model/feature_extractor.py

import torch
import torchvision.models as models
from torchvision import transforms
from PIL import Image
import numpy as np

class FeatureExtractor:
    def __init__(self):
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        # Load pretrained ResNet50 without the final classification layer
        resnet = models.resnet50(pretrained=True)
        # Remove the final fully connected layer (fc)
        self.model = torch.nn.Sequential(*list(resnet.children())[:-1])
        self.model.eval().to(self.device)

        # Standard ImageNet preprocessing
        self.transform = transforms.Compose([
            transforms.Resize(256),
            transforms.CenterCrop(224),
            transforms.ToTensor(),
            transforms.Normalize(
                mean=[0.485, 0.456, 0.406],
                std=[0.229, 0.224, 0.225]
            ),
        ])

    def extract(self, image: Image.Image):
        image = self.transform(image).unsqueeze(0).to(self.device)
        with torch.no_grad():
            features = self.model(image)
        features = features.squeeze().cpu().numpy()
        features = features.reshape(-1)  # flatten (2048,)

        # Normalize to unit vector (important for cosine similarity)
        norm = np.linalg.norm(features)
        if norm > 0:
            features = features / norm
        return features