OnboardingUsingRecordedVideo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "attempted-turning",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import cv2\n",
    "import time\n",
    "import logging\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import math\n",
    "from tqdm import tqdm\n",
    "import ipywidgets as widgets\n",
    "import multiprocessing as mp \n",
    "from tensorflow.keras.models import load_model\n",
    "\n",
    "import csv\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "connected-crown",
   "metadata": {},
   "outputs": [],
   "source": [
    "##Specify all the file paths for required files\n",
    "# Specify the paths for the 2 files\n",
    "protoFile = \"pose_deploy_linevec.prototxt\"\n",
    "\n",
    "weightsFile = \"pose_iter_440000.caffemodel\"\n",
    "# Read the network into Memory\n",
    "\n",
    "df=pd.read_csv('data_augmented.csv')\n",
    "df_label=pd.read_csv('PersonLabel_augmented.csv')\n",
    "\n",
    "modelFilePath='siamesenetwork.h5'\n",
    "\n",
    "\n",
    "net = cv2.dnn.readNetFromCaffe(protoFile, weightsFile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cardiac-breach",
   "metadata": {},
   "outputs": [],
   "source": [
    "def flattenList(inputList):\n",
    "    lst=[]\n",
    "    \n",
    "    for i in inputList:\n",
    "        #print(i)\n",
    "        if i is None:\n",
    "            lst.append(0)\n",
    "            lst.append(0)\n",
    "        else:\n",
    "            try:\n",
    "                for k in i:\n",
    "                    lst.append(k)\n",
    "            except:\n",
    "                lst.append(i)\n",
    "\n",
    "    return lst\n",
    "\n",
    "# a=[(445, 203), (528, 500), (278, 516), None, None, (806, 485), (862, 704), None, None, None, None, None, None, None, (417, 140), (528, 140), None, (640, 234)]\n",
    "\n",
    "# print(flattenList(a),len(flattenList(a))) \n",
    "# x=flattenList(a)\n",
    "# process_joints(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "confident-asset",
   "metadata": {},
   "outputs": [],
   "source": [
    "nPoints = 18\n",
    "POSE_PAIRS = [ [1,0],[1,2],[1,5],[2,3],[3,4],[5,6],[6,7],[1,8],[8,9],[9,10],[1,11],[11,12],[12,13],[0,14],[0,15],[14,16],[15,17]]\n",
    "\n",
    "inWidth =368     #Here we supply the spatial size that the Convolutional Neural Network expects.\n",
    "inHeight =368    #Here we supply the spatial size that the Convolutional Neural Network expects.\n",
    "threshold = 0.1\n",
    "\n",
    "#body_pose_list=[]\n",
    "count=10\n",
    "num_frames_no_kp_detected=count\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "allied-premiere",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 139, 180, 0, 0, 0, 0, 222, 180, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 139, 180, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 139, 180, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 695, 270, 695, 270, 0, 0, 0, 0, 695, 270, 723, 270, 0, 0, 695, 360, 667, 360, 695, 450, 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0, 0, 0, 222, 180, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 500, 270, 500, 270, 500, 270, 500, 360, 500, 270, 500, 270, 0, 0, 500, 360, 473, 450, 222, 180, 473, 360, 473, 450, 222, 180, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 473, 270, 473, 270, 0, 0, 0, 0, 473, 270, 0, 0, 0, 0, 473, 360, 473, 450, 166, 180, 473, 360, 473, 450, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 473, 360, 0, 0, 194, 180, 473, 360, 0, 0, 194, 180, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 166, 180, 0, 0, 0, 0, 166, 180, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 139, 180, 0, 0, 0, 0, 139, 180, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 139, 180, 0, 0, 0, 0, 139, 180, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 139, 180, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 166, 180, 0, 0, 0, 0, 194, 180, 0, 0, 0, 0, 0, 0, 0, 0], [445, 180, 445, 270, 445, 270, 445, 270, 445, 360, 417, 270, 417, 270, 417, 360, 417, 360, 445, 360, 417, 450, 417, 360, 417, 360, 417, 450, 0, 0, 0, 0, 445, 180, 445, 180], [0, 0, 417, 270, 417, 270, 417, 270, 417, 360, 417, 270, 445, 270, 417, 360, 417, 360, 389, 360, 389, 450, 417, 360, 417, 360, 417, 450, 0, 0, 0, 0, 0, 0, 0, 0], [389, 180, 417, 270, 417, 270, 445, 270, 417, 360, 389, 270, 389, 270, 389, 360, 417, 360, 417, 450, 417, 540, 417, 360, 389, 360, 417, 540, 0, 0, 389, 180, 417, 180, 389, 180], [389, 180, 417, 270, 417, 270, 417, 270, 417, 360, 389, 270, 389, 270, 389, 360, 417, 360, 417, 360, 417, 540, 417, 360, 389, 360, 417, 540, 0, 0, 389, 180, 417, 180, 389, 180], [389, 180, 389, 270, 389, 180, 417, 270, 361, 360, 389, 270, 389, 270, 389, 360, 417, 360, 361, 450, 417, 540, 417, 360, 417, 450, 445, 540, 0, 0, 389, 180, 389, 180, 389, 180], [389, 180, 389, 270, 389, 180, 389, 270, 361, 360, 389, 180, 389, 270, 389, 360, 389, 360, 333, 450, 333, 540, 417, 360, 417, 450, 445, 540, 389, 180, 389, 180, 389, 180, 389, 180], [361, 180, 389, 270, 389, 180, 417, 270, 389, 360, 361, 270, 361, 270, 389, 360, 389, 360, 417, 450, 445, 540, 389, 360, 389, 450, 445, 540, 0, 0, 361, 180, 389, 180, 361, 180], [0, 0, 389, 270, 361, 270, 361, 270, 361, 360, 361, 270, 389, 270, 389, 360, 361, 360, 333, 450, 333, 540, 389, 360, 389, 450, 417, 540, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 361, 270, 361, 270, 389, 360, 389, 360, 361, 270, 361, 360, 361, 360, 389, 360, 389, 450, 417, 540, 361, 360, 361, 450, 417, 540, 0, 0, 0, 0, 361, 180, 361, 180], [361, 180, 361, 270, 333, 270, 333, 270, 361, 360, 361, 270, 389, 270, 361, 360, 333, 360, 333, 450, 139, 180, 361, 360, 389, 450, 222, 180, 361, 180, 361, 180, 361, 180, 361, 180]]\n"
