build_model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MEMUAT DAN MEMPERSIAPKAN DATASET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "is_executing": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import cv2 as cv\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-21T10:01:52.994491Z",
     "start_time": "2023-07-21T10:01:52.978370700Z"
    }
   },
   "outputs": [],
   "source": [
    "# Fungsi untuk memuat dan mempersiapkan dataset\n",
    "def load_dataset(dataset_path, label_map):\n",
    "    images = []\n",
    "    labels = []\n",
    "\n",
    "    for dataset_type in ['train', 'validation']:\n",
    "        dataset_type_path = os.path.join(dataset_path, dataset_type)\n",
    "        for expression in os.listdir(dataset_type_path):\n",
    "            if expression not in label_map:\n",
    "                continue\n",
    "            expression_folder = os.path.join(dataset_type_path, expression)\n",
    "            for image_file in os.listdir(expression_folder):\n",
    "                image_path = os.path.join(expression_folder, image_file)\n",
    "                image = cv.imread(image_path, cv.IMREAD_GRAYSCALE)\n",
    "                if image is not None:\n",
    "                    image = cv.resize(image, (48, 48))\n",
    "                    images.append(image)\n",
    "                    labels.append(label_map[expression])\n",
    "\n",
    "    images = np.array(images)\n",
    "    labels = np.array(labels)\n",
    "\n",
    "    return images, labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-21T11:16:32.943758Z",
     "start_time": "2023-07-21T11:16:29.551832500Z"
    }
   },
   "outputs": [],
   "source": [
    "dataset_path = 'dataset'\n",
    "label_map = {'angry': 0, 'disgust': 1, 'fear': 2, 'happy': 3, 'neutral': 4, 'sad': 5, 'surprise': 6}\n",
    "\n",
    "images, labels = load_dataset(dataset_path, label_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-21T11:16:38.915610800Z",
     "start_time": "2023-07-21T11:16:38.899948400Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 72,  78,  81, ...,  52,  43,  40],\n",
       "       [ 65,  62,  59, ...,  55,  52,  45],\n",
       "       [ 47,  42,  60, ...,  47,  54,  49],\n",
       "       ...,\n",
       "       [ 91,  65,  46, ...,  74,  54,  43],\n",
       "       [ 76,  80,  76, ..., 108,  69,  46],\n",
       "       [ 77,  74,  81, ..., 105, 108,  82]], dtype=uint8)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "images[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-21T11:17:57.575834500Z",
     "start_time": "2023-07-21T11:17:57.553231400Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels[-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PEMROSESAN AWAL DATASET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-20T15:05:05.142577900Z",
     "start_time": "2023-07-20T15:05:04.747313900Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Membagi dataset menjadi data pelatihan dan data validasi\n",
    "X_train, X_val, y_train, y_val = train_test_split(images, labels, test_size=0.2, random_state=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-20T15:05:05.158204500Z",
     "start_time": "2023-07-20T15:05:05.142577900Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-20T15:05:07.188557600Z",
     "start_time": "2023-07-20T15:05:05.158204500Z"
    }
   },
   "outputs": [],
   "source": [
    "from keras.utils import to_categorical\n",
    "\n",
    "# Normalisasi data dan one-hot encoding pada label\n",
    "X_train = X_train / 255.0\n",
    "X_val = X_val / 255.0\n",
    "\n",
    "num_classes = 7\n",
    "y_train = to_categorical(y_train, num_classes)\n",
    "y_val = to_categorical(y_val, num_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-20T15:05:07.204184100Z",
     "start_time": "2023-07-20T15:05:07.188557600Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0., 0., 1.], dtype=float32)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-20T15:05:07.229935700Z",
     "start_time": "2023-07-20T15:05:07.204184100Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28709"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MEMBANGUN DAN MELATIH MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-20T15:05:07.261414300Z",
     "start_time": "2023-07-20T15:05:07.219859400Z"
    }
   },
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout\n",
    "\n",
    "# Fungsi untuk membangun model CNN\n",
    "def build_model(input_shape, num_classes):\n",
    "    model = Sequential()\n",
    "\n",
    "    model.add(Conv2D(32, (3, 3), activation='relu', input_shape=input_shape))\n",
    "    model.add(MaxPooling2D((2, 2)))\n",
    "\n",
    "    model.add(Conv2D(64, (3, 3), activation='relu'))\n",
    "    model.add(MaxPooling2D((2, 2)))\n",
    "\n",
    "    model.add(Conv2D(128, (3, 3), activation='relu'))\n",
    "    model.add(MaxPooling2D((2, 2)))\n",
    "\n",
    "    model.add(Conv2D(128, (3, 3), activation='relu'))\n",
    "    model.add(MaxPooling2D((2, 2)))\n",
    "\n",
    "    model.add(Flatten())\n",
    "\n",
