写一个调用yolov8的程序

YOLOv8 (You Only Look Once version 8) 是一种流行的实时目标检测算法，基于开源项目 Darknet 开发。要在 Python 中编写一个简单的 YOLOv8 程序，你需要安装必要的库，如 OpenCV、PyTorch及其Dlib等，并加载预训练模型。

以下是一个基本的步骤示例：

import cv2
import torch

# 安装必要的库（如果你还没安装）
!pip install opencv-python-headless torchvision yolo-v8-pytorch darkflow

from models.experimental import attempt_load
from utils.datasets import LoadStreams, LoadImages
from utils.general import non_max_suppression, check_img_size

# 加载预训练模型
model = attempt_load('yolov8.weights', map_location='cuda:0') if torch.cuda.is_available() else attempt_load('yolov8.weights', map_location='cpu')

# 设置输入大小
imgsz = 640

# 使用YoloV8进行实时检测
def detect_objects(streams, imgsz):
    for path, im in streams:
        try:
            img = preprocess_image(im, imgsz)
            results = model(img)
            output = non_max_suppression(results, conf_thres=0.5, iou_thres=0.5)
            draw_output(path, im, output)
        except Exception as e:
            print(f"Error during detection: {e}")

def preprocess_image(image, size):
    # 调整图像尺寸并进行前处理
    img = cv2.resize(image, (size, size))
    img = img[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB and HWC to CHW
    img = np.ascontiguousarray(img)
    return img

def draw_output(path, image, boxes):
    # 绘制预测结果到原图上
    for result in boxes:
        box, label, prob = result
        cv2.rectangle(image, (box[0], box[1]), (box[2], box[3]), (255, 0, 0), 2)
        cv2.putText(image, f'{label}: {prob:.2f}', (box[0] + 20, box[1] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)

# 初始化数据加载器
streams = LoadImages('your_video.mp4')  # 或者LoadStreams('path/to/images/*.jpg', img_size=imgsz)

# 开始实时检测
detect_objects(streams, imgsz)

注意替换 `your_video.mp4` 或 `'path/to/images/*.jpg'` 为你实际视频文件路径或图片目录。这个脚本将读取视频流并对其中的目标进行实时检测。