Face_Detection_Recognition_KNN&SVM.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LA88UHZ_Ery0"
      },
      "source": [
        "##**REQUIREMENTS**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z8FVzZsJCxBG",
        "outputId": "45bd3a3b-e0a6-42f9-80be-6b9ea47a4820"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting face_detection\n",
            "  Downloading face_detection-0.2.2.tar.gz (20 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: torch>=1.6 in /usr/local/lib/python3.10/dist-packages (from face_detection) (2.2.1+cu121)\n",
            "Requirement already satisfied: torchvision>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from face_detection) (0.17.1+cu121)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from face_detection) (1.25.2)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.6->face_detection) (3.14.0)\n",
            "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.6->face_detection) (4.11.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.6->face_detection) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.6->face_detection) (3.3)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.6->face_detection) (3.1.3)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=1.6->face_detection) (2023.6.0)\n",
            "Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch>=1.6->face_detection)\n",
            "  Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)\n",
            "Collecting nvidia-cuda-runtime-cu12==12.1.105 (from torch>=1.6->face_detection)\n",
            "  Using cached nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (823 kB)\n",
            "Collecting nvidia-cuda-cupti-cu12==12.1.105 (from torch>=1.6->face_detection)\n",
            "  Using cached nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (14.1 MB)\n",
            "Collecting nvidia-cudnn-cu12==8.9.2.26 (from torch>=1.6->face_detection)\n",
            "  Using cached nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl (731.7 MB)\n",
            "Collecting nvidia-cublas-cu12==12.1.3.1 (from torch>=1.6->face_detection)\n",
            "  Using cached nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)\n",
            "Collecting nvidia-cufft-cu12==11.0.2.54 (from torch>=1.6->face_detection)\n",
            "  Using cached nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl (121.6 MB)\n",
            "Collecting nvidia-curand-cu12==10.3.2.106 (from torch>=1.6->face_detection)\n",
            "  Using cached nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl (56.5 MB)\n",
            "Collecting nvidia-cusolver-cu12==11.4.5.107 (from torch>=1.6->face_detection)\n",
            "  Using cached nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (124.2 MB)\n",
            "Collecting nvidia-cusparse-cu12==12.1.0.106 (from torch>=1.6->face_detection)\n",
            "  Using cached nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)\n",
            "Collecting nvidia-nccl-cu12==2.19.3 (from torch>=1.6->face_detection)\n",
            "  Using cached nvidia_nccl_cu12-2.19.3-py3-none-manylinux1_x86_64.whl (166.0 MB)\n",
            "Collecting nvidia-nvtx-cu12==12.1.105 (from torch>=1.6->face_detection)\n",
            "  Using cached nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)\n",
            "Requirement already satisfied: triton==2.2.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.6->face_detection) (2.2.0)\n",
            "Collecting nvidia-nvjitlink-cu12 (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.6->face_detection)\n",
            "  Using cached nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (21.1 MB)\n",
            "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.10/dist-packages (from torchvision>=0.3.0->face_detection) (9.4.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.6->face_detection) (2.1.5)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.6->face_detection) (1.3.0)\n",
            "Building wheels for collected packages: face_detection\n",
            "  Building wheel for face_detection (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for face_detection: filename=face_detection-0.2.2-py3-none-any.whl size=25555 sha256=6816376d25fb67a13acb53ea330545176adc33863421601d1cceaecb15997ba1\n",
            "  Stored in directory: /root/.cache/pip/wheels/f9/14/a1/617e184738e71e46c1e75f068f67a911917ae5d02faeabc4e4\n",
            "Successfully built face_detection\n",
            "Installing collected packages: nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, face_detection\n",
            "Successfully installed face_detection-0.2.2 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.4.127 nvidia-nvtx-cu12-12.1.105\n"
          ]
        }
      ],
      "source": [
        "!pip install face_detection"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dPgGI2UlDGPD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7834ea3e-0798-4f16-b4cf-7219b5dd216f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting mtcnn\n",
            "  Downloading mtcnn-0.1.1-py3-none-any.whl (2.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 MB\u001b[0m \u001b[31m13.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: keras>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from mtcnn) (2.15.0)\n",
            "Requirement already satisfied: opencv-python>=4.1.0 in /usr/local/lib/python3.10/dist-packages (from mtcnn) (4.8.0.76)\n",
            "Requirement already satisfied: numpy>=1.21.2 in /usr/local/lib/python3.10/dist-packages (from opencv-python>=4.1.0->mtcnn) (1.25.2)\n",
            "Installing collected packages: mtcnn\n",
            "Successfully installed mtcnn-0.1.1\n"
          ]
        }
      ],
      "source": [
        "!pip install mtcnn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "73VsU_ESDIM0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "971595c0-04f7-42d9-af14-94f09b94195e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (1.2.2)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.25.2)\n",
            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.11.4)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.4.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (3.5.0)\n"
          ]
        }
      ],
      "source": [
        "!pip3 install scikit-learn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2a1Mq5fWDK-4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "008ff2ca-f596-488d-f240-900d991ffda3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: dlib in /usr/local/lib/python3.10/dist-packages (19.24.2)\n"
          ]
        }
      ],
      "source": [
        "!pip install dlib"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "w1CRLvEYDQrT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "37d55223-897f-4a38-fd59-b74c907d896d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: face-recognition in /usr/local/lib/python3.10/dist-packages (1.3.0)\n",
            "Requirement already satisfied: face-recognition-models>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from face-recognition) (0.3.0)\n",
            "Requirement already satisfied: Click>=6.0 in /usr/local/lib/python3.10/dist-packages (from face-recognition) (8.1.7)\n",
            "Requirement already satisfied: dlib>=19.7 in /usr/local/lib/python3.10/dist-packages (from face-recognition) (19.24.2)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from face-recognition) (1.25.2)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from face-recognition) (9.4.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install face-recognition"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6vmAmiGlEwtb"
      },
      "source": [
        "##**DETECTION AND CROPPING**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-XaBemO5DjcL"
      },
      "outputs": [],
      "source": [
        "import cv2\n",
        "import os\n",
        "from mtcnn import MTCNN  # Make sure you have installed mtcnn library\n",
        "from google.colab.patches import cv2_imshow\n",
        "\n",
        "# Load the image\n",
        "image = cv2.imread('/content/classroom3.jpg')\n",
        "\n",
        "# Load the pre-trained face detection model from MTCNN\n",
        "detector_mtcnn = MTCNN()\n",
        "\n",
        "# Detect faces using MTCNN\n",
        "faces_mtcnn = detector_mtcnn.detect_faces(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))\n",
        "\n",
        "# Create a folder to store cropped faces\n",
        "output_folder = 'cropped_faces2'\n",
        "os.makedirs(output_folder, exist_ok=True)\n",
        "\n",
        "# Crop and save each detected face\n",
        "for i, face in enumerate(faces_mtcnn):\n",
        "    x, y, width, height = face['box']\n",
        "    # Calculate new box coordinates\n",
        "    x_new = max(0, x - int(width * 0.25))\n",
        "    y_new = max(0, y - int(height * 0.25))\n",
        "    width_new = int(width * 1.5)\n",
        "    height_new = int(height * 1.5)\n",
        "    # Crop the face region from the image\n",
        "    cropped_face = image[y_new:y_new+height_new, x_new:x_new+width_new]\n",
        "    # Save the cropped face as a separate image\n",
