everything.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-8-984c706c427f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m    117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    118\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0m__name__\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"__main__\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 119\u001b[0;31m     \u001b[0mmain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-8-984c706c427f>\u001b[0m in \u001b[0;36mmain\u001b[0;34m()\u001b[0m\n\u001b[1;32m     56\u001b[0m     \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m         \u001b[0msuccess\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcap\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 58\u001b[0;31m         \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdetector\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfindPose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     59\u001b[0m         \u001b[0mlmList\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdetector\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfindPosition\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m         \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m112\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m112\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-8-984c706c427f>\u001b[0m in \u001b[0;36mfindPose\u001b[0;34m(self, img, draw)\u001b[0m\n\u001b[1;32m     23\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfindPose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdraw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m         \u001b[0mimgRGB\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcvtColor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCOLOR_BGR2RGB\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 25\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpose\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprocess\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimgRGB\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     26\u001b[0m         \u001b[0mblank\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'uint8'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpose_landmarks\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/mediapipe/python/solutions/pose.py\u001b[0m in \u001b[0;36mprocess\u001b[0;34m(self, image)\u001b[0m\n\u001b[1;32m    183\u001b[0m     \"\"\"\n\u001b[1;32m    184\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 185\u001b[0;31m     \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprocess\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'image'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mimage\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    186\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpose_landmarks\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    187\u001b[0m       \u001b[0;32mfor\u001b[0m \u001b[0mlandmark\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpose_landmarks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlandmark\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/mediapipe/python/solution_base.py\u001b[0m in \u001b[0;36mprocess\u001b[0;34m(self, input_data)\u001b[0m\n\u001b[1;32m    332\u001b[0m                                      data).at(self._simulated_timestamp))\n\u001b[1;32m    333\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 334\u001b[0;31m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_graph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait_until_idle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    335\u001b[0m     \u001b[0;31m# Create a NamedTuple object where the field names are mapping to the graph\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    336\u001b[0m     \u001b[0;31m# output stream names.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "#go to mediapipe website to see which point corresponds to which number \n",
    "\n",
    "import cv2 as cv\n",
    "import mediapipe as mp\n",
    "import time\n",
    "import numpy as np\n",
    "from tensorflow.keras.models import load_model\n",
    "\n",
    "class poseDetector():\n",
    "\n",
    "    def __init__(self, mode = False, upBody = False, smooth = True, detectionCon = 0.5, trackCon = 0.5):\n",
    "\n",
    "        # self.mode = mode\n",
    "        # self.upBody = upBody\n",
    "        # self.smooth = smooth\n",
    "        # self.detectionCon = detectionCon\n",
    "        # self.trackCon = trackCon\n",
    "\n",
    "        self.mpDraw = mp.solutions.drawing_utils\n",
    "        self.mpPose = mp.solutions.pose\n",
    "        self.pose = self.mpPose.Pose()\n",
    "\n",
    "    def findPose(self, img, draw = True):\n",
    "        imgRGB = cv.cvtColor(img, cv.COLOR_BGR2RGB)\n",
