Kaggle_X-Ray_Covid-19_prediction_using_Albumentations.ipynb

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    "outputId": "a79e4cc4-eb63-4b11-c1f5-ebe012926514"
   },
   "outputs": [],
   "source": [
    "!pip install -U git+https://github.com/albu/albumentations --no-cache-dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
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   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "import os\n",
    "import sys\n",
    "import cv2\n",
    "from PIL import Image\n",
    "import ast\n",
    "import shutil\n",
    "import random\n",
    "import time\n",
    "\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.layers import Conv2D\n",
    "from tensorflow.keras.layers import MaxPooling2D\n",
    "from tensorflow.keras.layers import AvgPool2D\n",
    "from tensorflow.keras.layers import Flatten\n",
    "from tensorflow.keras.layers import Dropout\n",
    "from tensorflow.keras.layers import BatchNormalization\n",
    "from tensorflow.keras.layers import Input\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "\n",
    "import albumentations as A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XlLcKuv21x3r",
    "outputId": "9072a8c0-946a-41e0-e3ed-a363d0fbff35"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0.3\n"
     ]
    }
   ],
   "source": [
    "print(A.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "WGzGRtchU7oa",
    "outputId": "1f14a812-9184-426d-c077-d6bea1183e34"
   },
   "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": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/content\n",
      "6334\n",
      "6054\n",
      "Convertng and combining classes. This will take ~10 secs\n",
      "Unpacking bboxes into separate columns. This will take ~20 secs\n",
      "All bboxes unpacked and scaled\n"
     ]
    }
   ],
   "source": [
    "!pwd\n",
    "\n",
    "train_image = pd.read_csv('/content/drive/MyDrive/train_image_level.csv')\n",
    "train_study = pd.read_csv('/content/drive/MyDrive/train_study_level.csv')\n",
    "\n",
    "print(len(train_image))\n",
    "print(len(train_study))\n",
    "\n",
    "#### Rename columns\n",
    "\n",
    "train_study['id'] = train_study['id'].str.replace('_study', '')\n",
    "\n",
    "train_study.rename(columns={'id': 'StudyInstanceUID'}, inplace=True)\n",
    "\n",
    "#### Train_result is the final dataframe for training\n",
    "\n",
    "train_result = train_image.merge(train_study, on='StudyInstanceUID', how='left')\n",
    "\n",
    "train_result['id'] = train_result['id'].str.replace('_image', '')\n",
    "\n",
    "#### Add Original dimensions for scaling bboxes\n",
    "\n",
    "original_dims = pd.read_csv('/content/drive/MyDrive/meta.csv')\n",
    "original_dims.rename(columns={'image_id': 'id'}, inplace=True)\n",
    "\n",
    "train_result_final = train_result.merge(original_dims, on='id', how='left')\n",
    "\n",
    "#### Add extensions to filenames\n",
    "train_result_final['id'] = train_result_final['id'] + '.jpg'\n",
    "\n",
    "### Scale bboxes proportionally\n",
    "\n",
    "train_result_final[\"boxes\"] = train_result_final[\"boxes\"].fillna(\"[{'x':0, 'y':0, 'width':1, 'height':1}]\")\n",
    "train_result_final[\"boxes\"] = train_result_final[\"boxes\"].apply(lambda x: ast.literal_eval(x))\n",
    "\n",
    "### Add classes (str and num)\n",
    "\n",
    "labels_dict = {'Negative for Pneumonia': [\"negative\", 0], 'Typical Appearance': [\"typical\", 1], 'Indeterminate Appearance': [\"indeterminate\", 2], 'Atypical Appearance': [\"atypical\", 3]}\n",
    "\n",
    "def convert_and_combine_classes(df):\n",
    "    for lbl in labels_dict:\n",
    "        if df[lbl]:\n",
    "            df['class'] = labels_dict[lbl][0]\n",
    "            df['class_num'] = labels_dict[lbl][1]\n",
    "    return df\n",
    "\n",
    "print(\"Convertng and combining classes. This will take ~10 secs\")\n",
    "train_result_final = train_result_final.apply(convert_and_combine_classes, axis=1)\n",
    "\n",
    "### Convert and scale bboxes coordinates\n",
    "\n",
    "def convert_bboxes_to_pascal_voc(df):\n",
    "    \"\"\" go from xmin,ymin,width,height --> xmin,ymin,xmax,ymax \"\"\"\n",
    "    for dictionary in df[\"boxes\"]:\n",
    "        df[\"xmin\"] = dictionary[\"x\"]\n",
    "        df[\"ymin\"] = dictionary[\"y\"]\n",
    "        df[\"xmax\"] = dictionary[\"x\"] + dictionary[\"width\"]\n",
    "        df[\"ymax\"] = dictionary[\"y\"] + dictionary[\"height\"]\n",
    "    return df\n",
    "\n",
    "def scale_bbox_coor(df):\n",
    "    # if df['class'] != 'negative':\n",
    "    df['xmin'] *= (1 / df['dim1'])\n",
    "    df['xmax'] *= (1 / df['dim1'])\n",
    "    df['ymin'] *= (1 / df['dim0'])\n",
    "    df['ymax'] *= (1 / df['dim0'])\n",
    "    return df\n",
    "\n",
    "print(\"Unpacking bboxes into separate columns. This will take ~20 secs\")\n",
    "train_result_final = train_result_final.apply(convert_bboxes_to_pascal_voc, axis=1)\n",
    "train_result_final = train_result_final.apply(scale_bbox_coor, axis=1)\n",
    "\n",
    "train_result_final = train_result_final.filter(['id', 'xmin', 'ymin', 'xmax', 'ymax', 'class'], axis=1)\n",
    "print(\"All bboxes unpacked and scaled\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 81
    },
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       "  <thead>\n",
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       "      <th>id</th>\n",
       "      <th>xmin</th>\n",
       "      <th>ymin</th>\n",
       "      <th>xmax</th>\n",
       "      <th>ymax</th>\n",
       "      <th>class</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.000333</td>\n",
       "      <td>0.000464</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "                    id  xmin  ymin      xmax      ymax     class\n",
       "5477  dc5b7d2810ab.jpg   0.0   0.0  0.000333  0.000464  negative"
      ]
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     "metadata": {
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   "source": [
    "train_result_final.loc[train_result_final['id'] == 'dc5b7d2810ab.jpg']"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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    "execution": {
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   "source": [
    "!rm -rf ./train\n",
    "!rm -rf ./val\n",
    "\n",
    "!mkdir -p ./train\n",
    "!mkdir -p ./val\n",
    "\n",
    "!mkdir -p ./train/negative\n",
    "!mkdir -p ./train/typical\n",
    "!mkdir -p ./train/indeterminate\n",
    "!mkdir -p ./train/atypical\n",
    "\n",
    "!mkdir -p ./val/negative\n",
    "!mkdir -p ./val/typical\n",
    "!mkdir -p ./val/indeterminate\n",
    "!mkdir -p ./val/atypical"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1ocHxfVBT6au"
