FaceMaskDetection_ipnb.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bb6ir8AwtwHi"
      },
      "source": [
        "# **Face Mask Detection**\n",
        "With mobile net v2 deployed using streamlit.\n",
        "Link: https://praathapj-facemaskdetection-app-b5c2wm.streamlit.app/"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c9w-pnbp-9Lw"
      },
      "source": [
        "# **Summary**\n",
        "* Used 1100 images for each class out of 3000 images due to computation constraints.\n",
        "* standardize the data by dividing with 255.\n",
        "* Use MobileNet since model faster that VGG Net and can be deployed with low computation requirement.\n",
        "* Achived 99% accuracy with balanced class.\n",
        "* Deployed using streamlit."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P6WRKTVuvGzC"
      },
      "source": [
        "# **Business Problem**\n",
        "\n",
        "In recent trend in world wide Lockdowns due to COVID19 outbreak, as Face Mask is became mandatory for everyone while roaming outside. Masks play a crucial role in protecting the health of individuals against respiratory diseases, as is one of the few precautions available for COVID-19 in the absence of immunization. \n",
        "\n",
        "Is it possible to create a model to detect people wearing masks, not wearing them."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OQBu6MZp9eng"
      },
      "source": [
        "# **Business objective and constraints**\n",
        "* Interpretablity is partially important.\n",
        "* Low latency requirement( couple of seconds )\n",
        "* Errors -> classification of both class is equally important."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YeXO6TAX02mr"
      },
      "source": [
        "# **Mapping to an ML problem**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_dtPLw0EvBfK"
      },
      "source": [
        "## Data aquisation from kaggle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hbVqht526EH3",
        "outputId": "1a60e973-f2c8-410b-9d76-9fc6227a1f3a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AmnhJfgO5zY4"
      },
      "outputs": [],
      "source": [
        "! pip install -q kaggle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LTsxvRk-6CyB"
      },
      "outputs": [],
      "source": [
        "! mkdir ~/.kaggle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AvOMRxTC6VnI"
      },
      "outputs": [],
      "source": [
        "! cp /content/drive/MyDrive/DS_DL_ML_AI_project/kaggle.json ~/.kaggle/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5aPKhhl16bBf"
      },
      "outputs": [],
      "source": [
        "! chmod 600 ~/.kaggle/kaggle.json"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GdpwsgNO7HNX",
        "outputId": "01099e03-a539-41d9-9d83-018b9f818bde"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading face-mask-dataset.zip to /content\n",
            " 98% 160M/163M [00:04<00:00, 35.4MB/s]\n",
            "100% 163M/163M [00:04<00:00, 34.3MB/s]\n"
          ]
        }
      ],
      "source": [
        "! kaggle datasets download -d omkargurav/face-mask-dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "75evU9m_7V0f",
        "outputId": "92bb1836-1955-4d27-e86d-5d50dc2bcbb8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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            "  inflating: data/without_mask/without_mask_996.jpg  \n",
            "  inflating: data/without_mask/without_mask_997.jpg  \n",
            "  inflating: data/without_mask/without_mask_998.jpg  \n",
            "  inflating: data/without_mask/without_mask_999.jpg  \n"
          ]
        }
      ],
      "source": [
        "# All the data in the ZIP will be unzipped in train folder.\n",
        "! unzip /content/face-mask-dataset.zip"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "0EZvXqqO1hRd"
      },
      "source": [
        "## Data Files Overview\n",
        "\n",
        "Data set consists of 7553 RGB images in 2 folders as with_mask and without_mask. Images are named as label with_mask and without_mask. Images of faces with mask are 3725 and images of faces without mask are 3828.\n",
        "\n",
        "But used 1100 images for each class due to computation constraints.\n",
        "\n",
        "Source: https://www.kaggle.com/datasets/omkargurav/face-mask-dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qi7S-4eTag8o"
      },
      "source": [
        "## ML Problem\n",
        "\n",
        "* Binary classification problem."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n4R_8UqobVqA"
      },
      "source": [
        "## Performance Metric\n",
        "\n",
        "* Binary Crossentropy."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OV8THOk7b245"
      },
      "source": [
        "##Train Test Split\n",
        "\n",
        "* Random split as there is no time stamps in the data.\n",
        "* Split with stratify since classification problem."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_7RYP8JQERK7"
      },
      "source": [
        "# **Data Loading, Cleaning and Prepration**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rX0OEDx9EY0u"
      },
      "source": [
        "## Import Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rRRE9ViOvLHJ"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import cv2\n",
        "import random\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from keras.applications import MobileNet\n",
        "from keras import Sequential\n",
        "from keras.layers import Dense\n",
        "\n",
        "\n",
        "# Libraries for capturing web cam on google colab\n",
        "# source: https://python.plainenglish.io/how-to-get-your-webcam-stream-in-colab-and-use-it-with-python-1f1d2c30df34\n",
        "from IPython.display import display, Javascript, Image\n",
        "from google.colab.output import eval_js\n",
        "import html\n",
        "from base64 import b64decode, b64encode\n",
        "import cv2\n",
        "import numpy as np\n",
        "import PIL\n",
        "import io"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FziPEwXB-WA_"
      },
      "source": [
        "## Read data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CqF5wRwkh3ZJ"
      },
      "outputs": [],
      "source": [
        "# Creating categories of data\n",
        "\n",
        "img_cat = [\"with_mask\",\"without_mask\"]\n",
        "\n",
        "# Read all the images\n",
        "data = []\n",
        "\n",
        "for cat in img_cat:\n",
        "  path = os.path.join(\"/content/data\",cat)\n",
        "\n",
        "  img_label = img_cat.index(cat) # Index of current category\n",
        "\n",
        "  img_count = 0 #to count the number of images in each category\n",
        "  \n",
        "  for file in os.listdir(path):\n",
        "\n",
        "    if img_count == 1100:\n",
        "      break\n",
        "    \n",
        "    # Read file\n",
        "    img_path = os.path.join(path,file)\n",
        "    img  = cv2.imread(img_path)\n",
        "    img = cv2.resize(img,(224,224)) ## VGG net accepts only image of this shape\n",
        "\n",
        "    # crete 2 feature column 1 -> to store array and 2-> image class\n",
        "    data.append([img,img_label])\n",
        "\n",
        "    img_count += 1\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "51bJWY1uLF8N"
      },
      "outputs": [],
      "source": [
        "# Since image_category is in order we need to shuffle for a generalized model\n",
        "random.shuffle(data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TqsNp4zTLY0a"
      },
      "outputs": [],
      "source": [
        "# Seperate dependent and independent variable\n",
        "\n",
        "x = []\n",
        "y = []\n",
        "\n",
        "for feature,label in data:\n",
        "  x.append(feature)\n",
        "  y.append(label)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "elTw_AT9Xu2M",
        "outputId": "dea04e1b-5efa-485e-e369-95afdcf722e1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Shape of x: (2200, 224, 224, 3)\n",
            "Shape of y: (2200,)\n"
          ]
        }
      ],
      "source": [
        "# Check the shape of input array\n",
        "\n",
        "x = np.array(x)\n",
        "print(\"Shape of x:\",x.shape)\n",
        "y = np.array(y)\n",
        "print(\"Shape of y:\",y.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cVJPW2GSYeZm"
      },
      "outputs": [],
      "source": [
        "# standardize the data\n",
        "x = x/255"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0M9YGzL8YeWC",
        "outputId": "d741899d-25fb-4718-cbb9-458b70ca6d06"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "shape of train: (1760, 224, 224, 3) (1760,)\n",
            "shape of test: (440, 224, 224, 3) (440,)\n"
          ]
        }
      ],
      "source": [
        "# Train test split\n",
        "\n",
        "x_train,x_test,y_train,y_test = train_test_split(x,y, test_size=0.2, random_state=35, stratify=y)\n",
        "\n",
        "print(\"shape of train:\",x_train.shape, y_train.shape)\n",
        "print(\"shape of test:\",x_test.shape, y_test.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1tl-IK_2-fds"
      },
      "source": [
        "# Predictive modelling"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zu-6csqCYeUV",
        "outputId": "705c6bfa-233e-4448-d60d-20afdd6cc069"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/mobilenet/mobilenet_1_0_224_tf.h5\n",
