UnderGraduate_Project.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "UnderGraduate_Project.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0WIhqrzAFMqT",
        "outputId": "ce97a2df-45fd-4554-a68c-8913714bd4c6"
      },
      "source": [
        "!nvidia-smi"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mon May 24 03:45:47 2021       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 465.19.01    Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   36C    P8     9W /  70W |      0MiB / 15109MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S5PMYUZu3NP2"
      },
      "source": [
        "import numpy as np\n",
        "from tensorflow.keras import layers\n",
        "from keras.preprocessing import image\n",
        "import tensorflow as tf \n",
        "from keras.models import Model,load_model\n",
        "from keras.utils import to_categorical\n",
        "import os\n",
        "import keras\n",
        "from keras.callbacks import ModelCheckpoint,ReduceLROnPlateau\n",
        "from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D, BatchNormalization\n",
        "from keras.callbacks import ModelCheckpoint,ReduceLROnPlateau, EarlyStopping\n",
        "from keras.layers import Lambda\n",
        "from keras.optimizers import Adam, SGD\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras import backend as K\n",
        "import random\n",
        "from PIL import Image \n",
        "from random import shuffle\n",
        "%load_ext tensorboard\n",
        "import datetime\n"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z5PwooDorNkc"
      },
      "source": [
        "**Prepare**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qfOiNyPJFcFY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a1c5d591-30fd-4fd4-b07a-f7fce3e2d4d6"
      },
      "source": [
        "import numpy as np\n",
        "from numpy import genfromtxt\n",
        "from numpy import asarray\n",
        "import math\n",
        "import copy\n",
        "import os\n",
        "from PIL import Image \n",
        "import cv2\n",
        "import shutil\n",
        "\n",
        "patch_size = 64 #input = 64x64\n",
        "label_size = 128 #output = 128x128\n",
        "\n",
        "#get RGGB bayer image\n",
        "def bayer_reverse(img):\n",
        "    height,width,c = img.shape;\n",
        "    tmp = np.zeros([height,width]);\n",
        "    for i in range( height ):\n",
        "        for j in range( width ):\n",
        "            if i % 2 == 0 :\n",
        "                if j % 2 == 0:\n",
        "                    tmp[i][j] = img[i][j][0];#R\n",
        "                else:\n",
        "                    tmp[i][j] = img[i][j][1];#G\n",
        "            else :\n",
        "                if j % 2 == 0:\n",
        "                    tmp[i][j] = img[i][j][1];#G\n",
        "                else:\n",
        "                    tmp[i][j] = img[i][j][2];#B\n",
        "\n",
        "    return tmp;\n",
        "\n",
        "#split image to prepare the train set\n",
        "def split(img,name,dir_path):\n",
        "    height,width,c = img.shape;    \n",
        "    count = 0;\n",
        "\n",
        "    for i in range(0, height, 30):\n",
        "        for j in range(0, width, 30):\n",
        "            if( i + label_size < height and j + label_size < width ):                \n",
        "                label = np.zeros([label_size,label_size,3])                \n",
        "                tmp2  = np.zeros([label_size,label_size,3])                \n",
        "                tmp2 = img[ i : i + label_size, j : j + label_size,:]\n",
        "                label_img = Image.fromarray(tmp2)\n",
        "\n",
        "                label[:,:,0] = tmp2[:,:,2]\n",
        "                label[:,:,1] = tmp2[:,:,1]\n",
        "                label[:,:,2] = tmp2[:,:,0]\n",
        "                label_path = os.path.join(dir_path,'label/'+name.split('.')[0] +'_'+str(count)+'.png')                                                \n",
        "                cv2.imwrite(label_path,label)\n",
        "\n",
        "                \n",
        "                zoom = label_img.resize((patch_size,patch_size), Image.BICUBIC)                 \n",
        "                tmp3 = np.zeros([patch_size,patch_size])\n",
        "                patch = np.zeros([patch_size,patch_size])\n",
        "\n",
        "                zoom2 = np.array(zoom)                \n",
        "                patch = bayer_reverse(zoom2)                \n",
        "                                           \n",
        "                patch_path = os.path.join(dir_path,'patch/'+name.split('.')[0] +'_'+str(count)+'.png')                          \n",
        "                cv2.imwrite(patch_path, patch)\n",
        "\n",
        "                count = count + 1    \n",
        "\n",
        "def main():\n",
        "    path = 'drive/My Drive/Colab Notebooks/undergraduate_project'\n",
        "\n",
        "    if os.path.exists(os.path.join(path,'patch')):\n",
        "      shutil.rmtree(os.path.join(path,'patch'))\n",
        "    os.makedirs(os.path.join(path,'patch'))\n",
        "\n",
        "    if os.path.exists(os.path.join(path,'label')):\n",
        "      shutil.rmtree(os.path.join(path,'label'))\n",
        "    os.makedirs(os.path.join(path,'label'))\n",
        "\n",
        "\n",
        "    dataset_path = os.path.join(path,'BSD200')\n",
        "    entries = os.listdir(dataset_path)\n",
        "    for entry in entries:\n",
        "        print(entry)\n",
        "        img_path = dataset_path + '/' + entry\n",
        "        img = Image.open(img_path)\n",
        "        img = np.array(img)   \n",
        "        split(img,entry,path)\n",
        "\n",
        "\n",
        "\n",
        "if __name__ == '__main__':\n",
        "    main()\n"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "geAXuo4nrFWD"
      },
      "source": [
        "**Training**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h_SV9g9WWp9d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "17b30a85-88f8-4206-d26f-48c198f61c76"
      },
      "source": [
        "import numpy as np\n",
        "from tensorflow.keras import layers\n",
        "from keras.preprocessing import image\n",
        "import tensorflow as tf \n",
        "from keras.models import Model,load_model\n",
        "from keras.utils import to_categorical\n",
        "import os\n",
        "import keras\n",
        "from keras.callbacks import ModelCheckpoint,ReduceLROnPlateau, EarlyStopping\n",
        "from keras.layers import Lambda\n",
        "from keras.optimizers import Adam\n",
        "from keras import backend as K\n",
        "import random\n",
        "from PIL import Image \n",
        "from random import shuffle\n",
        "\n",
        "train_image = []\n",
        "train_label = []\n",
        "patch_size = 64 \n",
