yolo-11 物体计数实例

最新推荐文章于 2025-05-19 20:42:06 发布

转载最新推荐文章于 2025-05-19 20:42:06 发布

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原文链接：https://docs.ultralytics.com/zh/guides/object-counting/#real-world-applications

文章标签：

#YOLO

转载于 ultralytics物体计数https://docs.ultralytics.com/zh/guides/object-counting/#what-is-object-counting

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。

函数solutions.ObjectCounter()

函数 solutions.ObjectCounter().count(img)

img：参与计算的图片帧。

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, 400)]  # For line counting
region_points = [(20, 400), (1080, 400), (1080, 360), (20, 360)]  # For rectangle region counting
# region_points = [(20, 400), (1080, 400), (1080, 360), (20, 360), (20, 400)]  # For polygon region counting

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

# Init ObjectCounter
counter = solutions.ObjectCounter(
    show=True,  # Display the output
    region=region_points,  # Pass region points
    model="yolo11n.pt",  # model="yolo11n-obb.pt" for object counting using YOLO11 OBB model.
    # classes=[0, 2],  # If you want to count specific classes i.e person and car with COCO pretrained model.
    # show_in=True,  # Display in counts
    # show_out=True,  # Display out counts
    # line_width=2,  # Adjust the line width for bounding boxes and text display
)

# 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()