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import sys
sys.path.append('droid_slam')
from tqdm import tqdm
import numpy as np
import torch
import lietorch
import cv2
import os
import glob
import time
import argparse
from torch.multiprocessing import Process
from droid import Droid
import torch.nn.functional as F
def show_image(image):
image = image.permute(1, 2, 0).cpu().numpy()
cv2.imshow('image', image / 255.0)
cv2.waitKey(1)
def image_stream(datapath, image_size=[320, 512], stereo=False, stride=1):
""" image generator """
K_l = np.array([458.654, 0.0, 367.215, 0.0, 457.296, 248.375, 0.0, 0.0, 1.0]).reshape(3,3)
d_l = np.array([-0.28340811, 0.07395907, 0.00019359, 1.76187114e-05, 0.0])
R_l = np.array([
0.999966347530033, -0.001422739138722922, 0.008079580483432283,
0.001365741834644127, 0.9999741760894847, 0.007055629199258132,
-0.008089410156878961, -0.007044357138835809, 0.9999424675829176
]).reshape(3,3)
P_l = np.array([435.2046959714599, 0, 367.4517211914062, 0, 0, 435.2046959714599, 252.2008514404297, 0, 0, 0, 1, 0]).reshape(3,4)
map_l = cv2.initUndistortRectifyMap(K_l, d_l, R_l, P_l[:3,:3], (752, 480), cv2.CV_32F)
K_r = np.array([457.587, 0.0, 379.999, 0.0, 456.134, 255.238, 0.0, 0.0, 1]).reshape(3,3)
d_r = np.array([-0.28368365, 0.07451284, -0.00010473, -3.555907e-05, 0.0]).reshape(5)
R_r = np.array([
0.9999633526194376, -0.003625811871560086, 0.007755443660172947,
0.003680398547259526, 0.9999684752771629, -0.007035845251224894,
-0.007729688520722713, 0.007064130529506649, 0.999945173484644
]).reshape(3,3)
P_r = np.array([435.2046959714599, 0, 367.4517211914062, -47.90639384423901, 0, 435.2046959714599, 252.2008514404297, 0, 0, 0, 1, 0]).reshape(3,4)
map_r = cv2.initUndistortRectifyMap(K_r, d_r, R_r, P_r[:3,:3], (752, 480), cv2.CV_32F)
intrinsics_vec = [435.2046959714599, 435.2046959714599, 367.4517211914062, 252.2008514404297]
ht0, wd0 = [480, 752]
# read all png images in folder
images_left = sorted(glob.glob(os.path.join(datapath, 'mav0/cam0/data/*.png')))[::stride]
images_right = [x.replace('cam0', 'cam1') for x in images_left]
for t, (imgL, imgR) in enumerate(zip(images_left, images_right)):
if stereo and not os.path.isfile(imgR):
continue
tstamp = float(imgL.split('/')[-1][:-4])
images = [cv2.remap(cv2.imread(imgL), map_l[0], map_l[1], interpolation=cv2.INTER_LINEAR)]
if stereo:
images += [cv2.remap(cv2.imread(imgR), map_r[0], map_r[1], interpolation=cv2.INTER_LINEAR)]
images = torch.from_numpy(np.stack(images, 0))
images = images.permute(0, 3, 1, 2).to("cuda:0", dtype=torch.float32)
images = F.interpolate(images, image_size, mode="bilinear", align_corners=False)
intrinsics = torch.as_tensor(intrinsics_vec).cuda()
intrinsics[0] *= image_size[1] / wd0
intrinsics[1] *= image_size[0] / ht0
intrinsics[2] *= image_size[1] / wd0
intrinsics[3] *= image_size[0] / ht0
yield stride*t, images, intrinsics
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("--datapath", help="path to euroc sequence")
parser.add_argument("--gt", help="path to gt file")
parser.add_argument("--weights", default="droid.pth")
parser.add_argument("--buffer", type=int, default=512)
parser.add_argument("--image_size", default=[320,512])
parser.add_argument("--disable_vis", action="store_true")
parser.add_argument("--stereo", action="store_true")
parser.add_argument("--beta", type=float, default=0.3)
parser.add_argument("--filter_thresh", type=float, default=2.4)
parser.add_argument("--warmup", type=int, default=15)
parser.add_argument("--keyframe_thresh", type=float, default=3.5)
parser.add_argument("--frontend_thresh", type=float, default=17.5)
parser.add_argument("--frontend_window", type=int, default=20)
parser.add_argument("--frontend_radius", type=int, default=2)
parser.add_argument("--frontend_nms", type=int, default=1)
parser.add_argument("--backend_thresh", type=float, default=24.0)
parser.add_argument("--backend_radius", type=int, default=2)
parser.add_argument("--backend_nms", type=int, default=2)
args = parser.parse_args()
torch.multiprocessing.set_start_method('spawn')
print("Running evaluation on {}".format(args.datapath))
print(args)
droid = Droid(args)
time.sleep(5)
for (t, image, intrinsics) in tqdm(image_stream(args.datapath, stereo=args.stereo, stride=2)):
droid.track(t, image, intrinsics=intrinsics)
traj_est = droid.terminate(image_stream(args.datapath, stride=1))
### run evaluation ###
import evo
from evo.core.trajectory import PoseTrajectory3D
from evo.tools import file_interface
from evo.core import sync
import evo.main_ape as main_ape
from evo.core.metrics import PoseRelation
images_list = sorted(glob.glob(os.path.join(args.datapath, 'mav0/cam0/data/*.png')))
tstamps = [float(x.split('/')[-1][:-4]) for x in images_list]
traj_est = PoseTrajectory3D(
positions_xyz=1.10 * traj_est[:,:3],
orientations_quat_wxyz=traj_est[:,3:],
timestamps=np.array(tstamps))
traj_ref = file_interface.read_tum_trajectory_file(args.gt)
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)
print(result)
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