   },
   "source": [
    "## Copy images to train dataset and move 20% of images from train to valid dataset (and add their names to val_filenames list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
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    "outputId": "fb16e343-9876-4f41-8789-beddb4035c3d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Copied 500 files\n",
      "Copied 1000 files\n",
      "Copied 1500 files\n"
     ]
    }
   ],
   "source": [
    "start_time = time.time()\n",
    "\n",
    "original_dataset_path = '/content/drive/MyDrive/siim_train_6334_jpg'\n",
    "\n",
    "i = 0\n",
    "for _, row in train_result_final.iterrows():\n",
    "    class_type = row['class']\n",
    "    source_file_path = os.path.join(original_dataset_path, row['id'])\n",
    "    dest_file_path = os.path.join('./train', class_type, row['id'])\n",
    "    shutil.copy(source_file_path, dest_file_path)\n",
    "    i += 1\n",
    "    if i % 500 == 0:\n",
    "      print(f\"Copied {i} files\")\n",
    "print(f\"Copied all {i} files\")\n",
    "print(f\"Took {(time.time() - start_time)/60:.3} minutes\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ZoqPe7ANMjR-"
   },
   "outputs": [],
   "source": [
    "## Move 20% of images from train to valid dataset (and add their names to val_filenames list)\n",
    "\n",
    "start_time = time.time()\n",
    "\n",
    "i = 0\n",
    "val_filenames = []\n",
    "for dirpath, dirname, filenames in os.walk('./train'):\n",
    "    if filenames:\n",
    "        shuffled_filenames = random.sample(filenames, len(filenames))\n",
    "        split_name = dirpath.split('/')[-1]\n",
    "        for filename in shuffled_filenames[int(len(shuffled_filenames)*0.8):]:\n",
    "            source_filename = os.path.join(dirpath, filename)\n",
    "            dest_filename = os.path.join('./val', split_name, filename)\n",
    "            shutil.move(source_filename, dest_filename)\n",
    "            val_filenames.append(filename)\n",
    "            i += 1\n",
    "            if i % 500 == 0:\n",
    "              print(f\"Moved {i} files\")\n",
    "print(f\"Moved all {i} files\")        \n",
    "print(f\"Took {(time.time() - start_time):.3} seconds\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oEpTqDinT6av"
   },
   "source": [
    "#### Add a column to train_result_final with split names (train/val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "for ind, row in train_result_final.iterrows():\n",
    "    if row['id'] in val_filenames:\n",
    "        train_result_final.at[ind, 'split'] = 'val'\n",
    "    else:\n",
    "        train_result_final.at[ind, 'split'] = 'train'\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
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       "      <td>0.169497</td>\n",
       "      <td>0.784909</td>\n",
       "      <td>0.674528</td>\n",
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       "      <td>0.000353</td>\n",
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       "      <td>0.586613</td>\n",
       "      <td>0.158236</td>\n",
       "      <td>0.788520</td>\n",
       "      <td>0.631647</td>\n",
       "      <td>typical</td>\n",
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       "      <td>0.859112</td>\n",
       "      <td>0.791288</td>\n",
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       "      <td>0.756622</td>\n",
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       "      <td>0.951066</td>\n",
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       "      <td>typical</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000287</td>\n",
       "      <td>0.000236</td>\n",
       "      <td>negative</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000293</td>\n",
       "      <td>0.000357</td>\n",
       "      <td>negative</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6331</th>\n",
       "      <td>ffd9b6cf2961.jpg</td>\n",
       "      <td>0.231886</td>\n",
       "      <td>0.302378</td>\n",
       "      <td>0.360457</td>\n",
       "      <td>0.657984</td>\n",
       "      <td>typical</td>\n",
       "      <td>train</td>\n",
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       "      <th>6332</th>\n",
       "      <td>ffdc682f7680.jpg</td>\n",
       "      <td>0.236328</td>\n",
       "      <td>0.454323</td>\n",
       "      <td>0.391927</td>\n",
       "      <td>0.676752</td>\n",
       "      <td>typical</td>\n",
       "      <td>train</td>\n",
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       "      <td>0.853204</td>\n",
       "      <td>0.607098</td>\n",
       "      <td>typical</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6334 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    id      xmin      ymin      xmax      ymax     class  split\n",
       "0     000a312787f2.jpg  0.527705  0.169497  0.784909  0.674528   typical    val\n",
       "1     000c3a3f293f.jpg  0.000000  0.000000  0.000353  0.000431  negative  train\n",
       "2     0012ff7358bc.jpg  0.586613  0.158236  0.788520  0.631647   typical  train\n",
       "3     001398f4ff4f.jpg  0.637617  0.619697  0.859112  0.791288  atypical    val\n",
       "4     001bd15d1891.jpg  0.756622  0.356667  0.951066  0.756667   typical  train\n",
       "...                ...       ...       ...       ...       ...       ...    ...\n",
       "6329  ffcc6edd9445.jpg  0.000000  0.000000  0.000287  0.000236  negative  train\n",
       "6330  ffd91a2c4ca0.jpg  0.000000  0.000000  0.000293  0.000357  negative  train\n",
       "6331  ffd9b6cf2961.jpg  0.231886  0.302378  0.360457  0.657984   typical  train\n",
       "6332  ffdc682f7680.jpg  0.236328  0.454323  0.391927  0.676752   typical  train\n",
       "6333  ffe942c8655f.jpg  0.544719  0.126606  0.853204  0.607098   typical  train\n",
       "\n",
       "[6334 rows x 7 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_result_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "019EKOjGok15"
   },
   "outputs": [],
   "source": [
    "train_df = train_result_final.loc[train_result_final['split'] == 'train'].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "s2ckUWOksV2F",
    "outputId": "4a78ae22-1276-4a49-cd9a-e748b64ee7e0"
   },
   "outputs": [
    {
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       "      <td>train</td>\n",
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       "      <td>0.360457</td>\n",
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       "      <td>typical</td>\n",
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       "      <th>5064</th>\n",
       "      <td>ffe942c8655f.jpg</td>\n",
