mostly fixed - last tests to run
This commit is contained in:
davidpagnon
parent
f356f16076
commit
a4f327b13f
@ -27,7 +27,7 @@ frame_range = [] # For example [10,300], or [] for all frames
|
||||
exclude_from_batch = [] # List of trials to be excluded from batch analysis, ['<participant_dir/trial_dir>', 'etc'].
|
||||
# e.g. ['S00_P00_Participant/S00_P00_T00_StaticTrial', 'S00_P00_Participant/S00_P00_T01_BalancingTrial']
|
||||
|
||||
# Only taken into account if pose_model is 'BODY_25_AUGMENTED' # Coming soon!
|
||||
## Only taken into account if pose_model is 'BODY_25_AUGMENTED' # Coming soon!
|
||||
participant_height = 1.7 # m
|
||||
participant_mass = 70 # kg
|
||||
|
||||
@ -123,11 +123,12 @@ single_person = true # false for multi-person analysis (not supported yet), true
|
||||
tracked_keypoint = 'Neck' # If the neck is not detected by the pose_model, check skeleton.py
|
||||
# and choose a stable point for tracking the person of interest (e.g., 'right_shoulder' with BLAZEPOSE)
|
||||
reproj_error_threshold_association = 20 # px
|
||||
likelihood_threshold_triangulation = 0.2
|
||||
|
||||
|
||||
[triangulation]
