Fastai4DEM-SRCNN.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "Fastai4DEM-SRCNN.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "machine_shape": "hm"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XbvTrtv2dG-V"
      },
      "source": [
        "FASTAI 4 DEM - SRCNN\n",
        "=========================\n",
        "**This is only a template, no better results could be achieved than with a simple bicubic upsampling.**\n",
        "\n",
        "If you achieve any proper results please get in contact.\n",
        "\n",
        "The implementation is based on  https://github.com/benymd/super_resolution.\n",
        "\n",
        "Paper: https://arxiv.org/abs/1501.00092\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rayuy-t3eOyV"
      },
      "source": [
        "# Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wPtNyKFheTGJ"
      },
      "source": [
        "Download the training and test data of the austrian DTM: https://www.openmaps.online/data/austria_dtm_fastai.zip\n",
        "\n",
        "License: Data by - Geoland.at (2019) - CC BY 4.0 (CC BY 4.0)\n",
        "\n",
        "High resolution images: 300*300px\n",
        "\n",
        "Low resolution image: 100*100px\n",
        "\n",
        "Upsampled to 300*300px with next neighbor or bicubic method\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NyE-lUubmmdb"
      },
      "source": [
        "# Setup"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mSDFfc5NM8j6"
      },
      "source": [
        "!pip install -Uqq fastbook\n",
        "import fastbook\n",
        "fastbook.setup_book()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NouTg_1FOiaR"
      },
      "source": [
        "!pip install rasterio"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t850JxBPntdx"
      },
      "source": [
        "# Data Loader"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8TJgqYlIDdFx"
      },
      "source": [
        "# adapt this parameter to the maximum height of your training dataset\n",
        "max_height = 3150\n",
        "# adapt the folder name to the low resolution images \n",
        "LR_PATH_TRAIN = \"/content/gdrive/MyDrive/fastai/data_100/lr_us_cc\""
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ft-MnjriNMD7"
      },
      "source": [
        "# read geotiffs\n",
        "import rasterio\n",
        "from fastbook import *\n",
        "from fastai.vision import *\n",
        "\n",
        "def raster2tensor(image):  \n",
        "    f = rasterio.open(image)\n",
        "    d1 = f.read(1)\n",
        "    tensorImage = TensorImage([d1])\n",
        "    # adapt \n",
        "    tensorImage = tensorImage / max_height\n",
        "    return tensorImage\n",
        "\n",
        "def raster2tensorHr(image):  \n",
        "    # adapt to replace the folder name to the high resolution images \n",
        "    image = str(image).replace(\"lr_us_cc\", \"hr\")\n",
        "    f = rasterio.open(image)\n",
        "    d1 = f.read(1)\n",
        "    tensorImage = TensorImage([d1])\n",
        "    tensorImage = tensorImage / max_height\n",
        "    return tensorImage "
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f8eKQyoDNSBn"
      },
      "source": [
        "# valid_pct = percent of the total images that is used for validation \n",
        "db = DataBlock(blocks=(TransformBlock(type_tfms=raster2tensor),\n",
        "                       TransformBlock(type_tfms=raster2tensorHr),\n",
        "                      ),\n",
        "               get_items = get_image_files,\n",
        "               splitter=RandomSplitter(valid_pct=0.1, seed = 42)\n",
        "              )"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gA3XMF_0NYcu"
      },
      "source": [
