Superresolution.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# SRGAN demonstration for QR code enhancement"
      ],
      "metadata": {
        "id": "o6XJVjdnSd43"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Installing dependencies+ Unzipping data"
      ],
      "metadata": {
        "id": "be07ZluASUkM"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nzcv5GOI964O",
        "outputId": "907ad4e6-3ff3-452b-def2-3cb134040eec",
        "scrolled": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Archive:  archive.zip\n",
            "replace qr_dataset/1002-v1.png? [y]es, [n]o, [A]ll, [N]one, [r]ename: ^C\n"
          ]
        }
      ],
      "source": [
        "!unzip archive.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "La9YjZki4X4E",
        "outputId": "7ebbddce-23fb-452f-f00b-2fe0a93f0ded"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2022-04-30 11:43:39--  https://raw.githubusercontent.com/PsVenom/QR-code-enhancement-using-SRGANs/main/main.py\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 2606:50c0:8003::154, 2606:50c0:8001::154, 2606:50c0:8000::154, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|2606:50c0:8003::154|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 3020 (2.9K) [text/plain]\n",
            "Saving to: ‘main.py.1’\n",
            "\n",
            "100%[======================================>] 3,020       --.-K/s   in 0s      \n",
            "\n",
            "2022-04-30 11:43:39 (17.1 MB/s) - ‘main.py.1’ saved [3020/3020]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!wget https://raw.githubusercontent.com/PsVenom/QR-code-enhancement-using-SRGANs/main/main.py"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XVxyXQxK2184"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "90EN9jfHSQPB",
        "outputId": "332223e8-50ff-4bc6-fe31-9c9b0689a680"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Defaulting to user installation because normal site-packages is not writeable\n",
            "Requirement already satisfied: opencv-python in /home/itv000740/.local/lib/python3.6/site-packages (4.5.5.64)\n",
            "Requirement already satisfied: numpy>=1.13.3 in /opt/anaconda3/envs/beakerx/lib/python3.6/site-packages (from opencv-python) (1.19.5)\n",
            "Defaulting to user installation because normal site-packages is not writeable\n",
            "Requirement already satisfied: tqdm in /home/itv000740/.local/lib/python3.6/site-packages (4.64.0)\n",
            "Requirement already satisfied: importlib-resources in /home/itv000740/.local/lib/python3.6/site-packages (from tqdm) (5.4.0)\n",
            "Requirement already satisfied: zipp>=3.1.0 in /opt/anaconda3/envs/beakerx/lib/python3.6/site-packages (from importlib-resources->tqdm) (3.4.1)\n",
            "Defaulting to user installation because normal site-packages is not writeable\n",
            "Requirement already satisfied: scikit-image in /home/itv000740/.local/lib/python3.6/site-packages (0.17.2)\n",
            "Requirement already satisfied: pillow!=7.1.0,!=7.1.1,>=4.3.0 in /home/itv000740/.local/lib/python3.6/site-packages (from scikit-image) (8.4.0)\n",
            "Requirement already satisfied: PyWavelets>=1.1.1 in /home/itv000740/.local/lib/python3.6/site-packages (from scikit-image) (1.1.1)\n",
            "Requirement already satisfied: numpy>=1.15.1 in /opt/anaconda3/envs/beakerx/lib/python3.6/site-packages (from scikit-image) (1.19.5)\n",
            "Requirement already satisfied: scipy>=1.0.1 in /home/itv000740/.local/lib/python3.6/site-packages (from scikit-image) (1.5.4)\n",
            "Requirement already satisfied: tifffile>=2019.7.26 in /home/itv000740/.local/lib/python3.6/site-packages (from scikit-image) (2020.9.3)\n",
            "Requirement already satisfied: networkx>=2.0 in /home/itv000740/.local/lib/python3.6/site-packages (from scikit-image) (2.5.1)\n",
            "Requirement already satisfied: imageio>=2.3.0 in /home/itv000740/.local/lib/python3.6/site-packages (from scikit-image) (2.15.0)\n",
            "Requirement already satisfied: matplotlib!=3.0.0,>=2.0.0 in /home/itv000740/.local/lib/python3.6/site-packages (from scikit-image) (3.3.4)\n",
            "Requirement already satisfied: cycler>=0.10 in /home/itv000740/.local/lib/python3.6/site-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image) (0.11.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /home/itv000740/.local/lib/python3.6/site-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image) (1.3.1)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in /opt/anaconda3/envs/beakerx/lib/python3.6/site-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image) (2.4.7)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /opt/anaconda3/envs/beakerx/lib/python3.6/site-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image) (2.8.1)\n",
            "Requirement already satisfied: decorator<5,>=4.3 in /home/itv000740/.local/lib/python3.6/site-packages (from networkx>=2.0->scikit-image) (4.4.2)\n",