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      "text/plain": [
       "                    id      xmin      ymin  ...      ymax          class  split\n",
       "0     000c3a3f293f.jpg  0.000000  0.000000  ...  0.000431       negative  train\n",
       "1     0012ff7358bc.jpg  0.586613  0.158236  ...  0.631647        typical  train\n",
       "2     001bd15d1891.jpg  0.756622  0.356667  ...  0.756667        typical  train\n",
       "3     0022227f5adf.jpg  0.608920  0.200199  ...  0.357556  indeterminate  train\n",
       "4     002e9b2128d0.jpg  0.649089  0.308455  ...  0.943843        typical  train\n",
       "...                ...       ...       ...  ...       ...            ...    ...\n",
       "5060  ffcc6edd9445.jpg  0.000000  0.000000  ...  0.000236       negative  train\n",
       "5061  ffd91a2c4ca0.jpg  0.000000  0.000000  ...  0.000357       negative  train\n",
       "5062  ffd9b6cf2961.jpg  0.231886  0.302378  ...  0.657984        typical  train\n",
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       "[5065 rows x 7 columns]"
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     "metadata": {
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     "output_type": "execute_result"
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   "source": [
    "train_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "nFD4Ukl2oknZ"
   },
   "outputs": [],
   "source": [
    "val_df = train_result_final.loc[train_result_final['split'] == 'val'].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "Wxelvl3dDpVJ",
    "outputId": "a4d1d25c-a1ea-44e2-e857-d2b3783214f2"
   },
   "outputs": [
    {
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       "      <td>val</td>\n",
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       "      <td>typical</td>\n",
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       "      <td>0.000409</td>\n",
       "      <td>0.000498</td>\n",
       "      <td>negative</td>\n",
       "      <td>val</td>\n",
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       "      <th>1265</th>\n",
       "      <td>ff339bf108b4.jpg</td>\n",
       "      <td>0.178519</td>\n",
       "      <td>0.623942</td>\n",
       "      <td>0.401121</td>\n",
       "      <td>0.822249</td>\n",
       "      <td>atypical</td>\n",
       "      <td>val</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1266</th>\n",
       "      <td>ff9f10a24c27.jpg</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000333</td>\n",
       "      <td>0.000333</td>\n",
       "      <td>negative</td>\n",
       "      <td>val</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1267</th>\n",
       "      <td>ffbeafe30b77.jpg</td>\n",
       "      <td>0.202574</td>\n",
       "      <td>0.522942</td>\n",
       "      <td>0.330293</td>\n",
       "      <td>0.650286</td>\n",
       "      <td>typical</td>\n",
       "      <td>val</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1268</th>\n",
       "      <td>ffcc16bbf428.jpg</td>\n",
       "      <td>0.172980</td>\n",
       "      <td>0.136946</td>\n",
       "      <td>0.308159</td>\n",
       "      <td>0.493596</td>\n",
       "      <td>typical</td>\n",
       "      <td>val</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1269 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    id      xmin      ymin      xmax      ymax     class split\n",
       "0     000a312787f2.jpg  0.527705  0.169497  0.784909  0.674528   typical   val\n",
       "1     001398f4ff4f.jpg  0.637617  0.619697  0.859112  0.791288  atypical   val\n",
       "2     0023f02ae886.jpg  0.000000  0.000000  0.000351  0.000429  negative   val\n",
       "3     007cf31356c6.jpg  0.152231  0.580000  0.448635  0.826667   typical   val\n",
       "4     00b767b49fcd.jpg  0.627437  0.460000  0.886690  0.771667   typical   val\n",
       "...                ...       ...       ...       ...       ...       ...   ...\n",
       "1264  ff0743bee789.jpg  0.000000  0.000000  0.000409  0.000498  negative   val\n",
       "1265  ff339bf108b4.jpg  0.178519  0.623942  0.401121  0.822249  atypical   val\n",
       "1266  ff9f10a24c27.jpg  0.000000  0.000000  0.000333  0.000333  negative   val\n",
       "1267  ffbeafe30b77.jpg  0.202574  0.522942  0.330293  0.650286   typical   val\n",
       "1268  ffcc16bbf428.jpg  0.172980  0.136946  0.308159  0.493596   typical   val\n",
       "\n",
       "[1269 rows x 7 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Number and percentage of all classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df_counts = train_df['class'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2021-07-14T09:16:42.115975Z",
     "iopub.status.busy": "2021-07-14T09:16:42.115463Z",
     "iopub.status.idle": "2021-07-14T09:16:42.127050Z",
     "shell.execute_reply": "2021-07-14T09:16:42.126123Z",
     "shell.execute_reply.started": "2021-07-14T09:16:42.115938Z"
    },
    "id": "MlIuOe7JT6a0",
    "outputId": "f73e9a86-38bd-4670-a938-934ee17d3630"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "typical          47.473950\n",
       "negative         27.407641\n",
       "indeterminate    17.492895\n",
       "atypical          7.625513\n",
       "Name: class, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_result_final['class'].value_counts(normalize=True) * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "I5k2NylET6a1"
   },
   "source": [
    "typical          47.47 0x\n",
    "\n",
    "negative         27.40 2x\n",
    "\n",
    "indeterminate    17.49 3x\n",
    "\n",
    "atypical          7.62 6x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Size of augmented train dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "O0vKmUhHtsFL"
   },
   "outputs": [],
   "source": [
    "augmented_train_set_length = train_df_counts['typical'] + train_df_counts['negative'] * 2 + train_df_counts['indeterminate'] * 3 + train_df_counts['atypical'] * 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "rg6Wjo9So_-A",
    "outputId": "59f6785b-029f-4001-c983-7d2eb9fd4391"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10155"
      ]
     },
     "execution_count": 24,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "augmented_train_set_length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-14T09:16:42.103617Z",
     "iopub.status.busy": "2021-07-14T09:16:42.103264Z",
     "iopub.status.idle": "2021-07-14T09:16:42.113762Z",
     "shell.execute_reply": "2021-07-14T09:16:42.112811Z",
     "shell.execute_reply.started": "2021-07-14T09:16:42.103580Z"
    },
    "id": "BdFLeNaET6ay"
   },
   "outputs": [],
   "source": [
    "def get_list_of_files(dirname):\n",
    "    list_of_files = os.listdir(dirname)\n",
    "    all_files = []\n",
    "    for file in list_of_files:\n",