        "dls = db.dataloaders(source=LR_PATH_TRAIN, bs=12, num_workers=2)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zOVPFlmPYved",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 699
        },
        "outputId": "30ea0217-43c9-4e5f-c655-7eb9922f06b6"
      },
      "source": [
        "# Batch is ok, but sometimes it is hard to see the diffences\n",
        "dls.show_batch(max_n = 1, cmap='gray', figsize = (100,100))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 7200x7200 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E7ujWJQ1eQjG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e40e1e07-07ef-4268-afa0-ccd2bc1c7156"
      },
      "source": [
        "# Shows the values, got the right datasets\n",
        "dls.one_batch()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(TensorImage([[[[0.3222, 0.3222, 0.3246,  ..., 0.2698, 0.2679, 0.2679],\n",
              "           [0.3222, 0.3222, 0.3246,  ..., 0.2698, 0.2679, 0.2679],\n",
              "           [0.3184, 0.3184, 0.3204,  ..., 0.2700, 0.2681, 0.2681],\n",
              "           ...,\n",
              "           [0.3295, 0.3295, 0.3286,  ..., 0.3415, 0.3417, 0.3417],\n",
              "           [0.3289, 0.3289, 0.3281,  ..., 0.3429, 0.3432, 0.3432],\n",
              "           [0.3289, 0.3289, 0.3281,  ..., 0.3429, 0.3432, 0.3432]]],\n",
              " \n",
              " \n",
              "         [[[0.3957, 0.3957, 0.3939,  ..., 0.3104, 0.3121, 0.3121],\n",
              "           [0.3957, 0.3957, 0.3939,  ..., 0.3104, 0.3121, 0.3121],\n",
              "           [0.3950, 0.3950, 0.3932,  ..., 0.3124, 0.3136, 0.3136],\n",
              "           ...,\n",
              "           [0.1888, 0.1888, 0.1893,  ..., 0.2171, 0.2173, 0.2173],\n",
              "           [0.1879, 0.1879, 0.1883,  ..., 0.2158, 0.2160, 0.2160],\n",
              "           [0.1879, 0.1879, 0.1883,  ..., 0.2158, 0.2160, 0.2160]]],\n",
              " \n",
              " \n",
              "         [[[0.5629, 0.5629, 0.5640,  ..., 0.6342, 0.6338, 0.6338],\n",
              "           [0.5629, 0.5629, 0.5640,  ..., 0.6342, 0.6338, 0.6338],\n",
              "           [0.5641, 0.5641, 0.5652,  ..., 0.6332, 0.6328, 0.6328],\n",
              "           ...,\n",
              "           [0.3121, 0.3121, 0.3133,  ..., 0.4208, 0.4197, 0.4197],\n",
              "           [0.3113, 0.3113, 0.3125,  ..., 0.4204, 0.4192, 0.4192],\n",
              "           [0.3113, 0.3113, 0.3125,  ..., 0.4204, 0.4192, 0.4192]]],\n",
              " \n",
              " \n",
              "         ...,\n",
              " \n",
              " \n",
              "         [[[0.5970, 0.5970, 0.5962,  ..., 0.5958, 0.5953, 0.5953],\n",
              "           [0.5970, 0.5970, 0.5962,  ..., 0.5958, 0.5953, 0.5953],\n",
              "           [0.5992, 0.5992, 0.5986,  ..., 0.5929, 0.5925, 0.5925],\n",
              "           ...,\n",
              "           [0.6986, 0.6986, 0.6978,  ..., 0.6915, 0.6894, 0.6894],\n",
              "           [0.6997, 0.6997, 0.6989,  ..., 0.6908, 0.6887, 0.6887],\n",
              "           [0.6997, 0.6997, 0.6989,  ..., 0.6908, 0.6887, 0.6887]]],\n",
              " \n",
              " \n",
              "         [[[0.6763, 0.6763, 0.6784,  ..., 0.5967, 0.5946, 0.5946],\n",
              "           [0.6763, 0.6763, 0.6784,  ..., 0.5967, 0.5946, 0.5946],\n",
              "           [0.6772, 0.6772, 0.6793,  ..., 0.5972, 0.5949, 0.5949],\n",
              "           ...,\n",
              "           [0.4917, 0.4917, 0.4917,  ..., 0.6507, 0.6544, 0.6544],\n",
              "           [0.4897, 0.4897, 0.4897,  ..., 0.6498, 0.6536, 0.6536],\n",
              "           [0.4897, 0.4897, 0.4897,  ..., 0.6498, 0.6536, 0.6536]]],\n",
              " \n",
