app.py

import streamlit as st
import cv2
import numpy as np
import numpy
import torch
from torchvision import transforms
from PIL import Image

# 这里你需要替换成你训练好的模型的路径和类名
model = torch.hub.load('ultralytics/yolov5', 'yolov5s')  # 加载 YOLOv5 模型
classes = model.names

def object_detection(img_path):
    """
    对图像进行物体识别。
    """
    results = model(img_path)
    return results.pandas().xyxy[0]

def show_results(results):
    """
    在 Streamlit 中显示物体识别结果。
    """
    for index, row in results.iterrows():
        left, top, right, bottom = row['xmin'], row['ymin'], row['xmax'], row['ymax']
        label = classes[int(row['class'])]
        st.write(f"物体：{label}, 位置：({left}, {top}) - ({right}, {bottom})")

if __name__ == "__main__":
    if st.button("选择图片"):
        uploaded_file = st.file_uploader("Choose an image file", type=["png", "jpg", "jpeg"])
        if uploaded_file is not None:
            img = Image.open(uploaded_file)
            img_array = np.array(img)
            results = object_detection(img_array)
            show_results(results)