        "label_size = 128\n",
        "\n",
        "dir_path = 'drive/My Drive/Colab Notebooks/undergraduate_project'\n",
        "patch_path = os.path.join(dir_path,'patch')\n",
        "entries = os.listdir(patch_path)\n",
        "for entry in entries:  \n",
        "  im = Image.open(patch_path+'/'+entry)\n",
        "  img = image.img_to_array(im)\n",
        "  # print(img.shape)\n",
        "  # img = img/255.\n",
        "  train_image.append(img)\n",
        "train_image= np.stack(train_image)\n",
        "\n",
        "print(train_image.shape)\n",
        "  \n",
        "\n",
        "label_path = os.path.join(dir_path,'label')\n",
        "entries = os.listdir(label_path)\n",
        "for entry in entries:  \n",
        "  im = Image.open(label_path+'/'+entry)\n",
        "  img = image.img_to_array(im)\n",
        "  # img = img/255.\n",
        "  train_label.append(img)\n",
        "train_label = np.stack(train_label)\n",
        "\n",
        "print(train_label.shape)\n",
        "\n",
        "\n",
        "\n",
        "index = [i for i in range(train_image.shape[0])]\n",
        "shuffle(index)\n",
        "train_image = train_image[index,:,:,:];\n",
        "train_label = train_label[index,:,:,:];\n",
        "\n",
        "# np.save('drive/My Drive/Colab Notebooks/undergraduate_project/train_image.npy', train_image)\n",
        "# np.save('drive/My Drive/Colab Notebooks/undergraduate_project/train_label.npy', train_label)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(16800, 64, 64, 1)\n",
            "(16800, 128, 128, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8ClU51qwkcz3"
      },
      "source": [
        "############################# Model Structure ################################################\n",
        "def create_model():\n",
        "  inputs = keras.Input(shape=(None,None,1))\n",
        "\n",
        "  ##Subpixel Construction\n",
        "  sub_layer_2 = Lambda(lambda x:tf.nn.space_to_depth(x,2)) \n",
        "  init = sub_layer_2(inputs=inputs)\n",
        "\n",
        "\n",
        "\n",
        "  ##Learning Residual (DCNN)\n",
        "  ####Conv 3x3x64x64 + PReLu\n",
        "  x = keras.layers.Conv2D(filters = 64, #feature map number\n",
        "                     kernel_size = 3, \n",
        "                     strides = 1,  # 2\n",
        "                     padding = 'same', \n",
        "                     activation = 'relu',\n",
        "                     input_shape = (None,None,1))(init)\n",
        "  x = keras.layers.Conv2D(filters = 64, #feature map number\n",
        "                     kernel_size = 3, \n",
        "                     strides = 1,  # 2\n",
        "                     padding = 'same', \n",
        "                     activation = 'relu',\n",
        "                     input_shape = (None,None,64))(x)\n",
        "  x = keras.layers.Conv2D(filters = 64, #feature map number\n",
        "                     kernel_size = 3, \n",
        "                     strides = 1,  # 2\n",
        "                     padding = 'same', \n",
        "                     activation = 'relu',\n",
        "                     input_shape = (None,None,64))(x)\n",
        "  # x = keras.layers.BatchNormalization()(x)  \n",
        "  # x = keras.layers.PReLU(alpha_initializer='zeros', alpha_regularizer=None, alpha_constraint=None, shared_axes=[1,2])(x)\n",
        "\n",
        "  ####Residual Block\n",
        "  for i in range(6):\n",
        "    start = x\n",
        "    Conv1 = keras.layers.Conv2D(filters=64,\n",
        "                        kernel_size = 1, \n",
        "                        strides = 1,  # 2\n",
        "                        padding = 'same',                                             \n",
        "                        input_shape = (None,None,64))(start)\n",
        "    Conv1 = keras.layers.LeakyReLU()(Conv1)\n",
        "    # Conv1_BN = keras.layers.BatchNormalization()(Conv1)\n",
        "    # Conv1_BN = Dropout(0.5)(Conv1_BN)\n",
        "    \n",
        "    # PReLu = keras.layers.PReLU(alpha_initializer='zeros', alpha_regularizer=None, alpha_constraint=None, shared_axes=[1,2])(Conv1)\n",
        "    Conv2 = keras.layers.Conv2D(filters=64,\n",
        "                        kernel_size = 3, \n",
        "                        strides = 1,  # 2\n",
        "                        padding = 'same',                                                \n",
        "                        input_shape = (None,None,64))(Conv1)\n",
        "    Conv2 = keras.layers.LeakyReLU()(Conv2)\n",
        "    # Conv2_BN = keras.layers.BatchNormalization()(Conv2)\n",
        "    # Conv2_BN = Dropout(0.5)(Conv2_BN)                        \n",
        "    # PReLu = keras.layers.PReLU(alpha_initializer='zeros', alpha_regularizer=None, alpha_constraint=None, shared_axes=[1,2])(Conv2)\n",
        "    Conv3 = keras.layers.Conv2D(filters=64,\n",
        "                        kernel_size = 1, \n",
        "                        strides = 1,  # 2\n",
        "                        padding = 'same',         \n",
        "                        input_shape = (None,None,64))(Conv2)\n",
        "    # Conv3 = keras.layers.LeakyReLU()(Conv3)\n",
        "    \n",
        "    # Concatenate\n",
        "    x = keras.layers.Add()([Conv3,x])\n",
        "    \n",
        "  ####Conv 3x3x64x64 + PReLu\n",
        "  x = keras.layers.Conv2D(filters = 64,\n",
        "                     kernel_size = 3, \n",
        "                     strides = 1,  # 2\n",
        "                     padding = 'same',\n",
        "                     activation = 'relu',\n",
        "                     input_shape = (None,None,64))(x)\n",
        "  x = keras.layers.Conv2D(filters = 64,\n",
        "                     kernel_size = 3, \n",
        "                     strides = 1,  # 2\n",
        "                     padding = 'same',\n",
        "                     activation = 'relu',\n",
        "                     input_shape = (None,None,64))(x)\n",
        "  x = keras.layers.Conv2D(filters = 64,\n",
        "                     kernel_size = 3, \n",
        "                     strides = 1,  # 2\n",
        "                     padding = 'same',\n",
        "                     activation = 'relu',\n",
        "                     input_shape = (None,None,64))(x)\n",
        "  # x = keras.layers.BatchNormalization()(x)                                                                 \n",
        "  # x = keras.layers.PReLU(alpha_initializer='zeros', alpha_regularizer=None, alpha_constraint=None, shared_axes=[1,2])(x)\n",
        "\n",
        "\n",
        "  ####Conv 3x3x64x48\n",
        "  x = keras.layers.Conv2D(filters = 64,\n",
        "                     kernel_size = 3, \n",
        "                     strides = 1,  \n",
        "                     padding = 'same',\n",