              " \n",
              "         [[[0.2996, 0.2996, 0.3003,  ..., 0.4935, 0.4936, 0.4936],\n",
              "           [0.2996, 0.2996, 0.3003,  ..., 0.4935, 0.4936, 0.4936],\n",
              "           [0.2996, 0.2996, 0.3004,  ..., 0.4929, 0.4928, 0.4928],\n",
              "           ...,\n",
              "           [0.2847, 0.2847, 0.2861,  ..., 0.4380, 0.4395, 0.4395],\n",
              "           [0.2850, 0.2850, 0.2864,  ..., 0.4370, 0.4384, 0.4384],\n",
              "           [0.2850, 0.2850, 0.2864,  ..., 0.4370, 0.4384, 0.4384]]]], device='cuda:0'),\n",
              " TensorImage([[[[0.3252, 0.3280, 0.3297,  ..., 0.2695, 0.2674, 0.2652],\n",
              "           [0.3203, 0.3222, 0.3237,  ..., 0.2701, 0.2679, 0.2657],\n",
              "           [0.3165, 0.3185, 0.3199,  ..., 0.2704, 0.2682, 0.2661],\n",
              "           ...,\n",
              "           [0.3303, 0.3294, 0.3285,  ..., 0.3415, 0.3416, 0.3416],\n",
              "           [0.3299, 0.3289, 0.3280,  ..., 0.3432, 0.3432, 0.3430],\n",
              "           [0.3295, 0.3285, 0.3276,  ..., 0.3448, 0.3450, 0.3449]]],\n",
              " \n",
              " \n",
              "         [[[0.3974, 0.3956, 0.3941,  ..., 0.3089, 0.3108, 0.3126],\n",
              "           [0.3976, 0.3957, 0.3938,  ..., 0.3102, 0.3121, 0.3138],\n",
              "           [0.3972, 0.3952, 0.3933,  ..., 0.3114, 0.3134, 0.3151],\n",
              "           ...,\n",
              "           [0.1883, 0.1888, 0.1894,  ..., 0.2174, 0.2174, 0.2174],\n",
              "           [0.1875, 0.1879, 0.1882,  ..., 0.2159, 0.2160, 0.2161],\n",
              "           [0.1866, 0.1868, 0.1871,  ..., 0.2142, 0.2142, 0.2145]]],\n",
              " \n",
              " \n",
              "         [[[0.5608, 0.5616, 0.5626,  ..., 0.6348, 0.6348, 0.6351],\n",
              "           [0.5621, 0.5629, 0.5640,  ..., 0.6341, 0.6338, 0.6338],\n",
              "           [0.5634, 0.5641, 0.5653,  ..., 0.6332, 0.6329, 0.6325],\n",
              "           ...,\n",
              "           [0.3110, 0.3123, 0.3135,  ..., 0.4210, 0.4198, 0.4184],\n",
              "           [0.3100, 0.3113, 0.3122,  ..., 0.4205, 0.4192, 0.4180],\n",
              "           [0.3088, 0.3099, 0.3112,  ..., 0.4203, 0.4189, 0.4176]]],\n",
              " \n",
              " \n",
              "         ...,\n",
              " \n",
              " \n",
              "         [[[0.5957, 0.5944, 0.5946,  ..., 0.5983, 0.5976, 0.5970],\n",
              "           [0.5983, 0.5970, 0.5969,  ..., 0.5952, 0.5953, 0.5951],\n",
              "           [0.6005, 0.5995, 0.5992,  ..., 0.5924, 0.5926, 0.5928],\n",
              "           ...,\n",
              "           [0.6990, 0.6986, 0.6978,  ..., 0.6915, 0.6893, 0.6874],\n",
              "           [0.7003, 0.6997, 0.6990,  ..., 0.6909, 0.6887, 0.6867],\n",
              "           [0.7016, 0.7011, 0.7003,  ..., 0.6902, 0.6881, 0.6860]]],\n",
              " \n",
              " \n",
              "         [[[0.6734, 0.6755, 0.6777,  ..., 0.5969, 0.5947, 0.5920],\n",
              "           [0.6743, 0.6763, 0.6784,  ..., 0.5968, 0.5946, 0.5921],\n",
              "           [0.6750, 0.6770, 0.6792,  ..., 0.5972, 0.5950, 0.5931],\n",
              "           ...,\n",
              "           [0.4919, 0.4922, 0.4921,  ..., 0.6500, 0.6558, 0.6610],\n",
              "           [0.4898, 0.4897, 0.4902,  ..., 0.6495, 0.6536, 0.6602],\n",
              "           [0.4876, 0.4874, 0.4887,  ..., 0.6481, 0.6521, 0.6575]]],\n",
              " \n",
              " \n",
              "         [[[0.2986, 0.2996, 0.3005,  ..., 0.4937, 0.4941, 0.4946],\n",
              "           [0.2986, 0.2996, 0.3004,  ..., 0.4935, 0.4936, 0.4936],\n",
