#feature_tracker = feature_tracker_factory(min_num_features=num_features, detector_type = FeatureDetectorTypes.AKAZE, descriptor_type = FeatureDescriptorTypes.AKAZE, tracker_type = tracker_type)
#feature_tracker = feature_tracker_factory(min_num_features=num_features, detector_type = FeatureDetectorTypes.SIFT, descriptor_type = FeatureDescriptorTypes.SIFT, tracker_type = tracker_type)
#feature_tracker = feature_tracker_factory(min_num_features=num_features, detector_type = FeatureDetectorTypes.SURF, descriptor_type = FeatureDescriptorTypes.SURF, tracker_type = tracker_type)
# create visual odometry object
vo = VisualOdometry(cam, grountruth, feature_tracker)
is_draw_traj_img = True
traj_img_size = 800
traj_img = np.zeros((traj_img_size, traj_img_size, 3), dtype=np.uint8)
half_traj_img_size = int(0.5 * traj_img_size)
draw_scale = 1
is_draw_3d = True
if use_pangolin:
display = Viewer3D()
else:
plt3d = Mplot3d(title='3D trajectory')
is_draw_err = True
err_plt = Mplot2d(xlabel='img id', ylabel='m', title='error')
is_draw_matched_points = True
matched_points_plt = Mplot2d(xlabel='img id',
ylabel='# matches',
title='# matches')
img_id = 0
while dataset.isOk():
img = dataset.getImage(img_id)
tracker_config['num_features'] = num_features
feature_tracker = feature_tracker_factory(**tracker_config)
# create visual odometry object
vo = VisualOdometry(cam, groundtruth, feature_tracker)
is_draw_traj_img = True
traj_img_size = 800
traj_img = np.zeros((traj_img_size, traj_img_size, 3), dtype=np.uint8)
half_traj_img_size = int(0.5*traj_img_size)
draw_scale = 1
is_draw_3d = True
if kUsePangolin:
viewer3D = Viewer3D()
else:
plt3d = Mplot3d(title='3D trajectory')
is_draw_err = True
err_plt = Mplot2d(xlabel='img id', ylabel='m',title='error')
is_draw_matched_points = True
matched_points_plt = Mplot2d(xlabel='img id', ylabel='# matches',title='# matches')
img_id = 0
while dataset.isOk():
img = dataset.getImage(img_id)
if img is not None:
import numpy as np
import os, cv2, json, math, time
import OpenGL.GL as gl
from scipy.cluster.hierarchy import ward, fcluster
from scipy.spatial.distance import pdist
# this file present date from json file and csv file o nterh map 3D
# and calculate the position of opints using DBSCAN and present in map
import viewer3D
from viewer3D import Viewer3D
viewer = Viewer3D()
def convert_to_array(s):
tab = []
t = s.split(' ')
for a in t:
if a != "":
tab.append(a)
return tab
def get_data_from_file(file_number):
json_file, csv_file = False, False
# check if json file exist
# if os.path.exists('data/object_in_'+str(file_number)+'.json'):
# # read the object and position in image form file
# json_file = True