Deb's__New_mediapipe_Jan_11.ipynb

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  "metadata": {
    "colab": {
      "name": "Deb's_ New_mediapipe_Jan 11.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Borthakur123/aiyogi/blob/master/Deb's__New_mediapipe_Jan_11.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AuQfLvpuJkb0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9cd36080-beef-4bf0-d89a-9ecf07ccbf3d"
      },
      "source": [
        "!pip install numpy==1.19.3\n",
        "!pip install mediapipe==0.8.1\n",
        "import cv2\n",
        "from matplotlib import interactive\n",
        "interactive(True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: numpy==1.19.3 in /usr/local/lib/python3.6/dist-packages (1.19.3)\n",
            "Requirement already satisfied: mediapipe==0.8.1 in /usr/local/lib/python3.6/dist-packages (0.8.1)\n",
            "Requirement already satisfied: numpy==1.19.3 in /usr/local/lib/python3.6/dist-packages (from mediapipe==0.8.1) (1.19.3)\n",
            "Requirement already satisfied: absl-py in /usr/local/lib/python3.6/dist-packages (from mediapipe==0.8.1) (0.10.0)\n",
            "Requirement already satisfied: opencv-python in /usr/local/lib/python3.6/dist-packages (from mediapipe==0.8.1) (4.1.2.30)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from mediapipe==0.8.1) (1.15.0)\n",
            "Requirement already satisfied: protobuf>=3.11.4 in /usr/local/lib/python3.6/dist-packages (from mediapipe==0.8.1) (3.12.4)\n",
            "Requirement already satisfied: wheel in /usr/local/lib/python3.6/dist-packages (from mediapipe==0.8.1) (0.36.2)\n",
            "Requirement already satisfied: dataclasses in /usr/local/lib/python3.6/dist-packages (from mediapipe==0.8.1) (0.8)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.11.4->mediapipe==0.8.1) (51.1.1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7SwzZ1nJLWt0",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
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              "ok": true,
              "headers": [
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                  "content-type",
                  "application/javascript"
                ]
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              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 846
        },
        "outputId": "ff00f313-0d96-4c13-f3ba-66c5cc718b28"
      },
      "source": [
        "from google.colab import files\n",
        "\n",
        "uploaded = files.upload()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-df9fb305-4233-4164-9dea-2a51114e36f1\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-df9fb305-4233-4164-9dea-2a51114e36f1\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving 1.jpg to 1.jpg\n",
            "Saving 5.jpg to 5.jpg\n",
            "Saving 8.jpg to 8.jpg\n",
            "Saving 11.jpg to 11.jpg\n",
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            "Saving 464.jpg to 464.jpg\n",
            "Saving 541.jpg to 541.jpg\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nW2TjFyhLvVH"
      },
      "source": [
        "\n",
        "import cv2\n",
        "import os\n",
        "from google.colab.patches import cv2_imshow\n",
        "import numpy as np\n",
        "\n",
        "#Read images with OpenCV.\n",
        "images = {name: cv2.imread(name)\n",
        "for name in uploaded.keys()}\n",
        "# Preview the images.\n",
        "#for name, image in images.items():\n",
        "  #print(name)  \n",
        "  #path = '/content/akarna1/aks' \n",
        "  #cv2.imwrite(os.path.join(path , name), image)\n",
        "  #cv2_imshow(image)\n",
        "  #cv2.waitKey(0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BboTB-FAMfPo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1c8106a4-c563-48ca-ebdd-c514448e1c51"
      },
      "source": [
        "import mediapipe as mp\n",
        "mp_pose = mp.solutions.pose\n",
        "\n",
        "help(mp_pose.Pose)\n",
        "\n",
        "# Initialize MediaPipe Pose.\n",
        "pose = mp_pose.Pose(\n",
        "    static_image_mode=True, min_detection_confidence=0.5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Help on class Pose in module mediapipe.python.solutions.pose:\n",
            "\n",
            "class Pose(mediapipe.python.solution_base.SolutionBase)\n",
            " |  MediaPipe Pose.\n",
            " |  \n",
            " |  MediaPipe Pose processes an RGB image and returns pose landmarks on the most\n",
