removed tracking option in pose estimation + fixed tests so that synchonization is not done in multiperson Demo + fixed multitab plots crashing

Pose2Sim/Demo_Batch/Config.toml

@@ -152,7 +152,7 @@ make_c3d = true # save triangulated data in c3d format in addition to trc
 
 [filtering]
 type = 'butterworth' # butterworth, kalman, gaussian, LOESS, median, butterworth_on_speed
-display_figures = false # true or false (lowercase) # fails when run multiple times https://github.com/superjax/plotWindow/issues/7
+display_figures = true # true or false (lowercase) 
 make_c3d = true # also save triangulated data in c3d format
 
    [filtering.butterworth]

Pose2Sim/Demo_Batch/Trial_1/Config.toml

@@ -152,7 +152,7 @@
 
 # [filtering]
 # type = 'butterworth' # butterworth, kalman, gaussian, LOESS, median, butterworth_on_speed
-# display_figures = false # true or false (lowercase) # fails when run multiple times https://github.com/superjax/plotWindow/issues/7
+# display_figures = true # true or false (lowercase) 
 # make_c3d = true # also save triangulated data in c3d format
 
    # [filtering.butterworth]

Pose2Sim/Demo_Batch/Trial_2/Config.toml

@@ -152,7 +152,7 @@ keypoints_to_consider = 'all' # 'all' if all points should be considered, for ex
 
 # [filtering]
 # type = 'butterworth' # butterworth, kalman, gaussian, LOESS, median, butterworth_on_speed
-# display_figures = false # true or false (lowercase) # fails when run multiple times https://github.com/superjax/plotWindow/issues/7
+# display_figures = true # true or false (lowercase) 
 # make_c3d = false # also save triangulated data in c3d format
 
    # [filtering.butterworth]

Pose2Sim/Demo_MultiPerson/Config.toml

@@ -152,7 +152,7 @@ make_c3d = false # save triangulated data in c3d format in addition to trc
 
 [filtering]
 type = 'butterworth' # butterworth, kalman, gaussian, LOESS, median, butterworth_on_speed
-display_figures = false # true or false (lowercase) # fails when run multiple times https://github.com/superjax/plotWindow/issues/7
+display_figures = true # true or false (lowercase) 
 make_c3d = false # also save triangulated data in c3d format
 
    [filtering.butterworth]

Pose2Sim/Demo_SinglePerson/Config.toml

@@ -152,7 +152,7 @@ make_c3d = true # save triangulated data in c3d format in addition to trc
 
 [filtering]
 type = 'butterworth' # butterworth, kalman, gaussian, LOESS, median, butterworth_on_speed
-display_figures = true # true or false (lowercase) # fails when run multiple times https://github.com/superjax/plotWindow/issues/7
+display_figures = true # true or false (lowercase) 
 make_c3d = true # also save triangulated data in c3d format
 
    [filtering.butterworth]

Pose2Sim/Utilities/tests.py

@@ -129,17 +129,19 @@ class TestWorkflow(unittest.TestCase):
         config_dict.get("synchronization").update({"display_sync_plots":False})
         config_dict['filtering']['display_figures'] = False
 
+        # Step by step
         Pose2Sim.calibration(config_dict)
         Pose2Sim.poseEstimation(config_dict)
-        Pose2Sim.synchronization(config_dict)
+        # Pose2Sim.synchronization(config_dict) # No synchronization for multi-person for now
         Pose2Sim.personAssociation(config_dict)
         Pose2Sim.triangulation(config_dict)
         Pose2Sim.filtering(config_dict)
         Pose2Sim.markerAugmentation(config_dict)
         # Pose2Sim.kinematics(config_dict)
 
+        # Run all
         config_dict.get("pose").update({"overwrite_pose":False})
-        Pose2Sim.runAll(config_dict)
+        Pose2Sim.runAll(config_dict, do_synchronization=False)
 
 
         ####################
@@ -149,7 +151,7 @@ class TestWorkflow(unittest.TestCase):
         project_dir = '../Demo_Batch'
         os.chdir(project_dir)
 
-        Pose2Sim.runAll()
+        Pose2Sim.runAll(do_synchronization=False)
 
 
 if __name__ == '__main__':

Pose2Sim/Utilities/trc_gaitevents.py

@@ -8,6 +8,10 @@
     ##################################################
     
     Determine gait on and off from a TRC file of point coordinates.
+    
+    N.B.: Could implement the methods listed there in the future. 
+          Please feel free to make a pull-request or keep me informed if you do so!
+
     Three available methods, each of them with their own pros and cons: 
 
     - "forward_coordinates": 