            "17225924/17225924 [==============================] - 1s 0us/step\n"
          ]
        }
      ],
      "source": [
        "# Instantiates the MobileNet architecture.\n",
        "\n",
        "mob_net = MobileNet()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_JJZxZhzXxsx",
        "outputId": "c3e3f3d0-25e0-40e3-99d3-d2148352062e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"mobilenet_1.00_224\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 224, 224, 3)]     0         \n",
            "                                                                 \n",
            " conv1 (Conv2D)              (None, 112, 112, 32)      864       \n",
            "                                                                 \n",
            " conv1_bn (BatchNormalizatio  (None, 112, 112, 32)     128       \n",
            " n)                                                              \n",
            "                                                                 \n",
            " conv1_relu (ReLU)           (None, 112, 112, 32)      0         \n",
            "                                                                 \n",
            " conv_dw_1 (DepthwiseConv2D)  (None, 112, 112, 32)     288       \n",
            "                                                                 \n",
            " conv_dw_1_bn (BatchNormaliz  (None, 112, 112, 32)     128       \n",
            " ation)                                                          \n",
            "                                                                 \n",
            " conv_dw_1_relu (ReLU)       (None, 112, 112, 32)      0         \n",
            "                                                                 \n",
            " conv_pw_1 (Conv2D)          (None, 112, 112, 64)      2048      \n",
            "                                                                 \n",
            " conv_pw_1_bn (BatchNormaliz  (None, 112, 112, 64)     256       \n",
            " ation)                                                          \n",
            "                                                                 \n",
            " conv_pw_1_relu (ReLU)       (None, 112, 112, 64)      0         \n",
            "                                                                 \n",
            " conv_pad_2 (ZeroPadding2D)  (None, 113, 113, 64)      0         \n",
            "                                                                 \n",
            " conv_dw_2 (DepthwiseConv2D)  (None, 56, 56, 64)       576       \n",
            "                                                                 \n",
            " conv_dw_2_bn (BatchNormaliz  (None, 56, 56, 64)       256       \n",
            " ation)                                                          \n",
            "                                                                 \n",
            " conv_dw_2_relu (ReLU)       (None, 56, 56, 64)        0         \n",
            "                                                                 \n",
            " conv_pw_2 (Conv2D)          (None, 56, 56, 128)       8192      \n",
            "                                                                 \n",
            " conv_pw_2_bn (BatchNormaliz  (None, 56, 56, 128)      512       \n",
            " ation)                                                          \n",
            "                                                                 \n",
            " conv_pw_2_relu (ReLU)       (None, 56, 56, 128)       0         \n",
            "                                                                 \n",
            " conv_dw_3 (DepthwiseConv2D)  (None, 56, 56, 128)      1152      \n",
            "                                                                 \n",
            " conv_dw_3_bn (BatchNormaliz  (None, 56, 56, 128)      512       \n",
            " ation)                                                          \n",
            "                                                                 \n",
            " conv_dw_3_relu (ReLU)       (None, 56, 56, 128)       0         \n",
            "                                                                 \n",
            " conv_pw_3 (Conv2D)          (None, 56, 56, 128)       16384     \n",
            "                                                                 \n",
            " conv_pw_3_bn (BatchNormaliz  (None, 56, 56, 128)      512       \n",
            " ation)                                                          \n",
            "                                                                 \n",
            " conv_pw_3_relu (ReLU)       (None, 56, 56, 128)       0         \n",
            "                                                                 \n",
            " conv_pad_4 (ZeroPadding2D)  (None, 57, 57, 128)       0         \n",
            "                                                                 \n",
            " conv_dw_4 (DepthwiseConv2D)  (None, 28, 28, 128)      1152      \n",
            "                                                                 \n",
            " conv_dw_4_bn (BatchNormaliz  (None, 28, 28, 128)      512       \n",
            " ation)                                                          \n",
            "                                                                 \n",
            " conv_dw_4_relu (ReLU)       (None, 28, 28, 128)       0         \n",
            "                                                                 \n",
            " conv_pw_4 (Conv2D)          (None, 28, 28, 256)       32768     \n",
            "                                                                 \n",
            " conv_pw_4_bn (BatchNormaliz  (None, 28, 28, 256)      1024      \n",
            " ation)                                                          \n",
            "                                                                 \n",
            " conv_pw_4_relu (ReLU)       (None, 28, 28, 256)       0         \n",
            "                                                                 \n",
            " conv_dw_5 (DepthwiseConv2D)  (None, 28, 28, 256)      2304      \n",
            "                                                                 \n",
            " conv_dw_5_bn (BatchNormaliz  (None, 28, 28, 256)      1024      \n",
            " ation)                                                          \n",
            "                                                                 \n",
            " conv_dw_5_relu (ReLU)       (None, 28, 28, 256)       0         \n",
            "                                                                 \n",
            " conv_pw_5 (Conv2D)          (None, 28, 28, 256)       65536     \n",
            "                                                                 \n",
            " conv_pw_5_bn (BatchNormaliz  (None, 28, 28, 256)      1024      \n",
            " ation)                                                          \n",
            "                                                                 \n",
            " conv_pw_5_relu (ReLU)       (None, 28, 28, 256)       0         \n",
            "                                                                 \n",
            " conv_pad_6 (ZeroPadding2D)  (None, 29, 29, 256)       0         \n",
            "                                                                 \n",
            " conv_dw_6 (DepthwiseConv2D)  (None, 14, 14, 256)      2304      \n",
            "                                                                 \n",
            " conv_dw_6_bn (BatchNormaliz  (None, 14, 14, 256)      1024      \n",
            " ation)                                                          \n",
            "                                                                 \n",
            " conv_dw_6_relu (ReLU)       (None, 14, 14, 256)       0         \n",
            "                                                                 \n",
            " conv_pw_6 (Conv2D)          (None, 14, 14, 512)       131072    \n",
            "                                                                 \n",
            " conv_pw_6_bn (BatchNormaliz  (None, 14, 14, 512)      2048      \n",
            " ation)                                                          \n",
            "                                                                 \n",
            " conv_pw_6_relu (ReLU)       (None, 14, 14, 512)       0         \n",
            "                                                                 \n",
            " conv_dw_7 (DepthwiseConv2D)  (None, 14, 14, 512)      4608      \n",
            "                                                                 \n",
            " conv_dw_7_bn (BatchNormaliz  (None, 14, 14, 512)      2048      \n",
            " ation)                                                          \n",
            "                                                                 \n",
            " conv_dw_7_relu (ReLU)       (None, 14, 14, 512)       0         \n",
            "                                                                 \n",
            " conv_pw_7 (Conv2D)          (None, 14, 14, 512)       262144    \n",
            "                                                                 \n",
            " conv_pw_7_bn (BatchNormaliz  (None, 14, 14, 512)      2048      \n",
