mask_detection.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.7"
    },
    "colab": {
      "name": "mask_detection.ipynb",
      "provenance": []
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E5C88koTu3Sy",
        "outputId": "049bed58-be74-48cd-f9cc-d62d5a054aa6"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GWqMAbi1u-2g",
        "outputId": "cdd829f2-f52f-4b56-bb46-6602a0c636f2"
      },
      "source": [
        "%cd drive/MyDrive\r\n",
        "%cp mask_dataset_7k.zip /content/"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Errno 2] No such file or directory: 'drive/MyDrive'\n",
            "/content/drive/MyDrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MlRTeZaKvUsp",
        "outputId": "bcd87788-310e-4243-db13-1694252abedf"
      },
      "source": [
        "%cd /content/\r\n",
        "!unzip mask_dataset_7k.zip"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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            "  inflating: mask_dataset_7k/with_mask/with_mask164.jpg  \n",
            " extracting: mask_dataset_7k/with_mask/with_mask165.jpeg  \n",
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            "  inflating: mask_dataset_7k/with_mask/with_mask201.jpg  \n",
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            "  inflating: mask_dataset_7k/with_mask/with_mask227.jpg  \n",
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            "  inflating: mask_dataset_7k/with_mask/with_mask231.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask232.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask233.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask234.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask235.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask236.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask237.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask238.jpg  \n",
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            "  inflating: mask_dataset_7k/with_mask/with_mask240.jpg  \n",
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            "  inflating: mask_dataset_7k/with_mask/with_mask246.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask247.jpg  \n",
            " extracting: mask_dataset_7k/with_mask/with_mask248.png  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask249.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask250.jpg  \n",
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            "  inflating: mask_dataset_7k/with_mask/with_mask253.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask254.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask255.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask256.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask257.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask258.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask259.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask260.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask261.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask262.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask263.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask264.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask265.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask266.jpg  \n",
            " extracting: mask_dataset_7k/with_mask/with_mask267.png  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask268.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask269.jpg  \n",
            " extracting: mask_dataset_7k/with_mask/with_mask270.png  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask271.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask272.jpg  \n",
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            "  inflating: mask_dataset_7k/with_mask/with_mask274.jpg  \n",
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            "  inflating: mask_dataset_7k/with_mask/with_mask276.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask277.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask278.jpg  \n",
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            "  inflating: mask_dataset_7k/with_mask/with_mask280.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask281.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask282.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask283.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask284.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask285.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask286.jpg  \n",
            " extracting: mask_dataset_7k/with_mask/with_mask287.png  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask288.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask289.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask290.jpg  \n",
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            "  inflating: mask_dataset_7k/with_mask/with_mask292.jpg  \n",
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            "  inflating: mask_dataset_7k/with_mask/with_mask295.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask296.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask297.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask298.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask299.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask300.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask301.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask302.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask303.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask304.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask305.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask306.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask307.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask308.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask309.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask310.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask311.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask312.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask313.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask314.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask315.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask316.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask317.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask318.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask319.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask320.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask321.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask322.jpg  \n",