Pose2Sim/common.py

@@ -22,9 +22,9 @@ import sys
 import matplotlib as mpl
 mpl.use('qt5agg')
 mpl.rc('figure', max_open_warning=0)
+from PyQt5.QtWidgets import QMainWindow, QApplication, QWidget, QTabWidget, QVBoxLayout
 from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
 from matplotlib.backends.backend_qt5agg import NavigationToolbar2QT as NavigationToolbar
-from PyQt5.QtWidgets import QMainWindow, QApplication, QWidget, QTabWidget, QVBoxLayout
 import warnings
 warnings.filterwarnings("ignore", category=UserWarning, module="c3d")
 
@@ -482,7 +482,8 @@ class plotWindow():
 
     def __init__(self, parent=None):
         self.app = QApplication(sys.argv)
-        self.MainWindow = QMainWindow()
+        if not self.app:
+            self.app = QApplication(sys.argv)
         self.MainWindow.__init__()
         self.MainWindow.setWindowTitle("Multitabs figure")
         self.canvases = []

Pose2Sim/filtering.py

@@ -319,7 +319,7 @@ def median_filter_1d(config_dict, frame_rate, col):
     return col_filtered
 
 
-def display_figures_fun(Q_unfilt, Q_filt, time_col, keypoints_names):
+def display_figures_fun(Q_unfilt, Q_filt, time_col, keypoints_names, person_id=0):
     '''
     Displays filtered and unfiltered data for comparison
 
@@ -334,6 +334,7 @@ def display_figures_fun(Q_unfilt, Q_filt, time_col, keypoints_names):
     '''
 
     pw = plotWindow()
+    pw.MainWindow.setWindowTitle('Person '+ str(person_id) + ' coordinates')
     for id, keypoint in enumerate(keypoints_names):
         f = plt.figure()
         
@@ -472,7 +473,7 @@ def filter_all(config_dict):
     trc_f_out = [f'{os.path.basename(t).split(".")[0]}_filt_{filter_type}.trc' for t in trc_path_in]
     trc_path_out = [os.path.join(pose3d_dir, t) for t in trc_f_out]
     
-    for t_in, t_out in zip(trc_path_in, trc_path_out):
+    for person_id, t_in, t_out in enumerate(zip(trc_path_in, trc_path_out)):
         # Read trc header
         with open(t_in, 'r') as trc_file:
             header = [next(trc_file) for line in range(5)]
@@ -489,7 +490,7 @@ def filter_all(config_dict):
         if display_figures:
             # Retrieve keypoints
             keypoints_names = pd.read_csv(t_in, sep="\t", skiprows=3, nrows=0).columns[2::3].to_numpy()
-            display_figures_fun(Q_coord, Q_filt, time_col, keypoints_names)
+            display_figures_fun(Q_coord, Q_filt, time_col, keypoints_names, person_id)
 
         # Reconstruct trc file with filtered coordinates
         with open(t_out, 'w') as trc_o:

Pose2Sim/poseEstimation.py

@@ -127,14 +127,13 @@ def sort_people_rtmlib(pose_tracker, keypoints, scores):
     return sorted_keypoints, sorted_scores
 
 
-def process_video(video_path, pose_tracker, tracking, output_format, save_video, save_images, display_detection, frame_range):
+def process_video(video_path, pose_tracker, output_format, save_video, save_images, display_detection, frame_range):
     '''
     Estimate pose from a video file
     
     INPUTS:
     - video_path: str. Path to the input video file
     - pose_tracker: PoseTracker. Initialized pose tracker object from RTMLib
-    - tracking: bool. Whether to give consistent person ID across frames
     - output_format: str. Output format for the pose estimation results ('openpose', 'mmpose', 'deeplabcut')
     - save_video: bool. Whether to save the output video
     - save_images: bool. Whether to save the output images
@@ -186,10 +185,6 @@ def process_video(video_path, pose_tracker, tracking, output_format, save_video,
                 # Perform pose estimation on the frame
                 keypoints, scores = pose_tracker(frame)
 
-                # Reorder keypoints, scores
-                if tracking:
-                    keypoints, scores = sort_people_rtmlib(pose_tracker, keypoints, scores)
-
                 # Save to json
                 if 'openpose' in output_format:
                     json_file_path = os.path.join(json_output_dir, f'{video_name_wo_ext}_{frame_idx:06d}.json')
@@ -225,7 +220,7 @@ def process_video(video_path, pose_tracker, tracking, output_format, save_video,
         cv2.destroyAllWindows()
 
 
-def process_images(image_folder_path, vid_img_extension, pose_tracker, tracking, output_format, fps, save_video, save_images, display_detection, frame_range):
+def process_images(image_folder_path, vid_img_extension, pose_tracker, output_format, fps, save_video, save_images, display_detection, frame_range):
     '''
     Estimate pose estimation from a folder of images
     