            " ation)                                                          \n",
            "                                                                 \n",
            " conv_pw_7_relu (ReLU)       (None, 14, 14, 512)       0         \n",
            "                                                                 \n",
            " conv_dw_8 (DepthwiseConv2D)  (None, 14, 14, 512)      4608      \n",
            "                                                                 \n",
            " conv_dw_8_bn (BatchNormaliz  (None, 14, 14, 512)      2048      \n",
            " ation)                                                          \n",
            "                                                                 \n",
            " conv_dw_8_relu (ReLU)       (None, 14, 14, 512)       0         \n",
            "                                                                 \n",
            " conv_pw_8 (Conv2D)          (None, 14, 14, 512)       262144    \n",
            "                                                                 \n",
            " conv_pw_8_bn (BatchNormaliz  (None, 14, 14, 512)      2048      \n",
            " ation)                                                          \n",
            "                                                                 \n",
            " conv_pw_8_relu (ReLU)       (None, 14, 14, 512)       0         \n",
            "                                                                 \n",
            " conv_dw_9 (DepthwiseConv2D)  (None, 14, 14, 512)      4608      \n",
            "                                                                 \n",
            " conv_dw_9_bn (BatchNormaliz  (None, 14, 14, 512)      2048      \n",
            " ation)                                                          \n",
            "                                                                 \n",
            " conv_dw_9_relu (ReLU)       (None, 14, 14, 512)       0         \n",
            "                                                                 \n",
            " conv_pw_9 (Conv2D)          (None, 14, 14, 512)       262144    \n",
            "                                                                 \n",
            " conv_pw_9_bn (BatchNormaliz  (None, 14, 14, 512)      2048      \n",
            " ation)                                                          \n",
            "                                                                 \n",
            " conv_pw_9_relu (ReLU)       (None, 14, 14, 512)       0         \n",
            "                                                                 \n",
            " conv_dw_10 (DepthwiseConv2D  (None, 14, 14, 512)      4608      \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv_dw_10_bn (BatchNormali  (None, 14, 14, 512)      2048      \n",
            " zation)                                                         \n",
            "                                                                 \n",
            " conv_dw_10_relu (ReLU)      (None, 14, 14, 512)       0         \n",
            "                                                                 \n",
            " conv_pw_10 (Conv2D)         (None, 14, 14, 512)       262144    \n",
            "                                                                 \n",
            " conv_pw_10_bn (BatchNormali  (None, 14, 14, 512)      2048      \n",
            " zation)                                                         \n",
            "                                                                 \n",
            " conv_pw_10_relu (ReLU)      (None, 14, 14, 512)       0         \n",
            "                                                                 \n",
            " conv_dw_11 (DepthwiseConv2D  (None, 14, 14, 512)      4608      \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv_dw_11_bn (BatchNormali  (None, 14, 14, 512)      2048      \n",
            " zation)                                                         \n",
            "                                                                 \n",
            " conv_dw_11_relu (ReLU)      (None, 14, 14, 512)       0         \n",
            "                                                                 \n",
            " conv_pw_11 (Conv2D)         (None, 14, 14, 512)       262144    \n",
            "                                                                 \n",
            " conv_pw_11_bn (BatchNormali  (None, 14, 14, 512)      2048      \n",
            " zation)                                                         \n",
            "                                                                 \n",
            " conv_pw_11_relu (ReLU)      (None, 14, 14, 512)       0         \n",
            "                                                                 \n",
            " conv_pad_12 (ZeroPadding2D)  (None, 15, 15, 512)      0         \n",
            "                                                                 \n",
            " conv_dw_12 (DepthwiseConv2D  (None, 7, 7, 512)        4608      \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv_dw_12_bn (BatchNormali  (None, 7, 7, 512)        2048      \n",
            " zation)                                                         \n",
            "                                                                 \n",
            " conv_dw_12_relu (ReLU)      (None, 7, 7, 512)         0         \n",
            "                                                                 \n",
            " conv_pw_12 (Conv2D)         (None, 7, 7, 1024)        524288    \n",
            "                                                                 \n",
            " conv_pw_12_bn (BatchNormali  (None, 7, 7, 1024)       4096      \n",
            " zation)                                                         \n",
            "                                                                 \n",
            " conv_pw_12_relu (ReLU)      (None, 7, 7, 1024)        0         \n",
            "                                                                 \n",
            " conv_dw_13 (DepthwiseConv2D  (None, 7, 7, 1024)       9216      \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv_dw_13_bn (BatchNormali  (None, 7, 7, 1024)       4096      \n",
            " zation)                                                         \n",
            "                                                                 \n",
            " conv_dw_13_relu (ReLU)      (None, 7, 7, 1024)        0         \n",
            "                                                                 \n",
            " conv_pw_13 (Conv2D)         (None, 7, 7, 1024)        1048576   \n",
            "                                                                 \n",
            " conv_pw_13_bn (BatchNormali  (None, 7, 7, 1024)       4096      \n",
            " zation)                                                         \n",
            "                                                                 \n",
            " conv_pw_13_relu (ReLU)      (None, 7, 7, 1024)        0         \n",
            "                                                                 \n",
            " global_average_pooling2d (G  (None, 1, 1, 1024)       0         \n",
            " lobalAveragePooling2D)                                          \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 1, 1, 1024)        0         \n",
            "                                                                 \n",
            " conv_preds (Conv2D)         (None, 1, 1, 1000)        1025000   \n",
            "                                                                 \n",
            " reshape_2 (Reshape)         (None, 1000)              0         \n",
            "                                                                 \n",
            " predictions (Activation)    (None, 1000)              0         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 4,253,864\n",
            "Trainable params: 4,231,976\n",
            "Non-trainable params: 21,888\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "# Summary of existing VGG model\n",
        "mob_net.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kMdJyZPWfw8i",
        "outputId": "281262f0-478f-4523-f65f-f8f3f850e90e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv1 (Conv2D)              (None, 112, 112, 32)      864       \n",
            "                                                                 \n",
            " conv1_bn (BatchNormalizatio  (None, 112, 112, 32)     128       \n",
            " n)                                                              \n",
            "                                                                 \n",
            " conv1_relu (ReLU)           (None, 112, 112, 32)      0         \n",
            "                                                                 \n",
            " conv_dw_1 (DepthwiseConv2D)  (None, 112, 112, 32)     288       \n",
            "                                                                 \n",
            " conv_dw_1_bn (BatchNormaliz  (None, 112, 112, 32)     128       \n",
            " ation)                                                          \n",
            "                                                                 \n",
            " conv_dw_1_relu (ReLU)       (None, 112, 112, 32)      0         \n",
