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 参考了这个：YOLOv8只检测人（或其他一种或者多种类别）_yolov8只检测指定类别-CSDN博客

1. 只检测人：predict的时候指定参数classes=[0]

2. 只画框不要标签：plot的时候传入labels=False

3. 标签中去掉置信度：result.plot(conf=False)

示例代码如下：

#!/usr/bin/env python
import contextlib
from datetime import datetime
from pathlib import Path# pip install opencv-python pillow ultralytics rich
import cv2
from ultralytics import YOLO  # type:ignore[import-untyped]with contextlib.suppress(ImportError):from rich import printmodel = YOLO("yolov8s.pt")
person_cls = 0  # 人员标签的类别ID
total = 200  # 总共截多少桢
interval = 20  # 每隔几桢推理一次
folder = Path(__file__).parent / "images"  # 未检测到人的图片存在这里
folder.mkdir(exist_ok=True)cap = cv2.VideoCapture(0)  # 打开电脑摄像头
count = 0
while True:if not cap.isOpened():print(f"Failed to open video capture with {cap=}")breakok, frame = cap.read()if not ok:continuecount += 1if count > total:print(f"Success to capture {total} frames~")breakelif count % interval != 0:continue# 通过指定classes参数，限定要检测的类别# result = model(frame, classes=[person_cls], save=False, show=False, verbose=False)[0]result = model(frame, classes=[person_cls], save=False, verbose=False)[0]has_person = bool(result.summary())if has_person:print(f"{count=}, result: {result.verbose()}")result.show()else:file = folder / f"{count}.jpg"content = cv2.imencode(".jpg", result.plot())[1].tobytes()file.write_bytes(content)print(datetime.now(), f"{count=}, result: {has_person}, save to {file}")
cap.release()
print("✨ Done.")