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small improvements on multi-person detection

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This commit is contained in:
davidpagnon 2024-03-04 18:46:56 +01:00
parent 757673d01c
commit 66df6bbd7c
4 changed files with 60 additions and 18 deletions
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2
Pose2Sim/Utilities/json_display_without_img.py
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@ -118,7 +118,7 @@ def json_display_without_img_func(**args):
scat.set_offsets(np.c_[X[frame], image_height-Y[frame]])
scat.set_array(CONF[frame])
if save == True or save=='True' or save == '1':
output_name = os.path.join(output_img_folder, f'{os.path.basename(output_img_folder)}_{frame}.png')
output_name = os.path.join(output_img_folder, f'{os.path.basename(output_img_folder)}_{str(frame).zfill(5)}.png')
plt.savefig(output_name)
return scat,

23
Pose2Sim/calibration.py
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@ -319,14 +319,15 @@ def read_intrinsic_yml(intrinsic_path):
N.B. : Size is calculated as twice the position of the optical center. Please correct in the .toml file if needed.
'''
intrinsic_yml = cv2.FileStorage(intrinsic_path, cv2.FILE_STORAGE_READ)
N = intrinsic_yml.getNode('names').size()
S, D, K = [], [], []
for i in range(N):
cam_number = intrinsic_yml.getNode('names').size()
N, S, D, K = [], [], [], []
for i in range(cam_number):
name = intrinsic_yml.getNode('names').at(i).string()
N.append(name)
K.append(intrinsic_yml.getNode(f'K_{name}').mat())
D.append(intrinsic_yml.getNode(f'dist_{name}').mat().flatten()[:-1])
S.append([K[i][0,2]*2, K[i][1,2]*2])
return S, K, D
return N, S, K, D
def read_extrinsic_yml(extrinsic_path):
@ -337,13 +338,14 @@ def read_extrinsic_yml(extrinsic_path):
- T (extrinsic translation)
'''
extrinsic_yml = cv2.FileStorage(extrinsic_path, cv2.FILE_STORAGE_READ)
N = extrinsic_yml.getNode('names').size()
R, T = [], []
for i in range(N):
cam_number = extrinsic_yml.getNode('names').size()
N, R, T = [], [], []
for i in range(cam_number):
name = extrinsic_yml.getNode('names').at(i).string()
N.append(name)
R.append(extrinsic_yml.getNode(f'R_{name}').mat().flatten()) # R_1 pour Rodrigues, Rot_1 pour matrice
T.append(extrinsic_yml.getNode(f'T_{name}').mat().flatten())
return R, T
return N, R, T
def calib_easymocap_fun(files_to_convert_paths, binning_factor=1):
@ -365,9 +367,8 @@ def calib_easymocap_fun(files_to_convert_paths, binning_factor=1):
'''
extrinsic_path, intrinsic_path = files_to_convert_paths
S, K, D = read_intrinsic_yml(intrinsic_path)
R, T = read_extrinsic_yml(extrinsic_path)
C = np.array(range(len(S)))
C, S, K, D = read_intrinsic_yml(intrinsic_path)
_, R, T = read_extrinsic_yml(extrinsic_path)
ret = [np.nan]*len(C)
return ret, C, S, D, K, R, T

