Object Counting using Ultralytics YOLO11

오브젝트 카운팅이란 무엇인가요?

Object counting with Ultralytics YOLO11 involves accurate identification and counting of specific objects in videos and camera streams. YOLO11 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 YOLO11

Watch: Class-wise Object Counting using Ultralytics YOLO11

오브젝트 카운팅의 장점은?

리소스 최적화: 개체 카운팅은 정확한 개수를 제공하고 재고 관리와 같은 애플리케이션에서 리소스 할당을 최적화하여 효율적인 리소스 관리를 용이하게 합니다.
보안 강화: 개체 카운팅은 개체를 정확하게 추적하고 계산하여 사전 위협 탐지를 지원함으로써 보안과 감시를 강화합니다.
정보에 기반한 의사 결정: 객체 카운팅은 의사 결정, 소매업, 교통 관리 및 기타 다양한 영역의 프로세스 최적화를 위한 귀중한 인사이트를 제공합니다.

실제 애플리케이션

물류	양식업

Conveyor Belt Packets Counting Using Ultralytics YOLO11	Fish Counting in Sea using Ultralytics YOLO11

Object Counting using YOLO11 Example

지역 내 카운트OBB Object Counting다각형으로 계산줄서서 계산특정 클래스

import cv2

from ultralytics import solutions

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(
    show=True,
    region=region_points,
    model="yolo11n.pt",
)

# Process video
while cap.isOpened():
    success, im0 = cap.read()
    if not success:
        print("Video frame is empty or video processing has been successfully completed.")
        break
    im0 = counter.count(im0)
    video_writer.write(im0)

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

import cv2

from ultralytics import solutions

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 or region 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(
    show=True,
    region=line_points,
    model="yolo11n-obb.pt",
)

# Process video
while cap.isOpened():
    success, im0 = cap.read()
    if not success:
        print("Video frame is empty or video processing has been successfully completed.")
        break
    im0 = counter.count(im0)
    video_writer.write(im0)

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

import cv2

from ultralytics import solutions

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), (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(
    show=True,
    region=region_points,
    model="yolo11n.pt",
)

# Process video
while cap.isOpened():
    success, im0 = cap.read()
    if not success:
        print("Video frame is empty or video processing has been successfully completed.")
        break
    im0 = counter.count(im0)
    video_writer.write(im0)

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

import cv2

from ultralytics import solutions

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
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(
    show=True,
    region=line_points,
    model="yolo11n.pt",
)

# Process video
while cap.isOpened():
    success, im0 = cap.read()
    if not success:
        print("Video frame is empty or video processing has been successfully completed.")
        break
    im0 = counter.count(im0)
    video_writer.write(im0)

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

import cv2

from ultralytics import solutions

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))

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

# Init Object Counter
counter = solutions.ObjectCounter(
    show=True,
    model="yolo11n.pt",
    classes=[0, 1],
)

# Process video
while cap.isOpened():
    success, im0 = cap.read()
    if not success:
        print("Video frame is empty or video processing has been successfully completed.")
        break
    im0 = counter.count(im0)
    video_writer.write(im0)

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

인수 `ObjectCounter`

다음은 표입니다. ObjectCounter 인수를 사용합니다:

이름	유형	기본값	설명
`model`	`str`	`None`	Path to Ultralytics YOLO Model File
`region`	`list`	`[(20, 400), (1260, 400)]`	계산 영역을 정의하는 포인트 목록입니다.
`line_width`	`int`	`2`	경계 상자의 선 두께입니다.
`show`	`bool`	`False`	비디오 스트림 표시 여부를 제어하는 플래그입니다.
`show_in`	`bool`	`True`	비디오 스트림에 인 카운트를 표시할지 여부를 제어하는 플래그입니다.
`show_out`	`bool`	`True`	비디오 스트림에 아웃 카운트를 표시할지 여부를 제어하는 플래그입니다.

인수 `model.track`

인수	유형	기본값	설명
`source`	`str`	`None`	Specifies the source directory for images or videos. Supports file paths and URLs.
`persist`	`bool`	`False`	Enables persistent tracking of objects between frames, maintaining IDs across video sequences.
`tracker`	`str`	`botsort.yaml`	Specifies the tracking algorithm to use, e.g., `bytetrack.yaml` 또는 `botsort.yaml`.
`conf`	`float`	`0.3`	Sets the confidence threshold for detections; lower values allow more objects to be tracked but may include false positives.
`iou`	`float`	`0.5`	Sets the Intersection over Union (IoU) threshold for filtering overlapping detections.
`classes`	`list`	`None`	Filters results by class index. For example, `classes=[0, 2, 3]` only tracks the specified classes.
`verbose`	`bool`	`True`	Controls the display of tracking results, providing a visual output of tracked objects.

