blobcity · pvtr-malli · Oct 7, 2021
diff --git a/FaceEmotionRecognition/README.md b/FaceEmotionRecognition/README.md
@@ -0,0 +1,8 @@
+# Face Emotion Recognition using python
+
+-   Model trainig can be found here --> train.ipynb
+-   You can get the trained model here - [Trained Model](https://drive.google.com/file/d/1-6O9vR9qvU18h7WEToyqMHXO2U_zleri/view?usp=sharing)
+-   You can take the model and try how this model work through webcam using this code ---> predict-emotion-using-webcam.ipynb
+
+**Dataset Used**:
+-   fer2013 (collected from kaggle) 
diff --git a/FaceEmotionRecognition/predict-emotion-using-webcam.ipynb b/FaceEmotionRecognition/predict-emotion-using-webcam.ipynb
@@ -0,0 +1,245 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting opencv-python\n",
+      "  Downloading https://files.pythonhosted.org/packages/70/a8/e52a82936be6d5696fb06c78450707c26dc13df91bb6bf49583bb9abbaa0/opencv_python-4.5.1.48-cp37-cp37m-win_amd64.whl (34.9MB)\n",
+      "Requirement already satisfied: numpy>=1.14.5 in d:\\anaconda\\installed_files\\lib\\site-packages (from opencv-python) (1.16.5)\n",
+      "Installing collected packages: opencv-python\n",
+      "Successfully installed opencv-python-4.5.1.48\n"
+     ]
+    }
+   ],
+   "source": [
+    "! pip install opencv-python"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import cv2\n",
+    "#tensorflow packages\n",
+    "from tensorflow.keras.models import load_model\n",
+    "from tensorflow.keras.preprocessing import image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Face Emotion Recognition\n",
+    "#Here i am using my trained model, that is trained and saved as a h5 file\n",
+    "faceDetection_model = 'D:\\pavi\\DeepLearningProjects\\Face_Emosion_Recognition\\pretrained_model\\Face_Detection_TrainedModel\\haarcascade_frontalface_default.xml'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"sequential_1\"\n",
+      "_________________________________________________________________\n",
+      "Layer (type)                 Output Shape              Param #   \n",
+      "=================================================================\n",
+      "conv2d_6 (Conv2D)            (None, 48, 48, 64)        1664      \n",
+      "_________________________________________________________________\n",
+      "conv2d_7 (Conv2D)            (None, 48, 48, 64)        102464    \n",
+      "_________________________________________________________________\n",
+      "batch_normalization_3 (Batch (None, 48, 48, 64)        256       \n",
+      "_________________________________________________________________\n",
+      "max_pooling2d_3 (MaxPooling2 (None, 24, 24, 64)        0         \n",
+      "_________________________________________________________________\n",
+      "conv2d_8 (Conv2D)            (None, 24, 24, 128)       73856     \n",
+      "_________________________________________________________________\n",
+      "conv2d_9 (Conv2D)            (None, 24, 24, 128)       147584    \n",
+      "_________________________________________________________________\n",
+      "batch_normalization_4 (Batch (None, 24, 24, 128)       512       \n",
+      "_________________________________________________________________\n",
+      "max_pooling2d_4 (MaxPooling2 (None, 12, 12, 128)       0         \n",
+      "_________________________________________________________________\n",
+      "conv2d_10 (Conv2D)           (None, 12, 12, 256)       295168    \n",
+      "_________________________________________________________________\n",
+      "conv2d_11 (Conv2D)           (None, 12, 12, 256)       590080    \n",
+      "_________________________________________________________________\n",
+      "batch_normalization_5 (Batch (None, 12, 12, 256)       1024      \n",
+      "_________________________________________________________________\n",
+      "max_pooling2d_5 (MaxPooling2 (None, 6, 6, 256)         0         \n",
+      "_________________________________________________________________\n",
+      "flatten_1 (Flatten)          (None, 9216)              0         \n",
+      "_________________________________________________________________\n",
+      "dense_3 (Dense)              (None, 1024)              9438208   \n",
+      "_________________________________________________________________\n",
+      "dropout_2 (Dropout)          (None, 1024)              0         \n",
+      "_________________________________________________________________\n",
+      "dense_4 (Dense)              (None, 1024)              1049600   \n",
+      "_________________________________________________________________\n",
+      "dropout_3 (Dropout)          (None, 1024)              0         \n",
+      "_________________________________________________________________\n",
+      "dense_5 (Dense)              (None, 7)                 7175      \n",
+      "=================================================================\n",
+      "Total params: 11,707,591\n",
+      "Trainable params: 11,706,695\n",
+      "Non-trainable params: 896\n",
