- Default names for Halpe_26, coco_133, coco_17: body_with_feet, whole_body, body

- Optional manual selection of backend and device

Author: davidpagnon

Sports2D/Demo/Config_demo.toml

@@ -48,11 +48,14 @@ result_dir = '' # If empty, project dir is current dir
 slowmo_factor = 1       # 1 for normal speed. For a video recorded at 240 fps and exported to 30 fps, it would be 240/30 = 8
 
 # Pose detection parameters
-pose_model = 'body_with_feet' # Only body_with_feet is available for now
+pose_model = 'Body_with_feet' # Only Body_with_feet is available for now
 mode = 'balanced'       # 'lightweight', 'balanced', or 'performance'
 det_frequency = 1       # Run person detection only every N frames, and inbetween track previously detected bounding boxes (keypoint detection is still run on all frames). 
                         # Equal to or greater than 1, can be as high as you want in simple uncrowded cases. Much faster, but might be less accurate. 
 tracking_mode = 'sports2d' # 'rtmlib' or 'sports2d'. 'sports2d' is generally much more accurate and comparable in speed
+device = 'auto' # 'auto', 'CPU', 'CUDA', 'MPS', 'ROCM'
+backend = 'auto' # 'auto', 'openvino', 'onnxruntime', 'opencv'
+
 
 # Processing parameters
 keypoint_likelihood_threshold = 0.3 # Keypoints whose likelihood is lower will not be taken into account

Sports2D/Sports2D.py

@@ -143,6 +143,8 @@
                                 'pose_model': 'body_with_feet',
                                 'mode': 'balanced',
                                 'det_frequency': 4,
+                                'device': 'auto',
+                                'backend': 'auto',
                                 'tracking_mode': 'sports2d',
                                 'keypoint_likelihood_threshold': 0.3,
                                 'average_likelihood_threshold': 0.5,
@@ -231,6 +233,8 @@
                 'mode': ["m", "light, balanced, or performance. balanced if not specified"],
                 'det_frequency': ["f", "run person detection only every N frames, and inbetween track previously detected bounding boxes. keypoint detection is still run on all frames.\n\
                                  Equal to or greater than 1, can be as high as you want in simple uncrowded cases. Much faster, but might be less accurate. 1 if not specified: detection runs on all frames"],
+                'backend': ["", "Backend for pose estimation can be 'auto', 'cpu', 'cuda', 'mps' (for MacOS), or 'rocm' (for AMD GPUs)"],
+                'device': ["", "Device for pose estimatino can be 'auto', 'openvino', 'onnxruntime', 'opencv'"],
                 'to_meters': ["M", "convert pixels to meters. true if not specified"],
                 'calib_on_person_id': ["", "person ID to calibrate on. 0 if not specified"],
                 'floor_angle': ["", "angle of the floor. 'auto' if not specified"],