eating_data_load_lstm.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mediapipe import solutions\n",
    "from mediapipe.framework.formats import landmark_pb2\n",
    "import numpy as np\n",
    "\n",
    "import mediapipe as mp\n",
    "from mediapipe.tasks import python\n",
    "from mediapipe.tasks.python import vision\n",
    "import glob\n",
    "from tqdm import tqdm\n",
    "from collections import OrderedDict, defaultdict\n",
    "from IPython.display import clear_output\n",
    "import time\n",
    "import os\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import glob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cuda')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n",
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "meta_txt='eating_data_info.txt'\n",
    "\n",
    "#filename, eating/non-eating, start, end\n",
    "\n",
    "with open(meta_txt, 'r') as f:\n",
    "    lines=f.readlines()\n",
    "\n",
    "eating=[]\n",
    "non_eating=[]\n",
    "for sid, line in enumerate(lines):\n",
    "    if 'non-eating' in line:\n",
    "        line=line.split()\n",
    "        se=line[1].split('=')[1].split(',')\n",
    "        s,e=int(se[0]),int(se[1])\n",
    "        non_eating.append( (line[0].replace('fn=','').strip(','), s,e) )\n",
    "    \n",
    "    elif 'eating' in line:\n",
    "        line=line.split()\n",
    "        se=line[1].split('=')[1].split(',')\n",
    "        s,e=int(se[0]),int(se[1])\n",
    "        eating.append( (line[0].replace('fn=','').strip(','), s,e) )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('videos/eating1.mp4', 15, 115),\n",
       " ('videos/eating3.mp4', 25, 150),\n",
       " ('videos/eating4.mp4', 50, 150),\n",
       " ('videos/eating4.mp4', 125, 225),\n",
       " ('videos/eating4.mp4', 390, 450),\n",
       " ('videos/eating5.mp4', 175, 250),\n",
       " ('videos/eating5.mp4', 325, 400),\n",
       " ('videos/eating5.mp4', 450, 550),\n",
       " ('videos/eating7.mp4', 25, 100),\n",
       " ('videos/mango.mp4', 75, 175),\n",
       " ('videos/reading2.mp4', 25, 140),\n",
       " ('videos/similar1.mp4', 0, 60),\n",
       " ('videos/sitting.mp4', 300, 375),\n",
       " ('videos/eating8.mp4', 90, 165),\n",
       " ('videos/eating8.mp4', 790, 865),\n",
       " ('videos/eating8.mp4', 1035, 1115),\n",
       " ('videos/talking2.mp4', 525, 625)]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('videos/eating1.mp4', 240, 340),\n",
       " ('videos/eating3.mp4', 175, 300),\n",
       " ('videos/eating4.mp4', 250, 350),\n",
       " ('videos/eating5.mp4', 550, 620),\n",
       " ('videos/eating6.mp4', 0, 250),\n",
       " ('videos/eating6.mp4', 250, 500),\n",
       " ('videos/eating7.mp4', 200, 340),\n",
       " ('videos/reading1.mp4', 0, 200),\n",
       " ('videos/reading1.mp4', 200, 445),\n",
       " ('videos/reading2.mp4', 250, 450),\n",
       " ('videos/similar3.mp4', 0, 150),\n",
       " ('videos/similar3.mp4', 150, 290),\n",
       " ('videos/sitting.mp4', 0, 200),\n",
       " ('videos/smartphone.mp4', 0, 250),\n",
       " ('videos/talking.mp4', 0, 150),\n",
       " ('videos/talking.mp4', 150, 300),\n",
       " ('videos/talking.mp4', 300, 450),\n",
       " ('videos/talking2.mp4', 0, 150)]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "non_eating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17, 18, 35)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(eating), len(non_eating), len(lines)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_imgs(video_fn, skip_frames=1):\n",
    "    video=cv2.VideoCapture(video_fn)\n",
    "    imgs=[]\n",
    "    frame_count=0\n",
    "    while(video.isOpened()):\n",
    "        ret, frame = video.read()\n",
    "        if ret==False: break\n",
    "        if frame_count%skip_frames==0:\n",
    "            imgs.append(frame)\n",
    "        frame_count+=1\n",
    "    video.release() \n",
    "    return imgs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def view_imgs_as_video(imgs, delay=0.05):\n",
    "    for img in imgs:\n",
    "        clear_output(wait=True)\n",
    "        plt.imshow(img[:,:,::-1])\n",
    "        plt.show()\n",
    "        time.sleep(delay)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 0, 'videos/eating1.mp4')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fn, s,e=eating[0]\n",