        "                     activation = 'relu',                   \n",
        "                     input_shape = (None,None,64))(x)\n",
        "  x = keras.layers.Conv2D(filters = 64,\n",
        "                     kernel_size = 3, \n",
        "                     strides = 1,  \n",
        "                     padding = 'same',\n",
        "                     activation = 'relu',                   \n",
        "                     input_shape = (None,None,64))(x)\n",
        "  x = keras.layers.Conv2D(filters = 48,\n",
        "                     kernel_size = 3, \n",
        "                     strides = 1,  \n",
        "                     padding = 'same',\n",
        "                     activation = 'relu',                   \n",
        "                     input_shape = (None,None,64))(x)\n",
        "  x = keras.layers.BatchNormalization()(x)\n",
        "\n",
        "  ###########Learning Residual (DCNN)############\n",
        "  \n",
        "\n",
        "  ##Recovery From Subpixel\n",
        "  sub_layer = Lambda(lambda x:tf.nn.depth_to_space(x,4)) \n",
        "  Residual_Output = sub_layer(inputs=x)\n",
        "  \n",
        "\n",
        "  ##Initial Prediction\n",
        "  R = Lambda(lambda x: x[:,:,:,0])(init)\n",
        "  G = Lambda(lambda x: x[:,:,:,1:3])(init)\n",
        "  G = Lambda(lambda x: K.mean(x, axis=3))(G)\n",
        "  B = Lambda(lambda x: x[:,:,:,3])(init)\n",
        "  # print(init.shape)\n",
        "  # print(R.shape)\n",
        "  # print(G.shape)\n",
        "  # print(B.shape)\n",
        "  R = Lambda(lambda x: tf.expand_dims(x, -1))(R)\n",
        "  G = Lambda(lambda x: tf.expand_dims(x, -1))(G)\n",
        "  B = Lambda(lambda x: tf.expand_dims(x, -1))(B)\n",
        "  \n",
        "  #rgb = tf.keras.backend.stack((R, G,B),axis =  3)\n",
        "  # print(R.shape)\n",
        "  rg = keras.layers.Concatenate(axis = 3)([R , G])\n",
        "  rgb = keras.layers.Concatenate(axis = 3)([rg,B])\n",
        "  # print(rgb.shape)\n",
        "  Coarse_Output = keras.layers.UpSampling2D(size=(4, 4), interpolation=\"bilinear\")(rgb) #size=4 , from W/2,H/2 ---> 2W,2H\n",
        "\n",
        "\n",
        "  ## + \n",
        "  outputs = keras.layers.Add()([Residual_Output,Coarse_Output])\n",
        "  #outputs = Residual_Output\n",
        " \n",
        "  model = keras.Model(inputs=inputs, outputs=outputs, name=\"JDMSR_model\")  \n",
        "  return model"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9V3myfmR3UaX"
      },
      "source": [
        "batch_size = 32\n",
        "lr = 0.001\n",
        "e_num = 50\n",
        "dir_path = 'drive/My Drive/Colab Notebooks/undergraduate_project'"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bsNBcy5C1Sgy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "befb7e3f-aced-47e3-dddf-629cbb40fbb1"
      },
      "source": [
        "model = create_model()\n",
        "model.summary()\n",
        "\n",
        "sgd = SGD(lr=lr, decay=1e-6, momentum=0.9, nesterov=True, clipnorm=1.0)\n",
        "\n",
        "model.compile(optimizer=Adam(), loss = 'mean_squared_error', metrics = ['accuracy'])\n",
        "\n",
        "checkpoint = ModelCheckpoint(os.path.join(dir_path,'model.hdf5'),verbose=1, monitor='loss', save_best_only=True, save_weights_only=True)\n",
        "rrp = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=2, verbose=1, mode='min', min_lr=0.000002)\n",
        "\n",
        "early_stopping = EarlyStopping(monitor='loss', patience=10, verbose=1, mode='auto')\n",
        "\n",
        "history = model.fit(train_image, train_label, epochs=e_num, batch_size=batch_size,verbose=1,validation_split = 0.1,callbacks=[checkpoint, rrp],shuffle = True)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(None, None, None, 4)\n",
            "(None, None, None)\n",
            "(None, None, None)\n",
            "(None, None, None)\n",
            "(None, None, None, 1)\n",
            "(None, None, None, 3)\n",
            "Model: \"JDMSR_model\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_4 (InputLayer)            [(None, None, None,  0                                            \n",
            "__________________________________________________________________________________________________\n",
            "lambda_27 (Lambda)              (None, None, None, 4 0           input_4[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_81 (Conv2D)              (None, None, None, 6 2368        lambda_27[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_82 (Conv2D)              (None, None, None, 6 36928       conv2d_81[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_83 (Conv2D)              (None, None, None, 6 36928       conv2d_82[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_84 (Conv2D)              (None, None, None, 6 4160        conv2d_83[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_36 (LeakyReLU)      (None, None, None, 6 0           conv2d_84[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_85 (Conv2D)              (None, None, None, 6 36928       leaky_re_lu_36[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_37 (LeakyReLU)      (None, None, None, 6 0           conv2d_85[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_86 (Conv2D)              (None, None, None, 6 4160        leaky_re_lu_37[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_21 (Add)                    (None, None, None, 6 0           conv2d_86[0][0]                  \n",
            "                                                                 conv2d_83[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_87 (Conv2D)              (None, None, None, 6 4160        add_21[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_38 (LeakyReLU)      (None, None, None, 6 0           conv2d_87[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_88 (Conv2D)              (None, None, None, 6 36928       leaky_re_lu_38[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_39 (LeakyReLU)      (None, None, None, 6 0           conv2d_88[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_89 (Conv2D)              (None, None, None, 6 4160        leaky_re_lu_39[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_22 (Add)                    (None, None, None, 6 0           conv2d_89[0][0]                  \n",
            "                                                                 add_21[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_90 (Conv2D)              (None, None, None, 6 4160        add_22[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_40 (LeakyReLU)      (None, None, None, 6 0           conv2d_90[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_91 (Conv2D)              (None, None, None, 6 36928       leaky_re_lu_40[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_41 (LeakyReLU)      (None, None, None, 6 0           conv2d_91[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_92 (Conv2D)              (None, None, None, 6 4160        leaky_re_lu_41[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_23 (Add)                    (None, None, None, 6 0           conv2d_92[0][0]                  \n",
