Merge branch 'main' of https://github.com/perfanalytics/pose2sim

Pose2Sim/Utilities/json_display_without_img.py

@@ -24,6 +24,7 @@
 import os
 import numpy as np
 import json
+import re
 import matplotlib.pyplot as plt 
 from matplotlib.animation import FuncAnimation, FileMovieWriter 
 import argparse
@@ -41,6 +42,21 @@ __status__ = "Development"
 
 
 ## FUNCTIONS
+def sort_stringlist_by_last_number(string_list):
+    '''
+    Sort a list of strings based on the last number in the string.
+    Works if other numbers in the string, if strings after number. Ignores alphabetical order.
+
+    Example: ['json1', 'js4on2.b', 'eypoints_0000003.json', 'ajson0', 'json10']
+    gives: ['ajson0', 'json1', 'js4on2.b', 'eypoints_0000003.json', 'json10']
+    '''
+    
+    def sort_by_last_number(s):
+        return int(re.findall(r'\d+', s)[-1])
+    
+    return sorted(string_list, key=sort_by_last_number)
+
+
 def save_inp_as_output(_img, c_name, dpi=100):
     h, w, _ = _img.shape
     fig, axes = plt.subplots(figsize=(h/dpi, w/dpi))
@@ -66,7 +82,7 @@ def json_display_without_img_func(**args):
 
     json_folder = os.path.realpath(args.get('json_folder'))
     json_fnames = [f for f in os.listdir(json_folder) if os.path.isfile(os.path.join(json_folder, f))]
-    json_fnames.sort(key=lambda f: int(f.split('_')[0])) # sort by frame number
+    json_fnames = sort_stringlist_by_last_number(json_fnames)
     
     output_img_folder =  args.get('output_img_folder')
     if output_img_folder==None: 

Pose2Sim/triangulation.py

@@ -178,7 +178,7 @@ def sort_people(Q_kpt_old, Q_kpt):
     # Compute distance between persons from one frame to another
     frame_by_frame_dist = []
     for comb in personsIDs_comb:
-        frame_by_frame_dist += [euclidean_distance(Q_kpt_old[comb[0]][:3],Q_kpt[comb[1]][:3])]
+        frame_by_frame_dist += [euclidean_distance(Q_kpt_old[comb[0]],Q_kpt[comb[1]])]
         
     # sort correspondences by distance
     minL, _, associated_tuples = min_with_single_indices(frame_by_frame_dist, personsIDs_comb)

README.md

@@ -659,10 +659,13 @@ Displays X, Y, Z coordinates of each 3D keypoint of a TRC file in a different ma
     <pre>
 
 [trc_from_easymocap.py](https://github.com/perfanalytics/pose2sim/blob/main/Pose2Sim/Utilities/trc_from_easymocap.py) 
-Convert EasyMocap results keypoints3d json files to .trc.
+Convert EasyMocap results keypoints3d .json files to .trc.
 
 [c3d_to_trc.py](https://github.com/perfanalytics/pose2sim/blob/main/Pose2Sim/Utilities/c3d_to_trc.py)
-Converts 3D point data of a .c3d file to a .trc file compatible with OpenSim. No analog data (force plates, emg) nor computed data (angles, powers, etc.) are retrieved.
+Converts 3D point data from a .c3d file to a .trc file compatible with OpenSim. No analog data (force plates, emg) nor computed data (angles, powers, etc.) are retrieved.
+
+[trc_to_c3d.py](https://github.com/perfanalytics/pose2sim/blob/main/Pose2Sim/Utilities/trc_to_c3d.py)
+Converts 3D point data from a .trc file to a .c3d file compatible with Visual3D.
 
 [trc_desample.py](https://github.com/perfanalytics/pose2sim/blob/main/Pose2Sim/Utilities/trc_desample.py)
 Undersamples a trc file.
@@ -772,7 +775,7 @@ You will be proposed a to-do list, but please feel absolutely free to propose yo
 &#9634; **Calibration:** Alternatively, self-calibrate with [OpenPose keypoints](https://ietresearch.onlinelibrary.wiley.com/doi/full/10.1049/cvi2.12130). Set world reference frame in the end.
 &#9634; **Calibration:** Convert [fSpy calibration](https://fspy.io/) based on vanishing point.
 
-&#9634; **Synchronization:** Synchronize cameras on 2D keypoint speeds. Cf [this draft script](https://github.com/perfanalytics/pose2sim/blob/draft/Pose2Sim/Utilities/synchronize_cams.py).
+&#10004; **Synchronization:** Synchronize cameras on keypoint speeds.
 
 &#10004; **Person Association:** Automatically choose the main person to triangulate.
 &#10004; **Person Association:** Multiple persons association. 1. Triangulate all the persons whose reprojection error is below a certain threshold (instead of only the one with minimum error), and then track in time with speed cf [Slembrouck 2020](https://link.springer.com/chapter/10.1007/978-3-030-40605-9_15)? or 2. Based on affinity matrices [Dong 2021](https://arxiv.org/pdf/1901.04111.pdf)? or 3. Based on occupancy maps [Yildiz 2012](https://link.springer.com/chapter/10.1007/978-3-642-35749-7_10)? or 4. With a neural network [Huang 2023](https://arxiv.org/pdf/2304.09471.pdf)?