41
Pose2Sim/personAssociation.py
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@ -59,6 +59,18 @@ __status__ = "Development"
## FUNCTIONS
def common_items_in_list(list1, list2):
'''
Do two lists have any items in common at the same index?
Returns True or False
'''
for i, j in enumerate(list1):
if j == list2[i]:
return True
return False
def min_with_single_indices(L, T):
'''
Let L be a list (size s) with T associated tuple indices (size s).
@ -121,6 +133,8 @@ def sort_people(Q_kpt_old, Q_kpt, nb_persons_to_detect):
'''
# Generate possible person correspondences across frames
if len(Q_kpt_old) < len(Q_kpt):
Q_kpt_old = np.concatenate((Q_kpt_old, [[0., 0., 0., 1.]]*(len(Q_kpt)-len(Q_kpt_old))))
personsIDs_comb = sorted(list(it.product(range(len(Q_kpt_old)),range(len(Q_kpt)))))
# Compute distance between persons from one frame to another
frame_by_frame_dist = []
@ -287,6 +301,27 @@ def best_persons_and_cameras_combination(config, json_files_framef, personsIDs_c
# print(comb_errors_below_thresh)
# print(Q_kpt)
if multi_person:
# sort combinations by error magnitude
errors_below_thresh_sorted = sorted(errors_below_thresh)
sorted_idx = np.array([errors_below_thresh.index(e) for e in errors_below_thresh_sorted])
comb_errors_below_thresh = np.array(comb_errors_below_thresh)[sorted_idx]
Q_kpt = np.array(Q_kpt)[sorted_idx]
# remove combinations with indices used several times for the same person
comb_errors_below_thresh = [c.tolist() for c in comb_errors_below_thresh]
comb = comb_errors_below_thresh.copy()
comb_ok = np.array([comb[0]])
for i, c1 in enumerate(comb):
idx_ok = np.array([not(common_items_in_list(c1, c2)) for c2 in comb[1:]])
try:
comb = np.array(comb[1:])[idx_ok]
comb_ok = np.concatenate((comb_ok, [comb[0]]))
except:
break
sorted_pruned_idx = [comb_errors_below_thresh.index(c.tolist()) for c in comb_ok]
errors_below_thresh = np.array(errors_below_thresh_sorted)[sorted_pruned_idx]
comb_errors_below_thresh = np.array(comb_errors_below_thresh)[sorted_pruned_idx]
Q_kpt = Q_kpt[sorted_pruned_idx]
# Remove indices already used for a person
personsIDs_combinations = np.array([personsIDs_combinations[i] for i in range(len(personsIDs_combinations))
if not np.array(
@ -365,6 +400,7 @@ def track_2d_all(config):
# Read config
project_dir = config.get('project').get('project_dir')
session_dir = os.path.realpath(os.path.join(project_dir, '..', '..'))
multi_person = config.get('project').get('multi_person')
pose_model = config.get('pose').get('pose_model')
tracked_keypoint = config.get('personAssociation').get('tracked_keypoint')
frame_range = config.get('project').get('frame_range')
@ -378,6 +414,11 @@ def track_2d_all(config):
pose_dir = os.path.join(project_dir, 'pose')
poseTracked_dir = os.path.join(project_dir, 'pose-associated')
if multi_person:
logging.info('\nMulti-person analysis selected. Note that you can set this option to false for faster runtime if you only need the main person in the scene.')
else:
logging.info('\nSingle-person analysis selected.')
# projection matrix from toml calibration file
P = computeP(calib_file, undistort=undistort_points)
calib_params = retrieve_calib_params(calib_file)

12
Pose2Sim/triangulation.py
View File

@ -669,7 +669,7 @@ def triangulate_all(config):
# Triangulation
Q_tot, error_tot, nb_cams_excluded_tot,id_excluded_cams_tot = [], [], [], []
for f in tqdm(range(*f_range)):
for f in tqdm(range(frames_nb)):
# Get x,y,likelihood values from files
json_tracked_files_f = [json_tracked_files[c][f] for c in range(n_cams)]
# print(json_tracked_files_f)
@ -718,10 +718,10 @@ def triangulate_all(config):
id_excluded_cams = [[id_excluded_cams[n][k] for k in range(keypoints_nb)] for n in range(nb_persons_to_detect)]
id_excluded_cams_tot.append(id_excluded_cams)
Q_tot = [pd.DataFrame([Q_tot[f][n] for f in range(*f_range)]) for n in range(nb_persons_to_detect)]
error_tot = [pd.DataFrame([error_tot[f][n] for f in range(*f_range)]) for n in range(nb_persons_to_detect)]
nb_cams_excluded_tot = [pd.DataFrame([nb_cams_excluded_tot[f][n] for f in range(*f_range)]) for n in range(nb_persons_to_detect)]
id_excluded_cams_tot = [pd.DataFrame([id_excluded_cams_tot[f][n] for f in range(*f_range)]) for n in range(nb_persons_to_detect)]
Q_tot = [pd.DataFrame([Q_tot[f][n] for f in range(frames_nb)]) for n in range(nb_persons_to_detect)]
error_tot = [pd.DataFrame([error_tot[f][n] for f in range(frames_nb)]) for n in range(nb_persons_to_detect)]
nb_cams_excluded_tot = [pd.DataFrame([nb_cams_excluded_tot[f][n] for f in range(frames_nb)]) for n in range(nb_persons_to_detect)]
id_excluded_cams_tot = [pd.DataFrame([id_excluded_cams_tot[f][n] for f in range(frames_nb)]) for n in range(nb_persons_to_detect)]
for n in range(nb_persons_to_detect):
error_tot[n]['mean'] = error_tot[n].mean(axis = 1)
@ -769,7 +769,7 @@ def triangulate_all(config):
trc_paths = [make_trc(config, Q_tot[n], keypoints_names, f_range, id_person=n) for n in range(len(Q_tot))]
# Reorder TRC files
if multi_person and reorder_trc:
if multi_person and reorder_trc and len(trc_paths)>1:
trc_id = retrieve_right_trc_order(trc_paths)
[os.rename(t, t+'.old') for t in trc_paths]
[os.rename(t+'.old', trc_paths[i]) for i, t in zip(trc_id,trc_paths)]