    "imgs=extract_imgs(fn)\n",
    "action_imgs=imgs[s:e:5]\n",
    "len(action_imgs), len(imgs), fn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "view_imgs_as_video(action_imgs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: Created TensorFlow Lite XNNPACK delegate for CPU.\n"
     ]
    }
   ],
   "source": [
    "pose = mp.solutions.pose.Pose(static_image_mode=False, min_detection_confidence=0.5, min_tracking_confidence=0.5)\n",
    "def extract_keypoints(image_rgb): \n",
    "    try:\n",
    "        results = pose.process(image_rgb)\n",
    "        landmarks=results.pose_landmarks.landmark\n",
    "    except Exception as e:\n",
    "        # print('Error file=', fn)\n",
    "        # print('Error=', e)\n",
    "        return None\n",
    "    xys=[]\n",
    "    for landmark in landmarks:\n",
    "        xys.append([landmark.x, landmark.y])\n",
    "    xys=np.array(xys)\n",
    "    return xys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "videos/eating1.mp4 0\n",
      "videos/eating3.mp4 0\n",
      "videos/eating4.mp4 0\n",
      "videos/eating4.mp4 0\n",
      "videos/eating4.mp4 0\n",
      "videos/eating5.mp4 0\n",
      "videos/eating5.mp4 0\n",
      "videos/eating5.mp4 0\n",
      "videos/eating7.mp4 0\n",
      "videos/mango.mp4 0\n",
      "videos/reading2.mp4 0\n",
      "videos/similar1.mp4 0\n",
      "videos/sitting.mp4 0\n",
      "videos/eating8.mp4 0\n",
      "videos/eating8.mp4 0\n",
      "videos/eating8.mp4 0\n",
      "videos/talking2.mp4 0\n"
     ]
    }
   ],
   "source": [
    "actions=[]\n",
    "for i, row in enumerate(eating):\n",
    "    fn,s,e=row\n",
    "    imgs=extract_imgs(fn)\n",
    "    skip_frames=4\n",
    "    if i==4 or i==11: skip_frames=3\n",
    "    action_imgs= imgs[s:e:skip_frames]\n",
    "    action=[]\n",
    "    for img in action_imgs: \n",
    "        image_rgb=cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "        xys=extract_keypoints(image_rgb)\n",
    "        xys=xys[:25].ravel() #first 25 keypoints\n",
    "        action.append(xys)\n",
    "\n",
    "    if len(action)==19:\n",
    "        action.append(action[-1])\n",
    "    action=action[:20]\n",
    "    action=np.array(action)\n",
    "    actions.append(action)\n",
    "    \n",
    "\n",
    "    print(fn, len(action))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17, 0)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_eating=np.array(actions)\n",
    "X_eating.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "videos/eating1.mp4 0\n",
      "videos/eating3.mp4 0\n",
      "videos/eating4.mp4 0\n",
      "videos/eating5.mp4 0\n",
      "videos/eating6.mp4 0\n",
      "videos/eating6.mp4 0\n",
      "videos/eating7.mp4 0\n",
      "videos/reading1.mp4 0\n",
      "videos/reading1.mp4 0\n",
      "videos/reading2.mp4 0\n",
      "videos/similar3.mp4 0\n",
      "videos/similar3.mp4 0\n",
      "videos/sitting.mp4 0\n",
      "videos/smartphone.mp4 0\n",
      "videos/talking.mp4 0\n",
      "videos/talking.mp4 0\n",
      "videos/talking.mp4 0\n",
      "videos/talking2.mp4 0\n"
     ]
    }
   ],
   "source": [
    "actions_non=[]\n",
    "for i, row in enumerate(non_eating):\n",
    "    fn,s,e=row\n",
    "    imgs=extract_imgs(fn)\n",
    "    skip_frames=4\n",
    "    if i==3: skip_frames=3\n",
    "     \n",
    "    action_imgs= imgs[s:e:skip_frames]\n",
    "    action=[]\n",
    "    for img in action_imgs: \n",
    "        image_rgb=cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "        xys=extract_keypoints(image_rgb)\n",
    "        if xys is None: \n",
    "            print('Error file=', fn)\n",
    "            continue\n",
    "        xys=xys[:25].ravel() #first 25 keypoints\n",
    "        action.append(xys)\n",
    "    \n",
    "    \n",
    "    action=action[:20]\n",
    "    action=np.array(action)\n",
    "    actions_non.append(action)\n",
    "\n",
    "    print(fn, len(action))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18, 0)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_not_eating=np.array(actions_non)\n",
