Face-Recognition.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip install face_recognition"
      ],
      "metadata": {
        "id": "WmOBr4oMfjwJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "from google.colab import files\n",
        "\n",
        "class MissingPersonDatabase:\n",
        "    def __init__(self):\n",
        "        self.database = {}\n",
        "\n",
        "    def add_person(self, name, current_photo, childhood_photo, info):\n",
        "        self.database[name] = {'current_photo': current_photo,\n",
        "                               'childhood_photo': childhood_photo,\n",
        "                               'info': info}\n",
        "        self.save_to_file()\n",
        "\n",
        "    def save_to_file(self, filename='missing_person_database.json'):\n",
        "        with open(filename, 'w') as file:\n",
        "            json.dump(self.database, file, indent=4)\n",
        "\n",
        "# Create an instance of the MissingPersonDatabase\n",
        "database = MissingPersonDatabase()\n",
        "\n",
        "# Function to handle adding a missing person\n",
        "def add_missing_person():\n",
        "    name = input(\"Enter person's name: \")\n",
        "    current_photo = files.upload()\n",
        "    childhood_photo = files.upload()\n",
        "    age = input(\"Enter person's age: \")\n",
        "    contact = input(\"Enter person's contact information: \")\n",
        "    info = {'age': age, 'contact': contact}\n",
        "    database.add_person(name, list(current_photo.keys())[0], list(childhood_photo.keys())[0], info)\n",
        "\n",
        "# Function to add multiple missing persons\n",
        "def add_multiple_missing_persons():\n",
        "    add_more = True\n",
        "    while add_more:\n",
        "        add_missing_person()\n",
        "        choice = input(\"Do you want to add another missing person? (yes/no): \").lower()\n",
        "        if choice != 'yes':\n",
        "            add_more = False\n",
        "\n",
        "# Add missing persons to the database\n",
        "add_multiple_missing_persons()\n"
      ],
      "metadata": {
        "id": "As8wx5EZbbUo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import cv2\n",
        "import face_recognition\n",
        "import numpy as np\n",
        "import json\n",
        "from google.colab import files\n",
        "\n",
        "class MissingPersonDatabase:\n",
        "    def __init__(self):\n",
        "        self.database = {}\n",
        "\n",
        "    def load_from_file(self, filename='missing_person_database.json'):\n",
        "        try:\n",
        "            with open(filename, 'r') as file:\n",
        "                self.database = json.load(file)\n",
        "        except FileNotFoundError:\n",
        "            print(\"Database file not found. Please make sure to create the database first.\")\n",
        "\n",
        "    def search_person(self, query_img):\n",
        "        if query_img is None:\n",
        "            return None, None\n",
        "\n",
        "        # Load known face encodings and names\n",
        "        known_face_encodings = []\n",
        "        known_face_names = []\n",
        "        for name, data in self.database.items():\n",
        "            current_img = face_recognition.load_image_file(data['current_photo'])\n",
        "            childhood_img = face_recognition.load_image_file(data['childhood_photo'])\n",
        "            current_face_encoding = face_recognition.face_encodings(current_img)\n",
        "            childhood_face_encoding = face_recognition.face_encodings(childhood_img)\n",
        "            if current_face_encoding:\n",
        "                known_face_encodings.append(current_face_encoding[0])\n",
        "                known_face_names.append(name)\n",
        "            if childhood_face_encoding:\n",
        "                known_face_encodings.append(childhood_face_encoding[0])\n",
        "                known_face_names.append(name)\n",
        "\n",
        "        # Find matches\n",
        "        face_locations = face_recognition.face_locations(query_img)\n",
        "        query_face_encodings = face_recognition.face_encodings(query_img, face_locations)\n",
        "        best_match = None\n",
        "        min_distance = float('inf')\n",
        "        for query_face_encoding in query_face_encodings:\n",
        "            distances = face_recognition.face_distance(known_face_encodings, query_face_encoding)\n",
        "            for i, distance in enumerate(distances):\n",
        "                if distance < min_distance:\n",
        "                    min_distance = distance\n",
        "                    best_match = (known_face_names[i], self.database[known_face_names[i]], distance)\n",
        "        if best_match and 1 - best_match[2] > 0.35:  # Check if similarity score is above threshold\n",
        "            return best_match[0], best_match[1], 1 - best_match[2]  # Name, Info, Similarity Score\n",
        "        return None, None, 1 - min_distance  # No match found, return None for name and info\n",
        "\n",
        "# Create an instance of the MissingPersonDatabase\n",
        "database = MissingPersonDatabase()\n",
        "\n",
        "# Load the database from file\n",
        "database.load_from_file()\n",
        "\n",
        "# Function to handle file upload and search\n",
        "def handle_upload():\n",
        "    uploaded = files.upload()\n",
        "    for filename in uploaded.keys():\n",
        "        query_img = cv2.imdecode(np.frombuffer(uploaded[filename], np.uint8), cv2.IMREAD_COLOR)\n",
        "        name, info, similarity = database.search_person(query_img)\n",
        "        if name:\n",
        "            print(\"Match found:\")\n",
        "            print(\"Name:\", name)\n",
        "            print(\"Age:\", info['info']['age'])\n",
        "            print(\"Contact:\", info['info']['contact'])\n",
        "            print(\"Similarity Score:\", similarity)\n",
        "        else:\n",
        "            print(\"No match found.\")\n",
        "            print(\"Lowest Similarity Score:\", similarity)\n",
        "\n",
        "# Perform file upload and search\n",
        "print(\"Upload the photo to search:\")\n",
        "handle_upload()"
      ],
      "metadata": {
        "id": "GeXby4y8bf4l"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}