            "                                                                 add_22[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_93 (Conv2D)              (None, None, None, 6 4160        add_23[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_42 (LeakyReLU)      (None, None, None, 6 0           conv2d_93[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_94 (Conv2D)              (None, None, None, 6 36928       leaky_re_lu_42[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_43 (LeakyReLU)      (None, None, None, 6 0           conv2d_94[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_95 (Conv2D)              (None, None, None, 6 4160        leaky_re_lu_43[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_24 (Add)                    (None, None, None, 6 0           conv2d_95[0][0]                  \n",
            "                                                                 add_23[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_96 (Conv2D)              (None, None, None, 6 4160        add_24[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_44 (LeakyReLU)      (None, None, None, 6 0           conv2d_96[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_97 (Conv2D)              (None, None, None, 6 36928       leaky_re_lu_44[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_45 (LeakyReLU)      (None, None, None, 6 0           conv2d_97[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_98 (Conv2D)              (None, None, None, 6 4160        leaky_re_lu_45[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_25 (Add)                    (None, None, None, 6 0           conv2d_98[0][0]                  \n",
            "                                                                 add_24[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_99 (Conv2D)              (None, None, None, 6 4160        add_25[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_46 (LeakyReLU)      (None, None, None, 6 0           conv2d_99[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_100 (Conv2D)             (None, None, None, 6 36928       leaky_re_lu_46[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_47 (LeakyReLU)      (None, None, None, 6 0           conv2d_100[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_101 (Conv2D)             (None, None, None, 6 4160        leaky_re_lu_47[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_26 (Add)                    (None, None, None, 6 0           conv2d_101[0][0]                 \n",
            "                                                                 add_25[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_102 (Conv2D)             (None, None, None, 6 36928       add_26[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_103 (Conv2D)             (None, None, None, 6 36928       conv2d_102[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_104 (Conv2D)             (None, None, None, 6 36928       conv2d_103[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "lambda_30 (Lambda)              (None, None, None, 2 0           lambda_27[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_105 (Conv2D)             (None, None, None, 6 36928       conv2d_104[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "lambda_29 (Lambda)              (None, None, None)   0           lambda_27[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "lambda_31 (Lambda)              (None, None, None)   0           lambda_30[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_106 (Conv2D)             (None, None, None, 6 36928       conv2d_105[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "lambda_33 (Lambda)              (None, None, None, 1 0           lambda_29[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "lambda_34 (Lambda)              (None, None, None, 1 0           lambda_31[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "lambda_32 (Lambda)              (None, None, None)   0           lambda_27[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_107 (Conv2D)             (None, None, None, 4 27696       conv2d_106[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_6 (Concatenate)     (None, None, None, 2 0           lambda_33[0][0]                  \n",
            "                                                                 lambda_34[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "lambda_35 (Lambda)              (None, None, None, 1 0           lambda_32[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_3 (BatchNor (None, None, None, 4 192         conv2d_107[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_7 (Concatenate)     (None, None, None, 3 0           concatenate_6[0][0]              \n",
            "                                                                 lambda_35[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "lambda_28 (Lambda)              (None, None, None, 3 0           batch_normalization_3[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "up_sampling2d_3 (UpSampling2D)  (None, None, None, 3 0           concatenate_7[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "add_27 (Add)                    (None, None, None, 3 0           lambda_28[0][0]                  \n",
            "                                                                 up_sampling2d_3[0][0]            \n",
            "==================================================================================================\n",
            "Total params: 560,240\n",
            "Trainable params: 560,144\n",
            "Non-trainable params: 96\n",
            "__________________________________________________________________________________________________\n",
            "Epoch 1/50\n",
            "473/473 [==============================] - 22s 43ms/step - loss: 360.3086 - accuracy: 0.8501 - val_loss: 331.7803 - val_accuracy: 0.8566\n",
            "\n",
            "Epoch 00001: loss improved from inf to 351.48135, saving model to drive/My Drive/Colab Notebooks/undergraduate_project/model.hdf5\n",
            "Epoch 2/50\n",
            "473/473 [==============================] - 20s 43ms/step - loss: 337.9326 - accuracy: 0.8536 - val_loss: 316.1272 - val_accuracy: 0.8573\n",
            "\n",
            "Epoch 00002: loss improved from 351.48135 to 332.83392, saving model to drive/My Drive/Colab Notebooks/undergraduate_project/model.hdf5\n",
            "Epoch 3/50\n",
            "473/473 [==============================] - 20s 43ms/step - loss: 318.5440 - accuracy: 0.8554 - val_loss: 300.4590 - val_accuracy: 0.8474\n",
            "\n",
            "Epoch 00003: loss improved from 332.83392 to 323.16632, saving model to drive/My Drive/Colab Notebooks/undergraduate_project/model.hdf5\n",
