Spaces:
Running
Running
File size: 4,586 Bytes
b7eedf7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 |
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
import torch.nn.functional as F
from droid import Droid
import matplotlib.pyplot as plt
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, use_depth=False, stride=1):
""" image generator """
fx, fy, cx, cy = np.loadtxt(os.path.join(datapath, 'calibration.txt')).tolist()
image_list = sorted(glob.glob(os.path.join(datapath, 'rgb', '*.png')))[::stride]
depth_list = sorted(glob.glob(os.path.join(datapath, 'depth', '*.png')))[::stride]
for t, (image_file, depth_file) in enumerate(zip(image_list, depth_list)):
image = cv2.imread(image_file)
depth = cv2.imread(depth_file, cv2.IMREAD_ANYDEPTH) / 5000.0
h0, w0, _ = image.shape
h1 = int(h0 * np.sqrt((384 * 512) / (h0 * w0)))
w1 = int(w0 * np.sqrt((384 * 512) / (h0 * w0)))
image = cv2.resize(image, (w1, h1))
image = image[:h1-h1%8, :w1-w1%8]
image = torch.as_tensor(image).permute(2, 0, 1)
depth = torch.as_tensor(depth)
depth = F.interpolate(depth[None,None], (h1, w1)).squeeze()
depth = depth[:h1-h1%8, :w1-w1%8]
intrinsics = torch.as_tensor([fx, fy, cx, cy])
intrinsics[0::2] *= (w1 / w0)
intrinsics[1::2] *= (h1 / h0)
if use_depth:
yield t, image[None], depth, intrinsics
else:
yield t, image[None], intrinsics
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("--datapath")
parser.add_argument("--weights", default="droid.pth")
parser.add_argument("--buffer", type=int, default=1024)
parser.add_argument("--image_size", default=[240, 320])
parser.add_argument("--disable_vis", action="store_true")
parser.add_argument("--beta", type=float, default=0.5)
parser.add_argument("--filter_thresh", type=float, default=2.0)
parser.add_argument("--warmup", type=int, default=8)
parser.add_argument("--keyframe_thresh", type=float, default=3.5)
parser.add_argument("--frontend_thresh", type=float, default=16.0)
parser.add_argument("--frontend_window", type=int, default=16)
parser.add_argument("--frontend_radius", type=int, default=1)
parser.add_argument("--frontend_nms", type=int, default=0)
parser.add_argument("--stereo", action="store_true")
parser.add_argument("--depth", action="store_true")
parser.add_argument("--backend_thresh", type=float, default=22.0)
parser.add_argument("--backend_radius", type=int, default=2)
parser.add_argument("--backend_nms", type=int, default=3)
args = parser.parse_args()
torch.multiprocessing.set_start_method('spawn')
print("Running evaluation on {}".format(args.datapath))
print(args)
# this can usually be set to 2-3 except for "camera_shake" scenes
# set to 2 for test scenes
stride = 1
tstamps = []
for (t, image, depth, intrinsics) in tqdm(image_stream(args.datapath, use_depth=True, stride=stride)):
if not args.disable_vis:
show_image(image[0])
if t == 0:
args.image_size = [image.shape[2], image.shape[3]]
droid = Droid(args)
droid.track(t, image, depth, intrinsics=intrinsics)
traj_est = droid.terminate(image_stream(args.datapath, use_depth=False, stride=stride))
### run evaluation ###
print("#"*20 + " Results...")
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
image_path = os.path.join(args.datapath, 'rgb')
images_list = sorted(glob.glob(os.path.join(image_path, '*.png')))[::stride]
tstamps = [float(x.split('/')[-1][:-4]) for x in images_list]
traj_est = PoseTrajectory3D(
positions_xyz=traj_est[:,:3],
orientations_quat_wxyz=traj_est[:,3:],
timestamps=np.array(tstamps))
gt_file = os.path.join(args.datapath, 'groundtruth.txt')
traj_ref = file_interface.read_tum_trajectory_file(gt_file)
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=False)
print(result.stats)
|