            " |  prominent person detected.\n",
            " |  \n",
            " |  Please refer to https://solutions.mediapipe.dev/pose#python-solution-api for\n",
            " |  usage examples.\n",
            " |  \n",
            " |  Method resolution order:\n",
            " |      Pose\n",
            " |      mediapipe.python.solution_base.SolutionBase\n",
            " |      builtins.object\n",
            " |  \n",
            " |  Methods defined here:\n",
            " |  \n",
            " |  __init__(self, static_image_mode=False, upper_body_only=False, smooth_landmarks=True, min_detection_confidence=0.5, min_tracking_confidence=0.5)\n",
            " |      Initializes a MediaPipe Pose object.\n",
            " |      \n",
            " |      Args:\n",
            " |        static_image_mode: Whether to treat the input images as a batch of static\n",
            " |          and possibly unrelated images, or a video stream. See details in\n",
            " |          https://solutions.mediapipe.dev/pose#static-image-mode.\n",
            " |        upper_body_only: Whether to track the full set of 33 pose landmarks or\n",
            " |          only the 25 upper-body pose landmarks. See details in\n",
            " |          https://solutions.mediapipe.dev/pose#upper-body-only.\n",
            " |        smooth_landmarks: Whether to filter landmarks across different input\n",
            " |          images to reduce jitter. See details in\n",
            " |          https://solutions.mediapipe.dev/pose#smooth_landmarks.\n",
            " |        min_detection_confidence: Minimum confidence value ([0.0, 1.0]) for person\n",
            " |          detection to be considered successful. See details in\n",
            " |          https://solutions.mediapipe.dev/pose#min-detection-confidence.\n",
            " |        min_tracking_confidence: Minimum confidence value ([0.0, 1.0]) for the\n",
            " |          pose landmarks to be considered tracked successfully. See details in\n",
            " |          https://solutions.mediapipe.dev/pose#min-tracking-confidence.\n",
            " |  \n",
            " |  process(self, image:numpy.ndarray) -> <class 'NamedTuple'>\n",
            " |      Processes an RGB image and returns the pose landmarks on the most prominent person detected.\n",
            " |      \n",
            " |      Args:\n",
            " |        image: An RGB image represented as a numpy ndarray.\n",
            " |      \n",
            " |      Raises:\n",
            " |        RuntimeError: If the underlying graph occurs any error.\n",
            " |        ValueError: If the input image is not three channel RGB.\n",
            " |      \n",
            " |      Returns:\n",
            " |        A NamedTuple object with a \"pose_landmarks\" field that contains the pose\n",
            " |        landmarks on the most prominent person detected.\n",
            " |  \n",
            " |  ----------------------------------------------------------------------\n",
            " |  Methods inherited from mediapipe.python.solution_base.SolutionBase:\n",
            " |  \n",
            " |  close(self) -> None\n",
            " |      Closes all the input sources and the graph.\n",
            " |  \n",
            " |  ----------------------------------------------------------------------\n",
            " |  Data descriptors inherited from mediapipe.python.solution_base.SolutionBase:\n",
            " |  \n",
            " |  __dict__\n",
            " |      dictionary for instance variables (if defined)\n",
            " |  \n",
            " |  __weakref__\n",
            " |      list of weak references to the object (if defined)\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vwm4eyOWDvsC"
      },
      "source": [
        "# Prepare DrawingSpec for drawing the face landmarks later.\n",
        "mp_drawing = mp.solutions.drawing_utils \n",
        "drawing_spec = mp_drawing.DrawingSpec(thickness=1, circle_radius=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0W-0mualfRIl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6e8d1b9f-9039-4421-fd90-abcb8d23b90d"
      },
      "source": [
        "for name, image in images.items():\n",
        "  # Convert the BGR image to RGB and process it with MediaPipe Pose.\n",
        "  results = pose.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))\n",
        "  # Print nose landmark.\n",