     ]
    },
    {
     "ename": "error",
     "evalue": "OpenCV(4.4.0) /private/var/folders/nz/vv4_9tw56nv9k3tkvyszvwg80000gn/T/pip-req-build-xjo66oc3/opencv/modules/highgui/src/window.cpp:376: error: (-215:Assertion failed) size.width>0 && size.height>0 in function 'imshow'\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31merror\u001b[0m                                     Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-ec516b8c5d21>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[0mhasFrame\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mframe\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[1;32m     13\u001b[0m     \u001b[0mframeCopy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframe\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m     \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'tf-pose-estimation result'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mframe\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     15\u001b[0m     \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwaitKey\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31merror\u001b[0m: OpenCV(4.4.0) /private/var/folders/nz/vv4_9tw56nv9k3tkvyszvwg80000gn/T/pip-req-build-xjo66oc3/opencv/modules/highgui/src/window.cpp:376: error: (-215:Assertion failed) size.width>0 && size.height>0 in function 'imshow'\n"
     ]
    }
   ],
   "source": [
    "body_pose_list=[]\n",
    "cap = cv2.VideoCapture('onboard_subject1_train.mov')\n",
    "while True:\n",
    "    #cap = cv2.VideoCapture('onboard_subject1.mp4')\n",
    "#     cap.set(3,1280)\n",
    "#     cap.set(4,1024)\n",
    "#     time.sleep(2)\n",
    "#     cap.set(15, -8.0)\n",
    "    t = time.time()\n",
    "    points = []\n",
    "\n",
    "    hasFrame, frame = cap.read()\n",
    "    frameCopy = np.copy(frame)\n",
    "    cv2.imshow('tf-pose-estimation result', frame)\n",
    "    cv2.waitKey(5)\n",
    "\n",
    "\n",
    "\n",
    "    frameWidth = frame.shape[1]\n",
    "    frameHeight = frame.shape[0]\n",
    "\n",
    "    inpBlob = cv2.dnn.blobFromImage(frame, 1.0 / 255, (inWidth, inHeight),(0, 0, 0), swapRB=False, crop=False)\n",
    "    net.setInput(inpBlob)\n",
    "    output = net.forward()\n",
    "    \n",
    "    H = output.shape[2] # Empty list to store the detected keypoints\n",
    "    W = output.shape[3] # Empty list to store the detected keypoints\n",
    "\n",
    "    for i in range(nPoints):\n",
    "        # confidence map of corresponding body's part.\n",
    "        probMap = output[0, i, :, :]\n",
    "\n",
    "        # Find global maxima of the probMap.\n",
    "        minVal, prob, minLoc, point = cv2.minMaxLoc(probMap)\n",
    "\n",
    "        # Scale the point to fit on the original image\n",
    "        x = (frameWidth * point[0]) / W\n",
    "        y = (frameHeight * point[1]) / H\n",
    "\n",
    "        if prob > threshold : \n",
    "            cv2.circle(frameCopy, (int(x), int(y)), 8, (0, 255, 255), thickness=-1, lineType=cv2.FILLED)\n",
    "            cv2.putText(frameCopy, \"{}\".format(i), (int(x), int(y)), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2, lineType=cv2.LINE_AA)\n",
    "\n",
    "            # Add the point to the list if the probability is greater than the threshold\n",
    "            points.append((int(x), int(y)))\n",
    "        else :\n",
    "            points.append(None)\n",
    "\n",
    "        # Draw Skeleton\n",
    "    for pair in POSE_PAIRS:\n",
    "        partA = pair[0]\n",
    "        partB = pair[1]\n",
    "\n",
    "        if points[partA] and points[partB]:\n",
    "            cv2.line(frame, points[partA], points[partB], (0, 255, 255), 3, lineType=cv2.LINE_AA)\n",
    "            cv2.circle(frame, points[partA], 8, (0, 0, 255), thickness=-1, lineType=cv2.FILLED)\n",
    "            cv2.circle(frame, points[partB], 8, (0, 0, 255), thickness=-1, lineType=cv2.FILLED)\n",
    "\n",
    "\n",
    "\n",
    "    cv2.putText(frame, \"time taken = {:.2f} sec\".format(time.time() - t), (50, 50), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 50, 0), 2, lineType=cv2.LINE_AA)\n",
    "    #cv2.putText(frame, \"OpenPose using OpenCV\", (50, 50), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 50, 0), 2, lineType=cv2.LINE_AA)\n",
    "    #cv2.imshow('Output-Keypoints', frameCopy)\n",
    "    #cv2.imshow('Output-Skeleton', frame)\n",
    "\n",
    "    if all([i==None for i in points]):\n",
    "        cv2.putText(frame, \"Auto Shut Down In :{}\".format(num_frames_no_kp_detected), (300, 300), cv2.FONT_HERSHEY_COMPLEX, 2, (255, 50, 0), 2, lineType=cv2.LINE_AA)\n",
    "        num_frames_no_kp_detected-=1\n",
    "    else:\n",
    "        num_frames_no_kp_detected=count\n",
    "\n",
    "    cv2.putText(frame, \"FPS: %f\" % (1.0 / (time.time() - t)), (50, 300),\n",
    "                cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)\n",
    "    cv2.imshow('tf-pose-estimation result', frame)\n",
