Computer Vision/FitPoseTracker/Fit_pose.ipynb

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  {
   "cell_type": "markdown",
   "id": "8a728133-4c13-443d-9860-0da101aa8f9a",
   "metadata": {},
   "source": [
    "# **Pose estimation and counter**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "17245a58-012e-476f-9e2b-6f0423cc4432",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: mediapipe in c:\\users\\arnab\\appdata\\roaming\\python\\python312\\site-packages (0.10.14)\n",
      "Requirement already satisfied: opencv-python in c:\\program files\\python\\lib\\site-packages (4.10.0.84)\n",
      "Requirement already satisfied: absl-py in c:\\users\\arnab\\appdata\\roaming\\python\\python312\\site-packages (from mediapipe) (2.1.0)\n",
      "Requirement already satisfied: attrs>=19.1.0 in c:\\users\\arnab\\appdata\\roaming\\python\\python312\\site-packages (from mediapipe) (24.2.0)\n",
      "Requirement already satisfied: flatbuffers>=2.0 in c:\\users\\arnab\\appdata\\roaming\\python\\python312\\site-packages (from mediapipe) (24.3.25)\n",
      "Requirement already satisfied: jax in c:\\users\\arnab\\appdata\\roaming\\python\\python312\\site-packages (from mediapipe) (0.4.31)\n",
      "Requirement already satisfied: jaxlib in c:\\users\\arnab\\appdata\\roaming\\python\\python312\\site-packages (from mediapipe) (0.4.31)\n",
      "Requirement already satisfied: matplotlib in c:\\users\\arnab\\appdata\\roaming\\python\\python312\\site-packages (from mediapipe) (3.9.2)\n",
      "Requirement already satisfied: numpy in c:\\users\\arnab\\appdata\\roaming\\python\\python312\\site-packages (from mediapipe) (1.26.4)\n",
      "Requirement already satisfied: opencv-contrib-python in c:\\users\\arnab\\appdata\\roaming\\python\\python312\\site-packages (from mediapipe) (4.10.0.84)\n",
      "Requirement already satisfied: protobuf<5,>=4.25.3 in c:\\users\\arnab\\appdata\\roaming\\python\\python312\\site-packages (from mediapipe) (4.25.4)\n",
      "Requirement already satisfied: sounddevice>=0.4.4 in c:\\users\\arnab\\appdata\\roaming\\python\\python312\\site-packages (from mediapipe) (0.5.0)\n",
      "Requirement already satisfied: CFFI>=1.0 in c:\\users\\arnab\\appdata\\roaming\\python\\python312\\site-packages (from sounddevice>=0.4.4->mediapipe) (1.17.0)\n",
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      "Requirement already satisfied: kiwisolver>=1.3.1 in c:\\users\\arnab\\appdata\\roaming\\python\\python312\\site-packages (from matplotlib->mediapipe) (1.4.5)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\arnab\\appdata\\roaming\\python\\python312\\site-packages (from matplotlib->mediapipe) (24.1)\n",
      "Requirement already satisfied: pillow>=8 in c:\\users\\arnab\\appdata\\roaming\\python\\python312\\site-packages (from matplotlib->mediapipe) (10.4.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\arnab\\appdata\\roaming\\python\\python312\\site-packages (from matplotlib->mediapipe) (3.1.2)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\arnab\\appdata\\roaming\\python\\python312\\site-packages (from matplotlib->mediapipe) (2.9.0.post0)\n",
      "Requirement already satisfied: pycparser in c:\\users\\arnab\\appdata\\roaming\\python\\python312\\site-packages (from CFFI>=1.0->sounddevice>=0.4.4->mediapipe) (2.22)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\arnab\\appdata\\roaming\\python\\python312\\site-packages (from python-dateutil>=2.7->matplotlib->mediapipe) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install mediapipe opencv-python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d5d4e6c6-31f2-4cab-8024-6f5c80d83ad5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import mediapipe as mp\n",
    "import numpy as np\n",
    "mp_drawing = mp.solutions.drawing_utils\n",
    "mp_pose = mp.solutions.pose"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5636254e-a9a6-4d79-909f-f57bab4f4997",
   "metadata": {},
   "source": [
    "Video feed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "87edefd0-75b9-4ff6-a2bb-1b0892487bc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "cap = cv2.VideoCapture(0)\n",
    "while cap.isOpened():\n",
    "    ret, frame = cap.read()\n",
    "    cv2.imshow('Mediapipe Feed', frame)\n",
    "\n",
    "    if cv2.waitKey(10) & 0xFF == ord('q'):\n",
    "        break\n",
    "\n",
    "cap.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed045341-c2e3-433a-afbe-8c480dcf16e1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "90ff6c8b-da5a-4bb7-b6a9-e8b3d13e07f6",
   "metadata": {},
   "source": [
