Migrated to nix for dependency managment

dodgeballtracker/logic.py

@@ -7,7 +7,7 @@ import cv2
 from dodgeballtracker.helper import create_video_writer, iter_frames
 import matplotlib
 import numpy as np
-from boxmot import DeepOCSORT
+from boxmot import DeepOcSort as DeepOCSORT
 
 
 
@@ -125,19 +125,20 @@ class DodgeballTracker:
 
     def _get_tracker(self):
         return DeepOCSORT(
-            model_weights=Path('lmbn_n_cuhk03_d.pt'),  # which ReID model to use
-            device=torch.device("mps"),
-            fp16=True,
+            Path('lmbn_n_cuhk03_d.pt'),  # which ReID model to use
+            torch.device("cpu"),
+            True,
         )
 
     def _get_detection_model(self):
         """
         load the pre-trained YOLOv8n model
         """
-        return YOLO("yolov8n.pt")
+        return YOLO("yolo11n.pt", verbose=True)
 
     def main(self):
         # define some constants
+        print("started")
 
         video_cap = cv2.VideoCapture(str(self.input_path))
         writer = create_video_writer(video_cap, str(self.output_path))
@@ -150,12 +151,15 @@ class DodgeballTracker:
             # run the YOLO model on the frame
             res = []
             detections = self.run_model_on_frame(model, frame)
+            print("ici?")
             for data in detections.boxes.data.tolist():
+                print("la?")
                 is_good_box = self.plot_boxe_to_frame(data, frame)
                 if is_good_box:
                     res.append(data)
 
             # show the frame to our screen
+            print("tracker?")
             data_ = tracker.update(np.array(res), frame)
             self.print_tracker_data(data_, i, ts)
 
@@ -163,8 +167,10 @@ class DodgeballTracker:
 
             if cv2.waitKey(1) == ord("q"):
                 break
+        print("fini")
 
         video_cap.release()
         writer.release()
         cv2.destroyAllWindows()
         self.persist_points()
+        print("finished")