    "    cv2.waitKey(1)\n",
    "\n",
    "\n",
    "\n",
    "    temp_kp_List=flattenList(points)\n",
    "    body_pose_list.append(temp_kp_List)\n",
    "    #normalized_kp_list=process_joints(temp_kp_List)\n",
    "    #body_pose_list.append(normalized_kp_list)\n",
    "    print(body_pose_list)\n",
    "\n",
    "    # if cv2.waitKey(2) & 0xFF == ord('q'):\n",
    "    #     break\n",
    "\n",
    "    # #if not hasFrame:\n",
    "    # #    break\n",
    "    if all([i==None for i in points]) and num_frames_no_kp_detected<1:\n",
    "        break\n",
    "    if len(body_pose_list)>95: #set max frames to capture\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "temporal-constitution",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_length(x1, y1, x2, y2):\n",
    "    return math.sqrt((x1-x2)*(x1-x2) + (y1-y2)*(y1-y2))\n",
    "\n",
    "def process_joints(x):\n",
    "\n",
    "    r = [0] * 36\n",
    "\n",
    "    refx = 0\n",
    "    refy = 0\n",
    "    reflength = 1   \n",
    "    \n",
    "\n",
    "    if x[2] != 0 or x[3] != 0:         #determine if neck position, which is the index 2 and index 3\n",
    "        refx = x[2]                # use the neck X, Y\n",
    "       # print('refx:',refx)\n",
    "        refy = x[3]\n",
    "       # print('refy:',refy)\n",
    "    elif (x[4] != 0 or x[5] != 0) and (x[10] != 0 or x[11] != 0):   #using shoulder position from to derive an estimate to neck position.\n",
    "        refx = (x[4] + x[10]) / 2  # estimate the neck X, Y from the mid point\n",
    "        refy = (x[5] + x[11]) / 2  # of the left/right shoulder  \n",
    "       # print('refx:',refx)\n",
    "       # print('refy:',refy)\n",
    "\n",
    "    elif (x[4] != 0 or x[5] != 0):   #estimate using only the left shoulder poin.\n",
    "        refx = x[4]  # estimate from left shoulder\n",
    "        refy = x[5]  # estimate from left shoulder\n",
    "   \n",
    "    elif (x[10] != 0 or x[11] != 0):   #estimate using only the right shoulder poin.\n",
    "        refx = x[10]  # estimate from right shoulder\n",
    "        refy = x[11]  # estimate from right shoulder\n",
    "\n",
    " \n",
    "    if x[16] != 0 and x[17] != 0:             \n",
    "        reflength = compute_length(x[16], x[17], refx, refy)   # neck to right hip\n",
    "    elif x[22] != 0 and x[23] != 0:\n",
    "        reflength = compute_length(x[22], x[23], refx, refy)   # neck to left hip \n",
    " \n",
    "    for i in range(0, 18):\n",
    "        if x[i*2] !=0:\n",
    "            r[i*2] = (x[i*2] - refx) / reflength\n",
    "        else :\n",
    "             r[i*2] = x[i*2]   #if the orignal coordinates are at 0, then just transfer to reference.\n",
    "        if x[i*2 + 1] !=0:\n",
    "            r[i*2 + 1] = -(x[i*2 + 1] - refy) / reflength\n",
    "        else :\n",
    "            r[i*2 + 1] = x[i*2 + 1]\n",
    " \n",
    "    \n",
    "    #print(r)\n",
    "    return r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "opening-steam",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 36)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalized_body_pose_list=[process_joints(eachframe)for eachframe in body_pose_list]\n",
    "\n",
    "bodyPoseArray=np.array(normalized_body_pose_list)\n",
    "bodyPoseArray.shape\n",
    "#bodyPoseArray[0:2,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "aggregate-transport",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(95, 36)\n",
      "(95, 36)\n",
      "(95, 36)\n",
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      " [0. 0. 0. ... 0. 0. 0.]\n",
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      " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]]\n"
     ]
    }
   ],
   "source": [
    "max_frames=95 #must set this to be same as trained model. \n",
    "\n",
    "#print(max_frames)\n",
    "def process_detected_frames(max_frames,bodyPoseArray):\n",
    "    num_frames_detected=bodyPoseArray.shape[0]\n",
    "    if num_frames_detected>max_frames:\n",
    "        bodyPoseArray=bodyPoseArray[0:max_frames,:]\n",
    "        return bodyPoseArray\n",
    "    else:\n",
    "        #zeroArray=np.zeros((max_frames,36))\n",
    "        zeroArray=np.zeros((max_frames,36))\n",
    "        print(zeroArray.shape)\n",
    "        zeroArray[:bodyPoseArray.shape[0],:bodyPoseArray.shape[1]]=bodyPoseArray\n",
    "        #print(np.transpose(zeroArray).shape)\n",
    "        print(zeroArray.shape)\n",
    "        return zeroArray\n",
    "\n",
    "processedData=process_detected_frames(max_frames,bodyPoseArray)\n",
    "print(processedData.shape)\n",
    "#processedData=processedData.reshape((1,max_frames,36))\n",
    "\n",
    "print(processedData,'\\n\\n',bodyPoseArray)\n",
    "\n",
    "#df_detected=process_detected_frames(max_frames,df) #pad the frames to fixed length or crop them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "collected-wagner",
   "metadata": {},
   "outputs": [
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       "..  ..                                                ...\n",
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   "source": [
    "processedData_list=[]\n",
    "for i,cell in enumerate(processedData):\n",
    "    processedData_list.append((i,cell))\n",
    "processedData_df=pd.DataFrame(processedData_list)\n",
