Reatime_Detection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "cVcf4sbRqGce"
   },
   "outputs": [],
   "source": [
    "import dlib\n",
    "import cv2\n",
    "from imutils import face_utils\n",
    "from scipy.spatial import distance \n",
    "import math\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import roc_curve, roc_auc_score, f1_score\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.naive_bayes import BernoulliNB\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.pipeline import Pipeline, FeatureUnion\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn import metrics\n",
    "import warnings\n",
    "from sklearn import preprocessing\n",
    "\n",
    "p = \"shape_predictor_68_face_landmarks.dat\"\n",
    "detector = dlib.get_frontal_face_detector()\n",
    "predictor = dlib.shape_predictor(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Tqi058dZqGco"
   },
   "outputs": [],
   "source": [
    "def eye_aspect_ratio(eye):\n",
    "\tA = distance.euclidean(eye[1], eye[5])\n",
    "\tB = distance.euclidean(eye[2], eye[4])\n",
    "\tC = distance.euclidean(eye[0], eye[3])\n",
    "\tear = (A + B) / (2.0 * C)\n",
    "\treturn ear\n",
    "\n",
    "def mouth_aspect_ratio(mouth):\n",
    "    A = distance.euclidean(mouth[14], mouth[18])\n",
    "    C = distance.euclidean(mouth[12], mouth[16])\n",
    "    mar = (A ) / (C)\n",
    "    return mar\n",
    "\n",
    "def circularity(eye):\n",
    "    A = distance.euclidean(eye[1], eye[4])\n",
    "    radius  = A/2.0\n",
    "    Area = math.pi * (radius ** 2)\n",
    "    p = 0\n",
    "    p += distance.euclidean(eye[0], eye[1])\n",
    "    p += distance.euclidean(eye[1], eye[2])\n",
    "    p += distance.euclidean(eye[2], eye[3])\n",
    "    p += distance.euclidean(eye[3], eye[4])\n",
    "    p += distance.euclidean(eye[4], eye[5])\n",
    "    p += distance.euclidean(eye[5], eye[0])\n",
    "    return 4 * math.pi * Area /(p**2)\n",
    "\n",
    "def mouth_over_eye(eye):\n",
    "    ear = eye_aspect_ratio(eye)\n",
    "    mar = mouth_aspect_ratio(eye)\n",
    "    mouth_eye = mar/ear\n",
    "    return mouth_eye\n",
    "\n",
    "\n",
    "def average(y_pred):\n",
    "    for i in range(len(y_pred)):\n",
    "        if i % 240 == 0 or (i+1) % 240 == 0:\n",
    "            pass\n",
    "        else: \n",
    "            average = float(y_pred[i-1] +  y_pred[i] + y_pred[i+1])/3\n",
    "            if average >= 0.5:\n",
    "                y_pred[i] = 1\n",
    "            else:\n",
    "                y_pred[i] = 0\n",
    "    return y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wluAHvusqGcu"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('totalwithmaininfo.csv',sep=',')\n",
    "df = df.drop(df.columns[0],axis=1)\n",
    "\n",
    "train_percentage = 17/22\n",
    "train_index = int(len(df)*train_percentage)\n",
    "test_index = len(df)-train_index\n",
    "\n",
    "df_train = df[:train_index]\n",
    "df_test = df[-test_index:]\n",
    "\n",
    "X_test = df_test.drop([\"Y\"],axis=1)\n",
    "y_test = df_test[\"Y\"]\n",
    "\n",
    "X_train = df_train.drop('Y',axis=1)\n",
    "y_train = df_train['Y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "W0LK5DCcqGc2",
    "outputId": "8e6f7e52-2343-4138-fd97-95f09398295c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.7216666666666667, 0.6570841889117043, 0.7083333333333333]\n",
      "[[1092  108]\n",
      " [ 560  640]]\n"
     ]
    }
   ],
   "source": [
    "acc3_list = []\n",
    "f1_score3_list = []\n",
    "roc_3_list = []\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "for i in range(1,30):\n",
    "    neigh = KNeighborsClassifier(n_neighbors=i)\n",
    "    neigh.fit(X_train, y_train) \n",
    "    pred_KN = neigh.predict(X_test)\n",
    "    pred_KN = average(pred_KN)\n",
    "    y_score_3 = neigh.predict_proba(X_test)[:,1]\n",
