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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.

<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/Ag2e-5_NpS0" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen> </iframe>
Watch: Object Counting using Ultralytics YOLOv8 		<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/Fj9TStNBVoY" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen> </iframe>
Watch: Class-wise 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
Conveyor Belt Packets Counting Using Ultralytics YOLOv8 Fish Counting in Sea using Ultralytics YOLOv8

!!! Example "Object Counting using YOLOv8 Example"

=== "Count in Region"

    ```python
    import cv2
    from ultralytics import YOLO, solutions

    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 = solutions.ObjectCounter(
        view_img=True,
        reg_pts=region_points,
        classes_names=model.names,
        draw_tracks=True,
        line_thickness=2,
    )

    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()
    ```

=== "Count in Polygon"

    ```python
    import cv2
    from ultralytics import YOLO, solutions

    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 as a polygon with 5 points
    region_points = [(20, 400), (1080, 404), (1080, 360), (20, 360), (20, 400)]

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

    # Init Object Counter
    counter = solutions.ObjectCounter(
        view_img=True,
        reg_pts=region_points,
        classes_names=model.names,
        draw_tracks=True,
        line_thickness=2,
    )

    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()
    ```

=== "Count in Line"

    ```python
    import cv2
    from ultralytics import YOLO, solutions

    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 = solutions.ObjectCounter(
        view_img=True,
        reg_pts=line_points,
        classes_names=model.names,
        draw_tracks=True,
        line_thickness=2,
    )

    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
    import cv2
    from ultralytics import YOLO, solutions

    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 = solutions.ObjectCounter(
        view_img=True,
        reg_pts=line_points,
        classes_names=model.names,
        draw_tracks=True,
        line_thickness=2,
    )

    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

Argument ObjectCounter

Here's a table with the ObjectCounter arguments:

Name Type Default Description
classes_names dict None Dictionary of class names.
reg_pts list [(20, 400), (1260, 400)] List of points defining the counting region.
count_reg_color tuple (255, 0, 255) RGB color of the counting region.
count_txt_color tuple (0, 0, 0) RGB color of the count text.
count_bg_color tuple (255, 255, 255) RGB color of the count text background.
line_thickness int 2 Line thickness for bounding boxes.
track_thickness int 2 Thickness of the track lines.
view_img bool False Flag to control whether to display the video stream.
view_in_counts bool True Flag to control whether to display the in counts on the video stream.
view_out_counts bool True Flag to control whether to display the out counts on the video stream.
draw_tracks bool False Flag to control whether to draw the object tracks.
track_color tuple None RGB color of the tracks.
region_thickness int 5 Thickness of the object counting region.
line_dist_thresh int 15 Euclidean distance threshold for line counter.
cls_txtdisplay_gap int 50 Display gap between each class count.

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