            " extracting: mask_dataset_7k/with_mask/with_mask323.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask324.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask325.jpg  \n",
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            "  inflating: mask_dataset_7k/with_mask/with_mask327.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask328.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask329.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask330.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask331.jpg  \n",
            " extracting: mask_dataset_7k/with_mask/with_mask332.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask333.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask334.png  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask335.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask336.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask337.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask338.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask339.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask340.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask341.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask342.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask343.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask344.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask345.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask346.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask347.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask348.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask349.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask350.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask351.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask352.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask353.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask354.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask355.jpg  \n",
            " extracting: mask_dataset_7k/with_mask/with_mask356.png  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask357.jpeg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask358.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask359.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask360.JPG  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask361.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask362.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask363.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask364.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask365.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask366.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask367.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask368.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask369.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask370.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask371.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask372.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask373.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask374.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask375.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask376.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask377.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask378.jpeg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask379.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask380.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask381.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask382.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask383.png  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask384.jpg  \n",
            " extracting: mask_dataset_7k/with_mask/with_mask385.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask386.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask387.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask388.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask389.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask390.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask391.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask392.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask393.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask394.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask395.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask396.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask397.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask398.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask399.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask400.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask401.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask402.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask403.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask404.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask405.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask406.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask407.jpg  \n",
            " extracting: mask_dataset_7k/with_mask/with_mask408.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask409.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask410.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask411.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask412.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask413.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask414.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask415.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask416.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask417.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask418.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask419.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask420.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask421.jpeg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask422.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask423.jpg  \n",