    "display(processedData_df)\n",
    "processedData_df.drop(columns=[0],axis=1, inplace=True)\n",
    "processedData_df=processedData_df.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "dried-venture",
   "metadata": {},
   "outputs": [],
   "source": [
    "processedData_df.reset_index(drop=True,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "continent-cylinder",
   "metadata": {},
   "outputs": [
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       "\n",
       "[1 rows x 95 columns]"
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     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
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  {
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   "id": "incident-fraction",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
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     "execution_count": 12,
     "metadata": {},
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    "type(processedData_df)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bulgarian-punch",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=95, step=1)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
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  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "usual-ethiopia",
   "metadata": {},
   "outputs": [
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     "execution_count": 14,
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    "stop=df.shape[1]\n",
    "processedData_df.columns=[str(i) for i in list(range(0,stop))]\n",
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   "cell_type": "code",
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   "id": "downtown-budget",
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    "scrolled": true
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     "data": {
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   "cell_type": "code",
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       "                                                  86  \\\n",
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       "                                                  92  \\\n",
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       "                                                  93  \\\n",
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       "\n",
       "                                                  94  \n",
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       "\n",
       "[5 rows x 95 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('data_augmented.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "nutritional-tampa",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12',\n",
       "       '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24',\n",
       "       '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36',\n",
       "       '37', '38', '39', '40', '41', '42', '43', '44', '45', '46', '47', '48',\n",
       "       '49', '50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '60',\n",
       "       '61', '62', '63', '64', '65', '66', '67', '68', '69', '70', '71', '72',\n",
       "       '73', '74', '75', '76', '77', '78', '79', '80', '81', '82', '83', '84',\n",
       "       '85', '86', '87', '88', '89', '90', '91', '92', '93', '94'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "intellectual-ceiling",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#df=df.append(processedData_df,ignore_index=True)\n",
    "df=pd.concat([df,processedData_df], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "brief-network",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>[0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, ...</td>\n",
       "      <td>[0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, ...</td>\n",
       "      <td>[0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[0.13302203273083382, 0.4700551967384218, 0.0,...</td>\n",
       "      <td>[0.09421312141340309, 0.45309470504857746, 0.0...</td>\n",
       "      <td>[0.12267374247517351, 0.39958600151310014, 0.0...</td>\n",
       "      <td>[0.10978048721253882, 0.39088555163453004, 0.0...</td>\n",
       "      <td>[0.09430960990988571, 0.37528561218620254, 0.0...</td>\n",
       "      <td>[0.14218348575425158, 0.4118172561654683, 0.0,...</td>\n",
       "      <td>[0.18382754309201266, 0.38373236086954554, 0.0...</td>\n",
       "      <td>[0.22785302668489213, 0.37481173403621754, 0.0...</td>\n",
       "      <td>[0.20807959053145783, 0.38680371184141904, 0.0...</td>\n",
       "      <td>[0.1861201892271649, 0.40442920401173105, 0.0,...</td>\n",
       "      <td>...</td>\n",
       "      <td>[0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, ...</td>\n",
       "      <td>[0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, ...</td>\n",
       "      <td>[0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, ...</td>\n",
       "      <td>[0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, ...</td>\n",
       "      <td>[0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, ...</td>\n",
       "      <td>[0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, ...</td>\n",
       "      <td>[0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, ...</td>\n",