    "X_not_eating.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(35, 0)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=np.concatenate((X_eating, X_not_eating))\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(35,)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=np.array([1]*X_eating.shape[0]+[0]*X_not_eating.shape[0])\n",
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([35, 0]), torch.Size([35, 1]))"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=torch.from_numpy(X).float()\n",
    "y=torch.from_numpy(y).reshape(-1,1)\n",
    "X.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([28, 0]),\n",
       " torch.Size([28, 1]),\n",
       " torch.Size([7, 0]),\n",
       " torch.Size([7, 1]))"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ids=np.arange(X.shape[0])\n",
    "np.random.shuffle(ids)\n",
    "train_ids=ids[:int(len(ids)*0.8)]\n",
    "test_ids=ids[int(len(ids)*0.8):]\n",
    "X_train, y_train=X[train_ids], y[train_ids]\n",
    "X_test, y_test=X[test_ids], y[test_ids]\n",
    "\n",
    "X_train.shape, y_train.shape, X_test.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([35, 0])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LSTM(nn.Module):\n",
    "    \n",
    "    def __init__(self,input_dim,hidden_dim,output_dim,layer_num, seq_len):\n",
    "        super(LSTM,self).__init__()\n",
    "        self.hidden_dim = hidden_dim\n",
    "        self.output_dim = output_dim\n",
    "        self.lstm = torch.nn.LSTM(input_dim,hidden_dim,layer_num,batch_first=True)\n",
    "        self.fc = torch.nn.Linear(hidden_dim,output_dim)\n",
    "        self.bn = nn.BatchNorm1d(seq_len)\n",
    "        \n",
    "    def forward(self,inputs):\n",
    "        x = self.bn(inputs)\n",
    "        lstm_out,(hn,cn) = self.lstm(x)\n",
    "        out = self.fc(lstm_out[:,-1,:])\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "n_data_size_train = X_train.shape[0]\n",
    "n_data_size_test = X_test.shape[0]\n",
    "\n",
    "def randomTrainingExampleBatch(batch_size,flag,num=-1):\n",
    "    if flag == 'train':\n",
    "        X = X_train\n",
    "        y = y_train\n",
    "        data_size = n_data_size_train\n",
    "    elif flag == 'test':\n",
    "        X = X_test\n",
    "        y = y_test\n",
    "        data_size = n_data_size_test\n",
    "    if num == -1:\n",
    "        ran_num = random.randint(0,data_size-batch_size)\n",
    "    else:\n",
    "        ran_num = num\n",
    "    pose_sequence_tensor = X[ran_num:(ran_num+batch_size)]\n",
    "    pose_sequence_tensor = pose_sequence_tensor\n",
    "    category_tensor = y[ran_num:ran_num+batch_size,:]\n",
    "    return category_tensor.long(),pose_sequence_tensor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LSTM(\n",
       "  (lstm): LSTM(50, 128, num_layers=3, batch_first=True)\n",
       "  (fc): Linear(in_features=128, out_features=2, bias=True)\n",
       "  (bn): BatchNorm1d(20, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       ")"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_hidden = 128\n",
    "n_joints = 25*2\n",
    "n_categories = 2\n",
    "n_layer = 3\n",
    "seq_len = 20\n",
    "rnn = LSTM(n_joints,n_hidden,n_categories,n_layer, seq_len)\n",
    "rnn.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "criterion = nn.CrossEntropyLoss()\n",
    "learning_rate = 0.0005\n",
    "optimizer = optim.SGD(rnn.parameters(),lr=learning_rate,momentum=0.9)\n",
    "#scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=10000, gamma=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "ename": "RuntimeError",
     "evalue": "input.size(-1) must be equal to input_size. Expected 50, got 0",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[1;32m/home/ns/activity_recog/eating_data_load_lstm.ipynb Cell 30\u001b[0m in \u001b[0;36m<cell line: 13>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     <a href='vscode-notebook-cell:/home/ns/activity_recog/eating_data_load_lstm.ipynb#X41sZmlsZQ%3D%3D?line=17'>18</a>\u001b[0m category_tensor \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39msqueeze(category_tensor)\n\u001b[1;32m     <a href='vscode-notebook-cell:/home/ns/activity_recog/eating_data_load_lstm.ipynb#X41sZmlsZQ%3D%3D?line=19'>20</a>\u001b[0m optimizer\u001b[39m.