Untitled1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 Physical GPUs, 1 Logical GPUs\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "gpus = tf.config.experimental.list_physical_devices('GPU')\n",
    "if gpus:\n",
    "  # Restrict TensorFlow to only allocate 1*X GB of memory on the first GPU\n",
    "    try:\n",
    "        tf.config.experimental.set_virtual_device_configuration(gpus[0],\n",
    "            [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=(1024*2))])\n",
    "        logical_gpus = tf.config.experimental.list_logical_devices('GPU')\n",
    "        print(len(gpus), \"Physical GPUs,\", len(logical_gpus), \"Logical GPUs\")\n",
    "    except RuntimeError as e:\n",
    "        # Virtual devices must be set before GPUs have been initialized\n",
    "        print(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "\n",
    "from tensorflow.keras import backend as K\n",
    "from tensorflow.keras.applications.vgg19 import VGG19\n",
    "\n",
    "from tensorflow.keras.layers import Add, BatchNormalization, Conv2D, Dense, Flatten, Input, LeakyReLU, PReLU, Lambda, GlobalAveragePooling2D\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.applications.vgg19 import VGG19\n",
    "import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import sys\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "\n",
    "from tensorflow.keras.layers import Conv2DTranspose\n",
    "\n",
    "from IPython import display\n",
    "\n",
    "def res_block(x_in, num_filters, momentum=0.8):\n",
    "    x = Conv2D(num_filters, kernel_size=3, padding='same')(x_in)\n",
    "    x = BatchNormalization(momentum=momentum)(x)\n",
    "    x = PReLU(shared_axes=[1, 2])(x)\n",
    "    x = Conv2D(num_filters, kernel_size=3, padding='same')(x)\n",
    "    x = BatchNormalization(momentum=momentum)(x)\n",
    "    x = Add()([x_in, x])\n",
    "    return x\n",
    "\n",
    "def resize_like(scale): # resizes input tensor wrt. ref_tensor\n",
    "    return Lambda(lambda inputs: tf.keras.backend.resize_images(inputs, scale, scale, \"channels_last\", \"nearest\"))\n",
    "\n",
    "# def up_sampling_block(model, kernal_size, filters, strides):\n",
    "    \n",
    "#     # In place of Conv2D and UpSampling2D we can also use Conv2DTranspose (Both are used for Deconvolution)\n",
    "#     # Even we can have our own function for deconvolution (i.e one made in Utils.py)\n",
    "#     #model = Conv2DTranspose(filters = filters, kernel_size = kernal_size, strides = strides, padding = \"same\")(model)\n",
    "#     model = Conv2D(filters = filters, kernel_size = kernal_size, strides = strides, padding = \"same\")(model)\n",
    "#     model = UpSampling2D(size = 2)(model)\n",
    "#     model = LeakyReLU(alpha = 0.2)(model)\n",
    "    \n",
    "#     return model\n",
    "\n",
    "def SubpixelConv2D(scale):\n",
    "    import tensorflow as tf\n",
    "    return Lambda(lambda x: tf.nn.depth_to_space(x, scale))\n",
    "\n",
    "def subpixel_upsampling_block(model, kernel_size, filters, strides, number):\n",
    "    model = Conv2D(filters, kernel_size=kernel_size, strides=strides, padding='same', name='upSample_Conv2d_'+str(number))(model)\n",
    "    model = SubpixelConv2D(4)(model)\n",
    "    model = PReLU(shared_axes=[1,2], name='upSamplePReLU_'+str(number))(model)\n",
    "    return model\n",
    "\n",
    "\n",
    "def upsampling_transpose_block(model, kernel_size, filters, strides):\n",
    "    model = Conv2DTranspose(filters = filters, kernel_size = kernel_size, strides = strides, padding = \"same\")(model)\n",
    "    model = LeakyReLU(alpha = 0.2)(model)    \n",
    "    return model\n",
    "\n",
    "def up_sampling_block(model, kernal_size, filters, strides, scale):\n",
    "    model = resize_like(scale)(model)\n",
    "    model = Conv2D(filters = filters, kernel_size = kernal_size, strides = strides, padding = \"same\")(model)\n",
    "    model = LeakyReLU(alpha = 0.2)(model)    \n",
    "    return model\n",
    "\n",
    "def discriminator_block(model, filters, kernel_size, strides):\n",
    "    \n",
    "    model = Conv2D(filters = filters, kernel_size = kernel_size, strides = strides, padding = \"same\")(model)\n",
    "    model = BatchNormalization(momentum = 0.5)(model)\n",
    "    model = LeakyReLU(alpha = 0.2)(model)\n",
    "    return model\n",
    "\n",
    "def norm(x):\n",
    "    return (x - 127.5) / 127.5\n",
    "\n",
    "\n",
    "def denorm(x):\n",
    "    return x * 127.5 + 127.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Generator(object):\n",
    "\n",
    "    def __init__(self, input_shape):\n",
    "\n",
    "        self.input_shape = input_shape\n",
    "\n",
    "    def generator(self, num_filters):\n",
    "\n",
    "        x_in = Input(shape=self.input_shape)\n",
    "        x = Lambda(norm)(x_in)\n",
    "        x = Conv2D(num_filters, kernel_size=9, padding='same')(x)\n",
    "        x = x_1 = PReLU(shared_axes=[1, 2])(x)\n",
    "\n",
    "        for _ in range(16):\n",
    "            x = res_block(x, num_filters)\n",
    "    \n",
    "        x = Conv2D(num_filters, kernel_size=3, padding='same')(x)\n",
    "        x = BatchNormalization(momentum=0.5)(x)\n",
    "        x = Add()([x_1, x])\n",
    "\n",
    "        x = upsampling_transpose_block(x, 3, 256, 2)\n",
