drone_vision/notebooks/Detection_Model.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Working Directory: /home/diptesh/IEEE/src/drone\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "print(\"Working Directory: \" + str(os.path.realpath(os.getcwd() + '../../../')))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qB_AsUt5HiUT",
        "outputId": "b6463981-fb79-4aa5-d116-a0dcf3a371a5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2827\n",
            "519\n",
            "98\n",
            "234\n"
          ]
        }
      ],
      "source": [
        "train_with_mask = '../../assets/Dataset/maskdata/maskdata/train/with_mask/'\n",
        "train_without_mask = '../../assets/Dataset/maskdata/maskdata/train/without_mask'\n",
        "test_with_mask = '../../assets/Dataset/maskdata/maskdata/test/with_mask'\n",
        "test_without_mask = '../../assets/Dataset/maskdata/maskdata/test/without_mask'\n",
        "print(len(os.listdir(train_with_mask)))\n",
        "print(len(os.listdir(train_without_mask)))\n",
        "print(len(os.listdir(test_with_mask)))\n",
        "print(len(os.listdir(test_without_mask)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473
        },
        "id": "o6KblXuEI8lk",
        "outputId": "7f968390-f85c-4929-f05c-e3d98c1f46d3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0\n",
            "(224, 224)\n",
            "1\n",
            "(224, 224)\n",
            "2\n",
            "(224, 224)\n",
            "3\n",
            "(224, 224)\n",
            "4\n",
            "(224, 224)\n",
            "5\n",
            "(224, 224)\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "#import sys\n",
        "from PIL import Image\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "main_dir = train_with_mask\n",
        "files = os.listdir(main_dir)\n",
        "# you can change file extension below to read other image types\n",
        "images_list = [i for i in files if i.endswith('.jpg')] ## output file names only\n",
        "i = 0\n",
        "for idx,image in enumerate(images_list):\n",
        "  if i>5:\n",
        "    break\n",
        "  i = i + 1\n",
        "  print(idx)\n",
        "  img = Image.open(main_dir + image)\n",
        "  print(img.size)\n",
        "  plt.imshow(img)\n",
        "  #img = img.resize((480, 600))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [],
      "source": [
        "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n",
        "os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import Model\n",
        "from tensorflow.keras.layers import Flatten, Conv2D, Dense, Dropout, AveragePooling2D\n",
        "from keras_preprocessing import image\n",
        "from keras_preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.applications import MobileNetV2\n",
        "# from tensorflow.keras.applications import InceptionV3\n",
        "from sklearn.metrics import classification_report\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "#from tensorflow.keras.applications.mobilenet_v2 import preprocess_input\n",
        "#from tensorflow.keras.preprocessing.image import img_to_array\n",
        "#from tensorflow.keras.models import load_model\n",
        "#from tensorflow.keras.layers import Conv2D, MaxPool2D, Input\n",
        "#from keras.applications import InceptionV3\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 imutils import paths\n",
        "#import argparse"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "OalgnkpRc7DH"
      },
      "outputs": [],
      "source": [
        "INIT_LR = 1e-4\n",
        "EPOCHS = 20\n",
        "BS = 32"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Found 2678 images belonging to 2 classes.\n",
            "Found 668 images belonging to 2 classes.\n",
            "Found 332 images belonging to 2 classes.\n",
            "(224, 224, 3)\n",
            "(224, 224, 3)\n",
            "(224, 224, 3)\n"
          ]
        }
      ],
      "source": [
        "train_DIR = '../../assets/Dataset/maskdata/maskdata/train'\n",
        "test_DIR = '../../assets/Dataset/maskdata/maskdata/test'\n",
        "\n",
        "# Image_gen = ImageDataGenerator(rescale=1.0/255.0,\n",
        "#                                horizontal_flip = True,\n",
        "#                                rotation_range = 50,\n",
        "#                                width_shift_range = 0.2,\n",
        "#                                height_shift_range = 0.2,\n",
        "#                                zoom_range = 0.2,\n",
        "#                                fill_mode = 'nearest',\n",
        "#                                validation_split = 0.2)\n",
        "\n",
        "Image_gen = ImageDataGenerator(rescale=1.0/255.0,width_shift_range = 0.2,height_shift_range = 0.2,zoom_range = 0.2, fill_mode = 'nearest',validation_split = 0.2 )\n",
        "\n",
        "train_generator = Image_gen.flow_from_directory(train_DIR,color_mode='rgb',\n",
        "                                                target_size = (224,224),\n",
        "                                                batch_size = 20,\n",
        "                                                class_mode = 'categorical',\n",
        "                                                subset = \"training\")\n",
        "\n",
        "val_generator = Image_gen.flow_from_directory(train_DIR,color_mode='rgb',\n",
        "                                                target_size = (224,224),\n",
