emotion_detection.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "VbNHWeNUNwfa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9793944b-6a0f-4c5b-8eb3-f78bb405aabc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "# MOUNT GOOGLE DRIVE FOR DATASET ACCESS\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount(\"/content/drive\")\n",
        "\n",
        "# UNMOUNT\n",
        "# drive.flush_and_unmount()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# IMPORTS\n",
        "\n",
        "# datascience\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import plotly.express as px\n",
        "\n",
        "# ML\n",
        "import cv2 as cv\n",
        "import tensorflow as tf\n",
        "import keras\n",
        "from keras.preprocessing import image\n",
        "from keras.models import Sequential \n",
        "from keras.layers import Conv2D, MaxPool2D, Flatten,Dense,Dropout,BatchNormalization\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "\n",
        "#System\n",
        "import os\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "metadata": {
        "id": "Jv2ZKxAJrI4A"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# DATASET DIRECTORIES\n",
        "\n",
        "#train_dir = \"/content/drive/MyDrive/datasets/emotion_detection/train\"\n",
        "#test_dir = \"/content/drive/MyDrive/datasets/emotion_detection/test\"\n",
        "\n",
        "train_dir=\"/content/drive/MyDrive/CV/train\"\n",
        "test_dir=\"/content/drive/MyDrive/CV/test\""
      ],
      "metadata": {
        "id": "3bM2MfcHrREz"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# AVAILABLE CLASSES\n",
        "\n",
        "emotions= os.listdir(train_dir)\n",
        "print(\"<================ SAMPLE IMAGES ==================>\")\n",
        "for i in range(1,len(emotions)+1):\n",
        "  path=train_dir+f\"/{emotions[i-1]}/\"\n",
        "  img = cv.imread(path+os.listdir(path)[0])\n",
        "  plt.subplot(3,3,i)\n",
        "  plt.tight_layout()\n",
        "  plt.imshow(img)\n",
        "  plt.title(emotions[i-1])\n",
        "  plt.xticks([])\n",
        "  plt.yticks([])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "id": "3uorF4EksOOl",
        "outputId": "530c8d98-004d-4ebf-97b9-d8283727c045"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<================ SAMPLE IMAGES ==================>\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 7 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# DATASET DISTRIBUTIUON\n",
        "\n",
        "%%time\n",
        "print(\"NUMBER OF IMAGES IN TRAINING DATASET : \")\n",
        "train_cnt=0\n",
        "for i in emotions:\n",
        "    cnt = len(os.listdir(train_dir+f\"/{i}/\"))\n",
        "    print(f\"{i} : \",cnt)\n",
        "    train_cnt+=cnt\n",
        "print(\"TOTAL : \",train_cnt)\n",
        "print(\"\\n\\nNUMBER OF IMAGES IN TESTING DATASET : \")\n",
        "test_cnt=0\n",
        "for i in emotions:\n",
        "    cnt = len(os.listdir(test_dir+f\"/{i}/\"))\n",
        "    print(f\"{i} : \",len(os.listdir(test_dir+f\"/{i}/\")))\n",
        "    test_cnt+=cnt\n",
        "print(\"TOTAL : \",test_cnt)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7z0Cn319spBe",
        "outputId": "f12374a7-7db8-4602-b978-2d6746baf04d"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "NUMBER OF IMAGES IN TRAINING DATASET : \n",
            "angry :  3995\n",
            "disgusted :  436\n",
            "fearful :  4097\n",
            "surprised :  3171\n",
            "neutral :  4965\n",
            "sad :  4830\n",
            "happy :  7215\n",
            "TOTAL :  28709\n",
            "\n",
            "\n",
            "NUMBER OF IMAGES IN TESTING DATASET : \n",
            "angry :  958\n",
            "disgusted :  111\n",
            "fearful :  1024\n",
            "surprised :  831\n",
            "neutral :  1233\n",
            "sad :  1252\n",
            "happy :  1774\n",
            "TOTAL :  7183\n",
            "CPU times: user 39 ms, sys: 12.5 ms, total: 51.4 ms\n",
            "Wall time: 541 ms\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#IMAGE DATA GENERATIONN\n",
        "\n",
        "#TRAINING DATA\n",
        "\n",
        "datagen = ImageDataGenerator(\n",
        "                                         width_shift_range = 0.1,\n",
        "                                         height_shift_range = 0.1,\n",
        "                                         horizontal_flip = True,\n",
        "                                         rescale = 1./255,\n",
        "                                         zoom_range = 0.2,\n",
        "                                         validation_split = 0.2\n",
        "                                        \n",
        "                                        )\n",
        "training_dataset = datagen.flow_from_directory(directory = train_dir,\n",
        "                                                    target_size = (48,48),\n",
        "                                                    batch_size = 32,\n",
        "                                                    color_mode = \"grayscale\",\n",
        "                                                    class_mode = \"categorical\"\n",
        "                                                   )\n",
        "datagen = ImageDataGenerator(rescale = 1./255,\n",
        "                                        )\n",
        "testing_dataset = datagen.flow_from_directory( directory = test_dir,\n",
        "                                                              target_size = (48,48),\n",
        "                                                              batch_size = 32,\n",
        "                                                              color_mode = \"grayscale\",\n",
        "                                                              class_mode = \"categorical\",\n",
        "                                                             )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nwo07AFDwlMo",
