emotion_first_run.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "emotion_first_run.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "oABgnAnjRMVy",
        "HSH73VeZRDY_",
        "EJQU_4obPn4I",
        "vNOMdeK9WOro"
      ],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/PranavKathar/DeepVision-Emotion-and-Gesture-Detection/blob/master/emotion_first_run.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Importing csv file from drive"
      ],
      "metadata": {
        "id": "oABgnAnjRMVy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rTZXwJxWYH4H",
        "outputId": "8c41e34d-a8e1-4598-8d9b-4f9462606b4b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np \n",
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "import cv2\n",
        "from tqdm import tqdm\n",
        "\n",
        "\n",
        "data = pd.read_csv(\"/content/drive/MyDrive/emotion_1/fer2013(1).csv\")\n",
        "data.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "zGAr_4qr1_Jp",
        "outputId": "a585a4e0-cff7-47a2-8e77-bccca6bb593e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-c7b96ce6-1302-4c22-a296-19b7c04ffb83\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>emotion</th>\n",
              "      <th>pixels</th>\n",
              "      <th>Usage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>70 80 82 72 58 58 60 63 54 58 60 48 89 115 121...</td>\n",
              "      <td>Training</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>151 150 147 155 148 133 111 140 170 174 182 15...</td>\n",
              "      <td>Training</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>231 212 156 164 174 138 161 173 182 200 106 38...</td>\n",
              "      <td>Training</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>24 32 36 30 32 23 19 20 30 41 21 22 32 34 21 1...</td>\n",
              "      <td>Training</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6</td>\n",
              "      <td>4 0 0 0 0 0 0 0 0 0 0 0 3 15 23 28 48 50 58 84...</td>\n",
              "      <td>Training</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c7b96ce6-1302-4c22-a296-19b7c04ffb83')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c7b96ce6-1302-4c22-a296-19b7c04ffb83 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c7b96ce6-1302-4c22-a296-19b7c04ffb83');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   emotion                                             pixels     Usage\n",
              "0        0  70 80 82 72 58 58 60 63 54 58 60 48 89 115 121...  Training\n",
              "1        0  151 150 147 155 148 133 111 140 170 174 182 15...  Training\n",
              "2        2  231 212 156 164 174 138 161 173 182 200 106 38...  Training\n",
              "3        4  24 32 36 30 32 23 19 20 30 41 21 22 32 34 21 1...  Training\n",
              "4        6  4 0 0 0 0 0 0 0 0 0 0 0 3 15 23 28 48 50 58 84...  Training"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np \n",
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "import cv2\n",
        "from tqdm import tqdm\n",
        "\n",
        "\n",
        "data = pd.read_csv(\"/content/drive/MyDrive/fer/fer2013.csv\")\n",
        "data.head()\n",
        "\n",
        "labels = []\n",
        "usage = []\n",
        "\n",
        "for i in data[\"emotion\"]:\n",
        "    labels.append(i)\n",
        "    \n",
        "for i in data[\"Usage\"]:\n",
        "    usage.append(i)\n",
        "    \n",
        "print(set(labels))\n",
        "print(set(usage))\n",
        "\n"
      ],
      "metadata": {
        "id": "l3xWhsCCqst6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7c8c8ae1-1fad-4b94-d557-09e32b92332f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{0, 1, 2, 3, 4, 5, 6}\n",
            "{'Training', 'PublicTest', 'PrivateTest'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Try this"
      ],
      "metadata": {
        "id": "HSH73VeZRDY_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#split function\n",
        "\n",
        "string = \"the gold deer\"\n",
        "print(string)\n",
        "folder = string.split('e')\n",
        "print(folder)\n"
      ],
      "metadata": {
        "id": "fAaFW4llvdKs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "df643283-39ad-41b5-e00b-f659939e9a04"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "the gold deer\n",
            "['th', ' gold d', '', 'r']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Converting numerical pixel data into image"