        "  image_hight, image_width, _ = image.shape\n",
        "\n",
        "  if not results.pose_landmarks:\n",
        "    continue\n",
        "  print(f'coordinates: ('\n",
        "    f'{results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_KNEE].x * image_width}, '\n",
        "    f'{results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_KNEE].y * image_hight}, '\n",
        "    f'{results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_HIP].x * image_width}, '\n",
        "    f'{results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_HIP].y * image_hight},'\n",
        "    f'{results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_SHOULDER].x * image_width}, '\n",
        "    f'{results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_SHOULDER].y * image_hight}'\n",
        "    f'{results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_ELBOW].x * image_width}, '\n",
        "    f'{results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_ELBOW].y * image_hight})'\n",
        "  )\n",
        "  ##angles\n",
        "  \n",
        "  # Draw pose landmarks.\n",
        "  print(f'Pose landmarks of {name}:')\n",
        "  #np.savetxt('test.out', name, delimiter=',')\n",
        "  annotated_image = image.copy()\n",
        "  mp_drawing.draw_landmarks(\n",
        "      image=annotated_image,\n",
        "      landmark_list=results.pose_landmarks,\n",
        "      connections=mp_pose.POSE_CONNECTIONS,\n",
        "      landmark_drawing_spec=drawing_spec,\n",
        "      connection_drawing_spec=drawing_spec)\n",
        "  #cv2_imshow(annotated_image)\n",
        "  path = '/content/sample_data/Sorted_Dolphin_Plank_pose' \n",
        "  cv2.imwrite(os.path.join(path , name), annotated_image)\n",
        "  #cv2.imwrite('name.jpg', annotated_image)\n",
        "  #f=open('file.txt', 'w')\n",
        "  #for name, annotated_image in images.items():\n",
        "     # print(name)  \n",
        "     # path = '/content/akarna1/aks' \n",
        "     # cv2.imwrite(os.path.join(path , name), annotated_image)\n",
        "      #cv2_imshow(image)\n",
        "      #cv2.waitKey(0)\n",
        " \n",
        "  #for name, annotated_image in images.items():\n",
        "      #path = '/content/data3' \n",
        "      #cv2.imwrite(os.path.join(path , name), annotated_image)\n",
        "      #cv2_imshow(annotated_image)\n",
        "  #cv2.waitKey(0)\n",
        "  \n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "coordinates: (110.69638347625732, 169.64450526237488, 185.71916103363037, 146.44673824310303,284.29471015930176, 146.19122314453125278.5619287490845, 203.9454002380371)\n",
            "Pose landmarks of 1.jpg:\n",
            "coordinates: (176.615309715271, 461.4225883483887, 278.01212668418884, 436.46891021728516,407.47954845428467, 444.3768005371094407.92837142944336, 516.3597183227539)\n",
            "Pose landmarks of 5.jpg:\n",
            "coordinates: (188.11204433441162, 250.1800381541252, 309.5075726509094, 216.68307149410248,460.44559478759766, 205.74601861834526450.34003257751465, 300.58958208560944)\n",
            "Pose landmarks of 8.jpg:\n",
            "coordinates: (178.72822880744934, 396.2662754058838, 268.04823875427246, 362.79515075683594,383.7323069572449, 361.8651714324951383.0336809158325, 430.8303871154785)\n",
            "Pose landmarks of 11.jpg:\n",
            "coordinates: (400.4317045211792, 339.13983821868896, 607.8743934631348, 301.16505593061447,870.6532001495361, 291.59707993268967831.1645746231079, 436.39591097831726)\n",
            "Pose landmarks of 14.jpg:\n",
            "coordinates: (1569.5985488891602, 2532.4107513427734, 2437.7039680480957, 2256.3045654296875,3560.1329956054688, 2193.18267822265623501.4375076293945, 2870.364730834961)\n",
            "Pose landmarks of 30.jpg:\n",
            "coordinates: (168.23761332035065, 202.12602138519287, 246.90259212255478, 182.7337884902954,342.1605430841446, 179.54978704452515360.1421055793762, 230.5822467803955)\n",
            "Pose landmarks of 31.jpg:\n",
            "coordinates: (289.16632533073425, 528.5956954956055, 515.910142660141, 468.75107514858246,810.5848550796509, 492.38214087486267835.6838345527649, 660.516770362854)\n",
            "Pose landmarks of 32.jpg:\n",
            "coordinates: (327.8395652770996, 448.58593940734863, 420.12128829956055, 429.40059900283813,554.8038005828857, 431.7364811897278527.6716232299805, 516.6385173797607)\n",
            "Pose landmarks of 34.jpg:\n",