            "Requirement already satisfied: six>=1.5 in /home/itv000740/.local/lib/python3.6/site-packages (from python-dateutil>=2.1->matplotlib!=3.0.0,>=2.0.0->scikit-image) (1.15.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install opencv-python\n",
        "!pip install tqdm\n",
        "!pip install scikit-image\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## looking at a single example"
      ],
      "metadata": {
        "id": "KNhApxR4T40b"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "pci9NDkb3cNd",
        "outputId": "a19573a9-3563-489a-c660-5de39b22afc6"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "import cv2 \n",
        "import os\n",
        "datadir = 'qr_dataset'\n",
        "# iterating over just one element\n",
        "for img in os.listdir(datadir): \n",
        "        img_array = cv2.imread(os.path.join(datadir,img) ,cv2.IMREAD_GRAYSCALE)  # convert to array\n",
        "        plt.imshow(img_array, cmap='gray')  # graph it\n",
        "        plt.show()  # display!\n",
        "\n",
        "        break  # we just want one for now so break"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## storing the images"
      ],
      "metadata": {
        "id": "5tR6pII5UuSg"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0nY4ha0V4w9B",
        "outputId": "894c3a14-3d27-451c-ddf6-45e66d09297d"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 10000/10000 [00:45<00:00, 218.88it/s]\n"
          ]
        }
      ],
      "source": [
        "#generalising this technique for all other images \n",
        "array = []\n",
        "array_small =[]\n",
        "from tqdm import tqdm\n",
        "def create_training_data():\n",
        "        for img in tqdm(list(os.listdir(datadir))):  # iterate over each image per dogs and cats\n",
        "            try:\n",
        "                img_array = cv2.imread(datadir+'/'+img ,cv2.IMREAD_COLOR)  # convert to array\n",
        "                new_array = cv2.resize(img_array, (128, 128))  # resize to normalize data size\n",
        "                array.append([new_array]) \n",
        "                array_small.append([cv2.resize(img_array, (32,32),\n",
        "                               interpolation=cv2.INTER_AREA)]) # add this to our training_data\n",
        "            except Exception as e:  # in the interest in keeping the output clean...\n",
        "                pass\n",
        "create_training_data()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-KEDC393Nbnz",
        "outputId": "ecec9641-4c0b-4cb9-c6e5-166f85266b72"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "10000"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(array)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dmIZORuSQJQC"
      },
      "outputs": [],
      "source": [
        "X = []\n",
        "Xs = []\n",
        "for features in array:\n",
        "    X.append(features)\n",
        "for features in array_small:\n",
        "    Xs.append(features)\n",
        "X = np.array(X).reshape(-1, 128, 128, 3)\n",
        "Xs = np.array(Xs).reshape(-1, 32, 32, 3)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Image Augmentation (offline)"
      ],
      "metadata": {
        "id": "0EHTQF3uXB-V"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7JWPVU2hEu6p"
      },
      "outputs": [],
      "source": [
        "#augmenting the data\n",
        "from keras.preprocessing.image import ImageDataGenerator #this generator will save files in a physical format\n",
        "from skimage import io\n",
        "datagen = ImageDataGenerator(        \n",
        "        rotation_range = 40,\n",
        "        shear_range = 0.2,\n",
        "        zoom_range = 0.2,\n",
        "        horizontal_flip = True,\n",
        "        brightness_range = (0.5, 1.5))\n",
        "\n",
        "for a in X:\n",
        " i = 0\n",
        " a = a.reshape((1, ) + a.shape)\n",
        " for batch in datagen.flow(a, batch_size=1,  save_to_dir= 'Augmented-images', save_prefix='dr', save_format='jpeg'):    \n",
        "  try:\n",
        "    i += 1   \n",
        "    if i>= 10:\n",
        "      break \n",
        "  except Exception:\n",
        "    print(\"error\")\n",
        "    pass\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## loading augmented images and adding them to image array"
      ],
      "metadata": {
        "id": "whAAOkPKXPXh"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_U8OAovJRI-s",
        "outputId": "c1dae1f2-fa78-4a1e-866e-de7b86ac5576"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 10000/10000 [00:56<00:00, 177.30it/s]\n"
          ]
        }
      ],
      "source": [
        "array=[]\n",
        "array_small=[]\n",
        "datadir = 'Augmented-images'\n",
        "create_training_data()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "kB23EmtWbn4O",
        "outputId": "3616a14e-002e-4af6-8287-7c70b3161f1f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f78bd81f0b8>"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "X1 =  []\n",
        "Xs1 = []\n",
