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demo_vio_whu.py
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import sys
sys.path.append('dbaf')
sys.path.append('dbaf/geoFunc')
from tqdm import tqdm
import numpy as np
import torch
import cv2
import os
import argparse
from dbaf import DBAFusion
import h5py
import pickle
import re
import math
import gtsam
import geoFunc.trans as trans
def show_image(image):
image = image.permute(1, 2, 0).cpu().numpy()
cv2.imshow('image', image / 255.0)
cv2.waitKey(1)
def image_stream(imagedir, imagestamp, enable_h5, h5path, calib, stride):
""" image generator """
calib = np.loadtxt(calib, delimiter=" ")
fx, fy, cx, cy = calib[:4]
K = np.eye(3)
K[0,0] = fx
K[0,2] = cx
K[1,1] = fy
K[1,2] = cy
if not enable_h5:
image_stamps = np.loadtxt(imagestamp,str,delimiter=',')
image_dict = dict(zip(image_stamps[:,1],image_stamps[:,0]))
image_list = list(image_dict)
ccount = 0
for t, imfile in enumerate(image_list):
tt = float(image_dict[imfile])
if int(tt*10)%2 == 1: continue
ccount += 1
image = cv2.imread(os.path.join(imagedir, imfile))
if len(calib) > 4:
image = cv2.undistort(image, K, calib[4:])
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)
intrinsics = torch.as_tensor([fx, fy, cx, cy])
intrinsics[0::2] *= (w1 / w0)
intrinsics[1::2] *= (h1 / h0)
yield tt, image[None], intrinsics
else:
raise Exception()
if __name__ == '__main__':
print(torch.cuda.device_count())
print(torch.cuda.is_available())
print(torch.cuda.current_device())
parser = argparse.ArgumentParser()
parser.add_argument("--imagedir", type=str, help="path to image directory")
parser.add_argument("--imagestamp", type=str, help="")
parser.add_argument("--imupath", type=str, help="")
parser.add_argument("--gtpath", type=str, help="")
parser.add_argument("--enable_h5", action="store_true", help="")
parser.add_argument("--h5path", type=str, help="")
parser.add_argument("--resultpath", type=str, help="")
parser.add_argument("--calib", type=str, help="path to calibration file")
parser.add_argument("--t0", default=0, type=int, help="starting frame")
parser.add_argument("--stride", default=3, type=int, help="frame stride")
parser.add_argument("--weights", default="droid.pth")
parser.add_argument("--buffer", type=int, default=80)
parser.add_argument("--image_size", default=[240, 320])
parser.add_argument("--max_factors", type=int, default=48, help="maximum active edges (which determines the GPU memory usage)")
parser.add_argument("--beta", type=float, default=0.3, help="weight for translation / rotation components of flow")
parser.add_argument("--filter_thresh", type=float, default=0.00, help="how much motion before considering new keyframe")
parser.add_argument("--warmup", type=int, default=8, help="number of warmup frames")
parser.add_argument("--vi_warmup", type=int, default=15, help="")
parser.add_argument("--keyframe_thresh", type=float, default=3.0, help="threshold to create a new keyframe")
parser.add_argument("--frontend_thresh", type=float, default=16.0, help="add edges between frames whithin this distance")
parser.add_argument("--frontend_window", type=int, default=25, help="frontend optimization window")
parser.add_argument("--active_window", type=int, default=8)
parser.add_argument("--inac_range", type=int, default=3)
parser.add_argument("--frontend_radius", type=int, default=2, help="force edges between frames within radius")
parser.add_argument("--frontend_nms", type=int, default=1, help="non-maximal supression of edges")
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)
parser.add_argument("--upsample", action="store_true")
parser.add_argument("--visual_only", type=int,default=0, help="wheter to disbale the IMU")
parser.add_argument("--far_threshold", type=float, default=0.02, help="far pixels would be downweighted (unit: m^-1)")
parser.add_argument("--translation_threshold", type=float, default=0.2, help="avoid the insertion of too close keyframes (unit: m)")
parser.add_argument("--mask_threshold", type=float, default=-1, help="downweight too close edges (unit: m)")
parser.add_argument("--skip_edge", type = str, default ="[]", help="whether to add 'skip' edges in the graph (for example, [-4,-5,-6] relative to the oldest active frame)")
