yolov8_test/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.

Watch: Object Counting using Ultralytics YOLOv8

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 using YOLOv8 Example"

=== "Region"

    ```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"
    w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))

    # Define region points
    region_points = [(20, 400), (1080, 404), (1080, 360), (20, 360)]

    # Video writer
    video_writer = cv2.VideoWriter("object_counting_output.avi",
                           cv2.VideoWriter_fourcc(*'mp4v'),
                           fps,
                           (w, h))

    # Init Object Counter
    counter = object_counter.ObjectCounter()
    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:
            print("Video frame is empty or video processing has been successfully completed.")
            break
        tracks = model.track(im0, persist=True, show=False)

        im0 = counter.start_counting(im0, tracks)
        video_writer.write(im0)

    cap.release()
    video_writer.release()
    cv2.destroyAllWindows()
    ```

=== "Line"

    ```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"
    w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))

    # Define line points
    line_points = [(20, 400), (1080, 400)]

    # Video writer
    video_writer = cv2.VideoWriter("object_counting_output.avi",
                           cv2.VideoWriter_fourcc(*'mp4v'),
                           fps,
                           (w, h))

    # Init Object Counter
    counter = object_counter.ObjectCounter()
    counter.set_args(view_img=True,
                     reg_pts=line_points,
                     classes_names=model.names,
                     draw_tracks=True)

    while cap.isOpened():
        success, im0 = cap.read()
        if not success:
            print("Video frame is empty or video processing has been successfully completed.")
            break
        tracks = model.track(im0, persist=True, show=False)

        im0 = counter.start_counting(im0, tracks)
        video_writer.write(im0)

    cap.release()
    video_writer.release()
    cv2.destroyAllWindows()
    ```

=== "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"
    w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))

    line_points = [(20, 400), (1080, 400)]  # line or region points
    classes_to_count = [0, 2]  # person and car classes for count

    # Video writer
    video_writer = cv2.VideoWriter("object_counting_output.avi",
                           cv2.VideoWriter_fourcc(*'mp4v'),
                           fps,
                           (w, h))

    # Init Object Counter
    counter = object_counter.ObjectCounter()
    counter.set_args(view_img=True,
                     reg_pts=line_points,
                     classes_names=model.names,
                     draw_tracks=True)

    while cap.isOpened():
        success, im0 = cap.read()
        if not success:
            print("Video frame is empty or video processing has been successfully completed.")
            break
        tracks = model.track(im0, persist=True, show=False,
                             classes=classes_to_count)

        im0 = counter.start_counting(im0, tracks)
        video_writer.write(im0)

    cap.release()
    video_writer.release()
    cv2.destroyAllWindows()
    ```

???+ 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 frames with counts
view_in_counts	bool	True	Display incounts only on video frame
view_out_counts	bool	True	Display outcounts only on video frame
line_thickness	int	2	Increase bounding boxes thickness
reg_pts	list	[(20, 400), (1260, 400)]	Points defining the Region Area
classes_names	dict	model.model.names	Dictionary of Class Names
region_color	RGB Color	(255, 0, 255)	Color of the Object counting Region or Line
track_thickness	int	2	Thickness of Tracking Lines
draw_tracks	bool	False	Enable drawing Track lines
track_color	RGB Color	(0, 255, 0)	Color for each track line
line_dist_thresh	int	15	Euclidean Distance threshold for line counter
count_txt_thickness	int	2	Thickness of Object counts text
count_txt_color	RGB Color	(0, 0, 0)	Foreground color for Object counts text
count_color	RGB Color	(255, 255, 255)	Background color for Object counts text
region_thickness	int	5	Thickness for object counter region or line

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]
verbose	bool	True	Display the object tracking results