|
||||
reproj_error_threshold_triangulation = 15 # px
|
||||
likelihood_threshold = 0.3
|
||||
likelihood_threshold_triangulation = 0.3
|
||||
min_cameras_for_triangulation = 2
|
||||
interpolation = 'cubic' #linear, slinear, quadratic, cubic, or none
|
||||
# 'none' if you don't want to interpolate missing points
|
||||
|
||||
@ -27,7 +27,7 @@
|
||||
# exclude_from_batch = [] # List of trials to be excluded from batch analysis, ['<participant_dir/trial_dir>', 'etc'].
|
||||
# e.g. ['S00_P00_Participant/S00_P00_T00_StaticTrial', 'S00_P00_Participant/S00_P00_T01_BalancingTrial']
|
||||
|
||||
# Only taken into account if pose_model is 'BODY_25_AUGMENTED' # Coming soon!
|
||||
## Only taken into account if pose_model is 'BODY_25_AUGMENTED' # Coming soon!
|
||||
# participant_height = 1.7 # m
|
||||
# participant_mass = 70 # kg
|
||||
|
||||
@ -124,11 +124,12 @@
|
||||
# tracked_keypoint = 'Neck' # If the neck is not detected by the pose_model, check skeleton.py
|
||||
## and choose a stable point for tracking the person of interest (e.g., 'right_shoulder' with BLAZEPOSE)
|
||||
# reproj_error_threshold_association = 20 # px
|
||||
# likelihood_threshold_triangulation = 0.2
|
||||
|
||||
|
||||
# [triangulation]
|
||||
# reproj_error_threshold_triangulation = 15 # px
|
||||
# likelihood_threshold = 0.3
|
||||
# likelihood_threshold_triangulation = 0.3
|
||||
# min_cameras_for_triangulation = 2
|
||||
# interpolation = 'cubic' #linear, slinear, quadratic, cubic, or none
|
||||
## 'none' if you don't want to interpolate missing points
|
||||
|
||||
@ -27,7 +27,7 @@
|
||||
# exclude_from_batch = [] # List of trials to be excluded from batch analysis, ['<participant_dir/trial_dir>', 'etc'].
|
||||
# e.g. ['S00_P00_Participant/S00_P00_T00_StaticTrial', 'S00_P00_Participant/S00_P00_T01_BalancingTrial']
|
||||
|
||||
# Only taken into account if pose_model is 'BODY_25_AUGMENTED' # Coming soon!
|
||||
## Only taken into account if pose_model is 'BODY_25_AUGMENTED' # Coming soon!
|
||||
# participant_height = 1.7 # m
|
||||
# participant_mass = 70 # kg
|
||||
|
||||
@ -124,11 +124,12 @@
|
||||
# tracked_keypoint = 'Neck' # If the neck is not detected by the pose_model, check skeleton.py
|
||||
## and choose a stable point for tracking the person of interest (e.g., 'right_shoulder' with BLAZEPOSE)
|
||||
# reproj_error_threshold_association = 20 # px
|
||||
# likelihood_threshold_triangulation = 0.2
|
||||
|
||||
|
||||
# [triangulation]
|
||||
# reproj_error_threshold_triangulation = 15 # px
|
||||
# likelihood_threshold = 0.3
|
||||
# likelihood_threshold_triangulation = 0.3
|
||||
# min_cameras_for_triangulation = 2
|
||||
# interpolation = 'cubic' #linear, slinear, quadratic, cubic, or none
|
||||
## 'none' if you don't want to interpolate missing points
|
||||
|
||||
@ -27,7 +27,7 @@
|
||||
# exclude_from_batch = [] # List of trials to be excluded from batch analysis, ['<participant_dir/trial_dir>', 'etc'].
|
||||
# e.g. ['S00_P00_Participant/S00_P00_T00_StaticTrial', 'S00_P00_Participant/S00_P00_T01_BalancingTrial']
|
||||
|
||||
# Only taken into account if pose_model is 'BODY_25_AUGMENTED' # Coming soon!
|
||||
## Only taken into account if pose_model is 'BODY_25_AUGMENTED' # Coming soon!
|
||||
# participant_height = 1.7 # m
|
||||
# participant_mass = 70 # kg
|
||||
|
||||
@ -124,11 +124,12 @@
|
||||
# tracked_keypoint = 'Neck' # If the neck is not detected by the pose_model, check skeleton.py
|
||||
## and choose a stable point for tracking the person of interest (e.g., 'right_shoulder' with BLAZEPOSE)
|
||||
# reproj_error_threshold_association = 20 # px
|
||||
# likelihood_threshold_triangulation = 0.2
|
||||
|
||||
|
||||
# [triangulation]
|
||||
# reproj_error_threshold_triangulation = 15 # px
|
||||
# likelihood_threshold = 0.3
|
||||
# likelihood_threshold_triangulation = 0.3
|
||||
# min_cameras_for_triangulation = 2
|
||||
# interpolation = 'cubic' #linear, slinear, quadratic, cubic, or none
|
||||
## 'none' if you don't want to interpolate missing points
|
||||
|
||||
@ -27,7 +27,7 @@ frame_range = [] # For example [10,300], or [] for all frames
|
||||
exclude_from_batch = [] # List of trials to be excluded from batch analysis, ['<participant_dir/trial_dir>', 'etc'].
|
||||
# e.g. ['S00_P00_Participant/S00_P00_T00_StaticTrial', 'S00_P00_Participant/S00_P00_T01_BalancingTrial']
|
||||
|
||||
# Only taken into account if pose_model is 'BODY_25_AUGMENTED' # Coming soon!
|
||||
## Only taken into account if pose_model is 'BODY_25_AUGMENTED' # Coming soon!
|
||||
participant_height = 1.7 # m
|
||||
participant_mass = 70 # kg
|
||||
|
||||
@ -123,11 +123,12 @@ single_person = true # false for multi-person analysis (not supported yet), true
|
||||
tracked_keypoint = 'Neck' # If the neck is not detected by the pose_model, check skeleton.py
|
||||
# and choose a stable point for tracking the person of interest (e.g., 'right_shoulder' with BLAZEPOSE)
|
||||
reproj_error_threshold_association = 20 # px
|
||||
likelihood_threshold_triangulation = 0.2
|
||||
|
||||
|
||||
[triangulation]
