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""" Step 4: Init new stracks"""
for inew in u_detection:
    track = detections[inew]
    if track.score < 0.7:
        continue
    else:
        track.activate(self.kalman_filter, self.frame_id) # Initiate a new track

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class ByteTrackArgument:
    track_thresh = 0.5 # High_threshold
    track_buffer = 50 # Number of frame lost tracklets are kept
    match_thresh = 0.8 # Matching threshold for first stage linear assignment
    aspect_ratio_thresh = 10.0 # Minimum bounding box aspect ratio
    min_box_area = 1.0 # Minimum bounding box area
    mot20 = False # If used, bounding boxes are not clipped.

MIN_THRESHOLD = 0.001

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trackers = [BYTETracker(ByteTrackArgument), BYTETracker(ByteTrackArgument), BYTETracker(ByteTrackArgument)]
frame_id = 0
results = []
history = deque()

while True:
    if frame_id % 20 == 0:
        print(f'Processing frame {frame_id}.')
    ret_val, online_im = cap.read()
    if ret_val:

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tracker = BYTETracker(args)
for image in images:
   dets = detector(image)
   online_targets = tracker.update(dets, info_imgs, img_size)

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### Load and Train YOLOv8 Model ###

# Load a model
model = YOLO("yolov8m.pt")  # load a pretrained model (recommended for training)

# Train the model
results = model.train(data=f"{HOME}/datasets/basketball.yaml", imgsz=TEST_SIZE, batch=BATCH_SIZE, epochs=EPOCHS, plots=True)

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!git clone https://github.com/ifzhang/ByteTrack.git
%cd ByteTrack
!pip3 install -r requirements.txt
!python3 setup.py develop
!pip3 install cython
!pip3 install 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
!pip3 install cython_bbox

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!yolo task=detect mode=predict model=./runs/detect/train/weights/best.pt conf=0.4 source=./datasets/aerial_view_ships.mp4 save=True

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from ultralytics import YOLO

model = YOLO("./runs/detect/train/weights/best.pt")

results = model.predict(source="./datasets/val/")

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!yolo task=detect mode=predict model=./runs/detect/train/weights/best.pt conf=0.4 source=./datasets/val/ save=True