            " extracting: mask_dataset_7k/with_mask/with_mask424.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask425.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask426.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask427.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask428.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask429.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask430.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask431.JPG  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask432.jpg  \n",
            " extracting: mask_dataset_7k/with_mask/with_mask433.png  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask434.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask435.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask436.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask437.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask438.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask439.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask440.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask441.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask442.jpg  \n",
            "  inflating: mask_dataset_7k/with_mask/with_mask443.jpg  \n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vI3GSgJ_ukI5"
      },
      "source": [
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.applications import MobileNetV2\n",
        "from tensorflow.keras.layers import AveragePooling2D\n",
        "from tensorflow.keras.layers import Dropout\n",
        "from tensorflow.keras.layers import Flatten\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras.layers import Input\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.applications.mobilenet_v2 import preprocess_input\n",
        "from tensorflow.keras.preprocessing.image import img_to_array\n",
        "from tensorflow.keras.preprocessing.image import load_img\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from sklearn.preprocessing import LabelBinarizer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report\n",
        "from imutils import paths\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import argparse\n",
        "import os"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gHt0AlW_ukI6",
        "outputId": "94c844e2-065f-4419-b7da-9ab66c8f04a7"
      },
      "source": [
        "imagePaths = list(paths.list_images('mask_dataset_7k'))\n",
        "data = []\n",
        "labels = []\n",
        "# loop over the image paths\n",
        "for imagePath in imagePaths:\n",
        "\t# extract the class label from the filename\n",
        "\tlabel = imagePath.split(os.path.sep)[-2]\n",
        "\t# load the input image (224x224) and preprocess it\n",
        "\timage = load_img(imagePath, target_size=(224, 224))\n",
        "\timage = img_to_array(image)\n",
        "\timage = preprocess_input(image)\n",
        "\t# update the data and labels lists, respectively\n",
        "\tdata.append(image)\n",
        "\tlabels.append(label)\n",
        "# convert the data and labels to NumPy arrays\n",
        "data = np.array(data, dtype=\"float32\")\n",
        "labels = np.array(labels)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/PIL/Image.py:932: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n",
            "  \"Palette images with Transparency expressed in bytes should be \"\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9MhDSjAwukI7"
      },
      "source": [
        "# perform one-hot encoding on the labels\n",
        "lb = LabelBinarizer()\n",
        "labels = lb.fit_transform(labels)\n",
        "labels = to_categorical(labels)\n",
        "# partition the data into training and testing splits using 80% of\n",
        "# the data for training and the remaining 20% for testing\n",
        "(trainX, testX, trainY, testY) = train_test_split(data, labels,test_size=0.20, stratify=labels, random_state=42)\n",
        "# construct the training image generator for data augmentation\n",
        "aug = ImageDataGenerator(\n",
        "    rotation_range=20,\n",
        "    zoom_range=0.15,\n",
        "    width_shift_range=0.2,\n",
        "    height_shift_range=0.2,\n",
        "    shear_range=0.15,\n",
        "    horizontal_flip=True,\n",
        "    fill_mode=\"nearest\")"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "byyBSmJ7ukI8",
        "outputId": "ca723fa7-bfee-4430-d55f-f12ccf75abbe"
      },
      "source": [
        "# load the MobileNetV2 network, ensuring the head FC layer sets are\n",
        "# left off\n",
        "mobilenet_model = MobileNetV2(weights=\"imagenet\", include_top=False,\n",
        "\tinput_tensor=Input(shape=(224, 224, 3)))\n",
        "# construct the head of the model that will be placed on top of the\n",
        "# the base model\n",
        "model_head = mobilenet_model.output\n",
        "model_head = AveragePooling2D(pool_size=(7, 7))(model_head)\n",
        "model_head = Flatten(name=\"flatten\")(model_head)\n",
        "model_head = Dense(128, activation=\"relu\")(model_head)\n",
        "model_head = Dropout(0.5)(model_head)\n",
        "model_head = Dense(2, activation=\"softmax\")(model_head)\n",
        "# place the head FC model on top of the base model (this will become\n",
        "# the actual model we will train)\n",
        "model = Model(inputs=mobilenet_model.input, outputs=model_head)\n",
        "# loop over all layers in the base model and freeze them so they will\n",
        "# *not* be updated during the first training process\n",
        "for layer in mobilenet_model.layers:\n",
        "\tlayer.trainable = False"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:`input_shape` is undefined or non-square, or `rows` is not in [96, 128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p70XrHY1ukI9",
        "outputId": "d68e8f53-a736-435e-ff01-97755e12e34b"
      },
      "source": [
        "learning_rate = 1e-4\n",
        "epochs = 20\n",
        "batch_size = 32\n",
        "\n",
        "# compile our model\n",
        "print(\"[INFO] compiling model...\")\n",
        "opt = Adam(lr=learning_rate, decay=learning_rate/ epochs)\n",
        "model.compile(loss=\"binary_crossentropy\", optimizer=opt,metrics=[\"accuracy\"])\n",
        "# train the head of the network\n",
        "print(\"[INFO] training head...\")\n",
        "model_result = model.fit(\n",
        "\taug.flow(trainX, trainY, batch_size=batch_size),\n",
        "\tsteps_per_epoch=len(trainX) // batch_size,\n",
