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使用对象计数Ultralytics YOLOv8

什么是对象计数？

使用 Ultralytics YOLOv8YOLOv8 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，可为人群分析和监控等各种场景提供高效、精确的物体计数。


观看： 使用对象计数Ultralytics YOLOv8
观看： 按类对象计数Ultralytics YOLOv8

物体计数的优势？

  • 资源优化：对象计数通过提供准确的计数和优化库存管理等应用中的资源分配，促进了高效的资源管理。
  • 增强安全性：物体计数可准确跟踪和计数实体，有助于主动探测威胁，从而增强安全性和监控能力。
  • 知情决策：物体计数为决策、优化零售、交通管理和其他各种领域的流程提供了宝贵的见解。

真实世界的应用

物流 水产养殖
使用传送带包装计数Ultralytics YOLOv8 利用海洋数鱼Ultralytics YOLOv8
使用传送带包装计数Ultralytics YOLOv8 利用海洋数鱼Ultralytics YOLOv8

使用YOLOv8 进行对象计数示例

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()

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()

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()

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()
区域可移动

点击区域边缘，可将其移动到框架内的任何位置

论据 ObjectCounter

下面的表格显示了 ObjectCounter 争论：

名称 类型 默认值 说明
names dict None 类名词典
reg_pts list [(20, 400), (1260, 400)] 定义计数区域的点列表。
line_thickness int 2 边界框的线条粗细
view_img bool False 用于控制是否显示视频流的标志。
view_in_counts bool True 用于控制是否在视频流中显示输入计数的标志。
view_out_counts bool True 用于控制是否在视频流中显示输出计数的标志。
draw_tracks bool False 标志，用于控制是否绘制对象轨迹。

论据 model.track

论据 类型 默认值 说明
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 或 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.

常见问题

如何使用Ultralytics YOLOv8 计数视频中的物体？

要使用Ultralytics YOLOv8 计数视频中的物体，可以按照以下步骤进行：

  1. 导入必要的程序库 (cv2, ultralytics).
  2. 加载预训练的YOLOv8 模型。
  3. 定义计算区域（如多边形、直线等）。
  4. 设置视频捕捉并初始化对象计数器。
  5. 处理每个帧，以跟踪定义区域内的对象并对其进行计数。

下面是一个简单的区域计数示例：

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")

在 "对象计数"部分探索更多配置和选项。

使用Ultralytics YOLOv8 进行物体计数有哪些优势？

使用Ultralytics YOLOv8 进行物体计数有几个优点：

  1. 资源优化：它通过提供精确的计数来促进有效的资源管理，帮助优化库存管理等行业的资源分配。
  2. 增强安全性：它通过准确跟踪和计算实体数量，帮助主动探测威胁，从而加强安全和监控。
  3. 知情决策：它为零售、交通管理等领域的决策和流程优化提供了宝贵的见解。

有关实际应用和代码示例，请访问对象计数的优势部分。

如何使用Ultralytics YOLOv8 对特定类别的对象进行计数？

要使用Ultralytics YOLOv8 统计特定类别的对象，您需要在跟踪阶段指定您感兴趣的类别。下面是Python 示例：

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])

在这个例子中 classes_to_count=[0, 2]，这意味着它会计算 0 和 2 (如人和车）。

在实时应用中，为什么要使用YOLOv8 而不是其他物体检测模型？

Ultralytics YOLOv8 与其他物体检测模型（如 Faster R-CNN、SSD 和以前的YOLO 版本）相比，它具有多项优势：

  1. 速度和效率： YOLOv8 具有实时处理能力，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：它为物体检测和跟踪任务提供了最先进的准确性，减少了误报的数量，提高了整个系统的可靠性。
  3. 易于集成： YOLOv8 可与各种平台和设备（包括移动设备和边缘设备）无缝集成，这对现代人工智能应用至关重要。
  4. 灵活性：支持对象检测、分割和跟踪等各种任务，可配置模型以满足特定用例的要求。

请查阅Ultralytics YOLOv8 文档，深入了解其功能和性能比较。

我能否将YOLOv8 用于人群分析和交通管理等高级应用？

Ultralytics YOLOv8 具有实时检测能力、可扩展性和集成灵活性，非常适合人群分析和交通管理等高级应用。其先进的功能可在动态环境中实现高精度的目标跟踪、计数和分类。使用实例包括

  • 人群分析：监控和管理大型集会，确保安全并优化人流。
  • 交通管理：跟踪和统计车辆，分析交通模式，实时管理交通拥堵。

有关详细信息和实施细节，请参阅YOLOv8 对象计数的实际应用指南。


📅创建于 9 个月前 ✏️2 天前已更新
翔宇 MatthewNoyce 里兹万-穆纳瓦 IvorZhu331 AyushExel

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