|
||||
reproj_error_threshold_triangulation = 15 # px
|
||||
likelihood_threshold = 0.3
|
||||
likelihood_threshold_triangulation = 0.3
|
||||
min_cameras_for_triangulation = 2
|
||||
interpolation = 'cubic' #linear, slinear, quadratic, cubic, or none
|
||||
# 'none' if you don't want to interpolate missing points
|
||||
|
||||
@ -29,7 +29,7 @@
|
||||
|
||||
# participant_height = 1.7 # m
|
||||
# participant_mass = 70 # kg
|
||||
# Only taken into account if pose_model is 'BODY_25_AUGMENTED' # Coming soon!
|
||||
## Only taken into account if pose_model is 'BODY_25_AUGMENTED' # Coming soon!
|
||||
|
||||
|
||||
## Take heart, calibration is not that complicated once you get the hang of it!
|
||||
@ -124,11 +124,12 @@
|
||||
# tracked_keypoint = 'Neck' # If the neck is not detected by the pose_model, check skeleton.py
|
||||
## and choose a stable point for tracking the person of interest (e.g., 'right_shoulder' with BLAZEPOSE)
|
||||
# reproj_error_threshold_association = 20 # px
|
||||
# likelihood_threshold_triangulation = 0.2
|
||||
|
||||
|
||||
# [triangulation]
|
||||
# reproj_error_threshold_triangulation = 15 # px
|
||||
# likelihood_threshold = 0.3
|
||||
# likelihood_threshold_triangulation = 0.3
|
||||
# min_cameras_for_triangulation = 2
|
||||
# interpolation = 'cubic' #linear, slinear, quadratic, cubic, or none
|
||||
## 'none' if you don't want to interpolate missing points
|
||||
|
||||
@ -28,7 +28,7 @@
|
||||
|
||||
# participant_height = 1.7 # m
|
||||
# participant_mass = 70 # kg
|
||||
# Only taken into account if pose_model is 'BODY_25_AUGMENTED' # Coming soon!
|
||||
## Only taken into account if pose_model is 'BODY_25_AUGMENTED' # Coming soon!
|
||||
|
||||
|
||||
## Take heart, calibration is not that complicated once you get the hang of it!
|
||||
@ -123,11 +123,12 @@
|
||||
# tracked_keypoint = 'Neck' # If the neck is not detected by the pose_model, check skeleton.py
|
||||
## and choose a stable point for tracking the person of interest (e.g., 'right_shoulder' with BLAZEPOSE)
|
||||
# reproj_error_threshold_association = 20 # px
|
||||
# likelihood_threshold_triangulation = 0.2
|
||||
|
||||
|
||||
# [triangulation]
|
||||
# reproj_error_threshold_triangulation = 15 # px
|
||||
# likelihood_threshold = 0.3
|
||||
# likelihood_threshold_triangulation = 0.3
|
||||
# min_cameras_for_triangulation = 2
|
||||
# interpolation = 'cubic' #linear, slinear, quadratic, cubic, or none
|
||||
## 'none' if you don't want to interpolate missing points
|
||||
|
||||
@ -28,7 +28,7 @@
|
||||
|
||||
# participant_height = 1.7 # m
|
||||
# participant_mass = 70 # kg
|
||||
# Only taken into account if pose_model is 'BODY_25_AUGMENTED' # Coming soon!
|
||||
## Only taken into account if pose_model is 'BODY_25_AUGMENTED' # Coming soon!
|
||||
|
||||
|
||||
## Take heart, calibration is not that complicated once you get the hang of it!
|
||||
@ -123,11 +123,12 @@
|
||||
# tracked_keypoint = 'Neck' # If the neck is not detected by the pose_model, check skeleton.py
|
||||
## and choose a stable point for tracking the person of interest (e.g., 'right_shoulder' with BLAZEPOSE)
|
||||
# reproj_error_threshold_association = 20 # px
|
||||
# likelihood_threshold_triangulation = 0.2
|
||||
|
||||
|
||||
# [triangulation]
|
||||
# reproj_error_threshold_triangulation = 15 # px
|
||||
# likelihood_threshold = 0.3
|
||||
# likelihood_threshold_triangulation = 0.3
|
||||
# min_cameras_for_triangulation = 2
|
||||
# interpolation = 'cubic' #linear, slinear, quadratic, cubic, or none
|
||||
## 'none' if you don't want to interpolate missing points
|
||||
|
||||
@ -28,7 +28,7 @@
|
||||
|
||||
# participant_height = 1.7 # m
|
||||
# participant_mass = 70 # kg
|
