def run_auv_iteration(self, measures, born_components, nav_status, swath_w, swath_l):
# pr_born = self.predict_birth(born_components)
sss_path = utils.evaluate_sss_path(nav_status, swath_w, swath_l)
predicted = self.predict_existing(nav_status, sss_path)
predicted.extend(born_components)
self.gm = predicted
self.auv_update(measures, nav_status, sss_path)
self.prune()
def run_auv_iteration(self, measures, born_components, nav_status, swath_w, swath_l):
# pr_born = self.predict_birth(born_components)
sss_path = utils.evaluate_sss_path(nav_status, swath_w, swath_l)
predicted = self.predict_existing(nav_status, sss_path)
predicted.extend(born_components)
self.gm = predicted
self.auv_update(measures, nav_status, sss_path)
self.prune()
#!/usr/bin/python
import itertools
import utils
import numpy as np
import gmphd
if __name__ == "__main__":
width = 50.0
length = 5.0
nav_status = np.array([10.0, 5.0, 1.57])
print(utils.evaluate_sss_path(nav_status, width, length))
# Test of the selection functions for features inside the fov of the sss
gmphd_components = [
gmphd.GmphdComponent(1, [10, 5], [1, 0, 0, 1]),
gmphd.GmphdComponent(1, [100, 1], [1, 0, 0, 1])
]
means = np.squeeze(np.asarray([comp._mean for comp in gmphd_components]))
print(means, means.shape)
sss_path = utils.evaluate_sss_path(nav_status, width, length)
gmm_mask = utils.inside_polygon(means, sss_path)
gmm_masked = list(itertools.compress(gmphd_components, gmm_mask))
print gmm_masked
#!/usr/bin/python
import itertools
import utils
import numpy as np
import gmphd
if __name__ == "__main__":
width = 50.0
length = 5.0
nav_status = np.array([10.0, 5.0, 1.57])
print(utils.evaluate_sss_path(nav_status, width, length))
# Test of the selection functions for features inside the fov of the sss
gmphd_components = [gmphd.GmphdComponent(1, [10,5],[1,0,0,1]),gmphd.GmphdComponent(1, [100,1],[1,0,0,1])]
means = np.squeeze(np.asarray([comp._mean for comp in gmphd_components]))
print(means,means.shape)
sss_path = utils.evaluate_sss_path(nav_status, width, length)
gmm_mask = utils.inside_polygon(means, sss_path)
gmm_masked = list(itertools.compress(gmphd_components, gmm_mask))
print gmm_masked