def _button_raw_orient_fired(self):
"""
update the external calibration with results of raw orientation, i.e.
the iterative process that adjust the initial guess' external
parameters (position and angle of cameras) without internal or
distortions.
See: https://github.com/openptv/openptv/liboptv/src/orientation.c#L591
"""
if self.need_reset:
self.reset_show_images()
self.need_reset = 0
# backup the ORI/ADDPAR files first
self.backup_ori_files()
# get manual points from cal_points and use ids from man_ori.par
for i_cam in range(self.n_cams):
selected_points = np.zeros((4, 3))
for i, cp_id in enumerate(self.cal_points['id']):
for j in range(4):
if cp_id == self.camera[i_cam].man_ori[j]:
selected_points[j, :] = self.cal_points['pos'][i, :]
continue
# in pixels:
manual_detection_points = np.array(
(self.camera[i_cam]._x, self.camera[i_cam]._y)).T
success = external_calibration(self.cals[i_cam], selected_points, \
manual_detection_points, self.cpar)
if success is False:
print("Initial guess has not been successful\n")
else:
self.camera[i_cam]._plot.overlays = []
self._project_cal_points(i_cam, color="red")
self._write_ori(i_cam)
self.status_text = "Orientation finished"
self.pass_raw_orient = True
def test_external_calibration(self):
"""External calibration using clicked points."""
ref_pts = np.array([[-40., -25., 8.], [40., -15., 0.], [40., 15., 0.],
[40., 0., 8.]])
# Fake the image points by back-projection
targets = convert_arr_metric_to_pixel(
image_coordinates(ref_pts, self.cal,
self.control.get_multimedia_params()),
self.control)
# Jigg the fake detections to give raw_orient some challenge.
targets[:, 1] -= 0.1
self.assertTrue(
external_calibration(self.cal, ref_pts, targets, self.control))
np.testing.assert_array_almost_equal(self.cal.get_angles(),
self.orig_cal.get_angles(),
decimal=4)
np.testing.assert_array_almost_equal(self.cal.get_pos(),
self.orig_cal.get_pos(),
decimal=3)
def test_external_calibration(self):
"""External calibration using clicked points."""
ref_pts = np.array([
[-40., -25., 8.],
[ 40., -15., 0.],
[ 40., 15., 0.],
[ 40., 0., 8.]])
# Fake the image points by back-projection
targets = convert_arr_metric_to_pixel(image_coordinates(
ref_pts, self.cal, self.control.get_multimedia_params()),
self.control)
# Jigg the fake detections to give raw_orient some challenge.
targets[:,1] -= 0.1
self.assertTrue(external_calibration(
self.cal, ref_pts, targets, self.control))
np.testing.assert_array_almost_equal(
self.cal.get_angles(), self.orig_cal.get_angles(),
decimal=4)
np.testing.assert_array_almost_equal(
self.cal.get_pos(), self.orig_cal.get_pos(),
decimal=3)