            "                                                                 \n",
            " conv_pw_1 (Conv2D)          (None, 112, 112, 64)      2048      \n",
            "                                                                 \n",
            " conv_pw_1_bn (BatchNormaliz  (None, 112, 112, 64)     256       \n",
            " ation)                                                          \n",
            "                                                                 \n",
            " conv_pw_1_relu (ReLU)       (None, 112, 112, 64)      0         \n",
            "                                                                 \n",
            " conv_pad_2 (ZeroPadding2D)  (None, 113, 113, 64)      0         \n",
            "                                                                 \n",
            " conv_dw_2 (DepthwiseConv2D)  (None, 56, 56, 64)       576       \n",
            "                                                                 \n",
            " conv_dw_2_bn (BatchNormaliz  (None, 56, 56, 64)       256       \n",
            " ation)                                                          \n",
            "                                                                 \n",
            " conv_dw_2_relu (ReLU)       (None, 56, 56, 64)        0         \n",
            "                                                                 \n",
            " conv_pw_2 (Conv2D)          (None, 56, 56, 128)       8192      \n",
            "                                                                 \n",
            " conv_pw_2_bn (BatchNormaliz  (None, 56, 56, 128)      512       \n",
            " ation)                                                          \n",
            "                                                                 \n",
            " conv_pw_2_relu (ReLU)       (None, 56, 56, 128)       0         \n",
            "                                                                 \n",
            " conv_dw_3 (DepthwiseConv2D)  (None, 56, 56, 128)      1152      \n",
            "                                                                 \n",
            " conv_dw_3_bn (BatchNormaliz  (None, 56, 56, 128)      512       \n",
            " ation)                                                          \n",
            "                                                                 \n",
            " conv_dw_3_relu (ReLU)       (None, 56, 56, 128)       0         \n",
            "                                                                 \n",
            " conv_pw_3 (Conv2D)          (None, 56, 56, 128)       16384     \n",
            "                                                                 \n",
            " conv_pw_3_bn (BatchNormaliz  (None, 56, 56, 128)      512       \n",
            " ation)                                                          \n",
            "                                                                 \n",
            " conv_pw_3_relu (ReLU)       (None, 56, 56, 128)       0         \n",
            "                                                                 \n",
            " conv_pad_4 (ZeroPadding2D)  (None, 57, 57, 128)       0         \n",
            "                                                                 \n",
            " conv_dw_4 (DepthwiseConv2D)  (None, 28, 28, 128)      1152      \n",
            "                                                                 \n",
            " conv_dw_4_bn (BatchNormaliz  (None, 28, 28, 128)      512       \n",
            " ation)                                                          \n",
            "                                                                 \n",
            " conv_dw_4_relu (ReLU)       (None, 28, 28, 128)       0         \n",
            "                                                                 \n",
            " conv_pw_4 (Conv2D)          (None, 28, 28, 256)       32768     \n",
            "                                                                 \n",
            " conv_pw_4_bn (BatchNormaliz  (None, 28, 28, 256)      1024      \n",
            " ation)                                                          \n",
            "                                                                 \n",
            " conv_pw_4_relu (ReLU)       (None, 28, 28, 256)       0         \n",
            "                                                                 \n",
            " conv_dw_5 (DepthwiseConv2D)  (None, 28, 28, 256)      2304      \n",
            "                                                                 \n",
            " conv_dw_5_bn (BatchNormaliz  (None, 28, 28, 256)      1024      \n",
            " ation)                                                          \n",
            "                                                                 \n",
            " conv_dw_5_relu (ReLU)       (None, 28, 28, 256)       0         \n",
            "                                                                 \n",
            " conv_pw_5 (Conv2D)          (None, 28, 28, 256)       65536     \n",
            "                                                                 \n",
            " conv_pw_5_bn (BatchNormaliz  (None, 28, 28, 256)      1024      \n",
            " ation)                                                          \n",
            "                                                                 \n",
            " conv_pw_5_relu (ReLU)       (None, 28, 28, 256)       0         \n",
            "                                                                 \n",
            " conv_pad_6 (ZeroPadding2D)  (None, 29, 29, 256)       0         \n",
            "                                                                 \n",
            " conv_dw_6 (DepthwiseConv2D)  (None, 14, 14, 256)      2304      \n",
            "                                                                 \n",
            " conv_dw_6_bn (BatchNormaliz  (None, 14, 14, 256)      1024      \n",
            " ation)                                                          \n",
            "                                                                 \n",
            " conv_dw_6_relu (ReLU)       (None, 14, 14, 256)       0         \n",
            "                                                                 \n",
            " conv_pw_6 (Conv2D)          (None, 14, 14, 512)       131072    \n",
            "                                                                 \n",
            " conv_pw_6_bn (BatchNormaliz  (None, 14, 14, 512)      2048      \n",
            " ation)                                                          \n",
            "                                                                 \n",
            " conv_pw_6_relu (ReLU)       (None, 14, 14, 512)       0         \n",
            "                                                                 \n",
            " conv_dw_7 (DepthwiseConv2D)  (None, 14, 14, 512)      4608      \n",
            "                                                                 \n",
            " conv_dw_7_bn (BatchNormaliz  (None, 14, 14, 512)      2048      \n",
            " ation)                                                          \n",
            "                                                                 \n",
            " conv_dw_7_relu (ReLU)       (None, 14, 14, 512)       0         \n",
            "                                                                 \n",
            " conv_pw_7 (Conv2D)          (None, 14, 14, 512)       262144    \n",
            "                                                                 \n",
            " conv_pw_7_bn (BatchNormaliz  (None, 14, 14, 512)      2048      \n",
            " ation)                                                          \n",
            "                                                                 \n",
            " conv_pw_7_relu (ReLU)       (None, 14, 14, 512)       0         \n",
            "                                                                 \n",
            " conv_dw_8 (DepthwiseConv2D)  (None, 14, 14, 512)      4608      \n",
            "                                                                 \n",
            " conv_dw_8_bn (BatchNormaliz  (None, 14, 14, 512)      2048      \n",
            " ation)                                                          \n",
            "                                                                 \n",
            " conv_dw_8_relu (ReLU)       (None, 14, 14, 512)       0         \n",
            "                                                                 \n",
            " conv_pw_8 (Conv2D)          (None, 14, 14, 512)       262144    \n",
            "                                                                 \n",
            " conv_pw_8_bn (BatchNormaliz  (None, 14, 14, 512)      2048      \n",
            " ation)                                                          \n",
            "                                                                 \n",
            " conv_pw_8_relu (ReLU)       (None, 14, 14, 512)       0         \n",
            "                                                                 \n",
            " conv_dw_9 (DepthwiseConv2D)  (None, 14, 14, 512)      4608      \n",
            "                                                                 \n",
            " conv_dw_9_bn (BatchNormaliz  (None, 14, 14, 512)      2048      \n",