자주 묻는 질문

How do I count objects in a video using Ultralytics YOLO11?

To count objects in a video using Ultralytics YOLO11, you can follow these steps:

필요한 라이브러리 가져오기(cv2, ultralytics).
카운팅 영역(예: 다각형, 선 등)을 정의합니다.
동영상 캡처를 설정하고 개체 카운터를 초기화합니다.
각 프레임을 처리하여 객체를 추적하고 정의된 영역 내에서 객체를 계산합니다.

다음은 한 지역에서 계산하는 간단한 예입니다:

import cv2

from ultralytics import solutions


def count_objects_in_region(video_path, output_video_path, model_path):
    """Count objects in a specific region within a video."""
    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))
    video_writer = cv2.VideoWriter(output_video_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))

    region_points = [(20, 400), (1080, 404), (1080, 360), (20, 360)]
    counter = solutions.ObjectCounter(show=True, region=region_points, model=model_path)

    while cap.isOpened():
        success, im0 = cap.read()
        if not success:
            print("Video frame is empty or video processing has been successfully completed.")
            break
        im0 = counter.count(im0)
        video_writer.write(im0)

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


count_objects_in_region("path/to/video.mp4", "output_video.avi", "yolo11n.pt")

개체 수 계산 섹션에서 더 많은 구성 및 옵션을 살펴보세요.

What are the advantages of using Ultralytics YOLO11 for object counting?

Using Ultralytics YOLO11 for object counting offers several advantages:

리소스 최적화: 정확한 카운트를 제공하여 효율적인 리소스 관리를 용이하게 하고, 재고 관리와 같은 산업에서 리소스 할당을 최적화하는 데 도움을 줍니다.
보안 강화: 엔티티를 정확하게 추적하고 계산하여 보안 및 감시를 강화하여 선제적인 위협 탐지를 지원합니다.
정보에 기반한 의사 결정: 리테일, 트래픽 관리 등의 영역에서 의사 결정을 위한 귀중한 인사이트를 제공하여 프로세스를 최적화합니다.

실제 애플리케이션과 코드 예제를 보려면 객체 카운팅의 장점 섹션을 참조하세요.

How can I count specific classes of objects using Ultralytics YOLO11?

To count specific classes of objects using Ultralytics YOLO11, you need to specify the classes you are interested in during the tracking phase. Below is a Python example:

import cv2

from ultralytics import solutions


def count_specific_classes(video_path, output_video_path, model_path, classes_to_count):
    """Count specific classes of objects in a video."""
    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))
    video_writer = cv2.VideoWriter(output_video_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))

    line_points = [(20, 400), (1080, 400)]
    counter = solutions.ObjectCounter(show=True, region=line_points, model=model_path, classes=classes_to_count)

    while cap.isOpened():
        success, im0 = cap.read()
        if not success:
            print("Video frame is empty or video processing has been successfully completed.")
            break
        im0 = counter.count(im0)
        video_writer.write(im0)

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


count_specific_classes("path/to/video.mp4", "output_specific_classes.avi", "yolo11n.pt", [0, 2])

이 예제에서는 classes_to_count=[0, 2]클래스의 객체를 계산한다는 의미입니다. 0 그리고 2 (예: 사람 및 자동차).

Why should I use YOLO11 over other object detection models for real-time applications?

Ultralytics YOLO11 provides several advantages over other object detection models like Faster R-CNN, SSD, and previous YOLO versions:

Speed and Efficiency: YOLO11 offers real-time processing capabilities, making it ideal for applications requiring high-speed inference, such as surveillance and autonomous driving.
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.
Ease of Integration: YOLO11 offers seamless integration with various platforms and devices, including mobile and edge devices, which is crucial for modern AI applications.
유연성: 특정 사용 사례 요구 사항을 충족하도록 구성 가능한 모델을 통해 객체 감지, 세분화, 추적과 같은 다양한 작업을 지원합니다.

Check out Ultralytics YOLO11 Documentation for a deeper dive into its features and performance comparisons.

Can I use YOLO11 for advanced applications like crowd analysis and traffic management?

Yes, Ultralytics YOLO11 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: