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import datetime
import glob
import os
import os.path as osp
from pathlib import Path
import cv2
import evo.main_ape as main_ape
import numpy as np
import torch
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.data_readers.tartan import test_split as val_split
from dpvo.dpvo import DPVO
from dpvo.plot_utils import plot_trajectory
from dpvo.utils import Timer
test_split = \
["MH%03d"%i for i in range(8)] + \
["ME%03d"%i for i in range(8)]
STRIDE = 1
fx, fy, cx, cy = [320, 320, 320, 240]
def show_image(image, t=0):
image = image.permute(1, 2, 0).cpu().numpy()
cv2.imshow('image', image / 255.0)
cv2.waitKey(t)
def video_iterator(imagedir, ext=".png", preload=True):
imfiles = glob.glob(osp.join(imagedir, "*{}".format(ext)))
data_list = []
for imfile in sorted(imfiles)[::STRIDE]:
image = torch.from_numpy(cv2.imread(imfile)).permute(2,0,1)
intrinsics = torch.as_tensor([fx, fy, cx, cy])
data_list.append((image, intrinsics))
for (image, intrinsics) in data_list:
yield image.cuda(), intrinsics.cuda()
@torch.no_grad()
def run(imagedir, cfg, network, viz=False, show_img=False):
slam = DPVO(cfg, network, ht=480, wd=640, viz=viz)
for t, (image, intrinsics) in enumerate(video_iterator(imagedir)):
if show_img:
show_image(image, 1)
with Timer("SLAM", enabled=False):
slam(t, image, intrinsics)
return slam.terminate()
def ate(traj_ref, traj_est):
assert isinstance(traj_ref, PoseTrajectory3D)
assert isinstance(traj_est, PoseTrajectory3D)
result = main_ape.ape(traj_ref, traj_est, est_name='traj',
pose_relation=PoseRelation.translation_part, align=True, correct_scale=True)
return result.stats["rmse"]
@torch.no_grad()
def evaluate(config, net, split="validation", trials=1, plot=False, save=False):
if config is None:
config = cfg
config.merge_from_file("config/default.yaml")
if not os.path.isdir("TartanAirResults"):
os.mkdir("TartanAirResults")
scenes = test_split if split=="test" else val_split
results = {}
all_results = []
for i, scene in enumerate(scenes):
results[scene] = []
for j in range(trials):
# estimated trajectory
if split == 'test':
scene_path = os.path.join("datasets/mono", scene)
traj_ref = osp.join("datasets/mono", "mono_gt", scene + ".txt")
elif split == 'validation':
scene_path = os.path.join("datasets/TartanAir", scene, "image_left")
traj_ref = osp.join("datasets/TartanAir", scene, "pose_left.txt")
# run the slam system
traj_est, tstamps = run(scene_path, config, net, viz=False, show_img=False)
PERM = [1, 2, 0, 4, 5, 3, 6] # ned -> xyz
traj_ref = np.loadtxt(traj_ref, delimiter=" ")[::STRIDE, PERM]
traj_est = PoseTrajectory3D(
positions_xyz=traj_est[:,:3],
orientations_quat_wxyz=traj_est[:, [6, 3, 4, 5]],
timestamps=tstamps)
traj_ref = PoseTrajectory3D(
positions_xyz=traj_ref[:,:3],
orientations_quat_wxyz=traj_ref[:,3:],
timestamps=tstamps)
# do evaluation
ate_score = ate(traj_ref, traj_est)
all_results.append(ate_score)
results[scene].append(ate_score)
if plot:
scene_name = '_'.join(scene.split('/')[1:]).title() if split == 'validation' else scene
Path("trajectory_plots").mkdir(exist_ok=True)
plot_trajectory(traj_est, traj_ref, f"TartanAir {scene_name.replace('_', ' ')} Trial #{j+1} (ATE: {ate_score:.03f})",
f"trajectory_plots/TartanAir_{scene_name}_Trial{j+1:02d}.pdf", align=True, correct_scale=True)
if save:
Path("saved_trajectories").mkdir(exist_ok=True)
file_interface.write_tum_trajectory_file(f"saved_trajectories/TartanAir_{scene_name}_Trial{j+1:02d}.txt", traj_est)
print(scene, sorted(results[scene]))
results_dict = dict([("Tartan/{}".format(k), np.median(v)) for (k, v) in results.items()])
# write output to file with timestamp
with open(osp.join("TartanAirResults", datetime.datetime.now().strftime('%m-%d-%I%p.txt')), "w") as f:
f.write(','.join([str(x) for x in all_results]))
xs = []
for scene in results:
x = np.median(results[scene])
xs.append(x)
ates = list(all_results)
results_dict["AUC"] = np.maximum(1 - np.array(ates), 0).mean()
results_dict["AVG"] = np.mean(xs)
return results_dict
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--viz', action="store_true")
parser.add_argument('--show_img', action="store_true")
parser.add_argument('--id', type=int, default=-1)
parser.add_argument('--weights', default="dpvo.pth")
parser.add_argument('--config', default="config/default.yaml")
parser.add_argument('--split', default="validation")
parser.add_argument('--trials', type=int, default=1)
parser.add_argument('--backend_thresh', type=float, default=18.0)
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("Running with config...")
print(cfg)
torch.manual_seed(1234)
if args.id >= 0:
scene_path = os.path.join("datasets/mono", test_split[args.id])
traj_est, tstamps = run(scene_path, cfg, args.weights, viz=args.viz, show_img=args.show_img)
traj_ref = osp.join("datasets/mono", "mono_gt", test_split[args.id] + ".txt")
traj_ref = np.loadtxt(traj_ref, delimiter=" ")[::STRIDE,[1, 2, 0, 4, 5, 3, 6]]
traj_est = PoseTrajectory3D(
positions_xyz=traj_est[:,:3],
orientations_quat_wxyz=traj_est[:, [6, 3, 4, 5]],
timestamps=tstamps)
traj_ref = PoseTrajectory3D(
positions_xyz=traj_ref[:,:3],
orientations_quat_wxyz=traj_ref[:,3:],
timestamps=tstamps)
# do evaluation
print(ate(traj_ref, traj_est))
else:
results = evaluate(cfg, args.weights, split=args.split, trials=args.trials, plot=args.plot, save=args.save_trajectory)
for k in results:
print(k, results[k])
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