        "                                                batch_size = 20,\n",
        "                                                class_mode = 'categorical',\n",
        "                                                subset = \"validation\")\n",
        "\n",
        "test_generator = Image_gen.flow_from_directory(test_DIR,color_mode='rgb',\n",
        "                                                target_size = (224,224),\n",
        "                                                batch_size = 20,\n",
        "                                                class_mode = 'categorical',\n",
        "                                                )\n",
        "\n",
        "print(train_generator.image_shape)\n",
        "print(val_generator.image_shape)\n",
        "print(test_generator.image_shape)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [],
      "source": [
        "input = keras.layers.Input(shape=(224,224,3))\n",
        "baseModel = MobileNetV2(weights=\"imagenet\", include_top=False, input_shape=(224,224,3), input_tensor= input)\n",
        "\n",
        "headModel = baseModel.output\n",
        "headModel = AveragePooling2D(pool_size=(7, 7))(headModel)\n",
        "headModel = Flatten()(headModel)\n",
        "headModel = Dense(128, activation=\"relu\")(headModel)\n",
        "headModel = Dropout(0.5)(headModel)\n",
        "headModel = Dense(2, activation=\"softmax\")(headModel)\n",
        "model = Model(inputs=baseModel.input, outputs=headModel)\n",
        "\n",
        "for layer in baseModel.layers:\n",
        "\tlayer.trainable = False"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"functional_1\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            [(None, 224, 224, 3) 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "Conv1_pad (ZeroPadding2D)       (None, 225, 225, 3)  0           input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "Conv1 (Conv2D)                  (None, 112, 112, 32) 864         Conv1_pad[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "bn_Conv1 (BatchNormalization)   (None, 112, 112, 32) 128         Conv1[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "Conv1_relu (ReLU)               (None, 112, 112, 32) 0           bn_Conv1[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_depthwise (Depthw (None, 112, 112, 32) 288         Conv1_relu[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_depthwise_BN (Bat (None, 112, 112, 32) 128         expanded_conv_depthwise[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_depthwise_relu (R (None, 112, 112, 32) 0           expanded_conv_depthwise_BN[0][0] \n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_project (Conv2D)  (None, 112, 112, 16) 512         expanded_conv_depthwise_relu[0][0\n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_project_BN (Batch (None, 112, 112, 16) 64          expanded_conv_project[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_1_expand (Conv2D)         (None, 112, 112, 96) 1536        expanded_conv_project_BN[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "block_1_expand_BN (BatchNormali (None, 112, 112, 96) 384         block_1_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_1_expand_relu (ReLU)      (None, 112, 112, 96) 0           block_1_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_1_pad (ZeroPadding2D)     (None, 113, 113, 96) 0           block_1_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_1_depthwise (DepthwiseCon (None, 56, 56, 96)   864         block_1_pad[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_1_depthwise_BN (BatchNorm (None, 56, 56, 96)   384         block_1_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_1_depthwise_relu (ReLU)   (None, 56, 56, 96)   0           block_1_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_1_project (Conv2D)        (None, 56, 56, 24)   2304        block_1_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_1_project_BN (BatchNormal (None, 56, 56, 24)   96          block_1_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_2_expand (Conv2D)         (None, 56, 56, 144)  3456        block_1_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_2_expand_BN (BatchNormali (None, 56, 56, 144)  576         block_2_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_2_expand_relu (ReLU)      (None, 56, 56, 144)  0           block_2_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_2_depthwise (DepthwiseCon (None, 56, 56, 144)  1296        block_2_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_2_depthwise_BN (BatchNorm (None, 56, 56, 144)  576         block_2_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_2_depthwise_relu (ReLU)   (None, 56, 56, 144)  0           block_2_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_2_project (Conv2D)        (None, 56, 56, 24)   3456        block_2_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_2_project_BN (BatchNormal (None, 56, 56, 24)   96          block_2_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_2_add (Add)               (None, 56, 56, 24)   0           block_1_project_BN[0][0]         \n",
