Image Search with OpenAI CLIP
Implement image search with the OpenAI CLIP Model and Supabase Vector.
The OpenAI CLIP Model was trained on a variety of (image, text)-pairs. You can use the CLIP model for:
- Text-to-Image / Image-To-Text / Image-to-Image / Text-to-Text Search
- You can fine-tune it on your own image and text data with the regular SentenceTransformers training code.
SentenceTransformers provides models that allow you to embed images and text into the same vector space. You can use this to find similar images as well as to implement image search.
You can find the full application code as a Python Poetry project on GitHub.
Create a new Python project with Poetry
Poetry provides packaging and dependency management for Python. If you haven't already, install poetry via pip:
_10pip install poetry
Then initialize a new project:
_10poetry new image-search
Setup Supabase project
If you haven't already, install the Supabase CLI, then initialize Supabase in the root of your newly created poetry project:
_10supabase init
Next, start your local Supabase stack:
_10supabase start
This will start up the Supabase stack locally and print out a bunch of environment details, including your local DB URL. Make a note of that for later user.
Install the dependencies
We will need to add the following dependencies to our project:
vecs: Supabase Vector Python Client.sentence-transformers: a framework for sentence, text and image embeddings (used with OpenAI CLIP model)matplotlib: for displaying our image result
_10poetry add vecs sentence-transformers matplotlib
Import the necessary dependencies
At the top of your main python script, import the dependencies and store your DB URL from above in a variable:
_10from PIL import Image_10from sentence_transformers import SentenceTransformer_10import vecs_10from matplotlib import pyplot as plt_10from matplotlib import image as mpimg_10_10DB_CONNECTION = "postgresql://postgres:postgres@localhost:54322/postgres"
Create embeddings for your images
In the root of your project, create a new folder called images and add some images. You can use the images from the example project on GitHub or you can find license free images on unsplash.
Next, create a seed method, which will create a new Supabase Vector Collection, generate embeddings for your images, and upsert the embeddings into your database:
_43def seed():_43 # create vector store client_43 vx = vecs.create_client(DB_CONNECTION)_43_43 # create a collection of vectors with 3 dimensions_43 images = vx.get_or_create_collection(name="image_vectors", dimension=512)_43_43 # Load CLIP model_43 model = SentenceTransformer('clip-ViT-B-32')_43_43 # Encode an image:_43 img_emb1 = model.encode(Image.open('./images/one.jpg'))_43 img_emb2 = model.encode(Image.open('./images/two.jpg'))_43 img_emb3 = model.encode(Image.open('./images/three.jpg'))_43 img_emb4 = model.encode(Image.open('./images/four.jpg'))_43_43 # add records to the *images* collection_43 images.upsert(_43 records=[_43 (_43 "one.jpg", # the vector's identifier_43 img_emb1, # the vector. list or np.array_43 {"type": "jpg"} # associated metadata_43 ), (_43 "two.jpg",_43 img_emb2,_43 {"type": "jpg"}_43 ), (_43 "three.jpg",_43 img_emb3,_43 {"type": "jpg"}_43 ), (_43 "four.jpg",_43 img_emb4,_43 {"type": "jpg"}_43 )_43 ]_43 )_43 print("Inserted images")_43_43 # index the collection for fast search performance_43 images.create_index()_43 print("Created index")
Add this method as a script in your pyproject.toml file:
_10[tool.poetry.scripts]_10seed = "image_search.main:seed"_10search = "image_search.main:search"
After activating the virtual environtment with poetry shell you can now run your seed script via poetry run seed. You can inspect the generated embeddings in your local database by visiting the local Supabase dashboard at localhost:54323, selecting the vecs schema, and the image_vectors database.
Perform an image search from a text query
With Supabase Vector we can easily query our embeddings. We can use either an image as search input or alternative we can generate an embedding from a string input and use that as the query input:
_23def search():_23 # create vector store client_23 vx = vecs.create_client(DB_CONNECTION)_23 images = vx.get_or_create_collection(name="image_vectors", dimension=512)_23_23 # Load CLIP model_23 model = SentenceTransformer('clip-ViT-B-32')_23 # Encode text query_23 query_string = "a bike in front of a red brick wall"_23 text_emb = model.encode(query_string)_23_23 # query the collection filtering metadata for "type" = "jpg"_23 results = images.query(_23 data=text_emb, # required_23 limit=1, # number of records to return_23 filters={"type": {"$eq": "jpg"}}, # metadata filters_23 )_23 result = results[0]_23 print(result)_23 plt.title(result)_23 image = mpimg.imread('./images/' + result)_23 plt.imshow(image)_23 plt.show()
By limiting the query to one result, we can show the most relevant image to the user. Finally we use matplotlib to show the image result to the user.
That's it, go ahead and test it out by running poetry run search and you will be presented with an image of a "bike in front of a red brick wall".
Conclusion
With just a couple of lines of Python you are able to implement image search as well as reverse image search using OpenAI's CLIP model and Supabase Vector.