    "        x = upsampling_transpose_block(x, 3, 256, 2)\n",
    "\n",
    "        x = Conv2D(3, kernel_size=9, padding='same', activation='tanh')(x)\n",
    "        x = Lambda(denorm)(x)\n",
    "        return Model(x_in, x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "a = Generator((64, 64, 3)).generator(64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            [(None, 64, 64, 3)]  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "lambda (Lambda)                 (None, 64, 64, 3)    0           input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d (Conv2D)                 (None, 64, 64, 64)   15616       lambda[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "p_re_lu (PReLU)                 (None, 64, 64, 64)   64          conv2d[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, 64, 64, 64)   36928       p_re_lu[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization (BatchNorma (None, 64, 64, 64)   256         conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "p_re_lu_1 (PReLU)               (None, 64, 64, 64)   64          batch_normalization[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, 64, 64, 64)   36928       p_re_lu_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, 64, 64, 64)   256         conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add (Add)                       (None, 64, 64, 64)   0           p_re_lu[0][0]                    \n",
      "                                                                 batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)               (None, 64, 64, 64)   36928       add[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_2 (BatchNor (None, 64, 64, 64)   256         conv2d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "p_re_lu_2 (PReLU)               (None, 64, 64, 64)   64          batch_normalization_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_4 (Conv2D)               (None, 64, 64, 64)   36928       p_re_lu_2[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_3 (BatchNor (None, 64, 64, 64)   256         conv2d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_1 (Add)                     (None, 64, 64, 64)   0           add[0][0]                        \n",
      "                                                                 batch_normalization_3[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_5 (Conv2D)               (None, 64, 64, 64)   36928       add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_4 (BatchNor (None, 64, 64, 64)   256         conv2d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "p_re_lu_3 (PReLU)               (None, 64, 64, 64)   64          batch_normalization_4[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_6 (Conv2D)               (None, 64, 64, 64)   36928       p_re_lu_3[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_5 (BatchNor (None, 64, 64, 64)   256         conv2d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_2 (Add)                     (None, 64, 64, 64)   0           add_1[0][0]                      \n",
      "                                                                 batch_normalization_5[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_7 (Conv2D)               (None, 64, 64, 64)   36928       add_2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_6 (BatchNor (None, 64, 64, 64)   256         conv2d_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "p_re_lu_4 (PReLU)               (None, 64, 64, 64)   64          batch_normalization_6[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_8 (Conv2D)               (None, 64, 64, 64)   36928       p_re_lu_4[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_7 (BatchNor (None, 64, 64, 64)   256         conv2d_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_3 (Add)                     (None, 64, 64, 64)   0           add_2[0][0]                      \n",
      "                                                                 batch_normalization_7[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_9 (Conv2D)               (None, 64, 64, 64)   36928       add_3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_8 (BatchNor (None, 64, 64, 64)   256         conv2d_9[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "p_re_lu_5 (PReLU)               (None, 64, 64, 64)   64          batch_normalization_8[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_10 (Conv2D)              (None, 64, 64, 64)   36928       p_re_lu_5[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_9 (BatchNor (None, 64, 64, 64)   256         conv2d_10[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_4 (Add)                     (None, 64, 64, 64)   0           add_3[0][0]                      \n",
      "                                                                 batch_normalization_9[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_11 (Conv2D)              (None, 64, 64, 64)   36928       add_4[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_10 (BatchNo (None, 64, 64, 64)   256         conv2d_11[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "p_re_lu_6 (PReLU)               (None, 64, 64, 64)   64          batch_normalization_10[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_12 (Conv2D)              (None, 64, 64, 64)   36928       p_re_lu_6[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_11 (BatchNo (None, 64, 64, 64)   256         conv2d_12[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_5 (Add)                     (None, 64, 64, 64)   0           add_4[0][0]                      \n",