            "                                                                 block_2_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_3_expand (Conv2D)         (None, 56, 56, 144)  3456        block_2_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_3_expand_BN (BatchNormali (None, 56, 56, 144)  576         block_3_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_3_expand_relu (ReLU)      (None, 56, 56, 144)  0           block_3_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_3_pad (ZeroPadding2D)     (None, 57, 57, 144)  0           block_3_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_3_depthwise (DepthwiseCon (None, 28, 28, 144)  1296        block_3_pad[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_3_depthwise_BN (BatchNorm (None, 28, 28, 144)  576         block_3_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_3_depthwise_relu (ReLU)   (None, 28, 28, 144)  0           block_3_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_3_project (Conv2D)        (None, 28, 28, 32)   4608        block_3_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_3_project_BN (BatchNormal (None, 28, 28, 32)   128         block_3_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_4_expand (Conv2D)         (None, 28, 28, 192)  6144        block_3_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_4_expand_BN (BatchNormali (None, 28, 28, 192)  768         block_4_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_4_expand_relu (ReLU)      (None, 28, 28, 192)  0           block_4_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_4_depthwise (DepthwiseCon (None, 28, 28, 192)  1728        block_4_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_4_depthwise_BN (BatchNorm (None, 28, 28, 192)  768         block_4_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_4_depthwise_relu (ReLU)   (None, 28, 28, 192)  0           block_4_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_4_project (Conv2D)        (None, 28, 28, 32)   6144        block_4_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_4_project_BN (BatchNormal (None, 28, 28, 32)   128         block_4_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_4_add (Add)               (None, 28, 28, 32)   0           block_3_project_BN[0][0]         \n",
            "                                                                 block_4_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_5_expand (Conv2D)         (None, 28, 28, 192)  6144        block_4_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_5_expand_BN (BatchNormali (None, 28, 28, 192)  768         block_5_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_5_expand_relu (ReLU)      (None, 28, 28, 192)  0           block_5_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_5_depthwise (DepthwiseCon (None, 28, 28, 192)  1728        block_5_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_5_depthwise_BN (BatchNorm (None, 28, 28, 192)  768         block_5_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_5_depthwise_relu (ReLU)   (None, 28, 28, 192)  0           block_5_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_5_project (Conv2D)        (None, 28, 28, 32)   6144        block_5_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_5_project_BN (BatchNormal (None, 28, 28, 32)   128         block_5_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_5_add (Add)               (None, 28, 28, 32)   0           block_4_add[0][0]                \n",
            "                                                                 block_5_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_6_expand (Conv2D)         (None, 28, 28, 192)  6144        block_5_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_6_expand_BN (BatchNormali (None, 28, 28, 192)  768         block_6_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_6_expand_relu (ReLU)      (None, 28, 28, 192)  0           block_6_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_6_pad (ZeroPadding2D)     (None, 29, 29, 192)  0           block_6_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_6_depthwise (DepthwiseCon (None, 14, 14, 192)  1728        block_6_pad[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_6_depthwise_BN (BatchNorm (None, 14, 14, 192)  768         block_6_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_6_depthwise_relu (ReLU)   (None, 14, 14, 192)  0           block_6_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_6_project (Conv2D)        (None, 14, 14, 64)   12288       block_6_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_6_project_BN (BatchNormal (None, 14, 14, 64)   256         block_6_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_7_expand (Conv2D)         (None, 14, 14, 384)  24576       block_6_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_7_expand_BN (BatchNormali (None, 14, 14, 384)  1536        block_7_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_7_expand_relu (ReLU)      (None, 14, 14, 384)  0           block_7_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_7_depthwise (DepthwiseCon (None, 14, 14, 384)  3456        block_7_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_7_depthwise_BN (BatchNorm (None, 14, 14, 384)  1536        block_7_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_7_depthwise_relu (ReLU)   (None, 14, 14, 384)  0           block_7_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_7_project (Conv2D)        (None, 14, 14, 64)   24576       block_7_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_7_project_BN (BatchNormal (None, 14, 14, 64)   256         block_7_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_7_add (Add)               (None, 14, 14, 64)   0           block_6_project_BN[0][0]         \n",
