Squat pose estimation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "from cv2 import destroyAllWindows\n",
    "import mediapipe as mp\n",
    "import numpy as np\n",
    "mp_drawing = mp.solutions.drawing_utils\n",
    "mp_drawing_styles = mp.solutions.drawing_styles\n",
    "mp_pose = mp.solutions.pose"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "STEP1"
   ]
  },
  {
   "attachments": {
    "pose_tracking_full_body_landmarks.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![pose_tracking_full_body_landmarks.png](attachment:pose_tracking_full_body_landmarks.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#mp_pose.POSE_CONNECTIONS\n",
    "\n",
    "#TODO: Add visibility parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def calculate_angle(a,b,c):\n",
    "    a = np.array(a) # First\n",
    "    b = np.array(b) # Mid\n",
    "    c = np.array(c) # End\n",
    "    \n",
    "    radians = np.arctan2(c[1]-b[1], c[0]-b[0]) - np.arctan2(a[1]-b[1], a[0]-b[0])\n",
    "    angle = np.abs(radians*180.0/np.pi)\n",
    "    \n",
    "    if angle >180.0:\n",
    "        angle = 360-angle\n",
    "        \n",
    "    return angle"
   ]
  },
  {
   "attachments": {
    "angles.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![angles.png](attachment:angles.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rescale_frame(frame, percent=50):\n",
    "    width = int(frame.shape[1] * percent/ 100)\n",
    "    height = int(frame.shape[0] * percent/ 100)\n",
    "    dim = (width, height)\n",
    "    return cv2.resize(frame, dim, interpolation =cv2.INTER_AREA)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Youtube video\n",
    "angle_min = []\n",
    "angle_min_hip = []\n",
    "cap = cv2.VideoCapture(\"squats_test_v2.mp4\")\n",
    "\n",
    "\n",
    "\n",
    "# Curl counter variables\n",
    "counter = 0 \n",
    "min_ang = 0\n",
    "max_ang = 0\n",
    "min_ang_hip = 0\n",
    "max_ang_hip = 0\n",
    "stage = None\n",
    "\n",
    "width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH) + 0.5)\n",
    "height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT) + 0.5)\n",
    "size = (640, 480)\n",
    "fourcc = cv2.VideoWriter_fourcc(*'MP4V')\n",
    "out = cv2.VideoWriter('output_video_.mp4', fourcc, 24, size)\n",
    "\n",
    "with mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) as pose:\n",
    "    while cap.isOpened():\n",
    "        ret, frame = cap.read()\n",
    "        if frame is not None:\n",
    "            frame_ = rescale_frame(frame, percent=75)\n",
    "        \n",
    "        # Recolor image to RGB\n",
    "        image = cv2.cvtColor(frame_, cv2.COLOR_BGR2RGB)\n",
    "        image.flags.writeable = False\n",
    "      \n",
    "        # Make detection\n",
    "        results = pose.process(image)\n",
    "    \n",
    "        # Recolor back to BGR\n",
    "        image.flags.writeable = True\n",
    "        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n",
    "        \n",
    "        # Extract landmarks\n",
    "        try:\n",
    "            landmarks = results.pose_landmarks.landmark\n",
    "            \n",
    "            # Get coordinates\n",
    "            shoulder = [landmarks[mp_pose.PoseLandmark.RIGHT_SHOULDER.value].x,landmarks[mp_pose.PoseLandmark.RIGHT_SHOULDER.value].y]\n",
    "            \"\"\"elbow = [landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value].x,landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value].y]\n",
    "            wrist = [landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].x,landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].y]\n",
    "            \"\"\"\n",
    "            \n",
    "            # Get coordinates\n",
    "            hip = [landmarks[mp_pose.PoseLandmark.RIGHT_HIP.value].x,landmarks[mp_pose.PoseLandmark.RIGHT_HIP.value].y]\n",
    "            knee = [landmarks[mp_pose.PoseLandmark.RIGHT_KNEE.value].x,landmarks[mp_pose.PoseLandmark.RIGHT_KNEE.value].y]\n",
    "            ankle = [landmarks[mp_pose.PoseLandmark.RIGHT_ANKLE.value].x,landmarks[mp_pose.PoseLandmark.RIGHT_ANKLE.value].y]\n",
    "            \n",
    "           \n",
    "            \n",
    "            # Calculate angle\n",
    "            angle = calculate_angle(shoulder, elbow, wrist)\n",
    "            \n",
