = []
for trial_num in range(args.trials):
traj_est, timestamps = run(cfg, args.network, scene_dir, scene, args.stride, args.viz, args.show_img)
traj_est = PoseTrajectory3D(
positions_xyz=traj_est[:,:3],
orientations_quat_wxyz=traj_est[:, [6, 3, 4, 5]],
timestamps=timestamps)
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:
Path("trajectory_plots").mkdir(exist_ok=True)
plot_trajectory(traj_est, traj_ref, f"TUM-RGBD Frieburg1 {scene} Trial #{trial_num+1} (ATE: {ate_score:.03f})",
f"trajectory_plots/TUM_RGBD_Frieburg1_{scene}_Trial{trial_num+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/TUM_RGBD_{scene}_Trial{trial_num+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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