    "    model.add(Dense(64, activation='relu'))\n",
    "    model.add(Dropout(0.5))\n",
    "\n",
    "    model.add(Dense(num_classes, activation='softmax'))\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-20T15:05:07.355365800Z",
     "start_time": "2023-07-20T15:05:07.229935700Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_4 (Conv2D)           (None, 46, 46, 32)        320       \n",
      "                                                                 \n",
      " max_pooling2d_4 (MaxPoolin  (None, 23, 23, 32)        0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " conv2d_5 (Conv2D)           (None, 21, 21, 64)        18496     \n",
      "                                                                 \n",
      " max_pooling2d_5 (MaxPoolin  (None, 10, 10, 64)        0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " conv2d_6 (Conv2D)           (None, 8, 8, 128)         73856     \n",
      "                                                                 \n",
      " max_pooling2d_6 (MaxPoolin  (None, 4, 4, 128)         0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " conv2d_7 (Conv2D)           (None, 2, 2, 128)         147584    \n",
      "                                                                 \n",
      " max_pooling2d_7 (MaxPoolin  (None, 1, 1, 128)         0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " flatten_1 (Flatten)         (None, 128)               0         \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 64)                8256      \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 64)                0         \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 7)                 455       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 248967 (972.53 KB)\n",
      "Trainable params: 248967 (972.53 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Membangun model\n",
    "input_shape = (48, 48, 1)\n",
    "model = build_model(input_shape, num_classes)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-20T15:41:15.975575100Z",
     "start_time": "2023-07-20T15:05:08.908825900Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "449/449 [==============================] - 23s 49ms/step - loss: 1.8059 - accuracy: 0.2524 - val_loss: 1.6576 - val_accuracy: 0.3420\n",
      "Epoch 2/10\n",
      "449/449 [==============================] - 23s 52ms/step - loss: 1.5582 - accuracy: 0.3925 - val_loss: 1.4043 - val_accuracy: 0.4514\n",
      "Epoch 3/10\n",
      "449/449 [==============================] - 23s 51ms/step - loss: 1.4002 - accuracy: 0.4604 - val_loss: 1.3112 - val_accuracy: 0.4955\n",
      "Epoch 4/10\n",
      "449/449 [==============================] - 22s 49ms/step - loss: 1.3139 - accuracy: 0.4999 - val_loss: 1.2864 - val_accuracy: 0.5001\n",
      "Epoch 5/10\n",
      "449/449 [==============================] - 22s 49ms/step - loss: 1.2594 - accuracy: 0.5230 - val_loss: 1.2471 - val_accuracy: 0.5104\n",
      "Epoch 6/10\n",
      "449/449 [==============================] - 22s 49ms/step - loss: 1.2070 - accuracy: 0.5443 - val_loss: 1.2189 - val_accuracy: 0.5330\n",
      "Epoch 7/10\n",
      "449/449 [==============================] - 22s 49ms/step - loss: 1.1625 - accuracy: 0.5610 - val_loss: 1.2053 - val_accuracy: 0.5382\n",
      "Epoch 8/10\n",
      "449/449 [==============================] - 22s 49ms/step - loss: 1.1208 - accuracy: 0.5816 - val_loss: 1.1939 - val_accuracy: 0.5475\n",
      "Epoch 9/10\n",
      "449/449 [==============================] - 22s 49ms/step - loss: 1.0892 - accuracy: 0.5925 - val_loss: 1.1862 - val_accuracy: 0.5429\n",
      "Epoch 10/10\n",
      "449/449 [==============================] - 22s 49ms/step - loss: 1.0446 - accuracy: 0.6083 - val_loss: 1.1822 - val_accuracy: 0.5543\n"
     ]
    }
   ],
   "source": [
    "from keras.optimizers import Adam\n",
    "\n",
    "# Mengompilasi dan melatih model\n",
    "model.compile(optimizer=Adam(), loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "# Melatih model dan menyimpan riwayat pelatihan\n",
    "history = model.fit(X_train, y_train, batch_size=64, epochs=10, validation_data=(X_val, y_val))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# VISUALISASI HASIL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Mendapatkan data pelatihan\n",
    "train_loss = history.history['loss']\n",
    "train_acc = history.history['accuracy']\n",
    "\n",
    "# Mendapatkan data validasi\n",
    "val_loss = history.history['val_loss']\n",
    "val_acc = history.history['val_accuracy']\n",
    "\n",
    "# Plot Loss\n",
    "plt.figure(figsize=(12, 4))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(range(1, len(train_loss) + 1), train_loss, label='Train Loss')\n",
    "plt.plot(range(1, len(val_loss) + 1), val_loss, label='Validation Loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('Train and Validation Loss')\n",
    "plt.legend()\n",
    "\n",
    "# Plot Accuracy\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(range(1, len(train_acc) + 1), train_acc, label='Train Accuracy')\n",
