MoveNet_SinglePose_Demo.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Masum06/Automated-Ataxia-Gait/blob/main/MoveNet_SinglePose_Demo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "toCy3v03Dwx7"
      },
      "source": [
        "##### Copyright 2021 The TensorFlow Hub Authors.\n",
        "\n",
        "Licensed under the Apache License, Version 2.0 (the \"License\");"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QKe-ubNcDvgv"
      },
      "outputs": [],
      "source": [
        "# Copyright 2021 The TensorFlow Hub Authors. All Rights Reserved.\n",
        "#\n",
        "# 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",
        "#     http://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.\n",
        "# =============================================================================="
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KqtQzBCpIJ7Y"
      },
      "source": [
        "# MoveNet: Ultra fast and accurate pose detection model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MCmFOosnSkCd"
      },
      "source": [
        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://www.tensorflow.org/hub/tutorials/movenet\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" />View on TensorFlow.org</a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/hub/blob/master/examples/colab/movenet.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://github.com/tensorflow/hub/blob/master/examples/colab/movenet.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View on GitHub</a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a href=\"https://storage.googleapis.com/tensorflow_docs/hub/examples/colab/movenet.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\" />Download notebook</a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a href=\"https://tfhub.dev/s?q=movenet\"><img src=\"https://www.tensorflow.org/images/hub_logo_32px.png\" />See TF Hub models</a>\n",
        "  </td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6x99e0aEY_d6"
      },
      "source": [
        "**[MoveNet](https://t.co/QpfnVL0YYI?amp=1)** is an ultra fast and accurate model that detects 17 keypoints of a body. The model is offered on [TF Hub](https://tfhub.dev/s?q=movenet) with two variants, known as Lightning and Thunder. Lightning is intended for latency-critical applications, while Thunder is intended for applications that require high accuracy. Both models run faster than real time (30+ FPS) on most modern desktops, laptops, and phones, which proves crucial for live fitness, health, and wellness applications.\n",
        "\n",
        "\n",
        "\n",
        "*Images downloaded from Pexels (https://www.pexels.com/)\n",
        "\n",
        "This Colab walks you through the details of how to load MoveNet, and run inference on the input image and video below.\n",
        "\n",
        "Note: check out the [live demo](https://storage.googleapis.com/tfjs-models/demos/pose-detection/index.html?model=movenet) for how the model works!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "10_zkgbZBkIE"
      },
      "source": [
        "# Human Pose Estimation with MoveNet"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9u_VGR6_BmbZ"
      },
      "source": [
        "## Visualization libraries & Imports"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TtcwSIcgbIVN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9ee8a9d9-b39c-4e63-b38c-1c5bdda583c4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Building wheel for tensorflow-docs (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "!pip install -q imageio\n",
        "!pip install -q opencv-python\n",
        "!pip install -q git+https://github.com/tensorflow/docs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9BLeJv-pCCld"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "import tensorflow_hub as hub\n",
        "from tensorflow_docs.vis import embed\n",
        "import numpy as np\n",
        "import cv2\n",
        "\n",
        "# Import matplotlib libraries\n",
        "from matplotlib import pyplot as plt\n",
        "from matplotlib.collections import LineCollection\n",
        "import matplotlib.patches as patches\n",
        "\n",
        "# Some modules to display an animation using imageio.\n",
        "import imageio\n",
        "from IPython.display import HTML, display "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bEJBMeRb3YUy"
      },
      "outputs": [],
      "source": [
        "#@title Helper functions for visualization\n",
        "\n",
        "# Dictionary that maps from joint names to keypoint indices.\n",
        "KEYPOINT_DICT = {\n",
        "    'nose': 0,\n",
        "    'left_eye': 1,\n",
        "    'right_eye': 2,\n",
        "    'left_ear': 3,\n",
        "    'right_ear': 4,\n",
        "    'left_shoulder': 5,\n",
        "    'right_shoulder': 6,\n",
        "    'left_elbow': 7,\n",
        "    'right_elbow': 8,\n",
        "    'left_wrist': 9,\n",
        "    'right_wrist': 10,\n",
        "    'left_hip': 11,\n",
        "    'right_hip': 12,\n",
        "    'left_knee': 13,\n",
        "    'right_knee': 14,\n",
        "    'left_ankle': 15,\n",
        "    'right_ankle': 16\n",
        "}\n",
        "\n",
        "# Maps bones to a matplotlib color name.\n",
        "KEYPOINT_EDGE_INDS_TO_COLOR = {\n",
        "    (0, 1): 'm',\n",
        "    (0, 2): 'c',\n",
        "    (1, 3): 'm',\n",
        "    (2, 4): 'c',\n",
        "    (0, 5): 'm',\n",
        "    (0, 6): 'c',\n",
        "    (5, 7): 'm',\n",
        "    (7, 9): 'm',\n",
        "    (6, 8): 'c',\n",
        "    (8, 10): 'c',\n",
        "    (5, 6): 'y',\n",
        "    (5, 11): 'm',\n",
        "    (6, 12): 'c',\n",
        "    (11, 12): 'y',\n",
        "    (11, 13): 'm',\n",
        "    (13, 15): 'm',\n",
        "    (12, 14): 'c',\n",
        "    (14, 16): 'c'\n",
        "}\n",
        "\n",
        "def _keypoints_and_edges_for_display(keypoints_with_scores,\n",
        "                                     height,\n",
        "                                     width,\n",
        "                                     keypoint_threshold=0.11):\n",
        "  \"\"\"Returns high confidence keypoints and edges for visualization.\n",
        "\n",
        "  Args:\n",
        "    keypoints_with_scores: A numpy array with shape [1, 1, 17, 3] representing\n",
        "      the keypoint coordinates and scores returned from the MoveNet model.\n",
        "    height: height of the image in pixels.\n",
        "    width: width of the image in pixels.\n",
        "    keypoint_threshold: minimum confidence score for a keypoint to be\n",
        "      visualized.\n",
        "\n",
        "  Returns:\n",
        "    A (keypoints_xy, edges_xy, edge_colors) containing:\n",
        "      * the coordinates of all keypoints of all detected entities;\n",
        "      * the coordinates of all skeleton edges of all detected entities;\n",
        "      * the colors in which the edges should be plotted.\n",
        "  \"\"\"\n",
        "  keypoints_all = []\n",
        "  keypoint_edges_all = []\n",
        "  edge_colors = []\n",
        "  num_instances, _, _, _ = keypoints_with_scores.shape\n",
        "  for idx in range(num_instances):\n",
        "    kpts_x = keypoints_with_scores[0, idx, :, 1]\n",
