going in the right direction.

object_tracking.py

@@ -1,8 +1,9 @@
 from ultralytics import YOLO
 import cv2
 from helper import create_video_writer, iter_frames, time_tracker
+from deep_sort_realtime.deepsort_tracker import DeepSort
 import matplotlib
-from numpy import arange
+import numpy as np
 
 Color = tuple[int, int, int]
 
@@ -45,6 +46,20 @@ def run_model_on_frame(model, frame):
     return model(frame)[0]
 
 
+def crazy_experiment(frame):
+    cv2.putText(frame,
+                "Hello world",
+                (50, 50),
+                cv2.FONT_HERSHEY_SIMPLEX,
+                2,
+                (0, 255, 0), 8)
+    cv2.line(frame, (0, 0), (511, 511), (255, 0, 0), 5)
+    pts = np.array([[50, 50], [500, 50], [500, 500], [50, 500], [50, 50]], np.int32)
+    pts = pts.reshape((-1, 1, 2))
+
+    cv2.polylines(frame, pts, True, (0, 255, 255), 8)
+
+
 def main(input_filename, output_filename, detected_box_objects):
     # define some constants
     detected_box_objects = sorted(detected_box_objects, reverse=True)
@@ -54,6 +69,7 @@ def main(input_filename, output_filename, detected_box_objects):
 
     # load the pre-trained YOLOv8n model
     model = YOLO("yolov8n.pt")
+    tracker = DeepSort(max_age=50)
 
     for frame in iter_frames(video_cap):
         with time_tracker():
@@ -75,7 +91,7 @@ def main(input_filename, output_filename, detected_box_objects):
 
 if __name__ == "__main__":
     detected_box_objects = [
-        DetectedObjectBox.from_viridis(threshold) for threshold in arange(0.5, 1, 0.05)
+        DetectedObjectBox.from_viridis(threshold) for threshold in np.arange(0.5, 1, 0.05)
     ]
 
     main("micro.mp4", "output.mp4", detected_box_objects)