      ],
      "metadata": {
        "id": "EJQU_4obPn4I"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "liT3nKyPqKfo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a4c46671-2174-48f2-c39d-896c77b2e9b0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "35887it [05:21, 111.62it/s]\n"
          ]
        }
      ],
      "source": [
        "count0 = 0\n",
        "count1 = 0\n",
        "count2 = 0\n",
        "count3 = 0\n",
        "count4 = 0\n",
        "count5 = 0\n",
        "count6 = 0\n",
        "\n",
        "for i, j, k in tqdm(zip(data[\"emotion\"], data[\"pixels\"], data[\"Usage\"])):\n",
        "    pixel = []\n",
        "    pixels = j.split(' ')\n",
        "    for m in pixels:\n",
        "        value = float(m)\n",
        "        pixel.append(value)\n",
        "    pixel = np.array(pixel)\n",
        "    img = pixel.reshape(48, 48)\n",
        "    \n",
        "    if k == \"Training\":\n",
        "        if not os.path.exists(\"drive/MyDrive/emotion_1/train\"):\n",
        "            os.mkdir(\"drive/MyDrive/emotion_1/train\")\n",
        "        if i == 0:\n",
        "            if not os.path.exists(\"drive/MyDrive/emotion_1/train/Angry\"):\n",
        "                os.mkdir(\"drive/MyDrive/emotion_1/train/Angry\")\n",
        "                path = \"drive/MyDrive/emotion_1/train/Angry/\" + str(count0) + \".jpg\"\n",
        "                cv2.imwrite(path , img)\n",
        "                count0 += 1\n",
        "            else:\n",
        "                path = \"drive/MyDrive/emotion_1/train/Angry/\" + str(count0) + \".jpg\"\n",
        "                cv2.imwrite(path , img)\n",
        "                count0 += 1    \n",
        " \n",
        "        if i == 1:\n",
        "            if not os.path.exists(\"drive/MyDrive/emotion_1/train/Disgust\"):\n",
        "                os.mkdir(\"drive/MyDrive/emotion_1/train/Disgust\")\n",
        "                path = \"drive/MyDrive/emotion_1/train/Disgust/\" + str(count1) + \".jpg\"\n",
        "                cv2.imwrite(path , img)\n",
        "                count1 += 1\n",
        "            else:\n",
        "                path = \"drive/MyDrive/emotion_1/train/Disgust/\" + str(count1) + \".jpg\"\n",
        "                cv2.imwrite(path , img)\n",
        "                count1 += 1\n",
        "        if i == 2:\n",
        "            if not os.path.exists(\"drive/MyDrive/emotion_1/train/Fear\"):\n",
        "                os.mkdir(\"drive/MyDrive/emotion_1/train/Fear\")\n",
        "                path = \"drive/MyDrive/emotion_1/train/Fear/\" + str(count2) + \".jpg\"\n",
        "                cv2.imwrite(path , img)\n",
        "                count2 += 1\n",
        "            else:\n",
        "                path = \"drive/MyDrive/emotion_1/train/Fear/\" + str(count2) + \".jpg\"\n",
        "                cv2.imwrite(path , img)\n",
        "                count2 += 1\n",
        "        if i == 3:\n",
        "            if not os.path.exists(\"drive/MyDrive/emotion_1/train/Happy\"):\n",
        "                os.mkdir(\"drive/MyDrive/emotion_1/train/Happy\")\n",
        "                path = \"drive/MyDrive/emotion_1/train/Happy/\" + str(count3) + \".jpg\"\n",
        "                cv2.imwrite(path , img)\n",
        "                count3 += 1\n",
        "            else:\n",
        "                path = \"drive/MyDrive/emotion_1/train/Happy/\" + str(count3) + \".jpg\"\n",
        "                cv2.imwrite(path , img)\n",
        "                count3 += 1\n",
        "        if i == 4:\n",
        "            if not os.path.exists(\"drive/MyDrive/emotion_1/train/Sad\"):\n",
        "                os.mkdir(\"drive/MyDrive/emotion_1/train/Sad\")\n",
        "                path = \"drive/MyDrive/emotion_1/train/Sad/\" + str(count4) + \".jpg\"\n",
        "                cv2.imwrite(path , img)\n",
        "                count4 += 1\n",
        "            else:\n",
        "                path = \"drive/MyDrive/emotion_1/train/Sad/\" + str(count4) + \".jpg\"\n",
        "                cv2.imwrite(path , img)\n",
        "                count4 += 1\n",
        "        if i == 5:\n",
        "            if not os.path.exists(\"drive/MyDrive/emotion_1/train/Surprise\"):\n",
        "                os.mkdir(\"drive/MyDrive/emotion_1/train/Surprise\")\n",