        "    kpts_y = keypoints_with_scores[0, idx, :, 0]\n",
        "    kpts_scores = keypoints_with_scores[0, idx, :, 2]\n",
        "    kpts_absolute_xy = np.stack(\n",
        "        [width * np.array(kpts_x), height * np.array(kpts_y)], axis=-1)\n",
        "    kpts_above_thresh_absolute = kpts_absolute_xy[\n",
        "        kpts_scores > keypoint_threshold, :]\n",
        "    keypoints_all.append(kpts_above_thresh_absolute)\n",
        "\n",
        "    for edge_pair, color in KEYPOINT_EDGE_INDS_TO_COLOR.items():\n",
        "      if (kpts_scores[edge_pair[0]] > keypoint_threshold and\n",
        "          kpts_scores[edge_pair[1]] > keypoint_threshold):\n",
        "        x_start = kpts_absolute_xy[edge_pair[0], 0]\n",
        "        y_start = kpts_absolute_xy[edge_pair[0], 1]\n",
        "        x_end = kpts_absolute_xy[edge_pair[1], 0]\n",
        "        y_end = kpts_absolute_xy[edge_pair[1], 1]\n",
        "        line_seg = np.array([[x_start, y_start], [x_end, y_end]])\n",
        "        keypoint_edges_all.append(line_seg)\n",
        "        edge_colors.append(color)\n",
        "  if keypoints_all:\n",
        "    keypoints_xy = np.concatenate(keypoints_all, axis=0)\n",
        "  else:\n",
        "    keypoints_xy = np.zeros((0, 17, 2))\n",
        "\n",
        "  if keypoint_edges_all:\n",
        "    edges_xy = np.stack(keypoint_edges_all, axis=0)\n",
        "  else:\n",
        "    edges_xy = np.zeros((0, 2, 2))\n",
        "  return keypoints_xy, edges_xy, edge_colors\n",
        "\n",
        "\n",
        "def draw_prediction_on_image(\n",
        "    image, keypoints_with_scores, crop_region=None, close_figure=False,\n",
        "    output_image_height=None):\n",
        "  \"\"\"Draws the keypoint predictions on image.\n",
        "\n",
        "  Args:\n",
        "    image: A numpy array with shape [height, width, channel] representing the\n",
        "      pixel values of the input image.\n",
        "    keypoints_with_scores: A numpy array with shape [1, 1, 17, 3] representing\n",
        "      the keypoint coordinates and scores returned from the MoveNet model.\n",
        "    crop_region: A dictionary that defines the coordinates of the bounding box\n",
        "      of the crop region in normalized coordinates (see the init_crop_region\n",
        "      function below for more detail). If provided, this function will also\n",
        "      draw the bounding box on the image.\n",
        "    output_image_height: An integer indicating the height of the output image.\n",
        "      Note that the image aspect ratio will be the same as the input image.\n",
        "\n",
        "  Returns:\n",
        "    A numpy array with shape [out_height, out_width, channel] representing the\n",
        "    image overlaid with keypoint predictions.\n",
        "  \"\"\"\n",
        "  height, width, channel = image.shape\n",
        "  aspect_ratio = float(width) / height\n",
        "  fig, ax = plt.subplots(figsize=(12 * aspect_ratio, 12))\n",
        "  # To remove the huge white borders\n",
        "  fig.tight_layout(pad=0)\n",
        "  ax.margins(0)\n",
        "  ax.set_yticklabels([])\n",
        "  ax.set_xticklabels([])\n",
        "  plt.axis('off')\n",
        "\n",
        "  im = ax.imshow(image)\n",
        "  line_segments = LineCollection([], linewidths=(4), linestyle='solid')\n",
        "  ax.add_collection(line_segments)\n",
        "  # Turn off tick labels\n",
        "  scat = ax.scatter([], [], s=60, color='#FF1493', zorder=3)\n",
        "\n",
        "  (keypoint_locs, keypoint_edges,\n",
        "   edge_colors) = _keypoints_and_edges_for_display(\n",
        "       keypoints_with_scores, height, width)\n",
        "\n",
        "  line_segments.set_segments(keypoint_edges)\n",
        "  line_segments.set_color(edge_colors)\n",
        "  if keypoint_edges.shape[0]:\n",
        "    line_segments.set_segments(keypoint_edges)\n",
        "    line_segments.set_color(edge_colors)\n",
        "  if keypoint_locs.shape[0]:\n",
        "    scat.set_offsets(keypoint_locs)\n",
        "\n",
        "  if crop_region is not None:\n",
        "    xmin = max(crop_region['x_min'] * width, 0.0)\n",
        "    ymin = max(crop_region['y_min'] * height, 0.0)\n",
        "    rec_width = min(crop_region['x_max'], 0.99) * width - xmin\n",
        "    rec_height = min(crop_region['y_max'], 0.99) * height - ymin\n",
        "    rect = patches.Rectangle(\n",
        "        (xmin,ymin),rec_width,rec_height,\n",
        "        linewidth=1,edgecolor='b',facecolor='none')\n",
        "    ax.add_patch(rect)\n",
        "\n",
        "  fig.canvas.draw()\n",
        "  image_from_plot = np.frombuffer(fig.canvas.tostring_rgb(), dtype=np.uint8)\n",
        "  image_from_plot = image_from_plot.reshape(\n",
        "      fig.canvas.get_width_height()[::-1] + (3,))\n",
        "  plt.close(fig)\n",
        "  if output_image_height is not None:\n",
        "    output_image_width = int(output_image_height / height * width)\n",
        "    image_from_plot = cv2.resize(\n",
        "        image_from_plot, dsize=(output_image_width, output_image_height),\n",
        "         interpolation=cv2.INTER_CUBIC)\n",
        "  return image_from_plot\n",
        "\n",
        "def to_gif(images, fps):\n",
        "  \"\"\"Converts image sequence (4D numpy array) to gif.\"\"\"\n",
        "  imageio.mimsave('./animation.gif', images, fps=fps)\n",
        "  return embed.embed_file('./animation.gif')\n",
        "\n",
        "def progress(value, max=100):\n",
        "  return HTML(\"\"\"\n",
        "      <progress\n",
        "          value='{value}'\n",
        "          max='{max}',\n",
        "          style='width: 100%'\n",
        "      >\n",
        "          {value}\n",
        "      </progress>\n",
        "  \"\"\".format(value=value, max=max))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UvrN0iQiOxhR"
      },
      "source": [
        "## Load Model from TF hub"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zeGHgANcT7a1"
      },
      "outputs": [],
      "source": [
        "model_name = \"movenet_lightning\" #@param [\"movenet_lightning\", \"movenet_thunder\", \"movenet_lightning_f16.tflite\", \"movenet_thunder_f16.tflite\", \"movenet_lightning_int8.tflite\", \"movenet_thunder_int8.tflite\"]\n",
        "\n",
        "if \"tflite\" in model_name:\n",
        "  if \"movenet_lightning_f16\" in model_name:\n",
        "    !wget -q -O model.tflite https://tfhub.dev/google/lite-model/movenet/singlepose/lightning/tflite/float16/4?lite-format=tflite\n",
        "    input_size = 192\n",
        "  elif \"movenet_thunder_f16\" in model_name:\n",
        "    !wget -q -O model.tflite https://tfhub.dev/google/lite-model/movenet/singlepose/thunder/tflite/float16/4?lite-format=tflite\n",
        "    input_size = 256\n",
        "  elif \"movenet_lightning_int8\" in model_name:\n",
        "    !wget -q -O model.tflite https://tfhub.dev/google/lite-model/movenet/singlepose/lightning/tflite/int8/4?lite-format=tflite\n",
        "    input_size = 192\n",
        "  elif \"movenet_thunder_int8\" in model_name:\n",
        "    !wget -q -O model.tflite https://tfhub.dev/google/lite-model/movenet/singlepose/thunder/tflite/int8/4?lite-format=tflite\n",
        "    input_size = 256\n",