    "            angle_knee = calculate_angle(hip, knee, ankle) #Knee joint angle\n",
    "            angle_knee = round(angle_knee,2)\n",
    "            \n",
    "            angle_hip = calculate_angle(shoulder, hip, knee)\n",
    "            angle_hip = round(angle_hip,2)\n",
    "            \n",
    "            hip_angle = 180-angle_hip\n",
    "            knee_angle = 180-angle_knee\n",
    "            \n",
    "            \n",
    "            angle_min.append(angle_knee)\n",
    "            angle_min_hip.append(angle_hip)\n",
    "            \n",
    "            #print(angle_knee)\n",
    "            # Visualize angle\n",
    "            \"\"\"cv2.putText(image, str(angle), \n",
    "                           tuple(np.multiply(elbow, [640, 480]).astype(int)), \n",
    "                           cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2, cv2.LINE_AA\n",
    "                                )\"\"\"\n",
    "                       \n",
    "                \n",
    "            cv2.putText(image, str(angle_knee), \n",
    "                           tuple(np.multiply(knee, [1500, 800]).astype(int)), \n",
    "                           cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255,255,255), 2, cv2.LINE_AA\n",
    "                                )\n",
    "            \n",
    "            cv2.putText(image, str(angle_hip), \n",
    "                           tuple(np.multiply(hip, [1500, 800]).astype(int)), \n",
    "                           cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2, cv2.LINE_AA\n",
    "                                )\n",
    "            \n",
    "            # Curl counter logic\n",
    "            if angle_knee > 169:\n",
    "                stage = \"up\"\n",
    "            if angle_knee <= 90 and stage =='up':\n",
    "                stage=\"down\"\n",
    "                counter +=1\n",
    "                print(counter)\n",
    "                min_ang  =min(angle_min)\n",
    "                max_ang = max(angle_min)\n",
    "                \n",
    "                min_ang_hip  =min(angle_min_hip)\n",
    "                max_ang_hip = max(angle_min_hip)\n",
    "                \n",
    "                print(min(angle_min), \" _ \", max(angle_min))\n",
    "                print(min(angle_min_hip), \" _ \", max(angle_min_hip))\n",
    "                angle_min = []\n",
    "                angle_min_hip = []\n",
    "        except:\n",
    "            pass\n",
    "        \n",
    "        # Render squat counter\n",
    "        # Setup status box\n",
    "        cv2.rectangle(image, (20,20), (435,160), (0,0,0), -1)\n",
    "        \n",
    "        # Rep data\n",
    "        \"\"\"cv2.putText(image, 'REPS', (15,12), \n",
    "                    cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255,255,255), 1, cv2.LINE_AA)\"\"\"\n",
    "        cv2.putText(image, \"Repetition : \" + str(counter), \n",
    "                    (30,60), \n",
    "                    cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255), 2, cv2.LINE_AA)\n",
    "        \n",
    "        # Stage data\n",
    "        \"\"\"cv2.putText(image, 'STAGE', (65,12), \n",
    "                    cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0,0,0), 1, cv2.LINE_AA)\"\"\"\n",
    "        \"\"\"cv2.putText(image, stage, \n",
    "                    (10,120), \n",
    "                    cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255), 2, cv2.LINE_AA)\"\"\"\n",
    "        \n",
    "        #Knee angle:\n",
    "        \"\"\"cv2.putText(image, 'Angle', (65,12), \n",
    "                    cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255,255,255), 1, cv2.LINE_AA)\"\"\"\n",
    "        cv2.putText(image, \"Knee-joint angle : \" + str(min_ang), \n",
    "                    (30,100), \n",
    "                    cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255), 2, cv2.LINE_AA)\n",
    "        \n",
    "        #Hip angle:\n",
    "        cv2.putText(image, \"Hip-joint angle : \" + str(min_ang_hip), \n",
    "                    (30,140), \n",
    "                    cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255), 2, cv2.LINE_AA)\n",
    "        \n",
    "\n",
    "        \n",
    "        \n",
    "        # Render detections\n",
