Face Expression Detector.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
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   "source": [
    "import cv2 \n",
    "import numpy as np\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.models.load_model('FaceExp60.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def label(arr):\n",
    "    labels = 'Angry Disgust Fear Happy Neutral Sad Surprise'.split()\n",
    "    pos = arr.argmax()\n",
    "    return str(labels[pos])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "face_cascade =  cv2.CascadeClassifier(\"haarcascade_frontalface_default.xml\")\n",
    "\n",
    "cap = cv2.VideoCapture(0)\n",
    "while cap.isOpened():\n",
    "    \n",
    "    _, frame = cap.read()\n",
    "    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "    faces = face_cascade.detectMultiScale(gray, 1.1, 4)\n",
    "    \n",
    "    for (x,y,w,h) in faces:\n",
    "        cv2.rectangle(frame, (x,y), (x+w, y+h), (0,255,0), 2)\n",
    "        \n",
    "        resized_frame = gray[y:y+h, x:x+w]\n",
    "        resized_gray = cv2.resize(resized_frame, (48,48))\n",
    "        resized_arr = tf.keras.preprocessing.image.img_to_array(resized_gray)\n",
    "        resized_arr = np.expand_dims(resized_arr, axis=0)\n",
    "        expression = label(model.predict(resized_arr))\n",
    "        cv2.putText(frame, expression, (x,y-15), cv2.FONT_HERSHEY_SIMPLEX, 1,(0,255,0), 2)\n",
    "\n",
    "    cv2.imshow(\"video\", frame)\n",
    "    \n",
    "    if cv2.waitKey(1) & 0xFF == 27:\n",
    "        break\n",
    "        \n",
    "cap.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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