+      "_________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "Emotion_Detction_model = 'D:\\pavi\\DeepLearningProjects\\Face_Emosion_Recognition\\pretrained_model\\Face_Emotion_model\\FER_vggnet.h5'\n",
+    "vggnet = load_model(Emotion_Detction_model)\n",
+    "vggnet.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "prediction ['Sad']\n",
+      "Sad\n"
+     ]
+    },
+    {
+     "ename": "TypeError",
+     "evalue": "only size-1 arrays can be converted to Python scalars",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-13-113054a609db>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     42\u001b[0m             \u001b[0mfontScale\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0.6\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     43\u001b[0m             \u001b[0mthickness\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 44\u001b[1;33m             \u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mputText\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mframe\u001b[0m \u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m \u001b[1;33m,\u001b[0m \u001b[0mlabel_position\u001b[0m \u001b[1;33m,\u001b[0m \u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mFONT_HERSHEY_SIMPLEX\u001b[0m \u001b[1;33m,\u001b[0m \u001b[0mfontScale\u001b[0m \u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m255\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m,\u001b[0m \u001b[0mthickness\u001b[0m \u001b[1;33m,\u001b[0m \u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mLINE_AA\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     45\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     46\u001b[0m             \u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mputText\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mframe\u001b[0m \u001b[1;33m,\u001b[0m \u001b[1;34m'No Face Detection'\u001b[0m \u001b[1;33m,\u001b[0m \u001b[0mlabel_position\u001b[0m \u001b[1;33m,\u001b[0m \u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mFONT_HERSHEY_SIMPLEX\u001b[0m \u001b[1;33m,\u001b[0m \u001b[1;36m0.6\u001b[0m \u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m255\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m,\u001b[0m \u001b[1;36m3\u001b[0m \u001b[1;33m,\u001b[0m\u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mLINE_AA\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mTypeError\u001b[0m: only size-1 arrays can be converted to Python scalars"
+     ]
+    }
+   ],
+   "source": [
+    "#defining the emotion classes for classification\n",
+    "classes = np.array((\"Angry\", \"Disgust\", \"Fear\", \"Happy\", \"Sad\", \"Surprise\", \"Neutral\"))\n",
+    "\n",
+    "#video capturing and classifing\n",
+    "\n",
+    "faceCascade = cv2.CascadeClassifier(faceDetection_model)\n",
+    "video_capture = cv2.VideoCapture(0)\n",
+    "\n",
+    "while True:\n",
+    "    ret,frame = video_capture.read()\n",
+    "    \n",
+    "    cv2.imshow('Original Video' , frame)\n",
+    "    \n",
+    "    gray = cv2.cvtColor(frame , cv2.COLOR_BGR2GRAY)\n",
+    "    \n",
+    "    face = faceCascade.detectMultiScale(gray ,scaleFactor=1.1 , minNeighbors=5,)\n",
+    "    \n",
+    "    #draw rectangle around the face and cut the face only\n",
+    "    for (x,y,w,h) in face:\n",
+    "        \n",
+    "        cv2.rectangle( frame , (x,y) , (x+w , y+h) , (0,255,255) , 2)\n",
+    "        face_img = gray[ y:(y+h) , x:(x+w)]\n",
+    "        x = cv2.resize(face_img, (48,48) , interpolation = cv2.INTER_AREA)\n",
+    "        \n",
+    "        if np.sum([x])!=0:\n",
+    "        #preprocessing\n",
+    "            x = x.astype('float')/255.0 \n",
+    "            x = image.img_to_array(x)\n",
+    "            x = np.expand_dims(x , axis = 0)\n",
+    "            \n",
+    "        \n",
+    "        #face_img = face_img.reshape(48,48)\n",
+    "        \n",
+    "        # prediction\n",
+    "            p = vggnet.predict(x)\n",
+    "            a = np.argmax(p,axis=1)\n",
+    "            print('prediction',classes[a])\n",
+    "            label = str(classes[a][0])\n",
+    "            print(label)\n",
+    "            label_position = (x-10,y-10)\n",
+    "            \n",
+    "            fontScale = 0.6\n",
+    "            thickness = 3\n",
+    "            cv2.putText(frame , label , label_position , cv2.FONT_HERSHEY_SIMPLEX , fontScale , (0,255,0) , thickness , cv2.LINE_AA)\n",
+    "        else:\n",
+    "            cv2.putText(frame , 'No Face Detection' , label_position , cv2.FONT_HERSHEY_SIMPLEX , 0.6 , (0,255,0) , 3 ,cv2.LINE_AA)\n",
+    "            \n",
+    "        #cv2.imshow('croped image' , face_img)\n",
+    "    #display the resulting frame \n",
+    "    \n",
+    "    cv2.imshow('Face Detected Video' , frame)\n",
+    "    \n",
+    "    #break the capturing\n",
+    "    if cv2.waitKey(1) & 0xFF == ord('q'):\n",
+    "        break\n",
+    "        \n",
+    "video_capture.release()\n",
+    "cv2.destroyAllWindows()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'sad'"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.7.4"
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+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}