    "# 1. Make Decisions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2b28a929-28d6-4350-a470-5c0e08d2921d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Arnab\\AppData\\Roaming\\Python\\Python312\\site-packages\\google\\protobuf\\symbol_database.py:55: UserWarning: SymbolDatabase.GetPrototype() is deprecated. Please use message_factory.GetMessageClass() instead. SymbolDatabase.GetPrototype() will be removed soon.\n",
      "  warnings.warn('SymbolDatabase.GetPrototype() is deprecated. Please '\n"
     ]
    }
   ],
   "source": [
    "cap = cv2.VideoCapture(0)\n",
    "\n",
    "#Setup mediapipe instance\n",
    "with mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) as pose:\n",
    "    while cap.isOpened():\n",
    "\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",
    "        # 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",
    "\n",
    "\n",
    "        cv2.imshow('Mediapipe Feed', image)\n",
    "    \n",
    "        if cv2.waitKey(10) & 0xFF ==ord('q'):\n",
    "             break\n",
    "\n",
    "cap.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "efcf1870-7c92-4361-a78d-ba74bd5a6ffb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "cd53208f-c3d6-491f-99fb-30571401e6f6",
   "metadata": {},
   "source": [
    "# 2. Determining joints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d068226e-8dbd-42c2-a29d-0de5f3a612f0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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      "z: 1.13919258\n",
      "visibility: 0.000203359305\n",
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      "y: 2.47312117\n",
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      ", x: 0.70879066\n",
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      "]\n"
     ]
    }
   ],
   "source": [
    "cap = cv2.VideoCapture(0)\n",
    "\n",
    "#Setup mediapipe instance\n",
    "with mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) as pose:\n",
    "    while cap.isOpened():\n",
    "\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",
    "            print(landmarks)\n",
    "        except: \n",
    "            pass\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), #for dots\n",
    "                               mp_drawing.DrawingSpec(color=(245,66,230), thickness=2, circle_radius=2) #for lines\n",
    "                                  )\n",
    "\n",
    "\n",
    "\n",
    "        cv2.imshow('Mediapipe Feed', image)\n",
    "    \n",
    "        if cv2.waitKey(10) & 0xFF ==ord('q'):\n",
    "             break\n",
    "\n",
    "cap.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ffed8907-caa7-447c-af4f-e96b8be9c4bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(landmarks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4044c519-55c7-411a-ba1e-7ad68af2ea7e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "14\n",
      "15\n",
      "16\n",
      "17\n",
      "18\n",
      "19\n",
      "20\n",
      "21\n",
      "22\n",
      "23\n",
      "24\n",
      "25\n",
      "26\n",
      "27\n",
      "28\n",
      "29\n",
      "30\n",
      "31\n",
      "32\n"
     ]
    }
   ],
   "source": [
    "for lndmrk in mp_pose.PoseLandmark:\n",
    "    print(lndmrk)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "395d62fd-971a-45d1-a180-c6ae3acdf1ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "x: 0.611578584\n",
       "y: 0.932002425\n",
       "z: -0.572983742\n",
       "visibility: 0.989971519"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7819e1e4-473e-4327-af45-866b061d2533",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<PoseLandmark.LEFT_SHOULDER: 11>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mp_pose.PoseLandmark.LEFT_SHOULDER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "835fe3fa-8f55-4953-bbfa-22f9bb092f41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mp_pose.PoseLandmark.LEFT_SHOULDER.value #giving the specific landmarks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "58be3a4f-3890-4531-b4b9-b8bfd7060f80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mp_pose.PoseLandmark.RIGHT_WRIST.value # Searching for index of any landmark in the body"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "80d536c9-9978-44bf-bfea-b91df20983be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mp_pose.PoseLandmark.LEFT_ELBOW.value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8df31162-9e15-4cdb-ac0f-97f5c799c711",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "65e4dce6-b8bc-438a-b88b-89b7bf3c9ea3",