      "                                                                 batch_normalization_11[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_13 (Conv2D)              (None, 64, 64, 64)   36928       add_5[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_12 (BatchNo (None, 64, 64, 64)   256         conv2d_13[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "p_re_lu_7 (PReLU)               (None, 64, 64, 64)   64          batch_normalization_12[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_14 (Conv2D)              (None, 64, 64, 64)   36928       p_re_lu_7[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_13 (BatchNo (None, 64, 64, 64)   256         conv2d_14[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_6 (Add)                     (None, 64, 64, 64)   0           add_5[0][0]                      \n",
      "                                                                 batch_normalization_13[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_15 (Conv2D)              (None, 64, 64, 64)   36928       add_6[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_14 (BatchNo (None, 64, 64, 64)   256         conv2d_15[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "p_re_lu_8 (PReLU)               (None, 64, 64, 64)   64          batch_normalization_14[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_16 (Conv2D)              (None, 64, 64, 64)   36928       p_re_lu_8[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_15 (BatchNo (None, 64, 64, 64)   256         conv2d_16[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_7 (Add)                     (None, 64, 64, 64)   0           add_6[0][0]                      \n",
      "                                                                 batch_normalization_15[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_17 (Conv2D)              (None, 64, 64, 64)   36928       add_7[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_16 (BatchNo (None, 64, 64, 64)   256         conv2d_17[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "p_re_lu_9 (PReLU)               (None, 64, 64, 64)   64          batch_normalization_16[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_18 (Conv2D)              (None, 64, 64, 64)   36928       p_re_lu_9[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_17 (BatchNo (None, 64, 64, 64)   256         conv2d_18[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_8 (Add)                     (None, 64, 64, 64)   0           add_7[0][0]                      \n",
      "                                                                 batch_normalization_17[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_19 (Conv2D)              (None, 64, 64, 64)   36928       add_8[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_18 (BatchNo (None, 64, 64, 64)   256         conv2d_19[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "p_re_lu_10 (PReLU)              (None, 64, 64, 64)   64          batch_normalization_18[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_20 (Conv2D)              (None, 64, 64, 64)   36928       p_re_lu_10[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_19 (BatchNo (None, 64, 64, 64)   256         conv2d_20[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_9 (Add)                     (None, 64, 64, 64)   0           add_8[0][0]                      \n",
      "                                                                 batch_normalization_19[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_21 (Conv2D)              (None, 64, 64, 64)   36928       add_9[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_20 (BatchNo (None, 64, 64, 64)   256         conv2d_21[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "p_re_lu_11 (PReLU)              (None, 64, 64, 64)   64          batch_normalization_20[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_22 (Conv2D)              (None, 64, 64, 64)   36928       p_re_lu_11[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_21 (BatchNo (None, 64, 64, 64)   256         conv2d_22[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_10 (Add)                    (None, 64, 64, 64)   0           add_9[0][0]                      \n",
      "                                                                 batch_normalization_21[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_23 (Conv2D)              (None, 64, 64, 64)   36928       add_10[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_22 (BatchNo (None, 64, 64, 64)   256         conv2d_23[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "p_re_lu_12 (PReLU)              (None, 64, 64, 64)   64          batch_normalization_22[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_24 (Conv2D)              (None, 64, 64, 64)   36928       p_re_lu_12[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_23 (BatchNo (None, 64, 64, 64)   256         conv2d_24[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_11 (Add)                    (None, 64, 64, 64)   0           add_10[0][0]                     \n",
