moved backend and device selection out of main function

Sports2D/process.py

@@ -221,21 +221,18 @@ def setup_video(video_file_path, save_vid, vid_output_path):
     return cap, out_vid, cam_width, cam_height, fps
 
 
-def setup_pose_tracker(det_frequency, mode, tracking):
+def setup_backend_device():
     '''
-    Set up the RTMLib pose tracker with the appropriate model and backend.
-    If CUDA is available, use it with ONNXRuntime backend; else use CPU with openvino
-
-    INPUTS:
-    - det_frequency: int. The frequency of pose detection (every N frames)
-    - mode: str. The mode of the pose tracker ('lightweight', 'balanced', 'performance')
-    - tracking: bool. Whether to track persons across frames with RTMlib tracker
+    Set up the backend and device for the pose tracker based on the availability of hardware acceleration.
+    TensorRT is not supported by RTMLib yet: https://github.com/Tau-J/rtmlib/issues/12
 
-    OUTPUTS:
-    - pose_tracker: PoseTracker. The initialized pose tracker object    
+    Selects the best option in the following order of priority:
+    1. GPU with CUDA and ONNXRuntime backend (if CUDAExecutionProvider is available)
+    2. GPU with ROCm and ONNXRuntime backend (if ROCMExecutionProvider is available, for AMD GPUs)
+    3. GPU with MPS or CoreML and ONNXRuntime backend (for macOS systems)
+    4. CPU with OpenVINO backend (default fallback)
     '''
 
-    # If CUDA is available, use it with ONNXRuntime backend; else use CPU with openvino
     try:
         import torch
         import onnxruntime as ort
@@ -262,6 +259,25 @@ def setup_pose_tracker(det_frequency, mode, tracking):
             device = 'cpu'
             backend = 'openvino'
             logging.info(f"\nNo valid CUDA installation found: using OpenVINO backend with CPU.")
+
+    return backend, device
+
+
+def setup_pose_tracker(det_frequency, mode, tracking):
+    '''
+    Set up the RTMLib pose tracker with the appropriate model and backend.
+    If CUDA is available, use it with ONNXRuntime backend; else use CPU with openvino
+
+    INPUTS:
+    - det_frequency: int. The frequency of pose detection (every N frames)
+    - mode: str. The mode of the pose tracker ('lightweight', 'balanced', 'performance')
+    - tracking: bool. Whether to track persons across frames with RTMlib tracker
+
+    OUTPUTS:
+    - pose_tracker: PoseTracker. The initialized pose tracker object    
+    '''
+
+    backend, device = setup_backend_device()
 
     # Initialize the pose tracker with Halpe26 model
     pose_tracker = PoseTracker(