              "           [0.2986, 0.2995, 0.3003,  ..., 0.4933, 0.4931, 0.4928],\n",
              "           ...,\n",
              "           [0.2832, 0.2851, 0.2865,  ..., 0.4380, 0.4395, 0.4409],\n",
              "           [0.2829, 0.2850, 0.2866,  ..., 0.4370, 0.4384, 0.4398],\n",
              "           [0.2828, 0.2847, 0.2865,  ..., 0.4359, 0.4372, 0.4387]]]], device='cuda:0'))"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-wVwsidxnzJi"
      },
      "source": [
        "# Random"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zHY9TpwBXfIk"
      },
      "source": [
        "# Taming the random\n",
        "\n",
        "import random\n",
        "import numpy as np\n",
        "import torch\n",
        "\n",
        "from fastai.vision import *\n",
        "\n",
        "\n",
        "def set_seeds():\n",
        "    random.seed(42)\n",
        "    np.random.seed(12345)\n",
        "    torch.manual_seed(1234)\n",
        "    torch.backends.cudnn.deterministic = True\n",
        "    torch.backends.cudnn.benchmark = False\n",
        "\n",
        "set_seeds()    "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Byih6hDloDvT"
      },
      "source": [
        "# SRCNN Simple"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WU4Vj5biUkQx"
      },
      "source": [
        "class SRCNN(nn.Module):\n",
        "    \"\"\" Image super-resolution using deep convolutional networks \"\"\"\n",
        "\n",
        "    def __init__(self):\n",
        "        super(SRCNN, self).__init__()\n",
        "        self.conv1 = nn.Conv2d(in_channels=1, out_channels=64, kernel_size=9, padding=4)\n",
        "        self.conv2 = nn.Conv2d(in_channels=64, out_channels=32, kernel_size=1, padding=0)\n",
        "        self.conv3 = nn.Conv2d(in_channels=32, out_channels=1, kernel_size=5, padding=2)\n",
        "        self.activate = nn.ReLU()\n",
        "\n",
        "    def forward(self, x):\n",
        "        h = self.activate(self.conv1(x))\n",
        "        h = self.activate(self.conv2(h))\n",
        "        return self.conv3(h)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t4Z-nVJyU05t"
      },
      "source": [
        "model = SRCNN()\n",
        "loss_func = MSELossFlat()\n",
        "metrics = [mse]\n",
        "learn = Learner(dls, model, loss_func=loss_func, metrics=metrics)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WDuTDrEu6QBC"
      },
      "source": [
        "# a lots of epochs are needed for proper results \n",
        "epoch = 10"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "g8AweIPIGP0B",
        "outputId": "dc2e1feb-6a22-4000-da33-c9dee4f75862"
      },
      "source": [
        "# Just to get an idea, the parameters are not used any further\n",
        "# Have a look here for further parameter tuning https://docs.fast.ai/callback.schedule.html#Learner.fit_one_cycle\n",
        "lrs = learn.lr_find(suggest_funcs=(minimum, steep, valley, slide))"
      ],
      "execution_count": null,
      "outputs": [

        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3pypwGHzVYVf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 777
        },
        "outputId": "fab85e2d-9aeb-4b13-f6e8-f86271e46d40"
      },
      "source": [
        "# load - adapt path and name\n",
        "#learn.load(\"/content/gdrive/MyDrive/fastai/filename.pth\")\n",
        "learn.fit_one_cycle(epoch, cbs=[ShowGraphCallback(), SaveModelCallback])\n",
        "# save - adapt path and name\n",
        "#learn.save(\"/content/gdrive/MyDrive/fastai/filename.pth\") "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>mse</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.001320</td>\n",
              "      <td>0.001530</td>\n",