    "        mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS,\n",
    "                                mp_drawing.DrawingSpec(color=(0,0,0), thickness=2, circle_radius=2), \n",
    "                                mp_drawing.DrawingSpec(color=(203,17,17), thickness=2, circle_radius=2) \n",
    "                                 )               \n",
    "        \n",
    "        out.write(image)\n",
    "        cv2.imshow('Mediapipe Feed', image)\n",
    "\n",
    "        if cv2.waitKey(10) & 0xFF == ord('q'):\n",
    "            cap.release()\n",
    "            out.release()\n",
    "            cv2.destroyAllWindows()\n",
    "            #break\n",
    "\n",
    "    cap.release()\n",
    "    out.release()\n",
    "    cv2.destroyAllWindows()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "# Youtube video2\n",
    "angle_min = []\n",
    "angle_min_hip = []\n",
    "\n",
    "cap = cv2.VideoCapture(\"squats_test_v2.mp4\")\n",
    "# Curl counter variables\n",
    "counter = 0 \n",
    "min_ang = 0\n",
    "max_ang = 0\n",
    "min_ang_hip = 0\n",
    "max_ang_hip = 0\n",
    "stage = None\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH) + 0.5)\n",
    "height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT) + 0.5)\n",
    "size = (640, 480)\n",
    "fourcc = cv2.VideoWriter_fourcc(*'MP4V')\n",
    "out = cv2.VideoWriter('output_video_.mp4', fourcc, 24, size)\n",
    "\n",
    "with mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) as pose:\n",
    "    while cap.isOpened():\n",
    "        ret, frame = cap.read()\n",
    "        if frame is not None:\n",
    "            frame_ = rescale_frame(frame, percent=55)\n",
    "        \n",
    "        # Recolor image to RGB\n",
    "        image = cv2.cvtColor(frame_, cv2.COLOR_BGR2RGB)\n",
    "        image.flags.writeable = False\n",
    "      \n",
    "        # Make detection\n",
    "        results = pose.process(image)\n",
    "    \n",
    "        # Recolor back to BGR\n",
    "        image.flags.writeable = True\n",
    "        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n",
    "        \n",
    "        # Extract landmarks\n",
    "        try:\n",
    "            landmarks = results.pose_landmarks.landmark\n",
    "            \n",
    "            # Get coordinates\n",
    "            shoulder = [landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].x,landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].y]\n",
    "            \"\"\"elbow = [landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value].x,landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value].y]\n",
    "            wrist = [landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].x,landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].y]\n",
    "            \"\"\"\n",
    "            \n",
    "            # Get coordinates\n",
    "            hip = [landmarks[mp_pose.PoseLandmark.LEFT_HIP.value].x,landmarks[mp_pose.PoseLandmark.LEFT_HIP.value].y]\n",
    "            knee = [landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].x,landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].y]\n",
    "            ankle = [landmarks[mp_pose.PoseLandmark.LEFT_ANKLE.value].x,landmarks[mp_pose.PoseLandmark.LEFT_ANKLE.value].y]\n",
    "            \n",
    "           \n",
    "            \n",
    "            # Calculate angle\n",
    "            angle = calculate_angle(shoulder, elbow, wrist)\n",
    "            \n",
    "            angle_knee = calculate_angle(hip, knee, ankle) #Knee joint angle\n",
    "            angle_knee = round(angle_knee,2)\n",
    "            \n",
    "            angle_hip = calculate_angle(shoulder, hip, knee)\n",
    "            angle_hip = round(angle_hip,2)\n",
    "            \n",
    "            hip_angle = 180-angle_hip\n",
    "            knee_angle = 180-angle_knee\n",
    "            \n",
    "            \n",
    "            angle_min.append(angle_knee)\n",
    "            angle_min_hip.append(angle_hip)\n",
    "            \n",
    "            print(angle_knee)\n",
    "            # Visualize angle\n",
    "            \"\"\"cv2.putText(image, str(angle), \n",
    "                           tuple(np.multiply(elbow, [640, 480]).astype(int)), \n",
    "                           cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2, cv2.LINE_AA\n",