       "      <td>[0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, ...</td>\n",
       "      <td>[0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, ...</td>\n",
       "      <td>[0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[0.2091141550638824, 0.3157836824543189, 0.0, ...</td>\n",
       "      <td>[0.22492385765393264, 0.28776158834513116, 0.0...</td>\n",
       "      <td>[0.09923219851277483, 0.30944928944164135, 0.0...</td>\n",
       "      <td>[0.17314204277430578, 0.3446916476772184, 0.0,...</td>\n",
       "      <td>[0.15465182266592792, 0.3936274254847282, 0.0,...</td>\n",
       "      <td>[0.20239531520282436, 0.3758250494131653, 0.0,...</td>\n",
       "      <td>[0.1808036850321451, 0.37455491477679015, 0.0,...</td>\n",
       "      <td>[0.21391976983223573, 0.37716067989382956, 0.0...</td>\n",
       "      <td>[0.18913569116945017, 0.3403862566677584, 0.0,...</td>\n",
       "      <td>[0.15853547615392832, 0.38089064555420366, 0.0...</td>\n",
       "      <td>...</td>\n",
       "      <td>[0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, ...</td>\n",
       "      <td>[0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, ...</td>\n",
       "      <td>[0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, ...</td>\n",
       "      <td>[0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, ...</td>\n",
       "      <td>[0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, ...</td>\n",
       "      <td>[0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, ...</td>\n",
       "      <td>[0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, ...</td>\n",
       "      <td>[0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, ...</td>\n",
       "      <td>[0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, ...</td>\n",
       "      <td>[0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 95 columns</p>\n",
       "</div>"
      ],
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       "                                                   1  \\\n",
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       "0  [0.20757447598040882, 0.28529764968514537, 0.0...  ...   \n",
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       "0  [0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, ...   \n",
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       "\n",
       "                                                  86  \\\n",
       "0  [0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, ...   \n",
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       "                                                  88  \\\n",
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       "                                                  89  \\\n",
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       "                                                  90  \\\n",
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       "                                                  92  \\\n",
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       "2  [0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, ...   \n",
       "3  [0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, ...   \n",
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       "\n",
       "                                                  93  \\\n",
       "0  [0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, ...   \n",
       "1  [0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, ...   \n",
       "2  [0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, ...   \n",
       "3  [0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, ...   \n",
       "4  [0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, ...   \n",
       "\n",
       "                                                  94  \n",
       "0  [0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, ...  \n",
       "1  [0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, ...  \n",
       "2  [0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, ...  \n",
       "3  [0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, ...  \n",
       "4  [0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, ...  \n",
       "\n",
       "[5 rows x 95 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "south-consensus",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(793, 95)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "reasonable-anniversary",
   "metadata": {},
   "outputs": [],
   "source": [
    "import ast\n",
    "\n",
    "NewPersonLabel=len(ast.literal_eval(df_label.loc[0].tolist()[0]))+1\n",
    "num_of_classes=NewPersonLabel\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "hawaiian-richmond",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PersonLabel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "    <tr>\n",
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       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                                         PersonLabel\n",
       "0  [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n",
       "1  [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n",
       "2  [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n",
       "3  [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n",
       "4  [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ..."
