NAFNet_demo.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rKu2--YjjiNd"
      },
      "source": [
        "論文  \n",
        "https://arxiv.org/abs/2204.04676<br>\n",
        "GitHub<br>\n",
        "https://github.com/megvii-research/NAFNet<br>\n",
        "<br>\n",
        "<a href=\"https://colab.research.google.com/github/kaz12tech/ai_demos/blob/master/NAFNet_demo.ipynb\" target=\"_blank\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YWhJj2RejiNg"
      },
      "source": [
        "# 環境セットアップ"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2IuH74fQjiNg"
      },
      "source": [
        "## GPU確認"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2PcII5fLdHnJ",
        "outputId": "3fd96959-59eb-4e0e-f7b5-6e4f5c994cb6"
      },
      "outputs": [],
      "source": [
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XjrCXTWkjiNi"
      },
      "source": [
        "## GitHubからコード取得"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R-Y5auuvnnnT",
        "outputId": "2934c5bd-4dd4-4200-d76d-afc6f75dd28e"
      },
      "outputs": [],
      "source": [
        "%cd /content\n",
        "\n",
        "!git clone https://github.com/megvii-research/NAFNet"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6pEp-7bNjiNj"
      },
      "source": [
        "## ライブラリのインストール"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LScsvO3UppqT",
        "outputId": "ce587902-c2e1-4e6a-e60f-ebe38e3d14a6"
      },
      "outputs": [],
      "source": [
        "%cd /content/NAFNet\n",
        "\n",
        "!pip install -r requirements.txt\n",
        "!pip install --upgrade --no-cache-dir gdown\n",
        "!python3 setup.py develop --no_cuda_ext"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nDsSRIjsjiNj"
      },
      "source": [
        "## ライブラリのインポート"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o2lxptJeo6kr",
        "outputId": "df597c70-0769-4729-ebcd-42f21b563ac7"
      },
      "outputs": [],
      "source": [
        "%cd /content/NAFNet\n",
        "\n",
        "import gdown\n",
        "\n",
        "import torch\n",
        "\n",
        "from basicsr.models import create_model\n",
        "from basicsr.utils import img2tensor as _img2tensor, tensor2img, imwrite\n",
        "from basicsr.utils.options import parse\n",
        "import numpy as np\n",
        "import cv2\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import os\n",
        "from google.colab import files\n",
        "import shutil\n",
        "import glob"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uoREcCRMjiNk"
      },
      "source": [
        "# 学習済みモデルのダウンロード\n",
        "Access denied with the following error:<br>\n",
        "が発生する場合、何回か実行"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QYmO4couoYhz",
        "outputId": "1341f812-90f5-4f83-e329-ffc15a803ff9"
      },
      "outputs": [],
      "source": [
        "%cd /content/NAFNet\n",
        "\n",
        "# Denoise\n",
        "if not os.path.exists(\"./experiments/pretrained_models/NAFNet-SIDD-width64.pth\"):\n",
        "  gdown.download('https://drive.google.com/uc?id=14Fht1QQJ2gMlk4N1ERCRuElg8JfjrWWR', \"./experiments/pretrained_models/\", quiet=False)\n",
        "# Deblur\n",
        "if not os.path.exists(\"./experiments/pretrained_models/NAFNet-REDS-width64.pth\"):\n",
        "  gdown.download('https://drive.google.com/uc?id=14D4V4raNYIOhETfcuuLI3bGLB-OYIv6X', \"./experiments/pretrained_models/\", quiet=False)\n",
        "# Super Resolution\n",
        "if not os.path.exists(\"./experiments/pretrained_models/NAFSSR-L_4x.pth\"):\n",
        "  gdown.download('https://drive.google.com/uc?id=1TIdQhPtBrZb2wrBdAp9l8NHINLeExOwb', \"./experiments/pretrained_models/\", quiet=False)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IYP8fIDCjiNl"
      },
      "source": [
        "# テスト画像のセットアップ"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iAm3NJEyHypU"
      },
      "source": [
        "## デモ用画像のダウンロード"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ljLvp7i_HLQc",
        "outputId": "6465b2e3-ba45-45fb-d447-a18a848fb265"
      },
      "outputs": [],
      "source": [
        "%cd /content/NAFNet\n",
        "\n",
        "# Denoise\n",
