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test_eth3d.py
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test_eth3d.py
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import sys
sys.path.append('/home/peiweipan/Projects/DroidSlam/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, 'color', '*.jpg')))[::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) / 1000.0
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=3.0)
parser.add_argument("--warmup", type=int, default=20)
parser.add_argument("--keyframe_thresh", type=float, default=2.0)
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=5)
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()
args.upsample = False
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 = 5
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))
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)
"""