        "  else:\n",
        "    raise ValueError(\"Unsupported model name: %s\" % model_name)\n",
        "\n",
        "  # Initialize the TFLite interpreter\n",
        "  interpreter = tf.lite.Interpreter(model_path=\"model.tflite\")\n",
        "  interpreter.allocate_tensors()\n",
        "\n",
        "  def movenet(input_image):\n",
        "    \"\"\"Runs detection on an input image.\n",
        "\n",
        "    Args:\n",
        "      input_image: A [1, height, width, 3] tensor represents the input image\n",
        "        pixels. Note that the height/width should already be resized and match the\n",
        "        expected input resolution of the model before passing into this function.\n",
        "\n",
        "    Returns:\n",
        "      A [1, 1, 17, 3] float numpy array representing the predicted keypoint\n",
        "      coordinates and scores.\n",
        "    \"\"\"\n",
        "    # TF Lite format expects tensor type of uint8.\n",
        "    input_image = tf.cast(input_image, dtype=tf.uint8)\n",
        "    input_details = interpreter.get_input_details()\n",
        "    output_details = interpreter.get_output_details()\n",
        "    interpreter.set_tensor(input_details[0]['index'], input_image.numpy())\n",
        "    # Invoke inference.\n",
        "    interpreter.invoke()\n",
        "    # Get the model prediction.\n",
        "    keypoints_with_scores = interpreter.get_tensor(output_details[0]['index'])\n",
        "    return keypoints_with_scores\n",
        "\n",
        "else:\n",
        "  if \"movenet_lightning\" in model_name:\n",
        "    module = hub.load(\"https://tfhub.dev/google/movenet/singlepose/lightning/4\")\n",
        "    input_size = 192\n",
        "  elif \"movenet_thunder\" in model_name:\n",
        "    module = hub.load(\"https://tfhub.dev/google/movenet/singlepose/thunder/4\")\n",
        "    input_size = 256\n",
        "  else:\n",
        "    raise ValueError(\"Unsupported model name: %s\" % model_name)\n",
        "\n",
        "  def movenet(input_image):\n",
        "    \"\"\"Runs detection on an input image.\n",
        "\n",
        "    Args:\n",
        "      input_image: A [1, height, width, 3] tensor represents the input image\n",
        "        pixels. Note that the height/width should already be resized and match the\n",
        "        expected input resolution of the model before passing into this function.\n",
        "\n",
        "    Returns:\n",
        "      A [1, 1, 17, 3] float numpy array representing the predicted keypoint\n",
        "      coordinates and scores.\n",
        "    \"\"\"\n",
        "    model = module.signatures['serving_default']\n",
        "\n",
        "    # SavedModel format expects tensor type of int32.\n",
        "    input_image = tf.cast(input_image, dtype=tf.int32)\n",
        "    # Run model inference.\n",
        "    outputs = model(input_image)\n",
        "    # Output is a [1, 1, 17, 3] tensor.\n",
        "    keypoints_with_scores = outputs['output_0'].numpy()\n",
        "    return keypoints_with_scores"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-h1qHYaqD9ap"
      },
      "source": [
        "## Single Image Example"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ymTVR2I9x22I"
      },
      "source": [
        "This session demonstrates the minumum working example of running the model on a **single image** to predict the 17 human keypoints."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5I3xBq80E3N_"
      },
      "source": [
        "### Load Input Image"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/gdrive')"
      ],
      "metadata": {
        "id": "iohYiz1E7XvX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e2ff7d02-c516-4c0e-eb79-f13f817c873b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!cp /gdrive/MyDrive/data/Collection2.zip ./"
      ],
      "metadata": {
        "id": "CucymTOm0DKn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!jar xvf Collection2.zip"
      ],
      "metadata": {
        "id": "ggkNP4Eg30NB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!rm Collection2.zip "
      ],
      "metadata": {
        "id": "xUUJMGiD8Yq5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ls -l"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l5TO-gXhxec5",
        "outputId": "1de3bc73-ef2a-488e-c4db-4707eb4ff710"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "total 16\n",
            "drwxr-xr-x 164 root root 12288 Jan 15 17:56 \u001b[0m\u001b[01;34mCollection2\u001b[0m/\n",
            "drwxr-xr-x   1 root root  4096 Jan  7 14:33 \u001b[01;34msample_data\u001b[0m/\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lJZYQ8KYFQ6x"
      },
      "outputs": [],
      "source": [
        "# Load the input image.\n",
        "image_path = 'per_1_001_cropped.png'\n",
        "image = tf.io.read_file(image_path)\n",
        "image = tf.image.decode_jpeg(image)[:,:,:3]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S_UWRdQxE6WN"
      },
      "source": [
        "### Run Inference"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Single Image"
      ],
      "metadata": {
        "id": "RYE11ex79fR5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab.patches import cv2_imshow"
      ],
      "metadata": {
        "id": "tK1DEFKPiZjv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cv2.imwrite(image_path.replace('.png', '_cropped.png'), cv2.imread('per_1_001.png')[:450, :])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 165
        },
        "id": "5mY4FbPOiZjw",
        "outputId": "c219786d-870c-4b9b-9fb3-e9aafb502b74"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-7-81c8e380726c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage_path\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'.png'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'_cropped.png'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'per_1_001.png'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m450\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'image_path' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VHmTwACwFW-v"
      },
      "outputs": [],
      "source": [
        "# Resize and pad the image to keep the aspect ratio and fit the expected size.\n",
        "image_height, image_width, _ = image.shape\n",
        "input_image = tf.expand_dims(image, axis=0)\n",
        "input_image = tf.image.resize_with_pad(input_image, input_size, input_size)\n",
        "# XXX = tf.convert_to_tensor(XXX[:,:,:3])\n",
        "\n",
        "# Run model inference.\n",
        "keypoints_with_scores = movenet(input_image)\n",
        "\n",
        "# Visualize the predictions with image.\n",
        "display_image = tf.expand_dims(image, axis=0)\n",
        "display_image = tf.cast(tf.image.resize_with_pad(\n",
        "    display_image, 1280, 1280), dtype=tf.int32)\n",
        "output_overlay = draw_prediction_on_image(\n",
        "    np.squeeze(display_image.numpy(), axis=0), keypoints_with_scores)\n",
        "\n",
        "output_overlay = cv2.cvtColor(output_overlay, cv2.COLOR_BGR2RGB)\n",
        "plt.figure(figsize=(5, 5))\n",
        "plt.imshow(output_overlay)\n",
        "# _ = plt.axis('off')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cv2.imwrite(image_path.replace('.png', '_pose.png'), output_overlay)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sk6D14qszizJ",