        "    output_path = os.path.join(output_folder, f'face_{i}.jpg')\n",
        "    cv2.imwrite(output_path, cropped_face)\n",
        "    # Draw rectangle around the face on the original image\n",
        "    cv2.rectangle(image, (x_new, y_new), (x_new + width_new, y_new + height_new), (0, 255, 0), 2)\n",
        "\n",
        "# Display the original image with bounding boxes\n",
        "cv2_imshow(image)\n",
        "cv2.waitKey(0)\n",
        "cv2.destroyAllWindows()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt: write a code to print every image in cropped_faces2 folder\n",
        "\n",
        "import os\n",
        "import cv2\n",
        "from google.colab.patches import cv2_imshow\n",
        "\n",
        "# Get the path of the cropped_faces2 folder\n",
        "cropped_faces_path = '/content/cropped_faces2'\n",
        "\n",
        "# Get a list of all files in the folder\n",
        "files = os.listdir(cropped_faces_path)\n",
        "\n",
        "# Iterate through the files and print each image\n",
        "for filename in files:\n",
        "  # Construct the full path of the image\n",
        "  image_path = os.path.join(cropped_faces_path, filename)\n",
        "\n",
        "  # Read the image using OpenCV\n",
        "  image = cv2.imread(image_path)\n",
        "\n",
        "  # Display the image using cv2_imshow\n",
        "  cv2_imshow(image)\n",
        "\n",
        "  # Wait for a key press before moving to the next image\n",
        "  cv2.waitKey(0)\n",
        "\n",
        "# Close all windows\n",
        "cv2.destroyAllWindows()\n"
      ],
      "metadata": {
        "id": "Dz1qlsZ19J05"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T5gbc4i6E33M"
      },
      "source": [
        "##**KNN**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "onXwWE7YDw5S"
      },
      "outputs": [],
      "source": [
        "import math\n",
        "from sklearn import neighbors\n",
        "import os\n",
        "import os.path\n",
        "import pickle\n",
        "from PIL import Image, ImageDraw\n",
        "import face_recognition\n",
        "from face_recognition.face_recognition_cli import image_files_in_folder\n",
        "\n",
        "ALLOWED_EXTENSIONS = {'png', 'jpg', 'jpeg'}\n",
        "\n",
        "def predict(X_img_path, knn_clf=None, model_path=None, distance_threshold=0.6):\n",
        "    \"\"\"\n",
        "    Recognizes faces in given image using a trained KNN classifier\n",
        "\n",
        "    :param X_img_path: path to image to be recognized\n",
        "    :param knn_clf: (optional) a knn classifier object. if not specified, model_save_path must be specified.\n",
        "    :param model_path: (optional) path to a pickled knn classifier. if not specified, model_save_path must be knn_clf.\n",
        "    :param distance_threshold: (optional) distance threshold for face classification. the larger it is, the more chance\n",
        "           of mis-classifying an unknown person as a known one.\n",
        "    :return: a list of names and face locations for the recognized faces in the image: [(name, bounding box), ...].\n",
        "        For faces of unrecognized persons, the name 'unknown' will be returned.\n",
        "    \"\"\"\n",
        "    if not os.path.isfile(X_img_path) or os.path.splitext(X_img_path)[1][1:] not in ALLOWED_EXTENSIONS:\n",
        "        raise Exception(\"Invalid image path: {}\".format(X_img_path))\n",
        "\n",
        "    if knn_clf is None and model_path is None:\n",
        "        raise Exception(\"Must supply knn classifier either thourgh knn_clf or model_path\")\n",
        "\n",
        "    # Load a trained KNN model (if one was passed in)\n",
        "    if knn_clf is None:\n",
        "        with open(model_path, 'rb') as f:\n",
        "            knn_clf = pickle.load(f)\n",
        "\n",
        "    # Load image file and find face locations\n",
        "    X_img = face_recognition.load_image_file(X_img_path)\n",
        "    X_face_locations = face_recognition.face_locations(X_img)\n",
        "\n",
        "    # If no faces are found in the image, return an empty result.\n",
        "    if len(X_face_locations) == 0:\n",
        "        return []\n",
        "\n",
        "    # Find encodings for faces in the test iamge\n",
        "    faces_encodings = face_recognition.face_encodings(X_img, known_face_locations=X_face_locations)\n",
        "\n",
        "    # Use the KNN model to find the best matches for the test face\n",
        "    closest_distances = knn_clf.kneighbors(faces_encodings, n_neighbors=1)\n",
        "    are_matches = [closest_distances[0][i][0] <= distance_threshold for i in range(len(X_face_locations))]\n",
        "\n",
        "    # Predict classes and remove classifications that aren't within the threshold\n",
        "    return [(pred, loc) if rec else (\"unknown\", loc) for pred, loc, rec in zip(knn_clf.predict(faces_encodings), X_face_locations, are_matches)]\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3Z8vdWf8EH2D",
        "outputId": "22cb079f-0846-41ed-cd1a-2faff26f59e8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Image_Name   True_Labels Predicted_Labels\n",
            "0       face0   60009220088      60009220088\n",
            "1       face1   60009210185      60009210185\n",
            "2       face2  unrecognised      60009220194\n",
            "3       face3   60009210164      60009220080\n",
            "4       face4  unrecognised      60009210202\n",
            "5       face5   60009210157      60009220088\n",
            "6       face6  unrecognised      60009210205\n",
            "7       face7   60009220174     unrecognised\n",
            "8       face8   60009210196      60009210196\n",
            "9       face9   60009210175      60009210175\n",
            "10     face10   60009210187      60009210187\n",
            "11     face11   60009220194      60009220194\n",
            "12     face12   60009210197      60009210197\n",
            "13     face13   60009210206      60009210206\n",
            "14     face14  unrecognised      60009210209\n",
            "15     face15   60009210123      60009210123\n",
            "16     face16   60009210202     unrecognised\n",
            "17     face17   60009220175      60009220175\n",
            "18     face18   60009210126      60009210156\n",
            "19     face19   60009210156      60009210156\n",
            "20     face20   60009210190      60009210190\n",
            "21     face21   60009210192      60009220175\n",
            "22     face22  unrecognised      60009210194\n",
            "23     face23   60009210210      60009210209\n",
            "24     face24  unrecognised      60009210209\n",
            "25     face25   60009210209     unrecognised\n",
            "26     face26   60009210188      60009210210\n",
            "27     face27   60009210204      60009210204\n",
            "28     face28   60009220080      60009220080\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Create a DataFrame with image names and true labels\n",
        "data = {\n",
        "    'Image_Name': ['face{}'.format(i) for i in range(29)],\n",
        "    'True_Labels': [\n",
        "        '60009220088', '60009210185', 'unrecognised', '60009210164', 'unrecognised',\n",
        "        '60009210157', 'unrecognised', '60009220174', '60009210196', '60009210175',\n",
        "        '60009210187', '60009220194', '60009210197', '60009210206', 'unrecognised', '60009210123',\n",
        "        '60009210202', '60009220175', '60009210126', '60009210156', '60009210190', '60009210192',\n",
        "        'unrecognised', '60009210210', 'unrecognised', '60009210209', '60009210188',\n",
        "        '60009210204', '60009220080'\n",
        "    ]\n",
        "}\n",
        "df = pd.DataFrame(data)\n",
        "\n",
        "# Load the trained model\n",
        "with open(\"trained_knn_model.clf\", 'rb') as f:\n",
        "    knn_clf = pickle.load(f)\n",
        "\n",
        "# Make predictions for each image\n",
        "predictions = []\n",
        "for i in range(29):\n",
        "    image_path = \"/content/cropped_faces2/face_{}.jpg\".format(i)\n",
        "    prediction = predict(image_path, knn_clf=knn_clf)\n",
        "    predictions.append(prediction)\n",
        "\n",
        "# Extract predicted labels from predictions\n",
        "predicted_labels = [pred[0][0] if pred else \"unrecognised\" for pred in predictions]\n",
        "\n",
        "# Add predicted labels to the DataFrame\n",
        "df['Predicted_Labels'] = predicted_labels\n",
        "\n",
        "print(df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 959
        },
        "id": "cFjM8hkmm_Bv",
        "outputId": "4d17c465-9ca2-4a15-c5b7-270d7074cef2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Image_Name   True_Labels Predicted_Labels\n",
              "0       face0   60009220088      60009220088\n",
              "1       face1   60009210185      60009210185\n",
              "2       face2  unrecognised      60009220194\n",
              "3       face3   60009210164      60009220080\n",
              "4       face4  unrecognised      60009210202\n",
