not_working_lip_detection.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Colab_25GBRAM_GPU_Techhawa.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "machine_shape": "hm"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dehb-i1oojeg"
      },
      "source": [
        "# Downloading Data From Kaggle"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CZZsjT-zj-zY"
      },
      "source": [
        " ! pip install -q kaggle"
      ],
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j2zVWXGckWrq"
      },
      "source": [
        "from google.colab import files"
      ],
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 56
        },
        "id": "hqQPLGqNkm4i",
        "outputId": "8a539551-c632-45b8-e4b3-ccfd1a4159fe"
      },
      "source": [
        "files.upload()\r\n",
        "\r\n"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-1828aec3-2a48-4721-bb3d-37a0438c5cb7\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-1828aec3-2a48-4721-bb3d-37a0438c5cb7\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "u53pfMG3lgYw",
        "outputId": "4375631f-0596-4ef2-d23b-326d5d47b6cf"
      },
      "source": [
        "import os\r\n",
        "os.getcwd()"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'/content'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d9EbL0zzoJGt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "719b4179-7e33-4a24-f792-ee68aff90dc7"
      },
      "source": [
        "!mkdir ~/.kaggle\r\n",
        "!cp kaggle.json ~/.kaggle/"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "mkdir: cannot create directory ‘/root/.kaggle’: File exists\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ohxw11RfoZ61"
      },
      "source": [
        "!chmod 600 ~/.kaggle/kaggle.json"
      ],
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Chr9odmkljLS"
      },
      "source": [
        "import os\r\n",
        "os.environ['KAGGLE_CONFIG_DIR'] = '/content'"
      ],
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W2VYY5vMnVyV",
        "outputId": "2b5433a5-0cff-499c-b763-d46482988f80"
      },
      "source": [
        "!kaggle datasets download -d olekslu/makeup-lips-segmentation-28k-samples"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Warning: Your Kaggle API key is readable by other users on this system! To fix this, you can run 'chmod 600 /content/kaggle.json'\n",
            "User cancelled operation\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BFFmDTMkouGU"
      },
      "source": [
        "# Extracting Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FYhN2B0Coh2K",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "outputId": "ffa8ac66-97ec-449a-cde4-765b2cecb9b0"
      },
      "source": [
        "from zipfile import ZipFile\r\n",
        "import os\r\n",
        "\r\n",
        "zip_file_path = os.path.join(os.getcwd(),'makeup-lips-segmentation-28k-samples.zip')\r\n",
        "zip_ref = ZipFile(zip_file_path, 'r')\r\n",
        "extract_path = os.path.join(os.getcwd(), 'lip')\r\n",
        "\r\n",
        "zip_ref.extractall(extract_path)\r\n",
        "zip_ref.close()"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-74-5a0d0f92319f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mextract_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetcwd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'lip'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mzip_ref\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextractall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mextract_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m \u001b[0mzip_ref\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.6/zipfile.py\u001b[0m in \u001b[0;36mextractall\u001b[0;34m(self, path, members, pwd)\u001b[0m\n\u001b[1;32m   1522\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1523\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mzipinfo\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmembers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1524\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_extract_member\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzipinfo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpwd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1525\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1526\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mclassmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.6/zipfile.py\u001b[0m in \u001b[0;36m_extract_member\u001b[0;34m(self, member, targetpath, pwd)\u001b[0m\n\u001b[1;32m   1577\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmember\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpwd\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpwd\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msource\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1578\u001b[0m              \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtargetpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"wb\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1579\u001b[0;31m             \u001b[0mshutil\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopyfileobj\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msource\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1580\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1581\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mtargetpath\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.6/shutil.py\u001b[0m in \u001b[0;36mcopyfileobj\u001b[0;34m(fsrc, fdst, length)\u001b[0m\n\u001b[1;32m     77\u001b[0m     \u001b[0;34m\"\"\"copy data from file-like object fsrc to file-like object fdst\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     78\u001b[0m     \u001b[0;32mwhile\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 79\u001b[0;31m         \u001b[0mbuf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfsrc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlength\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     80\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mbuf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     81\u001b[0m             \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.6/zipfile.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, n)\u001b[0m\n\u001b[1;32m    870\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_offset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    871\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_eof\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 872\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_read1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    873\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    874\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_readbuffer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.6/zipfile.py\u001b[0m in \u001b[0;36m_read1\u001b[0;34m(self, n)\u001b[0m\n\u001b[1;32m    946\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compress_type\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mZIP_DEFLATED\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    947\u001b[0m             \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMIN_READ_SIZE\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 948\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_decompressor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecompress\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    949\u001b[0m             self._eof = (self._decompressor.eof or\n\u001b[1;32m    950\u001b[0m                          \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compress_left\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mand\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M6qyCjkApnE5"
      },
      "source": [
        "home_directory = os.path.join(os.getcwd(),'lip','set-lipstick-original')\r\n",
        "\r\n",
        "current_train_directory_path = os.path.join(home_directory, '720p')\r\n",
        "current_mask_directory_path = os.path.join(home_directory, 'mask')\r\n",
        "\r\n",
        "dir_train_frame = os.path.join(os.getcwd(), 'lip','train_frames','train')\r\n",
        "dir_train_mask  = os.path.join(os.getcwd(), 'lip','train_masks', 'train')\r\n",
        "dir_val_frame   = os.path.join(os.getcwd(), 'lip','val_frames', 'val')\r\n",
        "dir_val_mask    = os.path.join(os.getcwd(), 'lip','val_masks', 'val')\r\n",
        "\r\n",
        "os.makedirs(dir_train_frame)\r\n",
        "os.makedirs(dir_train_mask)\r\n",
        "os.makedirs(dir_val_frame)\r\n",
        "os.makedirs(dir_val_mask)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tF_pQ024wWzO"
      },
      "source": [
        "# Renaming File"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V4zWGSX7s-mL"
      },
      "source": [
        "import re\r\n",
        "def rename_directory_content(path):\r\n",
        "    for fname in os.listdir(path):\r\n",
        "        new_name_l = re.split('(\\d+)',fname)\r\n",
        "        \r\n",
        "        new_name = new_name_l[1] + new_name_l[2] \r\n",
        "        old_path = os.path.join(path, fname)\r\n",
        "        new_path = os.path.join(path, new_name)\r\n",
        "\r\n",
        "        os.rename(old_path, new_path)\r\n",
        "\r\n",
        "rename_directory_content(current_train_directory_path)\r\n",
        "rename_directory_content(current_mask_directory_path)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zXnukiDZwaLN"
      },
      "source": [
        "# Finding (Frame, Mask) Pair"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jRlieKAsuozi"
      },
      "source": [
        "current_frame_name = [name.split('.')[0] for name in os.listdir(current_train_directory_path)]\r\n",
        "current_mask_name = [name.split('.')[0] for name in os.listdir(current_mask_directory_path)]\r\n",
        "\r\n",
        "print(f\"Length of frame: {len(current_frame_name)}\")\r\n",
        "print(f\"Length of Mask : {len(current_mask_name)}\")\r\n",
        "\r\n",
        "face_mask_intersection = set(current_frame_name).intersection(set(current_mask_name))                            \r\n",
        "len(face_mask_intersection)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jn8Bmu9swSPi"
      },
      "source": [
        "# Spliting Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zMYj6mjlusr4"
      },
      "source": [
        "import shutil\r\n",