    "plt.plot(range(1, len(val_acc) + 1), val_acc, label='Validation Accuracy')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.title('Train and Validation Accuracy')\n",
    "plt.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MENYIMPAN DAN MEMUAT MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-20T15:44:08.037471700Z",
     "start_time": "2023-07-20T15:44:07.995339Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Saved!\n"
     ]
    }
   ],
   "source": [
    "# save model\n",
    "model.save('model/model.keras')\n",
    "print('Model Saved!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-20T15:44:59.005826800Z",
     "start_time": "2023-07-20T15:44:58.849743500Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_4 (Conv2D)           (None, 46, 46, 32)        320       \n",
      "                                                                 \n",
      " max_pooling2d_4 (MaxPoolin  (None, 23, 23, 32)        0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " conv2d_5 (Conv2D)           (None, 21, 21, 64)        18496     \n",
      "                                                                 \n",
      " max_pooling2d_5 (MaxPoolin  (None, 10, 10, 64)        0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " conv2d_6 (Conv2D)           (None, 8, 8, 128)         73856     \n",
      "                                                                 \n",
      " max_pooling2d_6 (MaxPoolin  (None, 4, 4, 128)         0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " conv2d_7 (Conv2D)           (None, 2, 2, 128)         147584    \n",
      "                                                                 \n",
      " max_pooling2d_7 (MaxPoolin  (None, 1, 1, 128)         0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " flatten_1 (Flatten)         (None, 128)               0         \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 64)                8256      \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 64)                0         \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 7)                 455       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 248967 (972.53 KB)\n",
      "Trainable params: 248967 (972.53 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# load model\n",
    "from keras.models import load_model\n",
    "\n",
    "loadModel=load_model('model/model.keras')\n",
    "loadModel.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MENDETEKSI SECARA REAL-TIME"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-20T17:46:12.295035700Z",
     "start_time": "2023-07-20T17:46:02.756270100Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "# Mengakses kamera\n",
    "cap = cv.VideoCapture(0)\n",
    "\n",
    "# Inisialisasi Cascade Classifier untuk deteksi wajah\n",
    "face_cascade = cv.CascadeClassifier(cv.data.haarcascades + 'haarcascade_frontalface_default.xml')\n",
    "\n",
    "while True:\n",
    "    ret, frame = cap.read()\n",
    "\n",
    "    if not ret:\n",
    "        break\n",
    "\n",
    "    # Membuat mirror (flip horizontal) dari frame\n",
    "    frame_mirror = cv.flip(frame, 1)\n",
    "\n",
    "    # Konversi gambar ke grayscale\n",
    "    image_gray = cv.cvtColor(frame_mirror, cv.COLOR_BGR2GRAY)\n",
    "\n",
    "    # Deteksi wajah\n",
    "    faces = face_cascade.detectMultiScale(image_gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))\n",
    "\n",
    "    for (x, y, w, h) in faces:\n",
    "        face_roi = image_gray[y:y + h, x:x + w]\n",
    "        face_roi = cv.resize(face_roi, (48, 48))\n",
    "        face_roi = np.reshape(face_roi, (1, 48, 48, 1))\n",
    "        face_roi = face_roi / 255.0\n",
    "\n",
    "        # Menampilkan kotak di sekitar wajah\n",
    "        cv.rectangle(frame_mirror, (x, y), (x + w, y + h), (0, 255, 0), 2)\n",
    "\n",
    "        # Melakukan prediksi menggunakan model\n",
    "        prediction = loadModel.predict(face_roi)\n",
    "        label = np.argmax(prediction)\n",
    "        prob = np.max(prediction)\n",
    "\n",
    "        # Membuat dictionary invers untuk memetakan indeks label ke nama kelas\n",
    "        label_map_inverse = {v: k for k, v in label_map.items()}\n",
    "\n",
    "        # Menampilkan nama kelas\n",
    "        expression = label_map_inverse[label]\n",
    "\n",
    "        # Menampilkan hasil prediksi pada gambar\n",
    "        text = f'{expression}: {prob:.2f}'\n",
    "        cv.putText(frame_mirror, text, (x, y - 10), cv.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)\n",
    "\n",
    "    # Menampilkan hasil deteksi di jendela\n",
    "    cv.imshow('Facial Expression Detection', frame_mirror)\n",
    "\n",
    "    # Menghentikan program dengan menekan tombol 'q'\n",
    "    if cv.waitKey(1) & 0xFF == ord('q'):\n",
    "        break\n",
    "\n",
    "# Menutup kamera, menghancurkan jendela, dan menghentikan mode interaktif matplotlib\n",
    "cap.release()\n",
    "cv.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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