              "5       face5   60009210157      60009220088\n",
              "6       face6  unrecognised      60009210205\n",
              "7       face7   60009220174     unrecognised\n",
              "8       face8   60009210196      60009210196\n",
              "9       face9   60009210175      60009210175\n",
              "10     face10   60009210187      60009210187\n",
              "11     face11   60009220194      60009220194\n",
              "12     face12   60009210197      60009210197\n",
              "13     face13   60009210206      60009210206\n",
              "14     face14  unrecognised      60009210209\n",
              "15     face15   60009210123      60009210123\n",
              "16     face16   60009210202     unrecognised\n",
              "17     face17   60009220175      60009220175\n",
              "18     face18   60009210126      60009210156\n",
              "19     face19   60009210156      60009210156\n",
              "20     face20   60009210190      60009210190\n",
              "21     face21   60009210192      60009220175\n",
              "22     face22  unrecognised      60009210194\n",
              "23     face23   60009210210      60009210209\n",
              "24     face24  unrecognised      60009210209\n",
              "25     face25   60009210209     unrecognised\n",
              "26     face26   60009210188      60009210210\n",
              "27     face27   60009210204      60009210204\n",
              "28     face28   60009220080      60009220080"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7dd7b005-0ef4-4086-8a13-6af2d6b95f35\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Image_Name</th>\n",
              "      <th>True_Labels</th>\n",
              "      <th>Predicted_Labels</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>face0</td>\n",
              "      <td>60009220088</td>\n",
              "      <td>60009220088</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>face1</td>\n",
              "      <td>60009210185</td>\n",
              "      <td>60009210185</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>face2</td>\n",
              "      <td>unrecognised</td>\n",
              "      <td>60009220194</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>face3</td>\n",
              "      <td>60009210164</td>\n",
              "      <td>60009220080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>face4</td>\n",
              "      <td>unrecognised</td>\n",
              "      <td>60009210202</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>face5</td>\n",
              "      <td>60009210157</td>\n",
              "      <td>60009220088</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>face6</td>\n",
              "      <td>unrecognised</td>\n",
              "      <td>60009210205</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>face7</td>\n",
              "      <td>60009220174</td>\n",
              "      <td>unrecognised</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>face8</td>\n",
              "      <td>60009210196</td>\n",
              "      <td>60009210196</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>face9</td>\n",
              "      <td>60009210175</td>\n",
              "      <td>60009210175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>face10</td>\n",
              "      <td>60009210187</td>\n",
              "      <td>60009210187</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>face11</td>\n",
              "      <td>60009220194</td>\n",
              "      <td>60009220194</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>face12</td>\n",
              "      <td>60009210197</td>\n",
              "      <td>60009210197</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>face13</td>\n",
              "      <td>60009210206</td>\n",
              "      <td>60009210206</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>face14</td>\n",
              "      <td>unrecognised</td>\n",
              "      <td>60009210209</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>face15</td>\n",
              "      <td>60009210123</td>\n",
              "      <td>60009210123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>face16</td>\n",
              "      <td>60009210202</td>\n",
              "      <td>unrecognised</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>face17</td>\n",
              "      <td>60009220175</td>\n",
              "      <td>60009220175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>face18</td>\n",
              "      <td>60009210126</td>\n",
              "      <td>60009210156</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>face19</td>\n",
              "      <td>60009210156</td>\n",
              "      <td>60009210156</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>face20</td>\n",
              "      <td>60009210190</td>\n",
              "      <td>60009210190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>face21</td>\n",
              "      <td>60009210192</td>\n",
              "      <td>60009220175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>face22</td>\n",
              "      <td>unrecognised</td>\n",
              "      <td>60009210194</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>face23</td>\n",
              "      <td>60009210210</td>\n",
              "      <td>60009210209</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>face24</td>\n",
              "      <td>unrecognised</td>\n",
              "      <td>60009210209</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>face25</td>\n",
              "      <td>60009210209</td>\n",
              "      <td>unrecognised</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>face26</td>\n",
              "      <td>60009210188</td>\n",
              "      <td>60009210210</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>face27</td>\n",
              "      <td>60009210204</td>\n",
              "      <td>60009210204</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>face28</td>\n",
              "      <td>60009220080</td>\n",
              "      <td>60009220080</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7dd7b005-0ef4-4086-8a13-6af2d6b95f35')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-7dd7b005-0ef4-4086-8a13-6af2d6b95f35 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-7dd7b005-0ef4-4086-8a13-6af2d6b95f35');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-44b04cd1-54a2-403d-9cab-32f9254e6c72\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-44b04cd1-54a2-403d-9cab-32f9254e6c72')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-44b04cd1-54a2-403d-9cab-32f9254e6c72 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_562524b6-63f0-4d01-86bf-67c582a3d46b\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_562524b6-63f0-4d01-86bf-67c582a3d46b button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 29,\n  \"fields\": [\n    {\n      \"column\": \"Image_Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 29,\n        \"samples\": [\n          \"face27\",\n          \"face16\",\n          \"face12\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"True_Labels\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 24,\n        \"samples\": [\n          \"60009210187\",\n          \"60009210156\",\n          \"60009220088\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Predicted_Labels\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \"60009220088\",\n          \"60009210194\",\n          \"60009210156\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CdccgwojEIMI",
        "outputId": "c764eb8f-afa3-406a-daaf-62ecc3cd2c16"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "True Positives (TP): 14\n",
            "True Negatives (TN): 0\n",
            "False Positives (FP): 6\n",
            "False Negatives (FN): 9\n",
            "\n",
            "\n",
            "Accuracy: 0.4827586206896552\n",
            "Precision: 0.7\n",
            "Recall: 0.6086956521739131\n",
            "F1 Score: 0.6511627906976744\n",
            "\n",
            "\n",
            "Confusion Matrix:\n",
            "\t\tPredicted Unrecognised\tPredicted Recognised\n",
            "True Unrecognised\t0\t\t\t6\n",
            "True Recognised\t\t9\t\t\t14\n"
          ]
        }
      ],
      "source": [
        "# True Positives (TP): Images where both the true labels and predicted labels match.\n",
        "# True Negatives (TN): Images where both the true labels and predicted labels are 'unrecognised'.\n",
        "# False Positives (FP): Images where the true label is 'unrecognised' but the predicted label is not.\n",
        "# False Negatives (FN): Images where the true label exists but is predicted wrong or as 'unrecognised'.\n",
        "\n",
        "# Initialize counts\n",
        "TP = 0\n",
        "TN = 0\n",
        "FP = 0\n",
        "FN = 0\n",
        "\n",
        "# Iterate through each row in the DataFrame\n",
        "for index, row in df.iterrows():\n",
        "    true_label = row['True_Labels']\n",
        "    predicted_label = row['Predicted_Labels']\n",
        "\n",
        "    # Check if the true label is recognised\n",