        "outputId": "16768120-bdc8-4043-8903-6ac83f7089d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "xy, edes, color = _keypoints_and_edges_for_display(keypoints_with_scores, image_height, image_width)\n",
        "# xy, edes, color"
      ],
      "metadata": {
        "id": "o8XhOWVWZ0me"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(xy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "buNLJ2m-kM6Z",
        "outputId": "3eee4d8f-695c-42f9-f4f8-bf752f96c9ba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "17"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "img = cv2.imread('per_1_023.png')\n",
        "img.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N6Pxc6SxqZOp",
        "outputId": "3ed9a02b-ebe7-4045-a760-45d10b9f884f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(923, 451, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_56zQ0t2rUtq",
        "outputId": "d8b4b950-44fd-43f1-bc4c-16c42f010da3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ],
      "source": [
        "cv2.imwrite(\"output_image.png\", template_img)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rKm-B0eMYeg8"
      },
      "source": [
        "## Video (Image Sequence) Example"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gdPFXabLyiKv"
      },
      "source": [
        "This section demonstrates how to apply intelligent cropping based on detections from the previous frame when the input is a sequence of frames. This allows the model to devote its attention and resources to the main subject, resulting in much better prediction quality without sacrificing the speed.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "SYFdK-JHYhrv"
      },
      "outputs": [],
      "source": [
        "#@title Cropping Algorithm\n",
        "\n",
        "# Confidence score to determine whether a keypoint prediction is reliable.\n",
        "MIN_CROP_KEYPOINT_SCORE = 0.2\n",
        "\n",
        "def init_crop_region(image_height, image_width):\n",
        "  \"\"\"Defines the default crop region.\n",
        "\n",
        "  The function provides the initial crop region (pads the full image from both\n",
        "  sides to make it a square image) when the algorithm cannot reliably determine\n",
        "  the crop region from the previous frame.\n",
        "  \"\"\"\n",
        "  if image_width > image_height:\n",
        "    box_height = image_width / image_height\n",
        "    box_width = 1.0\n",
        "    y_min = (image_height / 2 - image_width / 2) / image_height\n",
        "    x_min = 0.0\n",
        "  else:\n",
        "    box_height = 1.0\n",
        "    box_width = image_height / image_width\n",
        "    y_min = 0.0\n",
        "    x_min = (image_width / 2 - image_height / 2) / image_width\n",
        "\n",
        "  return {\n",
        "    'y_min': y_min,\n",
        "    'x_min': x_min,\n",
        "    'y_max': y_min + box_height,\n",
        "    'x_max': x_min + box_width,\n",
        "    'height': box_height,\n",
        "    'width': box_width\n",
        "  }\n",
        "\n",
        "def torso_visible(keypoints):\n",
        "  \"\"\"Checks whether there are enough torso keypoints.\n",
        "\n",
        "  This function checks whether the model is confident at predicting one of the\n",
        "  shoulders/hips which is required to determine a good crop region.\n",
        "  \"\"\"\n",
        "  return ((keypoints[0, 0, KEYPOINT_DICT['left_hip'], 2] >\n",
        "           MIN_CROP_KEYPOINT_SCORE or\n",
        "          keypoints[0, 0, KEYPOINT_DICT['right_hip'], 2] >\n",
        "           MIN_CROP_KEYPOINT_SCORE) and\n",
        "          (keypoints[0, 0, KEYPOINT_DICT['left_shoulder'], 2] >\n",
        "           MIN_CROP_KEYPOINT_SCORE or\n",
        "          keypoints[0, 0, KEYPOINT_DICT['right_shoulder'], 2] >\n",
        "           MIN_CROP_KEYPOINT_SCORE))\n",
        "\n",
        "def determine_torso_and_body_range(\n",
        "    keypoints, target_keypoints, center_y, center_x):\n",
        "  \"\"\"Calculates the maximum distance from each keypoints to the center location.\n",
        "\n",
        "  The function returns the maximum distances from the two sets of keypoints:\n",
        "  full 17 keypoints and 4 torso keypoints. The returned information will be\n",
        "  used to determine the crop size. See determineCropRegion for more detail.\n",
        "  \"\"\"\n",
        "  torso_joints = ['left_shoulder', 'right_shoulder', 'left_hip', 'right_hip']\n",
        "  max_torso_yrange = 0.0\n",
        "  max_torso_xrange = 0.0\n",
        "  for joint in torso_joints:\n",
        "    dist_y = abs(center_y - target_keypoints[joint][0])\n",
        "    dist_x = abs(center_x - target_keypoints[joint][1])\n",
        "    if dist_y > max_torso_yrange:\n",
        "      max_torso_yrange = dist_y\n",
        "    if dist_x > max_torso_xrange:\n",
        "      max_torso_xrange = dist_x\n",
        "\n",
        "  max_body_yrange = 0.0\n",
        "  max_body_xrange = 0.0\n",
        "  for joint in KEYPOINT_DICT.keys():\n",
        "    if keypoints[0, 0, KEYPOINT_DICT[joint], 2] < MIN_CROP_KEYPOINT_SCORE:\n",
        "      continue\n",
        "    dist_y = abs(center_y - target_keypoints[joint][0]);\n",
        "    dist_x = abs(center_x - target_keypoints[joint][1]);\n",
        "    if dist_y > max_body_yrange:\n",
        "      max_body_yrange = dist_y\n",
        "\n",
        "    if dist_x > max_body_xrange:\n",
        "      max_body_xrange = dist_x\n",
        "\n",
        "  return [max_torso_yrange, max_torso_xrange, max_body_yrange, max_body_xrange]\n",
        "\n",
        "def determine_crop_region(\n",
        "      keypoints, image_height,\n",
        "      image_width):\n",
        "  \"\"\"Determines the region to crop the image for the model to run inference on.\n",
        "\n",
        "  The algorithm uses the detected joints from the previous frame to estimate\n",
        "  the square region that encloses the full body of the target person and\n",
        "  centers at the midpoint of two hip joints. The crop size is determined by\n",
        "  the distances between each joints and the center point.\n",
        "  When the model is not confident with the four torso joint predictions, the\n",
        "  function returns a default crop which is the full image padded to square.\n",
        "  \"\"\"\n",
        "  target_keypoints = {}\n",
        "  for joint in KEYPOINT_DICT.keys():\n",
        "    target_keypoints[joint] = [\n",
        "      keypoints[0, 0, KEYPOINT_DICT[joint], 0] * image_height,\n",
        "      keypoints[0, 0, KEYPOINT_DICT[joint], 1] * image_width\n",
        "    ]\n",
        "\n",
        "  if torso_visible(keypoints):\n",
        "    center_y = (target_keypoints['left_hip'][0] +\n",
        "                target_keypoints['right_hip'][0]) / 2;\n",
        "    center_x = (target_keypoints['left_hip'][1] +\n",
        "                target_keypoints['right_hip'][1]) / 2;\n",
        "\n",
        "    (max_torso_yrange, max_torso_xrange,\n",
        "      max_body_yrange, max_body_xrange) = determine_torso_and_body_range(\n",
        "          keypoints, target_keypoints, center_y, center_x)\n",
        "\n",