            "Epoch 4/50\n",
            "473/473 [==============================] - 20s 43ms/step - loss: 324.4653 - accuracy: 0.8554 - val_loss: 299.9597 - val_accuracy: 0.8641\n",
            "\n",
            "Epoch 00004: loss improved from 323.16632 to 313.94339, saving model to drive/My Drive/Colab Notebooks/undergraduate_project/model.hdf5\n",
            "Epoch 5/50\n",
            "473/473 [==============================] - 21s 44ms/step - loss: 302.4203 - accuracy: 0.8590 - val_loss: 288.3454 - val_accuracy: 0.8624\n",
            "\n",
            "Epoch 00005: loss improved from 313.94339 to 300.23679, saving model to drive/My Drive/Colab Notebooks/undergraduate_project/model.hdf5\n",
            "Epoch 6/50\n",
            "473/473 [==============================] - 21s 44ms/step - loss: 291.9516 - accuracy: 0.8595 - val_loss: 279.7542 - val_accuracy: 0.8600\n",
            "\n",
            "Epoch 00006: loss improved from 300.23679 to 290.05161, saving model to drive/My Drive/Colab Notebooks/undergraduate_project/model.hdf5\n",
            "Epoch 7/50\n",
            "473/473 [==============================] - 21s 44ms/step - loss: 279.5919 - accuracy: 0.8634 - val_loss: 265.6154 - val_accuracy: 0.8700\n",
            "\n",
            "Epoch 00007: loss improved from 290.05161 to 280.35379, saving model to drive/My Drive/Colab Notebooks/undergraduate_project/model.hdf5\n",
            "Epoch 8/50\n",
            "473/473 [==============================] - 21s 44ms/step - loss: 273.2975 - accuracy: 0.8654 - val_loss: 258.1857 - val_accuracy: 0.8640\n",
            "\n",
            "Epoch 00008: loss improved from 280.35379 to 271.03796, saving model to drive/My Drive/Colab Notebooks/undergraduate_project/model.hdf5\n",
            "Epoch 9/50\n",
            "473/473 [==============================] - 21s 44ms/step - loss: 267.5123 - accuracy: 0.8645 - val_loss: 250.7708 - val_accuracy: 0.8671\n",
            "\n",
            "Epoch 00009: loss improved from 271.03796 to 262.12027, saving model to drive/My Drive/Colab Notebooks/undergraduate_project/model.hdf5\n",
            "Epoch 10/50\n",
            "473/473 [==============================] - 21s 44ms/step - loss: 252.6419 - accuracy: 0.8673 - val_loss: 240.2674 - val_accuracy: 0.8704\n",
            "\n",
            "Epoch 00010: loss improved from 262.12027 to 253.64273, saving model to drive/My Drive/Colab Notebooks/undergraduate_project/model.hdf5\n",
            "Epoch 11/50\n",
            "473/473 [==============================] - 21s 44ms/step - loss: 248.1594 - accuracy: 0.8683 - val_loss: 234.1017 - val_accuracy: 0.8723\n",
            "\n",
            "Epoch 00011: loss improved from 253.64273 to 245.31236, saving model to drive/My Drive/Colab Notebooks/undergraduate_project/model.hdf5\n",
            "Epoch 12/50\n",
            "473/473 [==============================] - 21s 44ms/step - loss: 240.5745 - accuracy: 0.8696 - val_loss: 232.1732 - val_accuracy: 0.8642\n",
            "\n",
            "Epoch 00012: loss improved from 245.31236 to 237.45015, saving model to drive/My Drive/Colab Notebooks/undergraduate_project/model.hdf5\n",
            "Epoch 13/50\n",
            "473/473 [==============================] - 21s 44ms/step - loss: 229.9614 - accuracy: 0.8711 - val_loss: 222.3571 - val_accuracy: 0.8759\n",
            "\n",
            "Epoch 00013: loss improved from 237.45015 to 229.90886, saving model to drive/My Drive/Colab Notebooks/undergraduate_project/model.hdf5\n",
            "Epoch 14/50\n",
            "473/473 [==============================] - 21s 44ms/step - loss: 223.1509 - accuracy: 0.8734 - val_loss: 211.3189 - val_accuracy: 0.8759\n",
            "\n",
            "Epoch 00014: loss improved from 229.90886 to 222.70676, saving model to drive/My Drive/Colab Notebooks/undergraduate_project/model.hdf5\n",
            "Epoch 15/50\n",
            "473/473 [==============================] - 21s 44ms/step - loss: 220.4425 - accuracy: 0.8734 - val_loss: 209.4184 - val_accuracy: 0.8677\n",
            "\n",
            "Epoch 00015: loss improved from 222.70676 to 215.80957, saving model to drive/My Drive/Colab Notebooks/undergraduate_project/model.hdf5\n",
            "Epoch 16/50\n",
            "473/473 [==============================] - 21s 44ms/step - loss: 211.4759 - accuracy: 0.8764 - val_loss: 200.3584 - val_accuracy: 0.8744\n",
            "\n",
            "Epoch 00016: loss improved from 215.80957 to 209.21703, saving model to drive/My Drive/Colab Notebooks/undergraduate_project/model.hdf5\n",
            "Epoch 17/50\n",
            "473/473 [==============================] - 21s 44ms/step - loss: 205.2322 - accuracy: 0.8750 - val_loss: 192.3725 - val_accuracy: 0.8827\n",
            "\n",
            "Epoch 00017: loss improved from 209.21703 to 203.15285, saving model to drive/My Drive/Colab Notebooks/undergraduate_project/model.hdf5\n",
            "Epoch 18/50\n",
            "473/473 [==============================] - 21s 44ms/step - loss: 198.0108 - accuracy: 0.8778 - val_loss: 190.1053 - val_accuracy: 0.8765\n",
            "\n",
            "Epoch 00018: loss improved from 203.15285 to 197.06424, saving model to drive/My Drive/Colab Notebooks/undergraduate_project/model.hdf5\n",
            "Epoch 19/50\n",
            "473/473 [==============================] - 21s 44ms/step - loss: 193.6460 - accuracy: 0.8780 - val_loss: 180.8719 - val_accuracy: 0.8842\n",
            "\n",
            "Epoch 00019: loss improved from 197.06424 to 191.71815, saving model to drive/My Drive/Colab Notebooks/undergraduate_project/model.hdf5\n",
            "Epoch 20/50\n",
            "473/473 [==============================] - 21s 44ms/step - loss: 184.0026 - accuracy: 0.8806 - val_loss: 186.0751 - val_accuracy: 0.8759\n",
            "\n",
            "Epoch 00020: loss improved from 191.71815 to 186.55185, saving model to drive/My Drive/Colab Notebooks/undergraduate_project/model.hdf5\n",
            "Epoch 21/50\n",
            "473/473 [==============================] - 21s 44ms/step - loss: 180.4915 - accuracy: 0.8802 - val_loss: 175.1640 - val_accuracy: 0.8826\n",
            "\n",
            "Epoch 00021: loss improved from 186.55185 to 181.82996, saving model to drive/My Drive/Colab Notebooks/undergraduate_project/model.hdf5\n",
            "Epoch 22/50\n",
            "473/473 [==============================] - 21s 44ms/step - loss: 177.0190 - accuracy: 0.8811 - val_loss: 171.9865 - val_accuracy: 0.8763\n",
            "\n",
            "Epoch 00022: loss improved from 181.82996 to 177.30899, saving model to drive/My Drive/Colab Notebooks/undergraduate_project/model.hdf5\n",
            "Epoch 23/50\n",
            "473/473 [==============================] - 21s 44ms/step - loss: 175.6186 - accuracy: 0.8813 - val_loss: 174.3331 - val_accuracy: 0.8799\n",
            "\n",