    "        full_path = os.path.join(dirname, file)\n",
    "        all_files.append(full_path)        \n",
    "    return all_files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Z2GkO2RQzAiK",
    "outputId": "69bd1dfa-8c88-4ed1-f471-ea2bd37222bb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "386"
      ]
     },
     "execution_count": 26,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(get_list_of_files('./train/atypical/'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing augmentations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "x4dYtLJRAyeA"
   },
   "outputs": [],
   "source": [
    "def preprocess_image(filepath):\n",
    "    image_1d = cv2.imread(filepath)\n",
    "    image_3d = cv2.cvtColor(image_1d, cv2.COLOR_BGR2RGB)\n",
    "    image_arr = np.float32(image_3d)\n",
    "    image_arr /= 255.0\n",
    "    return image_arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Ob2-FGBQAzRL"
   },
   "outputs": [],
   "source": [
    "image = preprocess_image(atypical_images[4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 287
    },
    "id": "MzjEVhsEA5nH",
    "outputId": "0055e1cf-1f1a-4e6d-f9f4-78a06be30d7c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7ff0a0419cd0>"
      ]
     },
     "execution_count": 54,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 287
    },
    "id": "xbHYmB4w7ga8",
    "outputId": "e020401c-4475-4c8b-90e3-30a567332cb7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7ff09f67a6d0>"
      ]
     },
     "execution_count": 55,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "image_scaled = scale_function_07(image=image, bboxes=bbox_2d, class_labels=class_labels)['image']\n",
    "\n",
    "plt.imshow(image_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 287
    },
    "id": "5eaOuiHJ7hxm",
    "outputId": "36985a16-6bb0-4ad5-de90-8f524253ca98"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7ff09f089890>"
      ]
     },
     "execution_count": 57,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sheared_image = shear_function(image=image, bboxes=bbox_2d, class_labels=class_labels)['image']\n",
    "\n",
    "plt.imshow(sheared_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 287
    },
    "id": "a-CI4KQr7k2_",
    "outputId": "ec2594b9-5563-4dbd-b7f5-372b0e0c8bc6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7ff0a0451150>"
      ]
     },
     "execution_count": 58,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "flipped_image = horizontal_flip_function(image=image, bboxes=bbox_2d, class_labels=class_labels)['image']\n",
    "\n",
    "plt.imshow(flipped_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 287
    },
    "id": "0PMFGkWb7rw4",
    "outputId": "bb5ac4a6-1186-4b89-c232-dd053a49f4d9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7ff0a064cb50>"
      ]
     },
     "execution_count": 60,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "brightness_contrast_image = brightness_contrast_function(image=image, bboxes=bbox_2d, class_labels=class_labels)['image']\n",
    "\n",
    "plt.imshow(brightness_contrast_image)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kI6fMZ7SHteF"
   },
   "source": [
    "### End of testing section"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dVBK2ErRHrkf"
   },
   "source": [
    "### Real code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zIerblakT6a3"
   },
   "source": [
    "#### We have to perform 1, 2 and 5 augmentations for negative, indeterminate, atypical classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-14T09:16:42.136735Z",
     "iopub.status.busy": "2021-07-14T09:16:42.136364Z",
     "iopub.status.idle": "2021-07-14T09:16:42.151868Z",
     "shell.execute_reply": "2021-07-14T09:16:42.150985Z",
     "shell.execute_reply.started": "2021-07-14T09:16:42.136698Z"
    },
    "id": "o0HtLPBeT6a4"
   },
   "outputs": [],
   "source": [
    "bbox_params = A.BboxParams(format='albumentations', min_area=1024, min_visibility=0.5, label_fields=['class_labels'])\n",
    "\n",
    "scale_function_07 = A.Compose([A.Affine(scale=0.8, p=1)], bbox_params=bbox_params)\n",
    "scale_move_shear_function = A.Compose([A.Affine(scale=0.95, translate_percent=-0.1, shear=-10, p=1)], bbox_params=bbox_params)\n",
    "shear_function = A.Compose([A.Affine(shear=20, p=1)], bbox_params=bbox_params)\n",
    "brightness_contrast_function = A.Compose([A.RandomBrightnessContrast(p=1)], bbox_params=bbox_params)\n",
    "horizontal_flip_function = A.Compose([A.HorizontalFlip(p=1)], bbox_params=bbox_params)\n",
    "\n",
    "augmentations = [\"scale_function_07\", \"horizontal_flip_function\", \"brightness_contrast_function\", \"shear_function\", \"scale_move_shear_function\"]\n",
    "\n",
    "def preprocess_image(filepath):\n",
    "    image_1d = cv2.imread(filepath)\n",
    "    image_3d = cv2.cvtColor(image_1d, cv2.COLOR_BGR2RGB)\n",
    "    image_arr = np.float32(image_3d)\n",
    "    image_arr /= 255.0\n",
    "    return image_arr\n",
    "\n",
    "def augment_images_a_num_of_times(function_str, image_arr, bbox, class_labels):\n",
    "    augmented_image_dict = globals()[function_str](image=image_arr, bboxes=bbox, class_labels=class_labels)\n",
    "#         print(augmented_image_dict)\n",
    "    augmented_image = augmented_image_dict['image']\n",
    "    # If after augmentations our bboxes is outside of the new iamge's boundaries, \n",
    "    # we assign it to the negative class (i.e. no opacities are seen within augmented image)\n",
    "    if augmented_image_dict['bboxes']:\n",
    "        xmin, ymin, xmax, ymax = augmented_image_dict['bboxes'][0]\n",
    "        augmented_label = augmented_image_dict['class_labels'][0]\n",
    "    else:\n",
    "        xmin, ymin, xmax, ymax = 0, 0, 1/256.0, 1/256.0\n",
    "        augmented_label = 'negative'\n",
    "    \n",
    "    # Improve this!!!\n",
    "    if class_labels[0] == 'negative':\n",
    "        xmin, ymin, xmax, ymax = 0, 0, 1/256.0, 1/256.0\n",
    "        augmented_label = 'negative'\n",
    "\n",
    "    augmented_label = np.array([augmented_label], dtype='<U16')\n",
    "    augmented_bbox = np.array([xmin, ymin, xmax, ymax], dtype=np.float32)\n",
    "\n",
    "    return augmented_image, augmented_bbox, augmented_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "cmPCwPiU8EDE",
    "outputId": "f9e3567e-9478-4d06-f62a-8328adb9b9d9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5065"
      ]
     },
     "execution_count": 22,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_df.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "8_sDzswNbyt8"
   },
   "outputs": [],
   "source": [
    "classes_to_augments_dict = {'negative': 2, 'indeterminate': 3, 'atypical': 6}\n",
