2024-11-17-rev1-000-test.ipynb

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  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# Testing `2024-11-17-rev1-000` Model",
   "id": "f65cd8acf6ceeda7"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "import sys\n",
    "import os\n",
    "\n",
    "colab = 'google.colab' in sys.modules"
   ],
   "id": "8bbed4542154e55a",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "if colab:\n",
    "    os.environ['PROJECT_DIR'] = project_dir = '/content/Foggy-CycleGAN'\n",
    "    replace = True\n",
    "    if os.path.isdir(project_dir):\n",
    "        choice = input(\"Project already exists in folder \" +\n",
    "                       \"{}\\nDelete the files and pull again? Enter Y/(N):\\n\"\n",
    "                       .format(project_dir))\n",
    "        if choice.lower() == 'y':\n",
    "            !rm -r $PROJECT_DIR\n",
    "            print(\"Deleted folder {}\".format(project_dir))\n",
    "        else:\n",
    "            replace = False\n",
    "            print(\"Nothing was changed.\")\n",
    "    if replace:\n",
    "        !cd /content && git clone https://github.com/ghaiszaher/Foggy-CycleGAN.git\n",
    "        print(\"Project cloned to \" + project_dir)\n",
    "    os.chdir(project_dir)\n",
    "    print(\"Done.\")"
   ],
   "id": "febf3f49d71bb78a",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "from lib.tools import create_dir\n",
    "\n",
    "output = './weights/generator_clear2fog.weights.h5'\n",
    "if not os.path.exists(output):\n",
    "    import gdown\n",
    "    file_id = '10cBLNuofYGbyrC6DKRycD-fmx0Wh7BCr'\n",
    "    create_dir('./weights/')\n",
    "    print(\"Downloading weights...\")\n",
    "    gdown.download(f'https://drive.google.com/uc?id={file_id}', output, quiet=False)\n",
    "    print(\"Weights downloaded.\")\n",
    "else:\n",
    "    print(\"Weights already downloaded.\")"
   ],
   "id": "40bb017068d0d5a3",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "from lib.models import ModelsBuilder\n",
    "\n",
    "models_builder = ModelsBuilder()\n",
    "generator_clear2fog = models_builder.build_generator()\n",
    "generator_clear2fog.load_weights('./weights/generator_clear2fog.weights.h5')"
   ],
   "id": "d3ffc8f1b4f3f6db",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "from lib.plot import plot_clear2fog_intensity\n",
    "import tensorflow as tf\n",
    "from lib.dataset import DatasetInitializer\n",
    "\n",
    "datasetInit = DatasetInitializer(256, 256)\n",
    "\n",
    "input_path = './custom-images/input/image.png'  #@param {type:\"string\"}\n",
    "fog_intensity = 0.4  #@param {type:\"slider\", min:0.05, max:1.00, step:0.05}\n",
    "output_path = './custom-images/output/image.png'  #@param {type:\"string\"}\n",
    "\n",
    "image_clear = tf.io.decode_png(tf.io.read_file(input_path), channels=3)\n",
    "image_clear, _ = datasetInit.preprocess_image_test(image_clear, 0)\n",
    "\n",
    "fig, foggy_image = plot_clear2fog_intensity(generator_clear2fog, image_clear, fog_intensity)\n",
    "foggy_image_uint8 = tf.image.convert_image_dtype(foggy_image, dtype=tf.uint8)\n",
    "encoded_image = tf.io.encode_png(foggy_image_uint8)\n",
    "tf.io.write_file(output_path, encoded_image)\n",
    "print(f\"Saved foggy image to {output_path}\")"
   ],
   "id": "ed5c34bc882f2c05",
   "outputs": [],
   "execution_count": null
  }
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