        "for features in array:\n",
        "    X1.append(features)\n",
        "for features in array_small:\n",
        "    Xs1.append(features)\n",
        "X1 = np.array(X1).reshape(-1, 128, 128, 3)\n",
        "Xs1 = np.array(Xs1).reshape(-1, 32, 32, 3)\n",
        "plt.imshow(Xs1[0], cmap = 'gray')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MHjjgEwvp0d6",
        "outputId": "57ba7522-23ad-40f9-d04b-28aa23ee8489"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(20000, 128, 128, 3)"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X=np.concatenate((X,X1), axis = 0)\n",
        "Xs=np.concatenate((Xs,Xs1), axis=0)\n",
        "X.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Training and validation\n",
        "\n",
        "\n",
        "creating training split"
      ],
      "metadata": {
        "id": "Cg5cHI_PXaww"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uAaoxW29RvWK",
        "outputId": "50c2968c-0e43-4da1-fd8b-5256e061d302"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(13400, 32, 32, 3)"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train,X_valid,y_train, y_valid = train_test_split(Xs, X, test_size = 0.33, random_state = 12)\n",
        "X_train.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NgHtjWZzyUBJ"
      },
      "outputs": [],
      "source": [
        "from main import *"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "reQ4s3MfX4bI"
      },
      "source": [
        "## creating the final generative network"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q0YoqGwS97UZ",
        "outputId": "10388123-bcdb-4971-8996-d147705cd851"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            [(None, 32, 32, 3)]  0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d (Conv2D)                 (None, 32, 32, 64)   15616       input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "p_re_lu (PReLU)                 (None, 32, 32, 64)   64          conv2d[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_1 (Conv2D)               (None, 32, 32, 64)   36928       p_re_lu[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization (BatchNorma (None, 32, 32, 64)   256         conv2d_1[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "p_re_lu_1 (PReLU)               (None, 32, 32, 64)   64          batch_normalization[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_2 (Conv2D)               (None, 32, 32, 64)   36928       p_re_lu_1[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_1 (BatchNor (None, 32, 32, 64)   256         conv2d_2[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "add (Add)                       (None, 32, 32, 64)   0           p_re_lu[0][0]                    \n",
            "                                                                 batch_normalization_1[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_3 (Conv2D)               (None, 32, 32, 64)   36928       add[0][0]                        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_2 (BatchNor (None, 32, 32, 64)   256         conv2d_3[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "p_re_lu_2 (PReLU)               (None, 32, 32, 64)   64          batch_normalization_2[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_4 (Conv2D)               (None, 32, 32, 64)   36928       p_re_lu_2[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_3 (BatchNor (None, 32, 32, 64)   256         conv2d_4[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "add_1 (Add)                     (None, 32, 32, 64)   0           add[0][0]                        \n",
            "                                                                 batch_normalization_3[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_5 (Conv2D)               (None, 32, 32, 64)   36928       add_1[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_4 (BatchNor (None, 32, 32, 64)   256         conv2d_5[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "p_re_lu_3 (PReLU)               (None, 32, 32, 64)   64          batch_normalization_4[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_6 (Conv2D)               (None, 32, 32, 64)   36928       p_re_lu_3[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_5 (BatchNor (None, 32, 32, 64)   256         conv2d_6[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "add_2 (Add)                     (None, 32, 32, 64)   0           add_1[0][0]                      \n",
            "                                                                 batch_normalization_5[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_7 (Conv2D)               (None, 32, 32, 64)   36928       add_2[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_6 (BatchNor (None, 32, 32, 64)   256         conv2d_7[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "p_re_lu_4 (PReLU)               (None, 32, 32, 64)   64          batch_normalization_6[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_8 (Conv2D)               (None, 32, 32, 64)   36928       p_re_lu_4[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_7 (BatchNor (None, 32, 32, 64)   256         conv2d_8[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "add_3 (Add)                     (None, 32, 32, 64)   0           add_2[0][0]                      \n",