    "\n",
    "augmented_train_set_length = train_df_counts['typical']\n",
    "for key, value in classes_to_augments_dict.items():\n",
    "  augmented_train_set_length += train_df_counts[key] * value\n",
    "\n",
    "train_images = np.empty((augmented_train_set_length, 256, 256, 3), dtype=\"float32\")\n",
    "train_bboxes = np.empty((augmented_train_set_length, 4), dtype=\"float32\")\n",
    "train_labels = np.empty((augmented_train_set_length, 1), dtype='<U16')\n",
    "train_images_augment_functions = np.empty((augmented_train_set_length, 1), dtype='<U32')\n",
    "\n",
    "val_images = np.empty((len(val_filenames), 256, 256, 3), dtype=\"float32\")\n",
    "val_bboxes = np.empty((len(val_filenames), 4), dtype=\"float32\")\n",
    "val_labels = np.empty((len(val_filenames), 1), dtype='<U16')\n",
    "\n",
    "splits_to_lists_mapping = {'train': [train_images, train_bboxes, train_labels], 'val': [val_images, val_bboxes, val_labels]}\n",
    "\n",
    "def create_dataset(df):\n",
    "  df_len = len(df.index)\n",
    "  prev_ind = df_len\n",
    "  for ind, row in df.iterrows():\n",
    "      row = row.values\n",
    "      filename = row[0]\n",
    "      xmin = row[1]\n",
    "      ymin = row[2]\n",
    "      xmax = row[3]\n",
    "      ymax = row[4]\n",
    "      label = row[5]\n",
    "      split = row[6]\n",
    "      filepath = os.path.join('.', split, label, filename)\n",
    "      image = preprocess_image(filepath)\n",
    "      bbox = np.array([xmin, ymin, xmax, ymax], dtype=np.float32)\n",
    "\n",
    "      # Adding original (non-augmented) images from both splits to the dataset\n",
    "      splits_to_lists_mapping[split][0][ind, :, :, :] = image\n",
    "      splits_to_lists_mapping[split][1][ind, :] = bbox\n",
    "      splits_to_lists_mapping[split][2][ind, :] = label\n",
    "\n",
    "      if label == 'typical':\n",
    "        continue\n",
    "      # Adding augmented train images from small labels lists\n",
    "      if split == 'train':\n",
    "        if label == 'typical':\n",
    "        increase_coeff = classes_to_augments_dict[label]\n",
    "        for i in range(increase_coeff-1):\n",
    "          # We must feed Python lists to the augemnt functions because we check for truethy/falsy of bbox list and it's not possible with np arrays. \n",
    "          # we then convert them into numpy arrays in the functions themselves. \n",
    "          class_labels = [label]\n",
    "          bbox_2d = []\n",
    "          bbox_2d.append(bbox)\n",
    "          augmented_image, augmented_bbox, augmented_label = augment_images_a_num_of_times(augmentations[i], image, bbox_2d, class_labels)\n",
    "          splits_to_lists_mapping[split][0][prev_ind+i, :, :, :] = augmented_image\n",
    "          splits_to_lists_mapping[split][1][prev_ind+i, :] = augmented_bbox\n",
    "          splits_to_lists_mapping[split][2][prev_ind+i, :] = augmented_label\n",
    "          train_images_augment_functions[prev_ind+i, :] = (filename + '_' + augmentations[i].split('_function')[0])\n",
    "        prev_ind += (i+1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "qSSmSSHwH5n2",
    "outputId": "9c80f119-afe7-400e-d800-c12b11c5d1bd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Took 12.6 seconds\n"
     ]
    }
   ],
   "source": [
    "start_time = time.time()\n",
    "create_dataset(train_df)\n",
    "print(f\"Took {(time.time() - start_time):.3} seconds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "tD5R3V4uH5fk",
    "outputId": "672656d4-9237-403a-8e2f-3cbfee6a0112"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Took 1.37 seconds\n"
     ]
    }
   ],
   "source": [
    "start_time = time.time()\n",
    "create_dataset(val_df)\n",
    "print(f\"Took {(time.time() - start_time):.3} seconds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VyNZ2cnPPS1s",
    "outputId": "765422eb-5658-4e16-ab0c-c06b933bee3e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['negative'], dtype='<U16')"
      ]
     },
     "execution_count": 32,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Check if all labels are correctly added (without empty strings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "lRaFflFPJQqA"
   },
   "outputs": [],
   "source": [
    "i = 0\n",
    "train_labels_list = train_labels.tolist()\n",
    "for ind, lbl in enumerate(train_labels_list):\n",
    "  if lbl == ['']:\n",
    "    print(ind)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 287
    },
    "id": "CDzX4P9UIsY5",
    "outputId": "f8e85048-976d-4673-ae26-04c76e2a7ea1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f8de2e3f310>"
      ]
     },
     "execution_count": 35,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(train_images[5069])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xsR4SJdwIsR5"
   },
   "source": [
    "### One-hot encode labels "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-14T09:17:00.101230Z",
     "iopub.status.busy": "2021-07-14T09:17:00.100559Z",
     "iopub.status.idle": "2021-07-14T09:17:00.141160Z",
     "shell.execute_reply": "2021-07-14T09:17:00.140344Z",
     "shell.execute_reply.started": "2021-07-14T09:17:00.101177Z"
    },
    "id": "1qrslVu6T6a5"
   },
   "outputs": [],
   "source": [
    "lb = LabelBinarizer()\n",
    "lb.fit([\"negative\", \"typical\", \"indeterminate\", \"atypical\"])\n",
    "train_labels_onehot = lb.transform(train_labels)     \n",
    "val_labels_onehot = lb.transform(val_labels)     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "AVh289TMW7S4",
    "outputId": "ae5c1460-b5d0-40ac-a8eb-1eb3bef9c695"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, 0])"
      ]
     },
     "execution_count": 66,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels_onehot[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4x3NoB3YW60d",
    "outputId": "3beddc7b-f0dd-410e-ea3b-838cea9df54f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 1])"
      ]
     },
     "execution_count": 67,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_labels_onehot[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GKakmdfVT6a5"
   },
   "source": [
    "## Create model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-14T09:17:00.150631Z",
     "iopub.status.busy": "2021-07-14T09:17:00.150247Z",
     "iopub.status.idle": "2021-07-14T09:17:02.154212Z",
     "shell.execute_reply": "2021-07-14T09:17:02.153332Z",
     "shell.execute_reply.started": "2021-07-14T09:17:00.150593Z"
    },
    "id": "ZZ1m_6oPT6a5"
   },
   "outputs": [],
   "source": [
    "base_model = tf.keras.models.Sequential([\n",
    "    Conv2D(32, (3, 3), activation='relu', input_shape=(256, 256, 3)),\n",
    "    MaxPooling2D(2, 2),\n",
    "    Conv2D(64, (3, 3), activation='relu'),\n",