||||
# Only taken into account if pose_model is 'BODY_25_AUGMENTED' # Coming soon!
|
||||
## Only taken into account if pose_model is 'BODY_25_AUGMENTED' # Coming soon!
|
||||
|
||||
|
||||
## Take heart, calibration is not that complicated once you get the hang of it!
|
||||
@ -123,11 +123,12 @@
|
||||
# tracked_keypoint = 'Neck' # If the neck is not detected by the pose_model, check skeleton.py
|
||||
## and choose a stable point for tracking the person of interest (e.g., 'right_shoulder' with BLAZEPOSE)
|
||||
# reproj_error_threshold_association = 20 # px
|
||||
# likelihood_threshold_triangulation = 0.2
|
||||
|
||||
|
||||
# [triangulation]
|
||||
# reproj_error_threshold_triangulation = 15 # px
|
||||
# likelihood_threshold = 0.3
|
||||
# likelihood_threshold_triangulation = 0.3
|
||||
# min_cameras_for_triangulation = 2
|
||||
# interpolation = 'cubic' #linear, slinear, quadratic, cubic, or none
|
||||
## 'none' if you don't want to interpolate missing points
|
||||
|
||||
@ -29,7 +29,7 @@
|
||||
|
||||
# participant_height = 1.7 # m
|
||||
# participant_mass = 70 # kg
|
||||
# Only taken into account if pose_model is 'BODY_25_AUGMENTED' # Coming soon!
|
||||
## Only taken into account if pose_model is 'BODY_25_AUGMENTED' # Coming soon!
|
||||
|
||||
|
||||
## Take heart, calibration is not that complicated once you get the hang of it!
|
||||
@ -124,11 +124,12 @@
|
||||
# tracked_keypoint = 'Neck' # If the neck is not detected by the pose_model, check skeleton.py
|
||||
## and choose a stable point for tracking the person of interest (e.g., 'right_shoulder' with BLAZEPOSE)
|
||||
# reproj_error_threshold_association = 20 # px
|
||||
# likelihood_threshold_triangulation = 0.2
|
||||
|
||||
|
||||
# [triangulation]
|
||||
# reproj_error_threshold_triangulation = 15 # px
|
||||
# likelihood_threshold = 0.3
|
||||
# likelihood_threshold_triangulation = 0.3
|
||||
# min_cameras_for_triangulation = 2
|
||||
# interpolation = 'cubic' #linear, slinear, quadratic, cubic, or none
|
||||
## 'none' if you don't want to interpolate missing points
|
||||
|
||||
@ -29,7 +29,7 @@
|
||||
|
||||
# participant_height = 1.7 # m
|
||||
# participant_mass = 70 # kg
|
||||
# Only taken into account if pose_model is 'BODY_25_AUGMENTED' # Coming soon!
|
||||
## Only taken into account if pose_model is 'BODY_25_AUGMENTED' # Coming soon!
|
||||
|
||||
|
||||
## Take heart, calibration is not that complicated once you get the hang of it!
|
||||
@ -124,11 +124,12 @@
|
||||
# tracked_keypoint = 'Neck' # If the neck is not detected by the pose_model, check skeleton.py
|
||||
## and choose a stable point for tracking the person of interest (e.g., 'right_shoulder' with BLAZEPOSE)
|
||||
# reproj_error_threshold_association = 20 # px
|
||||
# likelihood_threshold_triangulation = 0.2
|
||||
|
||||
|
||||
# [triangulation]
|
||||
# reproj_error_threshold_triangulation = 15 # px
|
||||
# likelihood_threshold = 0.3
|
||||
# likelihood_threshold_triangulation = 0.3
|
||||
# min_cameras_for_triangulation = 2
|
||||
# interpolation = 'cubic' #linear, slinear, quadratic, cubic, or none
|
||||
## 'none' if you don't want to interpolate missing points
|
||||
|
||||
@ -29,7 +29,7 @@
|
||||
|
||||
# participant_height = 1.7 # m
|
||||
# participant_mass = 70 # kg
|
||||
# Only taken into account if pose_model is 'BODY_25_AUGMENTED' # Coming soon!
|
||||
## Only taken into account if pose_model is 'BODY_25_AUGMENTED' # Coming soon!
|
||||
|
||||
|
||||
## Take heart, calibration is not that complicated once you get the hang of it!
|
||||
@ -124,11 +124,12 @@
|
||||
# tracked_keypoint = 'Neck' # If the neck is not detected by the pose_model, check skeleton.py
|
||||