            "                                                                 batch_normalization_7[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_9 (Conv2D)               (None, 32, 32, 64)   36928       add_3[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_8 (BatchNor (None, 32, 32, 64)   256         conv2d_9[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "p_re_lu_5 (PReLU)               (None, 32, 32, 64)   64          batch_normalization_8[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_10 (Conv2D)              (None, 32, 32, 64)   36928       p_re_lu_5[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_9 (BatchNor (None, 32, 32, 64)   256         conv2d_10[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "add_4 (Add)                     (None, 32, 32, 64)   0           add_3[0][0]                      \n",
            "                                                                 batch_normalization_9[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_11 (Conv2D)              (None, 32, 32, 64)   36928       add_4[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_10 (BatchNo (None, 32, 32, 64)   256         conv2d_11[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "p_re_lu_6 (PReLU)               (None, 32, 32, 64)   64          batch_normalization_10[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_12 (Conv2D)              (None, 32, 32, 64)   36928       p_re_lu_6[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_11 (BatchNo (None, 32, 32, 64)   256         conv2d_12[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "add_5 (Add)                     (None, 32, 32, 64)   0           add_4[0][0]                      \n",
            "                                                                 batch_normalization_11[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_13 (Conv2D)              (None, 32, 32, 64)   36928       add_5[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_12 (BatchNo (None, 32, 32, 64)   256         conv2d_13[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "p_re_lu_7 (PReLU)               (None, 32, 32, 64)   64          batch_normalization_12[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_14 (Conv2D)              (None, 32, 32, 64)   36928       p_re_lu_7[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_13 (BatchNo (None, 32, 32, 64)   256         conv2d_14[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "add_6 (Add)                     (None, 32, 32, 64)   0           add_5[0][0]                      \n",
            "                                                                 batch_normalization_13[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_15 (Conv2D)              (None, 32, 32, 64)   36928       add_6[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_14 (BatchNo (None, 32, 32, 64)   256         conv2d_15[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "p_re_lu_8 (PReLU)               (None, 32, 32, 64)   64          batch_normalization_14[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_16 (Conv2D)              (None, 32, 32, 64)   36928       p_re_lu_8[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_15 (BatchNo (None, 32, 32, 64)   256         conv2d_16[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "add_7 (Add)                     (None, 32, 32, 64)   0           add_6[0][0]                      \n",
            "                                                                 batch_normalization_15[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_17 (Conv2D)              (None, 32, 32, 64)   36928       add_7[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_16 (BatchNo (None, 32, 32, 64)   256         conv2d_17[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "p_re_lu_9 (PReLU)               (None, 32, 32, 64)   64          batch_normalization_16[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_18 (Conv2D)              (None, 32, 32, 64)   36928       p_re_lu_9[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_17 (BatchNo (None, 32, 32, 64)   256         conv2d_18[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "add_8 (Add)                     (None, 32, 32, 64)   0           add_7[0][0]                      \n",
            "                                                                 batch_normalization_17[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_19 (Conv2D)              (None, 32, 32, 64)   36928       add_8[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_18 (BatchNo (None, 32, 32, 64)   256         conv2d_19[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "p_re_lu_10 (PReLU)              (None, 32, 32, 64)   64          batch_normalization_18[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_20 (Conv2D)              (None, 32, 32, 64)   36928       p_re_lu_10[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_19 (BatchNo (None, 32, 32, 64)   256         conv2d_20[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "add_9 (Add)                     (None, 32, 32, 64)   0           add_8[0][0]                      \n",