\u001b[39mzero_grad()\n\u001b[0;32m---> <a href='vscode-notebook-cell:/home/ns/activity_recog/eating_data_load_lstm.ipynb#X41sZmlsZQ%3D%3D?line=21'>22</a>\u001b[0m output \u001b[39m=\u001b[39m rnn(input_sequence)\n\u001b[1;32m     <a href='vscode-notebook-cell:/home/ns/activity_recog/eating_data_load_lstm.ipynb#X41sZmlsZQ%3D%3D?line=22'>23</a>\u001b[0m loss \u001b[39m=\u001b[39m criterion(output, category_tensor)\n\u001b[1;32m     <a href='vscode-notebook-cell:/home/ns/activity_recog/eating_data_load_lstm.ipynb#X41sZmlsZQ%3D%3D?line=23'>24</a>\u001b[0m loss\u001b[39m.\u001b[39mbackward()\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1496\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1497\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1498\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_pre_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1499\u001b[0m         \u001b[39mor\u001b[39;00m _global_backward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1500\u001b[0m         \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m     \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m   1502\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n",
      "\u001b[1;32m/home/ns/activity_recog/eating_data_load_lstm.ipynb Cell 30\u001b[0m in \u001b[0;36mLSTM.forward\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m     <a href='vscode-notebook-cell:/home/ns/activity_recog/eating_data_load_lstm.ipynb#X41sZmlsZQ%3D%3D?line=10'>11</a>\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mforward\u001b[39m(\u001b[39mself\u001b[39m,inputs):\n\u001b[1;32m     <a href='vscode-notebook-cell:/home/ns/activity_recog/eating_data_load_lstm.ipynb#X41sZmlsZQ%3D%3D?line=11'>12</a>\u001b[0m     x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mbn(inputs)\n\u001b[0;32m---> <a href='vscode-notebook-cell:/home/ns/activity_recog/eating_data_load_lstm.ipynb#X41sZmlsZQ%3D%3D?line=12'>13</a>\u001b[0m     lstm_out,(hn,cn) \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mlstm(x)\n\u001b[1;32m     <a href='vscode-notebook-cell:/home/ns/activity_recog/eating_data_load_lstm.ipynb#X41sZmlsZQ%3D%3D?line=13'>14</a>\u001b[0m     out \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfc(lstm_out[:,\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m,:])\n\u001b[1;32m     <a href='vscode-notebook-cell:/home/ns/activity_recog/eating_data_load_lstm.ipynb#X41sZmlsZQ%3D%3D?line=14'>15</a>\u001b[0m     \u001b[39mreturn\u001b[39;00m out\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1496\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1497\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1498\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_pre_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1499\u001b[0m         \u001b[39mor\u001b[39;00m _global_backward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1500\u001b[0m         \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m     \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m   1502\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/torch/nn/modules/rnn.py:810\u001b[0m, in \u001b[0;36mLSTM.forward\u001b[0;34m(self, input, hx)\u001b[0m\n\u001b[1;32m    806\u001b[0m     \u001b[39m# Each batch of the hidden state should match the input sequence that\u001b[39;00m\n\u001b[1;32m    807\u001b[0m     \u001b[39m# the user believes he/she is passing in.\u001b[39;00m\n\u001b[1;32m    808\u001b[0m     hx \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpermute_hidden(hx, sorted_indices)\n\u001b[0;32m--> 810\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcheck_forward_args(\u001b[39minput\u001b[39;49m, hx, batch_sizes)\n\u001b[1;32m    811\u001b[0m \u001b[39mif\u001b[39;00m batch_sizes \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m    812\u001b[0m     result \u001b[39m=\u001b[39m _VF\u001b[39m.