            "                                                                 block_7_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_8_expand (Conv2D)         (None, 14, 14, 384)  24576       block_7_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_8_expand_BN (BatchNormali (None, 14, 14, 384)  1536        block_8_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_8_expand_relu (ReLU)      (None, 14, 14, 384)  0           block_8_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_8_depthwise (DepthwiseCon (None, 14, 14, 384)  3456        block_8_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_8_depthwise_BN (BatchNorm (None, 14, 14, 384)  1536        block_8_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_8_depthwise_relu (ReLU)   (None, 14, 14, 384)  0           block_8_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_8_project (Conv2D)        (None, 14, 14, 64)   24576       block_8_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_8_project_BN (BatchNormal (None, 14, 14, 64)   256         block_8_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_8_add (Add)               (None, 14, 14, 64)   0           block_7_add[0][0]                \n",
            "                                                                 block_8_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_9_expand (Conv2D)         (None, 14, 14, 384)  24576       block_8_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_9_expand_BN (BatchNormali (None, 14, 14, 384)  1536        block_9_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_9_expand_relu (ReLU)      (None, 14, 14, 384)  0           block_9_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_9_depthwise (DepthwiseCon (None, 14, 14, 384)  3456        block_9_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_9_depthwise_BN (BatchNorm (None, 14, 14, 384)  1536        block_9_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_9_depthwise_relu (ReLU)   (None, 14, 14, 384)  0           block_9_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_9_project (Conv2D)        (None, 14, 14, 64)   24576       block_9_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_9_project_BN (BatchNormal (None, 14, 14, 64)   256         block_9_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_9_add (Add)               (None, 14, 14, 64)   0           block_8_add[0][0]                \n",
            "                                                                 block_9_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_10_expand (Conv2D)        (None, 14, 14, 384)  24576       block_9_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_10_expand_BN (BatchNormal (None, 14, 14, 384)  1536        block_10_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_10_expand_relu (ReLU)     (None, 14, 14, 384)  0           block_10_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_10_depthwise (DepthwiseCo (None, 14, 14, 384)  3456        block_10_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_10_depthwise_BN (BatchNor (None, 14, 14, 384)  1536        block_10_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_10_depthwise_relu (ReLU)  (None, 14, 14, 384)  0           block_10_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_10_project (Conv2D)       (None, 14, 14, 96)   36864       block_10_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_10_project_BN (BatchNorma (None, 14, 14, 96)   384         block_10_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_11_expand (Conv2D)        (None, 14, 14, 576)  55296       block_10_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_11_expand_BN (BatchNormal (None, 14, 14, 576)  2304        block_11_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_11_expand_relu (ReLU)     (None, 14, 14, 576)  0           block_11_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_11_depthwise (DepthwiseCo (None, 14, 14, 576)  5184        block_11_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_11_depthwise_BN (BatchNor (None, 14, 14, 576)  2304        block_11_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_11_depthwise_relu (ReLU)  (None, 14, 14, 576)  0           block_11_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_11_project (Conv2D)       (None, 14, 14, 96)   55296       block_11_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_11_project_BN (BatchNorma (None, 14, 14, 96)   384         block_11_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_11_add (Add)              (None, 14, 14, 96)   0           block_10_project_BN[0][0]        \n",
            "                                                                 block_11_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_12_expand (Conv2D)        (None, 14, 14, 576)  55296       block_11_add[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_12_expand_BN (BatchNormal (None, 14, 14, 576)  2304        block_12_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_12_expand_relu (ReLU)     (None, 14, 14, 576)  0           block_12_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_12_depthwise (DepthwiseCo (None, 14, 14, 576)  5184        block_12_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_12_depthwise_BN (BatchNor (None, 14, 14, 576)  2304        block_12_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_12_depthwise_relu (ReLU)  (None, 14, 14, 576)  0           block_12_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_12_project (Conv2D)       (None, 14, 14, 96)   55296       block_12_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_12_project_BN (BatchNorma (None, 14, 14, 96)   384         block_12_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_12_add (Add)              (None, 14, 14, 96)   0           block_11_add[0][0]               \n",