            "                                                                 batch_normalization_19[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_21 (Conv2D)              (None, 32, 32, 64)   36928       add_9[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_20 (BatchNo (None, 32, 32, 64)   256         conv2d_21[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "p_re_lu_11 (PReLU)              (None, 32, 32, 64)   64          batch_normalization_20[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_22 (Conv2D)              (None, 32, 32, 64)   36928       p_re_lu_11[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_21 (BatchNo (None, 32, 32, 64)   256         conv2d_22[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "add_10 (Add)                    (None, 32, 32, 64)   0           add_9[0][0]                      \n",
            "                                                                 batch_normalization_21[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_23 (Conv2D)              (None, 32, 32, 64)   36928       add_10[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_22 (BatchNo (None, 32, 32, 64)   256         conv2d_23[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "p_re_lu_12 (PReLU)              (None, 32, 32, 64)   64          batch_normalization_22[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_24 (Conv2D)              (None, 32, 32, 64)   36928       p_re_lu_12[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_23 (BatchNo (None, 32, 32, 64)   256         conv2d_24[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "add_11 (Add)                    (None, 32, 32, 64)   0           add_10[0][0]                     \n",
            "                                                                 batch_normalization_23[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_25 (Conv2D)              (None, 32, 32, 64)   36928       add_11[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_24 (BatchNo (None, 32, 32, 64)   256         conv2d_25[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "p_re_lu_13 (PReLU)              (None, 32, 32, 64)   64          batch_normalization_24[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_26 (Conv2D)              (None, 32, 32, 64)   36928       p_re_lu_13[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_25 (BatchNo (None, 32, 32, 64)   256         conv2d_26[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "add_12 (Add)                    (None, 32, 32, 64)   0           add_11[0][0]                     \n",
            "                                                                 batch_normalization_25[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_27 (Conv2D)              (None, 32, 32, 64)   36928       add_12[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_26 (BatchNo (None, 32, 32, 64)   256         conv2d_27[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "p_re_lu_14 (PReLU)              (None, 32, 32, 64)   64          batch_normalization_26[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_28 (Conv2D)              (None, 32, 32, 64)   36928       p_re_lu_14[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_27 (BatchNo (None, 32, 32, 64)   256         conv2d_28[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "add_13 (Add)                    (None, 32, 32, 64)   0           add_12[0][0]                     \n",
            "                                                                 batch_normalization_27[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_29 (Conv2D)              (None, 32, 32, 64)   36928       add_13[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_28 (BatchNo (None, 32, 32, 64)   256         conv2d_29[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "p_re_lu_15 (PReLU)              (None, 32, 32, 64)   64          batch_normalization_28[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_30 (Conv2D)              (None, 32, 32, 64)   36928       p_re_lu_15[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_29 (BatchNo (None, 32, 32, 64)   256         conv2d_30[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "add_14 (Add)                    (None, 32, 32, 64)   0           add_13[0][0]                     \n",
            "                                                                 batch_normalization_29[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_31 (Conv2D)              (None, 32, 32, 64)   36928       add_14[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_30 (BatchNo (None, 32, 32, 64)   256         conv2d_31[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "p_re_lu_16 (PReLU)              (None, 32, 32, 64)   64          batch_normalization_30[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_32 (Conv2D)              (None, 32, 32, 64)   36928       p_re_lu_16[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_31 (BatchNo (None, 32, 32, 64)   256         conv2d_32[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "add_15 (Add)                    (None, 32, 32, 64)   0           add_14[0][0]                     \n",