        "                path = \"drive/MyDrive/emotion_1/train/Surprise/\" + str(count5) + \".jpg\"\n",
        "                cv2.imwrite(path , img)\n",
        "                count5 += 1\n",
        "            else:\n",
        "                path = \"drive/MyDrive/emotion_1/train/Surprise/\" + str(count5) + \".jpg\"\n",
        "                cv2.imwrite(path , img)\n",
        "                count5 += 1\n",
        "        if i == 6:\n",
        "            if not os.path.exists(\"drive/MyDrive/emotion_1/train/Neutral\"):\n",
        "                os.mkdir(\"drive/MyDrive/emotion_1/train/Neutral\")\n",
        "                path = \"drive/MyDrive/emotion_1/train/Neutral/\" + str(count6) + \".jpg\"\n",
        "                cv2.imwrite(path , img)\n",
        "                count6 += 1\n",
        "            else:\n",
        "                path = \"drive/MyDrive/emotion_1/train/Neutral/\" + str(count6) + \".jpg\"\n",
        "                cv2.imwrite(path , img)\n",
        "                count6 += 1\n",
        "    else:\n",
        "        if not os.path.exists(\"drive/MyDrive/emotion_1/validation\"):\n",
        "            os.mkdir(\"drive/MyDrive/emotion_1/validation\")\n",
        "        if i == 0:\n",
        "            if not os.path.exists(\"drive/MyDrive/emotion_1/validation/Angry\"):\n",
        "                os.mkdir(\"drive/MyDrive/emotion_1/validation/Angry\")\n",
        "                path = \"drive/MyDrive/emotion_1/validation/Angry/\" + str(count0) + \".jpg\"\n",
        "                cv2.imwrite(path , img)\n",
        "                count0 += 1\n",
        "            else:\n",
        "                path = \"drive/MyDrive/emotion_1/validation/Angry/\" + str(count0) + \".jpg\"\n",
        "                cv2.imwrite(path , img)\n",
        "                count0 += 1\n",
        "        if i == 1:\n",
        "            if not os.path.exists(\"drive/MyDrive/emotion_1/validation/Disgust\"):\n",
        "                os.mkdir(\"drive/MyDrive/emotion_1/validation/Disgust\")\n",
        "                path = \"drive/MyDrive/emotion_1/validation/Disgust/\" + str(count1) + \".jpg\"\n",
        "                cv2.imwrite(path , img)\n",
        "                count1 += 1\n",
        "            else:\n",
        "                path = \"drive/MyDrive/emotion_1/validation/Disgust/\" + str(count1) + \".jpg\"\n",
        "                cv2.imwrite(path , img)\n",
        "                count1 += 1\n",
        "        if i == 2:\n",
        "            if not os.path.exists(\"drive/MyDrive/emotion_1/validation/Fear\"):\n",
        "                os.mkdir(\"drive/MyDrive/emotion_1/validation/Fear\")\n",
        "                path = \"drive/MyDrive/emotion_1/validation/Fear/\" + str(count2) + \".jpg\"\n",
        "                cv2.imwrite(path , img)\n",
        "                count2 += 1\n",
        "            else:\n",
        "                path = \"drive/MyDrive/emotion_1/validation/Fear/\" + str(count2) + \".jpg\"\n",
        "                cv2.imwrite(path , img)\n",
        "                count2 += 1\n",
        "        if i == 3:\n",
        "            if not os.path.exists(\"drive/MyDrive/emotion_1/validation/Happy\"):\n",
        "                os.mkdir(\"drive/MyDrive/emotion_1/validation/Happy\")\n",
        "                path = \"drive/MyDrive/emotion_1/validation/Happy/\" + str(count3) + \".jpg\"\n",
        "                cv2.imwrite(path , img)\n",
        "                count3 += 1\n",
        "            else:\n",
        "                path = \"drive/MyDrive/emotion_1/validation/Happy/\" + str(count3) + \".jpg\"\n",
        "                cv2.imwrite(path , img)\n",
        "                count3 += 1\n",
        "        if i == 4:\n",
        "            if not os.path.exists(\"drive/MyDrive/emotion_1/validation/Sad\"):\n",
        "                os.mkdir(\"drive/MyDrive/emotion_1/validation/Sad\")\n",
        "                path = \"drive/MyDrive/emotion_1/validation/Sad/\" + str(count4) + \".jpg\"\n",
        "                cv2.imwrite(path , img)\n",
        "                count4 += 1\n",
        "            else:\n",
        "                path = \"drive/MyDrive/emotion_1/validation/Sad/\" + str(count4) + \".jpg\"\n",
        "                cv2.imwrite(path , img)\n",
        "                count4 += 1\n",
