ScreenCapture屏幕图像类

import cv2
import mss
import numpy as npclass ScreenCapture:"""parameters----------screen_resolution : Tuple[int, int]屏幕宽高，分别为x，ycapture_region : Tuple[float, float]实际截图范围，分别为x，y，(1.0, 1.0)表示全屏检测，越低检测范围越小(始终保持屏幕中心为中心)window_name : str显示窗口名exit_code : int结束窗口的退出键值，为键盘各键对应的ASCII码值，默认是ESC键"""def __init__(self, screen_resolution=(1920, 1080), capture_region=(0.5, 0.5), window_name='test', exit_code=0x1B):self.screen_capture = mss.mss()  # 实例化mss，并使用高效模式 mon=-1, optimize=Trueself.screen_width = screen_resolution[0]  # 屏幕的宽self.screen_height = screen_resolution[1]  # 屏幕的高self.capture_region = capture_region  # 捕获区域self.screen_center_x, self.screen_center_y = self.screen_width // 2, self.screen_height // 2  # 屏幕中心点坐标# 截图区域self.capture_width, self.capture_height = int(self.screen_width * self.capture_region[0]), int(self.screen_height * self.capture_region[1])  # 宽高self.capture_left, self.capture_top = int(0 + self.screen_width // 2 * (1. - self.capture_region[0])), int(0 + self.screen_height // 2 * (1. - self.capture_region[1]))  # 原点self.display_window_width, self.display_window_height = self.screen_width // 3, self.screen_height // 3  # 显示窗口大小self.monitor_settings = {'left': self.capture_left,'top': self.capture_top,'width': self.capture_width,'height': self.capture_height}self.window_name = window_nameself.exit_code = exit_codeself.img = Nonedef grab_screen_mss(self, monitor=None):if monitor is None:monitor = self.monitor_settings# cap.grab截取图片，np.array将图片转为数组，cvtColor将BRGA转为BRG,去掉了透明通道return cv2.cvtColor(np.array(self.screen_capture.grab(monitor)), cv2.COLOR_BGRA2BGR)

YOLO模型根据图片的数组推断

import cv2
import os
import numpy as np
from ultralytics import YOLO
from PIL import Image, ImageDraw, ImageFont
from ScreenCapture import ScreenCapture
def run(sc):# 实例化 YOLO 模型model = YOLO(r'D:\GitProjects\ultralytics\runs\models\yolov8l.pt' )# 设置字体fontStyle = ImageFont.truetype(r'D:\GitProjects\ultralytics\runs\SIMSUN.ttf' , 48, encoding=' utf-8' )  # 中文字体文件# 循环体while True:# 截屏img = Image.fromarray(np.uint8(sc.grab_screen_mss()))# 实例化一个图像绘制对象draw = ImageDraw.Draw(img)# 利用模型进行图像预测results = model.predict(source=img, conf=0.75, iou=0.75)# 遍历结果，detectionfor result in results:# detectionif len(result.boxes.xyxy) > 0:boxes_conf = np.array(result.boxes.conf.tolist())# 确定检测结果的数量boxes_conf_n = len(boxes_conf)if boxes_conf_n > 0:boxes_xyxy = result.boxes.xyxy.tolist()boxes_cls = result.boxes.cls.tolist()boxes_xy_center = []for i, box_xyxy in enumerate(boxes_xyxy):draw.rectangle(box_xyxy, outline=(0, 0, 255), width=5)draw.text((int(box_xyxy[0]), int(box_xyxy[1]) - 20), str(int(boxes_cls[i])), (0, 0, 255),font=fontStyle)boxes_xy_center.append((((box_xyxy[0] + box_xyxy[2]) / 2) + sc.capture_left,((box_xyxy[1] + box_xyxy[3]) / 2) + sc.capture_top))cv2.namedWindow(sc.window_name, cv2.WINDOW_NORMAL)  # cv2.WINDOW_NORMAL 根据窗口大小设置图片大小cv2.resizeWindow(sc.window_name, sc.display_window_width, sc.display_window_height)cv2.imshow(sc.window_name, np.array(img))if cv2.waitKey(1) & 0XFF == sc.exit_code:  # 默认：ESCcv2.destroyAllWindows()os._exit(0)
if __name__ == '__main__':sc = ScreenCapture(capture_region=(1, 1))run(sc)