    "                                )\"\"\"\n",
    "                       \n",
    "                \n",
    "            cv2.putText(image, str(angle_knee), \n",
    "                           tuple(np.multiply(knee, [630,900]).astype(int)), \n",
    "                           cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255,9,0), 2, cv2.LINE_AA\n",
    "                                )\n",
    "            \n",
    "            cv2.putText(image, str(angle_hip), \n",
    "                           tuple(np.multiply(hip, [630,900]).astype(int)), \n",
    "                           cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0,0,255), 2, cv2.LINE_AA\n",
    "                                )\n",
    "            \n",
    "            # Curl counter logic\n",
    "            if angle_knee > 150:\n",
    "                stage = \"up\"\n",
    "            if angle_knee <= 90 and stage =='up':\n",
    "                stage=\"down\"\n",
    "                counter +=1\n",
    "                print(counter)\n",
    "                min_ang  =min(angle_min)\n",
    "                max_ang = max(angle_min)\n",
    "                \n",
    "                min_ang_hip  =min(angle_min_hip)\n",
    "                max_ang_hip = max(angle_min_hip)\n",
    "                \n",
    "                print(min(angle_min), \" _ \", max(angle_min))\n",
    "                print(min(angle_min_hip), \" _ \", max(angle_min_hip))\n",
    "                angle_min = []\n",
    "                angle_min_hip = []\n",
    "        except:\n",
    "            pass\n",
    "        \n",
    "        # Render squat counter\n",
    "        # Setup status box\n",
    "        cv2.rectangle(image, (20,20), (200,90), (0,0,0), -1)\n",
    "        \n",
    "        # Rep data\n",
    "        \"\"\"cv2.putText(image, 'REPS', (15,12), \n",
    "                    cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255,255,255), 1, cv2.LINE_AA)\"\"\"\n",
    "        cv2.putText(image, \"Repetition : \" + str(counter), \n",
    "                    (30,40), \n",
    "                    cv2.FONT_HERSHEY_SIMPLEX, 0.4, (255,255,255), 1, cv2.LINE_AA)\n",
    "        \n",
    "        # Stage data\n",
    "        \"\"\"cv2.putText(image, 'STAGE', (65,12), \n",
    "                    cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0,0,0), 1, cv2.LINE_AA)\"\"\"\n",
    "        \"\"\"cv2.putText(image, stage, \n",
    "                    (10,120), \n",
    "                    cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255), 2, cv2.LINE_AA)\"\"\"\n",
    "        \n",
    "        #Knee angle:\n",
    "        \"\"\"cv2.putText(image, 'Angle', (65,12), \n",
    "                    cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255,255,255), 1, cv2.LINE_AA)\"\"\"\n",
    "        cv2.putText(image, \"Knee-joint angle : \" + str(min_ang), \n",
    "                    (30,60), \n",
    "                    cv2.FONT_HERSHEY_SIMPLEX, 0.4, (255,255,255), 1, cv2.LINE_AA)\n",
    "        \n",
    "        #Hip angle:\n",
    "        cv2.putText(image, \"Hip-joint angle : \" + str(min_ang_hip), \n",
    "                    (30,80), \n",
    "                    cv2.FONT_HERSHEY_SIMPLEX, 0.4, (255,255,255), 1, cv2.LINE_AA)\n",
    "        \n",
    "\n",
    "        \n",
    "        \n",
    "        # Render detections\n",
    "        mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS,\n",
    "                                mp_drawing.DrawingSpec(color=(0,0,255), thickness=2, circle_radius=1), \n",
    "                                mp_drawing.DrawingSpec(color=(0,255,0), thickness=2, circle_radius=1) \n",
    "                                 )               \n",
    "        \n",
    "        out.write(image)\n",
    "        cv2.imshow('Mediapipe Feed', image)\n",
    "\n",
    "        if cv2.waitKey(10) & 0xFF == ord('q'):\n",
    "            cap.release()\n",
    "            out.release()\n",
    "            cv2.destroyAllWindows()\n",
    "            #break\n",
    "\n",
    "    cap.release()\n",
    "    out.release()\n",
    "    cv2.destroyAllWindows()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "544"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame_.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "x: 0.5446415\n",