            " ation)                                                          \n",
            "                                                                 \n",
            " conv_dw_9_relu (ReLU)       (None, 14, 14, 512)       0         \n",
            "                                                                 \n",
            " conv_pw_9 (Conv2D)          (None, 14, 14, 512)       262144    \n",
            "                                                                 \n",
            " conv_pw_9_bn (BatchNormaliz  (None, 14, 14, 512)      2048      \n",
            " ation)                                                          \n",
            "                                                                 \n",
            " conv_pw_9_relu (ReLU)       (None, 14, 14, 512)       0         \n",
            "                                                                 \n",
            " conv_dw_10 (DepthwiseConv2D  (None, 14, 14, 512)      4608      \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv_dw_10_bn (BatchNormali  (None, 14, 14, 512)      2048      \n",
            " zation)                                                         \n",
            "                                                                 \n",
            " conv_dw_10_relu (ReLU)      (None, 14, 14, 512)       0         \n",
            "                                                                 \n",
            " conv_pw_10 (Conv2D)         (None, 14, 14, 512)       262144    \n",
            "                                                                 \n",
            " conv_pw_10_bn (BatchNormali  (None, 14, 14, 512)      2048      \n",
            " zation)                                                         \n",
            "                                                                 \n",
            " conv_pw_10_relu (ReLU)      (None, 14, 14, 512)       0         \n",
            "                                                                 \n",
            " conv_dw_11 (DepthwiseConv2D  (None, 14, 14, 512)      4608      \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv_dw_11_bn (BatchNormali  (None, 14, 14, 512)      2048      \n",
            " zation)                                                         \n",
            "                                                                 \n",
            " conv_dw_11_relu (ReLU)      (None, 14, 14, 512)       0         \n",
            "                                                                 \n",
            " conv_pw_11 (Conv2D)         (None, 14, 14, 512)       262144    \n",
            "                                                                 \n",
            " conv_pw_11_bn (BatchNormali  (None, 14, 14, 512)      2048      \n",
            " zation)                                                         \n",
            "                                                                 \n",
            " conv_pw_11_relu (ReLU)      (None, 14, 14, 512)       0         \n",
            "                                                                 \n",
            " conv_pad_12 (ZeroPadding2D)  (None, 15, 15, 512)      0         \n",
            "                                                                 \n",
            " conv_dw_12 (DepthwiseConv2D  (None, 7, 7, 512)        4608      \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv_dw_12_bn (BatchNormali  (None, 7, 7, 512)        2048      \n",
            " zation)                                                         \n",
            "                                                                 \n",
            " conv_dw_12_relu (ReLU)      (None, 7, 7, 512)         0         \n",
            "                                                                 \n",
            " conv_pw_12 (Conv2D)         (None, 7, 7, 1024)        524288    \n",
            "                                                                 \n",
            " conv_pw_12_bn (BatchNormali  (None, 7, 7, 1024)       4096      \n",
            " zation)                                                         \n",
            "                                                                 \n",
            " conv_pw_12_relu (ReLU)      (None, 7, 7, 1024)        0         \n",
            "                                                                 \n",
            " conv_dw_13 (DepthwiseConv2D  (None, 7, 7, 1024)       9216      \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv_dw_13_bn (BatchNormali  (None, 7, 7, 1024)       4096      \n",
            " zation)                                                         \n",
            "                                                                 \n",
            " conv_dw_13_relu (ReLU)      (None, 7, 7, 1024)        0         \n",
            "                                                                 \n",
            " conv_pw_13 (Conv2D)         (None, 7, 7, 1024)        1048576   \n",
            "                                                                 \n",
            " conv_pw_13_bn (BatchNormali  (None, 7, 7, 1024)       4096      \n",
            " zation)                                                         \n",
            "                                                                 \n",
            " conv_pw_13_relu (ReLU)      (None, 7, 7, 1024)        0         \n",
            "                                                                 \n",
            " global_average_pooling2d (G  (None, 1, 1, 1024)       0         \n",
            " lobalAveragePooling2D)                                          \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 1, 1, 1024)        0         \n",
            "                                                                 \n",
            " conv_preds (Conv2D)         (None, 1, 1, 1000)        1025000   \n",
            "                                                                 \n",
            " reshape_2 (Reshape)         (None, 1000)              0         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 4,253,864\n",
            "Trainable params: 0\n",
            "Non-trainable params: 4,253,864\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "# We will perform transfer learning \n",
        "# last layer will predict 2 classes\n",
        "# wont train any parameters except last layer\n",
        "\n",
        "model = Sequential()\n",
        "\n",
        "for layer in mob_net.layers[:-1]: #all layer except last one.\n",
        "    layer.trainable=False # Making all the layers non trainable except ast one.\n",
        "    model.add(layer)\n",
        "\n",
        "# Summary of new model\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5zpiTWLdgIfJ",
        "outputId": "fabed869-d779-4aa6-8982-ca7cf511bf78"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv1 (Conv2D)              (None, 112, 112, 32)      864       \n",
            "                                                                 \n",
            " conv1_bn (BatchNormalizatio  (None, 112, 112, 32)     128       \n",
            " n)                                                              \n",
            "                                                                 \n",
            " conv1_relu (ReLU)           (None, 112, 112, 32)      0         \n",
            "                                                                 \n",
            " conv_dw_1 (DepthwiseConv2D)  (None, 112, 112, 32)     288       \n",
            "                                                                 \n",
            " conv_dw_1_bn (BatchNormaliz  (None, 112, 112, 32)     128       \n",
            " ation)                                                          \n",
            "                                                                 \n",
            " conv_dw_1_relu (ReLU)       (None, 112, 112, 32)      0         \n",
            "                                                                 \n",
            " conv_pw_1 (Conv2D)          (None, 112, 112, 64)      2048      \n",
            "                                                                 \n",
            " conv_pw_1_bn (BatchNormaliz  (None, 112, 112, 64)     256       \n",
            " ation)                                                          \n",
            "                                                                 \n",
            " conv_pw_1_relu (ReLU)       (None, 112, 112, 64)      0         \n",
            "                                                                 \n",
            " conv_pad_2 (ZeroPadding2D)  (None, 113, 113, 64)      0         \n",
            "                                                                 \n",
            " conv_dw_2 (DepthwiseConv2D)  (None, 56, 56, 64)       576       \n",
            "                                                                 \n",
            " conv_dw_2_bn (BatchNormaliz  (None, 56, 56, 64)       256       \n",
            " ation)                                                          \n",
            "                                                                 \n",
            " conv_dw_2_relu (ReLU)       (None, 56, 56, 64)        0         \n",
            "                                                                 \n",
            " conv_pw_2 (Conv2D)          (None, 56, 56, 128)       8192      \n",
            "                                                                 \n",