        "outputId": "ff5127a2-96b0-4322-b230-7519d6f38a42"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 28709 images belonging to 7 classes.\n",
            "Found 7183 images belonging to 7 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#MODEL\n",
        "model= Sequential()\n",
        "model.add(Conv2D(32,(3,3),padding='same',activation='relu',input_shape=(48,48,1)))\n",
        "model.add(Conv2D(64,(3,3),padding='same',activation='relu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPool2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "model.add(Conv2D(128,(3,3),padding='same',activation='relu'))\n",
        "model.add(Conv2D(256,(3,3),padding='same',activation='relu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPool2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "model.add(Conv2D(256,(3,3),padding='same',activation='relu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPool2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "model.add(Flatten()) \n",
        "\n",
        "model.add(Dense(100,activation = 'relu'))\n",
        "\n",
        "model.add(Dense(50,activation = 'relu'))\n",
        "\n",
        "model.add(Dense(225,activation = 'relu'))\n",
        "\n",
        "model.add(BatchNormalization())\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "model.add(Dense(7, activation='softmax'))"
      ],
      "metadata": {
        "id": "1gxwGTICxSY1"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FIcCIlU-x-qw",
        "outputId": "f1c5803c-850d-4315-fe84-ac4eb63599f8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 48, 48, 32)        320       \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 48, 48, 64)        18496     \n",
            "                                                                 \n",
            " batch_normalization (BatchN  (None, 48, 48, 64)       256       \n",
            " ormalization)                                                   \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 24, 24, 64)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 24, 24, 64)        0         \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 24, 24, 128)       73856     \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 24, 24, 256)       295168    \n",
            "                                                                 \n",
            " batch_normalization_1 (Batc  (None, 24, 24, 256)      1024      \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 12, 12, 256)      0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 12, 12, 256)       0         \n",
            "                                                                 \n",
            " conv2d_4 (Conv2D)           (None, 12, 12, 256)       590080    \n",
            "                                                                 \n",
            " batch_normalization_2 (Batc  (None, 12, 12, 256)      1024      \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPooling  (None, 6, 6, 256)        0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 6, 6, 256)         0         \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 9216)              0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 100)               921700    \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 50)                5050      \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 225)               11475     \n",
            "                                                                 \n",
            " batch_normalization_3 (Batc  (None, 225)              900       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 225)               0         \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 7)                 1582      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1,920,931\n",
            "Trainable params: 1,919,329\n",
            "Non-trainable params: 1,602\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(\n",
        "    optimizer = tf.keras.optimizers.Adam(lr=0.005), \n",
        "    loss='categorical_crossentropy', \n",
        "    metrics=['accuracy']\n",
        "  )"
      ],
      "metadata": {
        "id": "Wiwy6PqNyIzn"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#TRAINING\n",
        "history = model.fit(\n",
        "    training_dataset ,\n",
        "    epochs=1,\n",
        "    batch_size=32)"
      ],
      "metadata": {
        "id": "M247eh1ayPZ2",
        "outputId": "27477e49-b0e4-470c-eb08-7018d6277b22",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "898/898 [==============================] - 6910s 8s/step - loss: 1.8724 - accuracy: 0.2391\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#TESTING\n",
        "\n",
        "model.evaluate(testing_dataset)"
      ],
      "metadata": {
        "id": "Svygl1bIy-aO",
        "outputId": "79959afb-8716-420b-9808-58fb978df523",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "225/225 [==============================] - 1798s 8s/step - loss: 1.8211 - accuracy: 0.2609\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1.821070909500122, 0.2608937621116638]"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# MODEL EVALUATION\n",
        "\n",
        "train_acc = history.history['accuracy']\n",
        "train_loss = history.history['loss']\n",
        "\n",
        "plt.plot(train_acc,marker=\"*\",label=\"Acc Value\")\n",
        "plt.plot(train_loss,marker=\"*\",label=\"loss Value\")\n",
        "plt.grid(False)\n",
        "\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.ylabel(\"\")\n",
        "plt.legend([\"accuracy\",\"loss\"])\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "mHGG_Rn1zT8W",
        "outputId": "f768b45f-fe05-4a10-bad9-da1f054cc288",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#SAVING MODEL\n",
        "\n",
        "model.save(\"emotion_detection_model.h5\")"
      ],
      "metadata": {
        "id": "uLxktCEpzKD5"
      },
      "execution_count": 13,
      "outputs": []
    }
  ]
}