              "      <td>0.001530</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.001308</td>\n",
              "      <td>0.001471</td>\n",
              "      <td>0.001471</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.001213</td>\n",
              "      <td>0.001283</td>\n",
              "      <td>0.001283</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.001119</td>\n",
              "      <td>0.001184</td>\n",
              "      <td>0.001184</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.001040</td>\n",
              "      <td>0.000980</td>\n",
              "      <td>0.000980</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.000958</td>\n",
              "      <td>0.000862</td>\n",
              "      <td>0.000862</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.000886</td>\n",
              "      <td>0.000782</td>\n",
              "      <td>0.000782</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.000820</td>\n",
              "      <td>0.000735</td>\n",
              "      <td>0.000735</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.000767</td>\n",
              "      <td>0.000715</td>\n",
              "      <td>0.000715</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.000725</td>\n",
              "      <td>0.000711</td>\n",
              "      <td>0.000711</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Better model found at epoch 0 with valid_loss value: 0.001529725268483162.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Better model found at epoch 1 with valid_loss value: 0.0014711146941408515.\n",
            "Better model found at epoch 2 with valid_loss value: 0.0012834150111302733.\n",
            "Better model found at epoch 3 with valid_loss value: 0.0011842071544378996.\n",
            "Better model found at epoch 4 with valid_loss value: 0.000979761010967195.\n",
            "Better model found at epoch 5 with valid_loss value: 0.0008621765300631523.\n",
            "Better model found at epoch 6 with valid_loss value: 0.0007822176557965577.\n",
            "Better model found at epoch 7 with valid_loss value: 0.0007348398212343454.\n",
            "Better model found at epoch 8 with valid_loss value: 0.0007148834993131459.\n",
            "Better model found at epoch 9 with valid_loss value: 0.0007114557083696127.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RYVfaGsuGo4H"
      },
      "source": [
        "# Stats with independent test images"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "id": "cA6UP2O-IiRj",
        "outputId": "204b0a69-12a5-45f7-af89-7dce38d35da7"
      },
      "source": [
        "import os\n",
        "import sys\n",
        "import numpy as np\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from skimage.metrics import peak_signal_noise_ratio\n",
        "from skimage.metrics import structural_similarity\n",
        "import rasterio\n",
        "from rasterio.plot import reshape_as_image, reshape_as_raster, show\n",
        "from rasterio import Affine\n",
        "from rasterio.enums import Resampling\n",
        "from numpy import savetxt\n",
        "\n",
        "# adapt this parameter to the maximum height of your training dataset for PSNR processing\n",
        "max_height = 3150\n",
        "\n",
        "# adapt paths\n",
        "path_lr = '/content/gdrive/MyDrive/fastai/data_test_100/lr_us_cc'\n",
        "path_hr = '/content/gdrive/MyDrive/fastai/data_test_100/hr'\n",
        "\n",
        "meanAbsoluteError = []\n",
        "meanAbsoluteErrorCubic = []\n",
        "\n",
        "countMAE = 0\n",
        "countRMSE = 0\n",
        "countPSNR = 0\n",
        "countSSI = 0\n",
        "countMAESlice = 0\n",
        "countRMSESlice = 0\n",
        "countPSNRSlice = 0\n",
        "countSSISlice = 0\n",
        "totalMAE_lr = 0\n",