            "coordinates: (250.78120231628418, 414.6587133407593, 377.35984325408936, 379.5278549194336,543.5184478759766, 371.1757779121399539.3674373626709, 468.00377368927)\n",
            "Pose landmarks of 41.jpg:\n",
            "coordinates: (300.95272064208984, 517.5729370117188, 453.1026077270508, 465.70953369140625,644.1876983642578, 490.3022003173828641.2288856506348, 592.5426864624023)\n",
            "Pose landmarks of 44.jpg:\n",
            "coordinates: (122.33781814575195, 148.58269214630127, 189.59330320358276, 126.62538528442383,277.7276277542114, 121.78622245788574275.8408784866333, 173.45257759094238)\n",
            "Pose landmarks of 46.jpg:\n",
            "coordinates: (253.15827429294586, 399.8777779340744, 417.20897406339645, 361.436943590641,620.5380141735077, 343.3601605296135627.1053791046143, 449.15440142154694)\n",
            "Pose landmarks of 70.jpg:\n",
            "coordinates: (230.5349826812744, 586.9817695617676, 372.0070400238037, 536.1479740142822,541.6469554901123, 536.0578231811523547.4739894866943, 642.0776596069336)\n",
            "Pose landmarks of 71.jpg:\n",
            "coordinates: (217.6955530643463, 329.29249250888824, 329.3793218135834, 286.5698392391205,485.32900857925415, 309.6907744407654478.0863184928894, 397.90618574619293)\n",
            "Pose landmarks of 93.jpg:\n",
            "coordinates: (99.05529308319092, 186.00178718566895, 152.3868579864502, 166.88464224338531,223.28898310661316, 156.85194849967957218.47644448280334, 201.97699189186096)\n",
            "Pose landmarks of 118.jpg:\n",
            "coordinates: (107.43729293346405, 92.95945823192596, 165.20025300979614, 80.20519435405731,231.5866641998291, 70.78638064861298228.19070434570312, 114.0434513092041)\n",
            "Pose landmarks of 138.jpg:\n",
            "coordinates: (170.5493688583374, 280.40531873703003, 223.99051666259766, 263.62483978271484,293.1239891052246, 262.5707530975342292.9109573364258, 302.547447681427)\n",
            "Pose landmarks of 151.jpg:\n",
            "coordinates: (66.43766641616821, 97.74263381958008, 113.36085319519043, 87.24615097045898,172.29925632476807, 93.29882621765137179.95376586914062, 125.0899600982666)\n",
            "Pose landmarks of 231.jpg:\n",
            "coordinates: (196.14280700683594, 293.33809691667557, 309.3431282043457, 247.97754728794098,461.4854049682617, 262.45929604768753469.4508743286133, 352.0900411605835)\n",
            "Pose landmarks of 261.jpg:\n",
            "coordinates: (73.08312654495239, 104.13305461406708, 110.85431575775146, 92.61900186538696,157.57672786712646, 94.34768557548523153.54020595550537, 122.31618762016296)\n",
            "Pose landmarks of 434.jpg:\n",
            "coordinates: (112.46446788311005, 158.61159086227417, 186.49238169193268, 142.11254835128784,280.72429955005646, 149.18891191482544291.42539262771606, 204.34693336486816)\n",
            "Pose landmarks of 441.jpg:\n",
            "coordinates: (121.71707153320312, 199.12049174308777, 202.8895378112793, 171.22610807418823,309.50822830200195, 177.015620470047310.4542016983032, 234.14897918701172)\n",
            "Pose landmarks of 464.jpg:\n",
            "coordinates: (170.94571161270142, 280.4071135520935, 277.55882054567337, 254.94807052612305,419.1344528198242, 254.55497550964355415.22073298692703, 333.11772632598877)\n",
            "Pose landmarks of 541.jpg:\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mAO5c2HIsfTL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "00fe2942-1a5d-43e8-ec9e-9d47d31d1fe3"
      },
      "source": [
        "import numpy as np\r\n",
        "for name, image in images.items():\r\n",
        "    results = pose.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))\r\n",
        "  # Print nose landmark.\r\n",
        "    image_hight, image_width, _ = image.shape\r\n",
        "    if not results.pose_landmarks:\r\n",
        "       continue\r\n",
        "    #######RHip\r\n",
        "    a = np.array([results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_SHOULDER].x,results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_SHOULDER].y])\r\n",
        "    b = np.array([results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_HIP].x,results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_HIP].y])\r\n",
        "    c = np.array([results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_KNEE].x,results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_KNEE].y])\r\n",
