model_watermark_verify/tests/predict_batch.py

import onnxruntime as ort
import numpy as np
import os

from PIL import Image


# 读取并预处理图片
def process_image(image_path):
    import torchvision.transforms as T
    image = Image.open(image_path).convert("RGB")
    preprocess = T.Compose([
        T.Resize((224, 224)),
        T.ToTensor(),
        T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
    ])
    return preprocess(image).numpy()

# def process_image(image_path):
#     # 打开图像并转换为RGB
#     image = Image.open(image_path).convert("RGB")
#
#     # 调整图像大小
#     image = image.resize((224, 224))
#
#     # 转换为numpy数组并归一化
#     image_array = np.array(image) / 255.0  # 将像素值缩放到[0, 1]
#
#     # 进行标准化
#     mean = np.array([0.485, 0.456, 0.406])
#     std = np.array([0.229, 0.224, 0.225])
#     image_array = (image_array - mean) / std
#     image_array = image_array.transpose((2, 0, 1)).copy()
#
#     return image_array.astype(np.float32)


def batch_predict_images(model_path, image_dir, target_class, threshold=0.6, batch_size=10):
    """
    对指定图片文件夹图片进行批量检测
    :param model_path: onnx模型文件路径
    :param image_dir: 待推理的图像文件夹
    :param target_class: 目标分类
    :param threshold: 通过测试阈值
    :param batch_size: 每批图片数量
    :return: 检测结果
    """
    # 加载 ONNX 模型
    session = ort.InferenceSession(model_path)
    image_files = [f for f in os.listdir(image_dir) if f.lower().endswith(('.png', '.jpg', '.jpeg'))]
    results = {}
    input_name = session.get_inputs()[0].name

    for i in range(0, len(image_files), batch_size):
        correct_predictions = 0
        total_predictions = 0
        batch_files = image_files[i:i + batch_size]
        batch_images = []

        for image_file in batch_files:
            image_path = os.path.join(image_dir, image_file)
            image = process_image(image_path)
            batch_images.append(image)

        # 将批次图片堆叠成 (batch_size, 3, 224, 224) 维度
        batch_images = np.stack(batch_images)

        # 执行预测
        outputs = session.run(None, {input_name: batch_images})

        # 提取预测结果
        for j, image_file in enumerate(batch_files):
            predicted_class = np.argmax(outputs[0][j])  # 假设输出是每类的概率
            results[image_file] = predicted_class
            total_predictions += 1

            # 比较预测结果与目标分类
            if predicted_class == target_class:
                correct_predictions += 1

        print(f"Predicted batch {i // batch_size + 1}")
        # 计算准确率
        accuracy = correct_predictions / total_predictions if total_predictions > 0 else 0
        print(f"Accuracy: {accuracy * 100:.2f}%")
        if accuracy > threshold:
            return True
    return False


# 使用示例
image_dir = 'blackbox_models/vgg16/trigger/images/2'
model_path = 'blackbox_models/vgg16/vgg16.onnx'
target_class = 2  # 替换为您要检查的目标分类
batch_predict_images(model_path, image_dir, target_class)