       "y: 0.45685163\n",
       "z: 0.19262058\n",
       "visibility: 0.9993714"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "landmarks[mp_pose.PoseLandmark.LEFT_HIP.value]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Range of motion\n",
    "Partial squat (0-40 degrees knee angle)<br>\n",
    "Parallel squat (hips parallel to knees or 70-100 degrees knee angle)<br>\n",
    "Deep squat (full range or >100 degrees knee angle)<br><br>\n",
    "\n",
    "ref: https://www.raynersmale.com/blog/2014/1/31/optimising-your-squat\n",
    "\n",
    "TODO: Toque calculation:\n",
    "https://squatuniversity.com/2016/04/20/the-real-science-of-the-squat/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Todo: angles:\n",
    "Trunk angle,\n",
    "Shank angle\n",
    "Thigh segment angle\n",
    "Ankle segment angle\n",
    "ref: https://www.quinticsports.com/squatting_technique/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# webcam input\n",
    "\n",
    "cap = cv2.VideoCapture(0)\n",
    "# Curl counter variables\n",
    "counter = 0 \n",
    "stage = None\n",
    "\n",
    "\"\"\"width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH) + 0.5)\n",
    "height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT) + 0.5)\n",
    "size = (width, height)\n",
    "fourcc = cv2.VideoWriter_fourcc(*'MP4V')\n",
    "out = cv2.VideoWriter('your_video.mp4', fourcc, 10.0, size, True)\"\"\"\n",
    "\n",
    "with mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) as pose:\n",
    "    while cap.isOpened():\n",
    "        ret, frame = cap.read()\n",
    "        \n",
    "        # Recolor image to RGB\n",
    "        image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
    "        image.flags.writeable = False\n",
    "      \n",
    "        # Make detection\n",
    "        results = pose.process(image)\n",
    "    \n",
    "        # Recolor back to BGR\n",
    "        image.flags.writeable = True\n",
    "        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n",
    "        \n",
    "        # Extract landmarks\n",
    "        try:\n",
    "            landmarks = results.pose_landmarks.landmark\n",
    "            \n",
    "            # Get coordinates\n",
    "            shoulder = [landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].x,landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].y]\n",
    "            elbow = [landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value].x,landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value].y]\n",
    "            wrist = [landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].x,landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].y]\n",
    "            \n",
    "            \n",
    "            # Get coordinates\n",
    "            hip = [landmarks[mp_pose.PoseLandmark.LEFT_HIP.value].x,landmarks[mp_pose.PoseLandmark.LEFT_HIP.value].y]\n",
    "            knee = [landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].x,landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].y]\n",
    "            ankle = [landmarks[mp_pose.PoseLandmark.LEFT_ANKLE.value].x,landmarks[mp_pose.PoseLandmark.LEFT_ANKLE.value].y]\n",
    "            \n",
    "           \n",
    "            \n",
    "            # Calculate angle\n",
    "            angle = calculate_angle(shoulder, elbow, wrist)\n",
    "            \n",
    "            angle_knee = calculate_angle(hip, knee, ankle) #Knee joint angle\n",
    "            \n",
    "            angle_hip = calculate_angle(shoulder, hip, knee)\n",
    "            hip_angle = 180-angle_hip\n",
    "            knee_angle = 180-angle_knee\n",
    "            \n",
    "            # Visualize angle\n",
    "            \"\"\"cv2.putText(image, str(angle), \n",
    "                           tuple(np.multiply(elbow, [640, 480]).astype(int)), \n",
    "                           cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2, cv2.LINE_AA\n",
    "                                )\"\"\"\n",
    "                       \n",
    "                \n",
    "            cv2.putText(image, str(angle_knee), \n",
    "                           tuple(np.multiply(knee, [640, 480]).astype(int)), \n",
    "                           cv2.FONT_HERSHEY_SIMPLEX, 0.5, (79, 121, 66), 2, cv2.LINE_AA\n",