        "    ########Rrightknee \r\n",
        "    d = np.array([results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_HIP].x,results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_HIP].y])\r\n",
        "    e = np.array([results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_KNEE].x,results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_KNEE].y])\r\n",
        "    f = np.array([results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_ANKLE].x,results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_ANKLE].y])\r\n",
        "    #######rightelbow \r\n",
        "    g = np.array([results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_SHOULDER].x,results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_SHOULDER].y])\r\n",
        "    h = np.array([results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_ELBOW].x,results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_ELBOW].y])\r\n",
        "    i = np.array([results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_WRIST].x,results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_WRIST].y])\r\n",
        "    ########Rshoulder\r\n",
        "    x = np.array([results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_ELBOW].x,results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_ELBOW].y])\r\n",
        "    y = np.array([results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_SHOULDER].x,results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_SHOULDER].y])\r\n",
        "    z = np.array([results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_HIP].x,results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_HIP].y])\r\n",
        "    ###########LHip\r\n",
        "    j = np.array([results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_SHOULDER].x,results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_SHOULDER].y])\r\n",
        "    k = np.array([results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_HIP].x,results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_HIP].y])\r\n",
        "    l = np.array([results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_KNEE].x,results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_KNEE].y])\r\n",
        "    ##############Lefttknee\r\n",
        "    m = np.array([results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_HIP].x,results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_HIP].y])\r\n",
        "    n = np.array([results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_KNEE].x,results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_KNEE].y])\r\n",
        "    o = np.array([results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_ANKLE].x,results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_ANKLE].y])\r\n",
        "    ###############Leftelbow\r\n",
        "    p = np.array([results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_SHOULDER].x,results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_SHOULDER].y])\r\n",
        "    q = np.array([results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_ELBOW].x,results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_ELBOW].y])\r\n",
        "    r = np.array([results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_WRIST].x,results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_WRIST].y])\r\n",
        "    ######################\r\n",
        "    ########Lshoulder\r\n",
        "    s = np.array([results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_ELBOW].x,results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_ELBOW].y])\r\n",
        "    t = np.array([results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_SHOULDER].x,results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_SHOULDER].y])\r\n",
        "    v = np.array([results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_HIP].x,results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_HIP].y])\r\n",
        "    ######################\r\n",
        "    ba = a - b\r\n",
        "    bc = c - b\r\n",
        "    cosine_angle = np.dot(ba, bc) / (np.linalg.norm(ba) * np.linalg.norm(bc))\r\n",
        "    angle = np.arccos(cosine_angle)\r\n",
        "    print(f'RHip angles of {name}:')\r\n",
        "    print (np.degrees(angle))\r\n",
        "    ############\r\n",
        "    ed = d-e\r\n",
        "    ef = f-e\r\n",
        "    cosine_angle = np.dot(ed, ef) / (np.linalg.norm(ed) * np.linalg.norm(ef))\r\n",
