python YOLO v5实现图像模型检测代码

抱歉，作为AI语言模型，我无法生成实际代码。以下是YOLO v5的Python代码示例，供您参考：

1. 导入必要的库

import torch
import cv2
import numpy as np
from PIL import Image
from torchvision import transforms
from models.experimental import attempt_load
from utils.general import non_max_suppression, scale_coords
from utils.plots import plot_one_box

2. 定义函数

def detect(image_path, weights_path, conf_thres=0.25, iou_thres=0.45, device='cpu'):

    # Load model
    model = attempt_load(weights_path, map_location=device)
    # Set model to evaluation mode
    model.eval()

    # Define transforms for input image
    transform = transforms.Compose([
        transforms.Resize((640, 640)),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
    ])

    # Load image
    img = Image.open(image_path)

    # Apply transforms
    img = transform(img).unsqueeze(0)

    # Move image to device
    img = img.to(device)

    # Make predictions
    with torch.no_grad():
        # Get prediction
        pred = model(img)

        # Apply non-maximum suppression
        pred = non_max_suppression(pred, conf_thres, iou_thres)

    # Get image size
    img_size = img.shape[-2:]

    # Get prediction boxes and labels
    boxes = []
    labels = []
    scores = []

    for i, det in enumerate(pred):
        # Rescale boxes from 640 to image size
        det[:, :4] = scale_coords(img.shape[2:], det[:, :4], img_size).round()

        for *xyxy, conf, cls in reversed(det):
            label = model.names[int(cls)]
            score = float(conf)

            # Add box and label to lists
            boxes.append(xyxy)
            labels.append(label)
            scores.append(score)

    return boxes, labels, scores

3. 运行检测

if __name__ == '__main__':
    # Define device (GPU or CPU)
    device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')

    # Define paths to image and model weights
    image_path = 'path/to/image.jpg'
    weights_path = 'path/to/weights.pt'

    # Run detection
    boxes, labels, scores = detect(image_path, weights_path, device=device)

    # Load image
    img = cv2.imread(image_path)

    # Plot boxes on image
    for box, label, score in zip(boxes, labels, scores):
        plot_one_box(box, img, label=label, color=(0, 255, 0), line_thickness=2)

    # Show image
    cv2.imshow('Detection', img)
    cv2.waitKey(0)
    cv2.destroyAllWindows()