yolov5实时检测屏幕python代码

下面是一份使用 YOLOv5 进行实时屏幕检测的 Python 代码示例：

import cv2
import numpy as np
import torch
from models import *
from utils.datasets import *
from utils.utils import *

class ObjectDetection:
    def __init__(self, model_def, model_weights, class_path, conf_thres=0.5, nms_thres=0.5, img_size=416):
        self.device = torch.device("cv2.CAP_PROP_FRAME_WIDTH" if torch.cuda.is_available() else "cpu")

        # Set up model
        self.model = Darknet(model_def, img_size=img_size).to(self.device)

        if model_weights.endswith(".pt"):
            self.model.load_state_dict(torch.load(model_weights, map_location=self.device)["model"])
        else:
            _ = load_darknet_weights(self.model, model_weights)

        self.model.eval()  # Set in evaluation mode

        self.classes = load_classes(class_path)
        self.colors = np.random.randint(0, 255, size=(len(self.classes), 3), dtype="uint8")
        self.conf_thres = conf_thres
        self.nms_thres = nms_thres
        self.img_size = img_size

    def detect(self, frame):
        # Preprocess frame
        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
        frame = cv2.resize(frame, (self.img_size, self.img_size))
        frame = torch.from_numpy(frame).float().div(255.0).unsqueeze(0).to(self.device)

        # Inference
        with torch.no_grad():
            detections = self.model(frame)
            detections = non_max_suppression(detections, self.conf_thres, self.nms_thres)

        # Process detections
        for detection in detections:
            detection = detection[0]
            box = detection[:4].cpu().numpy()
            box[0] *= frame.shape[1]
            box[1] *= frame.shape[0]
            box[2] *= frame.shape[1]
            box[3] *= frame.shape[0]

            cls = int(detection[6])
            label = f"{self.classes[cls]}: {detection[5]:.2f}"

            color = self.colors[cls]
            cv2.rectangle(frame, (int(box[0]), int(box[1])), (int(box[2]), int(box[3])), color, 2)
            cv2.putText(frame, label, (int(box[0]), int(box[1]) - 10), cv2.FONT_