        "    crop_length_half = np.amax(\n",
        "        [max_torso_xrange * 1.9, max_torso_yrange * 1.9,\n",
        "          max_body_yrange * 1.2, max_body_xrange * 1.2])\n",
        "\n",
        "    tmp = np.array(\n",
        "        [center_x, image_width - center_x, center_y, image_height - center_y])\n",
        "    crop_length_half = np.amin(\n",
        "        [crop_length_half, np.amax(tmp)]);\n",
        "\n",
        "    crop_corner = [center_y - crop_length_half, center_x - crop_length_half];\n",
        "\n",
        "    if crop_length_half > max(image_width, image_height) / 2:\n",
        "      return init_crop_region(image_height, image_width)\n",
        "    else:\n",
        "      crop_length = crop_length_half * 2;\n",
        "      return {\n",
        "        'y_min': crop_corner[0] / image_height,\n",
        "        'x_min': crop_corner[1] / image_width,\n",
        "        'y_max': (crop_corner[0] + crop_length) / image_height,\n",
        "        'x_max': (crop_corner[1] + crop_length) / image_width,\n",
        "        'height': (crop_corner[0] + crop_length) / image_height -\n",
        "            crop_corner[0] / image_height,\n",
        "        'width': (crop_corner[1] + crop_length) / image_width -\n",
        "            crop_corner[1] / image_width\n",
        "      }\n",
        "  else:\n",
        "    return init_crop_region(image_height, image_width)\n",
        "\n",
        "def crop_and_resize(image, crop_region, crop_size):\n",
        "  \"\"\"Crops and resize the image to prepare for the model input.\"\"\"\n",
        "  boxes=[[crop_region['y_min'], crop_region['x_min'],\n",
        "          crop_region['y_max'], crop_region['x_max']]]\n",
        "  output_image = tf.image.crop_and_resize(\n",
        "      image, box_indices=[0], boxes=boxes, crop_size=crop_size)\n",
        "  return output_image\n",
        "\n",
        "def run_inference(movenet, image, crop_region, crop_size):\n",
        "  \"\"\"Runs model inferece on the cropped region.\n",
        "\n",
        "  The function runs the model inference on the cropped region and updates the\n",
        "  model output to the original image coordinate system.\n",
        "  \"\"\"\n",
        "  image_height, image_width, _ = image.shape\n",
        "  input_image = crop_and_resize(\n",
        "    tf.expand_dims(image, axis=0), crop_region, crop_size=crop_size)\n",
        "  # Run model inference.\n",
        "  keypoints_with_scores = movenet(input_image)\n",
        "  # Update the coordinates.\n",
        "  for idx in range(17):\n",
        "    keypoints_with_scores[0, 0, idx, 0] = (\n",
        "        crop_region['y_min'] * image_height +\n",
        "        crop_region['height'] * image_height *\n",
        "        keypoints_with_scores[0, 0, idx, 0]) / image_height\n",
        "    keypoints_with_scores[0, 0, idx, 1] = (\n",
        "        crop_region['x_min'] * image_width +\n",
        "        crop_region['width'] * image_width *\n",
        "        keypoints_with_scores[0, 0, idx, 1]) / image_width\n",
        "  return keypoints_with_scores"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L2JmA1xAEntQ"
      },
      "source": [
        "### Load Input Image Sequence"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CzJxbxDckWl2"
      },
      "outputs": [],
      "source": [
        "!wget -q -O dance.gif https://github.com/tensorflow/tfjs-models/raw/master/pose-detection/assets/dance_input.gif"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IxbMFZJUkd6W"
      },
      "outputs": [],
      "source": [
        "# Load the input image.\n",
        "image_path = 'dance.gif'\n",
        "image = tf.io.read_file(image_path)\n",
        "image = tf.image.decode_gif(image)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CJKeQ4siEtU9"
      },
      "source": [
        "### Run Inference with Cropping Algorithm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Jt5shUI6iUkg"
      },
      "outputs": [],
      "source": [
        "import time"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9B57XS0NZPIy",
        "outputId": "f3285dbe-e13a-4022-ae50-42c3c4ea1943"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-12-e2196c93e5a1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mstart\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m# Load the input image.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mnum_frames\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimage_height\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimage_width\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mcrop_region\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minit_crop_region\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage_height\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimage_width\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"loading done. \"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mstart\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'image' is not defined"
          ]
        }
      ],
      "source": [
        "start = time.time()\n",
        "# Load the input image.\n",
        "num_frames, image_height, image_width, _ = image.shape\n",
        "crop_region = init_crop_region(image_height, image_width)\n",
        "print(\"loading done. \", time.time() - start)\n",
        "\n",
        "output_images = []\n",
        "bar = display(progress(0, num_frames-1), display_id=True)\n",
        "for frame_idx in range(num_frames):\n",
        "  print(\"Frame:\",frame_idx, time.time() - start)\n",
        "  keypoints_with_scores = run_inference(\n",
        "      movenet, image[frame_idx, :, :, :], crop_region,\n",
        "      crop_size=[input_size, input_size])\n",
        "  output_images.append(draw_prediction_on_image(\n",
        "      image[frame_idx, :, :, :].numpy().astype(np.int32),\n",
        "      keypoints_with_scores, crop_region=None,\n",
        "      close_figure=True, output_image_height=300))\n",
        "  crop_region = determine_crop_region(\n",
        "      keypoints_with_scores, image_height, image_width)\n",
        "  bar.update(progress(frame_idx, num_frames-1))\n",
        "\n",
        "# Prepare gif visualization.\n",
        "print(\"Saving gif.\", time.time()- start)\n",
        "output = np.stack(output_images, axis=0)\n",
        "to_gif(output, fps=10) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "--30GSd8nAsM"
      },
      "outputs": [],
      "source": [
        "to_gif(output, fps=30)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3NUfhp2G2lfd"
      },
      "outputs": [],
      "source": [
        "tf.convert_to_tensor(image.numpy()).shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MIXQL21d9_wy"
      },
      "source": [
        "## From video"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Reading video"
      ],
      "metadata": {
        "id": "D2qA_QaMB42K"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YUQgCGZg-f6X"
      },
      "outputs": [],
      "source": [
        "import cv2\n",
        "import numpy as np\n",
        "\n",
        "# Create a VideoCapture object and read from input file\n",
        "# If the input is the camera, pass 0 instead of the video file name\n",
        "cap = cv2.VideoCapture('vid.mp4')\n",
        "\n",