        "val_index = len(face_mask_intersection) - 2048\r\n",
        "\r\n",
        "def single_instance_of_dataset(name, frame_initial, new_frame_initial,\r\n",
        "                               label_initial, new_label_initial):\r\n",
        "    def copy_file(source, destination):\r\n",
        "        try:\r\n",
        "            shutil.copyfile(source, destination)\r\n",
        "        except shutil.SameFileError as e:\r\n",
        "            print(e)\r\n",
        "        except IsADirectoryError as e:\r\n",
        "            print(e)\r\n",
        "        except PermissionError as e:\r\n",
        "            print(e)\r\n",
        "        except Exception as e:\r\n",
        "            print(e)\r\n",
        "    \r\n",
        "    frame_name = name + '.jpg'\r\n",
        "    label_name = name + '.png'\r\n",
        "\r\n",
        "    copy_file(os.path.join(frame_initial, frame_name), \r\n",
        "              os.path.join(new_frame_initial, frame_name))\r\n",
        "    \r\n",
        "    copy_file(os.path.join(label_initial, label_name),\r\n",
        "              os.path.join(new_label_initial, label_name))\r\n",
        "\r\n",
        "for index, name in enumerate(face_mask_intersection):\r\n",
        "  if index >= val_index:\r\n",
        "    single_instance_of_dataset(name, current_train_directory_path,\r\n",
        "                               dir_val_frame, current_mask_directory_path,\r\n",
        "                               dir_val_mask)\r\n",
        "  else:\r\n",
        "    single_instance_of_dataset(name, current_train_directory_path,\r\n",
        "                               dir_train_frame, current_mask_directory_path,\r\n",
        "                               dir_train_mask)\r\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MxY-7ZIF1xy0"
      },
      "source": [
        "print(len(os.listdir(dir_train_frame)))\r\n",
        "print(len(os.listdir(dir_train_mask)))\r\n",
        "print(len(os.listdir(dir_val_frame)))\r\n",
        "print(len(os.listdir(dir_val_mask)))\r\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ry8o7eMU2LtI"
      },
      "source": [
        "# Removing old directory structure"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wpgajt4B12f_"
      },
      "source": [
        "shutil.rmtree(home_directory)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YEUC9I4Z2cs6"
      },
      "source": [
        "# Creating ImageDatagen"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "npKAc06i2WGJ",
        "outputId": "cde9f702-8211-4f66-f4da-eec3dff76206"
      },
      "source": [
        "import tensorflow as tf\r\n",
        "print(tf.__version__)\r\n",
        "\r\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\r\n",
        "\r\n",
        "dir_train_frame = os.path.join(os.getcwd(), 'lip','train_frames','train')\r\n",
        "dir_train_mask  = os.path.join(os.getcwd(), 'lip','train_masks','train')\r\n",
        "dir_val_frame   = os.path.join(os.getcwd(), 'lip','val_frames','val')\r\n",
        "dir_val_mask    = os.path.join(os.getcwd(), 'lip','val_masks','val')\r\n",
        "\r\n"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.4.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7fYvUvlj2yFg"
      },
      "source": [
        "import matplotlib.image as mpimg\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "import cv2 as cv\r\n",
        "import numpy as np\r\n",
        "\r\n",
        "\r\n",
        "def adjust_gamma(image, gamma = 0.2):\r\n",
        "    table = np.array([\r\n",
        "                    ((i / 255.0) ** gamma) * 255 for i in np.arange(0,256)\r\n",
        "                    ]).astype('uint8')\r\n",
        "\r\n",
        "    return cv.LUT(image, table)\r\n",
        "\r\n",
        "def differnce_of_gaussian_filter(img, kernel_1 = (3,3), kernel_2 = (5,5), sigmoid_1 = 1.0, sigmoid_2 = 2.0):\r\n",
        "    low_sigma = cv.GaussianBlur(img, kernel_1, sigmoid_1)\r\n",
        "    high_sigma = cv.GaussianBlur(img, kernel_2, sigmoid_2)\r\n",
        "\r\n",
        "    return low_sigma - high_sigma\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "img_size = (512, 512)\r\n",
        "num_classes = 3\r\n",
        "batch_size = 30\r\n",
        "\r\n",
        "class LipData(tf.keras.utils.Sequence):\r\n",
        "    \"\"\"Helper to iterate over the data (as Numpy arrays).\"\"\"\r\n",
        "\r\n",
        "    def __init__(self, batch_size, img_size, input_img_paths, target_img_paths):\r\n",
        "        self.batch_size = batch_size\r\n",
        "        self.img_size = img_size\r\n",
        "        self.input_img_paths = input_img_paths\r\n",
        "        self.target_img_paths = target_img_paths\r\n",
        "\r\n",
        "    def __len__(self):\r\n",
        "        return len(self.target_img_paths) // self.batch_size\r\n",
        "\r\n",
        "    def __getitem__(self, idx):\r\n",
        "        \"\"\"Returns tuple (input, target) correspond to batch #idx.\"\"\"\r\n",
        "        i = idx * self.batch_size\r\n",
        "        batch_input_img_paths = self.input_img_paths[i : i + self.batch_size]\r\n",