    "    acc3_list.append(accuracy_score(y_test, pred_KN))\n",
    "    f1_score3_list.append(metrics.f1_score(y_test, pred_KN))\n",
    "    roc_3_list.append(metrics.roc_auc_score(y_test, y_score_3))\n",
    "    \n",
    "    \n",
    "neigh = KNeighborsClassifier(n_neighbors=acc3_list.index(max(acc3_list))+1)\n",
    "neigh.fit(X_train, y_train) \n",
    "pred_KN = neigh.predict(X_test)\n",
    "pred_KN = average(pred_KN)\n",
    "y_score_3 = neigh.predict_proba(X_test)[:,1]\n",
    "acc3 = accuracy_score(y_test, pred_KN)\n",
    "f1_score_3 = metrics.f1_score(y_test, pred_KN)\n",
    "roc_3 = metrics.roc_auc_score(y_test, y_score_3)\n",
    "print([acc3,f1_score_3,roc_3])\n",
    "print(confusion_matrix(y_test, pred_KN))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8vtx54-AqGc9"
   },
   "outputs": [],
   "source": [
    "def model(landmarks):\n",
    "\n",
    "    features = pd.DataFrame(columns=[\"EAR\",\"MAR\",\"Circularity\",\"MOE\"])\n",
    "\n",
    "    eye = landmarks[36:68]\n",
    "    ear = eye_aspect_ratio(eye)\n",
    "    mar = mouth_aspect_ratio(eye)\n",
    "    cir = circularity(eye)\n",
    "    mouth_eye = mouth_over_eye(eye)\n",
    "\n",
    "    df = features.append({\"EAR\":ear,\"MAR\": mar,\"Circularity\": cir,\"MOE\": mouth_eye},ignore_index=True)\n",
    "\n",
    "    df[\"EAR_N\"] = (df[\"EAR\"]-mean[\"EAR\"])/ std[\"EAR\"]\n",
    "    df[\"MAR_N\"] = (df[\"MAR\"]-mean[\"MAR\"])/ std[\"MAR\"]\n",
    "    df[\"Circularity_N\"] = (df[\"Circularity\"]-mean[\"Circularity\"])/ std[\"Circularity\"]\n",
    "    df[\"MOE_N\"] = (df[\"MOE\"]-mean[\"MOE\"])/ std[\"MOE\"]\n",
    "    \n",
    "    Result = neigh.predict(df)\n",
    "    if Result == 1:\n",
    "        Result_String = \"Drowsy\"\n",
    "    else:\n",
    "        Result_String = \"Alert\"\n",
    "    \n",
    "\n",
    "    return Result_String, df.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "AtZJFa5qqGdE"
   },
   "outputs": [],
   "source": [
    "def calibration():\n",
    "    data = []\n",
    "    cap = cv2.VideoCapture(0)\n",
    "\n",
    "    while True:\n",
    "        # Getting out image by webcam \n",
    "        _, image = cap.read()\n",
    "        # Converting the image to gray scale\n",
    "        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "        # Get faces into webcam's image\n",
    "        rects = detector(image, 0)\n",
    "\n",
    "        # For each detected face, find the landmark.\n",
    "        for (i, rect) in enumerate(rects):\n",
    "            # Make the prediction and transfom it to numpy array\n",
    "            shape = predictor(gray, rect)\n",
    "            shape = face_utils.shape_to_np(shape)\n",
    "            data.append(shape)\n",
    "            cv2.putText(image,\"Calibrating...\", bottomLeftCornerOfText, font, fontScale, fontColor,lineType)\n",
    "\n",
    "            # Draw on our image, all the finded cordinate points (x,y) \n",
    "            for (x, y) in shape:\n",
    "                cv2.circle(image, (x, y), 2, (0, 255, 0), -1)\n",
    "\n",
    "        # Show the image\n",
    "        cv2.imshow(\"Output\", image)\n",
    "\n",
    "        k = cv2.waitKey(5) & 0xFF\n",
    "        if k == 27:\n",
    "            break\n",
    "\n",
    "    cv2.destroyAllWindows()\n",
    "    cap.release()\n",
    "    \n",
    "    \n",
    "    features_test = []\n",
    "    for d in data:\n",
    "        eye = d[36:68]\n",
    "        ear = eye_aspect_ratio(eye)\n",
    "        mar = mouth_aspect_ratio(eye)\n",
    "        cir = circularity(eye)\n",
    "        mouth_eye = mouth_over_eye(eye)\n",
    "        features_test.append([ear, mar, cir, mouth_eye])\n",
    "    \n",
    "    features_test = np.array(features_test)\n",
    "    x = features_test\n",
    "    y = pd.DataFrame(x,columns=[\"EAR\",\"MAR\",\"Circularity\",\"MOE\"])\n",
