{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h2q27gKz1H20"
      },
      "source": [
        "##### Copyright 2023 The MediaPipe Authors. All Rights Reserved."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TUfAcER1oUS6"
      },
      "outputs": [],
      "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"
      },
      "source": [
        "# Hand Landmarks Detection with MediaPipe Tasks\n",
        "\n",
        "This notebook shows you how to use MediaPipe Tasks Python API to detect hand landmarks from images."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O6PN9FvIx614"
      },
      "source": [
        "## Preparation\n",
        "\n",
        "Let's start with installing MediaPipe."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gxbHBsF-8Y_l"
      },
      "outputs": [],
      "source": [
        "!pip install -q mediapipe"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a49D7h4TVmru"
      },
      "source": [
        "Then download an off-the-shelf model bundle. Check out the [MediaPipe documentation](https://developers.google.com/mediapipe/solutions/vision/hand_landmarker#models) for more information about this model bundle."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OMjuVQiDYJKF"
      },
      "outputs": [],
      "source": [
        "!wget -q https://storage.googleapis.com/mediapipe-models/hand_landmarker/hand_landmarker/float16/1/hand_landmarker.task"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YYKAJ5nDU8-I"
      },
      "source": [
        "## Visualization utilities"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "s3E6NFV-00Qt"
      },
      "outputs": [],
      "source": [
        "#@markdown We implemented some functions to visualize the hand landmark detection results. <br/> Run the following cell to activate the functions.\n",
        "import mediapipe as mp\n",
        "import numpy as np\n",
        "\n",
        "mp_hands = mp.tasks.vision.HandLandmarksConnections\n",
        "mp_drawing = mp.tasks.vision.drawing_utils\n",
        "mp_drawing_styles = mp.tasks.vision.drawing_styles\n",
        "\n",
        "MARGIN = 10  # pixels\n",
        "FONT_SIZE = 1\n",
        "FONT_THICKNESS = 1\n",
        "HANDEDNESS_TEXT_COLOR = (88, 205, 54) # vibrant green\n",
        "\n",
        "def draw_landmarks_on_image(rgb_image, detection_result):\n",
        "  hand_landmarks_list = detection_result.hand_landmarks\n",
        "  handedness_list = detection_result.handedness\n",
        "  annotated_image = np.copy(rgb_image)\n",
        "\n",
        "  # Loop through the detected hands to visualize.\n",
        "  for idx in range(len(hand_landmarks_list)):\n",
        "    hand_landmarks = hand_landmarks_list[idx]\n",
        "    handedness = handedness_list[idx]\n",
        "\n",
        "    # Draw the hand landmarks.\n",
        "    mp_drawing.draw_landmarks(\n",
        "      annotated_image,\n",
        "      hand_landmarks,\n",
        "      mp_hands.HAND_CONNECTIONS,\n",
        "      mp_drawing_styles.get_default_hand_landmarks_style(),\n",
        "      mp_drawing_styles.get_default_hand_connections_style())\n",
        "\n",
        "    # Get the top left corner of the detected hand's bounding box.\n",
        "    height, width, _ = annotated_image.shape\n",
        "    x_coordinates = [landmark.x for landmark in hand_landmarks]\n",
        "    y_coordinates = [landmark.y for landmark in hand_landmarks]\n",
        "    text_x = int(min(x_coordinates) * width)\n",
        "    text_y = int(min(y_coordinates) * height) - MARGIN\n",
        "\n",
        "    # Draw handedness (left or right hand) on the image.\n",
        "    cv2.putText(annotated_image, f\"{handedness[0].category_name}\",\n",
        "                (text_x, text_y), cv2.FONT_HERSHEY_DUPLEX,\n",
        "                FONT_SIZE, HANDEDNESS_TEXT_COLOR, FONT_THICKNESS, cv2.LINE_AA)\n",
        "\n",
        "  return annotated_image"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "83PEJNp9yPBU"
      },
      "source": [
        "## Download test image\n",
        "\n",
        "Let's grab a test image that we'll use later. The image is from [Unsplash](https://unsplash.com/photos/mt2fyrdXxzk)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tzXuqyIBlXer"
      },
      "outputs": [],
      "source": [
        "!wget -q -O image.jpg https://storage.googleapis.com/mediapipe-tasks/hand_landmarker/woman_hands.jpg\n",
        "\n",
        "import cv2\n",
        "from google.colab.patches import cv2_imshow\n",
        "\n",
        "img = cv2.imread(\"image.jpg\")\n",
        "cv2_imshow(img)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u-skLwMBmMN_"
      },
      "source": [
        "Optionally, you can upload your own image. If you want to do so, uncomment and run the cell below."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "etBjSdwImQPw"
      },
      "outputs": [],
      "source": [
        "# from google.colab import files\n",
        "# uploaded = files.upload()\n",
        "\n",
        "# for filename in uploaded:\n",
        "#   content = uploaded[filename]\n",
        "#   with open(filename, 'wb') as f:\n",
        "#     f.write(content)\n",
        "\n",
        "# if len(uploaded.keys()):\n",
        "#   IMAGE_FILE = next(iter(uploaded))\n",
        "#   print('Uploaded file:', IMAGE_FILE)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Iy4r2_ePylIa"
      },
      "source": [
        "## Running inference and visualizing the results\n",
        "\n",
        "Here are the steps to run hand landmark detection using MediaPipe.\n",
        "\n",
        "Check out the [MediaPipe documentation](https://developers.google.com/mediapipe/solutions/vision/hand_landmarker/python) to learn more about configuration options that this solution supports.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_JVO3rvPD4RN"
      },
      "outputs": [],
      "source": [
        "# STEP 1: Import the necessary modules.\n",
        "import mediapipe as mp\n",
        "from mediapipe.tasks import python\n",
        "from mediapipe.tasks.python import vision\n",
        "\n",
        "# STEP 2: Create an HandLandmarker object.\n",
        "base_options = python.BaseOptions(model_asset_path='hand_landmarker.task')\n",
        "options = vision.HandLandmarkerOptions(base_options=base_options,\n",
        "                                       num_hands=2)\n",
        "detector = vision.HandLandmarker.create_from_options(options)\n",
        "\n",
        "# STEP 3: Load the input image.\n",
        "image = mp.Image.create_from_file(\"image.jpg\")\n",
        "\n",
        "# STEP 4: Detect hand landmarks from the input image.\n",
        "detection_result = detector.detect(image)\n",
        "\n",
        "# STEP 5: Process the classification result. In this case, visualize it.\n",
        "annotated_image = draw_landmarks_on_image(image.numpy_view(), detection_result)\n",
        "cv2_imshow(cv2.cvtColor(annotated_image, cv2.COLOR_RGB2BGR))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SE6_sPCXaX3g"
      },
      "outputs": [],
      "source": []
    }
  ],
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      "collapsed_sections": [
        "h2q27gKz1H20"
      ],
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    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
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    "language_info": {
      "codemirror_mode": {
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