    "    MaxPooling2D(2, 2),\n",
    "    Conv2D(128, (3, 3), activation='relu'),\n",
    "    BatchNormalization(),\n",
    "    MaxPooling2D(2, 2),\n",
    "    Conv2D(256, (3, 3), activation='relu'),\n",
    "    MaxPooling2D(2, 2)\n",
    "])\n",
    "\n",
    "flatten = base_model.output\n",
    "flatten = Flatten()(flatten)\n",
    "\n",
    "# add dense layers header to predict bounding box coordinates\n",
    "bbox_head = Dense(128, activation=\"relu\", kernel_regularizer=tf.keras.regularizers.l1_l2(l1=0.01, l2=0.01))(flatten)\n",
    "bbox_head = Dropout(0.5)(bbox_head)\n",
    "bbox_head = Dense(64, activation=\"relu\")(bbox_head)\n",
    "bbox_head = Dropout(0.5)(bbox_head)\n",
    "bbox_head = Dense(32, activation=\"relu\")(bbox_head)\n",
    "bbox_head = Dense(4, activation=\"sigmoid\", name=\"bounding_box\")(bbox_head)\n",
    "\n",
    "# bbox_head = Dense(512, activation=\"relu\", kernel_regularizer=tf.keras.regularizers.l1_l2(l1=0.01, l2=0.01))(flatten)\n",
    "# bbox_head = Dropout(0.5)(bbox_head)\n",
    "# bbox_head = Dense(512, activation=\"relu\")(bbox_head)\n",
    "# bbox_head = Dropout(0.5)(bbox_head)\n",
    "# bbox_head = Dense(4, activation=\"sigmoid\", name=\"bounding_box\")(bbox_head)\n",
    "\n",
    "# add dense layers header to predict class labels\n",
    "label_head = Dense(512, activation=\"relu\")(flatten)\n",
    "label_head = Dropout(0.5)(label_head)\n",
    "label_head = Dense(512, activation=\"relu\", kernel_regularizer=tf.keras.regularizers.l1_l2(l1=0.01, l2=0.01))(label_head)\n",
    "label_head = Dropout(0.5)(label_head)\n",
    "label_head = Dense(4, activation=\"softmax\", name=\"class_label\")(label_head)\n",
    "\n",
    "# accept an input image and output bounding box coordinates and class labels\n",
    "model = Model(inputs=base_model.input, outputs=(bbox_head, label_head))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-14T09:17:02.155907Z",
     "iopub.status.busy": "2021-07-14T09:17:02.155549Z",
     "iopub.status.idle": "2021-07-14T09:17:02.174634Z",
     "shell.execute_reply": "2021-07-14T09:17:02.173693Z",
     "shell.execute_reply.started": "2021-07-14T09:17:02.155873Z"
    },
    "id": "OrZbQKmnT6a6"
   },
   "outputs": [],
   "source": [
    "# losses dictionary\n",
    "losses = {\"class_label\": \"categorical_crossentropy\", \"bounding_box\": \"mean_squared_error\",}\n",
    "\n",
    "# weights dictionary\n",
    "loss_weights = {\"class_label\": 1.0, \"bounding_box\": 1.0}\n",
    "\n",
    "opt = Adam(learning_rate=0.00001)\n",
    "model.compile(loss=losses, optimizer=opt, metrics=[\"accuracy\"], loss_weights=loss_weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-14T09:17:02.176716Z",
     "iopub.status.busy": "2021-07-14T09:17:02.176182Z",
     "iopub.status.idle": "2021-07-14T09:17:02.182867Z",
     "shell.execute_reply": "2021-07-14T09:17:02.181626Z",
     "shell.execute_reply.started": "2021-07-14T09:17:02.176672Z"
    },
    "id": "iBZg20HPT6a6"
   },
   "outputs": [],
   "source": [
    "# train targets dictionary\n",
    "train_targets = {\"class_label\": train_labels_onehot, \"bounding_box\": train_bboxes}\n",
    "\n",
    "# validation targets dictionary\n",
    "val_targets = {\"class_label\": val_labels_onehot, \"bounding_box\": val_bboxes}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Bm2wkC9GYA1A",
    "outputId": "56702c11-f162-43eb-d57a-88c86773d28c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10155"
      ]
     },
     "execution_count": 85,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ldkeU8pYX337",
    "outputId": "fe9a18b1-897e-4891-d369-cee5ba5dbbe3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_2\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_2 (InputLayer)            [(None, 256, 256, 3) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "block1_conv1 (Conv2D)           (None, 256, 256, 64) 1792        input_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "block1_conv2 (Conv2D)           (None, 256, 256, 64) 36928       block1_conv1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block1_pool (MaxPooling2D)      (None, 128, 128, 64) 0           block1_conv2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block2_conv1 (Conv2D)           (None, 128, 128, 128 73856       block1_pool[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block2_conv2 (Conv2D)           (None, 128, 128, 128 147584      block2_conv1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block2_pool (MaxPooling2D)      (None, 64, 64, 128)  0           block2_conv2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block3_conv1 (Conv2D)           (None, 64, 64, 256)  295168      block2_pool[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block3_conv2 (Conv2D)           (None, 64, 64, 256)  590080      block3_conv1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block3_conv3 (Conv2D)           (None, 64, 64, 256)  590080      block3_conv2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block3_conv4 (Conv2D)           (None, 64, 64, 256)  590080      block3_conv3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block3_pool (MaxPooling2D)      (None, 32, 32, 256)  0           block3_conv4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block4_conv1 (Conv2D)           (None, 32, 32, 512)  1180160     block3_pool[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block4_conv2 (Conv2D)           (None, 32, 32, 512)  2359808     block4_conv1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block4_conv3 (Conv2D)           (None, 32, 32, 512)  2359808     block4_conv2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block4_conv4 (Conv2D)           (None, 32, 32, 512)  2359808     block4_conv3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block4_pool (MaxPooling2D)      (None, 16, 16, 512)  0           block4_conv4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block5_conv1 (Conv2D)           (None, 16, 16, 512)  2359808     block4_pool[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block5_conv2 (Conv2D)           (None, 16, 16, 512)  2359808     block5_conv1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block5_conv3 (Conv2D)           (None, 16, 16, 512)  2359808     block5_conv2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block5_conv4 (Conv2D)           (None, 16, 16, 512)  2359808     block5_conv3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block5_pool (MaxPooling2D)      (None, 8, 8, 512)    0           block5_conv4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "flatten_2 (Flatten)             (None, 32768)        0           block5_pool[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "dense_13 (Dense)                (None, 128)          4194432     