        "\tvalidation_data=(testX, testY),\n",
        "\tvalidation_steps=len(testX) // batch_size,\n",
        "\tepochs=epochs)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[INFO] compiling model...\n",
            "[INFO] training head...\n",
            "Epoch 1/20\n",
            "202/202 [==============================] - 78s 333ms/step - loss: 0.4230 - accuracy: 0.8223 - val_loss: 0.0881 - val_accuracy: 0.9759\n",
            "Epoch 2/20\n",
            "202/202 [==============================] - 65s 322ms/step - loss: 0.0927 - accuracy: 0.9768 - val_loss: 0.0635 - val_accuracy: 0.9808\n",
            "Epoch 3/20\n",
            "202/202 [==============================] - 65s 321ms/step - loss: 0.0722 - accuracy: 0.9785 - val_loss: 0.0558 - val_accuracy: 0.9827\n",
            "Epoch 4/20\n",
            "202/202 [==============================] - 65s 321ms/step - loss: 0.0612 - accuracy: 0.9795 - val_loss: 0.0519 - val_accuracy: 0.9839\n",
            "Epoch 5/20\n",
            "202/202 [==============================] - 65s 322ms/step - loss: 0.0503 - accuracy: 0.9828 - val_loss: 0.0443 - val_accuracy: 0.9889\n",
            "Epoch 6/20\n",
            "202/202 [==============================] - 65s 322ms/step - loss: 0.0441 - accuracy: 0.9875 - val_loss: 0.0419 - val_accuracy: 0.9895\n",
            "Epoch 7/20\n",
            "202/202 [==============================] - 65s 323ms/step - loss: 0.0382 - accuracy: 0.9900 - val_loss: 0.0430 - val_accuracy: 0.9839\n",
            "Epoch 8/20\n",
            "202/202 [==============================] - 65s 321ms/step - loss: 0.0354 - accuracy: 0.9894 - val_loss: 0.0392 - val_accuracy: 0.9889\n",
            "Epoch 9/20\n",
            "202/202 [==============================] - 65s 321ms/step - loss: 0.0319 - accuracy: 0.9895 - val_loss: 0.0382 - val_accuracy: 0.9858\n",
            "Epoch 10/20\n",
            "202/202 [==============================] - 65s 321ms/step - loss: 0.0322 - accuracy: 0.9919 - val_loss: 0.0379 - val_accuracy: 0.9870\n",
            "Epoch 11/20\n",
            "202/202 [==============================] - 65s 320ms/step - loss: 0.0322 - accuracy: 0.9911 - val_loss: 0.0352 - val_accuracy: 0.9901\n",
            "Epoch 12/20\n",
            "202/202 [==============================] - 65s 322ms/step - loss: 0.0350 - accuracy: 0.9897 - val_loss: 0.0347 - val_accuracy: 0.9895\n",
            "Epoch 13/20\n",
            "202/202 [==============================] - 64s 319ms/step - loss: 0.0365 - accuracy: 0.9901 - val_loss: 0.0333 - val_accuracy: 0.9901\n",
            "Epoch 14/20\n",
            "202/202 [==============================] - 65s 321ms/step - loss: 0.0310 - accuracy: 0.9932 - val_loss: 0.0322 - val_accuracy: 0.9901\n",
            "Epoch 15/20\n",
            "202/202 [==============================] - 65s 321ms/step - loss: 0.0251 - accuracy: 0.9906 - val_loss: 0.0325 - val_accuracy: 0.9895\n",
            "Epoch 16/20\n",
            "202/202 [==============================] - 65s 322ms/step - loss: 0.0207 - accuracy: 0.9927 - val_loss: 0.0321 - val_accuracy: 0.9901\n",
            "Epoch 17/20\n",
            "202/202 [==============================] - 65s 321ms/step - loss: 0.0333 - accuracy: 0.9905 - val_loss: 0.0297 - val_accuracy: 0.9901\n",
            "Epoch 18/20\n",
            "202/202 [==============================] - 65s 320ms/step - loss: 0.0247 - accuracy: 0.9919 - val_loss: 0.0306 - val_accuracy: 0.9907\n",
            "Epoch 19/20\n",
            "202/202 [==============================] - 65s 321ms/step - loss: 0.0223 - accuracy: 0.9927 - val_loss: 0.0295 - val_accuracy: 0.9901\n",
            "Epoch 20/20\n",
            "202/202 [==============================] - 65s 320ms/step - loss: 0.0207 - accuracy: 0.9933 - val_loss: 0.0344 - val_accuracy: 0.9870\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fp8sG1QgukI9",
        "outputId": "2f79ac69-2590-4ef9-9979-fd8e9e47ecd8"
      },
      "source": [
        "# make predictions on the testing set\n",
        "print(\"[INFO] evaluating network...\")\n",
        "predIdxs = model.predict(testX, batch_size=batch_size)\n",
        "# for each image in the testing set we need to find the index of the\n",
        "# label with corresponding largest predicted probability\n",
        "predIdxs = np.argmax(predIdxs, axis=1)\n",
        "# show a nicely formatted classification report\n",
        "print(classification_report(testY.argmax(axis=1), predIdxs,target_names=lb.classes_))\n",
        "# serialize the model to disk\n",
        "print(\"[INFO] saving mask detector model...\")\n",
        "model.save('mask_detector.model', save_format=\"h5\")"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[INFO] evaluating network...\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "   with_mask       0.98      0.99      0.99       809\n",
            "without_mask       0.99      0.98      0.99       809\n",
            "\n",
            "    accuracy                           0.99      1618\n",
            "   macro avg       0.99      0.99      0.99      1618\n",
            "weighted avg       0.99      0.99      0.99      1618\n",
            "\n",
            "[INFO] saving mask detector model...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4S1iJoy43D0B"
      },
      "source": [
        "model.save('mask_detector.model', save_format=\"h5\")"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "k2KtBE55ukI-",
        "outputId": "d45f2630-f9aa-446e-d4d5-b08b842323ae"
      },
      "source": [
        "plt.style.use(\"ggplot\")\n",
        "plt.figure()\n",
        "plt.plot(np.arange(0, epochs), model_result.history[\"loss\"], label=\"train_loss\")\n",
        "plt.plot(np.arange(0, epochs), model_result.history[\"val_loss\"], label=\"val_loss\")\n",
        "plt.plot(np.arange(0, epochs), model_result.history[\"accuracy\"], label=\"train_acc\")\n",
        "plt.plot(np.arange(0, epochs), model_result.history[\"val_accuracy\"], label=\"val_acc\")\n",
        "plt.title(\"Training Loss and Accuracy\")\n",
        "plt.xlabel(\"Epoch #\")\n",
        "plt.ylabel(\"Loss/Accuracy\")\n",
        "plt.legend(loc=\"lower left\")\n",
        "plt.savefig('plot.png')"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XWt7zAECukI-"
      },
      "source": [
        ""
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "icDv9PF1ukI_"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}