    "                                )\n",
    "            \n",
    "            \"\"\"cv2.putText(image, str(angle_hip), \n",
    "                           tuple(np.multiply(hip, [640, 480]).astype(int)), \n",
    "                           cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2, cv2.LINE_AA\n",
    "                                )\"\"\"\n",
    "            \n",
    "            # Curl counter logic\n",
    "            if angle_knee > 169:\n",
    "                stage = \"UP\"\n",
    "            if angle_knee <= 90 and stage =='UP':\n",
    "                stage=\"DOWN\"\n",
    "                counter +=1\n",
    "                print(counter)\n",
    "        except:\n",
    "            pass\n",
    "        \n",
    "        # Render squat counter\n",
    "        # Setup status box\n",
    "        cv2.rectangle(image, (0,0), (225,73), (245,117,16), -1)\n",
    "        \n",
    "        # Rep data\n",
    "        cv2.putText(image, 'REPS', (15,12), \n",
    "                    cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0,0,0), 1, cv2.LINE_AA)\n",
    "        cv2.putText(image, str(counter), \n",
    "                    (10,60), \n",
    "                    cv2.FONT_HERSHEY_SIMPLEX, 2, (255,255,255), 2, cv2.LINE_AA)\n",
    "        \n",
    "        # Stage data\n",
    "        cv2.putText(image, 'STAGE', (65,12), \n",
    "                    cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0,0,0), 1, cv2.LINE_AA)\n",
    "        cv2.putText(image, stage, \n",
    "                    (60,60), \n",
    "                    cv2.FONT_HERSHEY_SIMPLEX, 2, (255,255,255), 2, cv2.LINE_AA)\n",
    "        \n",
    "        \n",
    "        # Render detections\n",
    "        mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS,\n",
    "                                mp_drawing.DrawingSpec(color=(245,117,66), thickness=2, circle_radius=2), \n",
    "                                mp_drawing.DrawingSpec(color=(245,66,230), thickness=2, circle_radius=2) \n",
    "                                 )               \n",
    "        \n",
    "        out.write(image)\n",
    "        cv2.imshow('Mediapipe Feed', image)\n",
    "\n",
    "        if cv2.waitKey(10) & 0xFF == ord('q'):\n",
    "            break\n",
    "\n",
    "    cap.release()\n",
    "    #out.release()\n",
    "    cv2.destroyAllWindows()\n",
    "    \n",
    "#destroyAllWindows()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(landmarks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PoseLandmark.NOSE\n",
      "PoseLandmark.LEFT_EYE_INNER\n",
      "PoseLandmark.LEFT_EYE\n",
      "PoseLandmark.LEFT_EYE_OUTER\n",
      "PoseLandmark.RIGHT_EYE_INNER\n",
      "PoseLandmark.RIGHT_EYE\n",
      "PoseLandmark.RIGHT_EYE_OUTER\n",
      "PoseLandmark.LEFT_EAR\n",
      "PoseLandmark.RIGHT_EAR\n",
      "PoseLandmark.MOUTH_LEFT\n",
      "PoseLandmark.MOUTH_RIGHT\n",
      "PoseLandmark.LEFT_SHOULDER\n",
      "PoseLandmark.RIGHT_SHOULDER\n",
      "PoseLandmark.LEFT_ELBOW\n",
      "PoseLandmark.RIGHT_ELBOW\n",
      "PoseLandmark.LEFT_WRIST\n",
      "PoseLandmark.RIGHT_WRIST\n",
      "PoseLandmark.LEFT_PINKY\n",
      "PoseLandmark.RIGHT_PINKY\n",
      "PoseLandmark.LEFT_INDEX\n",
      "PoseLandmark.RIGHT_INDEX\n",
      "PoseLandmark.LEFT_THUMB\n",
      "PoseLandmark.RIGHT_THUMB\n",
      "PoseLandmark.LEFT_HIP\n",
      "PoseLandmark.RIGHT_HIP\n",
      "PoseLandmark.LEFT_KNEE\n",
      "PoseLandmark.RIGHT_KNEE\n",
      "PoseLandmark.LEFT_ANKLE\n",
      "PoseLandmark.RIGHT_ANKLE\n",
      "PoseLandmark.LEFT_HEEL\n",
      "PoseLandmark.RIGHT_HEEL\n",
      "PoseLandmark.LEFT_FOOT_INDEX\n",
      "PoseLandmark.RIGHT_FOOT_INDEX\n"
     ]
    }
   ],
   "source": [
    "for lnmark in mp_pose.PoseLandmark:\n",
    "    print(lnmark)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dash is running on http://127.0.0.1:8050/\n",
      "\n",
      " * Serving Flask app \"__main__\" (lazy loading)\n",
      " * Environment: production\n",
      "   WARNING: This is a development server. Do not use it in a production deployment.\n",
      "   Use a production WSGI server instead.\n",
      " * Debug mode: off\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " * Running on http://127.0.0.1:8050/ (Press CTRL+C to quit)\n",
