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

comments	description	keywords
true	Learn to accurately identify and count objects in real-time using Ultralytics YOLOv8 for applications like crowd analysis and surveillance.	object counting, YOLOv8, Ultralytics, real-time object detection, AI, deep learning, object tracking, crowd analysis, surveillance, resource optimization

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

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

!!! 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,
        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,
        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,
        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,
        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
names	dict	None	Dictionary of classes 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

FAQ

How do I count objects in a video using Ultralytics YOLOv8?

To count objects in a video using Ultralytics YOLOv8, you can follow these steps:

1. Import the necessary libraries (cv2, ultralytics).
2. Load a pretrained YOLOv8 model.
3. Define the counting region (e.g., a polygon, line, etc.).
4. Set up the video capture and initialize the object counter.
5. Process each frame to track objects and count them within the defined region.

Here's a simple example for counting in a region:

import cv2

from ultralytics import YOLO, solutions


def count_objects_in_region(video_path, output_video_path, model_path):
    """Count objects in a specific region within a video."""
    model = YOLO(model_path)
    cap = cv2.VideoCapture(video_path)
    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))
    region_points = [(20, 400), (1080, 404), (1080, 360), (20, 360)]
    video_writer = cv2.VideoWriter(output_video_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))
    counter = solutions.ObjectCounter(
        view_img=True, reg_pts=region_points, 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_objects_in_region("path/to/video.mp4", "output_video.avi", "yolov8n.pt")

Explore more configurations and options in the Object Counting section.

What are the advantages of using Ultralytics YOLOv8 for object counting?

Using Ultralytics YOLOv8 for object counting offers several advantages:

1. Resource Optimization: It facilitates efficient resource management by providing accurate counts, helping optimize resource allocation in industries like inventory management.
2. Enhanced Security: It enhances security and surveillance by accurately tracking and counting entities, aiding in proactive threat detection.
3. Informed Decision-Making: It offers valuable insights for decision-making, optimizing processes in domains like retail, traffic management, and more.

For real-world applications and code examples, visit the Advantages of Object Counting section.

How can I count specific classes of objects using Ultralytics YOLOv8?

To count specific classes of objects using Ultralytics YOLOv8, you need to specify the classes you are interested in during the tracking phase. Below is a Python example:

import cv2

from ultralytics import YOLO, solutions


def count_specific_classes(video_path, output_video_path, model_path, classes_to_count):
    """Count specific classes of objects in a video."""
    model = YOLO(model_path)
    cap = cv2.VideoCapture(video_path)
    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)]
    video_writer = cv2.VideoWriter(output_video_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))
    counter = solutions.ObjectCounter(
        view_img=True, reg_pts=line_points, 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()


count_specific_classes("path/to/video.mp4", "output_specific_classes.avi", "yolov8n.pt", [0, 2])

In this example, classes_to_count=[0, 2], which means it counts objects of class 0 and 2 (e.g., person and car).

Why should I use YOLOv8 over other object detection models for real-time applications?

Ultralytics YOLOv8 provides several advantages over other object detection models like Faster R-CNN, SSD, and previous YOLO versions:

1. Speed and Efficiency: YOLOv8 offers real-time processing capabilities, making it ideal for applications requiring high-speed inference, such as surveillance and autonomous driving.
2. Accuracy: It provides state-of-the-art accuracy for object detection and tracking tasks, reducing the number of false positives and improving overall system reliability.
3. Ease of Integration: YOLOv8 offers seamless integration with various platforms and devices, including mobile and edge devices, which is crucial for modern AI applications.
4. Flexibility: Supports various tasks like object detection, segmentation, and tracking with configurable models to meet specific use-case requirements.

Check out Ultralytics YOLOv8 Documentation for a deeper dive into its features and performance comparisons.

Can I use YOLOv8 for advanced applications like crowd analysis and traffic management?

Yes, Ultralytics YOLOv8 is perfectly suited for advanced applications like crowd analysis and traffic management due to its real-time detection capabilities, scalability, and integration flexibility. Its advanced features allow for high-accuracy object tracking, counting, and classification in dynamic environments. Example use cases include:

• Crowd Analysis: Monitor and manage large gatherings, ensuring safety and optimizing crowd flow.
• Traffic Management: Track and count vehicles, analyze traffic patterns, and manage congestion in real-time.

For more information and implementation details, refer to the guide on Real World Applications of object counting with YOLOv8.