facial_expression_recog.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import load_model\n",
    "from time import sleep \n",
    "from keras.preprocessing.image import img_to_array\n",
    "from keras.preprocessing import image\n",
    "import cv2\n",
    "import numpy as np \n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "face_classifier =cv2.CascadeClassifier(r'C:\\Users\\hp\\Music detection system using emotion\\haarcascade_frontalface_default.xml')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier = load_model(r'C:\\Users\\hp\\Music detection system using emotion\\weights_7.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_labels=['angry','disgust','fear','happy','neutral','sad','suprise']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cap=cv2.VideoCapture(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# while True:\n",
    "#     ret,frame=cap.read()\n",
    "#     labels=[]\n",
    "#     gray=cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)\n",
    "#     faces=face_classifier.detectMultiScale(gray,1.3,5)\n",
    "    \n",
    "#     for(x,y,w,h) in faces:\n",
    "#         cv2.rectangle(frame,(x,y),(x+w,y+h),(255,0,0),2)\n",
    "#         roi_gray=gray[y:y+h,x:x+w]\n",
    "#         roi_gray=cv2.resize(roi_gray,(48,48),interpolation=cv2.INTER_AREA)\n",
    "        \n",
    "        \n",
    "#         if np.sum([roi_gray])!=0:\n",
    "#             roi=roi_gray.astype('float')/255.0\n",
    "#             roi=img_to_array(roi)\n",
    "#             roi=np.expand_dims(roi,axis=0)\n",
    "            \n",
    "            \n",
    "#             #make the prediction on the ROI then lookup the class\n",
    "            \n",
    "#             pred=classifier.predict(roi)[0]\n",
    "#             label=class_labels[pred.argmax()]\n",
    "#             label_position=(x,y)\n",
    "#             cv2.putText(frame,label,label_position,cv2.FONT_HERSHEY_SIMPLEX,2,(0,255,0),3)\n",
    "        \n",
    "#         else:\n",
    "#             cv2.putText(frame,'NO FACE FOUND',(20,60),cv2.FONT_HERSHEY_SIMPLEX,2,(0,255,0),3)\n",
    "            \n",
    "#     cv2.imshow('Emotion Detector',frame)\n",
    "#     if(cv2.waitKey(1)& 0xFF == ord('q')):\n",
    "#         break\n",
    "        \n",
    "\n",
    "# cap.release()\n",
    "# cv2.destroyAllWindows()\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "neutral\n"
     ]
    }
   ],
   "source": [
    "cap = cv2.VideoCapture(0)\n",
    "\n",
    "label = ''\n",
    "\n",
    "while True:\n",
    "    ret,frame = cap.read()\n",
    "    cv2.imshow(\"test window\", frame)\n",
    "\n",
    "    labels=[]\n",
    "    \n",
    "    gray=cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)\n",
    "    faces=face_classifier.detectMultiScale(gray,1.3,5)\n",
    "\n",
    "    for(x,y,w,h) in faces:\n",
    "        cv2.rectangle(frame,(x,y),(x+w,y+h),(255,0,0),2)\n",
    "        roi_gray=gray[y:y+h,x:x+w]\n",
    "        roi_gray=cv2.resize(roi_gray,(48,48),interpolation=cv2.INTER_AREA)\n",
    "        \n",
    "        \n",
    "        if np.sum([roi_gray])!=0:\n",
    "            roi=roi_gray.astype('float')/255.0\n",
    "            roi=img_to_array(roi)\n",
    "            roi=np.expand_dims(roi,axis=0)\n",
    "            \n",
    "            \n",
    "            #make the prediction on the ROI then lookup the class\n",
    "            \n",
    "            pred=classifier.predict(roi)[0]\n",
    "            label=class_labels[pred.argmax()]\n",
    "            label_position=(x,y)\n",
    "            cv2.putText(frame,label,label_position,cv2.FONT_HERSHEY_SIMPLEX,2,(0,255,0),3)\n",
    "        \n",
    "        else:\n",
    "            cv2.putText(frame,'NO FACE FOUND',(20,60),cv2.FONT_HERSHEY_SIMPLEX,2,(0,255,0),3)\n",
    "            \n",
    "    cv2.imshow('Emotion Detector',frame)\n",
    "\n",
    "    if cv2.waitKey(10) == ord('q'):\n",
    "        break\n",
    "\n",
    "print(label)    \n",
    "\n",
    "cap.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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