yolov8读取视频

很抱歉，yolov8并不是一个已知的模型或库，可能是您自己开发的模型或者笔误。如果您是想问如何使用YOLOv3或YOLOv4读取视频，可以使用OpenCV库中的VideoCapture类来实现。以下是一个使用YOLOv4读取视频并进行目标检测的示例代码：

import cv2
import numpy as np

# 加载YOLOv4模型
net = cv2.dnn.readNetFromDarknet('yolov4.cfg', 'yolov4.weights')
net.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA)
net.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA)

# 加载类别标签
classes = []
with open('coco.names', 'r') as f:
    classes = [line.strip() for line in f.readlines()]

# 读取视频
cap = cv2.VideoCapture('test.mp4')

while True:
    # 读取一帧
    ret, frame = cap.read()
    if not ret:
        break

    # 对帧进行目标检测
    blob = cv2.dnn.blobFromImage(frame, 1/255.0, (416, 416), swapRB=True, crop=False)
    net.setInput(blob)
    layer_names = net.getLayerNames()
    output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
    outputs = net.forward(output_layers)
    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 = 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)

    # 非极大值抑制
    indices = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)

    # 绘制检测结果
    for i in indices:
        i = i[0]
        x, y, w, h = boxes[i]
        label = 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)

    # 显示结果
    cv2.imshow('frame', frame)
    if cv2.waitKey(1) == ord('q'):
        break

# 释放资源
cap.release()
cv2.destroyAllWindows()