examples/face_stylizer/python/face_stylizer.ipynb

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      "source": [
        "##### Copyright 2023 The MediaPipe Authors. All Rights Reserved."
      ]
    },
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      "source": [
        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L_cQX8dWu4Dv"
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      "source": [
        "# Face Stylizer\n",
        "\n",
        "This notebook shows you how to use the MediaPipe Tasks Python API to generate stylize faces given a model and input image. This is an experimental feature, so please report any bugs or issues in this sample through the [GitHub sample repo](https://github.com/googlesamples/mediapipe)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O6PN9FvIx614"
      },
      "source": [
        "## Imports and Setup\n",
        "Let's start with installing MediaPipe and the related dependencies."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gxbHBsF-8Y_l"
      },
      "outputs": [],
      "source": [
        "!pip install -q mediapipe"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a49D7h4TVmru"
      },
      "source": [
        "## Download the face stylizer model"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The next thing you will need to do is download the face stylizer model that will be used for this demo. This model is already trained with a pre-determined style that will be used for face stylization."
      ],
      "metadata": {
        "id": "QHXsuWDxpOj4"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OMjuVQiDYJKF"
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      "outputs": [],
      "source": [
        "#@title Start downloading here.\n",
        "!wget -O face_stylizer.task -q https://storage.googleapis.com/mediapipe-models/face_stylizer/blaze_face_stylizer/float32/latest/face_stylizer_color_sketch.task"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "83PEJNp9yPBU"
      },
      "source": [
        "## Download a test image\n",
        "\n",
        "After downloading the model, you will need to download an image that you can use for testing! It's worth noting that while this is working with a single image, you can download a collection of images to store in the `IMAGE_FILENAMES` array."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tzXuqyIBlXer"
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      "source": [
        "import urllib\n",
        "IMAGE_FILENAMES = ['business-person.png']\n",
        "\n",
        "for name in IMAGE_FILENAMES:\n",
        "  url = f'https://storage.googleapis.com/mediapipe-assets/{name}'\n",
        "  urllib.request.urlretrieve(url, name)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P8XRmapjySMN"
      },
      "source": [
        "## Preview the downloaded image"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "You can ensure that everything has downloaded correctly by using the following code to display the downloaded image."
      ],
      "metadata": {
        "id": "Eu_q-Z03r-ed"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8rjHk72-lmHX"
      },
      "outputs": [],
      "source": [
        "import cv2\n",
        "from google.colab.patches import cv2_imshow\n",
        "import math\n",
        "\n",
        "# Height and width that will be used by the model\n",
        "DESIRED_HEIGHT = 480\n",
        "DESIRED_WIDTH = 480\n",
        "\n",
        "# Performs resizing and showing the image\n",
        "def resize_and_show(image):\n",
        "  h, w = image.shape[:2]\n",
        "  if h < w:\n",
        "    img = cv2.resize(image, (DESIRED_WIDTH, math.floor(h/(w/DESIRED_WIDTH))))\n",
        "  else:\n",
        "    img = cv2.resize(image, (math.floor(w/(h/DESIRED_HEIGHT)), DESIRED_HEIGHT))\n",
        "  cv2_imshow(img)\n",
        "\n",
        "\n",
        "# Preview the image(s)\n",
        "images = {name: cv2.imread(name) for name in IMAGE_FILENAMES}\n",
        "for name, image in images.items():\n",
        "  print(name)\n",
        "  resize_and_show(image)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Iy4r2_ePylIa"
      },
      "source": [
        "## Running inference and visualizing the results\n",
        "To run inference using the face stylizer MediaPipe Task, you will need to initialize the `FaceStylizer` using the model. Once the stylizer has been initialized, you can use the sytlize method to apply the pre-trained model style onto your image."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Yl_Oiye4mUuo"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import mediapipe as mp\n",
        "\n",
        "from mediapipe.tasks import python\n",
        "from mediapipe.tasks.python import vision\n",
        "\n",
        "\n",
        "# Create the options that will be used for FaceStylizer\n",
        "base_options = python.BaseOptions(model_asset_path='face_stylizer.task')\n",
        "options = vision.FaceStylizerOptions(base_options=base_options)\n",
        "\n",
        "# Create the face stylizer\n",
        "with vision.FaceStylizer.create_from_options(options) as stylizer:\n",
        "\n",
        "  # Loop through demo image(s)\n",
        "  for image_file_name in IMAGE_FILENAMES:\n",
        "\n",
        "    # Create the MediaPipe image file that will be stylized\n",
        "    image = mp.Image.create_from_file(image_file_name)\n",
        "    # Retrieve the stylized image\n",
        "    stylized_image = stylizer.stylize(image)\n",
        "\n",
        "    # Show the stylized image\n",
        "    rgb_stylized_image = cv2.cvtColor(stylized_image.numpy_view(), cv2.COLOR_BGR2RGB)\n",
        "    resize_and_show(rgb_stylized_image)"
      ]
    }
  ],
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