            "                                                                 block_12_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_13_expand (Conv2D)        (None, 14, 14, 576)  55296       block_12_add[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_13_expand_BN (BatchNormal (None, 14, 14, 576)  2304        block_13_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_13_expand_relu (ReLU)     (None, 14, 14, 576)  0           block_13_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_13_pad (ZeroPadding2D)    (None, 15, 15, 576)  0           block_13_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_13_depthwise (DepthwiseCo (None, 7, 7, 576)    5184        block_13_pad[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_13_depthwise_BN (BatchNor (None, 7, 7, 576)    2304        block_13_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_13_depthwise_relu (ReLU)  (None, 7, 7, 576)    0           block_13_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_13_project (Conv2D)       (None, 7, 7, 160)    92160       block_13_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_13_project_BN (BatchNorma (None, 7, 7, 160)    640         block_13_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_14_expand (Conv2D)        (None, 7, 7, 960)    153600      block_13_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_14_expand_BN (BatchNormal (None, 7, 7, 960)    3840        block_14_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_14_expand_relu (ReLU)     (None, 7, 7, 960)    0           block_14_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_14_depthwise (DepthwiseCo (None, 7, 7, 960)    8640        block_14_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_14_depthwise_BN (BatchNor (None, 7, 7, 960)    3840        block_14_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_14_depthwise_relu (ReLU)  (None, 7, 7, 960)    0           block_14_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_14_project (Conv2D)       (None, 7, 7, 160)    153600      block_14_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_14_project_BN (BatchNorma (None, 7, 7, 160)    640         block_14_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_14_add (Add)              (None, 7, 7, 160)    0           block_13_project_BN[0][0]        \n",
            "                                                                 block_14_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_15_expand (Conv2D)        (None, 7, 7, 960)    153600      block_14_add[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_15_expand_BN (BatchNormal (None, 7, 7, 960)    3840        block_15_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_15_expand_relu (ReLU)     (None, 7, 7, 960)    0           block_15_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_15_depthwise (DepthwiseCo (None, 7, 7, 960)    8640        block_15_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_15_depthwise_BN (BatchNor (None, 7, 7, 960)    3840        block_15_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_15_depthwise_relu (ReLU)  (None, 7, 7, 960)    0           block_15_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_15_project (Conv2D)       (None, 7, 7, 160)    153600      block_15_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_15_project_BN (BatchNorma (None, 7, 7, 160)    640         block_15_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_15_add (Add)              (None, 7, 7, 160)    0           block_14_add[0][0]               \n",
            "                                                                 block_15_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_16_expand (Conv2D)        (None, 7, 7, 960)    153600      block_15_add[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_16_expand_BN (BatchNormal (None, 7, 7, 960)    3840        block_16_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_16_expand_relu (ReLU)     (None, 7, 7, 960)    0           block_16_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_16_depthwise (DepthwiseCo (None, 7, 7, 960)    8640        block_16_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_16_depthwise_BN (BatchNor (None, 7, 7, 960)    3840        block_16_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_16_depthwise_relu (ReLU)  (None, 7, 7, 960)    0           block_16_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_16_project (Conv2D)       (None, 7, 7, 320)    307200      block_16_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_16_project_BN (BatchNorma (None, 7, 7, 320)    1280        block_16_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "Conv_1 (Conv2D)                 (None, 7, 7, 1280)   409600      block_16_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "Conv_1_bn (BatchNormalization)  (None, 7, 7, 1280)   5120        Conv_1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "out_relu (ReLU)                 (None, 7, 7, 1280)   0           Conv_1_bn[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d (AveragePooli (None, 1, 1, 1280)   0           out_relu[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "flatten (Flatten)               (None, 1280)         0           average_pooling2d[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "dense (Dense)                   (None, 128)          163968      flatten[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dropout (Dropout)               (None, 128)          0           dense[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "dense_1 (Dense)                 (None, 2)            258         dropout[0][0]                    \n",