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_label.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aquatic-triangle",
   "metadata": {},
   "source": [
    "\n",
    "# NewPersonLabel=len(ast.literal_eval(df_label.loc[0].tolist()[0]))+1\n",
    "# num_of_classes=NewPersonLabel\n",
    "\n",
    "def add_removePersonLabel(df_label,df,processedData_df=None): #True label for add\n",
    "    input1= input('Add or Remove Person?\\n'\\\n",
    "         'Type \"a\" to add.\\n'\\\n",
    "         'Type \"r\" to remove.')\n",
    "    y_class_list=[i+1 for i in np.argmax(df_label,axis=1)] #one hot encoding index 0 represent Person ID 1.\n",
    "    \n",
    "    #print(y_class_list)\n",
    "    y = []\n",
    "    y_onehot=[]\n",
    "    num_of_classes=len(set(y_class_list)) #current class list\n",
    "    index = 0\n",
    "    if input1=='a' and num_of_classes>=44: #first 44 classes are set aside as a permanent block for testing and debugging. \n",
    "        \n",
    "        print('New Person ID is :', num_of_classes+1) #Person ID starts from 1. Indexes start from 0.\n",
    "        #NewPersonLabel= int(input('Provide Person ID Number To add: '))\n",
    "        \n",
    "        #y_class_list=y_class_list.append(num_of_classes+1) #updated class list with new Person ID.\n",
    "        y_class_list.append(num_of_classes+1)\n",
    "#         oneHot_newPersonID= = [0] * (len(set(y_class_list))+1)\n",
    "#         oneHot_newPersonID[-1]=1\n",
    "        \n",
    "        if processedData_df is not None:\n",
    "            print('processedData_df exist')\n",
    "            df=pd.concat([df,processedData_df], axis=0)\n",
    "        else:\n",
    "            print('Missing Detection. Nothing Will Be Added')\n",
    "            return None ,None\n",
    "        \n",
    "        #print(type(y_class_list), type(y_class_list_updated), type(num_of_classes), y_class_list_updated)\n",
    "        for each in range(len(df)):\n",
    "            #print(y_class_list)\n",
    "            one_hot = [0] * (len(set(y_class_list)))\n",
    "            #print(one_hot)\n",
    "            if each<range(len(df))[-1]:\n",
    "                one_hot[np.argmax(df_label[each])]=1\n",
    "                y.append(one_hot)\n",
    "            else: \n",
    "                one_hot[-1]=1\n",
    "                y.append(one_hot)\n",
    "        y_onehot=np.array(y)\n",
    "        \n",
    "        return df,y_onehot\n",
    "        \n",
    "        \n",
    "    \n",
    "    elif input1=='r' and num_of_classes>=44:\n",
    "        print('Will Remove The Last Person ID. This is for testing only')\n",
    "        print('Remove Class and Data For Person ID :',  y_class_list[-1])\n",
    "        \n",
    "        #print(len(y_class_list))\n",
    "        \n",
    "        index_removed_class=len(y_class_list)-1\n",
    "        #print(index_removed_class)\n",
    "        del y_class_list[-1]\n",
    "        \n",
    "        print('y_class_list after removed :',len(y_class_list))\n",
    "        print(df.shape)\n",
    "        \n",
    "        df_dropped=df.drop([index_removed_class])\n",
    "        \n",
    "        print('df_dropped :',df_dropped.shape)\n",
    "        \n",
    "        for each in range(len(df_dropped)):\n",
    "            \n",
    "            one_hot = [0] * (len(set(y_class_list))+1)\n",
    "            #print(one_hot)\n",
    "            one_hot[np.argmax(df_label[each])]=1\n",
    "            y.append(one_hot)\n",
    "        \n",
    "        y_onehot=np.array(y)\n",
    "        return df_dropped,y_onehot\n",
    "\n",
    "    else: \n",
    "        print(\"You cannot edit the first 44 class. These classes are reserved for testing and debugging.\")\n",
    "    \n",
    "df,y_onehot=add_removePersonLabel(y,df,processedData_df)\n",
    "\n",
    "print(df.shape,y_onehot.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "talented-documentary",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# NewPersonLabel=len(ast.literal_eval(df_label.loc[0].tolist()[0]))+1\n",
    "# num_of_classes=NewPersonLabel\n",
    "\n",
    "def add_removePersonLabel(df_label): #True label for add\n",
    "    input1= input('Add or Remove Person?\\n'\\\n",
    "         'Type \"a\" to add.\\n'\\\n",
    "         'Type \"r\" to remove.')\n",
    "    \n",
    "    y = []\n",
    "    y_onehot=[]\n",
    "    index = 0\n",
    "    if input1=='a':\n",
    "        NewPersonLabel= int(input('Provide Person ID Number To add: '))\n",
    "        \n",
    "        #get current list of person ID label \n",
    "        for row in tqdm(df_label.itertuples(index=False)):\n",
    "            #print(row[0])\n",
    "            personID=(ast.literal_eval(row[0])).index(max(ast.literal_eval(row[0])))\n",
    "            y.append(personID)\n",
    "\n",
