124 lines
3.9 KiB
Python
124 lines
3.9 KiB
Python
import sys
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sys.path.append('droid_slam')
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from tqdm import tqdm
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import numpy as np
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import torch
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import lietorch
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import cv2
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import os
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import glob
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import time
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import argparse
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import torch.nn.functional as F
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from droid import Droid
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def show_image(image):
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image = image.permute(1, 2, 0).cpu().numpy()
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cv2.imshow('image', image / 255.0)
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cv2.waitKey(1)
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def image_stream(datapath, image_size=[320, 512]):
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""" image generator """
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fx, fy, cx, cy = 517.3, 516.5, 318.6, 255.3
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K_l = np.array([fx, 0.0, cx, 0.0, fy, cy, 0.0, 0.0, 1.0]).reshape(3,3)
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d_l = np.array([0.2624, -0.9531, -0.0054, 0.0026, 1.1633])
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# read all png images in folder
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images_list = sorted(glob.glob(os.path.join(datapath, 'rgb', '*.png')))[::2]
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for t, imfile in enumerate(images_list):
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image = cv2.imread(imfile)
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ht0, wd0, _ = image.shape
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image = cv2.undistort(image, K_l, d_l)
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image = cv2.resize(image, (320+32, 240+16))
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image = torch.from_numpy(image).permute(2,0,1)
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intrinsics = torch.as_tensor([fx, fy, cx, cy]).cuda()
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intrinsics[0] *= image.shape[2] / 640.0
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intrinsics[1] *= image.shape[1] / 480.0
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intrinsics[2] *= image.shape[2] / 640.0
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intrinsics[3] *= image.shape[1] / 480.0
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# crop image to remove distortion boundary
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intrinsics[2] -= 16
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intrinsics[3] -= 8
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image = image[:, 8:-8, 16:-16]
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yield t, image[None], intrinsics
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument("--datapath")
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parser.add_argument("--weights", default="droid.pth")
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parser.add_argument("--buffer", type=int, default=512)
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parser.add_argument("--image_size", default=[240, 320])
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parser.add_argument("--disable_vis", action="store_true")
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parser.add_argument("--beta", type=float, default=0.6)
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parser.add_argument("--filter_thresh", type=float, default=1.75)
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parser.add_argument("--warmup", type=int, default=12)
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parser.add_argument("--keyframe_thresh", type=float, default=2.25)
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parser.add_argument("--frontend_thresh", type=float, default=12.0)
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parser.add_argument("--frontend_window", type=int, default=25)
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parser.add_argument("--frontend_radius", type=int, default=2)
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parser.add_argument("--frontend_nms", type=int, default=1)
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parser.add_argument("--backend_thresh", type=float, default=15.0)
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parser.add_argument("--backend_radius", type=int, default=2)
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parser.add_argument("--backend_nms", type=int, default=3)
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args = parser.parse_args()
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args.stereo = False
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torch.multiprocessing.set_start_method('spawn')
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print("Running evaluation on {}".format(args.datapath))
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print(args)
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droid = Droid(args)
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time.sleep(5)
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tstamps = []
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for (t, image, intrinsics) in tqdm(image_stream(args.datapath)):
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if not args.disable_vis:
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show_image(image)
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droid.track(t, image, intrinsics=intrinsics)
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traj_est = droid.terminate(image_stream(args.datapath))
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### run evaluation ###
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print("#"*20 + " Results...")
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import evo
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from evo.core.trajectory import PoseTrajectory3D
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from evo.tools import file_interface
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from evo.core import sync
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import evo.main_ape as main_ape
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from evo.core.metrics import PoseRelation
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image_path = os.path.join(args.datapath, 'rgb')
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images_list = sorted(glob.glob(os.path.join(image_path, '*.png')))[::2]
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tstamps = [float(x.split('/')[-1][:-4]) for x in images_list]
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traj_est = PoseTrajectory3D(
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positions_xyz=traj_est[:,:3],
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orientations_quat_wxyz=traj_est[:,3:],
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timestamps=np.array(tstamps))
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gt_file = os.path.join(args.datapath, 'groundtruth.txt')
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traj_ref = file_interface.read_tum_trajectory_file(gt_file)
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traj_ref, traj_est = sync.associate_trajectories(traj_ref, traj_est)
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result = main_ape.ape(traj_ref, traj_est, est_name='traj',
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pose_relation=PoseRelation.translation_part, align=True, correct_scale=True)
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print(result)
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