            "                                                                 batch_normalization_31[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_33 (Conv2D)              (None, 32, 32, 64)   36928       add_15[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_32 (BatchNo (None, 32, 32, 64)   256         conv2d_33[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "add_16 (Add)                    (None, 32, 32, 64)   0           batch_normalization_32[0][0]     \n",
            "                                                                 p_re_lu[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_34 (Conv2D)              (None, 32, 32, 256)  147712      add_16[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "up_sampling2d (UpSampling2D)    (None, 64, 64, 256)  0           conv2d_34[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "p_re_lu_17 (PReLU)              (None, 64, 64, 256)  256         up_sampling2d[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_35 (Conv2D)              (None, 64, 64, 256)  590080      p_re_lu_17[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "up_sampling2d_1 (UpSampling2D)  (None, 128, 128, 256 0           conv2d_35[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "p_re_lu_18 (PReLU)              (None, 128, 128, 256 256         up_sampling2d_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_36 (Conv2D)              (None, 128, 128, 3)  62211       p_re_lu_18[0][0]                 \n",
            "==================================================================================================\n",
            "Total params: 2,044,291\n",
            "Trainable params: 2,040,067\n",
            "Non-trainable params: 4,224\n",
            "__________________________________________________________________________________________________\n",
            "Model: \"model_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_2 (InputLayer)         [(None, 128, 128, 3)]     0         \n",
            "_________________________________________________________________\n",
            "conv2d_37 (Conv2D)           (None, 128, 128, 64)      1792      \n",
            "_________________________________________________________________\n",
            "leaky_re_lu (LeakyReLU)      (None, 128, 128, 64)      0         \n",
            "_________________________________________________________________\n",
            "conv2d_38 (Conv2D)           (None, 64, 64, 64)        36928     \n",
            "_________________________________________________________________\n",
            "batch_normalization_33 (Batc (None, 64, 64, 64)        256       \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_1 (LeakyReLU)    (None, 64, 64, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_39 (Conv2D)           (None, 64, 64, 128)       73856     \n",
            "_________________________________________________________________\n",
            "batch_normalization_34 (Batc (None, 64, 64, 128)       512       \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_2 (LeakyReLU)    (None, 64, 64, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_40 (Conv2D)           (None, 32, 32, 128)       147584    \n",
            "_________________________________________________________________\n",
            "batch_normalization_35 (Batc (None, 32, 32, 128)       512       \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_3 (LeakyReLU)    (None, 32, 32, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_41 (Conv2D)           (None, 32, 32, 256)       295168    \n",
            "_________________________________________________________________\n",
            "batch_normalization_36 (Batc (None, 32, 32, 256)       1024      \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_4 (LeakyReLU)    (None, 32, 32, 256)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_42 (Conv2D)           (None, 16, 16, 256)       590080    \n",
            "_________________________________________________________________\n",
            "batch_normalization_37 (Batc (None, 16, 16, 256)       1024      \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_5 (LeakyReLU)    (None, 16, 16, 256)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_43 (Conv2D)           (None, 16, 16, 512)       1180160   \n",
            "_________________________________________________________________\n",