        "        if i == 5:\n",
        "            if not os.path.exists(\"drive/MyDrive/emotion_1/validation/Surprise\"):\n",
        "                os.mkdir(\"drive/MyDrive/emotion_1/validation/Surprise\")\n",
        "                path = \"drive/MyDrive/emotion_1/validation/Surprise/\" + str(count5) + \".jpg\"\n",
        "                cv2.imwrite(path , img)\n",
        "                count5 += 1\n",
        "            else:\n",
        "                path = \"drive/MyDrive/emotion_1/validation/Surprise/\" + str(count5) + \".jpg\"\n",
        "                cv2.imwrite(path , img)\n",
        "                count5 += 1\n",
        "        if i == 6:\n",
        "            if not os.path.exists(\"drive/MyDrive/emotion_1/validation/Neutral\"):\n",
        "                os.mkdir(\"drive/MyDrive/emotion_1/validation/Neutral\")\n",
        "                path = \"drive/MyDrive/emotion_1/validation/Neutral/\" + str(count6) + \".jpg\"\n",
        "                cv2.imwrite(path , img)\n",
        "                count6 += 1\n",
        "            else:\n",
        "                path = \"drive/MyDrive/emotion_1/validation/Neutral/\" + str(count6) + \".jpg\"\n",
        "                cv2.imwrite(path , img)\n",
        "                count6 += 1\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Try this"
      ],
      "metadata": {
        "id": "vNOMdeK9WOro"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "\n",
        "    pixel = []\n",
        "    pixels = j.split(' ')\n",
        "    for m in pixels:\n",
        "        value = float(m)\n",
        "        pixel.append(value)\n",
        "    pixel = np.array(pixel)\n",
        "    img = pixel.reshape(48, 48)"
      ],
      "metadata": {
        "id": "i06PdFCn-wvY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab.patches import cv2_imshow\n",
        "print(img)\n",
        "cv2_imshow(img)\n",
        "print(pixel)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "UH704wo8Vgqu",
        "outputId": "a39d117e-c138-4fb7-8f53-6bca9e485fdb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 19.  13.  14. ... 108.  95.  86.]\n",
            " [ 16.  17.  15. ... 105.  94.  90.]\n",
            " [ 10.   9.  10. ... 101.  93.  95.]\n",
            " ...\n",
            " [ 18.  14.  16. ...  55.  64.  95.]\n",
            " [ 15.  15.  13. ... 123. 171. 192.]\n",
            " [ 16.  14.  13. ... 189. 199. 201.]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=L size=48x48 at 0x7F8C914F4090>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 19.  13.  14. ... 189. 199. 201.]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Exploratory analysis of data"
      ],
      "metadata": {
        "id": "kftXo_h9ReoE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import matplotlib.pyplot as plt\n",
        "train_directory = \"drive/MyDrive/emotion_1/train/\"\n",
        "train_dir = os.listdir(train_directory)\n",
        "classes = []\n",
        "\n",
        "for folder in train_dir:\n",
        "    classes.append(folder)\n",
        "\n",
        "train_counts = []\n",
        "#drive/MyDrive/emotion_1/train/angry/\n",
        "for folder in train_dir:\n",
        "    class_path = train_directory + folder + \"/\"\n",
        "    count = 0\n",
        "    for file in os.listdir(class_path):\n",
        "        count +=1\n",
        "    \n",
        "    train_counts.append(count)\n",
        "    \n",
        "train_counts\n",
        "\n"
      ],
      "metadata": {
        "id": "eAm_RivcqhFC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "184a4be5-acf7-4d35-b034-ee859c879408"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[3995, 4097, 4830, 4965, 7215, 3171, 436]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## plotting our data"
      ],
      "metadata": {
        "id": "9_-gTS_NVO2u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# plt.bar(classes, train_counts, width=0.5)\n",
        "# plt.title(\"Bar Graph of Train Data\")\n",
        "# plt.xlabel(\"Classes\")\n",
        "# plt.ylabel(\"Counts\")\n",
        "\n",
        "\n",
        "plt.scatter(classes, train_counts)\n",
        "plt.plot(classes, train_counts, '-o')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "9Y_z6iwyOqIX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "14e05462-781e-4f4f-9ed5-da18b06c8947"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}