features_extraction.py

import torch
from torchvision import models, transforms
from PIL import Image
import pickle
import os
from tqdm import tqdm  # Import tqdm for the progress bar

# Load a pretrained ResNet model
model = models.resnet50(pretrained=True)
model = model.eval()

# Define preprocessing transforms
preprocess = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])

# Function to extract features from an image
def extract_features(image_path):
    image = Image.open(image_path).convert('RGB')
    input_tensor = preprocess(image)
    input_batch = input_tensor.unsqueeze(0)

    with torch.no_grad():
        output = model(input_batch)

    return output.squeeze().numpy()

# Directory containing your images
images_directory = "photos/"

# Process each image and save features
image_features = {}
for filename in tqdm(os.listdir(images_directory), desc="Processing Images"):
    if filename.endswith(".jpg") or filename.endswith(".png"):
        image_path = os.path.join(images_directory, filename)
        features = extract_features(image_path)
        image_features[filename] = features

# Save the features to a pickle file
output_file = "unsplash-25k-embeddings.pkl"
with open(output_file, 'wb') as f:
    pickle.dump(image_features, f)