      "127.0.0.1 - - [29/Sep/2021 23:06:56] \"GET / HTTP/1.1\" 200 -\n",
      "127.0.0.1 - - [29/Sep/2021 23:06:56] \"GET /_dash-layout HTTP/1.1\" 200 -\n",
      "127.0.0.1 - - [29/Sep/2021 23:06:56] \"GET /_dash-dependencies HTTP/1.1\" 200 -\n",
      "127.0.0.1 - - [29/Sep/2021 23:06:56] \"GET /video_feed HTTP/1.1\" 200 -\n",
      "Error on request:\n",
      "Traceback (most recent call last):\n",
      "  File \"C:\\Users\\Admin\\anaconda3\\Lib\\site-packages\\werkzeug\\serving.py\", line 319, in run_wsgi\n",
      "    execute(self.server.app)\n",
      "  File \"C:\\Users\\Admin\\anaconda3\\Lib\\site-packages\\werkzeug\\serving.py\", line 310, in execute\n",
      "    for data in application_iter:\n",
      "  File \"C:\\Users\\Admin\\anaconda3\\Lib\\site-packages\\werkzeug\\wsgi.py\", line 462, in __next__\n",
      "    return self._next()\n",
      "  File \"C:\\Users\\Admin\\anaconda3\\Lib\\site-packages\\werkzeug\\wrappers\\response.py\", line 49, in _iter_encoded\n",
      "    for item in iterable:\n",
      "  File \"<ipython-input-6-40b1029506a2>\", line 51, in gen\n",
      "    \n",
      "  File \"<ipython-input-6-40b1029506a2>\", line 40, in get_frame\n",
      "    \n",
      "cv2.error: OpenCV(4.5.3) C:\\Users\\runneradmin\\AppData\\Local\\Temp\\pip-req-build-u4kjpz2z\\opencv\\modules\\imgcodecs\\src\\loadsave.cpp:978: error: (-215:Assertion failed) !image.empty() in function 'cv::imencode'\n",
      "127.0.0.1 - - [29/Sep/2021 23:07:32] \"GET / HTTP/1.1\" 200 -\n",
      "127.0.0.1 - - [29/Sep/2021 23:07:33] \"GET /_dash-layout HTTP/1.1\" 200 -\n",
      "127.0.0.1 - - [29/Sep/2021 23:07:33] \"GET /_dash-dependencies HTTP/1.1\" 200 -\n",
      "127.0.0.1 - - [29/Sep/2021 23:07:33] \"GET /_favicon.ico?v=1.18.1 HTTP/1.1\" 200 -\n",
      "127.0.0.1 - - [29/Sep/2021 23:07:33] \"GET /video_feed HTTP/1.1\" 200 -\n",
      "Error on request:\n",
      "Traceback (most recent call last):\n",
      "  File \"C:\\Users\\Admin\\anaconda3\\Lib\\site-packages\\werkzeug\\serving.py\", line 319, in run_wsgi\n",
      "    execute(self.server.app)\n",
      "  File \"C:\\Users\\Admin\\anaconda3\\Lib\\site-packages\\werkzeug\\serving.py\", line 310, in execute\n",
      "    for data in application_iter:\n",
      "  File \"C:\\Users\\Admin\\anaconda3\\Lib\\site-packages\\werkzeug\\wsgi.py\", line 462, in __next__\n",
      "    return self._next()\n",
      "  File \"C:\\Users\\Admin\\anaconda3\\Lib\\site-packages\\werkzeug\\wrappers\\response.py\", line 49, in _iter_encoded\n",
      "    for item in iterable:\n",
      "  File \"<ipython-input-6-40b1029506a2>\", line 51, in gen\n",
      "    \n",
      "  File \"<ipython-input-6-40b1029506a2>\", line 40, in get_frame\n",
      "    \n",
      "cv2.error: OpenCV(4.5.3) C:\\Users\\runneradmin\\AppData\\Local\\Temp\\pip-req-build-u4kjpz2z\\opencv\\modules\\imgcodecs\\src\\loadsave.cpp:978: error: (-215:Assertion failed) !image.empty() in function 'cv::imencode'\n"
     ]
    }
   ],
   "source": [
    "\"\"\"#dash app for pose detection: live video\n",
    "#import sys\n",
    "import dash\n",
    "import asyncio\n",
    "import dash_core_components as dcc\n",
    "import dash_html_components as html\n",
    "\n",
    "from flask import Flask, Response\n",
    "import cv2\n",
    "\n",
    "class VideoCamera(object):\n",
    "    def __init__(self):\n",
    "        self.video = cv2.VideoCapture(\"youTube_video.mp4\")\n",
    "\n",
    "    def __del__(self):\n",
    "        self.video.release()\n",
    "        destroyAllWindows()\n",
    "\n",
    "    def get_frame(self):\n",
    "        \"\"\"\"\"\"with mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) as pose:\n",
    "            success, image = self.video.read()\n",
    "\n",
    "            # Recolor image to RGB\n",
    "            image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
    "            image.flags.writeable = False\n",
    "\n",
    "            # Make detection\n",
    "            results = pose.process(image)\n",
    "\n",
    "            # Recolor back to BGR\n",
    "            image.flags.writeable = True\n",
    "            image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n",
    "\n",
    "            # Render detections\n",
    "            mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS,\n",