\u001b[39mlstm(\u001b[39minput\u001b[39m, hx, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_flat_weights, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mbias, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mnum_layers,\n\u001b[1;32m    813\u001b[0m                       \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdropout, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtraining, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mbidirectional, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mbatch_first)\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/torch/nn/modules/rnn.py:730\u001b[0m, in \u001b[0;36mLSTM.check_forward_args\u001b[0;34m(self, input, hidden, batch_sizes)\u001b[0m\n\u001b[1;32m    725\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mcheck_forward_args\u001b[39m(\u001b[39mself\u001b[39m,  \u001b[39m# type: ignore[override]\u001b[39;00m\n\u001b[1;32m    726\u001b[0m                        \u001b[39minput\u001b[39m: Tensor,\n\u001b[1;32m    727\u001b[0m                        hidden: Tuple[Tensor, Tensor],\n\u001b[1;32m    728\u001b[0m                        batch_sizes: Optional[Tensor],\n\u001b[1;32m    729\u001b[0m                        ):\n\u001b[0;32m--> 730\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcheck_input(\u001b[39minput\u001b[39;49m, batch_sizes)\n\u001b[1;32m    731\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcheck_hidden_size(hidden[\u001b[39m0\u001b[39m], \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mget_expected_hidden_size(\u001b[39minput\u001b[39m, batch_sizes),\n\u001b[1;32m    732\u001b[0m                            \u001b[39m'\u001b[39m\u001b[39mExpected hidden[0] size \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m, got \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m'\u001b[39m)\n\u001b[1;32m    733\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcheck_hidden_size(hidden[\u001b[39m1\u001b[39m], \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mget_expected_cell_size(\u001b[39minput\u001b[39m, batch_sizes),\n\u001b[1;32m    734\u001b[0m                            \u001b[39m'\u001b[39m\u001b[39mExpected hidden[1] size \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m, got \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m'\u001b[39m)\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/torch/nn/modules/rnn.py:218\u001b[0m, in \u001b[0;36mRNNBase.check_input\u001b[0;34m(self, input, batch_sizes)\u001b[0m\n\u001b[1;32m    214\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mRuntimeError\u001b[39;00m(\n\u001b[1;32m    215\u001b[0m         \u001b[39m'\u001b[39m\u001b[39minput must have \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m dimensions, got \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m.\u001b[39mformat(\n\u001b[1;32m    216\u001b[0m             expected_input_dim, \u001b[39minput\u001b[39m\u001b[39m.\u001b[39mdim()))\n\u001b[1;32m    217\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39minput_size \u001b[39m!=\u001b[39m \u001b[39minput\u001b[39m\u001b[39m.\u001b[39msize(\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m):\n\u001b[0;32m--> 218\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mRuntimeError\u001b[39;00m(\n\u001b[1;32m    219\u001b[0m         \u001b[39m'\u001b[39m\u001b[39minput.size(-1) must be equal to input_size. Expected \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m, got \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m.\u001b[39mformat(\n\u001b[1;32m    220\u001b[0m             \u001b[39mself\u001b[39m\u001b[39m.\u001b[39minput_size, \u001b[39minput\u001b[39m\u001b[39m.\u001b[39msize(\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m)))\n",
      "\u001b[0;31mRuntimeError\u001b[0m: input.size(-1) must be equal to input_size. Expected 50, got 0"
     ]
    }
   ],
   "source": [
    "n_iters = 20000\n",
    "print_every = 1000\n",
    "plot_every = 1000\n",
    "batch_size = 4\n",
    "\n",
    "# Keep track of losses for plotting\n",
    "current_loss = 0\n",
    "all_losses = []\n",
    "\n",
    " \n",
    "start = time.time()\n",
    "\n",
    "for iter in range(1, n_iters + 1):\n",
    "   \n",
    "    category_tensor, input_sequence = randomTrainingExampleBatch(batch_size,'train')\n",
    "    input_sequence = input_sequence.to(device)\n",
    "    category_tensor = category_tensor.to(device)\n",
    "    category_tensor = torch.squeeze(category_tensor)\n",
    "    \n",
    "    optimizer.zero_grad()\n",
    "    \n",
    "    output = rnn(input_sequence)\n",
    "    loss = criterion(output, category_tensor)\n",
    "    loss.backward()\n",
    "    optimizer.step() \n",
    " \n",
    "    current_loss += loss.item()\n",
    "\n",
