yolov8怎么调用摄像头

你可以使用 OpenCV 库来调用摄像头，并将视频流传递给 YOLOv8 模型进行目标检测。下面是一个示例代码：

import cv2
import numpy as np

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

# 获取 YOLOv8 模型的输出层信息
layer_names = net.getLayerNames()
output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]

# 打开摄像头
cap = cv2.VideoCapture(0)

while True:
    # 读取摄像头的视频流
    ret, frame = cap.read()

    if not ret:
        break

    # 对视频流进行预处理
    blob = cv2.dnn.blobFromImage(frame, 0.00392, (416, 416), swapRB=True, crop=False)
    net.setInput(blob)

    # 运行 YOLOv8 模型进行目标检测
    outs = net.forward(output_layers)

    # 解析模型输出，绘制边界框
    class_ids = []
    confidences = []
    boxes = []
    for out in outs:
        for detection in out:
            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 = int(center_x - w / 2)
                y = int(center_y - h / 2)
                boxes.append([x, y, w, h])
                confidences.append(float(confidence))
                class_ids.append(class_id)

    indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
    font = cv2.FONT_HERSHEY_SIMPLEX
    colors = np.random.uniform(0, 255, size=(len(classes), 3))

    for i in range(len(boxes)):
        if i in indexes:
            x, y, w, h = boxes[i]
            label = str(classes[class_ids[i]])
            color = colors[class_ids[i]]
            cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2)
            cv2.putText(frame, label, (x, y - 10), font, 0.5, color, 2)

    # 显示输出视频流
    cv2.imshow("YOLOv8", frame)
    if cv2.waitKey(1) == ord('q'):
        break

# 释放摄像头和窗口
cap.release()
cv2.destroyAllWindows()

你需要将 `yolov8.weights` 和 `yolov8.cfg` 替换为你自己训练好的模型文件路径。这段代码会打开摄像头并实时显示目标检测的结果。按下 'q' 键即可退出程序。