    "        y.append(NewPersonLabel)\n",
    "        num_of_classes=len(set(y))\n",
    "        \n",
    "        #transform new list back to one hot encode format\n",
    "        for idx,value in enumerate(y):\n",
    "            one_hot = [0] * num_of_classes\n",
    "            print(value, type(value))\n",
    "            one_hot[int(value)-1] = 1  # index base from 0. so need to subtract by 1\n",
    "            y_onehot.append(one_hot)\n",
    "        y_onehot = np.array(y_onehot)\n",
    "        return y_onehot\n",
    "    \n",
    "    else:\n",
    "        input3= int(input('Provide Person ID Number To Remove: '))\n",
    "        \n",
    "        #get current list of person ID label\n",
    "        for row in tqdm(df_label.itertuples(index=False)):\n",
    "            #print(row[0])\n",
    "            personID=(ast.literal_eval(row[0])).index(max(ast.literal_eval(row[0])))\n",
    "            y.append(personID)\n",
    "        print(y)\n",
    "        y.remove(input3)\n",
    "        num_of_classes=len(set(y))\n",
    "\n",
    "        #transform new list back to one hot encode format\n",
    "        for idx,value in enumerate(y):\n",
    "            one_hot = [0] * num_of_classes\n",
    "            print(value)\n",
    "            one_hot[value-1] = 1  # index base from 0. so need to subtract by 1\n",
    "            y_onehot.append(one_hot)\n",
    "        y_onehot = np.array(y_onehot)\n",
    "        return y_onehot\n",
    "\n",
    "y_onehot=add_removePersonLabel(df_label)\n",
    "\n",
    "y_onehot.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "minus-international",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "y_onehot.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "homeless-arbor",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_label=y_onehot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "characteristic-viking",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cosmetic-theater",
   "metadata": {},
   "outputs": [],
   "source": [
    "import ast\n",
    "\n",
    "def load_x(data):\n",
    "    x = [] \n",
    "    for row in tqdm(data.itertuples(index=False)):\n",
    "        x_row=[]\n",
    "        for i in row:       \n",
    "            #print(ast.literal_eval(i))\n",
    "            try:\n",
    "                x_row.append(ast.literal_eval(i))    \n",
    "            except:\n",
    "                x_row.append(i)\n",
    "        x.append(x_row)\n",
    "    x = np.array(x)\n",
    "    print (\"Done.\")\n",
    "    return x\n",
    "\n",
    "# def load_y(data):\n",
    "        \n",
    "#     y = []\n",
    "#     index = 0\n",
    "#     for row in tqdm(data.itertuples(index=False)):\n",
    "#         for i in row:\n",
    "#           #print(ast.literal_eval(i))\n",
    "#             try:\n",
    "#                 y.append(ast.literal_eval(i))\n",
    "#             except:\n",
    "#                 y.append(i)\n",
    "#     y = np.array(y)\n",
    "#     print (\"Done.\")  \n",
    "#     return y\n",
    "\n",
    "x=load_x(df)\n",
    "print(x.shape)\n",
    "y=df_label\n",
    "# y=load_y(df_label)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "inside-girlfriend",
   "metadata": {},
   "outputs": [],
   "source": [
    "processedData.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "spread-adapter",
   "metadata": {},
   "outputs": [],
   "source": [
    "#for testing. Test case is for same person. Expected prediction should near to zero. \n",
    "model=load_model(modelFilePath,compile=False)\n",
    "max_frames=df.shape[1]\n",
    "temp1=x[-1,:max_frames,:]\n",
    "temp2=x[-1,:max_frames,:]\n",
    "temp1=temp1.reshape((1,max_frames,36))\n",
    "temp2=temp2.reshape((1,max_frames,36))\n",
    "print(temp1.shape)\n",
    "\n",
    "model.predict([temp1,temp2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "alien-burden",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "min_threshold=0.7\n",
    "qualifying_threshold=0.97\n",
    "#max_frames=95\n",
    "max_frames=95\n",
    "\n",
    "model=load_model(modelFilePath,compile=False) #since we are making inference only, set compile to False.\n",
    "x_cropped=x[:,:max_frames,:36]\n",
    "\n",
    "def make_pairs(modelFilePath, detectedData, x,y):\n",
    "    \n",
    "    y_class_list=[i+1 for i in np.argmax(y,axis=1)] # need to add 1 because ID of Person start from 1, not 0.\n",
    "    result=[]\n",
    "    personLabel_forRow = []\n",
    "    pair_class_labels=[]\n",
    "    predictPairResult=[]\n",
    "    \n",
    "    shape=(1,max_frames,36)\n",
    "    \n",
    "    #result=[model.predict([detectedData.reshape(shape),i.reshape(shape)])[0][0] for i in tqdm(x_cropped)]\n",
    " \n",
    "# #########\n",
    "#     result=[]\n",
    "#     bestScore=None\n",
    "#     index_bestscore=None\n",
    "#     detectedData=detectedData.reshape(shape)\n",
