实用指南：YOLO系列——实时屏幕检测

通过PIL的ImageGrab.grab可以截取屏幕,转换成BGR格式后就可以给YOLO进行检测，一旦屏幕上出现指定的内容，就会标记出来。

import cv2
from ultralytics import YOLO
from PIL import ImageGrab
import numpy as np
import cv2 as cv
model = YOLO("../yolov8n.pt")
a = (100,200,800,1000)#None 全屏，[100,200,800,1000]
a=None
while 1:
    scrn = ImageGrab.grab(bbox=a)
    #把RGB->BGR
    scrn = np.array(scrn)
    scrn = cv.cvtColor(scrn,cv2.COLOR_RGB2BGR)
    #这下YOLO可以用了 GBR
    results = model.predict(scrn)
    #results[0]保存了第0张图片的x,y,x,y坐标，conf每个目标的置信度，cls每个飙的泪飙
    # for box in results[0].boxes:
    #     print("坐标：",box.xyxy[0].tolist())
    #     print("置信度：",box.conf[0].item())
    #     print("类别ID:",box.cls[0].item())
    annotated_frame=results[0].plot()
    cv2.imshow('jian',annotated_frame) #窗口名jian,后面是检测到的帧信息
    if cv2.waitKey(1) &0xFF == ord('q'):
        break
cv2.destroyAllWindows()

屏幕一旦出现关注的目标就把屏幕保存，比如抓舞弊：

import cv2
from ultralytics import YOLO
from PIL import ImageGrab
import numpy as np
import cv2 as cv
model = YOLO(r"D:\PyCharm\LearningYOLO\da_fa_yolo\runs\detect\train4\weights\best.pt")
a = (100,200,800,1000)#None 全屏，[100,200,800,1000]
a=None
while 1:
    scrn = ImageGrab.grab(bbox=a)
    #把RGB->BGR
    scrn1 = np.array(scrn)
    scrn1 = cv.cvtColor(scrn1,cv2.COLOR_RGB2BGR)
    #这下YOLO可以用了 GBR
    results = model.predict(scrn)
    #results[0]保存了第0张图片的x,y,x,y坐标，conf每个目标的置信度，cls每个飙的泪飙
    # for box in results[0].boxes:
    #     print("坐标：",box.xyxy[0].tolist())
    #     print("置信度：",box.conf[0].item())
    #     print("类别ID:",box.cls[0].item())
    c=0
    t=500
    for box in results[0].boxes:
        if box.cls[0]== 0:
            print("找到了目标")
            # 保存截图
            scrn.save(fr"./t/{c}.png")
            # 发出蜂鸣
            winsound.Beep(1000,t) #蜂鸣的频率1000，维持时间ms
            c+=1
    annotated_frame = results[0].plot()
    cv2.imshow('jian',annotated_frame)
    if cv2.waitKey(1)&0xFF==ord('q'):
        break
cv2.destroyAllWindows()