            "batch_normalization_38 (Batc (None, 16, 16, 512)       2048      \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_6 (LeakyReLU)    (None, 16, 16, 512)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_44 (Conv2D)           (None, 8, 8, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "batch_normalization_39 (Batc (None, 8, 8, 512)         2048      \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_7 (LeakyReLU)    (None, 8, 8, 512)         0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 32768)             0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 1024)              33555456  \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_8 (LeakyReLU)    (None, 1024)              0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1)                 1025      \n",
            "=================================================================\n",
            "Total params: 38,249,281\n",
            "Trainable params: 38,245,569\n",
            "Non-trainable params: 3,712\n",
            "_________________________________________________________________\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg19/vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "80142336/80134624 [==============================] - 1s 0us/step\n",
            "80150528/80134624 [==============================] - 1s 0us/step\n",
            "Model: \"model_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_3 (InputLayer)         [(None, 128, 128, 3)]     0         \n",
            "_________________________________________________________________\n",
            "block1_conv1 (Conv2D)        (None, 128, 128, 64)      1792      \n",
            "_________________________________________________________________\n",
            "block1_conv2 (Conv2D)        (None, 128, 128, 64)      36928     \n",
            "_________________________________________________________________\n",
            "block1_pool (MaxPooling2D)   (None, 64, 64, 64)        0         \n",
            "_________________________________________________________________\n",
            "block2_conv1 (Conv2D)        (None, 64, 64, 128)       73856     \n",
            "_________________________________________________________________\n",
            "block2_conv2 (Conv2D)        (None, 64, 64, 128)       147584    \n",
            "_________________________________________________________________\n",
            "block2_pool (MaxPooling2D)   (None, 32, 32, 128)       0         \n",
            "_________________________________________________________________\n",
            "block3_conv1 (Conv2D)        (None, 32, 32, 256)       295168    \n",
            "_________________________________________________________________\n",
            "block3_conv2 (Conv2D)        (None, 32, 32, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_conv3 (Conv2D)        (None, 32, 32, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_conv4 (Conv2D)        (None, 32, 32, 256)       590080    \n",
            "=================================================================\n",
            "Total params: 2,325,568\n",
            "Trainable params: 2,325,568\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n"
          ]
        }
      ],
      "source": [
        "hr_shape = (y_train.shape[1], y_train.shape[2], y_train.shape[3])\n",
        "lr_shape = (X_train.shape[1], X_train.shape[2], X_train.shape[3])\n",
        "\n",
        "lr_ip = Input(shape=lr_shape)\n",
        "hr_ip = Input(shape=hr_shape)\n",
        "\n",
        "generator = generator(lr_ip, res_range = 16, upscale_range=2)\n",
        "generator.summary()\n",
        "\n",
        "discriminator = discriminator(hr_ip)\n",
        "discriminator.compile(loss=\"binary_crossentropy\", optimizer=\"adam\", metrics=['accuracy'])\n",
        "discriminator.summary()\n",
        "\n",
        "vgg = build_vgg((128,128,3))\n",
        "print(vgg.summary())\n",
        "vgg.trainable = False\n",
        "\n",
        "gan_model = create_comb(generator, discriminator, vgg, lr_ip, hr_ip)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MakDrAh6f8Xl",
        "outputId": "217fec05-20c9-42c1-cf33-bbbf1852db15"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"model_3\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            [(None, 32, 32, 3)]  0                                            \n",
            "__________________________________________________________________________________________________\n",
            "model (Functional)              (None, 128, 128, 3)  2044291     input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "input_2 (InputLayer)            [(None, 128, 128, 3) 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "model_1 (Functional)            (None, 1)            38249281    model[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "model_2 (Functional)            (None, 32, 32, 256)  2325568     model[0][0]                      \n",
            "==================================================================================================\n",
            "Total params: 42,619,140\n",
            "Trainable params: 2,040,067\n",
            "Non-trainable params: 40,579,073\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "gan_model.compile(loss=[\"binary_crossentropy\", \"mse\"], loss_weights=[1e-3, 1], optimizer=\"adam\")\n",
