yolov8调用摄像头实时监测

要使用YOLOv8调用摄像头进行实时监测，可以按照以下步骤进行：

1. 安装OpenCV和YOLOv8
2. 下载YOLOv8的权重文件和配置文件
3. 使用OpenCV打开摄像头
4. 对于每一帧图像，使用YOLOv8进行目标检测
5. 在图像上绘制检测结果并显示

下面是一个简单的示例代码：

import cv2
import numpy as np

# Load YOLOv8
net = cv2.dnn.readNet("yolov8.weights", "yolov8.cfg")

# Load classes
classes = []
with open("coco.names", "r") as f:
    classes = [line.strip() for line in f.readlines()]

# Set up camera
cap = cv2.VideoCapture(0)

while True:
    # Read frame from camera
    ret, frame = cap.read()

    # Create blob from frame
    blob = cv2.dnn.blobFromImage(frame, 1/255, (416, 416), swapRB=True, crop=False)

    # Set input to YOLOv8 network
    net.setInput(blob)

    # Get output layer names
    layer_names = net.getLayerNames()
    output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]

    # Run forward pass on YOLOv8 network
    outputs = net.forward(output_layers)

    # Process outputs
    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 > 0.5:
                center_x = int(detection[0] * frame.shape[1])
                center_y = int(detection[1] * frame.shape[0])
                w = int(detection[2] * frame.shape[1])
                h = int(detection[3] * frame.shape[0])
                x = center_x - w // 2
                y = center_y - h // 2
                boxes.append([x, y, w, h])
                confidences.append(float(confidence))
                class_ids.append(class_id)

    # Apply non-max suppression
    indices = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)

    # Draw boxes and labels on frame
    if len(indices) > 0:
        for i in indices.flatten():
            x, y, w, h = boxes[i]
            label = str(classes[class_ids[i]])
            confidence = confidences[i]
            color = (0, 255, 0)
            cv2.rectangle(frame, (x, y), (x+w, y+h), color, 2)
            cv2.putText(frame, f"{label} {confidence:.2f}", (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)

    # Show frame
    cv2.imshow("YOLOv8", frame)

    # Exit on 'q' key press
    if cv2.waitKey(1) == ord('q'):
        break

# Release resources
cap.release()
cv2.destroyAllWindows()