        "frame_list = []\n",
        "# Check if camera opened successfully\n",
        "if (cap.isOpened()== False): \n",
        "  print(\"Error opening video stream or file\")\n",
        "\n",
        "# Read until video is completed\n",
        "while(cap.isOpened()):\n",
        "  # Capture frame-by-frame\n",
        "  ret, frame = cap.read()\n",
        "  if ret == True:\n",
        "\n",
        "    # Display the resulting frame\n",
        "    # cv2.imshow('Frame',frame)\n",
        "    # print(frame.shape)\n",
        "    frame_list.append(frame)\n",
        "\n",
        "    # Press Q on keyboard to  exit\n",
        "    if cv2.waitKey(25) & 0xFF == ord('q'):\n",
        "      break\n",
        "\n",
        "  # Break the loop\n",
        "  else: \n",
        "    break\n",
        "\n",
        "# When everything done, release the video capture object\n",
        "cap.release()\n",
        "\n",
        "# Closes all the frames\n",
        "cv2.destroyAllWindows()\n",
        "\n",
        "image = tf.convert_to_tensor(np.array(frame_list))\n",
        "image.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Reading image directory"
      ],
      "metadata": {
        "id": "o9YwFEYIB73p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab.patches import cv2_imshow"
      ],
      "metadata": {
        "id": "cZKDnIHKDkSb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "data_dir = 'Collection2/'\n",
        "videos = os.listdir(data_dir)\n",
        "videos.sort()"
      ],
      "metadata": {
        "id": "G1XokCw7-VOx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "videos"
      ],
      "metadata": {
        "id": "DVYaYZNF5fFe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "keypoints_dict = {}"
      ],
      "metadata": {
        "id": "zed2KyuwLLrg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# !rm output_patient/.ipynb_checkpoints"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_wBiz2e6xruw",
        "outputId": "55208597-b68a-4ca8-c2ad-fc9b6ff1dec2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "rm: cannot remove 'output_patient/.ipynb_checkpoints': Is a directory\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5nANR6JT3X20"
      },
      "outputs": [],
      "source": [
        "bar = display(progress(0, len(videos)), display_id=True)\n",
        "\n",
        "for i in range(0, len(videos)):\n",
        "  video = videos[i]\n",
        "  frames = os.listdir(data_dir+video)\n",
        "  frames.sort()\n",
        "\n",
        "  images = []\n",
        "  for frame in frames:\n",
        "    image = cv2.imread(data_dir+video+'/'+frame)\n",
        "    images.append(image)\n",
        "\n",
        "  images = np.stack(images, axis=0)\n",
        "\n",
        "  image = tf.convert_to_tensor(images)\n",
        "  # Load the input image.\n",
        "  num_frames, image_height, image_width, _ = image.shape\n",
        "  crop_region = init_crop_region(image_height, image_width)\n",
        "\n",
        "  output_images = []\n",
        "  keypoints = []\n",
        "  \n",
        "\n",
        "  for frame_idx in range(num_frames):\n",
        "    keypoints_with_scores = run_inference(\n",
        "        movenet, image[frame_idx, :, :, :], crop_region,\n",
        "        crop_size=[input_size, input_size])\n",
        "    keypoints.append(_keypoints_and_edges_for_display(keypoints_with_scores, image_height, image_width))\n",
        "    # output_images.append(draw_prediction_on_image(\n",
        "    #     np.zeros((800, 400, 3)),\n",
        "    #     keypoints_with_scores, crop_region=None,\n",
        "    #     close_figure=True, output_image_height=300))\n",
        "    # crop_region = determine_crop_region(keypoints_with_scores, image_height, image_width)\n",
        "  bar.update(progress(i, len(videos)))\n",
        "\n",
        "  ### Prepare gif visualization.\n",
        "  ### print(\"Processing done!\")\n",
        "  keypoints_dict[video] = keypoints\n",
        "  # output = np.stack(output_images, axis=0).sum(0)\n",
        "  # cv2.imwrite('heatmaps/'+video+'.png', output)\n",
        "  # del output\n",
        "  # del output_images"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "outfile = open('keypoints_dict_c2.pkl','wb')\n",
        "pickle.dump(keypoints_dict, outfile)\n",
        "outfile.close() "
      ],
      "metadata": {
        "id": "mSeQ-NElLQfS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "\n",
        "files.download('keypoints_dict_c2.pkl')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "vVsWHgdiVhH5",
        "outputId": "5957d018-68ed-4e5e-b941-6e18d31d5dc7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_8225e9e1-7b4a-4e32-9939-63efbccc9e52\", \"keypoints_dict_c2.pkl\", 16954790)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cv2_imshow(output.sum(0)) ## Score 2"
      ],
      "metadata": {
        "id": "-X5lH6yeE2Gd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cv2_imshow(output.sum(0)) ## Score 2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "-9oq2Jb8DTjk",
        "outputId": "4987280c-19f7-4c2c-cd27-0bdbe6d24cac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=150x300 at 0x7FDF96423450>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "xy, edes, color = _keypoints_and_edges_for_display(keypoints[30], image_height, image_width)\n",
        "xy, edes, color"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9oIRptadwWaf",
        "outputId": "0340c284-6d89-46e9-be58-05c94bc97820"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([[213.22365 ,  66.641594],\n",
              "        [210.29501 ,  60.034412],\n",
              "        [220.9073  ,  60.5091  ],\n",
              "        [198.96542 ,  81.31019 ],\n",
              "        [235.9743  ,  87.62626 ],\n",
              "        [159.7259  , 153.59196 ],\n",
              "        [268.09225 , 169.42128 ],\n",
              "        [ 74.87421 , 273.6188  ],\n",
              "        [357.92154 , 298.35016 ],\n",
              "        [ 56.51405 , 374.72675 ],\n",
              "        [315.18936 , 411.51428 ],\n",
              "        [193.83533 , 383.76627 ],\n",
              "        [213.45277 , 384.39453 ],\n",
              "        [122.80112 , 558.20044 ],\n",
              "        [232.94377 , 590.39453 ],\n",
              "        [224.65964 , 735.75543 ],\n",
              "        [ 92.89634 , 694.8308  ]], dtype=float32),\n",
              " array([[[213.22365 ,  66.641594],\n",
              "         [210.29501 ,  60.034412]],\n",
              " \n",
              "        [[213.22365 ,  66.641594],\n",
              "         [220.9073  ,  60.5091  ]],\n",
              " \n",
              "        [[210.29501 ,  60.034412],\n",
              "         [198.96542 ,  81.31019 ]],\n",
              " \n",
              "        [[220.9073  ,  60.5091  ],\n",
              "         [235.9743  ,  87.62626 ]],\n",
              " \n",
              "        [[213.22365 ,  66.641594],\n",
              "         [159.7259  , 153.59196 ]],\n",
              " \n",
              "        [[213.22365 ,  66.641594],\n",
              "         [268.09225 , 169.42128 ]],\n",
              " \n",
              "        [[159.7259  , 153.59196 ],\n",
              "         [ 74.87421 , 273.6188  ]],\n",
