73 lines
2.1 KiB
Python
73 lines
2.1 KiB
Python
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import os
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import cv2
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import torch
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import torch.nn as nn
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from torchvision import models, transforms
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from sklearn.svm import SVC
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import joblib
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from PIL import Image
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import numpy as np
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# 加载 ResNet 模型并去掉最后一层
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model = models.resnet50(pretrained=False)
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modules = list(model.children())[:-1] # 去掉最后的全连接层
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model = nn.Sequential(*modules)
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# 加载训练好的 ResNet 模型权重
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model.load_state_dict(torch.load('resnet_feature_extractor.pth'))
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model.eval()
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# 使用 GPU
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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model = model.to(device)
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# 加载 SVM 分类器
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svm = joblib.load('svm_classifier.joblib')
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# 数据预处理
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transform = transforms.Compose([
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transforms.Resize((224, 224)),
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transforms.ToTensor(),
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transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
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])
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# 提取 ORB 特征
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def extract_orb_features(image_path):
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image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
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orb = cv2.ORB_create()
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keypoints, descriptors = orb.detectAndCompute(image, None)
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if descriptors is None:
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return np.zeros((1, 32)) # 如果没有检测到关键点，返回一个零数组
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return descriptors
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# 提取 ResNet 特征
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def extract_resnet_features(image):
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image = transform(image).unsqueeze(0) # 添加批次维度
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image = image.to(device)
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with torch.no_grad():
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features = model(image)
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features = features.view(features.size(0), -1) # 展平特征向量
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return features.cpu().numpy()
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# 推理函数
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def predict(image_path):
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# 提取 ORB 特征
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orb_features = extract_orb_features(image_path)
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orb_features = np.mean(orb_features, axis=0).reshape(1, -1) # 取平均值作为全局特征
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# 提取 ResNet 特征
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image = Image.open(image_path).convert('RGB')
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resnet_features = extract_resnet_features(image)
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# 特征融合
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features = np.hstack((resnet_features, orb_features))
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# 进行分类
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prediction = svm.predict(features)
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return 'real' if prediction == 0 else 'fake'
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# 示例推理调用
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image_path = 'demo.png'
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result = predict(image_path)
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print(f'The QR code is {result}.')
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