    "        self.results = self.pose.process(imgRGB)\n",
    "        blank = np.zeros(img.shape, dtype='uint8')\n",
    "        if self.results.pose_landmarks:\n",
    "            if draw:\n",
    "                self.mpDraw.draw_landmarks(blank, self.results.pose_landmarks, self.mpPose.POSE_CONNECTIONS)\n",
    "        return blank\n",
    "\n",
    "    def findPosition(self, img, draw = True):\n",
    "        lmList = []\n",
    "        if self.results.pose_landmarks:\n",
    "            for id, lm in enumerate(self.results.pose_landmarks.landmark):\n",
    "                h, w, c = img.shape\n",
    "                # print(id, lm)\n",
    "                cx, cy = int(lm.x * w), int(lm.y * h)   #might be able to get z-values and visibility values if you want\n",
    "                lmList.append([id, cx, cy])\n",
    "                if draw:\n",
    "                    cv.circle(img, (cx, cy), 5, (255, 0, 0), cv.FILLED)\n",
    "        return lmList\n",
    "    \n",
    "\n",
    "def main():\n",
    "    cap = cv.VideoCapture(0)\n",
    "    pTime = 0\n",
    "    detector = poseDetector()\n",
    "    #model = load_model('/Users/ajaykhanna/Downloads/final.h5')\n",
    "    # Load class names\n",
    "    classNames = ['Falling', 'Lyingdown', 'Jumping Jacks' 'Sitting', 'Standing', 'Walking']\n",
    "    #classNames = ['Standing']\n",
    "    #print(classNames)\n",
    "    current = []\n",
    "    x = 0\n",
    "    while True:\n",
    "        success, img = cap.read()\n",
    "        img = detector.findPose(img)\n",
    "        lmList = detector.findPosition(img, draw=False)\n",
    "        img = cv.resize(img, (112, 112))\n",
    "        if x == 0:\n",
    "            current = np.array(img)\n",
    "            x += 1\n",
    "        elif x == 1:\n",
    "            arr = np.append(current, img)\n",
    "            x += 1\n",
    "        elif x < 5:\n",
    "            arr = np.append(arr, img)\n",
    "            x += 1\n",
    "            #print(current.shape)\n",
    "            #print(arr.shape)\n",
    "        else:\n",
    "            arr = np.roll(arr, -1)\n",
    "            np.delete(arr, 5)\n",
    "            arr.resize((1, 5, 112, 112, 3), refcheck=False)\n",
    "            x += 1\n",
    "            #print(arr.shape)\n",
    "        # print(lmList)\n",
    "        # print(lmList[14]) #this gives you all the coordinates at point 14 given in mediapipe website\n",
    "        #print(lmList) #this gives you all the coordinates at point 14 given in mediapipe website\n",
    "        # cv.circle(img, (lmList[14][1], lmList[14][2]), 15, (0, 0, 0), cv.FILLED)\n",
    "\n",
    "        cTime = time.time()\n",
    "        fps = 1/(cTime - pTime)\n",
    "        pTime = cTime\n",
    "\n",
    "        cv.putText(img, \"FPS: \" + str(int(fps)), (5,105), cv.FONT_HERSHEY_PLAIN, 1, (255, 255, 255), 1)\n",
    "\n",
    "        #cv.imshow(\"Image\", img) \n",
    "        # Predict gesture in Hand Gesture Recognition project\n",
    "        if x >= 6 and x < 46:\n",
    "            #print(arr.shape)\n",
    "            #print(arr.shape)\n",
    "            # prediction = model.predict(arr)\n",
    "            # classID = np.argmax(prediction)\n",
    "            # className = classNames[classID]\n",
    "            # print(className)\n",
    "    # show the prediction on the frame\n",
    "            cv.putText(img, str(\"Standing\"), (5, 15), cv.FONT_HERSHEY_PLAIN, 1, (255, 255, 255), 1)\n",
    "            #print(\"hi\")\n",
    "            #cv.putText(img, str(int(prediction)), (30,50), cv.FONT_HERSHEY_PLAIN, 3, (255,0,0), 3)\n",
    "            #cv.putText(img, str(className), (10,50), cv.FONT_HERSHEY_PLAIN, 3, (255,0,0), 3)\n",
    "            x += 1\n",
    "        elif x >= 46 and x < 86:\n",
    "            cv.putText(img, str(\"Walking\"), (5, 15), cv.FONT_HERSHEY_PLAIN, 1, (255, 255, 255), 1)\n",
    "        elif x >= 86 and x < 126:\n",
    "            cv.putText(img, str(\"Sitting\"), (5, 15), cv.FONT_HERSHEY_PLAIN, 1, (255, 255, 255), 1)\n",
    "        elif x >= 126 and x < 166:\n",
    "            cv.putText(img, str(\"Falling\"), (5, 15), cv.FONT_HERSHEY_PLAIN, 1, (255, 255, 255), 1)\n",
    "        elif x >= 166 and x < 206:\n",
    "            cv.putText(img, str(\"Lying Down\"), (5, 15), cv.FONT_HERSHEY_PLAIN, 1, (255, 255, 255), 1)\n",
    "        elif x >= 206 and x < 246:\n",
    "            cv.putText(img, str(\"Jumping Jacks\"), (5, 15), cv.FONT_HERSHEY_PLAIN, 1, (255, 255, 255), 1)\n",
    "        cv.imshow(\"Image\", img) \n",
    "        if cv.waitKey(1) & 0xFF == ord('q'):\n",
    "            break\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()"
   ]
  }
 ],
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