## and choose a stable point for tracking the person of interest (e.g., 'right_shoulder' with BLAZEPOSE)
|
||||
# reproj_error_threshold_association = 20 # px
|
||||
# likelihood_threshold_triangulation = 0.2
|
||||
|
||||
|
||||
# [triangulation]
|
||||
# reproj_error_threshold_triangulation = 15 # px
|
||||
# likelihood_threshold = 0.3
|
||||
# likelihood_threshold_triangulation = 0.3
|
||||
# min_cameras_for_triangulation = 2
|
||||
# interpolation = 'cubic' #linear, slinear, quadratic, cubic, or none
|
||||
## 'none' if you don't want to interpolate missing points
|
||||
|
||||
@ -29,7 +29,7 @@
|
||||
|
||||
# participant_height = 1.7 # m
|
||||
# participant_mass = 70 # kg
|
||||
# Only taken into account if pose_model is 'BODY_25_AUGMENTED' # Coming soon!
|
||||
## Only taken into account if pose_model is 'BODY_25_AUGMENTED' # Coming soon!
|
||||
|
||||
|
||||
## Take heart, calibration is not that complicated once you get the hang of it!
|
||||
@ -124,11 +124,12 @@
|
||||
# tracked_keypoint = 'Neck' # If the neck is not detected by the pose_model, check skeleton.py
|
||||
## and choose a stable point for tracking the person of interest (e.g., 'right_shoulder' with BLAZEPOSE)
|
||||
# reproj_error_threshold_association = 20 # px
|
||||
# likelihood_threshold_triangulation = 0.2
|
||||
|
||||
|
||||
# [triangulation]
|
||||
# reproj_error_threshold_triangulation = 15 # px
|
||||
# likelihood_threshold = 0.3
|
||||
# likelihood_threshold_triangulation = 0.3
|
||||
# min_cameras_for_triangulation = 2
|
||||
# interpolation = 'cubic' #linear, slinear, quadratic, cubic, or none
|
||||
## 'none' if you don't want to interpolate missing points
|
||||
|
||||
@ -114,7 +114,7 @@ def best_persons_and_cameras_combination(config, json_files_framef, personsIDs_c
|
||||
|
||||
error_threshold_tracking = config.get('personAssociation').get('reproj_error_threshold_association')
|
||||
min_cameras_for_triangulation = config.get('triangulation').get('min_cameras_for_triangulation')
|
||||
likelihood_threshold = config.get('triangulation').get('likelihood_threshold')
|
||||
likelihood_threshold = config.get('triangulation').get('likelihood_threshold_association')
|
||||
undistort_points = config.get('triangulation').get('undistort_points')
|
||||
|
||||
n_cams = len(json_files_framef)
|
||||
|
||||
@ -204,7 +204,7 @@ def recap_triangulate(config, error, nb_cams_excluded, keypoints_names, cam_excl
|
||||
cam_names = np.array([calib[c].get('name') for c in list(calib.keys())])
|
||||
cam_names = cam_names[list(cam_excluded_count.keys())]
|
||||
error_threshold_triangulation = config.get('triangulation').get('reproj_error_threshold_triangulation')
|
||||
likelihood_threshold = config.get('triangulation').get('likelihood_threshold')
|
||||
likelihood_threshold = config.get('triangulation').get('likelihood_threshold_triangulation')
|
||||
show_interp_indices = config.get('triangulation').get('show_interp_indices')
|
||||
interpolation_kind = config.get('triangulation').get('interpolation')
|
||||
handle_LR_swap = config.get('triangulation').get('handle_LR_swap')
|
||||
@ -252,7 +252,7 @@ def recap_triangulate(config, error, nb_cams_excluded, keypoints_names, cam_excl
|
||||
str_cam_excluded_count += f'Camera {k}: {int(np.round(v*100))}%, '
|
||||
logging.info(str_cam_excluded_count)
|
||||
|
||||
logging.info(f'\nLimb swapping was {"handled" if handle_LR_swap else "not handled"}.')
|
||||
logging.info(f'Limb swapping was {"handled" if handle_LR_swap else "not handled"}.')
|
||||
logging.info(f'Lens distortions were {"taken into account" if undistort_points else "not taken into account"}.')
|
||||
|
||||
logging.info(f'\n3D coordinates are stored at {trc_path}.')