        "if not os.path.exists(\"./demo_input/noisy-demo-0.png\"):\n",
        "  gdown.download('https://drive.google.com/uc?id=1uKwZUgeGfBYLlPKllSuzgGUItlzb40hm', \"demo_input/\", quiet=False)\n",
        "\n",
        "# Deblur\n",
        "if not os.path.exists(\"./demo_input/blurry-reds-0.jpg\"):\n",
        "  gdown.download('https://drive.google.com/uc?id=1kWjrGsAvh4gOA_gn7rB9vnnQVfRINwEn', \"demo_input/\", quiet=False)\n",
        "\n",
        "# Super Resolution\n",
        "if not os.path.exists(\"./demo_input/Middlebury_lr_x4_sword2_l.png\"):\n",
        "  gdown.download('https://drive.google.com/uc?id=15MLvll3frPC2ICjTUnXvnLe58D_dONXc', \"demo_input/\", quiet=False)\n",
        "if not os.path.exists(\"./demo_input/Middlebury_lr_x4_sword2_r.png\"):\n",
        "  gdown.download('https://drive.google.com/uc?id=1tedRtTen7LFHXsaC4ddwwg8ZkbEHPL0b', \"demo_input/\", quiet=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j1HVhLKlS5pF"
      },
      "source": [
        "## デモ用画像のアップロード\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "huzn7-0olYd_"
      },
      "source": [
        "### Denoise\n",
        "[luminance noise画像](https://assets-global.website-files.com/60e4d0d0155e62117f4faef3/620c027c4aabcea88b2285d2_Example%20of%20luminance%20noise.jpg)<br>\n",
        "[Chromatic noise画像](https://assets-global.website-files.com/60e4d0d0155e62117f4faef3/620c02ce030b773058eaf663_Bill-Maynard-photo-of-a-mink.jpeg)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168,
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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",
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "ok": true,
              "status": 200,
              "status_text": ""
            }
          }
        },
        "id": "OuYhAjM6S86t",
        "outputId": "af889097-c233-48a7-fee8-b33e3a7b3d5b"
      },
      "outputs": [],
      "source": [
        "%cd /content/NAFNet\n",
        "\n",
        "denoise_upload_folder = 'upload/denoise_input'\n",
        "denoise_result_folder = 'upload/denoise_output'\n",
        "\n",
        "if os.path.isdir(denoise_upload_folder):\n",
        "    shutil.rmtree(denoise_upload_folder)\n",
        "if os.path.isdir(denoise_result_folder):\n",
        "    shutil.rmtree(denoise_result_folder)\n",
        "os.makedirs(denoise_upload_folder)\n",
        "os.makedirs(denoise_result_folder)\n",
        "\n",
        "# upload images\n",
        "uploaded = files.upload()\n",
        "for filename in uploaded.keys():\n",
        "  dst_path = os.path.join(denoise_upload_folder, filename)\n",
        "  print(f'move {filename} to {dst_path}')\n",
        "  shutil.move(filename, dst_path)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ULKxptOAlW7l"
      },
      "source": [
        "### Deblur\n",
        "[Blur画像](https://pbblogassets.s3.amazonaws.com/uploads/2019/10/09143411/motion-blur-cover.jpg)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 115,
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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",
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "ok": true,
              "status": 200,
              "status_text": ""
            }
          }
        },
        "id": "qx3u5RTYlaWW",
        "outputId": "dde52cd6-356b-4a16-8f1e-4e00d907b8c6"
      },
      "outputs": [],
      "source": [
        "%cd /content/NAFNet\n",
        "\n",
        "deblur_upload_folder = 'upload/deblur_input'\n",
        "deblur_result_folder = 'upload/deblur_output'\n",
        "\n",
        "if os.path.isdir(deblur_upload_folder):\n",
        "    shutil.rmtree(deblur_upload_folder)\n",
        "if os.path.isdir(deblur_result_folder):\n",
        "    shutil.rmtree(deblur_result_folder)\n",
        "os.makedirs(deblur_upload_folder)\n",
        "os.makedirs(deblur_result_folder)\n",
        "\n",
        "# upload images\n",
        "uploaded = files.upload()\n",
        "for filename in uploaded.keys():\n",
        "  dst_path = os.path.join(deblur_upload_folder, filename)\n",
        "  print(f'move {filename} to {dst_path}')\n",
        "  shutil.move(filename, dst_path)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dY-rL-eWrUxn"