            " conv_pw_2_bn (BatchNormaliz  (None, 56, 56, 128)      512       \n",
            " ation)                                                          \n",
            "                                                                 \n",
            " conv_pw_2_relu (ReLU)       (None, 56, 56, 128)       0         \n",
            "                                                                 \n",
            " conv_dw_3 (DepthwiseConv2D)  (None, 56, 56, 128)      1152      \n",
            "                                                                 \n",
            " conv_dw_3_bn (BatchNormaliz  (None, 56, 56, 128)      512       \n",
            " ation)                                                          \n",
            "                                                                 \n",
            " conv_dw_3_relu (ReLU)       (None, 56, 56, 128)       0         \n",
            "                                                                 \n",
            " conv_pw_3 (Conv2D)          (None, 56, 56, 128)       16384     \n",
            "                                                                 \n",
            " conv_pw_3_bn (BatchNormaliz  (None, 56, 56, 128)      512       \n",
            " ation)                                                          \n",
            "                                                                 \n",
            " conv_pw_3_relu (ReLU)       (None, 56, 56, 128)       0         \n",
            "                                                                 \n",
            " conv_pad_4 (ZeroPadding2D)  (None, 57, 57, 128)       0         \n",
            "                                                                 \n",
            " conv_dw_4 (DepthwiseConv2D)  (None, 28, 28, 128)      1152      \n",
            "                                                                 \n",
            " conv_dw_4_bn (BatchNormaliz  (None, 28, 28, 128)      512       \n",
            " ation)                                                          \n",
            "                                                                 \n",
            " conv_dw_4_relu (ReLU)       (None, 28, 28, 128)       0         \n",
            "                                                                 \n",
            " conv_pw_4 (Conv2D)          (None, 28, 28, 256)       32768     \n",
            "                                                                 \n",
            " conv_pw_4_bn (BatchNormaliz  (None, 28, 28, 256)      1024      \n",
            " ation)                                                          \n",
            "                                                                 \n",
            " conv_pw_4_relu (ReLU)       (None, 28, 28, 256)       0         \n",
            "                                                                 \n",
            " conv_dw_5 (DepthwiseConv2D)  (None, 28, 28, 256)      2304      \n",
            "                                                                 \n",
            " conv_dw_5_bn (BatchNormaliz  (None, 28, 28, 256)      1024      \n",
            " ation)                                                          \n",
            "                                                                 \n",
            " conv_dw_5_relu (ReLU)       (None, 28, 28, 256)       0         \n",
            "                                                                 \n",
            " conv_pw_5 (Conv2D)          (None, 28, 28, 256)       65536     \n",
            "                                                                 \n",
            " conv_pw_5_bn (BatchNormaliz  (None, 28, 28, 256)      1024      \n",
            " ation)                                                          \n",
            "                                                                 \n",
            " conv_pw_5_relu (ReLU)       (None, 28, 28, 256)       0         \n",
            "                                                                 \n",
            " conv_pad_6 (ZeroPadding2D)  (None, 29, 29, 256)       0         \n",
            "                                                                 \n",
            " conv_dw_6 (DepthwiseConv2D)  (None, 14, 14, 256)      2304      \n",
            "                                                                 \n",
            " conv_dw_6_bn (BatchNormaliz  (None, 14, 14, 256)      1024      \n",
            " ation)                                                          \n",
            "                                                                 \n",
            " conv_dw_6_relu (ReLU)       (None, 14, 14, 256)       0         \n",
            "                                                                 \n",
            " conv_pw_6 (Conv2D)          (None, 14, 14, 512)       131072    \n",
            "                                                                 \n",
            " conv_pw_6_bn (BatchNormaliz  (None, 14, 14, 512)      2048      \n",
            " ation)                                                          \n",
            "                                                                 \n",
            " conv_pw_6_relu (ReLU)       (None, 14, 14, 512)       0         \n",
            "                                                                 \n",
            " conv_dw_7 (DepthwiseConv2D)  (None, 14, 14, 512)      4608      \n",
            "                                                                 \n",
            " conv_dw_7_bn (BatchNormaliz  (None, 14, 14, 512)      2048      \n",
            " ation)                                                          \n",
            "                                                                 \n",
            " conv_dw_7_relu (ReLU)       (None, 14, 14, 512)       0         \n",
            "                                                                 \n",
            " conv_pw_7 (Conv2D)          (None, 14, 14, 512)       262144    \n",
            "                                                                 \n",
            " conv_pw_7_bn (BatchNormaliz  (None, 14, 14, 512)      2048      \n",
            " ation)                                                          \n",
            "                                                                 \n",
            " conv_pw_7_relu (ReLU)       (None, 14, 14, 512)       0         \n",
            "                                                                 \n",
            " conv_dw_8 (DepthwiseConv2D)  (None, 14, 14, 512)      4608      \n",
            "                                                                 \n",
            " conv_dw_8_bn (BatchNormaliz  (None, 14, 14, 512)      2048      \n",
            " ation)                                                          \n",
            "                                                                 \n",
            " conv_dw_8_relu (ReLU)       (None, 14, 14, 512)       0         \n",
            "                                                                 \n",
            " conv_pw_8 (Conv2D)          (None, 14, 14, 512)       262144    \n",
            "                                                                 \n",
            " conv_pw_8_bn (BatchNormaliz  (None, 14, 14, 512)      2048      \n",
            " ation)                                                          \n",
            "                                                                 \n",
            " conv_pw_8_relu (ReLU)       (None, 14, 14, 512)       0         \n",
            "                                                                 \n",
            " conv_dw_9 (DepthwiseConv2D)  (None, 14, 14, 512)      4608      \n",
            "                                                                 \n",
            " conv_dw_9_bn (BatchNormaliz  (None, 14, 14, 512)      2048      \n",
            " ation)                                                          \n",
            "                                                                 \n",
            " conv_dw_9_relu (ReLU)       (None, 14, 14, 512)       0         \n",
            "                                                                 \n",
            " conv_pw_9 (Conv2D)          (None, 14, 14, 512)       262144    \n",
            "                                                                 \n",
            " conv_pw_9_bn (BatchNormaliz  (None, 14, 14, 512)      2048      \n",
            " ation)                                                          \n",
            "                                                                 \n",
            " conv_pw_9_relu (ReLU)       (None, 14, 14, 512)       0         \n",
            "                                                                 \n",
            " conv_dw_10 (DepthwiseConv2D  (None, 14, 14, 512)      4608      \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv_dw_10_bn (BatchNormali  (None, 14, 14, 512)      2048      \n",
            " zation)                                                         \n",
            "                                                                 \n",
            " conv_dw_10_relu (ReLU)      (None, 14, 14, 512)       0         \n",
            "                                                                 \n",
            " conv_pw_10 (Conv2D)         (None, 14, 14, 512)       262144    \n",
            "                                                                 \n",