        "    if 'unrecognised' in true_label.lower():\n",
        "        # Check if the predicted label is also 'unrecognised'\n",
        "        if 'unrecognised' in predicted_label.lower():\n",
        "            TN += 1\n",
        "        else:\n",
        "            FP += 1\n",
        "    else:\n",
        "        # Check if the predicted label matches the true label\n",
        "        if str(true_label) == predicted_label:\n",
        "            TP += 1\n",
        "        elif((str(true_label) != predicted_label)and(str(true_label)!='unrecognised')):\n",
        "            FN += 1\n",
        "\n",
        "# Print counts\n",
        "print(\"True Positives (TP):\", TP)\n",
        "print(\"True Negatives (TN):\", TN)\n",
        "print(\"False Positives (FP):\", FP)\n",
        "print(\"False Negatives (FN):\", FN)\n",
        "\n",
        "# Calculate accuracy\n",
        "accuracy = (TP + TN) / (TP + TN + FP + FN)\n",
        "print(\"\\n\\nAccuracy:\", accuracy)\n",
        "\n",
        "# Calculate precision\n",
        "precision = TP / (TP + FP)\n",
        "print(\"Precision:\", precision)\n",
        "\n",
        "# Calculate recall\n",
        "recall = TP / (TP + FN)\n",
        "print(\"Recall:\", recall)\n",
        "\n",
        "# Calculate F1 score\n",
        "f1_score = 2 * (precision * recall) / (precision + recall)\n",
        "print(\"F1 Score:\", f1_score)\n",
        "\n",
        "# Print confusion matrix\n",
        "print(\"\\n\\nConfusion Matrix:\")\n",
        "print(\"\\t\\tPredicted Unrecognised\\tPredicted Recognised\")\n",
        "print(\"True Unrecognised\\t{}\\t\\t\\t{}\".format(TN, FP))\n",
        "print(\"True Recognised\\t\\t{}\\t\\t\\t{}\".format(FN, TP))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "LW59EmbiEMJ6",
        "outputId": "09f8be05-82e7-4425-8c28-908ff995438a"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Define labels for the confusion matrix\n",
        "labels = ['Unrecognised', 'Recognised']\n",
        "\n",
        "# Create confusion matrix\n",
        "confusion_matrix = np.array([[TN, FP], [FN, TP]])\n",
        "\n",
        "# Plot confusion matrix\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.imshow(confusion_matrix, interpolation='nearest', cmap=plt.cm.Blues)\n",
        "plt.title('Confusion Matrix')\n",
        "plt.colorbar()\n",
        "tick_marks = np.arange(len(labels))\n",
        "plt.xticks(tick_marks, labels, rotation=45)\n",
        "plt.yticks(tick_marks, labels)\n",
        "plt.xlabel('Predicted Label')\n",
        "plt.ylabel('True Label')\n",
        "\n",
        "# Add text annotations\n",
        "for i in range(len(labels)):\n",
        "    for j in range(len(labels)):\n",
        "        plt.text(j, i, str(confusion_matrix[i, j]), ha='center', va='center', color='white')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rZngEPdqGQwr"
      },
      "source": [
        "##**SVM**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1HO8XVhQFGRC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e2250884-8968-48fc-c0f5-a9d8efca2126"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Image_Name   True_Labels Predicted_Labels_SVM\n",
            "0       face0   60009220088          60009220088\n",
            "1       face1   60009210185          60009210185\n",
            "2       face2  unrecognised          60009220194\n",
            "3       face3    6000921064          60009210164\n",
            "4       face4  unrecognised          60009210187\n",
            "5       face5   60009210157          60009210157\n",
            "6       face6  unrecognised          60009210123\n",
            "7       face7   60009220174         unrecognised\n",
            "8       face8   60009210196          60009210175\n",
            "9       face9   60009210175          60009210196\n",
            "10     face10   60009210187          60009210187\n",
            "11     face11   60009220194          60009220194\n",
            "12     face12   60009210197          60009210197\n",
            "13     face13   60009210206          60009210206\n",
            "14     face14  unrecognised          60009210202\n",
            "15     face15   60009210123          60009210123\n",
            "16     face16   60009210202         unrecognised\n",
            "17     face17   60009220175          60009220175\n",
            "18     face18   60009210126          60009210126\n",
            "19     face19   60009210156          60009210156\n",
            "20     face20   60009210190          60009210190\n",
            "21     face21   60009210192          60009210192\n",
            "22     face22  unrecognised          60009210126\n",
            "23     face23   60009210210          60009210210\n",
            "24     face24  unrecognised          60009220088\n",
            "25     face25   60009210209         unrecognised\n",
            "26     face26   60009210188          60009210210\n",
            "27     face27   60009210204          60009210204\n",
            "28     face28   60009220080          60009220080\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import pickle\n",
        "import os\n",
        "import face_recognition\n",
        "from sklearn import svm\n",
        "\n",
        "# Load the trained SVM model\n",
        "with open(\"svm_model.pkl\", 'rb') as f:\n",
        "    svm_clf = pickle.load(f)\n",
        "\n",
        "# Function to predict labels for each image\n",
        "def predict_labels_svm(image_dir, svm_classifier):\n",
        "    predictions = []\n",
        "\n",
        "    # Loop through each image in the directory\n",
        "    for i in range(29):\n",
        "        image_path = os.path.join(image_dir, \"face_{}.jpg\".format(i))\n",
        "\n",
        "        # Load the image\n",
        "        image = face_recognition.load_image_file(image_path)\n",
        "\n",
        "        # Find face encodings\n",
        "        face_encodings = face_recognition.face_encodings(image)\n",
        "\n",
        "        # Predict label using SVM classifier\n",
        "        if len(face_encodings) == 1:\n",
        "            prediction = svm_classifier.predict(face_encodings)[0]\n",
        "            predictions.append(prediction)\n",
        "        else:\n",
        "            predictions.append(\"unrecognised\")\n",
        "\n",
        "    return predictions\n",
        "\n",
        "# Directory containing cropped images\n",
        "image_dir = \"/content/cropped_faces2\"\n",
        "\n",
        "# Make predictions using SVM model\n",
        "predicted_labels_svm = predict_labels_svm(image_dir, svm_clf)\n",
        "\n",
        "# Create DataFrame with image names, true labels, and predicted labels\n",
        "data = {\n",
        "    'Image_Name': ['face{}'.format(i) for i in range(29)],\n",
        "    'True_Labels': [\n",
        "        '60009220088', '60009210185', 'unrecognised', '60009210164', 'unrecognised',\n",
        "        '60009210157', 'unrecognised', '60009220174', '60009210196', '60009210175',\n",
        "        '60009210187', '60009220194', '60009210197', '60009210206', 'unrecognised', '60009210123',\n",
        "        '60009210202', '60009220175', '60009210126', '60009210156', '60009210190', '60009210192',\n",
        "        'unrecognised', '60009210210', 'unrecognised', '60009210209', '60009210188',\n",
        "        '60009210204', '60009220080'\n",
        "    ],\n",
        "    'Predicted_Labels_SVM': predicted_labels_svm\n",
        "}\n",
        "\n",
        "# Create DataFrame\n",
        "df_svm_predictions = pd.DataFrame(data)\n",
        "\n",
        "print(df_svm_predictions)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 959
        },
        "id": "FWZmMCCdoFyt",
        "outputId": "3927643a-015c-4f5a-a88c-6f916e14938e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Image_Name   True_Labels Predicted_Labels_SVM\n",
              "0       face0   60009220088          60009220088\n",
              "1       face1   60009210185          60009210185\n",
              "2       face2  unrecognised          60009220194\n",
              "3       face3    6000921064          60009210164\n",
              "4       face4  unrecognised          60009210187\n",
              "5       face5   60009210157          60009210157\n",
              "6       face6  unrecognised          60009210123\n",
              "7       face7   60009220174         unrecognised\n",
              "8       face8   60009210196          60009210175\n",
              "9       face9   60009210175          60009210196\n",
              "10     face10   60009210187          60009210187\n",
              "11     face11   60009220194          60009220194\n",
              "12     face12   60009210197          60009210197\n",
              "13     face13   60009210206          60009210206\n",
              "14     face14  unrecognised          60009210202\n",
              "15     face15   60009210123          60009210123\n",
              "16     face16   60009210202         unrecognised\n",
              "17     face17   60009220175          60009220175\n",
              "18     face18   60009210126          60009210126\n",