            "Epoch 00023: loss improved from 177.30899 to 173.00632, saving model to drive/My Drive/Colab Notebooks/undergraduate_project/model.hdf5\n",
            "Epoch 24/50\n",
            "473/473 [==============================] - 21s 44ms/step - loss: 170.0455 - accuracy: 0.8822 - val_loss: 169.4879 - val_accuracy: 0.8850\n",
            "\n",
            "Epoch 00024: loss improved from 173.00632 to 169.08142, saving model to drive/My Drive/Colab Notebooks/undergraduate_project/model.hdf5\n",
            "Epoch 25/50\n",
            "473/473 [==============================] - 21s 44ms/step - loss: 164.3646 - accuracy: 0.8842 - val_loss: 164.4122 - val_accuracy: 0.8829\n",
            "\n",
            "Epoch 00025: loss improved from 169.08142 to 165.39799, saving model to drive/My Drive/Colab Notebooks/undergraduate_project/model.hdf5\n",
            "Epoch 26/50\n",
            "473/473 [==============================] - 21s 44ms/step - loss: 164.3681 - accuracy: 0.8834 - val_loss: 156.8237 - val_accuracy: 0.8723\n",
            "\n",
            "Epoch 00026: loss improved from 165.39799 to 162.17035, saving model to drive/My Drive/Colab Notebooks/undergraduate_project/model.hdf5\n",
            "Epoch 27/50\n",
            "473/473 [==============================] - 21s 44ms/step - loss: 158.6748 - accuracy: 0.8835 - val_loss: 156.9992 - val_accuracy: 0.8856\n",
            "\n",
            "Epoch 00027: loss improved from 162.17035 to 159.22566, saving model to drive/My Drive/Colab Notebooks/undergraduate_project/model.hdf5\n",
            "Epoch 28/50\n",
            "473/473 [==============================] - 21s 44ms/step - loss: 157.0533 - accuracy: 0.8851 - val_loss: 153.1727 - val_accuracy: 0.8840\n",
            "\n",
            "Epoch 00028: loss improved from 159.22566 to 156.15144, saving model to drive/My Drive/Colab Notebooks/undergraduate_project/model.hdf5\n",
            "Epoch 29/50\n",
            "473/473 [==============================] - 21s 44ms/step - loss: 155.9867 - accuracy: 0.8849 - val_loss: 174.7640 - val_accuracy: 0.8712\n",
            "\n",
            "Epoch 00029: loss improved from 156.15144 to 153.79781, saving model to drive/My Drive/Colab Notebooks/undergraduate_project/model.hdf5\n",
            "Epoch 30/50\n",
            "473/473 [==============================] - 21s 44ms/step - loss: 151.0975 - accuracy: 0.8858 - val_loss: 162.5229 - val_accuracy: 0.8814\n",
            "\n",
            "Epoch 00030: loss improved from 153.79781 to 151.47592, saving model to drive/My Drive/Colab Notebooks/undergraduate_project/model.hdf5\n",
            "\n",
            "Epoch 00030: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
            "Epoch 31/50\n",
            "473/473 [==============================] - 21s 44ms/step - loss: 149.7582 - accuracy: 0.8857 - val_loss: 145.4221 - val_accuracy: 0.8885\n",
            "\n",
            "Epoch 00031: loss improved from 151.47592 to 148.00504, saving model to drive/My Drive/Colab Notebooks/undergraduate_project/model.hdf5\n",
            "Epoch 32/50\n",
            "473/473 [==============================] - 21s 44ms/step - loss: 147.5485 - accuracy: 0.8878 - val_loss: 144.3963 - val_accuracy: 0.8898\n",
            "\n",
            "Epoch 00032: loss improved from 148.00504 to 147.40195, saving model to drive/My Drive/Colab Notebooks/undergraduate_project/model.hdf5\n",
            "Epoch 33/50\n",
            "473/473 [==============================] - 21s 44ms/step - loss: 146.1959 - accuracy: 0.8877 - val_loss: 144.4821 - val_accuracy: 0.8890\n",
            "\n",
            "Epoch 00033: loss improved from 147.40195 to 146.91032, saving model to drive/My Drive/Colab Notebooks/undergraduate_project/model.hdf5\n",
            "Epoch 34/50\n",
            "473/473 [==============================] - 21s 44ms/step - loss: 145.5408 - accuracy: 0.8875 - val_loss: 144.2033 - val_accuracy: 0.8926\n",
            "\n",
            "Epoch 00034: loss improved from 146.91032 to 146.62445, saving model to drive/My Drive/Colab Notebooks/undergraduate_project/model.hdf5\n",
            "Epoch 35/50\n",
            "473/473 [==============================] - 21s 44ms/step - loss: 146.7371 - accuracy: 0.8882 - val_loss: 143.7904 - val_accuracy: 0.8897\n",
            "\n",
            "Epoch 00035: loss improved from 146.62445 to 146.49678, saving model to drive/My Drive/Colab Notebooks/undergraduate_project/model.hdf5\n",
            "Epoch 36/50\n",
            "473/473 [==============================] - 21s 44ms/step - loss: 146.8480 - accuracy: 0.8865 - val_loss: 142.6587 - val_accuracy: 0.8912\n",
            "\n",
            "Epoch 00036: loss improved from 146.49678 to 146.11626, saving model to drive/My Drive/Colab Notebooks/undergraduate_project/model.hdf5\n",
            "Epoch 37/50\n",
            "473/473 [==============================] - 21s 44ms/step - loss: 143.3960 - accuracy: 0.8883 - val_loss: 144.3209 - val_accuracy: 0.8919\n",
            "\n",
            "Epoch 00037: loss improved from 146.11626 to 145.95982, saving model to drive/My Drive/Colab Notebooks/undergraduate_project/model.hdf5\n",
            "Epoch 38/50\n",
            "473/473 [==============================] - 21s 44ms/step - loss: 147.2725 - accuracy: 0.8861 - val_loss: 143.4070 - val_accuracy: 0.8903\n",
            "\n",
            "Epoch 00038: loss improved from 145.95982 to 145.56067, saving model to drive/My Drive/Colab Notebooks/undergraduate_project/model.hdf5\n",
            "\n",
            "Epoch 00038: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.\n",
            "Epoch 39/50\n",
            "473/473 [==============================] - 21s 44ms/step - loss: 144.0724 - accuracy: 0.8891 - val_loss: 143.0420 - val_accuracy: 0.8906\n",
            "\n",
            "Epoch 00039: loss improved from 145.56067 to 145.20686, saving model to drive/My Drive/Colab Notebooks/undergraduate_project/model.hdf5\n",
            "Epoch 40/50\n",
            "473/473 [==============================] - 21s 44ms/step - loss: 143.2315 - accuracy: 0.8880 - val_loss: 142.2857 - val_accuracy: 0.8912\n",
            "\n",
            "Epoch 00040: loss improved from 145.20686 to 145.03192, saving model to drive/My Drive/Colab Notebooks/undergraduate_project/model.hdf5\n",
            "Epoch 41/50\n",
            "473/473 [==============================] - 21s 44ms/step - loss: 144.6916 - accuracy: 0.8873 - val_loss: 142.9106 - val_accuracy: 0.8917\n",
            "\n",
            "Epoch 00041: loss did not improve from 145.03192\n",
            "Epoch 42/50\n",
            "473/473 [==============================] - 21s 44ms/step - loss: 146.3753 - accuracy: 0.8879 - val_loss: 141.7767 - val_accuracy: 0.8921\n",
            "\n",
            "Epoch 00042: loss improved from 145.03192 to 144.97876, saving model to drive/My Drive/Colab Notebooks/undergraduate_project/model.hdf5\n",