      "                                                                 batch_normalization_23[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_25 (Conv2D)              (None, 64, 64, 64)   36928       add_11[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_24 (BatchNo (None, 64, 64, 64)   256         conv2d_25[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "p_re_lu_13 (PReLU)              (None, 64, 64, 64)   64          batch_normalization_24[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_26 (Conv2D)              (None, 64, 64, 64)   36928       p_re_lu_13[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_25 (BatchNo (None, 64, 64, 64)   256         conv2d_26[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_12 (Add)                    (None, 64, 64, 64)   0           add_11[0][0]                     \n",
      "                                                                 batch_normalization_25[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_27 (Conv2D)              (None, 64, 64, 64)   36928       add_12[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_26 (BatchNo (None, 64, 64, 64)   256         conv2d_27[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "p_re_lu_14 (PReLU)              (None, 64, 64, 64)   64          batch_normalization_26[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_28 (Conv2D)              (None, 64, 64, 64)   36928       p_re_lu_14[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_27 (BatchNo (None, 64, 64, 64)   256         conv2d_28[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_13 (Add)                    (None, 64, 64, 64)   0           add_12[0][0]                     \n",
      "                                                                 batch_normalization_27[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_29 (Conv2D)              (None, 64, 64, 64)   36928       add_13[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_28 (BatchNo (None, 64, 64, 64)   256         conv2d_29[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "p_re_lu_15 (PReLU)              (None, 64, 64, 64)   64          batch_normalization_28[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_30 (Conv2D)              (None, 64, 64, 64)   36928       p_re_lu_15[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_29 (BatchNo (None, 64, 64, 64)   256         conv2d_30[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_14 (Add)                    (None, 64, 64, 64)   0           add_13[0][0]                     \n",
      "                                                                 batch_normalization_29[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_31 (Conv2D)              (None, 64, 64, 64)   36928       add_14[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_30 (BatchNo (None, 64, 64, 64)   256         conv2d_31[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "p_re_lu_16 (PReLU)              (None, 64, 64, 64)   64          batch_normalization_30[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_32 (Conv2D)              (None, 64, 64, 64)   36928       p_re_lu_16[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_31 (BatchNo (None, 64, 64, 64)   256         conv2d_32[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_15 (Add)                    (None, 64, 64, 64)   0           add_14[0][0]                     \n",
      "                                                                 batch_normalization_31[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_33 (Conv2D)              (None, 64, 64, 64)   36928       add_15[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_32 (BatchNo (None, 64, 64, 64)   256         conv2d_33[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_16 (Add)                    (None, 64, 64, 64)   0           p_re_lu[0][0]                    \n",
      "                                                                 batch_normalization_32[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose (Conv2DTranspo (None, 128, 128, 256 147712      add_16[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu (LeakyReLU)         (None, 128, 128, 256 0           conv2d_transpose[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_1 (Conv2DTrans (None, 256, 256, 256 590080      leaky_re_lu[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_1 (LeakyReLU)       (None, 256, 256, 256 0           conv2d_transpose_1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_34 (Conv2D)              (None, 256, 256, 3)  62211       leaky_re_lu_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "lambda_1 (Lambda)               (None, 256, 256, 3)  0           conv2d_34[0][0]                  \n",
      "==================================================================================================\n",
      "Total params: 2,043,779\n",
      "Trainable params: 2,039,555\n",
      "Non-trainable params: 4,224\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "a.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = Image.open('00008af0-bad0-11e8-b2b8-ac1f6b6435d0.png').resize((128, 128), Image.BICUBIC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def patchify(im, patch_shape):\n",
    "    res = np.array(im).shape\n",
    "    patches = []\n",
    "    for i in range(res[0]//patch_shape):\n",
    "        x1 = patch_shape*i\n",