            "==================================================================================================\n",
            "Total params: 2,422,210\n",
            "Trainable params: 164,226\n",
            "Non-trainable params: 2,257,984\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[INFO] compiling model...\n",
            "[INFO] training head...\n",
            "Epoch 1/20\n",
            "134/134 - 25s - loss: 0.3311 - accuracy: 0.8663 - val_loss: 0.1259 - val_accuracy: 0.9551\n",
            "Epoch 2/20\n",
            "134/134 - 24s - loss: 0.1192 - accuracy: 0.9552 - val_loss: 0.0888 - val_accuracy: 0.9671\n",
            "Epoch 3/20\n",
            "134/134 - 24s - loss: 0.0896 - accuracy: 0.9671 - val_loss: 0.0664 - val_accuracy: 0.9760\n",
            "Epoch 4/20\n",
            "134/134 - 25s - loss: 0.0601 - accuracy: 0.9768 - val_loss: 0.0652 - val_accuracy: 0.9760\n",
            "Epoch 5/20\n",
            "134/134 - 25s - loss: 0.0530 - accuracy: 0.9806 - val_loss: 0.0529 - val_accuracy: 0.9805\n",
            "Epoch 6/20\n",
            "134/134 - 25s - loss: 0.0442 - accuracy: 0.9869 - val_loss: 0.0507 - val_accuracy: 0.9790\n",
            "Epoch 7/20\n",
            "134/134 - 25s - loss: 0.0441 - accuracy: 0.9828 - val_loss: 0.0479 - val_accuracy: 0.9790\n",
            "Epoch 8/20\n",
            "134/134 - 25s - loss: 0.0354 - accuracy: 0.9892 - val_loss: 0.0565 - val_accuracy: 0.9746\n",
            "Epoch 9/20\n",
            "134/134 - 25s - loss: 0.0280 - accuracy: 0.9899 - val_loss: 0.0534 - val_accuracy: 0.9746\n",
            "Epoch 10/20\n",
            "134/134 - 25s - loss: 0.0298 - accuracy: 0.9884 - val_loss: 0.0426 - val_accuracy: 0.9805\n",
            "Epoch 11/20\n",
            "134/134 - 26s - loss: 0.0294 - accuracy: 0.9881 - val_loss: 0.0518 - val_accuracy: 0.9790\n",
            "Epoch 12/20\n",
            "134/134 - 26s - loss: 0.0247 - accuracy: 0.9925 - val_loss: 0.0674 - val_accuracy: 0.9731\n",
            "Epoch 13/20\n",
            "134/134 - 24s - loss: 0.0265 - accuracy: 0.9895 - val_loss: 0.0592 - val_accuracy: 0.9775\n",
            "Epoch 14/20\n",
            "134/134 - 23s - loss: 0.0247 - accuracy: 0.9918 - val_loss: 0.0633 - val_accuracy: 0.9820\n",
            "Epoch 15/20\n",
            "134/134 - 23s - loss: 0.0243 - accuracy: 0.9925 - val_loss: 0.0545 - val_accuracy: 0.9716\n",
            "Epoch 16/20\n",
            "134/134 - 23s - loss: 0.0294 - accuracy: 0.9877 - val_loss: 0.0474 - val_accuracy: 0.9880\n",
            "Epoch 17/20\n",
            "134/134 - 22s - loss: 0.0210 - accuracy: 0.9922 - val_loss: 0.0572 - val_accuracy: 0.9775\n",
            "Epoch 18/20\n",
            "134/134 - 22s - loss: 0.0149 - accuracy: 0.9951 - val_loss: 0.0536 - val_accuracy: 0.9805\n",
            "Epoch 19/20\n",
            "134/134 - 23s - loss: 0.0223 - accuracy: 0.9918 - val_loss: 0.0458 - val_accuracy: 0.9820\n",
            "Epoch 20/20\n",
            "134/134 - 22s - loss: 0.0197 - accuracy: 0.9922 - val_loss: 0.0548 - val_accuracy: 0.9760\n"
          ]
        }
      ],
      "source": [
        "print(\"[INFO] compiling model...\")\n",
        "opt = Adam(learning_rate=INIT_LR, decay=INIT_LR / EPOCHS)\n",
        "model.compile(loss=\"binary_crossentropy\", optimizer=opt,\n",
        "\tmetrics=[\"accuracy\"])\n",
        "# train the head of the network\n",
        "print(\"[INFO] training head...\")\n",
        "H = model.fit(train_generator, batch_size=BS, validation_data=val_generator, epochs=EPOCHS,verbose = 2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "17/17 - 2s - loss: 0.0380 - accuracy: 0.9819\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "[0.03803173452615738, 0.9819276928901672]"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.evaluate(test_generator,batch_size= BS, verbose=2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[INFO] evaluating network...\n",
            "17/17 - 2s\n"
          ]
        }
      ],
      "source": [
        "print(\"[INFO] evaluating network...\")\n",
        "predIdxs = model.predict(test_generator, batch_size=BS,verbose=2, )\n",
        "\n",
        "predIdxs = np.argmax(predIdxs, axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.27      0.28      0.27        98\n",
            "           1       0.69      0.69      0.69       234\n",
            "\n",
            "    accuracy                           0.57       332\n",
            "   macro avg       0.48      0.48      0.48       332\n",
            "weighted avg       0.57      0.57      0.57       332\n",
            "\n"
          ]
        }
      ],
      "source": [
        "test_labels=test_generator.classes \n",
        "print(classification_report(test_labels, predIdxs))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {},
      "outputs": [],
      "source": [
        "# model.save(\"../saved_model\", save_format=\"h5\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f7e29c2ad60>"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "N = EPOCHS\n",
        "plt.style.use(\"ggplot\")\n",
        "plt.figure()\n",
        "plt.plot(np.arange(0, N), H.history[\"loss\"], label=\"train_loss\")\n",