    "    \n",
    "#     randomRunTimes=3\n",
    "#     randomRunTimes_result=[]\n",
    "#     for run in range(0,randomRunTimes):\n",
    "#         idx=np.random.choice(range(0,len(x_cropped)),30)\n",
    "#         for i in idx:\n",
    "#             score=model.predict([detectedData,x_cropped[i].reshape(shape)])[0][0]\n",
    "#             randomRunTimes_result.append(score)\n",
    "#     if max(randomRunTimes_result)<0.7 or np.isnan(np.array(randomRunTimes_result)).all() :\n",
    "#         personID='Not Detected'\n",
    "#         message='No Person Detected.'\n",
    "#         score='-'\n",
    "#         print('Person ID Predicted : ',personID,'\\t','Message : ',message,'\\t',' Score:',score )\n",
    "#         return personID,score\n",
    "    \n",
    "#     for i,row in enumerate(tqdm(x_cropped)):\n",
    "#         #print(type(row), row.shape)\n",
    "#         score=model.predict([detectedData,row.reshape(shape)])[0][0]\n",
    "#         if score>qualifying_threshold:\n",
    "#                 #print(i, y_class_list[i], y_class_list)\n",
    "#                 personID=y_class_list[i]\n",
    "#                 message='This Person Is Authorized To Enter'\n",
    "#                 print('Person ID Predicted : ',y_class_list[i],'\\t','Message : ',message,'\\t',' Score:',score )\n",
    "#                 return personID, score\n",
    "#         else:\n",
    "#             result.append(score)\n",
    "    \n",
    "# ############    \n",
    "    detectedData=detectedData.reshape(shape)\n",
    "    for idxA in tqdm(range(len(x))):\n",
    "        \n",
    "        rowData=x[idxA]\n",
    "        rowData=rowData[:max_frames,:36]\n",
    "        rowData=rowData.reshape((1,max_frames,36))\n",
    "        \n",
    "        \n",
    "        #print(rowData.shape, detectedData.shape)  \n",
    "        \n",
    "        predictedResult=(model.predict([detectedData,rowData]))[0][0]\n",
    "        #print(type(predictedResult))\n",
    "        result.append(predictedResult)\n",
    "    \n",
    "        #print(predictedResult)\n",
    "    \n",
    "    ##bestResult_rowIndex=max(zip(result,range(len(result))))[1]  \n",
    "                                            #uncomment above line to tune for threshold.\n",
    "                                            #to use, comment out next line. \n",
    "                                            #take some random web cam video and run prediction.\n",
    "                                            #the result the set he benchmark for min threshold.\n",
    "                                            #any prediction above threshold is positive\n",
    "    ##bestResult_rowIndex=min(zip(result,range(len(result))))[1]  #Use for constrastive loss.\n",
    "    ##print(result)\n",
    "    bestResult_rowIndex=result.index(max(result))\n",
    "    print('bestResult_rowIndex', bestResult_rowIndex)\n",
    "    ##bestResult_rowIndex=max(zip(result,range(len(result))))[1]\n",
    "    dissimilarscore=result[bestResult_rowIndex]\n",
    "    ##print(type(confidence))\n",
    "    predictedClass=y_class_list[bestResult_rowIndex]\n",
    "    \n",
    "    if np.isnan(dissimilarscore):\n",
    "        personID='Not Detected'\n",
    "        message='No Person Detected.'\n",
    "    elif dissimilarscore>=threshold:    #need to change inequality sign for constrastive loss.\n",
    "        personID=predictedClass\n",
    "        message='This Person Is Authorized To Enter'\n",
    "    elif dissimilarscore<threshold:    #need to change inequality sign for constrastive loss.\n",
    "        personID='A Person Detected'\n",
    "        message='NOT AUTHORIZED. DO NOT ENTER!'\n",
    "    \n",
    "    print('Person ID Predicted : ',personID,'\\t','Message : ',message,'\\t',' Score:',dissimilarscore )\n",
    "    return predictedClass, result\n",
    "\n",
    "        \n",
    "predictedClass,result=make_pairs(modelFilePath,processedData,x,y)        \n",
    "\n",
    "\n",
    "#predictedClass,result=make_pairs(modelFilePath,zeroArray,x,y)  #use for testing.       \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cognitive-shower",
   "metadata": {},
   "outputs": [],
   "source": [
    "fil_name1 = 'data_augmented_onboarded'\n",
    "\n",
    "x_data = x.tolist()\n",
    "with open(fil_name1+'.csv', 'w', newline='') as csvfile:\n",
    "    writer = csv.writer(csvfile, delimiter=',')\n",
    "    writer.writerows(x_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "hollow-fundamentals",
   "metadata": {},
   "outputs": [],
   "source": [
    "fil_name2 = 'PersonLabel_augmented_onboarded'\n",
    "np.savetxt(fil_name2+'.csv',y,delimiter=\",\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "copyrighted-northwest",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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