model_watermark_verify/tests/classfication_model_watermark_detect_test.py

import os
import onnxruntime as ort
from torchvision import transforms
from PIL import Image
import numpy as np

# 模型加载
onnx_model_path = 'your_model_path.onnx'
session = ort.InferenceSession(onnx_model_path)

# 图像预处理
preprocess = transforms.Compose([
    transforms.Resize((224, 224)),  # 根据你的模型输入大小调整
    transforms.ToTensor(),
])

# 触发集目录和嵌入位置的TXT文件路径
trigger_dir = 'path_to_trigger_images'
location_file = 'path_to_location_txt.txt'

# 读取嵌入位置的TXT文件
embedding_positions = {}
with open(location_file, 'r') as file:
    for line in file:
        # 假设TXT文件中每行的格式是: 文件名 x y width height
        filename, x, y, width, height = line.strip().split()
        embedding_positions[filename] = (int(x), int(y), int(width), int(height))

# 扫描触发集目录并处理每张图像
watermark_success_rates = []
for img_name in os.listdir(trigger_dir):
    if img_name in embedding_positions:
        # 加载图像
        img_path = os.path.join(trigger_dir, img_name)
        image = Image.open(img_path).convert('RGB')

        # 获取嵌入位置
        x, y, width, height = embedding_positions[img_name]

        # 裁剪出嵌入二维码的区域
        cropped_image = image.crop((x, y, x + width, y + height))

        # 图像预处理
        input_tensor = preprocess(cropped_image).unsqueeze(0).numpy()  # 增加batch维度并转换为numpy

        # 获取ONNX模型的输入名称
        input_name = session.get_inputs()[0].name

        # 模型推理
        output = session.run(None, {input_name: input_tensor})

        # 假设你有一个期望的标签，比如`expected_label`
        predicted_label = np.argmax(output[0])
        expected_label = 1  # 根据实际情况设置

        # 判断预测是否正确
        is_correct = (predicted_label == expected_label)
        watermark_success_rates.append(is_correct)

# 计算整体水印成功率
overall_success_rate = np.mean(watermark_success_rates)

# 输出结果
threshold = 0.9  # 设置成功率阈值
if overall_success_rate > threshold:
    print(f"模型中嵌入了水印，水印成功率: {overall_success_rate * 100:.2f}%")
else:
    print("模型中未嵌入水印。")