              "19     face19   60009210156          60009210156\n",
              "20     face20   60009210190          60009210190\n",
              "21     face21   60009210192          60009210192\n",
              "22     face22  unrecognised          60009210126\n",
              "23     face23   60009210210          60009210210\n",
              "24     face24  unrecognised          60009220088\n",
              "25     face25   60009210209         unrecognised\n",
              "26     face26   60009210188          60009210210\n",
              "27     face27   60009210204          60009210204\n",
              "28     face28   60009220080          60009220080"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fb9d6e58-46dc-4826-94ec-4e3360d59a6c\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Image_Name</th>\n",
              "      <th>True_Labels</th>\n",
              "      <th>Predicted_Labels_SVM</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>face0</td>\n",
              "      <td>60009220088</td>\n",
              "      <td>60009220088</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>face1</td>\n",
              "      <td>60009210185</td>\n",
              "      <td>60009210185</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>face2</td>\n",
              "      <td>unrecognised</td>\n",
              "      <td>60009220194</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>face3</td>\n",
              "      <td>6000921064</td>\n",
              "      <td>60009210164</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>face4</td>\n",
              "      <td>unrecognised</td>\n",
              "      <td>60009210187</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>face5</td>\n",
              "      <td>60009210157</td>\n",
              "      <td>60009210157</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>face6</td>\n",
              "      <td>unrecognised</td>\n",
              "      <td>60009210123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>face7</td>\n",
              "      <td>60009220174</td>\n",
              "      <td>unrecognised</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>face8</td>\n",
              "      <td>60009210196</td>\n",
              "      <td>60009210175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>face9</td>\n",
              "      <td>60009210175</td>\n",
              "      <td>60009210196</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>face10</td>\n",
              "      <td>60009210187</td>\n",
              "      <td>60009210187</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>face11</td>\n",
              "      <td>60009220194</td>\n",
              "      <td>60009220194</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>face12</td>\n",
              "      <td>60009210197</td>\n",
              "      <td>60009210197</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>face13</td>\n",
              "      <td>60009210206</td>\n",
              "      <td>60009210206</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>face14</td>\n",
              "      <td>unrecognised</td>\n",
              "      <td>60009210202</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>face15</td>\n",
              "      <td>60009210123</td>\n",
              "      <td>60009210123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>face16</td>\n",
              "      <td>60009210202</td>\n",
              "      <td>unrecognised</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>face17</td>\n",
              "      <td>60009220175</td>\n",
              "      <td>60009220175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>face18</td>\n",
              "      <td>60009210126</td>\n",
              "      <td>60009210126</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>face19</td>\n",
              "      <td>60009210156</td>\n",
              "      <td>60009210156</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>face20</td>\n",
              "      <td>60009210190</td>\n",
              "      <td>60009210190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>face21</td>\n",
              "      <td>60009210192</td>\n",
              "      <td>60009210192</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>face22</td>\n",
              "      <td>unrecognised</td>\n",
              "      <td>60009210126</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>face23</td>\n",
              "      <td>60009210210</td>\n",
              "      <td>60009210210</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>face24</td>\n",
              "      <td>unrecognised</td>\n",
              "      <td>60009220088</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>face25</td>\n",
              "      <td>60009210209</td>\n",
              "      <td>unrecognised</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>face26</td>\n",
              "      <td>60009210188</td>\n",
              "      <td>60009210210</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>face27</td>\n",
              "      <td>60009210204</td>\n",
              "      <td>60009210204</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>face28</td>\n",
              "      <td>60009220080</td>\n",
              "      <td>60009220080</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fb9d6e58-46dc-4826-94ec-4e3360d59a6c')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-fb9d6e58-46dc-4826-94ec-4e3360d59a6c button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-fb9d6e58-46dc-4826-94ec-4e3360d59a6c');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-bcaf4cc2-833a-47d9-90ac-d365f0989e43\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-bcaf4cc2-833a-47d9-90ac-d365f0989e43')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-bcaf4cc2-833a-47d9-90ac-d365f0989e43 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_360292d3-e8ef-4831-854b-321df5f5447b\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_svm_predictions')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_360292d3-e8ef-4831-854b-321df5f5447b button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_svm_predictions');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_svm_predictions",
              "summary": "{\n  \"name\": \"df_svm_predictions\",\n  \"rows\": 29,\n  \"fields\": [\n    {\n      \"column\": \"Image_Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 29,\n        \"samples\": [\n          \"face27\",\n          \"face16\",\n          \"face12\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"True_Labels\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 24,\n        \"samples\": [\n          \"60009210187\",\n          \"60009210156\",\n          \"60009220088\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Predicted_Labels_SVM\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 21,\n        \"samples\": [\n          \"60009220088\",\n          \"60009210192\",\n          \"60009210156\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "df_svm_predictions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aKbhbHIqGfdx",
        "outputId": "89e3d742-5be8-46b5-9351-0cd72e13795f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "True Positives (TP): 16\n",
            "True Negatives (TN): 0\n",
            "False Positives (FP): 6\n",
            "False Negatives (FN): 7\n",
            "\n",
            "\n",
            "Accuracy: 0.5517241379310345\n",
            "Precision: 0.7272727272727273\n",
            "Recall: 0.6956521739130435\n",
            "F1 Score: 0.711111111111111\n",
            "\n",
            "\n",
            "Confusion Matrix:\n",
            "\t\tPredicted Unrecognised\tPredicted Recognised\n",
            "True Unrecognised\t0\t\t\t6\n",
            "True Recognised\t\t7\t\t\t16\n"
          ]
        }
      ],
      "source": [
        "# Initialize counts\n",
        "TP = 0\n",
        "TN = 0\n",
        "FP = 0\n",
        "FN = 0\n",
        "\n",
        "# Iterate through each row in the DataFrame\n",
        "for index, row in df_svm_predictions.iterrows():\n",
        "    true_label = row['True_Labels']\n",
        "    predicted_label = row['Predicted_Labels_SVM']\n",
        "\n",
        "    # Check if the true label is recognised\n",
        "    if 'unrecognised' in true_label.lower():\n",
        "        # Check if the predicted label is also 'unrecognised'\n",
        "        if 'unrecognised' in predicted_label.lower():\n",
        "            TN += 1\n",
        "        else:\n",
        "            FP += 1\n",
        "    else:\n",
        "        # Check if the predicted label matches the true label\n",
        "        if str(true_label) == predicted_label:\n",
        "            TP += 1\n",
        "        else:\n",
        "            FN += 1\n",
        "\n",
        "# Print counts\n",
        "print(\"True Positives (TP):\", TP)\n",
        "print(\"True Negatives (TN):\", TN)\n",
        "print(\"False Positives (FP):\", FP)\n",
        "print(\"False Negatives (FN):\", FN)\n",
        "\n",
        "# Calculate accuracy\n",