            " conv_pw_10_bn (BatchNormali  (None, 14, 14, 512)      2048      \n",
            " zation)                                                         \n",
            "                                                                 \n",
            " conv_pw_10_relu (ReLU)      (None, 14, 14, 512)       0         \n",
            "                                                                 \n",
            " conv_dw_11 (DepthwiseConv2D  (None, 14, 14, 512)      4608      \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv_dw_11_bn (BatchNormali  (None, 14, 14, 512)      2048      \n",
            " zation)                                                         \n",
            "                                                                 \n",
            " conv_dw_11_relu (ReLU)      (None, 14, 14, 512)       0         \n",
            "                                                                 \n",
            " conv_pw_11 (Conv2D)         (None, 14, 14, 512)       262144    \n",
            "                                                                 \n",
            " conv_pw_11_bn (BatchNormali  (None, 14, 14, 512)      2048      \n",
            " zation)                                                         \n",
            "                                                                 \n",
            " conv_pw_11_relu (ReLU)      (None, 14, 14, 512)       0         \n",
            "                                                                 \n",
            " conv_pad_12 (ZeroPadding2D)  (None, 15, 15, 512)      0         \n",
            "                                                                 \n",
            " conv_dw_12 (DepthwiseConv2D  (None, 7, 7, 512)        4608      \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv_dw_12_bn (BatchNormali  (None, 7, 7, 512)        2048      \n",
            " zation)                                                         \n",
            "                                                                 \n",
            " conv_dw_12_relu (ReLU)      (None, 7, 7, 512)         0         \n",
            "                                                                 \n",
            " conv_pw_12 (Conv2D)         (None, 7, 7, 1024)        524288    \n",
            "                                                                 \n",
            " conv_pw_12_bn (BatchNormali  (None, 7, 7, 1024)       4096      \n",
            " zation)                                                         \n",
            "                                                                 \n",
            " conv_pw_12_relu (ReLU)      (None, 7, 7, 1024)        0         \n",
            "                                                                 \n",
            " conv_dw_13 (DepthwiseConv2D  (None, 7, 7, 1024)       9216      \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv_dw_13_bn (BatchNormali  (None, 7, 7, 1024)       4096      \n",
            " zation)                                                         \n",
            "                                                                 \n",
            " conv_dw_13_relu (ReLU)      (None, 7, 7, 1024)        0         \n",
            "                                                                 \n",
            " conv_pw_13 (Conv2D)         (None, 7, 7, 1024)        1048576   \n",
            "                                                                 \n",
            " conv_pw_13_bn (BatchNormali  (None, 7, 7, 1024)       4096      \n",
            " zation)                                                         \n",
            "                                                                 \n",
            " conv_pw_13_relu (ReLU)      (None, 7, 7, 1024)        0         \n",
            "                                                                 \n",
            " global_average_pooling2d (G  (None, 1, 1, 1024)       0         \n",
            " lobalAveragePooling2D)                                          \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 1, 1, 1024)        0         \n",
            "                                                                 \n",
            " conv_preds (Conv2D)         (None, 1, 1, 1000)        1025000   \n",
            "                                                                 \n",
            " reshape_2 (Reshape)         (None, 1000)              0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 1)                 1001      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 4,254,865\n",
            "Trainable params: 1,001\n",
            "Non-trainable params: 4,253,864\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "# Add last classification layer with sigmoid activation\n",
        "\n",
        "model.add(Dense(1,activation=\"sigmoid\"))\n",
        "\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r4ECR-Pngy2n",
        "outputId": "407435e9-9f4f-4d37-cef2-1828d436b40b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/7\n",
            "55/55 [==============================] - 16s 87ms/step - loss: 0.2598 - accuracy: 0.8983 - val_loss: 0.1088 - val_accuracy: 0.9591\n",
            "Epoch 2/7\n",
            "55/55 [==============================] - 3s 50ms/step - loss: 0.0685 - accuracy: 0.9739 - val_loss: 0.0474 - val_accuracy: 0.9818\n",
            "Epoch 3/7\n",
            "55/55 [==============================] - 3s 51ms/step - loss: 0.0412 - accuracy: 0.9835 - val_loss: 0.0407 - val_accuracy: 0.9841\n",
            "Epoch 4/7\n",
            "55/55 [==============================] - 3s 50ms/step - loss: 0.0271 - accuracy: 0.9920 - val_loss: 0.0368 - val_accuracy: 0.9864\n",
            "Epoch 5/7\n",
            "55/55 [==============================] - 3s 48ms/step - loss: 0.0197 - accuracy: 0.9949 - val_loss: 0.0363 - val_accuracy: 0.9886\n",
            "Epoch 6/7\n",
            "55/55 [==============================] - 3s 50ms/step - loss: 0.0152 - accuracy: 0.9972 - val_loss: 0.0265 - val_accuracy: 0.9886\n",
            "Epoch 7/7\n",
            "55/55 [==============================] - 3s 50ms/step - loss: 0.0121 - accuracy: 0.9989 - val_loss: 0.0257 - val_accuracy: 0.9932\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fef8019ed90>"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.compile(optimizer='Adam',loss='binary_crossentropy', metrics=['accuracy'])\n",
        "model.fit(x_train,y_train, epochs=7, validation_data = (x_test,y_test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hOTe-prB3LHW"
      },
      "outputs": [],
      "source": [
        "# Stream local webcam to google colab\n",
        "# source: https://python.plainenglish.io/how-to-get-your-webcam-stream-in-colab-and-use-it-with-python-1f1d2c30df34\n",
        "\n",
        "# create a function to convert this image object from JS to an OpenCV image to be able to use it in your Python code.\n",
        "\n",
        "def jsob_to_image(js_object):\n",
        "  # decode base64 image\n",
        "  image_bytes = b64decode(js_object.split(',')[1])\n",
        "  # convert bytes to numpy array\n",
        "  img_array = np.frombuffer(image_bytes, dtype=np.uint8)\n",
        "  # convert numpy array into OpenCV BGR \n",
        "  frame = cv2.imdecode(img_array, flags=1)\n",
        "\n",
        "  return frame"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mexScSkT3LEx"
      },
      "outputs": [],
      "source": [
        "# to stream the image\n",
        "\n",
        "def video_stream():\n",
        "  js = Javascript('''\n",
        "    var video;\n",
        "    var div = null;\n",
        "    var stream;\n",
        "    var captureCanvas;\n",
        "    var imgElement;\n",
        "    var labelElement;\n",
        "    \n",
        "    var pendingResolve = null;\n",
        "    var shutdown = false;\n",
        "    \n",
        "    function removeDom() {\n",
        "       stream.getVideoTracks()[0].stop();\n",
        "       video.remove();\n",
        "       div.remove();\n",
        "       video = null;\n",
        "       div = null;\n",
        "       stream = null;\n",
        "       imgElement = null;\n",
        "       captureCanvas = null;\n",
        "       labelElement = null;\n",
        "    }\n",
        "    \n",
        "    function onAnimationFrame() {\n",
        "      if (!shutdown) {\n",
        "        window.requestAnimationFrame(onAnimationFrame);\n",
        "      }\n",
        "      if (pendingResolve) {\n",
        "        var result = \"\";\n",
        "        if (!shutdown) {\n",
        "          captureCanvas.getContext('2d').drawImage(video, 0, 0, 640, 480);\n",
        "          result = captureCanvas.toDataURL('image/jpeg', 0.8)\n",
        "        }\n",
        "        var lp = pendingResolve;\n",
        "        pendingResolve = null;\n",
        "        lp(result);\n",
        "      }\n",
        "    }\n",
        "    \n",
        "    async function createDom() {\n",
        "      if (div !== null) {\n",
        "        return stream;\n",
        "      }\n",
        "      div = document.createElement('div');\n",
        "      div.style.border = '2px solid black';\n",
        "      div.style.padding = '3px';\n",
        "      div.style.width = '100%';\n",
        "      div.style.maxWidth = '600px';\n",
        "      document.body.appendChild(div);\n",
        "      \n",