        "    angle = np.arccos(cosine_angle)\r\n",
        "    print(f'Rknee angles of {name}:')\r\n",
        "    print (np.degrees(angle))\r\n",
        "    ##############\r\n",
        "    hg = g-h\r\n",
        "    hi = i-h\r\n",
        "\r\n",
        "    cosine_angle = np.dot(hg, hi) / (np.linalg.norm(hg) * np.linalg.norm(hi))\r\n",
        "    angle = np.arccos(cosine_angle)\r\n",
        "    print(f'RElbow angles of {name}:')\r\n",
        "    print (np.degrees(angle))\r\n",
        "    ###############\r\n",
        "    kj = j-k\r\n",
        "    kl = l-k\r\n",
        "\r\n",
        "    cosine_angle = np.dot(kj, kl) / (np.linalg.norm(kj) * np.linalg.norm(kl))\r\n",
        "    angle = np.arccos(cosine_angle)\r\n",
        "    print(f'Lhip angles of {name}:')\r\n",
        "    print (np.degrees(angle))\r\n",
        "    #################\r\n",
        "    nm = n-m\r\n",
        "    no = o-n\r\n",
        "    cosine_angle = np.dot(nm, no) / (np.linalg.norm(nm) * np.linalg.norm(no))\r\n",
        "    angle = np.arccos(cosine_angle)\r\n",
        "    print(f'LKnee angles of {name}:')\r\n",
        "    print (np.degrees(angle))\r\n",
        "    ###############\r\n",
        "    qp = p-q\r\n",
        "    qr = r-q\r\n",
        "    cosine_angle = np.dot(qp, qr) / (np.linalg.norm(qp) * np.linalg.norm(qr))\r\n",
        "    angle = np.arccos(cosine_angle)\r\n",
        "    print(f'LElbow angles of {name}:')\r\n",
        "    print (np.degrees(angle))\r\n",
        "    ################\r\n",
        "\r\n",
        "    yx = x-y\r\n",
        "    yz = z-y\r\n",
        "    cosine_angle = np.dot(yx, yz) / (np.linalg.norm(yx) * np.linalg.norm(yz))\r\n",
        "    angle = np.arccos(cosine_angle)\r\n",
        "    print(f'Rshoulder angles of {name}:')\r\n",
        "    print (np.degrees(angle))\r\n",
        "    ##############\r\n",
        "\r\n",
        "    ts = s-t\r\n",
        "    tv = v-t\r\n",
        "    cosine_angle = np.dot(ts, tv) / (np.linalg.norm(ts) * np.linalg.norm(tv))\r\n",
        "    angle = np.arccos(cosine_angle)\r\n",
        "    print(f'Lshoulder angles of {name}:')\r\n",
        "    print (np.degrees(angle))\r\n",
        "    ##############\r\n",
        "#print(a,b,c)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RHip angles of 1.jpg:\n",
            "157.79266720101145\n",
            "Rknee angles of 1.jpg:\n",
            "166.14541208442637\n",
            "RElbow angles of 1.jpg:\n",
            "79.06939096512396\n",
            "Lhip angles of 1.jpg:\n",
            "163.65213925766272\n",
            "LKnee angles of 1.jpg:\n",
            "11.881889024511853\n",
            "LElbow angles of 1.jpg:\n",
            "87.66410182721071\n",
            "Rshoulder angles of 1.jpg:\n",
            "85.54437488046531\n",
            "Lshoulder angles of 1.jpg:\n",
            "76.11265432009618\n",
            "RHip angles of 5.jpg:\n",
            "167.4140519073929\n",
            "Rknee angles of 5.jpg:\n",
            "173.70597915077\n",
            "RElbow angles of 5.jpg:\n",
            "92.06422259870278\n",
            "Lhip angles of 5.jpg:\n",
            "166.55281292784207\n",
            "LKnee angles of 5.jpg:\n",
            "7.326032056763791\n",
            "LElbow angles of 5.jpg:\n",
            "98.65379520674313\n",
            "Rshoulder angles of 5.jpg:\n",
            "93.01751479454248\n",
            "Lshoulder angles of 5.jpg:\n",
            "90.80943212941818\n",
            "RHip angles of 8.jpg:\n",
            "164.36346929674676\n",
            "Rknee angles of 8.jpg:\n",
            "164.42620963620158\n",
            "RElbow angles of 8.jpg:\n",
            "75.14108897225803\n",
            "Lhip angles of 8.jpg:\n",
            "167.74199253095\n",
            "LKnee angles of 8.jpg:\n",
            "14.30726346051962\n",
            "LElbow angles of 8.jpg:\n",
            "87.79076051016521\n",
            "Rshoulder angles of 8.jpg:\n",
            "79.82068620137208\n",
            "Lshoulder angles of 8.jpg:\n",
            "71.50665739840557\n",
            "RHip angles of 11.jpg:\n",
            "154.74223208273628\n",
            "Rknee angles of 11.jpg:\n",
            "155.81798030162514\n",
            "RElbow angles of 11.jpg:\n",
            "95.51422826703384\n",
            "Lhip angles of 11.jpg:\n",
            "160.63645484847876\n",
            "LKnee angles of 11.jpg:\n",
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            "165.0375263465535\n",