        "accuracy = (TP + TN) / (TP + TN + FP + FN)\n",
        "print(\"\\n\\nAccuracy:\", accuracy)\n",
        "\n",
        "# Calculate precision\n",
        "precision = TP / (TP + FP)\n",
        "print(\"Precision:\", precision)\n",
        "\n",
        "# Calculate recall\n",
        "recall = TP / (TP + FN)\n",
        "print(\"Recall:\", recall)\n",
        "\n",
        "# Calculate F1 score\n",
        "f1_score = 2 * (precision * recall) / (precision + recall)\n",
        "print(\"F1 Score:\", f1_score)\n",
        "\n",
        "# Print confusion matrix\n",
        "print(\"\\n\\nConfusion Matrix:\")\n",
        "print(\"\\t\\tPredicted Unrecognised\\tPredicted Recognised\")\n",
        "print(\"True Unrecognised\\t{}\\t\\t\\t{}\".format(TN, FP))\n",
        "print(\"True Recognised\\t\\t{}\\t\\t\\t{}\".format(FN, TP))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "qSZJs0JfGutG",
        "outputId": "4fcf8e3a-9a78-4ccf-9cde-8cce1df591b0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Define labels for the confusion matrix\n",
        "labels = ['Unrecognised', 'Recognised']\n",
        "\n",
        "# Create confusion matrix\n",
        "confusion_matrix = np.array([[TN, FP], [FN, TP]])\n",
        "\n",
        "# Plot confusion matrix\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.imshow(confusion_matrix, interpolation='nearest', cmap=plt.cm.Blues)\n",
        "plt.title('Confusion Matrix')\n",
        "plt.colorbar()\n",
        "tick_marks = np.arange(len(labels))\n",
        "plt.xticks(tick_marks, labels, rotation=45)\n",
        "plt.yticks(tick_marks, labels)\n",
        "plt.xlabel('Predicted Label')\n",
        "plt.ylabel('True Label')\n",
        "\n",
        "# Add text annotations\n",
        "for i in range(len(labels)):\n",
        "    for j in range(len(labels)):\n",
        "        plt.text(j, i, str(confusion_matrix[i, j]), ha='center', va='center', color='white')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**SVM with tuned parameters**"
      ],
      "metadata": {
        "id": "uc06_qEX_TfH"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RAnSgg04G1M2",
        "outputId": "7ccc4b6c-7e98-407a-8f8e-6273438fc01b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training SVM classifier with hyperparameter tuning...\n",
            "Skipping /content/drive/MyDrive/Dataset/60009210202/60009210202 10.jpg - No face found.\n",
            "Skipping /content/drive/MyDrive/Dataset/60009210187/60009210187 5.jpg - No face found.\n",
            "Skipping /content/drive/MyDrive/Dataset/60009210190/60009210190 6.jpg - No face found.\n",
            "Skipping /content/drive/MyDrive/Dataset/60009210209/60009210209 7.jpg - No face found.\n",
            "Skipping /content/drive/MyDrive/Dataset/60009210209/60009210209 9.jpg - No face found.\n",
            "Skipping /content/drive/MyDrive/Dataset/60009210194/60009210194 10.jpg - No face found.\n",
            "Skipping /content/drive/MyDrive/Dataset/60009210156/60009210156 5.jpg - No face found.\n",
            "Fitting 5 folds for each of 48 candidates, totalling 240 fits\n",
            "Best Parameters: {'C': 1, 'gamma': 1, 'kernel': 'rbf'}\n",
            "Training complete!\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "import pickle\n",
        "import face_recognition\n",
        "from sklearn import svm\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "def train_svm(train_dir, model_save_path=None, verbose=False):\n",
        "    \"\"\"\n",
        "    Trains an SVM classifier for face recognition.\n",
        "\n",
        "    :param train_dir: Directory that contains a sub-directory for each known person, with its name.\n",
        "    :param model_save_path: (optional) Path to save the trained model on disk.\n",
        "    :param verbose: Verbosity of training.\n",
        "    :return: Trained SVM classifier.\n",
        "    \"\"\"\n",
        "    encodings = []\n",
        "    labels = []\n",
        "\n",
        "    # Loop through each person in the training set\n",
        "    for person_dir in os.listdir(train_dir):\n",
        "        if not os.path.isdir(os.path.join(train_dir, person_dir)):\n",
        "            continue\n",
        "\n",
        "        # Loop through each training image for the current person\n",
        "        for img_name in os.listdir(os.path.join(train_dir, person_dir)):\n",
        "            img_path = os.path.join(train_dir, person_dir, img_name)\n",
        "\n",
        "            # Load the image and find face encodings\n",
        "            image = face_recognition.load_image_file(img_path)\n",
        "            face_encodings = face_recognition.face_encodings(image)\n",
        "\n",
        "            # Ensure only one face is present in the image\n",
        "            if len(face_encodings) == 1:\n",
        "                encodings.append(face_encodings[0])\n",
        "                labels.append(person_dir)\n",
        "            elif len(face_encodings) < 1:\n",
        "                if verbose:\n",
        "                    print(f\"Skipping {img_path} - No face found.\")\n",
        "            else:\n",
        "                if verbose:\n",
        "                    print(f\"Skipping {img_path} - Multiple faces found.\")\n",
        "\n",
        "    # Define parameter grid for hyperparameter tuning\n",
        "    param_grid = {\n",
        "        'C': [0.1, 1, 10],\n",
        "        'gamma': [0.001, 0.01, 0.1, 1],\n",
        "        'kernel': ['linear', 'poly', 'rbf', 'sigmoid']\n",
        "    }\n",
        "\n",
        "    # Perform grid search with cross-validation\n",
        "    grid_search = GridSearchCV(svm.SVC(), param_grid, cv=5, verbose=verbose)\n",
        "    grid_search.fit(encodings, labels)\n",
        "\n",
        "    # Get the best parameters and best estimator\n",
        "    best_params = grid_search.best_params_\n",
        "    best_estimator = grid_search.best_estimator_\n",
        "\n",
        "    print(\"Best Parameters:\", best_params)\n",
        "\n",
        "    # Save the best estimator (model)\n",
        "    if model_save_path:\n",
        "        with open(model_save_path, 'wb') as f:\n",
        "            pickle.dump(best_estimator, f)\n",
        "\n",
        "    return best_estimator\n",
        "\n",
        "# Example usage:\n",
        "train_dir = '/content/drive/MyDrive/Dataset'\n",
        "model_save_path = 'svm_model_best_params.pkl'\n",
        "\n",
        "print(\"Training SVM classifier with hyperparameter tuning...\")\n",
        "svm_classifier = train_svm(train_dir, model_save_path=model_save_path, verbose=True)\n",
        "print(\"Training complete!\")\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import pickle\n",
        "import os\n",
        "import face_recognition\n",
        "from sklearn import svm\n",
        "\n",
        "# Load the trained SVM model\n",
        "with open(\"/content/svm_model_best_params.pkl\", 'rb') as f:\n",
        "    svm_clf = pickle.load(f)\n",
        "\n",
        "# Function to predict labels for each image\n",
        "def predict_labels_svm(image_dir, svm_classifier):\n",
        "    predictions = []\n",
        "\n",
        "    # Loop through each image in the directory\n",
        "    for i in range(29):\n",
        "        image_path = os.path.join(image_dir, \"face_{}.jpg\".format(i))\n",
        "\n",
        "        # Load the image\n",
        "        image = face_recognition.load_image_file(image_path)\n",
        "\n",
        "        # Find face encodings\n",
        "        face_encodings = face_recognition.face_encodings(image)\n",
        "\n",
        "        # Predict label using SVM classifier\n",
        "        if len(face_encodings) == 1:\n",
        "            prediction = svm_classifier.predict(face_encodings)[0]\n",
        "            predictions.append(prediction)\n",
        "        else:\n",
        "            predictions.append(\"unrecognised\")\n",
        "\n",
        "    return predictions\n",
        "\n",
        "# Directory containing cropped images\n",
        "image_dir = \"/content/cropped_faces2\"\n",
        "\n",
        "# Make predictions using SVM model\n",
        "predicted_labels_svm = predict_labels_svm(image_dir, svm_clf)\n",
        "\n",
        "# Create DataFrame with image names, true labels, and predicted labels\n",
        "data = {\n",
        "    'Image_Name': ['face{}'.format(i) for i in range(29)],\n",
        "    'True_Labels': [\n",
        "        '60009220088', '60009210185', 'unrecognised', '60009210164', 'unrecognised',\n",
        "        '60009210157', 'unrecognised', '60009220174', '60009210196', '60009210175',\n",
        "        '60009210187', '60009220194', '60009210197', '60009210206', 'unrecognised', '60009210123',\n",
        "        '60009210202', '60009220175', '60009210126', '60009210156', '60009210190', '60009210192',\n",
        "        'unrecognised', '60009210210', 'unrecognised', '60009210209', '60009210188',\n",
        "        '60009210204', '60009220080'\n",
        "    ],\n",
        "    'Predicted_Labels_SVM': predicted_labels_svm\n",
        "}\n",
        "\n",
