使用 Ultralytics 进行目标计数

什么是目标计数？

使用Ultralytics YOLO11进行目标计数涉及在视频和摄像头流中准确地识别和计数特定对象。YOLO11 擅长实时应用，凭借其最先进的算法和深度学习能力，为诸如人群分析和监控等多种场景提供高效且精确的对象计数。

Definition 1

目标计数（Object Counting）是计算机视觉领域中的基本任务，它旨在识别并计算图像或视频中的对象数量。此过程通常涉及到多个步骤，包括对象的检测、分类以及计数。在实际应用中，对象计数技术被广泛应用于人群密度估计、交通监控、野生动物调查等多个领域。

什么是目标计数？

使用Ultralytics YOLOv11进行对象计数涉及在视频和摄像头流中准确识别并计数特定对象。YOLOv11在实时应用中表现出色，凭借其先进的算法和深度学习能力，为人群分析和监控等不同场景提供高效且精确的目标计数服务。

Watch: Object Counting using Ultralytics YOLOv8

Watch: Class-wise Object Counting using Ultralytics YOLO11

目标计数的优势？

• 资源优化：目标计数通过提供精确的计数，帮助实现高效的资源管理，并优化如库存管理等应用中的资源分配。

• 增强安全性：目标计数通过准确追踪和计数实体，增强了安全监控能力，有助于主动威胁检测。

• 明智决策：目标计数为决策提供了宝贵的见解，优化了零售、交通管理和多个其他领域的流程。

Real World Applications

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

使用 YOLO11 进行目标计数的示例

CLI

# Run a counting example
yolo solutions count show=True

# Pass a source video
yolo solutions count source="path/to/video/file.mp4"

# Pass region coordinates
yolo solutions count region=[(20, 400), (1080, 404), (1080, 360), (20, 360)]

Python

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()

OBB Object Counting

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()

Count in Polygon

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()

Count in Line

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()

Specific Classes

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()

ObjectCounter 参数

ObjectCounter 参数表：

名称	类型	默认值	描述
model	str	None	Ultralytics YOLO 模型文件的路径
region	list	[(20, 400), (1260, 400)]	定义计数区域的点列表
line_width	int	2	边界框的线条粗细
show	bool	False	控制是否显示视频流的标志
show_in	bool	True	控制是否在视频流上显示进入计数的标志
show_out	bool	True	控制是否在视频流上显示离开计数的标志

常见问题解答

如何使用 Ultralytics YOLO11 在视频中计数对象？

要使用 Ultralytics YOLO11 在视频中进行对象计数，您可以按照以下步骤操作：

1. 导入必要的库（cv2, ultralytics）。

2. 定义计数区域（例如，多边形、线条等）。

3. 设置视频捕获并初始化对象计数器。

4. 处理每一帧以跟踪对象并在定义的区域内进行计数。

以下是在特定区域内计数的简单示例：

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")

在这个例子中，classes_to_count=[0, 2] 表示它统计类别为 0 和 2 的对象（例如，人和汽车）。

为什么在实时应用中选择 YOLO11 而不是其他目标检测模型？

Ultralytics YOLO11相较于Faster R-CNN、SSD以及之前的YOLO版本，提供了以下几个优势：

1. 速度与效率：YOLO11提供实时处理能力，非常适合需要高速推理的应用，如监控和自动驾驶。

2. 准确性：它在目标检测和跟踪任务上提供了最先进的准确性，减少了误报的数量，并提高了系统的整体可靠性。

3. 易于集成：YOLO11可以无缝集成到各种平台和设备中，包括移动和边缘设备，这对于现代AI应用至关重要。

4. 灵活性：支持各种任务，如目标检测、分割和跟踪，并且可以通过配置模型来满足特定的用例需求。

查看 Ultralytics YOLO11文档，深入了解其功能和性能比较。

可以运用YOLOv11进行高级应用，如人群分析和交通管理吗？

是的，Ultralytics YOLO11非常适合用于高级应用，如人群分析和交通管理，因为它具备实时检测能力、可扩展性和集成灵活性。其先进的功能允许在动态环境中进行高精度的对象跟踪、计数和分类。例如使用案例包括：

• 人群分析：监控和管理大型集会，确保安全并优化人群流动。

• 交通管理：追踪和统计车辆，分析交通模式，并实时管理拥堵情况。