ultralytics/docs/en/guides/object-counting.md

comments	description	keywords
true	Object Counting Using Ultralytics YOLOv8	Ultralytics, YOLOv8, Object Detection, Object Counting, Object Tracking, Notebook, IPython Kernel, CLI, Python SDK

Object Counting using Ultralytics YOLOv8 🚀

What is Object Counting?

Object counting with Ultralytics YOLOv8 involves accurate identification and counting of specific objects in videos and camera streams. YOLOv8 excels in real-time applications, providing efficient and precise object counting for various scenarios like crowd analysis and surveillance, thanks to its state-of-the-art algorithms and deep learning capabilities.

Advantages of Object Counting?

• Resource Optimization: Object counting facilitates efficient resource management by providing accurate counts, and optimizing resource allocation in applications like inventory management.
• Enhanced Security: Object counting enhances security and surveillance by accurately tracking and counting entities, aiding in proactive threat detection.
• Informed Decision-Making: Object counting offers valuable insights for decision-making, optimizing processes in retail, traffic management, and various other domains.

Real World Applications

Logistics	Aquaculture
Conveyor Belt Packets Counting Using Ultralytics YOLOv8	Fish Counting in Sea using Ultralytics YOLOv8

!!! Example "Object Counting Example"

=== "Object Counting"
    ```python
    from ultralytics import YOLO
    from ultralytics.solutions import object_counter
    import cv2

    model = YOLO("yolov8n.pt")
    cap = cv2.VideoCapture("path/to/video/file.mp4")
    assert cap.isOpened(), "Error reading video file"

    counter = object_counter.ObjectCounter()  # Init Object Counter
    region_points = [(20, 400), (1080, 404), (1080, 360), (20, 360)]
    counter.set_args(view_img=True,
                     reg_pts=region_points,
                     classes_names=model.names,
                     draw_tracks=True)

    while cap.isOpened():
        success, im0 = cap.read()
        if not success:
            exit(0)
        tracks = model.track(im0, persist=True, show=False)
        im0 = counter.start_counting(im0, tracks)
    ```

=== "Object Counting with Specific Classes"
    ```python
    from ultralytics import YOLO
    from ultralytics.solutions import object_counter
    import cv2

    model = YOLO("yolov8n.pt")
    cap = cv2.VideoCapture("path/to/video/file.mp4")
    assert cap.isOpened(), "Error reading video file"

    classes_to_count = [0, 2]
    counter = object_counter.ObjectCounter()  # Init Object Counter
    region_points = [(20, 400), (1080, 404), (1080, 360), (20, 360)]
    counter.set_args(view_img=True,
                     reg_pts=region_points,
                     classes_names=model.names,
                     draw_tracks=True)

    while cap.isOpened():
        success, im0 = cap.read()
        if not success:
            exit(0)
        tracks = model.track(im0, persist=True,
                            show=False,
                            classes=classes_to_count)
        im0 = counter.start_counting(im0, tracks)
    ```

=== "Object Counting with Save Output"
    ```python
    from ultralytics import YOLO
    from ultralytics.solutions import object_counter
    import cv2

    model = YOLO("yolov8n.pt")
    cap = cv2.VideoCapture("path/to/video/file.mp4")
    assert cap.isOpened(), "Error reading video file"

    video_writer = cv2.VideoWriter("object_counting.avi",
                                   cv2.VideoWriter_fourcc(*'mp4v'),
                                   int(cap.get(5)),
                                   (int(cap.get(3)), int(cap.get(4))))

    counter = object_counter.ObjectCounter()  # Init Object Counter
    region_points = [(20, 400), (1080, 404), (1080, 360), (20, 360)]
    counter.set_args(view_img=True,
                     reg_pts=region_points,
                     classes_names=model.names,
                     draw_tracks=True)

    while cap.isOpened():
        success, im0 = cap.read()
        if not success:
            exit(0)
        tracks = model.track(im0, persist=True, show=False)
        im0 = counter.start_counting(im0, tracks)
        video_writer.write(im0)

    video_writer.release()
    ```

???+ tip "Region is Movable"

You can move the region anywhere in the frame by clicking on its edges

Optional Arguments set_args

Name	Type	Default	Description
view_img	bool	False	Display the frame with counts
line_thickness	int	2	Increase the thickness of count value
reg_pts	list	(20, 400), (1080, 404), (1080, 360), (20, 360)	Region Area Points
classes_names	dict	model.model.names	Classes Names Dict
region_color	tuple	(0, 255, 0)	Region Area Color
track_thickness	int	2	Tracking line thickness
draw_tracks	bool	False	Draw Tracks lines

Arguments model.track

Name	Type	Default	Description
source	im0	None	source directory for images or videos
persist	bool	False	persisting tracks between frames
tracker	str	botsort.yaml	Tracking method 'bytetrack' or 'botsort'
conf	float	0.3	Confidence Threshold
iou	float	0.5	IOU Threshold
classes	list	None	filter results by class, i.e. classes=0, or classes=[0,2,3]