    "    df_means = y.mean(axis=0)\n",
    "    df_std = y.std(axis=0)\n",
    "    \n",
    "    return df_means,df_std\n",
    "\n",
    "font                   = cv2.FONT_HERSHEY_SIMPLEX\n",
    "bottomLeftCornerOfText = (10,400)\n",
    "fontScale              = 1\n",
    "fontColor              = (255,255,255)\n",
    "lineType               = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "C-hbYdBYqGdK"
   },
   "outputs": [],
   "source": [
    "def live():\n",
    "    cap = cv2.VideoCapture(0)\n",
    "    data = []\n",
    "    result = []\n",
    "    while True:\n",
    "        # Getting out image by webcam \n",
    "        _, image = cap.read()\n",
    "        # Converting the image to gray scale\n",
    "        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "        rects = detector(image, 0)\n",
    "\n",
    "        for (i, rect) in enumerate(rects):\n",
    "            # Make the prediction and transfom it to numpy array\n",
    "            shape = predictor(gray, rect)\n",
    "            shape = face_utils.shape_to_np(shape)\n",
    "            Result_String, features = model(shape)\n",
    "            cv2.putText(image,Result_String, bottomLeftCornerOfText, font, fontScale, fontColor,lineType)\n",
    "            data.append (features)\n",
    "            result.append(Result_String)\n",
    "\n",
    "            for (x, y) in shape:\n",
    "                cv2.circle(image, (x, y), 2, (0, 255, 0), -1)\n",
    "\n",
    "        cv2.imshow(\"Output\", image)\n",
    "\n",
    "        k = cv2.waitKey(300) & 0xFF\n",
    "        if k == 27:\n",
    "            break\n",
    "\n",
    "    cv2.destroyAllWindows()\n",
    "    cap.release()\n",
    "    \n",
    "    return data,result\n",
    "\n",
    "font                   = cv2.FONT_HERSHEY_SIMPLEX\n",
    "bottomLeftCornerOfText = (10,400)\n",
    "fontScale              = 1\n",
    "fontColor              = (255,255,255)\n",
    "lineType               = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "RFG6Gde9qGdR"
   },
   "outputs": [],
   "source": [
    "mean, std = calibration()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5doI5hPAqGdY"
   },
   "outputs": [],
   "source": [
    "features, result = live()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "88ctUIfVqGdd",
    "outputId": "423f8e3c-3410-489c-b5ba-8de565c77d9b"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Time (Frames Captured)')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "features =np.vstack(features)\n",
    "y = pd.DataFrame(features,columns=[\"EAR\",\"MAR\",\"Circularity\",\"MOE\",\"EAR_N\",\"MAR_N\",\"Circularity_N\",\"MOE_N\"])\n",
    "y = y.drop(columns=[\"EAR_N\",\"MAR_N\",\"Circularity_N\",\"MOE_N\"])\n",
    "\n",
    "x = y.values #returns a numpy array\n",
    "min_max_scaler = preprocessing.MinMaxScaler()\n",
    "x_scaled = min_max_scaler.fit_transform(x)\n",
    "y = pd.DataFrame(x_scaled,columns=[\"Eye Aspect Ratio\",\"Mouth Aspect Ratio\",\"Eye Circularity\",\"Mouth over Eye\"])\n",
    "\n",
    "\n",
    "y [\"Result\"] = result\n",
    "\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(nrows=2,\n",
    "                                ncols=1,\n",
    "                                sharex=True,\n",
    "                                sharey=False,\n",
    "                                figsize=(15, 8))\n",
    "\n",
    "ax1.set_title(\"Normalised Features\")\n",
    "#ax1.plot(y[\"Eye Aspect Ratio\"])\n",
    "#ax1.plot(y[\"Mouth Aspect Ratio\"])\n",
    "ax1.plot(y[\"Eye Circularity\"])\n",
    "ax1.plot(y[\"Mouth over Eye\"])\n",
    "ax1.legend(loc=\"best\")\n",
    "ax1.set_ylabel('Feature Value')\n",
    "\n",
    "\n",
    "ax2.plot(y[\"Result\"],marker = '.', color = \"Black\")\n",
    "ax2.set_xlabel('Time (Frames Captured)')"
   ]
  }
 ],
 "metadata": {
  "colab": {
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