image validation against truth set

image-validation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "from urllib3.exceptions import NotOpenSSLWarning\n",
    "warnings.filterwarnings(\"ignore\", category=NotOpenSSLWarning)\n",
    "\n",
    "import os\n",
    "import random\n",
    "import torch\n",
    "from PIL import Image\n",
    "from transformers import CLIPProcessor, CLIPModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      ".venv/lib/python3.9/site-packages/urllib3/__init__.py:35: NotOpenSSLWarning: urllib3 v2 only supports OpenSSL 1.1.1+, currently the 'ssl' module is compiled with 'LibreSSL 2.8.3'. See: https://github.com/urllib3/urllib3/issues/3020\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# Step 1: Load CLIP model and processor\n",
    "model = CLIPModel.from_pretrained(\"openai/clip-vit-base-patch32\")\n",
    "processor = CLIPProcessor.from_pretrained(\"openai/clip-vit-base-patch32\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 2: Generate embeddings for truth set\n",
    "\n",
    "truth_set_directory = \"truth_set\"\n",
    "\n",
    "truth_images = [\n",
    "    os.path.join(truth_set_directory, f)\n",
    "    for f in os.listdir(truth_set_directory)\n",
    "    if os.path.isfile(os.path.join(truth_set_directory, f))\n",
    "]\n",
    "\n",
    "\n",
    "truth_embeddings = []\n",
    "\n",
    "for image_path in truth_images:\n",
    "    image = Image.open(image_path).convert(\"RGB\")\n",
    "    inputs = processor(images=image, return_tensors=\"pt\", padding=True)\n",
    "    with torch.no_grad():\n",
    "        embedding = model.get_image_features(**inputs).squeeze()\n",
    "    truth_embeddings.append(embedding)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Average the truth set embeddings\n",
    "truth_set_avg_embedding = torch.stack(truth_embeddings).mean(dim=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 3: Generate embedding for query image\n",
    "\n",
    "def get_random_query_image(truth_set_directory):\n",
    "    files = [\n",
    "        os.path.join(truth_set_directory, f)\n",
    "        for f in os.listdir(truth_set_directory)\n",
    "        if os.path.isfile(os.path.join(truth_set_directory, f))\n",
    "    ]\n",
    "    \n",
    "    if not files:\n",
    "        raise ValueError(f\"No files found in the directory: {truth_set_directory}\")\n",
    "    \n",
    "    query_image = random.choice(files)\n",
    "    return query_image\n",
    "\n",
    "truth_set_directory = \"truth_set\"\n",
    "query_image_path = get_random_query_image(truth_set_directory)\n",
    "query_image = Image.open(query_image_path).convert(\"RGB\")\n",
    "query_inputs = processor(images=query_image, return_tensors=\"pt\", padding=True)\n",
    "with torch.no_grad():\n",
    "    query_embedding = model.get_image_features(**query_inputs).squeeze()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Query image is similar to the truth set: True\n"
     ]
    }
   ],
   "source": [
    "# Compare using cosine similarity\n",
    "cosine_sim = torch.nn.functional.cosine_similarity(query_embedding, truth_set_avg_embedding, dim=0)\n",
    "threshold = 0.8\n",
    "is_similar = cosine_sim > threshold\n",
    "\n",
    "print(f\"Query image is similar to the truth set: {is_similar.item()}\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}