      },
      "source": [
        "### Super Resolution\n",
        "[small画像](https://www.google.com/url?sa=i&url=https%3A%2F%2Fs.awa.fm%2Ftrack%2Fd9a068a1ba12b36aa983&psig=AOvVaw39U0Eb-ZfxdmYXxSnH33IX&ust=1651852662698000&source=images&cd=vfe&ved=0CAwQjRxqFwoTCMDK14HdyPcCFQAAAAAdAAAAABAD)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 115,
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "ok": true,
              "status": 200,
              "status_text": ""
            }
          }
        },
        "id": "vfQDMBAVrX0q",
        "outputId": "15dec920-67aa-434f-961c-c4e199f36b0b"
      },
      "outputs": [],
      "source": [
        "%cd /content/NAFNet\n",
        "\n",
        "sr_upload_folder = 'upload/sr_input'\n",
        "sr_result_folder = 'upload/sr_output'\n",
        "\n",
        "if os.path.isdir(sr_upload_folder):\n",
        "    shutil.rmtree(sr_upload_folder)\n",
        "if os.path.isdir(sr_result_folder):\n",
        "    shutil.rmtree(sr_result_folder)\n",
        "os.makedirs(sr_upload_folder)\n",
        "os.makedirs(sr_result_folder)\n",
        "\n",
        "# upload images\n",
        "uploaded = files.upload()\n",
        "for filename in uploaded.keys():\n",
        "  dst_path = os.path.join(sr_upload_folder, filename)\n",
        "  print(f'move {filename} to {dst_path}')\n",
        "  shutil.move(filename, dst_path)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sX6G62L_nDjK"
      },
      "source": [
        "# 画像系Utility関数定義"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b1WNTef3nG8-"
      },
      "outputs": [],
      "source": [
        "def imread(img_path):\n",
        "  img = cv2.imread(img_path)\n",
        "  img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
        "  return img\n",
        "def img2tensor(img, bgr2rgb=False, float32=True):\n",
        "    img = img.astype(np.float32) / 255.\n",
        "    return _img2tensor(img, bgr2rgb=bgr2rgb, float32=float32)\n",
        "\n",
        "def display(img1, img2):\n",
        "  fig = plt.figure(figsize=(25, 10))\n",
        "  ax1 = fig.add_subplot(1, 2, 1) \n",
        "  plt.title('Input image', fontsize=16)\n",
        "  ax1.axis('off')\n",
        "  ax2 = fig.add_subplot(1, 2, 2)\n",
        "  plt.title('NAFNet output', fontsize=16)\n",
        "  ax2.axis('off')\n",
        "  ax1.imshow(img1)\n",
        "  ax2.imshow(img2)\n",
        "\n",
        "def single_image_inference(model, img, save_path):\n",
        "      model.feed_data(data={'lq': img.unsqueeze(dim=0)})\n",
        "\n",
        "      if model.opt['val'].get('grids', False):\n",
        "          model.grids()\n",
        "\n",
        "      model.test()\n",
        "\n",
        "      if model.opt['val'].get('grids', False):\n",
        "          model.grids_inverse()\n",
        "\n",
        "      visuals = model.get_current_visuals()\n",
        "      sr_img = tensor2img([visuals['result']])\n",
        "      imwrite(sr_img, save_path)\n",
        "\n",
        "def sr_display(LR_l, LR_r, SR_l, SR_r):\n",
        "  h,w = SR_l.shape[:2]\n",
        "  LR_l = cv2.resize(LR_l, (w,h), interpolation=cv2.INTER_CUBIC)\n",
        "  LR_r = cv2.resize(LR_r, (w,h), interpolation=cv2.INTER_CUBIC)\n",
        "  fig = plt.figure(figsize=(w//40, h//40))\n",
        "  ax1 = fig.add_subplot(2, 2, 1) \n",
        "  plt.title('Input image (Left)', fontsize=16)\n",
        "  ax1.axis('off')\n",
        "  ax2 = fig.add_subplot(2, 2, 2)\n",
        "  plt.title('NAFSSR output (Left)', fontsize=16)\n",
        "  ax2.axis('off')\n",
        "  ax1.imshow(LR_l)\n",
        "  ax2.imshow(SR_l)\n",
        "\n",
        "  ax3 = fig.add_subplot(2, 2, 3) \n",
        "  plt.title('Input image (Right)', fontsize=16)\n",
        "  ax3.axis('off')\n",
        "  ax4 = fig.add_subplot(2, 2, 4)\n",
        "  plt.title('NAFSSR output (Right)', fontsize=16)\n",
        "  ax4.axis('off')\n",
        "  ax3.imshow(LR_r)\n",
        "  ax4.imshow(SR_r)\n",
        "\n",
        "  plt.subplots_adjust(wspace=0.04, hspace=0.04)\n",
        "\n",
        "def stereo_image_inference(model, img_l, img_r, save_path):\n",