        "           \n",
        "      video = document.createElement('video');\n",
        "      video.style.display = 'block';\n",
        "      video.width = div.clientWidth - 6;\n",
        "      video.setAttribute('playsinline', '');\n",
        "      video.onclick = () => { shutdown = true; };\n",
        "      stream = await navigator.mediaDevices.getUserMedia(\n",
        "          {video: { facingMode: \"environment\"}});\n",
        "      div.appendChild(video);\n",
        "      imgElement = document.createElement('img');\n",
        "      imgElement.style.position = 'absolute';\n",
        "      imgElement.style.zIndex = 1;\n",
        "      imgElement.onclick = () => { shutdown = true; };\n",
        "      div.appendChild(imgElement);\n",
        "      \n",
        "      const instruction = document.createElement('div');\n",
        "      instruction.innerHTML = \n",
        "          '<span style=\"blue: red; font-weight: bold;\">' +\n",
        "          'click here to stop the video</span>';\n",
        "      div.appendChild(instruction);\n",
        "      instruction.onclick = () => { shutdown = true; };\n",
        "      \n",
        "      video.srcObject = stream;\n",
        "      await video.play();\n",
        "      captureCanvas = document.createElement('canvas');\n",
        "      captureCanvas.width = 640; \n",
        "      captureCanvas.height = 480; \n",
        "      window.requestAnimationFrame(onAnimationFrame);\n",
        "      \n",
        "      return stream;\n",
        "    }\n",
        "    async function stream_frame() {\n",
        "      if (shutdown) {\n",
        "        removeDom();\n",
        "        shutdown = false;\n",
        "        return '';\n",
        "      }\n",
        "      var preCreate = Date.now();\n",
        "      stream = await createDom();\n",
        "      \n",
        "      var preShow = Date.now();\n",
        "  \n",
        "            \n",
        "      \n",
        "      var preCapture = Date.now();\n",
        "      var result = await new Promise(function(resolve, reject) {\n",
        "        pendingResolve = resolve;\n",
        "      });\n",
        "      shutdown = false;\n",
        "      \n",
        "      return {'create': preShow - preCreate, \n",
        "              'show': preCapture - preShow, \n",
        "              'capture': Date.now() - preCapture,\n",
        "              'img': result};\n",
        "    }\n",
        "    ''')\n",
        "\n",
        "  display(js)\n",
        "  \n",
        "def video_frame():\n",
        "  data = eval_js('stream_frame()')\n",
        "  return data\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3Up6L2J_pECS"
      },
      "outputs": [],
      "source": [
        "# detection functin\n",
        "def detect_face_mask(img):\n",
        "\n",
        "  img = cv2.resize(img,(224,224))\n",
        "  img = img/255 # standardize img\n",
        "  pred = (model.predict(img.reshape(1,224,224,3)) > 0.5).astype(\"int32\")\n",
        "\n",
        "  return pred[0][0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 503
        },
        "id": "J5a-l1xf3LBk",
        "outputId": "dfa501f4-00fb-4847-c80f-32a42436b35d"
      },
      "outputs": [
        {
          "data": {
            "application/javascript": "\n    var video;\n    var div = null;\n    var stream;\n    var captureCanvas;\n    var imgElement;\n    var labelElement;\n    \n    var pendingResolve = null;\n    var shutdown = false;\n    \n    function removeDom() {\n       stream.getVideoTracks()[0].stop();\n       video.remove();\n       div.remove();\n       video = null;\n       div = null;\n       stream = null;\n       imgElement = null;\n       captureCanvas = null;\n       labelElement = null;\n    }\n    \n    function onAnimationFrame() {\n      if (!shutdown) {\n        window.requestAnimationFrame(onAnimationFrame);\n      }\n      if (pendingResolve) {\n        var result = \"\";\n        if (!shutdown) {\n          captureCanvas.getContext('2d').drawImage(video, 0, 0, 640, 480);\n          result = captureCanvas.toDataURL('image/jpeg', 0.8)\n        }\n        var lp = pendingResolve;\n        pendingResolve = null;\n        lp(result);\n      }\n    }\n    \n    async function createDom() {\n      if (div !== null) {\n        return stream;\n      }\n      div = document.createElement('div');\n      div.style.border = '2px solid black';\n      div.style.padding = '3px';\n      div.style.width = '100%';\n      div.style.maxWidth = '600px';\n      document.body.appendChild(div);\n      \n           \n      video = document.createElement('video');\n      video.style.display = 'block';\n      video.width = div.clientWidth - 6;\n      video.setAttribute('playsinline', '');\n      video.onclick = () => { shutdown = true; };\n      stream = await navigator.mediaDevices.getUserMedia(\n          {video: { facingMode: \"environment\"}});\n      div.appendChild(video);\n      imgElement = document.createElement('img');\n      imgElement.style.position = 'absolute';\n      imgElement.style.zIndex = 1;\n      imgElement.onclick = () => { shutdown = true; };\n      div.appendChild(imgElement);\n      \n      const instruction = document.createElement('div');\n      instruction.innerHTML = \n          '<span style=\"blue: red; font-weight: bold;\">' +\n          'click here to stop the video</span>';\n      div.appendChild(instruction);\n      instruction.onclick = () => { shutdown = true; };\n      \n      video.srcObject = stream;\n      await video.play();\n      captureCanvas = document.createElement('canvas');\n      captureCanvas.width = 640; \n      captureCanvas.height = 480; \n      window.requestAnimationFrame(onAnimationFrame);\n      \n      return stream;\n    }\n    async function stream_frame() {\n      if (shutdown) {\n        removeDom();\n        shutdown = false;\n        return '';\n      }\n      var preCreate = Date.now();\n      stream = await createDom();\n      \n      var preShow = Date.now();\n  \n            \n      \n      var preCapture = Date.now();\n      var result = await new Promise(function(resolve, reject) {\n        pendingResolve = resolve;\n      });\n      shutdown = false;\n      \n      return {'create': preShow - preCreate, \n              'show': preCapture - preShow, \n              'capture': Date.now() - preCapture,\n              'img': result};\n    }\n    ",
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1/1 [==============================] - 1s 1s/step\n",
            "No Mask\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "No Mask\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "No Mask\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "No Mask\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "No Mask\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "No Mask\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "No Mask\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "No Mask\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "Mask\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "No Mask\n",
            "1/1 [==============================] - 0s 35ms/step\n",
            "No Mask\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "No Mask\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "No Mask\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "No Mask\n"
          ]
        }
      ],
      "source": [
        "# Get the webcam stream and forward it to python \n",
        "video_stream()\n",
        "\n",
        "while True:\n",
        "    frame_js = video_frame()\n",
        "    if not frame_js:\n",
        "        break\n",
        "    img = jsob_to_image(frame_js[\"img\"])\n",
        "\n",
        "    y_pred = detect_face_mask(img)\n",
        "\n",
        "    if y_pred == 0:\n",
        "      print(\"Mask\")\n",
        "    if y_pred == 1:\n",
        "      print(\"No Mask\")\n",
        "\n",
        "    "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FPhxLuzw_y_7"
      },
      "source": [
        "# Modelling Observation\n",
        "\n",
        "* Achieved 99% accuracy in both train and test data using MobileNet."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "elQ8FIjCDpmI"
      },
      "source": [
        "# Deployment\n",
        "* Save required models,variables for deployment."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q0kYo2PhJZ6l"
      },
      "source": [
        "**Save model**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "I4drAupB0-TL"
      },
      "outputs": [],
      "source": [
        "# To save in regular tensor flow format\n",
        "#model.save('/content/drive/MyDrive/DS_DL_ML_AI_project/FaceMaskDetection/lite_model')\n",
        "\n",
        "# To save in old keras format \n",
        "model.save(\"/content/drive/MyDrive/DS_DL_ML_AI_project/FaceMaskDetection/faceMaskDetect.hdf5\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ouL2Tu36RV_H"
      },
      "source": [
        "# **Conclusion**\n",
        "* The above model has 99% accuracy.\n",
        "* To continue the project:\n",
        "* Obtain more data set with has images of 'mask not worn properly'.\n",
        "* Also include images which has face covered without mask as 'No Mask'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Tv9UCMU-RXbZ"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": [],
      "toc_visible": true
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}