Various small fixes

README.md

@@ -237,9 +237,9 @@ Will be much faster, with no impact on accuracy. However, the installation takes
 <br>
 
 #### Customize your output:
-- Choose whether you want video, images, trc pose file, and angle mot file:
+- Choose whether you want video, images, trc pose file, angle mot file, and real-time display:
   ```cmd
-  sports2d --save_vid false --save_img true --save_trc false --save_mot true
+  sports2d --save_vid false --save_img true --save_pose false --save_angles true --show_realtime_results false
   ```
 - Choose which angles you need:
   ```cmd
@@ -415,7 +415,7 @@ If you want to contribute to Sports2D, please follow [this guide](https://docs.g
 - [x] Run again without pose estimation with the option `--load_trc` for px .trc file.
 - [x] **Convert positions to meters** by providing the person height, a calibration file, or 3D points [to click on the image](https://stackoverflow.com/questions/74248955/how-to-display-the-coordinates-of-the-points-clicked-on-the-image-in-google-cola)
 - [ ] Perform **Inverse kinematics and dynamics** with OpenSim (cf. [Pose2Sim](https://github.com/perfanalytics/pose2sim), but in 2D). Update [this model](https://github.com/davidpagnon/Sports2D/blob/main/Sports2D/Utilities/2D_gait.osim) (add arms, markers, remove muscles and contact spheres). Add pipeline example.
-- [ ] Run with the options `--load_trc` and `--compare` to visually compare motion with a trc file. If run with a webcam input, the user can follow the motion of the trc file. Further calculation can then be done to compare specific variables.
+- [ ] Run with the option `--compare_to` to visually compare motion with a trc file. If run with a webcam input, the user can follow the motion of the trc file. Further calculation can then be done to compare specific variables.
 - [ ] **Colab version**: more user-friendly, usable on a smartphone.
 - [ ] **GUI applications** for Windows, Mac, and Linux, as well as for Android and iOS.
 

Sports2D/process.py

@@ -1559,10 +1559,10 @@ def process_fun(config_dict, video_file, time_range, frame_rate, result_dir):
                 if save_img:
                     cv2.imwrite(str((img_output_dir / f'{output_dir_name}_{frame_count:06d}.png')), img)
 
-            if save_pose:
-                all_frames_X.append(np.array(valid_X))
-                all_frames_Y.append(np.array(valid_Y))
-                all_frames_scores.append(np.array(valid_scores))
+            all_frames_X.append(np.array(valid_X))
+            all_frames_Y.append(np.array(valid_Y))
+            all_frames_scores.append(np.array(valid_scores))
+
             if save_angles and calculate_angles:
                 all_frames_angles.append(np.array(valid_angles))
             if video_file=='webcam' and save_vid:   # To adjust framerate of output video
@@ -1587,26 +1587,25 @@ def process_fun(config_dict, video_file, time_range, frame_rate, result_dir):
 
 
     # Post-processing: Interpolate, filter, and save pose and angles
+    all_frames_X_homog = make_homogeneous(all_frames_X)
+    all_frames_X_homog = all_frames_X_homog[...,keypoints_ids]
+    all_frames_Y_homog = make_homogeneous(all_frames_Y)
+    all_frames_Y_homog = all_frames_Y_homog[...,keypoints_ids]
+    all_frames_Z_homog = pd.DataFrame(np.zeros_like(all_frames_X_homog)[:,0,:], columns=keypoints_names)
+    all_frames_scores = make_homogeneous(all_frames_scores)
+
     frame_range = [0,frame_count] if video_file == 'webcam' else frame_range
     all_frames_time = pd.Series(np.linspace(frame_range[0]/fps/slowmo_factor, frame_range[1]/fps/slowmo_factor, frame_count+1), name='time')
-
+    if not multiperson:
+        detected_persons = [get_personID_with_highest_scores(all_frames_scores)]
+    else:
+        detected_persons = range(all_frames_X_homog.shape[1])
+
+    # Post-processing pose
     if save_pose:
         logging.info('\nPost-processing pose:')
-        # Select only the keypoints that are in the model from skeletons.py, invert Y axis, divide pixel values by 1000
-        all_frames_X_homog = make_homogeneous(all_frames_X)
-        all_frames_X_homog = all_frames_X_homog[...,keypoints_ids]
-        all_frames_Y_homog = make_homogeneous(all_frames_Y)
-        all_frames_Y_homog = all_frames_Y_homog[...,keypoints_ids]
-        all_frames_scores = make_homogeneous(all_frames_scores)
-
-        all_frames_Z_homog = pd.DataFrame(np.zeros_like(all_frames_X_homog)[:,0,:], columns=keypoints_names)
-
-        # Process pose for each person
-        if not multiperson:
-            detected_persons = [get_personID_with_highest_scores(all_frames_scores)]
-        else:
-            detected_persons = range(all_frames_X_homog.shape[1])
 
+        # Process pose for each person
         trc_data = []
         trc_data_unfiltered = []
         for i in detected_persons:
@@ -1706,7 +1705,6 @@ def process_fun(config_dict, video_file, time_range, frame_rate, result_dir):
                     cx, cy = xy_origin_estim
                 else:
                     cx, cy = xy_origin
-                print(xy_origin_estim, xy_origin)
                 logging.info(f'Using height of person #{calib_on_person_id} ({person_height_m}m) to convert coordinates in meters. '
                              f'Floor angle: {np.degrees(-floor_angle_estim) if not floor_angle=="auto" else f"auto (estimation: {round(np.degrees(-floor_angle_estim),2)}°)"}, '
                              f'xy_origin: {xy_origin if not xy_origin=="auto" else f"auto (estimation: {[round(c,2) for c in xy_origin_estim]})"}.')
@@ -1775,7 +1773,7 @@ def process_fun(config_dict, video_file, time_range, frame_rate, result_dir):
 
 
 
-    # Angles post-processing
+    # Post-processing angles
     if save_angles and calculate_angles:
         logging.info('\nPost-processing angles:')
         all_frames_angles = make_homogeneous(all_frames_angles)

setup.cfg

@@ -1,6 +1,6 @@
 [metadata]
 name = sports2d
-version = 0.5.0
+version = 0.5.1
 author = David Pagnon
 author_email = [email protected]
 description = Detect pose and compute 2D joint angles from a video.