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from itertools import count
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)
# From https://github.com/utiasSTARS/pykitti/blob/d3e1bb81676e831886726cc5ed79ce1f049aef2c/pykitti/utils.py#L68
def read_calib_file(filepath):
"""Read in a calibration file and parse into a dictionary."""
data = {}
with open(filepath, 'r') as f:
for line in f.readlines():
key, value = line.split(':', 1)
# The only non-float values in these files are dates, which
# we don't care about anyway
try:
data[key] = np.array([float(x) for x in value.split()])
except ValueError:
pass
return data
def kitti_image_stream(queue, kittidir, sequence, stride, skip=0):
""" image generator """
images_dir = kittidir / "dataset" / "sequences" / sequence
image_list = sorted((images_dir / "image_2").glob("*.png"))[skip::stride]
calib = read_calib_file(images_dir / "calib.txt")
intrinsics = calib['P0'][[0, 5, 2, 6]]
for t, imfile in enumerate(image_list):
image_left = cv2.imread(str(imfile))
H, W, _ = image_left.shape
H, W = (H - H%4, W - W%4)
image_left = image_left[..., :H, :W, :]
queue.put((t, image_left, intrinsics))
queue.put((-1, image_left, intrinsics))
@torch.no_grad()
def run(cfg, network, kittidir, sequence, stride=1, viz=False, show_img=False):
slam = None
queue = Queue(maxsize=8)
reader = Process(target=kitti_image_stream, args=(queue, kittidir, sequence, stride, 0))
reader.start()
for step in count(start=1):
(t, image, intrinsics) = queue.get()
if t < 0: break
image = torch.as_tensor(image, device='cuda').permute(2,0,1)
intrinsics = torch.as_tensor(intrinsics, dtype=torch.float, device='cuda')
if show_img:
show_image(image, 1)
if slam is None:
slam = DPVO(cfg, network, ht=image.shape[-2], wd=image.shape[-1], viz=viz)
intrinsics = intrinsics.cuda()
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('--kittidir', type=Path, default="datasets/KITTI")
parser.add_argument('--backend_thresh', type=float, default=32.0)
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)
kitti_scenes = [f"{i:02d}" for i in range(11)]
results = {}
for scene in kitti_scenes:
groundtruth = args.kittidir / "dataset" / "poses" / f"{scene}.txt"
poses_ref = file_interface.read_kitti_poses_file(groundtruth)
print(f"Evaluating KITTI {scene} with {poses_ref.num_poses // args.stride} poses")
scene_results = []
for trial_num in range(args.trials):
traj_est, timestamps = run(cfg, args.network, args.kittidir, 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 * args.stride)
traj_ref = PoseTrajectory3D(
positions_xyz=poses_ref.positions_xyz,
orientations_quat_wxyz=poses_ref.orientations_quat_wxyz,
timestamps=np.arange(poses_ref.num_poses, dtype=np.float64))
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:
plot_trajectory(traj_est, traj_ref, f"kitti sequence {scene} Trial #{trial_num+1}", f"trajectory_plots/kitti_seq{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/KITTI_{scene}.txt", traj_est)
# file_interface.write_kitti_poses_file(f"saved_trajectories/{scene}.txt", traj_est) # standard kitti format
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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