    "    print('iter: %d, loss: %.3f' % (iter, loss.item()))\n",
    "    \n",
    "    \n",
    "    # Add current loss avg to list of losses\n",
    "    if iter % plot_every == 0:\n",
    "        all_losses.append(current_loss / plot_every)\n",
    "        current_loss = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f6d12ddd550>]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(all_losses)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of 7 test data: 57.143%\n"
     ]
    }
   ],
   "source": [
    "#test \n",
    "\n",
    "rnn.eval()\n",
    "\n",
    "n_correct = 0\n",
    "n_total = 0\n",
    "for i in range(n_data_size_test):\n",
    "    category_tensor, input_sequence = randomTrainingExampleBatch(1,'test',i)\n",
    "    input_sequence = input_sequence.to(device)\n",
    "    category_tensor = category_tensor.to(device)\n",
    "    category_tensor = torch.squeeze(category_tensor)\n",
    "    output = rnn(input_sequence)\n",
    "    po=output.argmax(axis=1)\n",
    "    guess=po.item()\n",
    "    category = category_tensor\n",
    "    if category == guess:\n",
    "        n_correct += 1\n",
    "    n_total += 1\n",
    "\n",
    "print('Accuracy of %d test data: %.3f%%' % (n_data_size_test, n_correct / n_total * 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of 7 test data: 100.000%\n"
     ]
    }
   ],
   "source": [
    "#test \n",
    "n_correct = 0\n",
    "n_total = 0\n",
    "for i in range(n_data_size_test):\n",
    "    category_tensor, input_sequence = randomTrainingExampleBatch(1,'train',i)\n",
    "    input_sequence = input_sequence.to(device)\n",
    "    category_tensor = category_tensor.to(device)\n",
    "    category_tensor = torch.squeeze(category_tensor)\n",
    "    output = rnn(input_sequence)\n",
    "    po=output.argmax(axis=1)\n",
    "    guess=po.item()\n",
    "    category = category_tensor\n",
    "    if category == guess:\n",
    "        n_correct += 1\n",
    "    n_total += 1\n",
    "\n",
    "print('Accuracy of %d test data: %.3f%%' % (n_data_size_test, n_correct / n_total * 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### load a video and predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fn, s,e=eating[3]\n",
    "fn, s,e=non_eating[5]\n",
    "imgs=extract_imgs(fn)\n",
    "action_imgs=imgs[s:e:5]\n",
    "len(action_imgs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "view_imgs_as_video(action_imgs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20, 50)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "action=[]\n",
    "for img in action_imgs: \n",
    "    image_rgb=cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "    xys=extract_keypoints(image_rgb)\n",
    "    xys=xys[:25].ravel()\n",
    "    action.append(xys)\n",
    "action=np.array(action)[:20]\n",
    "action.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 20, 50])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tx=torch.from_numpy(action).float()\n",
    "tx=tx.unsqueeze(0).to(device)\n",
    "tx.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prediction= 0\n"
     ]
    }
   ],
   "source": [
    "output = rnn(tx).cpu().detach().numpy()\n",
    "po=output.argmax(axis=1)\n",
    "prediction=po[0]\n",
    "print('prediction=', prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "412"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fn='eat_output.avi' \n",
    "\n",
    "video=cv2.VideoCapture(fn)\n",
    "imgs=[]\n",
    "while(video.isOpened()):\n",
    "    ret, frame = video.read()\n",
    "    if ret==False: break\n",
    "    imgs.append(frame)\n",
    "video.release() \n",
    "\n",
    "len(imgs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_imgs(video_fn, skip_frames=1):\n",
    "    video=cv2.VideoCapture(video_fn)\n",
    "    imgs=[]\n",
    "    frame_count=0\n",
    "    while(video.isOpened()):\n",
    "        ret, frame = video.read()\n",
    "        if ret==False: break\n",
    "        if frame_count%skip_frames==0:\n",
    "            imgs.append(frame)\n",
    "        frame_count+=1\n",
    "    video.release() \n",
    "    return imgs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "103"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imgs=extract_imgs(fn, skip_frames=4)\n",
