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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/github/Snehil-Shah/MultiModal-Vector-Semantic-Search-Engine/blob/main/images.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "aH0U6JkEbAcg"
},
"source": [
"# Image to Semantic Embeddings\n",
"\n",
"**Aim**: Encode around 50k jpg/jpeg images into vector embeddings using a vision tranformer model and upsert them into a vector database for clustering and querying"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "CFLaAyqCbAch"
},
"outputs": [],
"source": [
"!pip install jupyter pandas qdrant_client pyarrow datasets"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "j5o4d0jbbAci"
},
"source": [
"# Load Dataset\n",
"This is the Open Images Dataset by CVDFoundation which hosts over 9 mil images. We will be working with a smaller subset.\n",
"\n",
"The dataset currently is a tsv file, with the first column representing a URL to a hosted jpg/jpeg image."
]
},
{
"cell_type": "code",
"source": [
"import pandas as pd\n",
"data = pd.read_csv('open-images-dataset-validation.tsv', sep='\\t', header=None).reset_index()\n",
"print(data.shape, data.head(), sep=\"\\n\")"
],
"metadata": {
"id": "j97T0MIBeEDe",
"outputId": "df823427-2859-40f6-c171-f92b5a84361b",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": 98,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"(41620, 4)\n",
" index 0 1 \\\n",
"0 0 https://c2.staticflickr.com/6/5606/15611395595... 2038323 \n",
"1 1 https://c6.staticflickr.com/3/2808/10351094034... 1762125 \n",
"2 2 https://c2.staticflickr.com/9/8089/8416776003_... 9059623 \n",
"3 3 https://farm3.staticflickr.com/568/21452126474... 2306438 \n",
"4 4 https://farm4.staticflickr.com/1244/677743874_... 6571968 \n",
"\n",
" 2 \n",
"0 I4V4qq54NBEFDwBqPYCkDA== \n",
"1 38x6O2LAS75H1vUGVzIilg== \n",
"2 4ksF8TuGWGcKul6Z/6pq8g== \n",
"3 R+6Cs525mCUT6RovHPWREg== \n",
"4 JnkYas7iDJu+pb81tfqVow== \n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"## Download the images\n",
"We need the image data locally to feed it to the model"
],
"metadata": {
"id": "M-Esbnhy6KTU"
}
},
{
"cell_type": "code",
"source": [
"import urllib\n",
"import os\n",
"\n",
"def download_file(url):\n",
" basename = os.path.basename(url)\n",
" target_path = f\"./images/{basename}\"\n",
" if not os.path.exists(target_path):\n",
" try:\n",
" urllib.request.urlretrieve(url, target_path)\n",
" except urllib.error.HTTPError:\n",
" return None\n",
" return target_path"
],
"metadata": {
"id": "cK_63ubnieI6"
},
"execution_count": 99,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"# The Model\n",
"We will be using a pre-trained model. Contrastive Language-Image Pre-training (CLIP) model developed by OpenAI is a multi-modal Vision Transformer model that can extract the visual features from the image into vector embeddings\n",
"\n",
"We will be storing these vector embeddings in a vector space database, where images will be clustered based on their semantic information ready for querying"
],
"metadata": {
"id": "0WrAbzxP6khy"
}
},
{
"cell_type": "code",
"source": [
"from sentence_transformers import SentenceTransformer\n",
"model = SentenceTransformer(\"clip-ViT-B-32\")"
],
"metadata": {
"id": "pHYk-KdmlJxz"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"# The Vector Database\n",
"\n",
"Qdrant is an open-source vector database, where we can store vector embeddings and query nearest neighbours of a given embedding to create a recommendation/semantic search engine\n",
"\n",
"We start by initializing the Qdrant client and connecting to the cluster hosted on Qdrant Cloud"
],
"metadata": {
"id": "2h7jMch58ADV"
}
},
{
"cell_type": "code",
"source": [
"from qdrant_client import QdrantClient\n",
"from qdrant_client.http import models as rest\n",
"from google.colab import userdata\n",
"\n",
"qdrant_client = QdrantClient(\n",
" url = userdata.get('QDRANT_CLUSTER_URL'),\n",
" api_key = userdata.get('QDRANT_CLUSTER_API_KEY'),\n",
")\n",
"qdrant_client.recreate_collection(\n",
" collection_name=\"images\",\n",
" vectors_config = rest.VectorParams(size=512, distance = rest.Distance.COSINE),\n",
")"
],
"metadata": {
"id": "nAObCg-yrzpC"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Function to upsert an embedding to the collection"
],
"metadata": {
"id": "zGbMrsDL_HH-"
}
},
{
"cell_type": "code",
"source": [
"def upsert_to_db(id, vector, payload):\n",
" qdrant_client.upsert(\n",
" collection_name=\"images\",\n",
" points=[\n",
" rest.PointStruct(\n",
" id=id,\n",
" vector=vector.tolist(),\n",
" payload=payload\n",
" )\n",
" ]\n",
")"
],
"metadata": {
"id": "mjTRm85dr13p"
},
"execution_count": 76,
"outputs": []
},
{
"cell_type": "code",
"source": [
"for i, link in data.iloc[:, :2].iterrows():\n",
" img = download_file(link[0])\n",
" if(img):\n",
" embedding = model.encode(str(img))\n",
" upsert_to_db(i,embedding, {\"link\":link[0]})\n",
" print(f\"upserted {i}\")"
],
"metadata": {
"id": "MvFEc4MgwSLW"
},
"execution_count": null,
"outputs": []
}
],
"metadata": {
"language_info": {
"name": "python"
},
"colab": {
"provenance": [],
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"nbformat": 4,
"nbformat_minor": 0
} |