def test_match_detection_to_ref(self):
"""Match detection to reference (sortgrid)"""
xyz_input = np.array([(10, 10, 10),
(200, 200, 200),
(600, 800, 100),
(20, 10, 2000),
(30, 30, 30)], dtype=float)
coords_count = len(xyz_input)
xy_img_pts_metric = image_coordinates(
xyz_input, self.calibration, self.control.get_multimedia_params())
xy_img_pts_pixel = convert_arr_metric_to_pixel(
xy_img_pts_metric, control=self.control)
# convert to TargetArray object
target_array = TargetArray(coords_count)
for i in range(coords_count):
target_array[i].set_pnr(i)
target_array[i].set_pos(
(xy_img_pts_pixel[i][0], xy_img_pts_pixel[i][1]))
# create randomized target array
indices = range(coords_count)
shuffled_indices = range(coords_count)
while indices == shuffled_indices:
random.shuffle(shuffled_indices)
rand_targ_array = TargetArray(coords_count)
for i in range(coords_count):
rand_targ_array[shuffled_indices[i]].set_pos(target_array[i].pos())
rand_targ_array[shuffled_indices[i]].set_pnr(target_array[i].pnr())
# match detection to reference
matched_target_array = match_detection_to_ref(cal=self.calibration,
ref_pts=xyz_input,
img_pts=rand_targ_array,
cparam=self.control)
# assert target array is as before
for i in range(coords_count):
if matched_target_array[i].pos() != target_array[i].pos() \
or matched_target_array[i].pnr() != target_array[i].pnr():
self.fail()
# pass ref_pts and img_pts with non-equal lengths
with self.assertRaises(ValueError):
match_detection_to_ref(cal=self.calibration,
ref_pts=xyz_input,
img_pts=TargetArray(coords_count - 1),
cparam=self.control)
def test_match_detection_to_ref(self):
"""Match detection to reference (sortgrid)"""
xyz_input = np.array([(10, 10, 10), (200, 200, 200), (600, 800, 100),
(20, 10, 2000), (30, 30, 30)],
dtype=float)
coords_count = len(xyz_input)
xy_img_pts_metric = image_coordinates(
xyz_input, self.calibration, self.control.get_multimedia_params())
xy_img_pts_pixel = convert_arr_metric_to_pixel(xy_img_pts_metric,
control=self.control)
# convert to TargetArray object
target_array = TargetArray(coords_count)
for i in range(coords_count):
target_array[i].set_pnr(i)
target_array[i].set_pos(
(xy_img_pts_pixel[i][0], xy_img_pts_pixel[i][1]))
# create randomized target array
indices = range(coords_count)
shuffled_indices = range(coords_count)
while indices == shuffled_indices:
random.shuffle(shuffled_indices)
rand_targ_array = TargetArray(coords_count)
for i in range(coords_count):
rand_targ_array[shuffled_indices[i]].set_pos(target_array[i].pos())
rand_targ_array[shuffled_indices[i]].set_pnr(target_array[i].pnr())
# match detection to reference
matched_target_array = match_detection_to_ref(cal=self.calibration,
ref_pts=xyz_input,
img_pts=rand_targ_array,
cparam=self.control)
# assert target array is as before
for i in range(coords_count):
if matched_target_array[i].pos() != target_array[i].pos() \
or matched_target_array[i].pnr() != target_array[i].pnr():
self.fail()
# pass ref_pts and img_pts with non-equal lengths
with self.assertRaises(ValueError):
match_detection_to_ref(cal=self.calibration,
ref_pts=xyz_input,
img_pts=TargetArray(coords_count - 1),
cparam=self.control)
def _button_sort_grid_fired(self):
"""
Uses sortgrid function of liboptv to match between the
calibration points in the fixp target file and the targets
detected in the images
"""
if self.need_reset:
self.reset_show_images()
self.need_reset = 0
self.sorted_targs = []
for i_cam in range(self.n_cams):
# if len(self.cal_points) > len(self.detections[i_cam]):
# raise ValueError("Insufficient detected points, need at least as"
# "many as fixed points")
targs = match_detection_to_ref(self.cals[i_cam],
self.cal_points['pos'],
self.detections[i_cam], self.cpar)
x, y, pnr = [], [], []
for t in targs:
if t.pnr() != -999:
pnr.append(t.pnr())
x.append(t.pos()[0])
y.append(t.pos()[1])
self.sorted_targs.append(targs)
self.camera[i_cam]._plot.overlays = []
self.camera[i_cam].plot_num_overlay(x, y, pnr)
self.status_text = "Sort grid finished."
self.pass_sortgrid = True