    "len(imgs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for img in imgs[22:42]:\n",
    "    img=cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "    plt.imshow(img)\n",
    "    plt.show()\n",
    "    time.sleep(0.1)\n",
    "    clear_output(wait=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20, 50)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "action=[]\n",
    "for img in imgs[22:42]: \n",
    "    image_rgb=cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "    xys=extract_keypoints(image_rgb)\n",
    "    xys=xys[:25].ravel() #first 25 keypoints\n",
    "    action.append(xys)\n",
    "action=np.array(action)\n",
    "action.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 20, 50])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tx=torch.from_numpy(action).float()\n",
    "tx=tx.unsqueeze(0).to(device)\n",
    "tx.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 20, 50])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tx=torch.from_numpy(action).float()\n",
    "tx=tx.unsqueeze(0).to(device)\n",
    "tx.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prediction= 1\n"
     ]
    }
   ],
   "source": [
    "output = rnn(tx).cpu().detach().numpy()\n",
    "po=output.argmax(axis=1)\n",
    "prediction=po[0]\n",
    "print('prediction=', prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### live video"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import deque\n",
    "\n",
    "labels=['non-eating', 'eating']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_points25(image, xys, keys=None):\n",
    "    mp_drawing = mp.solutions.drawing_utils\n",
    "    drawing_spec = mp_drawing.DrawingSpec(color=(245, 117, 66), thickness=2, circle_radius=2)\n",
    " \n",
    "    if keys==None: keys=[ i for i in range(25) ]\n",
    "    \n",
    "    xs,ys=xys[:25], xys[25:]\n",
    "    for key in keys:\n",
    "        x,y= xs[key], ys[key]\n",
    "        x_px, y_px = int(x * image.shape[1]), int(y * image.shape[0])\n",
    "        cv2.circle(image, (x_px, y_px), drawing_spec.circle_radius, drawing_spec.color, drawing_spec.thickness)\n",
    "    return image "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f6cfc360550>"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "image=draw_points25(image_rgb.copy(), xys)\n",
    "plt.imshow(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50,)"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xys.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "vid = cv2.VideoCapture(0)\n",
    "\n",
    "# maintain a running queue fo 20 frames\n",
    "kps= deque(maxlen=20)\n",
    "\n",
    "# frames=[]\n",
    "skip_frames=4\n",
    "frame_count=0\n",
    "seq_len=20\n",
    "\n",
    "while(True):\n",
    "      \n",
    "    ret, frame = vid.read()\n",
    "    if ret==False: \n",
    "        print('no frame')\n",
    "        continue\n",
    "\n",
    "    # cv2.imshow('frame', frame)\n",
    "\n",
    "    image_rgb=cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
    "    xys=extract_keypoints(image_rgb)\n",
    "    xys=xys[:25].ravel() #first 25 keypoints\n",
    "    frame=draw_points25(frame, xys)\n",
    "\n",
    "    frame_count+=1\n",
    "    if frame_count%skip_frames==0:\n",
    "        # image_rgb=cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
    "        # xys=extract_keypoints(image_rgb)\n",
    "        # xys=xys[:25].ravel() #first 25 keypoints\n",
    "        kps.append(xys)\n",
    "\n",
    "        frame_count=0\n",
    "\n",
    "        # frame=draw_points25(frame, xys)\n",
    "        # plt.imshow(image)\n",
    "    \n",
    "    if len(kps)==seq_len:\n",
    "        # print('predicting...')\n",
    "        action=np.array(kps)\n",
    "        tx=torch.from_numpy(action).float()\n",
    "        tx=tx.unsqueeze(0).to(device)\n",
    "        output = rnn(tx).cpu().detach().numpy()\n",
    "        po=output.argmax(axis=1)\n",
    "        prediction=po[0]\n",
    "        # print('prediction=', prediction)\n",
    "\n",
    "        #draw text on the image\n",
    "        font = cv2.FONT_HERSHEY_SIMPLEX\n",
    "        org = (50, 50)\n",
    "        fontScale = 1\n",
    "        color = (255, 0, 0)\n",
    "        thickness = 2\n",
    "        frame = cv2.putText(frame, \"pred=\"+labels[prediction], org, font,  \n",