        "plt.plot(np.arange(0, N), H.history[\"val_loss\"], label=\"val_loss\")\n",
        "plt.plot(np.arange(0, N), H.history[\"accuracy\"], label=\"train_acc\")\n",
        "plt.plot(np.arange(0, N), H.history[\"val_accuracy\"], label=\"val_acc\")\n",
        "plt.plot(np.arange(0, N), H.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\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mask:  0.99320084\n",
            "Non Mask:  0.006799141\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "k = \"../../assets/test_photos/7.jpg\"\n",
        "import cv2\n",
        "from keras_preprocessing import image\n",
        "images = image.load_img(k, target_size=(224, 224))\n",
        "x1 = image.img_to_array(images)\n",
        "#x1 = image.rgb_to_grayscale(x1)\n",
        "x = np.expand_dims(x1, axis=0)\n",
        "x = x/255.0\n",
        "plt.imshow(images)\n",
        "plt.grid(None)\n",
        "k = model.predict(x)\n",
        "print(\"Mask: \",k[0][0])\n",
        "print(\"Non Mask: \",k[0][1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--------------------------------------------------\n",
            "Frozen model layers: \n",
            "yourInputName\n",
            "functional_1/Conv1_pad/Pad/paddings\n",
            "functional_1/Conv1_pad/Pad\n",
            "functional_1/Conv1/Conv2D/ReadVariableOp/resource\n",
            "functional_1/Conv1/Conv2D/ReadVariableOp\n",
            "functional_1/Conv1/Conv2D\n",
            "functional_1/bn_Conv1/ReadVariableOp/resource\n",
            "functional_1/bn_Conv1/ReadVariableOp\n",
            "functional_1/bn_Conv1/ReadVariableOp_1/resource\n",
            "functional_1/bn_Conv1/ReadVariableOp_1\n",
            "functional_1/bn_Conv1/FusedBatchNormV3/ReadVariableOp/resource\n",
            "functional_1/bn_Conv1/FusedBatchNormV3/ReadVariableOp\n",
            "functional_1/bn_Conv1/FusedBatchNormV3/ReadVariableOp_1/resource\n",
            "functional_1/bn_Conv1/FusedBatchNormV3/ReadVariableOp_1\n",
            "functional_1/bn_Conv1/FusedBatchNormV3\n",
            "functional_1/Conv1_relu/Relu6\n",
            "functional_1/expanded_conv_depthwise/depthwise/ReadVariableOp/resource\n",
            "functional_1/expanded_conv_depthwise/depthwise/ReadVariableOp\n",
            "functional_1/expanded_conv_depthwise/depthwise\n",
            "functional_1/expanded_conv_depthwise_BN/ReadVariableOp/resource\n",
            "functional_1/expanded_conv_depthwise_BN/ReadVariableOp\n",
            "functional_1/expanded_conv_depthwise_BN/ReadVariableOp_1/resource\n",
            "functional_1/expanded_conv_depthwise_BN/ReadVariableOp_1\n",
            "functional_1/expanded_conv_depthwise_BN/FusedBatchNormV3/ReadVariableOp/resource\n",
            "functional_1/expanded_conv_depthwise_BN/FusedBatchNormV3/ReadVariableOp\n",
            "functional_1/expanded_conv_depthwise_BN/FusedBatchNormV3/ReadVariableOp_1/resource\n",
            "functional_1/expanded_conv_depthwise_BN/FusedBatchNormV3/ReadVariableOp_1\n",
            "functional_1/expanded_conv_depthwise_BN/FusedBatchNormV3\n",
            "functional_1/expanded_conv_depthwise_relu/Relu6\n",
            "functional_1/expanded_conv_project/Conv2D/ReadVariableOp/resource\n",
            "functional_1/expanded_conv_project/Conv2D/ReadVariableOp\n",
            "functional_1/expanded_conv_project/Conv2D\n",
            "functional_1/expanded_conv_project_BN/ReadVariableOp/resource\n",
            "functional_1/expanded_conv_project_BN/ReadVariableOp\n",
            "functional_1/expanded_conv_project_BN/ReadVariableOp_1/resource\n",
            "functional_1/expanded_conv_project_BN/ReadVariableOp_1\n",
            "functional_1/expanded_conv_project_BN/FusedBatchNormV3/ReadVariableOp/resource\n",
            "functional_1/expanded_conv_project_BN/FusedBatchNormV3/ReadVariableOp\n",
            "functional_1/expanded_conv_project_BN/FusedBatchNormV3/ReadVariableOp_1/resource\n",
            "functional_1/expanded_conv_project_BN/FusedBatchNormV3/ReadVariableOp_1\n",
            "functional_1/expanded_conv_project_BN/FusedBatchNormV3\n",
            "functional_1/block_1_expand/Conv2D/ReadVariableOp/resource\n",
            "functional_1/block_1_expand/Conv2D/ReadVariableOp\n",
            "functional_1/block_1_expand/Conv2D\n",
            "functional_1/block_1_expand_BN/ReadVariableOp/resource\n",
            "functional_1/block_1_expand_BN/ReadVariableOp\n",
            "functional_1/block_1_expand_BN/ReadVariableOp_1/resource\n",
            "functional_1/block_1_expand_BN/ReadVariableOp_1\n",
            "functional_1/block_1_expand_BN/FusedBatchNormV3/ReadVariableOp/resource\n",
            "functional_1/block_1_expand_BN/FusedBatchNormV3/ReadVariableOp\n",
            "functional_1/block_1_expand_BN/FusedBatchNormV3/ReadVariableOp_1/resource\n",
            "functional_1/block_1_expand_BN/FusedBatchNormV3/ReadVariableOp_1\n",
            "functional_1/block_1_expand_BN/FusedBatchNormV3\n",
            "functional_1/block_1_expand_relu/Relu6\n",
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            "functional_1/Conv_1_bn/FusedBatchNormV3/ReadVariableOp\n",
            "functional_1/Conv_1_bn/FusedBatchNormV3/ReadVariableOp_1/resource\n",
            "functional_1/Conv_1_bn/FusedBatchNormV3/ReadVariableOp_1\n",
            "functional_1/Conv_1_bn/FusedBatchNormV3\n",
            "functional_1/out_relu/Relu6\n",
            "functional_1/average_pooling2d/AvgPool\n",
