How to Create an Image Search Engine with OpenAI CLIP and Python

In today’s digital world, image search engines play a crucial role in various applications like e-commerce, content management systems, and social media platforms. Traditional methods for image search rely on text-based metadata or manually annotated tags, which can be time-consuming and error-prone.

But thanks to recent advancements in deep learning, we now have powerful models that can understand both images and text simultaneously. One such model is OpenAI’s CLIP (Contrastive Language-Image Pretraining), which can be used to create an image search engine with remarkable accuracy.

In this tutorial, we will walk through the process of building an image search engine using OpenAI CLIP and Python. By the end of this tutorial, you will have a clear understanding of how to leverage CLIP’s capabilities to build your own image search engine.

Prerequisites

To follow along with this tutorial, you will need the following:

Python 3.6 or later installed on your machine
A basic understanding of Python programming
Familiarity with the command line interface (CLI)
An internet connection to download the necessary libraries
Optional: A GPU-enabled machine for faster processing (recommended but not required)

Let’s get started!

Step 1: Set up the Environment

First, let’s set up the Python environment by creating a virtual environment and installing the necessary packages.

Open your command line interface (CLI).

Create a new directory for your project:

mkdir image_search_engine
cd image_search_engine

Set up a virtual environment:

python3 -m venv env
source env/bin/activate

Install the required packages:
```
pip install torch torchvision ftfy regex requests tqdm Pillow
```
If you have a GPU-enabled machine, you can install torch with GPU support by following the instructions on the official pytorch website: https://pytorch.org/get-started/locally/

Great! Now our environment is all set up to build our image search engine.

Step 2: Collect Image Data

To create an image search engine, we need a dataset of images. In this tutorial, we will use the CIFAR-10 dataset as a sample dataset for demonstration purposes. CIFAR-10 consists of 60,000 32×32 color images in 10 classes.

Download the CIFAR-10 dataset:

mkdir data
cd data
wget https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz
tar -xf cifar-10-python.tar.gz

Now we need to preprocess the images into a format suitable for CLIP:

from PIL import Image
import numpy as np
import pickle

def preprocess_image(image):
   image = np.array(image)
   image = image.astype('float32') / 255.0
   image = (image - 0.5) / 0.5  # normalize between -1 and +1
   return image

def preprocess_cifar10(data_path, save_path):
   with open(data_path, 'rb') as file:
       data = pickle.load(file, encoding='bytes')

   images = np.array(data[b'data'])
   labels = np.array(data[b'labels'])

   num_images = len(images)
   preprocessed_images = []

   for i in range(num_images):
       image = images[i].reshape(3, 32, 32)
       image = np.transpose(image, (1, 2, 0))
       image = preprocess_image(image)
       preprocessed_images.append(image)

   with open(save_path, 'wb') as file:
       pickle.dump((preprocessed_images, labels), file)

preprocess_cifar10('cifar-10-batches-py/data_batch_1', 'cifar10_preprocessed.pkl')

This will preprocess the CIFAR-10 dataset and save it as a pickled file named cifar10_preprocessed.pkl.

Excellent! We now have our preprocessed dataset ready, and we can move on to the next step.

Step 3: Prepare CLIP Model

Next, we need to download the pre-trained CLIP model released by OpenAI and load it into our Python environment.

Download the CLIP model:

mkdir models
cd models
wget https://openai.clip.models.s3-us-west-2.amazonaws.com/vqgan/vqgan_imagenet_f16_16384.yaml
wget https://openai.clip.models.s3-us-west-2.amazonaws.com/vqgan/vqgan_imagenet_f16_16384.ckpt

Install the necessary libraries to load the CLIP model:
```
pip install git+https://github.com/openai/CLIP.git
```
Note: It may take a while to install the dependencies and download the necessary files.

Load the CLIP model in Python:

import torch
import clip

device = "cuda" if torch.cuda.is_available() else "cpu"
clip_model, preprocess = clip.load("openai/clip-vit-base-patch32", device=device)

This will download the necessary files and load the CLIP model into memory.

Brilliant! We have successfully set up the CLIP model. Now onto the exciting part – searching for images!

Step 4: Search for Images

Now that we have our preprocessed dataset and CLIP model ready, let’s build the image search engine. We’ll write a Python function that takes an input image and returns similar images from the dataset. The similarity is determined based on the text prompts provided to CLIP.

Here’s how our function will work:

Convert the input image into a feature vector using the CLIP model.
Compute the cosine similarity between the input image feature vector and the feature vectors of all dataset images.
Return the top k most similar images based on cosine similarity.

Let’s write the code for our image search function:

def search_images(input_image, dataset_images, k=5):
    # Preprocess input image
    input_tensor = preprocess(input_image).unsqueeze(0).to(device)

    # Compute feature vector for the input image
    with torch.no_grad():
        input_features = clip_model.encode_image(input_tensor).float()

    # Compute cosine similarity between input image and dataset images
    similarities = (input_features @ dataset_images.T).squeeze(0)

    # Get indices of top k most similar images
    top_indices = similarities.argsort(descending=True)[:k]

    # Return top k most similar images
    return [dataset_images[i] for i in top_indices]

Let’s test our search function on a sample image from the CIFAR-10 dataset:

import matplotlib.pyplot as plt

with open('data/cifar10_preprocessed.pkl', 'rb') as file:
    dataset_images, labels = pickle.load(file)

index = 42  # Choose a random index
sample_image = dataset_images[index]

similar_images = search_images(sample_image, dataset_images, k=5)

# Display the input image
plt.subplot(1, 6, 1)
plt.imshow(sample_image)
plt.title("Input Image")
plt.axis("off")

# Display the top 5 similar images
for i, image in enumerate(similar_images):
    plt.subplot(1, 6, i + 2)
    plt.imshow(image)
    plt.title(f"Similar Image {i+1}")
    plt.axis("off")

plt.show()

This code will display the input image and the top 5 similar images based on the CLIP model’s understanding of the images. You can modify the index and k values to explore the results for different images.

Congratulations! You have successfully built your own image search engine using OpenAI CLIP. You can now experiment with different images and see how CLIP performs.

Conclusion

In this tutorial, you learned how to create an image search engine using OpenAI CLIP and Python. We walked through the process of setting up the environment, pre-processing image data, loading the CLIP model, and using it to search for similar images. With CLIP’s remarkable capability to understand both images and text, you can build powerful image search engines that can revolutionize various applications.

Feel free to explore further by experimenting with other datasets, fine-tuning CLIP with custom images, or integrating the search engine into your existing projects. The possibilities are endless!

Now it’s time for you to unleash the power of CLIP and build your own image search engine. Happy coding!