Detection_Webcam.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from keras.datasets import mnist\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Activation, Flatten\n",
    "from keras.layers import Convolution2D, MaxPooling2D\n",
    "from keras.utils import np_utils\n",
    "from keras.optimizers import SGD\n",
    "import skimage\n",
    "import glob\n",
    "import numpy\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "from skimage.measure import compare_ssim\n",
    "from sklearn.metrics.cluster import normalized_mutual_info_score\n",
    "from sklearn.metrics.cluster import mutual_info_score\n",
    "from PIL import Image\n",
    "from keras.models import load_model\n",
    "from keras import backend as K\n",
    "import heapq\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "faceCascade = cv2.CascadeClassifier('haarcascade_frontalcatface.xml')\n",
    "\n",
    "video_capture = cv2.VideoCapture(0)\n",
    "\n",
    "m = load_model('emotion1.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Emotion Rank\n",
      "1. Happy\n",
      "2. Fear\n",
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      "1. Neutral\n",
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    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-60005dc91848>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     67\u001b[0m     \u001b[0;31m# cv2.resizeWindow('Video',640,600)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 69\u001b[0;31m     \u001b[0;32mif\u001b[0m \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwaitKey\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m&\u001b[0m \u001b[0;36m0xFF\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mord\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'q'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     70\u001b[0m         \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "while True:\n",
    "    # Capture frame-by-frame\n",
    "    ret, frame = video_capture.read()\n",
    "\n",
    "    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n",
    "    gray = cv2.equalizeHist(gray)\n",
    "    faces = faceCascade.detectMultiScale(\n",
    "        gray,\n",
    "        scaleFactor=1.1,\n",
    "        minNeighbors=5,\n",
    "        minSize=(30, 30),\n",
    "        flags=cv2.CASCADE_SCALE_IMAGE\n",
    "    )\n",
    "\n",
    "    for face in faces:\n",
    "        x,y,w,h = face\n",
    "        roi_gray = gray[y:y+h, x:x+w]\n",
    "        resize_img = cv2.resize(roi_gray,(48,48))\n",
    "        \n",
    "        img_arr = np.array(resize_img)\n",
    "        img_arr = img_arr.astype('float32')\n",
    "        img_arr -= np.mean(img_arr)\n",
    "        img_arr /= np.std(img_arr)\n",
    "        img_arr = img_arr.reshape(1,48,48, 1)\n",
    "        \n",
    "        \n",
    "\n",
    "        \n",
    "        prediction = m.predict(img_arr)\n",
    "\n",
    "\n",
    "\n",
    "        labels = {0:'Angry', 1:'Disgust', 2:'Fear', 3:'Happy', 4:'Sad', 5:'Surprise', 6:'Neutral'}\n",
    "        predictions_arr = m.predict(img_arr)\n",
    "        predictions = predictions_arr[0].tolist()\n",
    "        max_index = heapq.nlargest(3, range(len(predictions)), key=predictions.__getitem__)\n",
    "        print('Emotion Rank')\n",
    "        print('1.',labels[max_index[0]])\n",
    "        print('2.',labels[max_index[1]])\n",
    "        print('3.',labels[max_index[2]])\n",
    "\n",
    "        if (labels[max_index[0]]==\"Happy\"):\n",
    "            cv2.putText(frame,\"Happy\", (x,y), cv2.FONT_ITALIC, 3, (0,0,255))\n",
    "\n",
    "        elif (labels[max_index[0]]==\"Angry\"):\n",
    "            cv2.putText(frame,\"Angry\", (x,y), cv2.FONT_HERSHEY_SIMPLEX, 3, (0,0,255))\n",
    "\n",
    "        elif (labels[max_index[0]]==\"Disgust\"):\n",
    "            cv2.putText(frame,\"Disgust\", (x,y), cv2.FONT_HERSHEY_SIMPLEX, 3, (0,0,255))\n",
    "\n",
    "        elif (labels[max_index[0]]==\"Fear\"):\n",
    "            cv2.putText(frame,\"Fear\", (x,y), cv2.FONT_HERSHEY_SIMPLEX, 3, (0,0,255))\n",
    "\n",
    "        elif (labels[max_index[0]]==\"Sad\"):\n",
    "            cv2.putText(frame,\"Sad\", (x,y), cv2.FONT_HERSHEY_SIMPLEX,3, (0,0,255))\n",
    "\n",
    "        elif (labels[max_index[0]]==\"Surprise\"):\n",
    "            cv2.putText(frame,\"Surprise\", (x,y), cv2.FONT_HERSHEY_SIMPLEX, 3, (0,0,255))\n",
    "\n",
    "        else:\n",
    "            cv2.putText(frame,\"Neutral\", (x,y), cv2.FONT_ITALIC,3, (0,0,255))\n",
    "\n",
    "\n",
    "        cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)\n",
    "    # Display the resulting frame\n",
    "    cv2.imshow('Video', frame)\n",
    "    # cv2.resizeWindow('Video',640,600)\n",
    "\n",
    "    if cv2.waitKey(1) & 0xFF == ord('q'):\n",
    "        break\n",
    "\n",
    "# When everything is done, release the capture\n",
    "video_capture.release()\n",
    "cv2.destroyAllWindows()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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