        "# Create DataFrame\n",
        "df_svm_predictions = pd.DataFrame(data)\n",
        "\n",
        "print(df_svm_predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yfJnxAKB9fLv",
        "outputId": "82a811db-ce7d-4593-d209-73c9c3c7847f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Image_Name   True_Labels Predicted_Labels_SVM\n",
            "0       face0   60009220088          60009220088\n",
            "1       face1   60009210185          60009210185\n",
            "2       face2  unrecognised          60009220194\n",
            "3       face3   60009210164          60009210164\n",
            "4       face4  unrecognised          60009210187\n",
            "5       face5   60009210157          60009210157\n",
            "6       face6  unrecognised          60009210123\n",
            "7       face7   60009220174         unrecognised\n",
            "8       face8   60009210196          60009210196\n",
            "9       face9   60009210175          60009210175\n",
            "10     face10   60009210187          60009210187\n",
            "11     face11   60009220194          60009220194\n",
            "12     face12   60009210197          60009210197\n",
            "13     face13   60009210206          60009210206\n",
            "14     face14  unrecognised          60009210210\n",
            "15     face15   60009210123          60009210123\n",
            "16     face16   60009210202         unrecognised\n",
            "17     face17   60009220175          60009220175\n",
            "18     face18   60009210126          60009210126\n",
            "19     face19   60009210156          60009210156\n",
            "20     face20   60009210190          60009210190\n",
            "21     face21   60009210192          60009210192\n",
            "22     face22  unrecognised          60009210126\n",
            "23     face23   60009210210          60009210210\n",
            "24     face24  unrecognised          60009220088\n",
            "25     face25   60009210209         unrecognised\n",
            "26     face26   60009210188          60009210210\n",
            "27     face27   60009210204          60009210204\n",
            "28     face28   60009220080          60009220080\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_svm_predictions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 959
        },
        "id": "om6APT9P-g8y",
        "outputId": "88e1cf68-f18e-4d10-e5ff-e65f966f8728"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Image_Name   True_Labels Predicted_Labels_SVM\n",
              "0       face0   60009220088          60009220088\n",
              "1       face1   60009210185          60009210185\n",
              "2       face2  unrecognised          60009220194\n",
              "3       face3   60009210164          60009210164\n",
              "4       face4  unrecognised          60009210187\n",
              "5       face5   60009210157          60009210157\n",
              "6       face6  unrecognised          60009210123\n",
              "7       face7   60009220174         unrecognised\n",
              "8       face8   60009210196          60009210196\n",
              "9       face9   60009210175          60009210175\n",
              "10     face10   60009210187          60009210187\n",
              "11     face11   60009220194          60009220194\n",
              "12     face12   60009210197          60009210197\n",
              "13     face13   60009210206          60009210206\n",
              "14     face14  unrecognised          60009210210\n",
              "15     face15   60009210123          60009210123\n",
              "16     face16   60009210202         unrecognised\n",
              "17     face17   60009220175          60009220175\n",
              "18     face18   60009210126          60009210126\n",
              "19     face19   60009210156          60009210156\n",
              "20     face20   60009210190          60009210190\n",
              "21     face21   60009210192          60009210192\n",
              "22     face22  unrecognised          60009210126\n",
              "23     face23   60009210210          60009210210\n",
              "24     face24  unrecognised          60009220088\n",
              "25     face25   60009210209         unrecognised\n",
              "26     face26   60009210188          60009210210\n",
              "27     face27   60009210204          60009210204\n",
              "28     face28   60009220080          60009220080"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ba279905-b98a-4260-bd96-e44fcd1dbe8b\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Image_Name</th>\n",
              "      <th>True_Labels</th>\n",
              "      <th>Predicted_Labels_SVM</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>face0</td>\n",
              "      <td>60009220088</td>\n",
              "      <td>60009220088</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>face1</td>\n",
              "      <td>60009210185</td>\n",
              "      <td>60009210185</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>face2</td>\n",
              "      <td>unrecognised</td>\n",
              "      <td>60009220194</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>face3</td>\n",
              "      <td>60009210164</td>\n",
              "      <td>60009210164</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>face4</td>\n",
              "      <td>unrecognised</td>\n",
              "      <td>60009210187</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>face5</td>\n",
              "      <td>60009210157</td>\n",
              "      <td>60009210157</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>face6</td>\n",
              "      <td>unrecognised</td>\n",
              "      <td>60009210123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>face7</td>\n",
              "      <td>60009220174</td>\n",
              "      <td>unrecognised</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>face8</td>\n",
              "      <td>60009210196</td>\n",
              "      <td>60009210196</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>face9</td>\n",
              "      <td>60009210175</td>\n",
              "      <td>60009210175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>face10</td>\n",
              "      <td>60009210187</td>\n",
              "      <td>60009210187</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>face11</td>\n",
              "      <td>60009220194</td>\n",
              "      <td>60009220194</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>face12</td>\n",
              "      <td>60009210197</td>\n",
              "      <td>60009210197</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>face13</td>\n",
              "      <td>60009210206</td>\n",
              "      <td>60009210206</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>face14</td>\n",
              "      <td>unrecognised</td>\n",
              "      <td>60009210210</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>face15</td>\n",
              "      <td>60009210123</td>\n",
              "      <td>60009210123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>face16</td>\n",
              "      <td>60009210202</td>\n",
              "      <td>unrecognised</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>face17</td>\n",
              "      <td>60009220175</td>\n",
              "      <td>60009220175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>face18</td>\n",
              "      <td>60009210126</td>\n",
              "      <td>60009210126</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>face19</td>\n",
              "      <td>60009210156</td>\n",
              "      <td>60009210156</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>face20</td>\n",
              "      <td>60009210190</td>\n",
              "      <td>60009210190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>face21</td>\n",
              "      <td>60009210192</td>\n",
              "      <td>60009210192</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>face22</td>\n",
              "      <td>unrecognised</td>\n",
              "      <td>60009210126</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>face23</td>\n",
              "      <td>60009210210</td>\n",
              "      <td>60009210210</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>face24</td>\n",
              "      <td>unrecognised</td>\n",
              "      <td>60009220088</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>face25</td>\n",
              "      <td>60009210209</td>\n",
              "      <td>unrecognised</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>face26</td>\n",
              "      <td>60009210188</td>\n",
              "      <td>60009210210</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>face27</td>\n",
              "      <td>60009210204</td>\n",
              "      <td>60009210204</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>face28</td>\n",
              "      <td>60009220080</td>\n",
