Emotion_recogniser.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import cv2\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Dropout, Flatten\n",
    "from tensorflow.keras.layers import Conv2D\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.layers import MaxPooling2D\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.preprocessing import image\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "import numpy as np\n",
    "from tensorflow.keras.models import load_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#cuttle-environment-disable emotion-rec\n",
    "\n",
    "train_dir = 'emofiy/train'\n",
    "val_dir = 'emofiy/test'\n",
    "train_datagen = ImageDataGenerator(rescale=1./255)\n",
    "val_datagen = ImageDataGenerator(rescale=1./255)\n",
    "\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "        train_dir,\n",
    "        target_size=(48,48),\n",
    "        batch_size=64,\n",
    "        color_mode=\"grayscale\",\n",
    "        class_mode='categorical')\n",
    "\n",
    "validation_generator = val_datagen.flow_from_directory(\n",
    "        val_dir,\n",
    "        target_size=(48,48),\n",
    "        batch_size=64,\n",
    "        color_mode=\"grayscale\",\n",
    "        class_mode='categorical')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#cuttle-environment-disable emotion-rec\n",
    "\n",
    "emotion_model = Sequential()\n",
    "emotion_model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(48,48,1)))\n",
    "emotion_model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))\n",
    "emotion_model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "emotion_model.add(Dropout(0.25))\n",
    "emotion_model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))\n",
    "emotion_model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "emotion_model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))\n",
    "emotion_model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "emotion_model.add(Dropout(0.25))\n",
    "emotion_model.add(Flatten())\n",
    "emotion_model.add(Dense(1024, activation='relu'))\n",
    "emotion_model.add(Dropout(0.5))\n",
    "emotion_model.add(Dense(7, activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#cuttle-environment-disable emotion-rec\n",
    "\n",
    "emotion_model.compile(loss='categorical_crossentropy',optimizer=Adam(lr=0.0001, decay=1e-6),metrics=['accuracy'])\n",
    "emotion_model_info = emotion_model.fit_generator(\n",
    "        train_generator,\n",
    "        steps_per_epoch=28709 // 64,\n",
    "        epochs=80,\n",
    "        validation_data=validation_generator,\n",
    "        validation_steps=7178 // 64)\n",
    "emotion_model.save('model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def emotion_analysis(emotion):\n",
    "    objects = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral']\n",
    "    return objects[emotion]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "emotion_model = load_model('model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#cuttle-environment-set-config emotion-rec method=POST route=/api/emotion response=output\n",
    "file = open('test.jpeg', 'rb') #cuttle-environment-assign emotion-rec request.files['file']\n",
    "file_string = file.read()\n",
    "npimg = np.fromstring(file_string, np.uint8)    \n",
    "img = cv2.imdecode(npimg, cv2.IMREAD_UNCHANGED)\n",
    "img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\n",
    "x = cv2.resize(img, (48,48))\n",
    "x = image.img_to_array(x)\n",
    "x = np.expand_dims(x, axis = 0)\n",
    "custom = emotion_model.predict(x)\n",
    "output = custom.argmax(axis=1) \n",
    "output = emotion_analysis(output[0])"
   ]
  }
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