eye-blink-detection-4-comparison.ipynb

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   "source": [
    "# EYE BLINK DETECTION:\n",
    "\n",
    "# 4) Comparison of 4 models\n",
    "\n",
    "1. Simple model\n",
    "2. Adaptive model\n",
    "3. ML model\n",
    "4. ML model from RLDD"
   ]
  },
  {
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   "execution_count": 1,
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    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "from matplotlib import gridspec\n",
    "%matplotlib inline\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "\n",
    "# import utility functions\n",
    "from utils import *\n",
    "from utils3 import *"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
    "collapsed": true
   },
   "source": [
    "Load outputs of basic model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#define path and dataset_name\n",
    "path1 = \"../input/eye-blink-detection-1-simple-model\"\n",
    "dataset_name = \"talking\"\n",
    "\n",
    "# load datasets\n",
    "c_pred1, b_pred1, df1, c_test1, b_test1, s_str1 = load_datasets(path1, dataset_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "Load outputs of adaptive model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#define path and dataset_name\n",
    "path2 = \"../input/eye-blink-detection-2-adaptive-model-v2\"\n",
    "dataset_name = \"talking\"\n",
    "\n",
    "# load datasets\n",
    "c_pred2, b_pred2, df2, c_test2, b_test2, s_str2 = load_datasets(path2, dataset_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "Load outputs of ML models:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#define path\n",
    "path2 = \"../input/../input/eye-blink-detection-3-ml-model-part2/\"\n",
    "\n",
    "# load datasets\n",
    "c_pred3 = pd.read_pickle(path2+'red_df_ml.pkl')\n",
    "c_pred4 = pd.read_pickle(path2+'red_df_ml2.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "\n",
    "Load annotations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test = pd.read_pickle(\"../input/eye-blink-detection-3-ml-model-part1/test/test_set.pkl\")\n",
    "ground_truth = df_test['blink_annot'].tolist() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#EAR values\n",
    "data1 = df1['avg_ear']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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   ],
   "source": [
    "# Create traces\n",
    "fig = go.Figure()\n",
    "fig.add_trace(go.Scatter(x=np.arange(len(data1)),y=data1,\n",
    "                    mode='lines+markers',name='EAR'))\n",
    "fig.add_trace(go.Scatter(x=np.arange(len(data1)),y=[float('nan') if x==0 else x-0.98 for x in ground_truth],\n",
    "                    mode='markers',name='ground_truth'))\n",
    "fig.add_trace(go.Scatter(x=np.arange(len(data1)),y=[float('nan') if x==0 else x-0.99 for x in c_pred1],\n",
    "                    mode='markers',name='simple model (Precision:46% Recall:97% F1:62%)'))\n",
    "fig.add_trace(go.Scatter(x=np.arange(len(data1)),y=[float('nan')]*222+[float('nan') if x==0 else x-1.00 for x in c_pred2],\n",
    "                    mode='markers',name='adaptive model (Precision:61% Recall:68% F1:64%)'))\n",
    "fig.add_trace(go.Scatter(x=np.arange(len(data1)),y=[float('nan') if x==0 else x-1.01 for x in c_pred3],\n",
    "                    mode='markers',name='ML model (Precision:86% Recall:87% F1:86%)'))\n",
    "fig.add_trace(go.Scatter(x=np.arange(len(data1)),y=[float('nan') if x==0 else x-1.02 for x in c_pred4],\n",
    "                    mode='markers',name='ML model-RLDD (Precision:50% Recall:97% F1:66%)'))\n",
    "fig.update_layout(\n",
    "    autosize=False,\n",
    "    width=1000,\n",
    "    height=500,\n",
    "    legend=dict(x=0, y=-0.4))\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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