              "      <td>60009220080</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ba279905-b98a-4260-bd96-e44fcd1dbe8b')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ba279905-b98a-4260-bd96-e44fcd1dbe8b button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-ba279905-b98a-4260-bd96-e44fcd1dbe8b');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-85d012ff-add6-4996-a361-9cd92000371b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-85d012ff-add6-4996-a361-9cd92000371b')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-85d012ff-add6-4996-a361-9cd92000371b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_efc220a0-8de0-4541-b7de-5f5f5fd0188d\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_svm_predictions')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_efc220a0-8de0-4541-b7de-5f5f5fd0188d button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_svm_predictions');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_svm_predictions",
              "summary": "{\n  \"name\": \"df_svm_predictions\",\n  \"rows\": 29,\n  \"fields\": [\n    {\n      \"column\": \"Image_Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 29,\n        \"samples\": [\n          \"face27\",\n          \"face16\",\n          \"face12\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"True_Labels\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 24,\n        \"samples\": [\n          \"60009210187\",\n          \"60009210156\",\n          \"60009220088\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Predicted_Labels_SVM\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \"60009220088\",\n          \"60009210192\",\n          \"60009210156\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize counts\n",
        "TP = 0\n",
        "TN = 0\n",
        "FP = 0\n",
        "FN = 0\n",
        "\n",
        "# Iterate through each row in the DataFrame\n",
        "for index, row in df_svm_predictions.iterrows():\n",
        "    true_label = row['True_Labels']\n",
        "    predicted_label = row['Predicted_Labels_SVM']\n",
        "\n",
        "    # Check if the true label is recognised\n",
        "    if 'unrecognised' in true_label.lower():\n",
        "        # Check if the predicted label is also 'unrecognised'\n",
        "        if 'unrecognised' in predicted_label.lower():\n",
        "            TN += 1\n",
        "        else:\n",
        "            FP += 1\n",
        "    else:\n",
        "        # Check if the predicted label matches the true label\n",
        "        if str(true_label) == predicted_label:\n",
        "            TP += 1\n",
        "        else:\n",
        "            FN += 1\n",
        "\n",
        "# Print counts\n",
        "print(\"True Positives (TP):\", TP)\n",
        "print(\"True Negatives (TN):\", TN)\n",
        "print(\"False Positives (FP):\", FP)\n",
        "print(\"False Negatives (FN):\", FN)\n",
        "\n",
        "# Calculate accuracy\n",
        "accuracy = (TP + TN) / (TP + TN + FP + FN)\n",
        "print(\"\\n\\nAccuracy:\", accuracy)\n",
        "\n",
        "# Calculate precision\n",
        "precision = TP / (TP + FP)\n",
        "print(\"Precision:\", precision)\n",
        "\n",
        "# Calculate recall\n",
        "recall = TP / (TP + FN)\n",
        "print(\"Recall:\", recall)\n",
        "\n",
        "# Calculate F1 score\n",
        "f1_score = 2 * (precision * recall) / (precision + recall)\n",
        "print(\"F1 Score:\", f1_score)\n",
        "\n",
        "# Print confusion matrix\n",
        "print(\"\\n\\nConfusion Matrix:\")\n",
        "print(\"\\t\\tPredicted Unrecognised\\tPredicted Recognised\")\n",
        "print(\"True Unrecognised\\t{}\\t\\t\\t{}\".format(TN, FP))\n",
        "print(\"True Recognised\\t\\t{}\\t\\t\\t{}\".format(FN, TP))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NCOiVRJk-PSQ",
        "outputId": "8cb01ddc-d04b-4801-f258-d4687250c9bd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "True Positives (TP): 19\n",
            "True Negatives (TN): 0\n",
            "False Positives (FP): 6\n",
            "False Negatives (FN): 4\n",
            "\n",
            "\n",
            "Accuracy: 0.6551724137931034\n",
            "Precision: 0.76\n",
            "Recall: 0.8260869565217391\n",
            "F1 Score: 0.7916666666666667\n",
            "\n",
            "\n",
            "Confusion Matrix:\n",
            "\t\tPredicted Unrecognised\tPredicted Recognised\n",
            "True Unrecognised\t0\t\t\t6\n",
            "True Recognised\t\t4\t\t\t19\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Define labels for the confusion matrix\n",
        "labels = ['Unrecognised', 'Recognised']\n",
        "\n",
        "# Create confusion matrix\n",
        "confusion_matrix = np.array([[TN, FP], [FN, TP]])\n",
        "\n",
        "# Plot confusion matrix\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.imshow(confusion_matrix, interpolation='nearest', cmap=plt.cm.Reds)\n",
        "plt.title('Confusion Matrix')\n",
        "plt.colorbar()\n",
        "tick_marks = np.arange(len(labels))\n",
        "plt.xticks(tick_marks, labels, rotation=45)\n",
        "plt.yticks(tick_marks, labels)\n",
        "plt.xlabel('Predicted Label')\n",
        "plt.ylabel('True Label')\n",
        "\n",
        "# Add text annotations\n",
        "for i in range(len(labels)):\n",
        "    for j in range(len(labels)):\n",
        "        plt.text(j, i, str(confusion_matrix[i, j]), ha='center', va='center', color='white')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "YN1Q_r3--mVz",
        "outputId": "8931c83d-44e2-4a12-ea56-50d25fa5254b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def train(train_dir, model_save_path=None, n_neighbors=None, knn_algo='ball_tree', verbose=False):\n",
        "    \"\"\"\n",
        "    Trains a k-nearest neighbors classifier for face recognition.\n",
        "\n",
        "    :param train_dir: directory that contains a sub-directory for each known person, with its name.\n",
        "\n",
        "     (View in source code to see train_dir example tree structure)\n",
        "\n",
        "     Structure:\n",
        "        <train_dir>/\n",
        "        ├── <person1>/\n",
        "        │   ├── <somename1>.jpeg\n",
        "        │   ├── <somename2>.jpeg\n",
        "        │   ├── ...\n",
        "        ├── <person2>/\n",
        "        │   ├── <somename1>.jpeg\n",
        "        │   └── <somename2>.jpeg\n",
        "        └── ...\n",
        "\n",
        "    :param model_save_path: (optional) path to save model on disk\n",
        "    :param n_neighbors: (optional) number of neighbors to weigh in classification. Chosen automatically if not specified\n",
        "    :param knn_algo: (optional) underlying data structure to support knn.default is ball_tree\n",
        "    :param verbose: verbosity of training\n",
        "    :return: returns knn classifier that was trained on the given data.\n",
        "    \"\"\"\n",
        "    X = []\n",
        "    y = []\n",
        "\n",
        "    # Loop through each person in the training set\n",
        "    for class_dir in os.listdir(train_dir):\n",
        "        if not os.path.isdir(os.path.join(train_dir, class_dir)):\n",
        "            continue\n",
        "\n",
        "        # Loop through each training image for the current person\n",
        "        for img_path in image_files_in_folder(os.path.join(train_dir, class_dir)):\n",
        "            image = face_recognition.load_image_file(img_path)\n",
        "            face_bounding_boxes = face_recognition.face_locations(image)\n",
        "\n",
        "            if len(face_bounding_boxes) != 1:\n",
        "                # If there are no people (or too many people) in a training image, skip the image.\n",
        "                if verbose:\n",
        "                    print(\"Image {} not suitable for training: {}\".format(img_path, \"Didn't find a face\" if len(face_bounding_boxes) < 1 else \"Found more than one face\"))\n",
        "            else:\n",
        "                # Add face encoding for current image to the training set\n",
        "                X.append(face_recognition.face_encodings(image, known_face_locations=face_bounding_boxes)[0])\n",
        "                y.append(class_dir)\n",
        "\n",
        "    # Determine how many neighbors to use for weighting in the KNN classifier\n",
        "    if n_neighbors is None:\n",
        "        n_neighbors = int(round(math.sqrt(len(X))))\n",
        "        if verbose:\n",
        "            print(\"Chose n_neighbors automatically:\", n_neighbors)\n",
        "\n",
        "    # Create and train the KNN classifier\n",
        "    knn_clf = neighbors.KNeighborsClassifier(n_neighbors=n_neighbors, algorithm=knn_algo, weights='distance')\n",
        "    knn_clf.fit(X, y)\n",
        "\n",
        "    # Save the trained KNN classifier\n",
        "    if model_save_path is not None:\n",
        "        with open(model_save_path, 'wb') as f:\n",
        "            pickle.dump(knn_clf, f)\n",
        "\n",
        "    return knn_clf"
      ],
      "metadata": {
        "id": "bWvPWquKFItm"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}