Facial_Emotion_Recognition_Application.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n",
      "WARNING: Logging before flag parsing goes to stderr.\n",
      "W0705 01:33:15.945192 140475246520064 deprecation_wrapper.py:119] From /home/rahul/anaconda3/envs/pyenv37/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
      "\n",
      "W0705 01:33:15.974661 140475246520064 deprecation_wrapper.py:119] From /home/rahul/anaconda3/envs/pyenv37/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
      "\n",
      "W0705 01:33:15.993576 140475246520064 deprecation_wrapper.py:119] From /home/rahul/anaconda3/envs/pyenv37/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:3976: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
      "\n",
      "W0705 01:33:16.029979 140475246520064 deprecation_wrapper.py:119] From /home/rahul/anaconda3/envs/pyenv37/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:3980: The name tf.nn.avg_pool is deprecated. Please use tf.nn.avg_pool2d instead.\n",
      "\n",
      "W0705 01:33:16.082128 140475246520064 deprecation_wrapper.py:119] From /home/rahul/anaconda3/envs/pyenv37/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:131: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
      "\n",
      "W0705 01:33:16.083196 140475246520064 deprecation_wrapper.py:119] From /home/rahul/anaconda3/envs/pyenv37/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
      "\n",
      "W0705 01:33:16.094805 140475246520064 deprecation.py:506] From /home/rahul/anaconda3/envs/pyenv37/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
      "W0705 01:33:16.307290 140475246520064 deprecation_wrapper.py:119] From /home/rahul/anaconda3/envs/pyenv37/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:174: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
      "\n",
      "W0705 01:33:16.679035 140475246520064 deprecation_wrapper.py:119] From /home/rahul/anaconda3/envs/pyenv37/lib/python3.7/site-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
      "\n",
      "W0705 01:33:16.777793 140475246520064 deprecation.py:323] From /home/rahul/anaconda3/envs/pyenv37/lib/python3.7/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
     ]
    }
   ],
   "source": [
    "# Loading our existing trained model to test on Real-Time Human Face images \n",
    "\n",
    "import numpy as np\n",
    "import os\n",
    "import cv2 as cv\n",
    "import matplotlib.pyplot as plt\n",
    "from keras.models import load_model\n",
    "\n",
    "model=load_model('facial_emotion_recognition_cnn.h5')\n",
    "model.load_weights('facial_emotion_recognition_cnn_weights.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename='FaridaJalal_2.jpg'\n",
    "path='dataset/test/'\n",
    "\n",
    "image=cv.imread(path+filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(298, 256, 3)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(298, 256)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image=cv.cvtColor(image,cv.COLOR_BGR2GRAY)\n",
    "image.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(48, 48)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image=cv.resize(image,(48,48),interpolation=cv.INTER_AREA)\n",
    "image.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fc29e6799e8>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(image,cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 48, 48, 1)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image=np.array(image)\n",
    "image=image.reshape(1,48,48,1)\n",
    "image.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Method to pre-process the input image like converting it into grayscale image, reshaping it etc...\n",
    "\n",
    "def preprocess_input_image(path,filename):\n",
    "    image=cv.imread(path+filename)\n",
    "    image=cv.cvtColor(image,cv.COLOR_BGR2GRAY)\n",
    "    image=cv.resize(image,(48,48),interpolation=cv.INTER_AREA)\n",
    "    plt.imshow(image,cmap='gray')\n",
    "    image=np.array(image)\n",
    "    image=image.reshape(1,48,48,1)\n",
    "    return image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "image=preprocess_input_image(path,filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=model.predict(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "prob=np.argmax(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function that returns the actual emotion name predicted the CNN model from the input image \n",
    "\n",
    "def get_actual_emotion_name(image):\n",
    "    y_pred=model.predict(image)\n",
    "    probability=np.argmax(y_pred)\n",
    "    if probability==0: print('Its an Angry Face')\n",
    "    elif probability==1: print('Its a Disgust Face')\n",
    "    elif probability==2: print('Its a Fear Face')\n",
    "    elif probability==3: print('Its a Happy Face')\n",
    "    elif probability==4: print('Its a Sad Face')\n",
    "    elif probability==5: print('Its a Surprise Face')\n",
    "    elif probability==6: print('Its a Neutral Face')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Its a Fear Face\n"
     ]
    }
   ],
   "source": [
    "get_actual_emotion_name(prob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "filename='Irani_37.jpg'                                                       # Image name\n",
    "path='dataset/test/'                                                          # Destination Directory\n",
    "image=preprocess_input_image(path,filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Its an Angry Face\n"
     ]
    }
   ],
   "source": [
    "get_actual_emotion_name(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is not a very well-performing model as it has given a testing accuracy of 58% approx.."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}