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demo.py
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demo.py
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import cv2
import numpy as np
import glob
import os.path as osp
import os
import torch
from multiprocessing import Process, Queue
from dpvo.utils import Timer
from dpvo.dpvo import DPVO
from dpvo.config import cfg
from dpvo.stream import image_stream, video_stream
SKIP = 0
def show_image(image, t=0):
image = image.permute(1, 2, 0).cpu().numpy()
cv2.imshow('image', image / 255.0)
cv2.waitKey(t)
@torch.no_grad()
def run(cfg, network, imagedir, calib, stride=1, skip=0, viz=False, timeit=False):
slam = None
queue = Queue(maxsize=8)
if os.path.isdir(imagedir):
reader = Process(target=image_stream, args=(queue, imagedir, calib, stride, skip))
else:
reader = Process(target=video_stream, args=(queue, imagedir, calib, stride, skip))
reader.start()
while 1:
(t, image, intrinsics) = queue.get()
if t < 0: break
image = torch.from_numpy(image).permute(2,0,1).cuda()
intrinsics = torch.from_numpy(intrinsics).cuda()
if slam is None:
slam = DPVO(cfg, network, ht=image.shape[1], wd=image.shape[2], viz=viz)
image = image.cuda()
intrinsics = intrinsics.cuda()
with Timer("SLAM", enabled=timeit):
slam(t, image, intrinsics)
for _ in range(12):
slam.update()
reader.join()
print()
return slam.terminate()
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--network', type=str, default='dpvo.pth')
parser.add_argument('--imagedir', type=str)
parser.add_argument('--calib', type=str)
parser.add_argument('--stride', type=int, default=2)
parser.add_argument('--skip', type=int, default=0)
parser.add_argument('--config', default="config/default.yaml")
parser.add_argument('--timeit', action='store_true')
parser.add_argument('--viz', action="store_true")
args = parser.parse_args()
cfg.merge_from_file(args.config)
print("Running with config...")
print(cfg)
run(cfg, args.network, args.imagedir, args.calib, args.stride, args.skip, args.viz, args.timeit)