yolov5实时检测屏幕python代码

以下是使用 YOLOv5 实时检测屏幕的 Python 代码示例：

import cv2
import numpy as np
import time
from PIL import ImageGrab

# 加载 YOLOv5 检测模型
net = cv2.dnn.readNet("yolov5.weights", "yolov5.cfg")
classes = []
with open("coco.names", "r") as f:
    classes = [line.strip() for line in f.readlines()]
    
# 设置输入图片大小
input_size = (640, 640)

# 循环检测屏幕
while True:
    start_time = time.time()
    
    # 获取屏幕截图
    img = ImageGrab.grab(bbox=(0, 0, 1920, 1080))
    
    # 将 PIL 图片转换为 OpenCV 图片
    img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
    
    # 对图片进行预处理
    img = cv2.resize(img, input_size)
    img = img.transpose((2, 0, 1))
    img = np.expand_dims(img, axis=0)
    img = img / 255.0
    
    # 运行 YOLOv5 检测
    net.setInput(img)
    outputs = net.forward()
    
    # 处理检测结果
    conf_threshold = 0.5
    nms_threshold = 0.4
    boxes = []
    confidences = []
    class_ids = []
    for output in outputs:
        for detection in output:
            scores = detection[5:]
            class_id = np.argmax(scores)
            confidence = scores[class_id]
            if confidence > conf_threshold:
                center_x = int(detection[0] * input_size[0])
                center_y = int(detection[1] * input_size[1])
                width = int(detection[2] * input_size[0])
                height = int(detection[3] * input_size[1])
                left = int(center_x - width / 2)
                top = int(center_y - height / 2)
                boxes.append([left, top, width, height])
                confidences.append(float(confidence))
                class_ids.append(class_id)
    
    indices = cv2.dnn.NMSBoxes(boxes, confidences, conf_threshold, nms_threshold)
    for i in indices:
        i = i[0]
        box = boxes[i]
        left = box[0]
        top = box[1]
        width = box[2]
        height = box[3]
        label = classes[class_ids[i]]
        cv2.rectangle(img, (left, top), (left + width, top + height), (0, 255, 0), 2)
        cv2.putText(img, label, (left, top - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
        
    # 显示检测结果
    cv2.imshow("Screen", img)
    cv2.waitKey(1)
    
    end_time = time.time()
    fps = 1 / (end_time - start_time)
    print("FPS: {:.2f}".format(fps))

需要注意的是，该代码仅供参考，具体的实现方式可能需要根据具体情况进行修改。同时，该代码可能会占用较高的 CPU 和内存资源，需要注意设备性能。