        "totalMAE_sr = 0\n",
        "totalRMSE_lr = 0\n",
        "totalRMSE_sr = 0\n",
        "totalMAX_lr = 0\n",
        "totalMAX_sr = 0\n",
        "fileCounter = 0 \n",
        "\n",
        "# read low resolution images\n",
        "for file in os.listdir(path_lr):  \n",
        "    current = os.path.join(path_lr, file)\n",
        "    fileCounter += 1;\n",
        "    x, y = [], []\n",
        "    with rasterio.open(current) as src:\n",
        "        arrayLR = src.read(\n",
        "            out_shape=(\n",
        "                src.count,\n",
        "                int(src.height),\n",
        "                int(src.width)\n",
        "            )\n",
        "        )        \n",
        "    max_val_lr = np.amax(arrayLR)\n",
        "    min_val_lr = np.amin(arrayLR)\n",
        "    arrayLR = reshape_as_image(arrayLR)\n",
        "    nsamples, nx, ny = arrayLR.shape\n",
        "    arrayLR = arrayLR.reshape((nsamples,nx*ny))\n",
        "   \n",
        "    f = rasterio.open(current)\n",
        "    d1 = f.read(1)\n",
        "    tensorImage = TensorImage([d1])\n",
        "    tensorImage = (tensorImage - min_val_lr) / (max_val_lr - min_val_lr)\n",
        "    arraySR = learn.predict(tensorImage)\n",
        "    arraySR = arraySR[0].numpy()\n",
        "\n",
        "    # recalculate real world values for sr image\n",
        "    arraySR = arraySR * (max_val_lr - min_val_lr) + min_val_lr\n",
        "    arraySR = np.squeeze(arraySR, axis=0)\n",
        "   \n",
        "    # maximal value of sr image    \n",
        "    max_val_sr = np.amax(arraySR)\n",
        "\n",
        "    # load hr image to compare images    \n",
        "    currentHr = os.path.join(path_hr, file)\n",
        "    with rasterio.open(currentHr) as src:\n",
        "        arrayHR = src.read(\n",
        "            out_shape=(\n",
        "                src.count,\n",
        "                int(src.height),\n",
        "                int(src.width)\n",
        "            )\n",
        "        )\n",
        " \n",
        "    arrayHR = reshape_as_image(arrayHR)\n",
        "    nsamples, nx, ny = arrayHR.shape\n",
        "    arrayHR = arrayHR.reshape((nsamples,nx*ny))\n",
        "\n",
        "    # slice 15 pixels from border because border effects\n",
        "    slice_val = 15\n",
        "    HrSlice = arrayHR[slice_val:-slice_val, slice_val:-slice_val]\n",
        "    SrSlice = arraySR[slice_val:-slice_val, slice_val:-slice_val]\n",
        "    LrSlice = arrayLR[slice_val:-slice_val, slice_val:-slice_val]\n",
        "    \n",
        "    # calculate the metrics for sr image\n",
        "    MAE_sr = mean_absolute_error(arrayHR, arraySR)\n",
        "    RMSE_sr = mean_squared_error(arrayHR, arraySR, squared=False)\n",
        "    SSI_sr = structural_similarity(arrayHR, arraySR)\n",
        "    MAE_sr_slice = mean_absolute_error(HrSlice, SrSlice)\n",
        "    RMSE_sr_slice = mean_squared_error(HrSlice, SrSlice, squared=False)\n",
        "    SSI_sr_slice = structural_similarity(HrSlice, SrSlice)\n",
        "    MAX_sr = np.amax(abs(arrayHR - arraySR))\n",
        "    MAX_sr_slice = np.amax(abs(HrSlice - SrSlice))\n",
        "    # adapt data_range to the maximal overall value  of the dataset\n",
        "    PSNR_sr = peak_signal_noise_ratio(arrayHR, arraySR, data_range=max_height)\n",
        "    PSNR_sr_slice = peak_signal_noise_ratio(HrSlice, SrSlice, data_range=max_height)\n",
        "    \n",
        "    # calculate the metrics for lr image\n",
        "    MAE_lr = mean_absolute_error(arrayHR, arrayLR)\n",
        "    RMSE_lr = mean_squared_error(arrayHR, arrayLR, squared=False)\n",
        "    SSI_lr = structural_similarity(arrayHR, arrayLR)\n",