              " \n",
              "        [[ 74.87421 , 273.6188  ],\n",
              "         [ 56.51405 , 374.72675 ]],\n",
              " \n",
              "        [[268.09225 , 169.42128 ],\n",
              "         [357.92154 , 298.35016 ]],\n",
              " \n",
              "        [[357.92154 , 298.35016 ],\n",
              "         [315.18936 , 411.51428 ]],\n",
              " \n",
              "        [[159.7259  , 153.59196 ],\n",
              "         [268.09225 , 169.42128 ]],\n",
              " \n",
              "        [[159.7259  , 153.59196 ],\n",
              "         [193.83533 , 383.76627 ]],\n",
              " \n",
              "        [[268.09225 , 169.42128 ],\n",
              "         [213.45277 , 384.39453 ]],\n",
              " \n",
              "        [[193.83533 , 383.76627 ],\n",
              "         [213.45277 , 384.39453 ]],\n",
              " \n",
              "        [[193.83533 , 383.76627 ],\n",
              "         [122.80112 , 558.20044 ]],\n",
              " \n",
              "        [[122.80112 , 558.20044 ],\n",
              "         [224.65964 , 735.75543 ]],\n",
              " \n",
              "        [[213.45277 , 384.39453 ],\n",
              "         [232.94377 , 590.39453 ]],\n",
              " \n",
              "        [[232.94377 , 590.39453 ],\n",
              "         [ 92.89634 , 694.8308  ]]], dtype=float32),\n",
              " ['m',\n",
              "  'c',\n",
              "  'm',\n",
              "  'c',\n",
              "  'm',\n",
              "  'c',\n",
              "  'm',\n",
              "  'm',\n",
              "  'c',\n",
              "  'c',\n",
              "  'y',\n",
              "  'm',\n",
              "  'c',\n",
              "  'y',\n",
              "  'm',\n",
              "  'm',\n",
              "  'c',\n",
              "  'c'])"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Drawing keypoints in image"
      ],
      "metadata": {
        "id": "SA1HLptITq-y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "infile = open('keypoints_dict.pkl','rb')\n",
        "keypoints_dict = pickle.load(infile)\n",
        "infile.close()"
      ],
      "metadata": {
        "id": "BcuqH-Eh170J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import cv2\n",
        "from google.colab.patches import cv2_imshow\n",
        "colors = {'c': (255,255,0), 'y': (0,255,255), 'm':(255,0,255)}"
      ],
      "metadata": {
        "id": "0iEc8swFT7_F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for key in keypoints_dict:\n",
        "  img = np.zeros([400, 200, 3])\n",
        "  for i in range(len(keypoints_dict[key])):\n",
        "    for (edge, color) in zip(keypoints_dict[key][i][1], \\\n",
        "                             keypoints_dict[key][i][2]):\n",
        "      # edge = edge + 0.5\n",
        "      edge = edge/2 + 0.5 # small\n",
        "      edge = edge.astype(int)\n",
        "      x1, y1 = edge[0]\n",
        "      x2, y2 = edge[1]\n",
        "      line_thickness = 1\n",
        "      cv2.line(img, (x1, y1), (x2, y2), (0, 255-i*255/180, i*255/180), thickness=line_thickness)\n",
        "    # print(x1, y1, x2, y2, color)\n",
        "  # img = cv2.resize(img, (150, 300)) \n",
        "  cv2.imwrite('merged_images_time/'+key+'.png', img)"
      ],
      "metadata": {
        "id": "T_w3ETVfgnou"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Imagenet\n",
        "for key in keypoints_dict:\n",
        "  img = np.zeros([100, 100, 3])\n",
        "  for i in range(len(keypoints_dict[key])):\n",
        "    for point in keypoints_dict[key][i][0]:\n",
        "      # point = point/2 + 0.5\n",
        "      # point = point.astype(int)\n",
        "      (x, y) = point\n",
        "      x = x/4+0.5\n",
        "      y = y/8+0.5\n",
        "      x = int(x)\n",
        "      y = int(y)\n",
        "      if y<100 and x<100:\n",
        "        img[y,x] += [0.5*i*255/180,(255-i*255/180)*0.5,0]\n",
        "  img = cv2.blur(img,(5,5))\n",
        "  # img = cv2.GaussianBlur(img, (5,5),0) \n",
        "  cv2.imwrite('imagenet_ataxia/'+key+'.png', img) "
      ],
      "metadata": {
        "id": "i0Oep4DNB34P"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.array([1,2])*np.array([0.5, 0.25])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0dVMVS2J9P1z",
        "outputId": "f39adf4c-ed0f-43aa-bd9f-c83eef2601a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.5, 0.5])"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Imagenet full pose\n",
        "cg = np.array([200, 300])\n",
        "for key in keypoints_dict:\n",
        "  img = np.zeros([100, 100, 3])\n",
        "  for i in range(len(keypoints_dict[key])):\n",
        "    shift_vector = cg - keypoints_dict[key][i][0][[5,6,11,12]].mean(0)\n",
        "    shifted_edges = keypoints_dict[key][i][1] + shift_vector\n",
        "    for e in range(len(shifted_edges)):\n",
        "      edge = shifted_edges[e]\n",
        "      # edge = edge/2 + 0.5 # small\n",
        "      # edge = edge.astype(int)\n",
        "      x1, y1 = (edge[0]*np.array([1/4, 1/8])).astype(int)\n",
        "      x2, y2 = (edge[1]*np.array([1/4, 1/8])).astype(int)\n",
        "      line_thickness = 1\n",
        "      \n",
        "      cv2.line(img, (x1, y1), (x2, y2), (0,(255-i*255/180),i*255/180) , thickness=line_thickness)\n",
        "\n",
        "  # img = cv2.blur(img,(5,5))\n",
        "  # img = cv2.GaussianBlur(img, (5,5),0) \n",
        "  cv2.imwrite('imagenet_ataxia_lines/'+key+'.png', img) \n",
        "  cv2.imwrite('imagenet_ataxia_lines/'+key+'_f.png', cv2.flip(img, 1)) "
      ],
      "metadata": {
        "id": "ZQgLarJ77x2j"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "img.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SzIsbd17-Myl",
        "outputId": "98b0841d-502b-45ca-de6e-603004dfc62e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(100, 100, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "keypoints_dict['gait_EU-01-093.mp4'][1][0][[5,6,11,12]].mean(0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wfyI52F4T3jS",
        "outputId": "df4d1cef-83bb-4760-9f19-0c466ab14117"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([195.24075, 284.639  ], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# cg = keypoints_dict[key][1][0][[5,6,11,12]].mean(0)\n"
      ],
      "metadata": {
        "id": "m7gYVyTLFhZZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Adjusting center of gravity"
      ],
      "metadata": {
        "id": "wRo-OC0nuQ_8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir cg_adjusted"
      ],
      "metadata": {
        "id": "JppxuUgaF3ZX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cg = np.array([200, 300])\n",
        "alpha = 0.5\n",
        "for key in keypoints_dict:\n",
        "  img = np.zeros([400, 200])\n",
        "  num_frames = len(keypoints_dict[key])\n",
        "  for i in range(num_frames):\n",
        "    shift_vector = cg - keypoints_dict[key][i][0][[5,6,11,12]].mean(0)\n",
        "    shifted_edges = keypoints_dict[key][i][1] + shift_vector\n",
        "    overlay = np.zeros([400, 200])\n",
        "    prev_edges = shifted_edges\n",
        "    for e in range(len(shifted_edges)):\n",
        "      edge = shifted_edges[e]\n",