|
||||
@ -341,28 +341,27 @@ def triangulation_from_best_cameras(config, coords_2D_kpt, coords_2D_kpt_swapped
|
||||
print('x_files ', repr(x_files))
|
||||
print('x_files_filt ', repr(x_files_filt))
|
||||
print('id_cams_off_tot ', id_cams_off_tot)
|
||||
print('id_cams_off ', id_cams_off)
|
||||
|
||||
x_files_filt = [ np.array([ xx for ii, xx in enumerate(x) if not np.isnan(xx) and not xx==0. ]) for x in x_files_filt ]
|
||||
y_files_filt = [ np.array([ xx for ii, xx in enumerate(x) if not np.isnan(xx) and not xx==0. ]) for x in y_files_filt ]
|
||||
x_files_swapped_filt = [ np.array([ xx for ii, xx in enumerate(x) if not np.isnan(xx) and not xx==0. ]) for x in x_files_swapped_filt ]
|
||||
y_files_swapped_filt = [ np.array([ xx for ii, xx in enumerate(x) if not np.isnan(xx) and not xx==0. ]) for x in y_files_swapped_filt ]
|
||||
likelihood_files_filt = [ np.array([ xx for ii, xx in enumerate(x) if not np.isnan(xx) and not xx==0. ]) for x in likelihood_files_filt ]
|
||||
# print('nb_cams_off_tot ', nb_cams_off_tot)
|
||||
print('x_files_filt ', repr(x_files_filt))
|
||||
# Triangulate 2D points
|
||||
Q_filt = [weighted_triangulation(projection_matrices_filt[i], x_files_filt[i], y_files_filt[i], likelihood_files_filt[i]) for i in range(len(id_cams_off))]
|
||||
|
||||
# Reprojection
|
||||
if undistort_points:
|
||||
coords_2D_kpt_calc_filt = np.array([[cv2.projectPoints(np.array(Q_filt[i][:-1]), calib_params_R_filt[i][j], calib_params_T_filt[i][j], calib_params_K_filt[i][j], calib_params_dist_filt[i][j])[0].ravel()
|
||||
for j in range(n_cams-nb_cams_off_tot)]
|
||||
for i in range(len(id_cams_off))])
|
||||
coords_2D_kpt_calc_filt = [[coords_2D_kpt_calc_filt[i,:,0], coords_2D_kpt_calc_filt[i,:,1]] for i in range(len(id_cams_off))]
|
||||
coords_2D_kpt_calc_filt = [np.array([cv2.projectPoints(np.array(Q_filt[i][:-1]), calib_params_R_filt[i][j], calib_params_T_filt[i][j], calib_params_K_filt[i][j], calib_params_dist_filt[i][j])[0].ravel()
|
||||
for j in range(n_cams-nb_cams_excluded_filt[i])])
|
||||
for i in range(len(id_cams_off))]
|
||||
coords_2D_kpt_calc_filt = [[coords_2D_kpt_calc_filt[i][:,0], coords_2D_kpt_calc_filt[i][:,1]] for i in range(len(id_cams_off))]
|
||||
else:
|
||||
coords_2D_kpt_calc_filt = [reprojection(projection_matrices_filt[i], Q_filt[i]) for i in range(len(id_cams_off))]
|
||||
coords_2D_kpt_calc_filt = np.array(coords_2D_kpt_calc_filt, dtype=object)
|
||||
x_calc_filt = coords_2D_kpt_calc_filt[:,0]
|
||||
print('x_calc_filt ', x_calc_filt)
|
||||
y_calc_filt = coords_2D_kpt_calc_filt[:,1]
|
||||
|
||||
# Reprojection error
|
||||
@ -389,6 +388,7 @@ def triangulation_from_best_cameras(config, coords_2D_kpt, coords_2D_kpt_swapped
|
||||
print('nb_cams_off ', nb_cams_off, 'n_cams_swapped ', n_cams_swapped, 'nb_cams_off_tot ', nb_cams_off_tot)
|
||||
# Create subsets
|
||||
id_cams_swapped = np.array(list(it.combinations(range(n_cams-nb_cams_off_tot), n_cams_swapped)))
|
||||
print('id_cams_swapped ', id_cams_swapped)
|
||||
x_files_filt_off_swap = np.array([[x] * len(id_cams_swapped) for x in x_files_filt])
|
||||
y_files_filt_off_swap = np.array([[y] * len(id_cams_swapped) for y in y_files_filt])
