emotion_detection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import load_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "network_loaded = load_model('models/emodet.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "network_loaded.compile(loss = 'categorical_crossentropy', optimizer='Adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "face_detector = cv2.CascadeClassifier('models/haarcascade_frontalface_default.xml')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Single person image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "image = cv2.imread('data/Images/gabriel.png')\n",
    "cv2.imshow('abc',image)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[162,  40, 128, 128]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "original_image = image.copy()\n",
    "faces = face_detector.detectMultiScale(original_image)\n",
    "faces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 283ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roi = image[40:40 + 128, 162:162 + 128]\n",
    "cv2.imshow('abc',roi)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()\n",
    "roi = cv2.resize(roi, (48, 48))\n",
    "roi = roi / 255\n",
    "roi = np.expand_dims(roi, axis = 0)\n",
    "probs = network_loaded.predict(roi)\n",
    "result = np.argmax(probs)\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multiple faces in image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "image = cv2.imread('data/Images/faces_emotions.png')\n",
    "cv2.imshow('abc',image)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "faces = face_detector.detectMultiScale(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "emotions = ['Angry', 'Disgust', 'Fear', 'Happy', 'Neutral', 'Sad', 'Surprise']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "for (x, y, w, h) in faces:\n",
    "  cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 1)\n",
    "  roi = image[y:y + h, x:x + w]\n",
    "  roi = cv2.resize(roi, (48, 48))\n",
    "  roi = roi / 255\n",
    "  roi = np.expand_dims(roi, axis = 0)\n",
    "  prediction = network_loaded.predict(roi, verbose=0)\n",
    "  cv2.putText(image, emotions[np.argmax(prediction)], (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0,255,0), 2, cv2.LINE_AA)\n",
    "cv2.imshow('abc',image)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Videos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True (360, 640, 3)\n"
     ]
    }
   ],
   "source": [
    "cap = cv2.VideoCapture('data/Videos/emotion_test01.mp4')\n",
    "connected, video = cap.read()\n",
    "print(connected, video.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_path = 'results/Videos/emotion_test01_result.avi'\n",
    "fourcc = cv2.VideoWriter_fourcc(*'XVID')\n",
    "fps = 24\n",
    "output_video = cv2.VideoWriter(save_path, fourcc, fps, (video.shape[1], video.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "End\n"
     ]
    }
   ],
   "source": [
    "while (cv2.waitKey(1) < 0):\n",
    "  connected, frame = cap.read()\n",
    "  if not connected:\n",
    "    break\n",
    "  faces = face_detector.detectMultiScale(frame, scaleFactor=1.2, minNeighbors=5, minSize=(30,30))\n",
    "  if len(faces) > 0:\n",
    "    for (x, y, w, h) in faces:\n",
    "      frame = cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)\n",
    "      roi = frame[y:y + h, x:x + w]\n",
    "      roi = cv2.resize(roi, (48, 48))\n",
    "      roi = roi / 255\n",
    "      roi = np.expand_dims(roi, axis = 0)\n",
    "      prediction = network_loaded.predict(roi, verbose=0)\n",
    "\n",
    "      if prediction is not None:\n",
    "        result = np.argmax(prediction)\n",
    "        cv2.putText(frame, emotions[result], (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255,255,255), 1, cv2.LINE_AA)\n",
    "  output_video.write(frame)\n",
    "\n",
    "print('End')\n",
    "output_video.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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