    "                   fontScale, color, thickness, cv2.LINE_AA)\n",
    "    \n",
    "    cv2.imshow('frame', frame)\n",
    "\n",
    "\n",
    "    if cv2.waitKey(1) & 0xFF == ord('q'):\n",
    "        break\n",
    "  \n",
    "\n",
    "vid.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(frames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "vid.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "mp_drawing = mp.solutions.drawing_utils\n",
    "mp_pose = mp.solutions.pose\n",
    "pose = mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x55c3a59e48f0) is not the object's thread (0x55c3a5de7780).\n",
      "Cannot move to target thread (0x55c3a59e48f0)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cap = cv2.VideoCapture(0)\n",
    "\n",
    "\n",
    "kps= deque(maxlen=20)\n",
    "\n",
    "# frames=[]\n",
    "skip_frames=4\n",
    "frame_count=0\n",
    "seq_len=20\n",
    "\n",
    "\n",
    "while cap.isOpened():\n",
    "     # read frame\n",
    "     _, frame = cap.read()\n",
    "     try:\n",
    "          # resize the frame for portrait video\n",
    "          # frame = cv2.resize(frame, (350, 600))\n",
    "          # convert to RGB\n",
    "          frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
    "\n",
    "          # process the frame for pose detection\n",
    "          pose_results = pose.process(frame_rgb)\n",
    "          landmarks=pose_results.pose_landmarks.landmark\n",
    "          landmarks=landmarks[:25]\n",
    "\n",
    "\n",
    "          frame_count+=1\n",
    "          if frame_count%skip_frames==0:\n",
    "               xys=[]\n",
    "               for landmark in landmarks:\n",
    "                    xys.append([landmark.x, landmark.y])\n",
    "               xys=np.array(xys).ravel()\n",
    "               # xys=xys[:25].ravel() #first 25 keypoints\n",
    "               kps.append(xys) \n",
    "               frame_count=0\n",
    "    \n",
    "          if len(kps)==seq_len:\n",
    "               # print('predicting...')\n",
    "               action=np.array(kps)\n",
    "               tx=torch.from_numpy(action).float()\n",
    "               tx=tx.unsqueeze(0).to(device)\n",
    "               output = rnn(tx).cpu().detach().numpy()\n",
    "               po=output.argmax(axis=1)\n",
    "               prediction=po[0]\n",
    "               # print('prediction=', prediction)\n",
    "\n",
    "               #draw text on the image\n",
    "               font = cv2.FONT_HERSHEY_SIMPLEX\n",
    "               org = (50, 50)\n",
    "               fontScale = 1\n",
    "               color = (255, 0, 0)\n",
    "               thickness = 2\n",
    "               frame = cv2.putText(frame, \"pred=\"+labels[prediction], org, font,  \n",
    "                         fontScale, color, thickness, cv2.LINE_AA)\n",
    "               \n",
    "\n",
    "\n",
    "          # draw skeleton on the frame\n",
    "          mp_drawing.draw_landmarks(frame, pose_results.pose_landmarks, mp_pose.POSE_CONNECTIONS)\n",
    "          # display the frame\n",
    "          cv2.imshow('Output', frame)\n",
    "     except Exception as e:\n",
    "          print(e)\n",
    "          break\n",
    "\n",
    "     if cv2.waitKey(1) == ord('q'):\n",
    "          break\n",
    "          \n",
    "cap.release()\n",
    "cv2.destroyAllWindows()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[x: 0.52103590965271\n",
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       " visibility: 0.99962317943573,\n",
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       " visibility: 0.4819241464138031,\n",
       " x: 0.5937321186065674\n",
       " y: 1.1434460878372192\n",
       " z: -0.03702221065759659\n",
       " visibility: 0.03966718912124634,\n",
       " x: 0.36515265703201294\n",
       " y: 1.1535890102386475\n",
       " z: 0.03851062059402466\n",
       " visibility: 0.047628842294216156]"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "landmarks[:25]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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   "language": "python",
   "name": "python3"
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