        "gan_model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MbCubodIdQz6"
      },
      "outputs": [],
      "source": [
        "batch_size = 1  \n",
        "train_lr_batches = []\n",
        "train_hr_batches = []\n",
        "for it in range(int(y_train.shape[0] / batch_size)):\n",
        "    start_idx = it * batch_size\n",
        "    end_idx = start_idx + batch_size\n",
        "    train_hr_batches.append(y_train[start_idx:end_idx])\n",
        "    train_lr_batches.append(X_train[start_idx:end_idx])\n",
        "    "
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "creating training loop for 5 epochs"
      ],
      "metadata": {
        "id": "G1W2EVOjXwKF"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "J4WwGCNEDJfR"
      },
      "outputs": [],
      "source": [
        "epochs = 5\n",
        "#Enumerate training over epochs\n",
        "for e in range(epochs):\n",
        "    \n",
        "    fake_label = np.zeros((batch_size, 1)) # Assign a label of 0 to all fake (generated images)\n",
        "    real_label = np.ones((batch_size,1)) # Assign a label of 1 to all real images.\n",
        "    \n",
        "    #Create empty lists to populate gen and disc losses. \n",
        "    g_losses = []\n",
        "    d_losses = []\n",
        "    \n",
        "    #Enumerate training over batches. \n",
        "    for b in tqdm(range(len(train_hr_batches))):\n",
        "        lr_imgs = train_lr_batches[b] #Fetch a batch of LR images for training\n",
        "        hr_imgs = train_hr_batches[b] #Fetch a batch of HR images for training\n",
        "        \n",
        "        fake_imgs = generator.predict_on_batch(lr_imgs) #Fake images\n",
        "        \n",
        "        #First, train the discriminator on fake and real HR images. \n",
        "        discriminator.trainable = True\n",
        "        d_loss_gen = discriminator.train_on_batch(fake_imgs, fake_label)\n",
        "        d_loss_real = discriminator.train_on_batch(hr_imgs, real_label)\n",
        "        \n",
        "        #Now, train the generator by fixing discriminator as non-trainable\n",
        "        discriminator.trainable = False\n",
        "        \n",
        "        #Average the discriminator loss, just for reporting purposes. \n",
        "        d_loss = 0.5 * np.add(d_loss_gen, d_loss_real) \n",
        "        \n",
        "        #Extract VGG features, to be used towards calculating loss\n",
        "        image_features = vgg.predict(hr_imgs)\n",
        "     \n",
        "        #Train the generator via GAN. \n",
        "        #Remember that we have 2 losses, adversarial loss and content (VGG) loss\n",
        "        g_loss, _, _ = gan_model.train_on_batch([lr_imgs, hr_imgs], [real_label, image_features])\n",
        "        \n",
        "        #Save losses to a list so we can average and report. \n",
        "        d_losses.append(d_loss)\n",
        "        g_losses.append(g_loss)\n",
        "        \n",
        "    #Convert the list of losses to an array to make it easy to average    \n",
        "    g_losses = np.array(g_losses)\n",
        "    d_losses = np.array(d_losses)\n",
        "    \n",
        "    #Calculate the average losses for generator and discriminator\n",
        "    g_loss = np.sum(g_losses, axis=0) / len(g_losses)\n",
        "    d_loss = np.sum(d_losses, axis=0) / len(d_losses)\n",
        "    \n",
        "    #Report the progress during training. \n",
        "    print(\"epoch:\", e+1 ,\"g_loss:\", g_loss, \"d_loss:\", d_loss)\n",
        "\n",
        "    if (e+1) % 10 == 0: #Change the frequency for model saving, if needed\n",
        "        #Save the generator after every n epochs (Usually 10 epochs)\n",
        "        generator.save(\"gen_e_\"+ str(e+1) +\".h5\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## checking the generator output"
      ],
      "metadata": {
        "id": "5WTq5yA1X1d7"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pDyuMUArSQPP",
        "outputId": "614cb9ee-a290-4c17-c131-32c9a87396b2"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "from keras.models import load_model\n",
        "from numpy.random import randint\n",
        "\n",
        "[X1, X2] = [X_valid, y_valid]\n",
        "ix = randint(0, len(X1), 1)\n",
        "src_image, tar_image = X1[ix], X2[ix]\n",
        "gen_image = generator.predict(src_image)\n",
        "\n",
        "plt.figure(figsize=(16, 8))\n",
        "plt.subplot(231)\n",
        "plt.title('LR Image')\n",
        "plt.imshow(lri)\n",
        "plt.subplot(232)\n",
        "plt.title('Superresolution')\n",
        "plt.imshow(cv2.cvtColor(gen_image[0,:,:,:], cv2.COLOR_BGR2GRAY),cmap = 'gray')\n",
        "plt.subplot(233)\n",
        "plt.title('Orig. HR image')\n",
        "plt.imshow(hri)\n",
        "\n",
        "plt.show()\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "Superresolution.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.12"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}