# 使用 Ultralytics 进行目标计数 ## 什么是目标计数？ 使用[Ultralytics YOLO11](https://github.com/ultralytics/ultralytics/)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](https://www.ultralytics.com/glossary/deep-learning-dl)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。 ````{prf:definition} :label: object-counting 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](https://github.com/ultralytics/ultralytics/)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](https://www.ultralytics.com/glossary/deep-learning-dl)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](https://github.com/ultralytics/docs/releases/download/0/conveyor-belt-packets-counting.avif) | ![Fish Counting in Sea using Ultralytics YOLO11](https://github.com/ultralytics/docs/releases/download/0/fish-counting-in-sea-using-ultralytics-yolov8.avif) | | 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 `````{tab-set} ````{tab-item} CLI ```bash # 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)] ``` ```` ````{tab-item} Python ```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() ``` ```` ````{tab-item} OBB Object Counting ```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)) # 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() ``` ```` ````{tab-item} Count in Polygon ```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), (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() ``` ```` ````{tab-item} Count in Line ```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 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() ``` ```` ````{tab-item} Specific Classes ```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)) # 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： ```python 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](https://www.ultralytics.com/glossary/object-detection)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](https://docs.ultralytics.com/models/yolo11/)，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。