|
||||
for id_off in range(len(id_cams_off)): # for each configuration with nb_cams_off_tot removed
|
||||
@ -403,17 +403,17 @@ def triangulation_from_best_cameras(config, coords_2D_kpt, coords_2D_kpt_swapped
|
||||
|
||||
# Reprojection
|
||||
if undistort_points:
|
||||
coords_2D_kpt_calc_off_swap = np.array([[[cv2.projectPoints(np.array(Q_filt_off_swap[id_off,id_swapped][:-1]), calib_params_R_filt[id_off][j], calib_params_T_filt[id_off][j], calib_params_K_filt[id_off][j], calib_params_dist_filt[id_off][j])[0].ravel()
|
||||
coords_2D_kpt_calc_off_swap = [np.array([[cv2.projectPoints(np.array(Q_filt_off_swap[id_off][id_swapped][:-1]), calib_params_R_filt[id_off][j], calib_params_T_filt[id_off][j], calib_params_K_filt[id_off][j], calib_params_dist_filt[id_off][j])[0].ravel()
|
||||
for j in range(n_cams-nb_cams_off_tot)]
|
||||
for id_swapped in range(len(id_cams_swapped))]
|
||||
for id_off in range(len(id_cams_off))])
|
||||
coords_2D_kpt_calc_off_swap = np.array([[[coords_2D_kpt_calc_off_swap[id_off,id_swapped,:,0], coords_2D_kpt_calc_off_swap[id_off,id_swapped,:,1]]
|
||||
for id_swapped in range(len(id_cams_swapped))])
|
||||
for id_off in range(len(id_cams_off))]
|
||||
coords_2D_kpt_calc_off_swap = np.array([[[coords_2D_kpt_calc_off_swap[id_off][id_swapped,:,0], coords_2D_kpt_calc_off_swap[id_off][id_swapped,:,1]]
|
||||
for id_swapped in range(len(id_cams_swapped))]
|
||||
for id_off in range(len(id_cams_off))])
|
||||
else:
|
||||
coords_2D_kpt_calc_off_swap = np.array([[reprojection(projection_matrices_filt[id_off], Q_filt_off_swap[id_off, id_swapped])
|
||||
coords_2D_kpt_calc_off_swap = np.array([[reprojection(projection_matrices_filt[id_off], Q_filt_off_swap[id_off][id_swapped])
|
||||
for id_swapped in range(len(id_cams_swapped))]
|
||||
for id_off in range(len(id_cams_off))])
|
||||
for id_off in range(len(id_cams_off))]))
|
||||
x_calc_off_swap = coords_2D_kpt_calc_off_swap[:,:,0]
|
||||
y_calc_off_swap = coords_2D_kpt_calc_off_swap[:,:,1]
|
||||
|
||||
@ -528,7 +528,7 @@ def triangulate_all(config):
|
||||
session_dir = os.path.realpath(os.path.join(project_dir, '..', '..'))
|
||||
pose_model = config.get('pose').get('pose_model')
|
||||
frame_range = config.get('project').get('frame_range')
|
||||
likelihood_threshold = config.get('triangulation').get('likelihood_threshold')
|
||||
likelihood_threshold = config.get('triangulation').get('likelihood_threshold_triangulation')
|
||||
interpolation_kind = config.get('triangulation').get('interpolation')
|
||||
interp_gap_smaller_than = config.get('triangulation').get('interp_if_gap_smaller_than')
|
||||
show_interp_indices = config.get('triangulation').get('show_interp_indices')
|
||||
@ -619,7 +619,6 @@ def triangulate_all(config):
|
||||
Q_tot.append(np.concatenate(Q))
|
||||
error_tot.append(error)
|
||||
nb_cams_excluded_tot.append(nb_cams_excluded)
|
||||
print('LKJHLKJH ', id_excluded_cams)
|
||||
id_excluded_cams = [item for sublist in id_excluded_cams for item in sublist]
|
||||
id_excluded_cams_tot.append(id_excluded_cams)
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user