            "Epoch 43/50\n",
            "473/473 [==============================] - 21s 44ms/step - loss: 144.6365 - accuracy: 0.8870 - val_loss: 142.2157 - val_accuracy: 0.8930\n",
            "\n",
            "Epoch 00043: loss did not improve from 144.97876\n",
            "Epoch 44/50\n",
            "473/473 [==============================] - 21s 44ms/step - loss: 145.7680 - accuracy: 0.8876 - val_loss: 142.3558 - val_accuracy: 0.8928\n",
            "\n",
            "Epoch 00044: loss did not improve from 144.97876\n",
            "\n",
            "Epoch 00044: ReduceLROnPlateau reducing learning rate to 2e-06.\n",
            "Epoch 45/50\n",
            "473/473 [==============================] - 21s 44ms/step - loss: 143.5581 - accuracy: 0.8891 - val_loss: 142.4333 - val_accuracy: 0.8907\n",
            "\n",
            "Epoch 00045: loss improved from 144.97876 to 144.92647, saving model to drive/My Drive/Colab Notebooks/undergraduate_project/model.hdf5\n",
            "Epoch 46/50\n",
            "473/473 [==============================] - 21s 44ms/step - loss: 142.1353 - accuracy: 0.8878 - val_loss: 142.6860 - val_accuracy: 0.8911\n",
            "\n",
            "Epoch 00046: loss did not improve from 144.92647\n",
            "Epoch 47/50\n",
            "473/473 [==============================] - 21s 44ms/step - loss: 147.3557 - accuracy: 0.8874 - val_loss: 141.9942 - val_accuracy: 0.8915\n",
            "\n",
            "Epoch 00047: loss did not improve from 144.92647\n",
            "Epoch 48/50\n",
            "473/473 [==============================] - 21s 44ms/step - loss: 143.6205 - accuracy: 0.8882 - val_loss: 142.4388 - val_accuracy: 0.8912\n",
            "\n",
            "Epoch 00048: loss improved from 144.92647 to 144.82123, saving model to drive/My Drive/Colab Notebooks/undergraduate_project/model.hdf5\n",
            "Epoch 49/50\n",
            "473/473 [==============================] - 21s 44ms/step - loss: 145.8656 - accuracy: 0.8881 - val_loss: 141.9183 - val_accuracy: 0.8911\n",
            "\n",
            "Epoch 00049: loss did not improve from 144.82123\n",
            "Epoch 50/50\n",
            "473/473 [==============================] - 21s 44ms/step - loss: 146.2467 - accuracy: 0.8868 - val_loss: 142.2861 - val_accuracy: 0.8912\n",
            "\n",
            "Epoch 00050: loss did not improve from 144.82123\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7TVUNhlptZdZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "outputId": "46714046-43c1-4ab4-f8ec-2570b3f214d0"
      },
      "source": [
        "# Plotting\n",
        "import matplotlib.pyplot as plt\n",
        "fig = plt.figure()\n",
        "plt.plot()\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('Accuracy - 2')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='lower right')\n",
        "plt.savefig('model_accuracy2.png')\n",
        "plt.show()\n",
        "\n",
        "plt.plot()\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('Loss - 2')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper right')\n",
        "plt.savefig('model_loss2.png')\n",
        "plt.show()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pSBqGz65q-7S"
      },
      "source": [
        "**Validation**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bcbfQPmoq2W1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2e032437-44e3-4f13-fb4f-990ca945fdeb"
      },
      "source": [
        "from keras.models import load_model\n",
        "from keras.layers import Lambda\n",
        "from keras.preprocessing import image\n",
        "import keras\n",
        "import tensorflow as tf \n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from PIL import Image \n",
        "from keras import backend as K\n",
        "import os\n",
        "import math\n",
        "import shutil\n",
        "\n",
        "width_change = 0\n",
        "height_change = 0\n",
        "\n",
        "dir_path = 'drive/My Drive/Colab Notebooks/undergraduate_project'\n",
        "model = create_model()\n",
        "model.load_weights(os.path.join(dir_path,'model.hdf5'))\n",
        "\n",
        "input_path = os.path.join(dir_path, 'Set14')\n",
        "output_path = os.path.join(dir_path, 'output')\n",
        "output_path2 = os.path.join(dir_path, 'output2')\n",
        "output_path3 = os.path.join(dir_path, 'output3')\n",
        "\n",
        "if os.path.exists(output_path):\n",
        "    shutil.rmtree(output_path)\n",
        "os.makedirs(output_path)\n",
        "\n",
        "if os.path.exists(output_path2):\n",
        "    shutil.rmtree(output_path2)\n",
        "os.makedirs(output_path2)\n",
        "\n",
        "if os.path.exists(output_path3):\n",
        "    shutil.rmtree(output_path3)\n",
        "os.makedirs(output_path3)\n",
        "\n",
        "entries = os.listdir(input_path)\n",
        "\n",
        "for entry in entries:\n",
        "    # Test Image\n",
        "    print('-----------')\n",
        "    path = input_path+'/'+entry\n",
        "    test_image = Image.open(path)\n",
        "    print(test_image.size)\n",
        "    \n",
        "    if test_image.size[0] % 2 != 0: #odd size\n",
        "      test_image = test_image.resize((test_image.size[0]-1, test_image.size[1]), Image.BICUBIC)\n",
        "    if test_image.size[1] % 2 != 0:\n",
        "      test_image = test_image.resize((test_image.size[0], test_image.size[1]-1), Image.BICUBIC)\n",
        "    path = output_path+'/'+entry\n",
        "    test_image.save(path)\n",
        "    print(test_image.size)\n",
        "\n",
        "    test_image = test_image.resize((test_image.size[0]//2, test_image.size[1]//2), Image.BICUBIC)\n",
        "    print(test_image.size)\n",
        "    test_image_array = np.array(test_image)\n",
        "    test_image_array = bayer_reverse(test_image_array)\n",
        "    test_image_array = test_image_array[:,:,np.newaxis]\n",
        "    test_image = image.array_to_img(test_image_array)\n",
        "    test_image.save(output_path3+'/'+entry)\n",
        "    # print(test_image.shape)\n",
        "    print(test_image.size)\n",
        "    \n",
        "    \n",
        "    # print(test_image.shape)\n",
        "    test_image = np.array(test_image)\n",
        "    test_image = test_image[np.newaxis,:,:]\n",
        "    try:\n",
        "      out = model.predict(test_image)\n",
        "    except:\n",
        "      path = input_path+'/'+entry\n",
        "      test_image = Image.open(path)\n",
        "      if (test_image.size[0]//2)%2 != 0:\n",
        "        test_image = test_image.resize(((test_image.size[0]//2) - 1, test_image.size[1]//2), Image.BICUBIC)\n",
        "      if (test_image.size[1]//2)%2 != 0:\n",
        "        test_image = test_image.resize((test_image.size[0]//2 , (test_image.size[1]//2) - 1), Image.BICUBIC)\n",
        "      test_image_array = np.array(test_image)\n",
