mediapipe_yoga_detection_test.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/LoriSchuan-dev/feature_engineering_project2021/blob/main/mediapipe_yoga_detection_test.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "u4DO3fQkeeI-"
   },
   "outputs": [],
   "source": [
    "#!pip install mediapipe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "l9wDWG__h8UW"
   },
   "outputs": [],
   "source": [
    "import mediapipe as mp\n",
    "from urllib import request\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "NP6jKSKwiGCG"
   },
   "outputs": [],
   "source": [
    "#Test Images\n",
    "url_list = [#'https://i0.wp.com/cdn-prod.medicalnewstoday.com/content/images/articles/327/327086/a-woman-doing-the-bow-yoga-pose.jpg',\n",
    "            #'https://i0.wp.com/www.yogabasics.com/yogabasics2017/wp-content/uploads/2013/11/bow-pose-024.jpg',\n",
    "            #'https://cdn.prod.openfit.com/uploads/2019/05/13173628/bow-pose-dhanurasana-brent-laffoon-yoga521.jpg',\n",
    "            # 'https://cdn-prod.medicalnewstoday.com/content/images/articles/327/327086/supine-spinal-twist-yoga-pose.jpg',\n",
    "            #'https://www.artofliving.org/sites/www.artofliving.org/files/styles/original_image/public/wysiwyg_imageupload/1-Ten%20Challenging%20Yoga%20Asanas.jpg.webp?itok=OJgnpb3T',\n",
    "            #'https://www.artofliving.org/sites/www.artofliving.org/files/styles/original_image/public/wysiwyg_imageupload/2-Ten%20Challenging%20Yoga%20Asanas.jpg.webp?itok=z8l14C8v',\n",
    "            #'https://www.healthshots.com/wp-content/uploads/2020/09/Malasana-lead.jpg'\n",
    "            'https://www.yogajournal.com/wp-content/uploads/2020/12/ccd03542-1.jpg'\n",
    "            ]\n",
    "            \n",
    "for idx in range(len(url_list)):\n",
    "    os.system(\"curl \" + url_list[idx] + \" > test_\"+str(idx+1)+\".jpg\")\n",
    "\n",
    "image_list = ['test_'+str(i+1)+'.jpg' for i in range(len(url_list))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "8_csQ7UArjhl"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test_1.jpg\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "# from google.colab.patches import cv2_imshow\n",
    "import math\n",
    "import numpy as np\n",
    "\n",
    "DESIRED_HEIGHT = 480\n",
    "DESIRED_WIDTH = 480\n",
    "\n",
    "def resize_and_show(image):\n",
    "    h, w = image.shape[:2]\n",
    "    if h < w:\n",
    "        img = cv2.resize(image, (DESIRED_WIDTH, math.floor(h/(w/DESIRED_WIDTH))))\n",
    "    else:\n",
    "        img = cv2.resize(image, (math.floor(w/(h/DESIRED_HEIGHT)), DESIRED_HEIGHT))\n",
    "    \n",
    "    #cv2_imshow(img)\n",
    "    cv2.imshow(\"img\", img)\n",
    "    cv2.waitKey(0)\n",
    "    cv2.destroyAllWindows()\n",
    "\n",
    "\n",
    "# Read images with OpenCV.\n",
    "images = {name: cv2.imread(name) for name in image_list}\n",
    "# Preview the images.\n",
    "for name, image in images.items():\n",
    "    print(name)   \n",
    "    resize_and_show(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "qHdzTc9jp3rD"
   },
   "outputs": [],
   "source": [
    "mp_pose = mp.solutions.pose\n",
    "mp_drawing = mp.solutions.drawing_utils \n",
    "mp_drawing_styles = mp.solutions.drawing_styles\n",
    "\n",
    "# help(mp_pose.Pose)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "aC0RT0-Kp9DL"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading model to C:\\Users\\Cooper\\Documents\\GitHub\\feature_engineering_project2021\\env\\lib\\site-packages\\mediapipe/modules/pose_landmark/pose_landmark_heavy.tflite\n",
      "Nose coordinates: (955.3149938583374, 128.96952778100967)\n",
      "Pose landmarks of test_1.jpg:\n"
     ]
    }
   ],
   "source": [
    "# Run MediaPipe Pose and draw pose landmarks.\n",
    "with mp_pose.Pose(\n",
    "    static_image_mode=True, min_detection_confidence=0.2, model_complexity=2) as pose: \n",
    "    #You can change parameters mdc =>(0~1) model_complexity => 0,1,2\n",
    "    #default mdc = 0.5 mc = 2\n",
    "    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",
    "    \n",
    "      # Print nose landmark.\n",
    "        image_hight, image_width, _ = image.shape\n",
    "        if not results.pose_landmarks:\n",
    "            continue\n",
    "        print(\n",
    "            f'Nose coordinates: ('\n",
    "            f'{results.pose_landmarks.landmark[mp_pose.PoseLandmark.NOSE].x * image_width}, '\n",
    "            f'{results.pose_landmarks.landmark[mp_pose.PoseLandmark.NOSE].y * image_hight})'\n",
    "        )\n",
    "\n",
    "        # Draw pose landmarks.\n",
    "        print(f'Pose landmarks of {name}:')\n",
    "        annotated_image = image.copy()\n",
    "        mp_drawing.draw_landmarks(\n",
    "            annotated_image,\n",
    "            results.pose_landmarks,\n",
    "            mp_pose.POSE_CONNECTIONS,\n",
    "            landmark_drawing_spec=mp_drawing_styles.get_default_pose_landmarks_style())\n",
    "        resize_and_show(annotated_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "SawZsHqjsEL7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nose world landmark:\n",
      "x: -0.03469572588801384\n",
      "y: -0.6034910678863525\n",