parser.add_argument("--save_pkl", action="store_true")
parser.add_argument("--pklpath", default="result.pkl", help="path to saved reconstruction")
parser.add_argument("--show_plot", action="store_true", help="plot the image/trajectory during running")
parser.add_argument("--use_gnss", action="store_true")
parser.add_argument("--gnsspath", type=str, help="")
parser.add_argument("--use_odo", action="store_true")
parser.add_argument("--odopath", type=str, help="")
parser.add_argument("--use_zupt", action="store_true")
args = parser.parse_args()
args.skip_edge = eval(args.skip_edge)
args.stereo = False
dbaf = None
all_gt ={}
Ti0i1 =np.array([[ 9.99902524e-01, 1.39619889e-02, -7.31054713e-05, 1.00000000e-02],
[-1.39621803e-02, 9.99888818e-01, -5.23545345e-03, -2.05000000e-01],
[ 0.00000000e+00, 5.23596383e-03, 9.99986292e-01, -5.00000000e-02],
[ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.00000000e+00]])
Ten0 = None
is_ref_set = False
fp = open(args.gtpath,'rt')
while True:
line = fp.readline().strip()
if line == '':break
if line[0] == '#' :continue
line = re.sub('\s\s+',' ',line)
elem = line.split(' ')
sod = float(elem[1])
if sod not in all_gt.keys():
all_gt[sod] ={}
all_gt[sod]['X0'] = float(elem[2])
all_gt[sod]['Y0'] = float(elem[3])
all_gt[sod]['Z0'] = float(elem[4])
all_gt[sod]['VX0'] = float(elem[15])
all_gt[sod]['VY0'] = float(elem[16])
all_gt[sod]['VZ0'] = float(elem[17])
all_gt[sod]['ATTX0']= float(elem[25])
all_gt[sod]['ATTY0']= float(elem[26])
all_gt[sod]['ATTZ0']= -float(elem[24])
Ren = trans.Cen([all_gt[sod]['X0'],all_gt[sod]['Y0'],all_gt[sod]['Z0']])
ani0 = [all_gt[sod]['ATTX0']/180*math.pi,\
all_gt[sod]['ATTY0']/180*math.pi,\
all_gt[sod]['ATTZ0']/180*math.pi]
Rni0 = trans.att2m(ani0)
Rei0 = np.matmul(Ren,Rni0)
tei0 = np.array([all_gt[sod]['X0'],all_gt[sod]['Y0'],all_gt[sod]['Z0']])
Tei0 = np.eye(4,4)
Tei0[0:3,0:3] = Rei0
Tei0[0:3,3] = tei0
if not is_ref_set:
is_ref_set = True
Ten0 = np.eye(4,4)
Ten0[0:3,0:3] = trans.Cen(tei0)
Ten0[0:3,3] = tei0
Tn0i0 = np.matmul(np.linalg.inv(Ten0),Tei0)
Tn0i1 = np.matmul(Tn0i0,Ti0i1)
all_gt[sod]['T'] = Tn0i1
all_gt_keys =sorted(all_gt.keys())
fp.close()
all_imu = np.loadtxt(args.imupath,delimiter=' ')
if args.use_gnss and os.path.isfile(args.gnsspath):
fix_map = {b'Fixed':1.0,b'Float':0.0}
all_gnss = np.genfromtxt(args.gnsspath,converters={16: lambda x: fix_map[x]})
else:
all_gnss = []
if args.use_odo and os.path.isfile(args.odopath):
all_odo = np.genfromtxt(args.odopath)
all_odo = all_odo[np.fabs(all_odo[:,0] - np.round(all_odo[:,0]))<0.001]
np.random.seed(12345)
all_odo[:,1:] += np.random.randn(all_odo.shape[0],3)*0.05
else:
all_odo = []
tstamps = []
# try:
for (t, image, intrinsics) in tqdm(image_stream(args.imagedir, args.imagestamp, args.enable_h5,\
args.h5path, args.calib, args.stride)):
if args.show_plot:
show_image(image[0])
if dbaf is None:
args.image_size = [image.shape[2], image.shape[3]]
dbaf = DBAFusion(args)
dbaf.frontend.all_imu = all_imu
dbaf.frontend.all_stamp = np.loadtxt(args.imagestamp,str,delimiter=',')
dbaf.frontend.all_gnss = all_gnss
dbaf.frontend.all_odo = all_odo
if len(all_gt) > 0:
dbaf.frontend.all_gt = all_gt
dbaf.frontend.all_gt_keys = all_gt_keys
dbaf.video.Ti1c = np.array(
[0.99988370,-0.00563944,-0.01418468,-0.15590000,
0.01424932,0.01159187,0.99983149,0.63466000,
-0.00547407,-0.99991712,0.01167088,0.04605000,
0.00000000,0.00000000,0.00000000,1.00000000]).reshape([4,4])
dbaf.video.tbg = np.array([-0.0125, -0.26, 0.2091])
dbaf.video.Tbc = gtsam.Pose3(dbaf.video.Ti1c)
dbaf.video.state.set_imu_params([ 0.0003924 * 25,0.000205689024915 * 25, 0.004905 * 10, 0.000001454441043 * 25])
if args.use_gnss:
dbaf.video.init_pose_sigma = np.array([1.0, 1.0, 10.0,10.0,10.0,10.0])
else:
dbaf.video.init_pose_sigma = np.array([[0.1, 0.1, 0.0001, 0.0001,0.0001,0.0001],
[1.0, 1.0, 0.0001, 10.0, 10.0, 10.0]])
dbaf.video.init_bias_sigma = np.array([1.0,1.0,1.0, 0.1, 0.1, 0.1])
dbaf.frontend.translation_threshold = args.translation_threshold
dbaf.frontend.graph.mask_threshold = args.mask_threshold
dbaf.track(t, image, intrinsics=intrinsics)
dbaf.save_vis_easy()
dbaf.terminate()