        "      prev_edge = prev_edges[e]\n",
        "      shift = edge - prev_edges\n",
        "      edge = edge/2 + 0.5 # small\n",
        "      edge = edge.astype(int)\n",
        "      x1, y1 = edge[0]\n",
        "      x2, y2 = edge[1]\n",
        "      line_thickness = 3\n",
        "      \n",
        "      cv2.line(overlay, (x1, y1), (x2, y2), 50 , thickness=line_thickness)\n",
        "      #(0,(255-i*255/180),i*255/180)\n",
        "      \n",
        "      # img = cv2.addWeighted(overlay, alpha, img, 1 - alpha, 0)\n",
        "    img += overlay\n",
        "    # print(x1, y1, x2, y2, color)\n",
        "  # img = cv2.resize(img, (150, 300)) \n",
        "  # img = cv2.blur(img,(5,5))\n",
        "  cv2.imwrite('cg_adjusted/'+key+'.png', img)"
      ],
      "metadata": {
        "id": "RNNuntUwEaRH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cv2_imshow(img) # Without shifting"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "RZX5iCkEKMys",
        "outputId": "dcff1f5e-c3b0-41b9-e626-cb8c516ed9af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=L size=200x400 at 0x7F1BF9C781D0>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cv2_imshow(img) # Without shifting"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "-yQSwTdEI-lT",
        "outputId": "cfd140fc-a3b0-4e09-852b-37a723a1da7c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=200x400 at 0x7FF9A8E71D10>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cv2_imshow(img1) # with shifting"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "07aurVIShIxh",
        "outputId": "e35d66fe-e394-4f22-e413-e94fb797a5f5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=200x400 at 0x7F110360F150>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!zip black_n_white.zip black_n_white/*"
      ],
      "metadata": {
        "id": "Ik34chtPiqCi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f9ff8cb1-e476-4656-8a9b-5cb7d18e5151"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  adding: black_n_white/gait_AB-01-001.mp4.png (deflated 12%)\n",
            "  adding: black_n_white/gait_AB-01-002.mp4.png (deflated 12%)\n",
            "  adding: black_n_white/gait_AB-01-003.mp4.png (deflated 14%)\n",
            "  adding: black_n_white/gait_AB-01-004.mp4.png (deflated 12%)\n",
            "  adding: black_n_white/gait_CO-01-002.mp4.png (deflated 13%)\n",
            "  adding: black_n_white/gait_CO-01-003.mp4.png (deflated 9%)\n",
            "  adding: black_n_white/gait_CO-01-004.mp4.png (deflated 13%)\n",
            "  adding: black_n_white/gait_CO-01-005.mp4.png (deflated 9%)\n",
            "  adding: black_n_white/gait_CO-01-006.mp4.png (deflated 11%)\n",
            "  adding: black_n_white/gait_CO-01-007.mp4.png (deflated 12%)\n",
            "  adding: black_n_white/gait_CO-01-008.mp4.png (deflated 8%)\n",
            "  adding: black_n_white/gait_CO-01-009.mp4.png (deflated 14%)\n",
            "  adding: black_n_white/gait_EU-01-034.mp4.png (deflated 8%)\n",
            "  adding: black_n_white/gait_EU-01-076.mp4.png (deflated 6%)\n",
            "  adding: black_n_white/gait_EU-01-085.mp4.png (deflated 10%)\n",
            "  adding: black_n_white/gait_EU-01-093.mp4.png (deflated 15%)\n",
            "  adding: black_n_white/gait_HM-01-007.mp4.png (deflated 11%)\n",
            "  adding: black_n_white/gait_HM-01-010.mp4.png (deflated 10%)\n",
            "  adding: black_n_white/gait_HM-01-011.mp4.png (deflated 11%)\n",
            "  adding: black_n_white/gait_HM-01-012.mp4.png (deflated 12%)\n",
            "  adding: black_n_white/gait_HM-01-014.mp4.png (deflated 12%)\n",
            "  adding: black_n_white/gait_HM-01-015.mp4.png (deflated 9%)\n",
            "  adding: black_n_white/gait_HM-01-016.mp4.png (deflated 11%)\n",
            "  adding: black_n_white/gait_HM-01-018.mp4.png (deflated 9%)\n",
            "  adding: black_n_white/gait_HM-01-022.mp4.png (deflated 8%)\n",
            "  adding: black_n_white/gait_HM-01-026.mp4.png (deflated 11%)\n",
            "  adding: black_n_white/gait_HM-01-027.mp4.png (deflated 11%)\n",
            "  adding: black_n_white/gait_HM-01-028.mp4.png (deflated 9%)\n",
            "  adding: black_n_white/gait_HM-01-030.mp4.png (deflated 11%)\n",
            "  adding: black_n_white/gait_HM-01-031.mp4.png (deflated 7%)\n",
            "  adding: black_n_white/gait_HM-01-033.mp4.png (deflated 10%)\n",
            "  adding: black_n_white/gait_HM-01-034.mp4.png (deflated 13%)\n",
            "  adding: black_n_white/gait_HM-01-037.mp4.png (deflated 7%)\n",
            "  adding: black_n_white/gait_HM-01-039.mp4.png (deflated 13%)\n",
            "  adding: black_n_white/gait_HM-01-040.mp4.png (deflated 14%)\n",
            "  adding: black_n_white/gait_HM-01-047.mp4.png (deflated 14%)\n",
            "  adding: black_n_white/gait_HM-01-048.mp4.png (deflated 11%)\n",
            "  adding: black_n_white/gait_HM-01-049.mp4.png (deflated 12%)\n",
            "  adding: black_n_white/gait_HM-01-051.mp4.png (deflated 14%)\n",
            "  adding: black_n_white/gait_HM-01-052.mp4.png (deflated 16%)\n",
            "  adding: black_n_white/gait_HM-01-057.mp4.png (deflated 10%)\n",
            "  adding: black_n_white/gait_HM-01-058.mp4.png (deflated 10%)\n",
            "  adding: black_n_white/gait_HM-01-061.mp4.png (deflated 5%)\n",
            "  adding: black_n_white/gait_HM-01-065.mp4.png (deflated 8%)\n",
            "  adding: black_n_white/gait_MN-01-023.mp4.png (deflated 13%)\n",
            "  adding: black_n_white/gait_MN-01-024.mp4.png (deflated 12%)\n",
            "  adding: black_n_white/gait_MN-01-025.mp4.png (deflated 10%)\n",
            "  adding: black_n_white/gait_MN-01-026.mp4.png (deflated 11%)\n",
            "  adding: black_n_white/gait_MN-01-027.mp4.png (deflated 13%)\n",
            "  adding: black_n_white/gait_MN-01-029.mp4.png (deflated 8%)\n",
            "  adding: black_n_white/gait_MN-01-031.mp4.png (deflated 13%)\n",
            "  adding: black_n_white/gait_MN-01-032.mp4.png (deflated 9%)\n",
            "  adding: black_n_white/gait_MN-01-034.mp4.png (deflated 14%)\n",
            "  adding: black_n_white/gait_MN-01-035.mp4.png (deflated 10%)\n",
            "  adding: black_n_white/gait_UC-01-204.mp4.png (deflated 8%)\n",
            "  adding: black_n_white/gait_UC-01-205.mp4.png (deflated 7%)\n",
            "  adding: black_n_white/gait_UC-01-207.mp4.png (deflated 10%)\n",
            "  adding: black_n_white/gait_UC-01-208.mp4.png (deflated 10%)\n",
            "  adding: black_n_white/gait_UC-01-209.mp4.png (deflated 11%)\n",
            "  adding: black_n_white/gait_UC-01-211.mp4.png (deflated 13%)\n",
            "  adding: black_n_white/gait_UF-01-074.mp4.png (deflated 9%)\n",
            "  adding: black_n_white/gait_UF-01-075.mp4.png (deflated 10%)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Steadying all images"
      ],
      "metadata": {
        "id": "bKE_e4jbDI64"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "3Jq-w2-xDIPq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "oudeiwKQqAmq"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [
        "RYE11ex79fR5",
        "L2JmA1xAEntQ",
        "CJKeQ4siEtU9",
        "D2qA_QaMB42K"
      ],
      "name": "MoveNet_SinglePose_Demo.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}