        "    MAE_lr_slice = mean_absolute_error(HrSlice, LrSlice)\n",
        "    RMSE_lr_slice = mean_squared_error(HrSlice, LrSlice, squared=False)\n",
        "    SSI_lr_slice = structural_similarity(HrSlice, LrSlice)\n",
        "    MAX_lr = np.amax(abs(arrayHR - arrayLR))\n",
        "    MAX_lr_slice = np.amax(abs(HrSlice - LrSlice))\n",
        "    # adapt data_range to the maximal overall value  of the dataset\n",
        "    PSNR_lr = peak_signal_noise_ratio(arrayHR, arrayLR, data_range=max_height)\n",
        "    PSNR_lr_slice = peak_signal_noise_ratio(HrSlice, LrSlice, data_range=max_height)\n",
        "    \n",
        "    # MAE count improved images\n",
        "    if MAE_sr < MAE_lr:\n",
        "        countMAE += 1\n",
        "    if MAE_sr_slice < MAE_lr_slice:\n",
        "        countMAESlice += 1    \n",
        "    # RMSE count improved images\n",
        "    if RMSE_sr < RMSE_lr:\n",
        "        countRMSE += 1\n",
        "    if RMSE_sr_slice < RMSE_lr_slice:\n",
        "        countRMSESlice += 1 \n",
        "    # PSNR count imporved images, higher values are better\n",
        "    if PSNR_sr > PSNR_lr:\n",
        "        countPSNR += 1\n",
        "    if PSNR_sr_slice > PSNR_lr_slice:\n",
        "        countPSNRSlice += 1    \n",
        "    # SSI count imporved images, higher values are better\n",
        "    if SSI_sr > SSI_lr:\n",
        "        countSSI += 1\n",
        "    if SSI_sr_slice > SSI_lr_slice:\n",
        "        countSSISlice += 1\n",
        "\n",
        "    totalMAE_lr = totalMAE_lr + MAE_lr_slice\n",
        "    totalMAE_sr = totalMAE_sr + MAE_sr_slice\n",
        "    totalRMSE_lr = totalRMSE_lr + RMSE_lr_slice\n",
        "    totalRMSE_sr = totalRMSE_sr + RMSE_sr_slice        \n",
        "    totalMAX_lr = totalMAX_lr + MAX_lr_slice\n",
        "    totalMAX_sr = totalMAX_sr + MAX_sr_slice \n",
        "\n",
        "    # print metrics for every tile if desired\n",
        "    #print(file ,' SR MaxError: ', MAX_sr_slice  ,' LR MaxError: ', MAX_lr_slice, ' Difference MaxError LR - SR: ', MAX_lr_slice - MAX_sr_slice)\n",
        "    #print(file ,' SR MAE: ', MAE_sr_slice ,' LR MAE: ', MAE_lr_slice, ' Difference LR - SR: ', MAE_lr_slice - MAE_sr_slice)\n",
        "    #print(file ,' SR RMSE: ', RMSE_sr_slice ,' LR RMSE: ', RMSE_lr_slice, ' Difference LR - SR: ', RMSE_lr_slice - RMSE_sr_slice)\n",
        "    #print(file ,' SR PSNR: ', PSNR_sr_slice ,' LR PSNR: ', PSNR_lr_slice, ' Difference LR - SR: ', PSNR_lr_slice - PSNR_sr_slice)\n",
        "\n",
        "print('Mean MAE LR', totalMAE_lr/fileCounter);\n",
        "print('Mean MAE SR', totalMAE_sr/fileCounter);\n",
        "print('Difference LR - SR MAE ', totalMAE_lr/fileCounter - totalMAE_sr/fileCounter)\n",
        "print('Mean RMSE LR', totalRMSE_lr/fileCounter);\n",
        "print('Mean RMSE SR', totalRMSE_sr/fileCounter);\n",
        "print('Difference SR - LR RMSE ', totalRMSE_lr/fileCounter - totalRMSE_sr/fileCounter)\n",
        "print('MAX LR', totalMAX_lr/fileCounter);\n",
        "print('MAX SR', totalMAX_sr/fileCounter);\n",
        "print('Difference LR - SR MAX ', totalMAX_lr/fileCounter - totalMAX_sr/fileCounter)"
      ],
      "execution_count": null,
      "outputs": [


        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean MAE LR 2.63192657828331\n",
            "Mean MAE SR 19.325050439834595\n",
            "Difference LR - SR MAE  -16.693123861551285\n",
            "Mean RMSE LR 3.3373901045322416\n",
            "Mean RMSE SR 33.0080516242981\n",
            "Difference SR - LR RMSE  -29.670661519765858\n",
            "MAX LR 29.542468872070312\n",
            "MAX SR 154.45137084960936\n",
            "Difference LR - SR MAX  -124.90890197753905\n"
          ]
        }
      ]
    }
  ]
}