      "z: -0.20691804587841034\n",
      "visibility: 0.999894380569458\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "frozenset({(15, 21), (16, 20), (18, 20), (3, 7), (14, 16), (23, 25), (28, 30), (11, 23), (27, 31), (6, 8), (15, 17), (24, 26), (16, 22), (4, 5), (5, 6), (29, 31), (12, 24), (23, 24), (0, 1), (9, 10), (1, 2), (0, 4), (11, 13), (30, 32), (28, 32), (15, 19), (16, 18), (25, 27), (26, 28), (12, 14), (17, 19), (2, 3), (11, 12), (27, 29), (13, 15)})\n",
      "x: -0.03469572588801384\n",
      "y: -0.6034910678863525\n",
      "z: -0.20691804587841034\n",
      "visibility: 0.999894380569458\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Run MediaPipe Pose and plot 3d pose world landmarks.\n",
    "\n",
    "with mp_pose.Pose(static_image_mode=True,\n",
    "                  min_detection_confidence=0.5,\n",
    "                  model_complexity=2) as pose:\n",
    "    \n",
    "    for name, image in images.items():\n",
    "        results = pose.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))\n",
    "\n",
    "        # Print the real-world 3D coordinates of nose in meters with the origin at\n",
    "        # the center between hips.\n",
    "        print('Nose world landmark:'),\n",
    "        print(results.pose_world_landmarks.landmark[mp_pose.PoseLandmark.NOSE])\n",
    "\n",
    "            # Plot pose world landmarks.\n",
    "        #fig, ax = \n",
    "        mp_drawing.plot_landmarks(results.pose_world_landmarks, mp_pose.POSE_CONNECTIONS)\n",
    "        print(mp_pose.POSE_CONNECTIONS)\n",
    "        print(results.pose_world_landmarks.landmark[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "i3q082v1cbIP"
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'NoneType' object has no attribute 'canvas'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_1548/2568474818.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[1;31m# Animate\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 10\u001b[1;33m \u001b[0mani\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0manimation\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mFuncAnimation\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0manimate\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mframes\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minterval\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mblit\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     11\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m \u001b[0mHTML\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mani\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_html5_video\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Documents\\GitHub\\feature_engineering_project2021\\env\\lib\\site-packages\\matplotlib\\animation.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, fig, func, frames, init_func, fargs, save_count, cache_frame_data, **kwargs)\u001b[0m\n\u001b[0;32m   1703\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_save_seq\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1704\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1705\u001b[1;33m         \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1706\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1707\u001b[0m         \u001b[1;31m# Need to reset the saved seq, since right now it will contain data\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Documents\\GitHub\\feature_engineering_project2021\\env\\lib\\site-packages\\matplotlib\\animation.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, fig, interval, repeat_delay, repeat, event_source, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1464\u001b[0m         \u001b[1;31m# sharing timers between animation objects for syncing animations.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1465\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mevent_source\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1466\u001b[1;33m             \u001b[0mevent_source\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnew_timer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minterval\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_interval\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1467\u001b[0m         \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mevent_source\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mevent_source\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1468\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'canvas'"
     ]
    }
   ],
   "source": [
    "from matplotlib import animation\n",
    "from IPython.display import HTML\n",
    "from mpl_toolkits.mplot3d import axes3d\n",
    "\n",
    "def animate(i):\n",
    "    ax.view_init(elev=0., azim=3.6*i)\n",
    "    return fig,\n",
    "\n",
    "# Animate\n",
    "ani = animation.FuncAnimation(fig, animate, frames=100, interval=100, blit=True)    \n",
    "\n",
    "HTML(ani.to_html5_video())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "vs35CnpmgHIn"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "include_colab_link": true,
   "name": "mediapipe_yoga_detection_test",
   "private_outputs": true,
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}