flatten_2[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dense_10 (Dense)                (None, 128)          4194432     flatten_2[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dropout_6 (Dropout)             (None, 128)          0           dense_13[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dense_11 (Dense)                (None, 64)           8256        dense_10[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dense_14 (Dense)                (None, 64)           8256        dropout_6[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dense_12 (Dense)                (None, 32)           2080        dense_11[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dropout_7 (Dropout)             (None, 64)           0           dense_14[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bounding_box (Dense)            (None, 4)            132         dense_12[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "class_label (Dense)             (None, 4)            260         dropout_7[0][0]                  \n",
      "==================================================================================================\n",
      "Total params: 28,432,232\n",
      "Trainable params: 8,407,848\n",
      "Non-trainable params: 20,024,384\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "6CTNpKAuee41"
   },
   "outputs": [],
   "source": [
    "callback = tf.keras.callbacks.EarlyStopping(monitor='val_class_label_accuracy', patience=3, restore_best_weights=True, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2021-07-14T09:17:02.185210Z",
     "iopub.status.busy": "2021-07-14T09:17:02.184699Z"
    },
    "id": "4qr0NbEST6a6",
    "outputId": "4295febb-d18c-42c6-d448-afc8584777da"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "635/635 [==============================] - 73s 113ms/step - loss: 1.4319 - bounding_box_loss: 0.0838 - class_label_loss: 1.3482 - bounding_box_accuracy: 0.4587 - class_label_accuracy: 0.3412 - val_loss: 1.3504 - val_bounding_box_loss: 0.0835 - val_class_label_loss: 1.2669 - val_bounding_box_accuracy: 0.5847 - val_class_label_accuracy: 0.5138\n",
      "Epoch 2/20\n",
      "635/635 [==============================] - 72s 113ms/step - loss: 1.3502 - bounding_box_loss: 0.0768 - class_label_loss: 1.2734 - bounding_box_accuracy: 0.4799 - class_label_accuracy: 0.3999 - val_loss: 1.3293 - val_bounding_box_loss: 0.0806 - val_class_label_loss: 1.2487 - val_bounding_box_accuracy: 0.5823 - val_class_label_accuracy: 0.5256\n",
      "Epoch 3/20\n",
      "635/635 [==============================] - 72s 114ms/step - loss: 1.3240 - bounding_box_loss: 0.0742 - class_label_loss: 1.2498 - bounding_box_accuracy: 0.4978 - class_label_accuracy: 0.4225 - val_loss: 1.3021 - val_bounding_box_loss: 0.0792 - val_class_label_loss: 1.2228 - val_bounding_box_accuracy: 0.5477 - val_class_label_accuracy: 0.5437\n",
      "Epoch 4/20\n",
      "635/635 [==============================] - 72s 114ms/step - loss: 1.3056 - bounding_box_loss: 0.0729 - class_label_loss: 1.2327 - bounding_box_accuracy: 0.5207 - class_label_accuracy: 0.4443 - val_loss: 1.3402 - val_bounding_box_loss: 0.0803 - val_class_label_loss: 1.2599 - val_bounding_box_accuracy: 0.5965 - val_class_label_accuracy: 0.5169\n",
      "Epoch 5/20\n",
      "635/635 [==============================] - 72s 114ms/step - loss: 1.2858 - bounding_box_loss: 0.0713 - class_label_loss: 1.2145 - bounding_box_accuracy: 0.5205 - class_label_accuracy: 0.4527 - val_loss: 1.2934 - val_bounding_box_loss: 0.0800 - val_class_label_loss: 1.2134 - val_bounding_box_accuracy: 0.6076 - val_class_label_accuracy: 0.5508\n",
      "Epoch 6/20\n",
      "635/635 [==============================] - 72s 114ms/step - loss: 1.2745 - bounding_box_loss: 0.0700 - class_label_loss: 1.2045 - bounding_box_accuracy: 0.5245 - class_label_accuracy: 0.4700 - val_loss: 1.2729 - val_bounding_box_loss: 0.0778 - val_class_label_loss: 1.1951 - val_bounding_box_accuracy: 0.6060 - val_class_label_accuracy: 0.5556\n",
      "Epoch 7/20\n",
      "635/635 [==============================] - 72s 114ms/step - loss: 1.2562 - bounding_box_loss: 0.0686 - class_label_loss: 1.1876 - bounding_box_accuracy: 0.5240 - class_label_accuracy: 0.4752 - val_loss: 1.2977 - val_bounding_box_loss: 0.0794 - val_class_label_loss: 1.2183 - val_bounding_box_accuracy: 0.5926 - val_class_label_accuracy: 0.5469\n",
      "Epoch 8/20\n",
      "635/635 [==============================] - 72s 114ms/step - loss: 1.2449 - bounding_box_loss: 0.0672 - class_label_loss: 1.1777 - bounding_box_accuracy: 0.5301 - class_label_accuracy: 0.4798 - val_loss: 1.2464 - val_bounding_box_loss: 0.0821 - val_class_label_loss: 1.1643 - val_bounding_box_accuracy: 0.5950 - val_class_label_accuracy: 0.5650\n",
      "Epoch 9/20\n",
      "635/635 [==============================] - 72s 114ms/step - loss: 1.2289 - bounding_box_loss: 0.0663 - class_label_loss: 1.1626 - bounding_box_accuracy: 0.5339 - class_label_accuracy: 0.4974 - val_loss: 1.2562 - val_bounding_box_loss: 0.0771 - val_class_label_loss: 1.1790 - val_bounding_box_accuracy: 0.5626 - val_class_label_accuracy: 0.5619\n",
      "Epoch 10/20\n",
      "635/635 [==============================] - 72s 114ms/step - loss: 1.2231 - bounding_box_loss: 0.0652 - class_label_loss: 1.1579 - bounding_box_accuracy: 0.5309 - class_label_accuracy: 0.4991 - val_loss: 1.2527 - val_bounding_box_loss: 0.0798 - val_class_label_loss: 1.1729 - val_bounding_box_accuracy: 0.5934 - val_class_label_accuracy: 0.5650\n",
      "Epoch 11/20\n",
      "635/635 [==============================] - 72s 114ms/step - loss: 1.2134 - bounding_box_loss: 0.0641 - class_label_loss: 1.1493 - bounding_box_accuracy: 0.5433 - class_label_accuracy: 0.5037 - val_loss: 1.2189 - val_bounding_box_loss: 0.0790 - val_class_label_loss: 1.1399 - val_bounding_box_accuracy: 0.5957 - val_class_label_accuracy: 0.5729\n",
      "Epoch 12/20\n",
      "635/635 [==============================] - 72s 114ms/step - loss: 1.1995 - bounding_box_loss: 0.0634 - class_label_loss: 1.1361 - bounding_box_accuracy: 0.5474 - class_label_accuracy: 0.5114 - val_loss: 1.2569 - val_bounding_box_loss: 0.0778 - val_class_label_loss: 1.1791 - val_bounding_box_accuracy: 0.5823 - val_class_label_accuracy: 0.5658\n",
      "Epoch 13/20\n",
      "635/635 [==============================] - 72s 114ms/step - loss: 1.1932 - bounding_box_loss: 0.0621 - class_label_loss: 1.1311 - bounding_box_accuracy: 0.5378 - class_label_accuracy: 0.5143 - val_loss: 1.2270 - val_bounding_box_loss: 0.0772 - val_class_label_loss: 1.1498 - val_bounding_box_accuracy: 0.5626 - val_class_label_accuracy: 0.5666\n",
      "Epoch 14/20\n",
