pythonproject/qrtrain_v1.py

import cv2
import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import models, transforms
from torch.utils.data import DataLoader, Dataset
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score
from PIL import Image
import numpy as np
import os
from tqdm import tqdm
import joblib

# 自定义数据集类
class QRCodeDataset(Dataset):
    def __init__(self, root_dir, transform=None):
        self.root_dir = root_dir
        self.transform = transform
        self.image_paths = []
        self.labels = []

        for label in ['real', 'fake']:
            label_dir = os.path.join(root_dir, label)
            for img_name in os.listdir(label_dir):
                if img_name.endswith(('.png', '.jpg', '.jpeg')):  # 只添加图像文件
                    self.image_paths.append(os.path.join(label_dir, img_name))
                    self.labels.append(0 if label == 'real' else 1)

    def __len__(self):
        return len(self.image_paths)

    def __getitem__(self, idx):
        img_path = self.image_paths[idx]
        image = Image.open(img_path)
        label = self.labels[idx]

        if self.transform:
            image = self.transform(image)

        return image, label

# 数据预处理
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])

# 加载数据集
train_dataset = QRCodeDataset(root_dir='/Users/jasonwong/Downloads/二维码测试1/data/train', transform=transform)
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)

val_dataset = QRCodeDataset(root_dir='/Users/jasonwong/Downloads/二维码测试1/data/val', transform=transform)
val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False)

# 加载预训练的 ResNet 模型并去掉最后一层
model = models.resnet50(pretrained=True)
modules = list(model.children())[:-1]  # 去掉最后的全连接层
model = nn.Sequential(*modules)

# 使用 GPU
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = model.to(device)

# 提取 ORB 特征
def extract_orb_features(image_path):
    image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
    orb = cv2.ORB_create()
    keypoints, descriptors = orb.detectAndCompute(image, None)
    if descriptors is None:
        return np.zeros((1, 32))  # 如果没有检测到关键点，返回一个零数组
    return descriptors

# 提取 ResNet 特征
def extract_resnet_features(loader):
    model.eval()
    features = []
    labels = []
    with torch.no_grad():
        for inputs, label in tqdm(loader, desc="Extracting ResNet features"):
            inputs = inputs.to(device)
            output = model(inputs)
            output = output.view(output.size(0), -1)  # 展平特征向量
            features.append(output.cpu())
            labels.append(label)
    return torch.cat(features), torch.cat(labels)

# 从训练和验证数据集中提取 ResNet 特征
train_resnet_features, train_labels = extract_resnet_features(train_loader)
val_resnet_features, val_labels = extract_resnet_features(val_loader)

# 从训练和验证数据集中提取 ORB 特征
def extract_orb_features_from_dataset(dataset):
    features = []
    for img_path in tqdm(dataset.image_paths, desc="Extracting ORB features"):
        orb_features = extract_orb_features(img_path)
        features.append(np.mean(orb_features, axis=0))  # 取平均值作为全局特征
    return np.array(features)

train_orb_features = extract_orb_features_from_dataset(train_dataset)
val_orb_features = extract_orb_features_from_dataset(val_dataset)

# 特征融合
train_features = np.hstack((train_resnet_features.numpy(), train_orb_features))
val_features = np.hstack((val_resnet_features.numpy(), val_orb_features))

# 使用 SVM 分类器进行训练和评估
svm = SVC(kernel='linear')
svm.fit(train_features, train_labels)

val_preds = svm.predict(val_features)
val_acc = accuracy_score(val_labels, val_preds)
print(f'Validation Accuracy: {val_acc:.4f}')

# 保存 ResNet 模型特征提取部分
torch.save(model.state_dict(), 'resnet_feature_extractor.pth')

# 保存 SVM 分类器
joblib.dump(svm, 'svm_classifier.joblib')