        "      img = torch.cat([img_l, img_r], dim=0)\n",
        "      model.feed_data(data={'lq': img.unsqueeze(dim=0)})\n",
        "\n",
        "      if model.opt['val'].get('grids', False):\n",
        "          model.grids()\n",
        "\n",
        "      model.test()\n",
        "\n",
        "      if model.opt['val'].get('grids', False):\n",
        "          model.grids_inverse()\n",
        "\n",
        "      visuals = model.get_current_visuals()\n",
        "      img_L = visuals['result'][:,:3]\n",
        "      img_R = visuals['result'][:,3:]\n",
        "      img_L, img_R = tensor2img([img_L, img_R])\n",
        "      \n",
        "      imwrite(img_L, save_path.format('L'))\n",
        "      imwrite(img_R, save_path.format('R'))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "34lIUD6qT-MC"
      },
      "source": [
        "# Denoise"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GZQ6CHE3TuA2"
      },
      "source": [
        "## モデルのビルド"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iSZAnN4uTxOr",
        "outputId": "733d0225-3384-4d78-cb70-b81aa0c4a82b"
      },
      "outputs": [],
      "source": [
        "%cd /content/NAFNet\n",
        "\n",
        "opt_path = 'options/test/SIDD/NAFNet-width64.yml'\n",
        "opt = parse(opt_path, is_train=False)\n",
        "opt['dist'] = False\n",
        "NAFNet = create_model(opt)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "itbGctnrUGPQ"
      },
      "source": [
        "## ダウンロード画像のDenoise"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "id": "aGG3ehvGUA5J",
        "outputId": "f1576864-8d7d-41df-b3f8-fb19a6b55c7f"
      },
      "outputs": [],
      "source": [
        "%cd /content/NAFNet\n",
        "\n",
        "input_path = 'demo_input/noisy-demo-0.png'\n",
        "output_path = 'demo_output/noisy-demo-0.png'\n",
        "\n",
        "img_input = imread(input_path)\n",
        "inp = img2tensor(img_input)\n",
        "single_image_inference(NAFNet, inp, output_path)\n",
        "img_output = imread(output_path)\n",
        "display(img_input, img_output)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7uJQZidYUJc2"
      },
      "source": [
        "## アップロード画像のDenoise"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 870
        },
        "id": "2BCUJ9q1UN1F",
        "outputId": "74bf69e1-b9d1-4b95-bb8a-0b915414f324"
      },
      "outputs": [],
      "source": [
        "%cd /content/NAFNet\n",
        "\n",
        "input_list = sorted(glob.glob(os.path.join(denoise_upload_folder, '*')))\n",
        "\n",
        "for input_path in input_list:\n",
        "  img_input = imread(input_path)\n",
        "  inp = img2tensor(img_input)\n",
        "  output_path = os.path.join(denoise_result_folder, os.path.basename(input_path))\n",
        "  single_image_inference(NAFNet, inp, output_path)\n",
        "\n",
        "output_list = sorted(glob.glob(os.path.join(denoise_result_folder, '*')))\n",
        "for input_path, output_path in zip(input_list, output_list):\n",
        "  img_input = imread(input_path)\n",
        "  img_output = imread(output_path)\n",
        "  display(img_input, img_output)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uhV8pJ8ako5i"
      },
      "source": [
        "# Deblur"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jp8eUKhTkrkq"
      },
      "source": [
        "## モデルのビルド"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EyioFk8RkrG5",
        "outputId": "be09f943-91f4-4cc7-d5ec-a55a4dd7e6f8"
      },
      "outputs": [],
      "source": [
        "%cd /content/NAFNet\n",
        "\n",
        "opt_path = 'options/test/REDS/NAFNet-width64.yml'\n",
        "opt = parse(opt_path, is_train=False)\n",
        "opt['dist'] = False\n",
        "NAFNet = create_model(opt)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1SQhRY90k1d9"
      },
      "source": [
        "## ダウンロード画像のDeblur"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        },
        "id": "PRVHJt94k0Yo",
        "outputId": "3579b680-69f0-43ca-c748-a2e2b659b2b9"
      },
      "outputs": [],
      "source": [
        "%cd /content/NAFNet\n",
        "\n",
        "input_path = 'demo_input/blurry-reds-0.jpg'\n",
