Emotion_Model_RealTime_Tester.ipynb

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    {
     "ename": "ImportError",
     "evalue": "cannot import name 'load_img' from 'keras.preprocessing.image' (c:\\Users\\Dell\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\keras\\preprocessing\\image.py)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "Cell \u001b[1;32mIn [1], line 7\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mwarnings\u001b[39;00m\n\u001b[0;32m      6\u001b[0m warnings\u001b[38;5;241m.\u001b[39mfilterwarnings(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m----> 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpreprocessing\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mimage\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m load_img, img_to_array \n\u001b[0;32m      8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodels\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m  load_model\n\u001b[0;32m      9\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n",
      "\u001b[1;31mImportError\u001b[0m: cannot import name 'load_img' from 'keras.preprocessing.image' (c:\\Users\\Dell\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\keras\\preprocessing\\image.py)"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import cv2\n",
    "import numpy as np\n",
    "from keras.preprocessing import image\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "from keras.preprocessing.image import load_img, img_to_array \n",
    "from keras.models import  load_model\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "\n",
    "# load model\n",
    "model = load_model(\"final_model.h5\")\n",
    "\n",
    "\n",
    "face_haar_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ad462d4c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['neutral', 'happy', 'neutral', 'happy', 'happy', 'happy', 'happy', 'neutral', 'happy', 'happy', 'happy', 'happy', 'happy', 'happy', 'neutral', 'neutral', 'neutral', 'neutral', 'neutral', 'neutral', 'neutral', 'neutral', 'neutral', 'neutral', 'neutral', 'neutral', 'neutral', 'happy', 'neutral', 'neutral', 'neutral', 'neutral', 'neutral', 'neutral', 'neutral', 'neutral', 'neutral', 'neutral', 'neutral', 'neutral', 'neutral', 'happy', 'happy', 'neutral', 'happy', 'neutral', 'happy', 'neutral', 'neutral', 'neutral', 'neutral', 'neutral', 'neutral', 'happy', 'neutral', 'neutral', 'neutral', 'happy', 'neutral', 'neutral', 'neutral']\n"
     ]
    }
   ],
   "source": [
    "i=0\n",
    "GR_dict={0:(0,255,0),1:(0,0,255)}\n",
    "\n",
    "model = tf.keras.models.load_model('final_model.h5')\n",
    "face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')\n",
    "\n",
    "output=[]\n",
    "cap = cv2.VideoCapture(0)\n",
    "\n",
    "while (i<=50):\n",
    "    ret, img = cap.read()\n",
    "    #gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n",
    "    faces = face_cascade.detectMultiScale(img,1.05,5)\n",
    "\n",
    "    for x,y,w,h in faces:\n",
    "\n",
    "        face_img = img[y:y+h,x:x+w] \n",
    "\n",
    "        resized = cv2.resize(face_img,(224,224))\n",
    "        reshaped=resized.reshape(1, 224,224,3)/255\n",
    "        predictions = model.predict(reshaped)\n",
    "\n",
    "        # find max indexed array\n",
    "        max_index = np.argmax(predictions[0])\n",
    "\n",
    "        emotions = ('angry', 'disgust', 'fear', 'happy', 'sad', 'neutral', 'surprise')\n",
    "        predicted_emotion = emotions[max_index]\n",
    "        output.append(predicted_emotion)\n",
    "            \n",
    "            \n",
    "            \n",
    "        cv2.rectangle(img,(x,y),(x+w,y+h),GR_dict[1],2)\n",
    "        cv2.rectangle(img,(x,y-40),(x+w,y),GR_dict[1],-1)\n",
    "        cv2.putText(img, predicted_emotion, (x, y-10),cv2.FONT_HERSHEY_SIMPLEX,0.8,(255,255,255),2)\n",
    "    i = i+1\n",
    "\n",
    "    cv2.imshow('LIVE', img)\n",
    "    key = cv2.waitKey(1)\n",
    "    if key == 27: \n",
    "        cap.release()\n",
    "        cv2.destroyAllWindows()\n",
    "        break\n",
    "print(output)\n",
    "cap.release()\n",
    "cv2.destroyAllWindows()\n",
    "final_output1 = st.mode(output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5bb899a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'neutral'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statistics as st\n",
    "final_output = st.mode(output)\n",
    "final_output"
   ]
  }
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