yolov8调用图片

您可以使用以下代码调用YOLOv8模型来处理图像：

import cv2
import torch
from models import Darknet
from utils.utils import load_classes, non_max_suppression, rescale_boxes

# 加载模型
model = Darknet('cfg/yolov3.cfg')
model.load_weights('weights/yolov3.weights')
model.eval()

# 加载类别标签
classes = load_classes('data/coco.names')

# 加载图像
image = cv2.imread('image.jpg')

# 图像预处理
img_tensor = torch.from_numpy(image / 255.0).permute(2, 0, 1).float().unsqueeze(0)
input_img = img_tensor.cuda()

# 模型推理
with torch.no_grad():
    detections = model(input_img)
    detections = non_max_suppression(detections, conf_thres=0.5, nms_thres=0.4)

# 后处理
if detections is not None and len(detections) > 0:
    detections = rescale_boxes(detections[0], image.shape[:2])
    for x1, y1, x2, y2, conf, cls_conf, cls_pred in detections:
        print(f'预测类别: {classes[int(cls_pred)]}, 置信度: {cls_conf.item()}')

# 显示结果
cv2.imshow('Image', image)
cv2.waitKey(0)
cv2.destroyAllWindows()

确保您已经安装了必要的依赖项，并且已经下载了YOLOv3的权重文件。您可以替换`'cfg/yolov3.cfg'`和`'weights/yolov3.weights'`为YOLOv8的配置文件和权重文件路径。