        "      test_image_array = bayer_reverse(test_image_array)\n",
        "      test_image_array = test_image_array[:,:,np.newaxis]\n",
        "      test_image = image.array_to_img(test_image_array)\n",
        "      print(test_image.size)\n",
        "      test_image.save(output_path3+'/'+entry)\n",
        "      test_image = np.array(test_image)\n",
        "      test_image = test_image[np.newaxis,:,:]\n",
        "      out = model.predict(test_image)\n",
        "\n",
        "\n",
        "    # print(out.shape)\n",
        "    out = out[0]\n",
        "    out = image.array_to_img(out)\n",
        "    path = output_path2+'/'+entry\n",
        "    out.save(path)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "-----------\n",
            "(276, 276)\n",
            "(276, 276)\n",
            "(138, 138)\n",
            "(138, 138)\n",
            "-----------\n",
            "(500, 362)\n",
            "(500, 362)\n",
            "(250, 181)\n",
            "(250, 181)\n",
            "(250, 180)\n",
            "-----------\n",
            "(250, 361)\n",
            "(250, 360)\n",
            "(125, 180)\n",
            "(125, 180)\n",
            "(124, 180)\n",
            "-----------\n",
            "(512, 512)\n",
            "(512, 512)\n",
            "(256, 256)\n",
            "(256, 256)\n",
            "-----------\n",
            "(529, 656)\n",
            "(528, 656)\n",
            "(264, 328)\n",
            "(264, 328)\n",
            "-----------\n",
            "(500, 480)\n",
            "(500, 480)\n",
            "(250, 240)\n",
            "(250, 240)\n",
            "-----------\n",
            "(512, 512)\n",
            "(512, 512)\n",
            "(256, 256)\n",
            "(256, 256)\n",
            "-----------\n",
            "(720, 576)\n",
            "(720, 576)\n",
            "(360, 288)\n",
            "(360, 288)\n",
            "-----------\n",
            "(352, 288)\n",
            "(352, 288)\n",
            "(176, 144)\n",
            "(176, 144)\n",
            "-----------\n",
            "(512, 512)\n",
            "(512, 512)\n",
            "(256, 256)\n",
            "(256, 256)\n",
            "-----------\n",
            "(352, 288)\n",
            "(352, 288)\n",
            "(176, 144)\n",
            "(176, 144)\n",
            "-----------\n",
            "(768, 512)\n",
            "(768, 512)\n",
            "(384, 256)\n",
            "(384, 256)\n",
            "-----------\n",
            "(512, 512)\n",
            "(512, 512)\n",
            "(256, 256)\n",
            "(256, 256)\n",
            "-----------\n",
            "(586, 391)\n",
            "(586, 390)\n",
            "(293, 195)\n",
            "(293, 195)\n",
            "(146, 96)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aY5gyote19PB"
      },
      "source": [
        "**Performance**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lzVpCLEFoX8T",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7b937e4e-a866-4b73-a806-864c43f0f1b3"
      },
      "source": [
        "import cv2\n",
        "import numpy as np\n",
        "import math\n",
        "import os\n",
        "from google.colab.patches import cv2_imshow\n",
        "from skimage.measure import compare_ssim\n",
        "from skimage.measure import compare_ssim as ssim\n",
        "\n",
        "def calculate_psnr(original, contrast):\n",
        "    mse = np.mean((original - contrast) ** 2)\n",
        "    if mse == 0:\n",
        "        return 100\n",
        "    max_value = 255.0\n",
        "    return 10 * math.log10(max_value**2 / mse)    \n",
        "    \n",
        "\n",
        "path = 'drive/My Drive/Colab Notebooks/undergraduate_project'\n",
        "input_path = os.path.join(path,'output')\n",
        "output_path = os.path.join(path,'output2')\n",
        "entries = os.listdir(input_path)\n",
        "count = 0\n",
        "total_psnr = 0.\n",
        "total_ssim = 0.\n",
        "\n",
        "for entry in entries:  \n",
        "  img1 = cv2.imread(os.path.join(input_path,entry))\n",
        "  img2 = cv2.imread(os.path.join(output_path,entry))\n",
        "  img1 = cv2.resize(img1, (img2.shape[1], img2.shape[0]), interpolation=cv2.INTER_CUBIC)\n",
        "    \n",
        "\n",
        "\n",
        "  # PSNR\n",
        "  psnr = calculate_psnr(img1,img2)\n",
        "  print(\"PSNR-{0}: {1:.10f}dB\".format(entry,psnr))\n",
        "\n",
        "  # SSIM\n",
        "  ssim = compare_ssim(img1, img2, data_range=255.0 - 0.0,multichannel=True)\n",
        "  print(\"SSIM-{0}: {1:.10f}\".format(entry,ssim))\n",
        "\n",
        "  total_psnr += psnr\n",
        "  total_ssim += ssim\n",
        "  count += 1\n",
        "print(count)\n",
        "\n",
        "total_psnr = total_psnr / count\n",
        "total_ssim = total_ssim / count\n",
        "print(\"\\n=====================================\")\n",
        "print(\"Average PSNR:{:.10f}\".format(total_psnr))\n",
        "print(\"Average SSIM:{:.10f}\".format(total_ssim))\n",
        "\n",
        "\n"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "PSNR-face.png: 30.7175799418dB\n",
            "SSIM-face.png: 0.7285968782\n",
            "PSNR-flowers.png: 28.2964579959dB\n",
            "SSIM-flowers.png: 0.7460853863\n",
            "PSNR-comic.png: 28.4667205267dB\n",
            "SSIM-comic.png: 0.7815435156\n",
            "PSNR-bridge.png: 29.1586364744dB\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:37: UserWarning: DEPRECATED: skimage.measure.compare_ssim has been moved to skimage.metrics.structural_similarity. It will be removed from skimage.measure in version 0.18.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "SSIM-bridge.png: 0.7743857701\n",
            "PSNR-ppt3.png: 26.5963296188dB\n",
            "SSIM-ppt3.png: 0.8120912609\n",
            "PSNR-baboon.png: 28.1462514599dB\n",
            "SSIM-baboon.png: 0.5342114740\n",
            "PSNR-lenna.png: 30.0123106826dB\n",
            "SSIM-lenna.png: 0.8119088738\n",
            "PSNR-barbara.png: 31.1326112686dB\n",
            "SSIM-barbara.png: 0.7882059427\n",
            "PSNR-coastguard.png: 28.6310326601dB\n",
            "SSIM-coastguard.png: 0.7374095463\n",
            "PSNR-man.png: 27.7408623332dB\n",
            "SSIM-man.png: 0.7355200456\n",
            "PSNR-foreman.png: 29.3971100719dB\n",
            "SSIM-foreman.png: 0.9125499234\n",
            "PSNR-monarch.png: 30.8464345236dB\n",
            "SSIM-monarch.png: 0.9219950456\n",
            "PSNR-pepper.png: 29.1363846228dB\n",
            "SSIM-pepper.png: 0.7262895442\n",
            "PSNR-zebra.png: 28.4301735792dB\n",
            "SSIM-zebra.png: 0.6481505979\n",
            "14\n",
            "\n",
            "=====================================\n",
            "Average PSNR:29.0506354114\n",
            "Average SSIM:0.7613531289\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}