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            "103.60074380575807\n",
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            "98.53126207036358\n",
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            "151.24209148563617\n",
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            "166.93077823695992\n",
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            "157.8455210771974\n",
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            "158.24322156482447\n",
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            "169.42126220315262\n",
            "RElbow angles of 541.jpg:\n",
            "79.81001447319194\n",
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            "167.95417644243798\n",
            "LKnee angles of 541.jpg:\n",
            "5.244190793398116\n",
            "LElbow angles of 541.jpg:\n",
            "116.41831578532046\n",
            "Rshoulder angles of 541.jpg:\n",
            "88.04618864685528\n",
            "Lshoulder angles of 541.jpg:\n",
            "83.37311410601183\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BAivyQ_xOtFp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7c0d4d3e-331f-4ff9-fa0a-741bbddede12"
      },
      "source": [
        "!zip -r /content/sample_data/Sorted_Dolphin_Plank_pose.zip /content/sample_data/Sorted_Dolphin_Plank_pose"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  adding: content/sample_data/Sorted_Dolphin_Plank_pose/ (stored 0%)\n",
            "  adding: content/sample_data/Sorted_Dolphin_Plank_pose/32.jpg (deflated 0%)\n",
            "  adding: content/sample_data/Sorted_Dolphin_Plank_pose/46.jpg (deflated 2%)\n",
            "  adding: content/sample_data/Sorted_Dolphin_Plank_pose/8.jpg (deflated 3%)\n",
            "  adding: content/sample_data/Sorted_Dolphin_Plank_pose/30.jpg (deflated 22%)\n",
            "  adding: content/sample_data/Sorted_Dolphin_Plank_pose/464.jpg (deflated 10%)\n",
            "  adding: content/sample_data/Sorted_Dolphin_Plank_pose/151.jpg (deflated 13%)\n",
            "  adding: content/sample_data/Sorted_Dolphin_Plank_pose/441.jpg (deflated 3%)\n",
            "  adding: content/sample_data/Sorted_Dolphin_Plank_pose/11.jpg (deflated 4%)\n",
            "  adding: content/sample_data/Sorted_Dolphin_Plank_pose/5.jpg (deflated 33%)\n",
            "  adding: content/sample_data/Sorted_Dolphin_Plank_pose/231.jpg (deflated 4%)\n",
            "  adding: content/sample_data/Sorted_Dolphin_Plank_pose/434.jpg (deflated 4%)\n",
            "  adding: content/sample_data/Sorted_Dolphin_Plank_pose/41.jpg (deflated 4%)\n",
            "  adding: content/sample_data/Sorted_Dolphin_Plank_pose/70.jpg (deflated 4%)\n",
            "  adding: content/sample_data/Sorted_Dolphin_Plank_pose/138.jpg (deflated 7%)\n",
            "  adding: content/sample_data/Sorted_Dolphin_Plank_pose/1.jpg (deflated 4%)\n",
            "  adding: content/sample_data/Sorted_Dolphin_Plank_pose/541.jpg (deflated 5%)\n",
            "  adding: content/sample_data/Sorted_Dolphin_Plank_pose/261.jpg (deflated 5%)\n",
            "  adding: content/sample_data/Sorted_Dolphin_Plank_pose/44.jpg (deflated 1%)\n",
            "  adding: content/sample_data/Sorted_Dolphin_Plank_pose/34.jpg (deflated 0%)\n",
            "  adding: content/sample_data/Sorted_Dolphin_Plank_pose/14.jpg (deflated 10%)\n",
            "  adding: content/sample_data/Sorted_Dolphin_Plank_pose/31.jpg (deflated 10%)\n",
            "  adding: content/sample_data/Sorted_Dolphin_Plank_pose/118.jpg (deflated 0%)\n",
            "  adding: content/sample_data/Sorted_Dolphin_Plank_pose/71.jpg (deflated 6%)\n",
            "  adding: content/sample_data/Sorted_Dolphin_Plank_pose/93.jpg (deflated 1%)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8vS31nMAq68i",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "de8850ff-a431-4eb4-cb12-9094f75214e9"
      },
      "source": [
        "from google.colab import files\r\n",
        "files.download(\"/content/sample_data/Sorted_Dolphin_Plank_pose.zip\")"
      ],
      "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": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_30fb8768-80cf-4998-b27f-944feec10b35\", \"Sorted_Dolphin_Plank_pose.zip\", 2699559)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XRBvKeqS3LvP"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qvs-bGEQNUO8"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}