    "                                      mp_drawing.DrawingSpec(color=(245, 117, 66), thickness=2, circle_radius=2),\n",
    "                                      mp_drawing.DrawingSpec(color=(245, 66, 230), thickness=2, circle_radius=2)\n",
    "                                      )\"\"\"\"\"\"\n",
    "        success, image = self.video.read()\n",
    "        _, jpeg = cv2.imencode('.jpg', image)\n",
    "        return jpeg.tobytes()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def gen(camera):\n",
    "    \n",
    "    while True:\n",
    "        #if sys.platform == \"win32\" and (3, 8, 0) <= sys.version_info < (3, 9, 0):asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())\n",
    "        \n",
    "        frame = camera.get_frame()\n",
    "        \n",
    "        yield (b'--frame\\r\\n'\n",
    "               b'Content-Type: image/jpeg\\r\\n\\r\\n' + frame + b'\\r\\n\\r\\n')\n",
    "\n",
    "server = Flask(__name__)\n",
    "app = dash.Dash(__name__, server=server)\n",
    "\n",
    "@server.route('/video_feed')\n",
    "def video_feed():\n",
    "    return Response(gen(VideoCamera()),\n",
    "                    mimetype='multipart/x-mixed-replace; boundary=frame')\n",
    "\n",
    "app.layout = html.Div([\n",
    "    html.H1(\"Webcam Test\"),\n",
    "    html.Img(style={'width': '30%', 'padding': 10},src=\"/video_feed\")\n",
    "])\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    app.run_server(debug=False)\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"#dash app for youtube video\n",
    "#import sys\n",
    "import dash\n",
    "import asyncio\n",
    "import dash_core_components as dcc\n",
    "import dash_html_components as html\n",
    "\n",
    "from flask import Flask, Response\n",
    "import cv2\n",
    "\n",
    "class VideoCamera(object):\n",
    "    def __init__(self):\n",
    "        self.video = cv2.VideoCapture(\"youTube_video.mp4\")\n",
    "\n",
    "    def __del__(self):\n",
    "        self.video.release()\n",
    "        destroyAllWindows()\n",
    "\n",
    "    def get_frame(self):\n",
    "        \"\"\"\"\"\"with mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) as pose:\n",
    "            success, image = self.video.read()\n",
    "\n",
    "            # Recolor image to RGB\n",
    "            image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
    "            image.flags.writeable = False\n",
    "\n",
    "            # Make detection\n",
    "            results = pose.process(image)\n",
    "\n",
    "            # Recolor back to BGR\n",
    "            image.flags.writeable = True\n",
    "            image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n",
    "\n",
    "            # Render detections\n",
    "            mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS,\n",
    "                                      mp_drawing.DrawingSpec(color=(245, 117, 66), thickness=2, circle_radius=2),\n",
    "                                      mp_drawing.DrawingSpec(color=(245, 66, 230), thickness=2, circle_radius=2)\n",
    "                                      )\"\"\"\"\"\"\n",
    "        success, image = self.video.read()\n",
    "        _, jpeg = cv2.imencode('.jpg', image)\n",
    "        return jpeg.tobytes()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def gen(camera):\n",
    "    \n",
    "    while True:\n",
    "        #if sys.platform == \"win32\" and (3, 8, 0) <= sys.version_info < (3, 9, 0):asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())\n",
    "        \n",
    "        frame = camera.get_frame()\n",
    "        \n",
    "        yield (b'--frame\\r\\n'\n",
    "               b'Content-Type: image/jpeg\\r\\n\\r\\n' + frame + b'\\r\\n\\r\\n')\n",
    "\n",
    "server = Flask(__name__)\n",
    "app = dash.Dash(__name__, server=server)\n",
    "\n",
    "@server.route('/video_feed')\n",
    "def video_feed():\n",
    "    return Response(gen(VideoCamera()),\n",
    "                    mimetype='multipart/x-mixed-replace; boundary=frame')\n",
    "\n",
    "app.layout = html.Div([\n",
    "    html.H1(\"Webcam Test\"),\n",
    "    html.Img(style={'width': '30%', 'padding': 10},src=\"/video_feed\")\n",
    "])\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    app.run_server(debug=False)\"\"\""
   ]
  }
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