            "functional_1/flatten/Const\n",
            "functional_1/flatten/Reshape\n",
            "functional_1/dense/MatMul/ReadVariableOp/resource\n",
            "functional_1/dense/MatMul/ReadVariableOp\n",
            "functional_1/dense/MatMul\n",
            "functional_1/dense/BiasAdd/ReadVariableOp/resource\n",
            "functional_1/dense/BiasAdd/ReadVariableOp\n",
            "functional_1/dense/BiasAdd\n",
            "functional_1/dense/Relu\n",
            "functional_1/dropout/Identity\n",
            "functional_1/dense_1/MatMul/ReadVariableOp/resource\n",
            "functional_1/dense_1/MatMul/ReadVariableOp\n",
            "functional_1/dense_1/MatMul\n",
            "functional_1/dense_1/BiasAdd/ReadVariableOp/resource\n",
            "functional_1/dense_1/BiasAdd/ReadVariableOp\n",
            "functional_1/dense_1/BiasAdd\n",
            "functional_1/dense_1/Softmax\n",
            "Identity\n",
            "--------------------------------------------------\n",
            "Frozen model inputs: \n",
            "[<tf.Tensor 'yourInputName:0' shape=(None, 224, 224, 3) dtype=float32>]\n",
            "Frozen model outputs: \n",
            "[<tf.Tensor 'Identity:0' shape=(None, 2) dtype=float32>]\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "'../frozen_models/frozen_graph.pb'"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2\n",
        "full_model = tf.function(lambda x: model(x))\n",
        "full_model = full_model.get_concrete_function(\n",
        "    tf.TensorSpec(model.inputs[0].shape, model.inputs[0].dtype, name=\"yourInputName\"))\n",
        "# Get frozen ConcreteFunction\n",
        "frozen_func = convert_variables_to_constants_v2(full_model)\n",
        "frozen_func.graph.as_graph_def()\n",
        "layers = [op.name for op in frozen_func.graph.get_operations()]\n",
        "print(\"-\" * 50)\n",
        "print(\"Frozen model layers: \")\n",
        "for layer in layers:\n",
        "    print(layer)\n",
        "print(\"-\" * 50)\n",
        "print(\"Frozen model inputs: \")\n",
        "print(frozen_func.inputs)\n",
        "print(\"Frozen model outputs: \")\n",
        "print(frozen_func.outputs)\n",
        "# Save frozen graph from frozen ConcreteFunction to hard drive\n",
        "tf.io.write_graph(graph_or_graph_def=frozen_func.graph,\n",
        "                  logdir=\"../frozen_models\",\n",
        "                  name=\"frozen_graph.pb\",\n",
        "                  as_text=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'../frozen_models/model.pbtxt'"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pb_file = '../frozen_models/frozen_graph.pb'\n",
        "graph_def = tf.compat.v1.GraphDef()\n",
        "\n",
        "try:\n",
        "    with tf.io.gfile.GFile(pb_file, 'rb') as f:\n",
        "        graph_def.ParseFromString(f.read())\n",
        "except:\n",
        "    with tf.gfile.FastGFile(pb_file, 'rb') as f:\n",
        "        graph_def.ParseFromString(f.read())\n",
        "\n",
        "# Delete weights\n",
        "for i in reversed(range(len(graph_def.node))):\n",
        "    if graph_def.node[i].op == 'Const':\n",
        "        del graph_def.node[i]\n",
        "\n",
        "graph_def.library.Clear()\n",
        "\n",
        "tf.compat.v1.train.write_graph(graph_def, \"../frozen_models\", 'model.pbtxt', as_text=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(183, 275, 3)\n",
            "(183, 275, 3)\n",
            "Non-mask:  0.006304144\n",
            "Mask:  0.9936959\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import cv2\n",
        "import numpy as np\n",
        "# Load a model imported from Tensorflow\n",
        "tensorflowNet = cv2.dnn.readNetFromTensorflow('../frozen_models/frozen_graph.pb', '../frozen_models/model.pbtxt')\n",
        "# Input image\n",
        "img = cv2.imread('../../assets/test_photos/10.jpg', cv2.IMREAD_COLOR)\n",
        "print(img.shape)\n",
        "img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) #(224,224, 3) = (224,224, 1)\n",
        "img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) #(224,224,1) = (224,224,3)\n",
        "print(img.shape)\n",
        "image = img.astype(np.float32)\n",
        "rows, cols, channels = img.shape\n",
        "\n",
        "mean = np.array([1.0, 1.0, 1.0]) * 127.5\n",
        "scale = 1 / 127.5\n",
        "\n",
        "# Use the given image as input, which needs to be blob(s).\n",
        "blob = cv2.dnn.blobFromImage(image, scale, size=(224, 224), mean=mean, swapRB=True, crop=False)\n",
        "tensorflowNet.setInput(blob)\n",
        "\n",
        "# Runs a forward pass to compute the net output\n",
        "networkOutput = tensorflowNet.forward()[0]\n",
        "print(\"Non-mask: \", networkOutput[1])\n",
        "print(\"Mask: \", networkOutput[0])\n",
        "img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
        "plt.imshow(img)\n",
        "plt.grid(None)\n",
        "\n",
        "# cv2.imshow('Image', img)\n",
        "# cv2.waitKey(0)\n",
        "# cv2.destroyAllWindows() "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "name": "Detection Model.ipynb",
      "provenance": []
    },
    "interpreter": {
      "hash": "ed8a1b81e69b1030a74b9a4a45363a04eae80c0ef826b057bac6153d77c110c0"
    },
    "kernelspec": {
      "display_name": "Python 3.8.10 64-bit ('tensorFlow': conda)",
      "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": "3.8.10"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
}