Deteccion.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7fa2e0e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-05-27 22:01:57.443110: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "#Importando Bibliotecas\n",
    "import cv2\n",
    "import vlc\n",
    "import numpy as np\n",
    "from keras.models import load_model\n",
    "\n",
    "#Inicializando Variables\n",
    "video_capture = cv2.VideoCapture(0)\n",
    "eyeLeft = cv2.CascadeClassifier('/Users/alejandro/miniforge3/pkgs/libopencv-4.5.5-py39h86e1ac9_9/share/opencv4/haarcascades/haarcascade_lefteye_2splits.xml')\n",
    "eyeRight = cv2.CascadeClassifier('/Users/alejandro/miniforge3/pkgs/libopencv-4.5.5-py39h86e1ac9_9/share/opencv4/haarcascades/haarcascade_righteye_2splits.xml')\n",
    "data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)\n",
    "model = load_model('keras_model.h5')\n",
    "p = vlc.MediaPlayer(\"wakeup.mp3\")\n",
    "left_x, left_y, left_w, left_h = 0,0,0,0\n",
    "right_x, right_y, right_w, right_h = 0,0,0,0\n",
    "contador = 0\n",
    "\n",
    "#Ejecutando Video\n",
    "while True:\n",
    "    ret, frame = video_capture.read()\n",
    "    height,width = frame.shape[:2]\n",
    "    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n",
    "    \n",
    "    #Mostrando Contador\n",
    "    cv2.rectangle(frame, (0, 0), (width, int(height*0.1)), (0,0,0), -1)\n",
    "    cv2.putText(frame,'Contador: '+str(contador),(int(width*0.65), int(height*0.08)), cv2.FONT_HERSHEY_SIMPLEX, 2, (255,255,255), 2)\n",
    "    \n",
    "    #Identificando el Ojo Derecho\n",
    "    ojo_der = eyeRight.detectMultiScale(\n",
    "        gray,\n",
    "        scaleFactor=1.15,\n",
    "        minNeighbors=3,\n",
    "        minSize=(30, 30)\n",
    "    )\n",
    "    for (x, y, w, h) in ojo_der:\n",
    "        #cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)\n",
    "        right_x, right_y, right_w, right_h = x, y, w, h\n",
    "        break\n",
    "    \n",
    "    #Identificando el Ojo Izquierdo\n",
    "    ojo_izq = eyeLeft.detectMultiScale(\n",
    "        gray,\n",
    "        scaleFactor=1.15,\n",
    "        minNeighbors=3,\n",
    "        minSize=(30, 30)\n",
    "    )\n",
    "    for (x, y, w, h) in ojo_izq:\n",
    "        #cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)\n",
    "        left_x, left_y, left_w, left_h = x, y, w, h\n",
    "        break\n",
    "    \n",
    "    #Identificando Coordenadas x,y iniciales y finales para extraer la foto de los ojos\n",
    "    if (left_x > right_x):\n",
    "        start_x, end_x = right_x, (left_x+left_w)\n",
    "    else: \n",
    "        start_x, end_x = left_x, (right_x+right_w)\n",
    "\n",
    "    if (left_y > right_y):\n",
    "        start_y, end_y = right_y, (left_y+left_h)\n",
    "    else:\n",
    "        start_y, end_y = left_y, (right_y+right_h)\n",
    "    \n",
    "    \n",
    "    #Algoritmo de deteccion de sueño\n",
    "    if ((end_x-start_x)>120 and (end_y-start_y)<200):\n",
    "        start_x, start_y, end_x, end_y = start_x-30, start_y-50, end_x+30, end_y+50\n",
    "        cv2.rectangle(frame, (start_x, start_y), (end_x, end_y), (0, 255, 0), 2)\n",
    "        img = frame[start_y:end_y, start_x:end_x]\n",
    "        imagen = cv2.resize(img, (224, 224))\n",
    "        imagen_normalizada = (imagen.astype(np.float32) / 127.0) - 1\n",
    "        data[0] = imagen_normalizada\n",
    "        prediction = model.predict(data)\n",
    "        \n",
    "        if (list(prediction)[0][1]>=0.95):\n",
    "            cv2.putText(frame,'Durmiendo : '+str(round(list(prediction)[0][1],3)),(10, int(height*0.08)), cv2.FONT_HERSHEY_SIMPLEX, 2, (255,255,255), 2)\n",
    "            contador+=1\n",
    "        \n",
    "        if (list(prediction)[0][0]>=0.95):\n",
    "            cv2.putText(frame,'Despierto',(10, int(height*0.08)), cv2.FONT_HERSHEY_SIMPLEX, 2, (255,255,255), 2)\n",
    "            contador-=1\n",
    "            \n",
    "        if contador>=15:\n",
    "            contador=15\n",
    "            p.play()\n",
    "        elif (contador>0 and contador<15):\n",
    "            p.stop()\n",
    "        elif contador<0:\n",
    "            contador=0\n",
    "            \n",
    "    #Mostrar el video de la webcam\n",
    "    cv2.imshow('video', frame)\n",
    "\n",
    "    #Tecla de salida - acaba la transmisión\n",
    "    if cv2.waitKey(1) & 0xFF == ord('q'):\n",
    "        video_capture.release()\n",
    "        cv2.destroyAllWindows()\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6bf7196",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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