        "output_path = 'demo_output/blurry-reds-0.jpg'\n",
        "\n",
        "img_input = imread(input_path)\n",
        "inp = img2tensor(img_input)\n",
        "single_image_inference(NAFNet, inp, output_path)\n",
        "img_output = imread(output_path)\n",
        "display(img_input, img_output)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KgStlMkOlpf7"
      },
      "source": [
        "## アップロード画像のDeblur"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        },
        "id": "ZuUrJCd6lrnu",
        "outputId": "00da0505-9801-4e01-b16c-871d06399895"
      },
      "outputs": [],
      "source": [
        "%cd /content/NAFNet\n",
        "\n",
        "input_list = sorted(glob.glob(os.path.join(deblur_upload_folder, '*')))\n",
        "\n",
        "for input_path in input_list:\n",
        "  img_input = imread(input_path)\n",
        "  inp = img2tensor(img_input)\n",
        "  output_path = os.path.join(deblur_result_folder, os.path.basename(input_path))\n",
        "  single_image_inference(NAFNet, inp, output_path)\n",
        "\n",
        "output_list = sorted(glob.glob(os.path.join(deblur_result_folder, '*')))\n",
        "for input_path, output_path in zip(input_list, output_list):\n",
        "  img_input = imread(input_path)\n",
        "  img_output = imread(output_path)\n",
        "  display(img_input, img_output)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "laxZpw9YrhDP"
      },
      "source": [
        "# Super Resolution"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ALI-JeQIri_u"
      },
      "source": [
        "## モデルのビルド"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "za5ULvAgrio1",
        "outputId": "f8dc15a9-76a0-4633-9e8c-38efe48796f5"
      },
      "outputs": [],
      "source": [
        "opt_path = 'options/test/NAFSSR/NAFSSR-L_4x.yml'\n",
        "opt = parse(opt_path, is_train=False)\n",
        "opt['dist'] = False\n",
        "NAFSSR = create_model(opt)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4GmTSosSrolU"
      },
      "source": [
        "## ダウンロード画像のSR"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 719
        },
        "id": "v05IqhNGroPa",
        "outputId": "4e63ac12-b87a-42d2-edec-b672dddb91f5"
      },
      "outputs": [],
      "source": [
        "%cd /content/NAFNet\n",
        "\n",
        "input_path_l = 'demo_input/Middlebury_lr_x4_sword2_l.png'\n",
        "input_path_r = 'demo_input/Middlebury_lr_x4_sword2_r.png'\n",
        "output_path = 'demo_output/Middlebury_sr_x4_sword2_{}.png'\n",
        "\n",
        "img_l = imread(input_path_l)\n",
        "inp_l = img2tensor(img_l)\n",
        "img_r = imread(input_path_r)\n",
        "inp_r = img2tensor(img_r)\n",
        "stereo_image_inference(NAFSSR, inp_l, inp_r, output_path)\n",
        "SR_l = imread(output_path.format('L'))\n",
        "SR_r = imread(output_path.format('R'))\n",
        "sr_display(img_l, img_r, SR_l, SR_r)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yTVrdrbNsB2-"
      },
      "source": [
        "## アップロード画像のSR"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "id": "3e_2erjusF0M",
        "outputId": "a3095549-4882-4105-b3a4-1111ffc2106d"
      },
      "outputs": [],
      "source": [
        "%cd /content/NAFNet\n",
        "\n",
        "input_list = sorted(glob.glob(os.path.join(sr_upload_folder, '*')))\n",
        "\n",
        "for input_path in input_list:\n",
        "  img_l = imread(input_path)\n",
        "  inp_l = img2tensor(img_l)\n",
        "  img_r = imread(input_path)\n",
        "  inp_r = img2tensor(img_r)\n",
        "  stereo_image_inference(NAFSSR, inp_l, inp_r, os.path.join(sr_result_folder, \"sr_result{}.png\"))\n",
        "\n",
        "output_list = sorted(glob.glob(os.path.join(sr_result_folder, '*')))\n",
        "for input_path, output_path in zip(input_list, output_list):\n",
        "  img_input = imread(input_path)\n",
        "  img_output = imread(output_path)\n",
        "  display(img_input, img_output)"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "name": "NAFNet_demo.ipynb",
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "orig_nbformat": 4
  },
  "nbformat": 4,
  "nbformat_minor": 0
}