@@ -233,7 +228,6 @@ def process_images(image_folder_path, vid_img_extension, pose_tracker, tracking,
     - image_folder_path: str. Path to the input image folder
     - vid_img_extension: str. Extension of the image files
     - pose_tracker: PoseTracker. Initialized pose tracker object from RTMLib
-    - tracking: bool. Whether to give consistent person ID across frames
     - output_format: str. Output format for the pose estimation results ('openpose', 'mmpose', 'deeplabcut')
     - save_video: bool. Whether to save the output video
     - save_images: bool. Whether to save the output images
@@ -276,17 +270,6 @@ def process_images(image_folder_path, vid_img_extension, pose_tracker, tracking,
             # Perform pose estimation on the image
             keypoints, scores = pose_tracker(frame)
             
-            # Reorder keypoints, scores
-            if tracking:
-                max_id = max(pose_tracker.track_ids_last_frame)
-                num_frames, num_points, num_coordinates = keypoints.shape
-                keypoints_filled = np.zeros((max_id+1, num_points, num_coordinates))
-                scores_filled = np.zeros((max_id+1, num_points))
-                keypoints_filled[pose_tracker.track_ids_last_frame] = keypoints
-                scores_filled[pose_tracker.track_ids_last_frame] = scores
-                keypoints = keypoints_filled
-                scores = scores_filled            
-            
             # Extract frame number from the filename
             if 'openpose' in output_format:
                 json_file_path = os.path.join(json_output_dir, f"{os.path.splitext(os.path.basename(image_file))[0]}_{frame_idx:06d}.json")
@@ -361,9 +344,7 @@ def rtm_estimator(config_dict):
     save_images = True if 'to_images' in config_dict['pose']['save_video'] else False
     display_detection = config_dict['pose']['display_detection']
     overwrite_pose = config_dict['pose']['overwrite_pose']
-
     det_frequency = config_dict['pose']['det_frequency']
-    tracking = config_dict['pose']['tracking']
 
     # Determine frame rate
     video_files = glob.glob(os.path.join(video_dir, '*'+vid_img_extension))
@@ -408,9 +389,6 @@ def rtm_estimator(config_dict):
     else:
         raise ValueError(f"Invalid det_frequency: {det_frequency}. Must be an integer greater or equal to 1.")
 
-    if tracking:
-        logging.info(f'Pose estimation will attempt to give consistent person IDs across frames.\n')
-
     # Select the appropriate model based on the model_type
     if pose_model.upper() == 'HALPE_26':
         ModelClass = BodyWithFeet
@@ -433,7 +411,7 @@ def rtm_estimator(config_dict):
         mode=mode,
         backend=backend,
         device=device,
-        tracking=tracking,
+        tracking=False,
         to_openpose=False)
 
 
@@ -454,7 +432,7 @@ def rtm_estimator(config_dict):
             logging.info(f'Found video files with extension {vid_img_extension}.')
             for video_path in video_files:
                 pose_tracker.reset()
-                process_video(video_path, pose_tracker, tracking, output_format, save_video, save_images, display_detection, frame_range)
+                process_video(video_path, pose_tracker, output_format, save_video, save_images, display_detection, frame_range)
 
         else:
             # Process image folders
@@ -463,4 +441,4 @@ def rtm_estimator(config_dict):
             for image_folder in image_folders:
                 pose_tracker.reset()
                 image_folder_path = os.path.join(video_dir, image_folder)
-                process_images(image_folder_path, vid_img_extension, pose_tracker, tracking, output_format, frame_rate, save_video, save_images, display_detection, frame_range)
+                process_images(image_folder_path, vid_img_extension, pose_tracker, output_format, frame_rate, save_video, save_images, display_detection, frame_range)

Pose2Sim/synchronization.py

@@ -177,11 +177,6 @@ def time_lagged_cross_corr(camx, camy, lag_range, show=True, ref_cam_id=0, cam_i
     if isinstance(lag_range, int):
         lag_range = [-lag_range, lag_range]
 
-    import hashlib
-    print(repr(list(camx)), repr(list(camy)))
-    hashlib.md5(pd.util.hash_pandas_object(camx).values).hexdigest()
-    hashlib.md5(pd.util.hash_pandas_object(camy).values).hexdigest()
-
     pearson_r = [camx.corr(camy.shift(lag)) for lag in range(lag_range[0], lag_range[1])]
     offset = int(np.floor(len(pearson_r)/2)-np.argmax(pearson_r))
     if not np.isnan(pearson_r).all():