      "635/635 [==============================] - 72s 114ms/step - loss: 1.1824 - bounding_box_loss: 0.0609 - class_label_loss: 1.1215 - bounding_box_accuracy: 0.5490 - class_label_accuracy: 0.5118 - val_loss: 1.2097 - val_bounding_box_loss: 0.0766 - val_class_label_loss: 1.1331 - val_bounding_box_accuracy: 0.5839 - val_class_label_accuracy: 0.5816\n",
      "Epoch 15/20\n",
      "635/635 [==============================] - 72s 114ms/step - loss: 1.1689 - bounding_box_loss: 0.0596 - class_label_loss: 1.1092 - bounding_box_accuracy: 0.5448 - class_label_accuracy: 0.5242 - val_loss: 1.2155 - val_bounding_box_loss: 0.0769 - val_class_label_loss: 1.1386 - val_bounding_box_accuracy: 0.5839 - val_class_label_accuracy: 0.5768\n",
      "Epoch 16/20\n",
      "635/635 [==============================] - 72s 114ms/step - loss: 1.1727 - bounding_box_loss: 0.0589 - class_label_loss: 1.1137 - bounding_box_accuracy: 0.5368 - class_label_accuracy: 0.5319 - val_loss: 1.2158 - val_bounding_box_loss: 0.0784 - val_class_label_loss: 1.1374 - val_bounding_box_accuracy: 0.5792 - val_class_label_accuracy: 0.5674\n",
      "Epoch 17/20\n",
      "635/635 [==============================] - 72s 114ms/step - loss: 1.1634 - bounding_box_loss: 0.0579 - class_label_loss: 1.1055 - bounding_box_accuracy: 0.5442 - class_label_accuracy: 0.5348 - val_loss: 1.1966 - val_bounding_box_loss: 0.0779 - val_class_label_loss: 1.1187 - val_bounding_box_accuracy: 0.5839 - val_class_label_accuracy: 0.5831\n",
      "Epoch 18/20\n",
      "635/635 [==============================] - 72s 114ms/step - loss: 1.1544 - bounding_box_loss: 0.0568 - class_label_loss: 1.0976 - bounding_box_accuracy: 0.5451 - class_label_accuracy: 0.5387 - val_loss: 1.1927 - val_bounding_box_loss: 0.0767 - val_class_label_loss: 1.1160 - val_bounding_box_accuracy: 0.5902 - val_class_label_accuracy: 0.5816\n",
      "Epoch 19/20\n",
      "635/635 [==============================] - 72s 114ms/step - loss: 1.1485 - bounding_box_loss: 0.0559 - class_label_loss: 1.0925 - bounding_box_accuracy: 0.5487 - class_label_accuracy: 0.5401 - val_loss: 1.2027 - val_bounding_box_loss: 0.0769 - val_class_label_loss: 1.1258 - val_bounding_box_accuracy: 0.5934 - val_class_label_accuracy: 0.5776\n",
      "Epoch 20/20\n",
      "635/635 [==============================] - 72s 114ms/step - loss: 1.1445 - bounding_box_loss: 0.0546 - class_label_loss: 1.0899 - bounding_box_accuracy: 0.5456 - class_label_accuracy: 0.5423 - val_loss: 1.1751 - val_bounding_box_loss: 0.0788 - val_class_label_loss: 1.0963 - val_bounding_box_accuracy: 0.5823 - val_class_label_accuracy: 0.5847\n"
     ]
    }
   ],
   "source": [
    "H = model.fit(\n",
    "    train_images, train_targets,\n",
    "    validation_data=(val_images, val_targets),\n",
    "    batch_size=16,\n",
    "    epochs=20,\n",
    "    callbacks=[callback],\n",
    "    verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "dkS2KADzE1Pm"
   },
   "outputs": [],
   "source": [
    "model.save('4_layers_CNN', save_format=\"h5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use VGG19 model from Keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "57iaE6rJeocg"
   },
   "outputs": [],
   "source": [
    "# load the VGG16 network\n",
    "model = tf.keras.applications.vgg19.VGG19(weights='imagenet', include_top=False, input_tensor=Input(shape=(256, 256, 3)), classes=4)\n",
    "\n",
    "# freeze all VGG layers\n",
    "model.trainable = False\n",
    "\n",
    "# flatten the max-pooling output of VGG\n",
    "flatten = model.output\n",
    "flatten = Flatten()(flatten)\n",
    "\n",
    "# add dense layers header to predict bounding box coordinates\n",
    "bbox_head = Dense(128, activation=\"relu\")(flatten)\n",
    "bbox_head = Dense(64, activation=\"relu\")(bbox_head)\n",
    "bbox_head = Dense(32, activation=\"relu\")(bbox_head)\n",
    "bbox_head = Dense(4, activation=\"sigmoid\", name=\"bounding_box\")(bbox_head)\n",
    "\n",
    "# add a second dense layers head to predict class labels\n",
    "label_head = Dense(128, activation=\"relu\")(flatten)\n",
    "label_head = Dropout(0.5)(label_head)\n",
    "label_head = Dense(64, activation=\"relu\")(label_head)\n",
    "label_head = Dropout(0.5)(label_head)\n",
    "label_head = Dense(len(lb.classes_), activation=\"softmax\", name=\"class_label\")(label_head)\n",
    "\n",
    "# accept an input image and output bounding box coordinates and class labels\n",
    "model = Model(inputs=model.input, outputs=(bbox_head, label_head))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Go up to compile the model and train it!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save the VGG19 model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('VGG19', save_format=\"h5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "SSPTuJkQSHwu",
    "outputId": "1da16849-2e0f-4aa2-cf38-0dc929794095"
   },
   "outputs": [],
   "source": [
    "# plot the total loss, label loss, and bounding box loss\n",
    "loss_names = [\"loss\", \"class_label_loss\", \"bounding_box_loss\"]\n",
    "N = np.arange(0, 20)\n",
    "plt.style.use(\"ggplot\")\n",
    "(fig, ax) = plt.subplots(3, 1, figsize=(13, 13))\n",
    "\n",
    "# loop over the loss names\n",
    "for (i, l) in enumerate(loss_names):\n",
    "    # plot the loss for both the training and validation data\n",
    "    title = \"Loss for {}\".format(l) if l != \"loss\" else \"Total loss\"\n",
    "    ax[i].set_title(title)\n",
    "    ax[i].set_xlabel(\"Epoch #\")\n",
    "    ax[i].set_ylabel(\"Loss\")\n",
    "    ax[i].plot(N, H.history[l], label=l)\n",
    "    ax[i].plot(N, H.history[\"val_\" + l], label=\"val_\" + l)\n",
    "    ax[i].legend()\n",
    "\n",
    "# save the losses figure and create a new figure for the accuracies\n",
    "plt.tight_layout()\n",
    "plot_path = os.path.join('.', \"losses_2_layers_.png\")\n",
    "plt.savefig(plot_path)\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 506
    },
    "id": "_ES1gAMuZB5L",
    "outputId": "d751bf02-fc52-4b3d-a385-abfd82750802"
   },
   "outputs": [],
   "source": [
    "# create a new figure for the accuracies\n",
    "plt.style.use(\"ggplot\")\n",
    "plt.figure()\n",
    "plt.plot(N, H.history[\"class_label_accuracy\"], label=\"class_label_accuracy\")\n",
    "plt.plot(N, H.history[\"val_class_label_accuracy\"], label=\"val_class_label_accuracy\")\n",
    "plt.title(\"Class Label Accuracy\")\n",
    "plt.xlabel(\"Epoch #\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.legend(loc=\"lower left\")\n",
    "\n",
    "# save the accuracies plot\n",
    "plot_path = os.path.join('.', \"accuracies_2_layers_.png\")\n",
    "plt.savefig(plot_path)"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "covid19-inflate-dataset-albumentations.ipynb",
   "provenance": []
  },
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