    "        for j in range(res[1]//patch_shape):\n",
    "            x2 = patch_shape*j\n",
    "            img= im[x1:x1+patch_shape, x2:x2+patch_shape, :]\n",
    "            patches.append(img)\n",
    "        x2 = 0\n",
    "    return np.array(patches)\n",
    "\n",
    "def unpatchify(patches, imsize):\n",
    "    assert len(patches.shape) == 4\n",
    "    nb_patches_w = imsize[1]//patches.shape[2]\n",
    "    nb_patches_h = imsize[0]//patches.shape[1]\n",
    "    img_final = np.zeros((patches.shape[1]*nb_patches_w, patches.shape[2]*nb_patches_h, 3))\n",
    "    for i in range(nb_patches_w):\n",
    "        x1 = patches.shape[1]*i\n",
    "        for j in range(nb_patches_h):\n",
    "            x2 = patches.shape[2]*j\n",
    "            img_final[x1:x1+patches.shape[1], x2:x2+patches.shape[2], :] = patches[i*nb_patches_h+j] \n",
    "        x2 = 0\n",
    "    return img_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# a(np.array(img).reshape(1,64,64,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'cv2' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-8-49f870191559>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# import cv2\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mvideo\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mVideoCapture\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'zebrafish.mp4'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mfps\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvideo\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mCAP_PROP_FPS\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'cv2' is not defined"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "import time\n",
    "video = cv2.VideoCapture('zebrafish.mp4')\n",
    "\n",
    "fps = video.get(cv2.CAP_PROP_FPS)\n",
    "print (\"Frames per second using video.get(cv2.CAP_PROP_FPS) : {0}\".format(fps))\n",
    "\n",
    "num_frames = 120;\n",
    "print (\"Capturing {0} frames\".format(num_frames))\n",
    "counter = 0\n",
    "start = time.time()\n",
    "while(True) :\n",
    "    ret, frame = video.read()\n",
    "    if(not ret):\n",
    "        break\n",
    "    counter+=1\n",
    "end = time.time()\n",
    "seconds = end - start\n",
    "print(\"Time taken : {0} seconds\".format(seconds))\n",
    "\n",
    "    # Calculate frames per second\n",
    "fps  = counter/ seconds\n",
    "print(\"Estimated frames per second : {0}\".format(fps))\n",
    "video.release()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "frame rate 6.274270677004995e-10\n",
      "frame rate 6.274270675880245e-10\n",
      "frame rate 6.274270675177023e-10\n",
      "frame rate 6.274270674373369e-10\n",
      "frame rate 6.274270673648999e-10\n",
      "frame rate 6.274270672969099e-10\n",
      "frame rate 6.274270671738484e-10\n",
      "frame rate 6.274270670794033e-10\n",
      "frame rate 6.27427066985421e-10\n",
      "frame rate 6.27427066875373e-10\n",
      "frame rate 6.27427066779248e-10\n",
      "frame rate 6.274270666790345e-10\n",
      "frame rate 6.27427066564866e-10\n",
      "frame rate 6.274270664113264e-10\n",
      "frame rate 6.274270662832969e-10\n",
      "frame rate 6.274270661693248e-10\n",
      "frame rate 6.274270660389176e-10\n",
      "frame rate 6.274270659281826e-10\n",
      "frame rate 6.274270658136867e-10\n",
      "frame rate 6.274270657138369e-10\n",
      "frame rate 6.274270656371421e-10\n",
      "frame rate 6.274270655489485e-10\n",
      "frame rate 6.274270654753653e-10\n",
      "frame rate 6.274270653813823e-10\n",
      "frame rate 6.274270652903757e-10\n",
      "frame rate 6.274270652065227e-10\n",
      "frame rate 6.274270651343874e-10\n",
      "frame rate 6.2742706507077e-10\n",
      "frame rate 6.274270649860916e-10\n",
      "frame rate 6.274270649226559e-10\n",
      "frame rate 6.274270648347026e-10\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import cv2\n",
    "from PIL import ImageGrab\n",
    "\n",
    "last_time = time.time()\n",
    "while(True):\n",
    "    # 800x600 windowed mode\n",
    "    t1 = time.time()\n",
    "    printscreen =  np.array(ImageGrab.grab(bbox=(100,50,500,370)).resize((64, 64), Image.BICUBIC))\n",
    "    t2 = time.time()\n",
    "    output = a(printscreen.reshape(1, 64, 64, 3))\n",
    "    t3 = time.time() - (t2 - t1)\n",
    "    print('frame rate {}'.format(1/(t3)))\n",
    "    last_time = time.time()\n",
    "    cv2.imshow('window',cv2.cvtColor(printscreen, cv2.COLOR_BGR2RGB))\n",
    "\n",
    "    if cv2.waitKey(25) & 0xFF == ord('q'):\n",
    "        cv2.destroyAllWindows()\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=256x256 at 0x17063F4A6D8>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image.fromarray(np.uint8(output.numpy().reshape(256, 256, 3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3345448970794678\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "t1 = time.time()\n",
    "patches = patchify(np.array(img, dtype=np.float32), 64)\n",
    "p = patches[0].reshape(1, 64, 64, 3)\n",
    "output = a(patches)\n",
    "v = unpatchify(output.numpy(), [512, 512])\n",
    "print(time.time()-t1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "t1 = time.time()\n",
    "a = img.resize((512, 512), Image.NEAREST)\n",
    "print(time.time()-t1)"
   ]
  }
 ],
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