   "metadata": {},
   "source": [
    "# 3. Calculating Angles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "905d0c23-a732-4d88-822c-557940d881a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "09a94148-7d40-4474-85aa-b7ede3e521f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "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]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5c717130-05ef-4552-b8fc-91f4e2b4148f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([0.6115785837173462, 0.9320024251937866],\n",
       " [0.7614896297454834, 1.1660879850387573],\n",
       " [0.799742579460144, 1.3479408025741577])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shoulder, elbow, wrist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e1cecc48-cf6d-41ee-b1da-e573cca6f389",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "159.24308806743588"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calculate_angle(shoulder, elbow, wrist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d18b2a36-8d4f-41df-af89-3d2ec6805294",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(487, 559)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuple(np.multiply(elbow,[640, 480]).astype(int))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "76e10f39-42bf-4e6c-93a1-b7a31b352e78",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "cap = cv2.VideoCapture(0)\n",
    "\n",
    "#Setup mediapipe instance\n",
    "with mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) as pose:\n",
    "    while cap.isOpened():\n",
    "\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",
    "\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",
    "            #Get angles\n",
    "            angle = calculate_angle(shoulder, elbow, wrist)\n",
    "            \n",
    "            #Visualize\n",
    "            cv2.putText(image, str(angle), (int(elbow[0] * 640), int(elbow[1] * 480)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2, cv2.LINE_AA)\n",
    "            print(landmarks)\n",
    "        except: \n",
    "            pass\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",
    "\n",
    "\n",
    "        cv2.imshow('Mediapipe Feed', image)\n",
    "    \n",
    "        if cv2.waitKey(10) & 0xFF ==ord('q'):\n",
    "             break\n",
    "\n",
    "cap.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8bccdf60-2e70-45af-b162-dafeaf2170d1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "656cfabf-e1b8-4d63-93f5-cbf81eef44f9",
   "metadata": {},
   "source": [
    "# Curl counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "37178b69-0532-4b31-bb85-c926faf55ca9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "cap = cv2.VideoCapture(0)\n",
    "\n",
    "#Curl counter variables\n",
    "counter = 0\n",
    "stage = None\n",
    "\n",
    "#Setup mediapipe instance\n",
    "with mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) as pose:\n",
    "    while cap.isOpened():\n",
    "\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",
    "\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",
    "            #Get angles\n",
    "            angle = calculate_angle(shoulder, elbow, wrist)\n",
    "            \n",
    "            #Visualize\n",
    "            cv2.putText(image, str(angle), (int(elbow[0] * 640), int(elbow[1] * 480)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2, cv2.LINE_AA)\n",
    "\n",
    "            #Curl counter logic\n",
    "            if angle >160:\n",
    "                stage = \"    Down\"\n",
    "            elif angle<30 and stage == 'Down':\n",
    "                stage=\"    Up\"\n",
    "                counter +=1\n",
    "                print(counter)\n",
    "        \n",
    "        except: \n",
    "            pass\n",
    "\n",
    "        #Render Curl counter\n",
    "        #Setup status box\n",
    "        cv2.rectangle(image,(0,0), (225,73), (245,117,16), -1)\n",
    "\n",
    "\n",
    "        #Creating 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",
    "        #Creating 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",
    "        # 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",
    "\n",
    "\n",
    "        cv2.imshow('Mediapipe Feed', image)\n",
    "    \n",
    "        if cv2.waitKey(10) & 0xFF ==ord('q'):\n",
    "             break\n",
    "\n",
    "cap.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0fd349ed-6f54-4bbb-b817-adf02dad5ddd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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