        "        batch_target_img_paths = self.target_img_paths[i : i + self.batch_size]\r\n",
        "        x = np.zeros((self.batch_size,) + self.img_size + (3,), dtype=\"float32\")\r\n",
        "        for j, path in enumerate(batch_input_img_paths):\r\n",
        "            img = mpimg.imread(path)\r\n",
        "            img = cv.resize(img, self.img_size)\r\n",
        "            img = adjust_gamma(image)\r\n",
        "            img = differnce_of_gaussian_filter(img)\r\n",
        "            img = img / 255.\r\n",
        "            x[j] = img\r\n",
        "        y = np.zeros((self.batch_size,) + self.img_size + (1,), dtype=\"uint8\")\r\n",
        "        for j, path in enumerate(batch_target_img_paths):\r\n",
        "            img = cv.imread(path, cv.IMREAD_GRAYSCALE)\r\n",
        "            img = cv.resize(img, self.img_size)\r\n",
        "            img = img / 255.\r\n",
        "            img = np.expand_dims(img, -1)\r\n",
        "            y[j] = img\r\n",
        "\r\n",
        "        return x, y"
      ],
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GWayH8JZCjAB"
      },
      "source": [
        "train_frame_name = sorted(\r\n",
        "    [\r\n",
        "        os.path.join(dir_train_frame, fname)\r\n",
        "        for fname in os.listdir(dir_train_frame)\r\n",
        "    ]\r\n",
        ")\r\n",
        "\r\n",
        "\r\n",
        "train_label_name = sorted(\r\n",
        "    [\r\n",
        "        os.path.join(dir_train_mask, fname)\r\n",
        "        for fname in os.listdir(dir_train_mask)\r\n",
        "    ]\r\n",
        ")\r\n",
        "\r\n",
        "val_frame_name = sorted(\r\n",
        "    [\r\n",
        "        os.path.join(dir_val_frame, fname)\r\n",
        "        for fname in os.listdir(dir_val_frame)\r\n",
        "    ]\r\n",
        ")\r\n",
        "\r\n",
        "\r\n",
        "val_label_name = sorted(\r\n",
        "    [\r\n",
        "        os.path.join(dir_val_mask, fname)\r\n",
        "        for fname in os.listdir(dir_val_mask)\r\n",
        "    ]\r\n",
        ")\r\n",
        "\r\n",
        "\r\n",
        "train_gen = LipData(batch_size, img_size,\r\n",
        "                     train_frame_name,\r\n",
        "                     train_label_name)\r\n",
        "\r\n",
        "val_gen = LipData(batch_size, img_size,\r\n",
        "                   val_frame_name,\r\n",
        "                   val_label_name)      "
      ],
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JhfCPou-CT7b"
      },
      "source": [
        "train_frame_datagen = ImageDataGenerator(rescale= 1./255,\r\n",
        "                                         width_shift_range= 0.2,\r\n",
        "                                         height_shift_range= 0.2,\r\n",
        "                                         rotation_range= 40,\r\n",
        "                                         horizontal_flip= True,\r\n",
        "                                         fill_mode = 'nearest',\r\n",
        "                                         zoom_range = 0.2,\r\n",
        "                                         shear_range = 0.2)\r\n",
        "\r\n",
        "train_mask_datagen = ImageDataGenerator(rescale = 1./255,\r\n",
        "                                        width_shift_range= 0.2,\r\n",
        "                                        height_shift_range= 0.2,\r\n",
        "                                        rotation_range= 40,\r\n",
        "                                        horizontal_flip= True,\r\n",
        "                                        fill_mode = 'nearest',\r\n",
        "                                        zoom_range = 0.2,\r\n",
        "                                        shear_range = 0.2)\r\n",
        "\r\n",
        "val_frame_datagen = ImageDataGenerator(rescale= 1./255)\r\n",
        "val_mask_datagen = ImageDataGenerator(rescale = 1./255)\r\n",
        "\r\n",
        "img_size = (640, 360)\r\n",
        "\r\n",
        "train_frame_generator = train_frame_datagen.flow_from_directory(dir_train_frame,\r\n",
        "                                                                batch_size = 20,\r\n",
        "                                                                target_size = img_size)\r\n",
        "\r\n",
        "train_mask_generator = train_mask_datagen.flow_from_directory(dir_train_mask,\r\n",
        "                                                              batch_size = 20,\r\n",
        "                                                              target_size = img_size)\r\n",
        "\r\n",
        "val_frame_generator = val_frame_datagen.flow_from_directory(dir_val_frame,\r\n",
        "                                                            batch_size = 20,\r\n",
        "                                                            target_size = img_size)\r\n",
        "\r\n",
        "val_mask_generator = val_mask_datagen.flow_from_directory(dir_val_mask,\r\n",
        "                                                          batch_size = 20,\r\n",
        "                                                          target_size = img_size)\r\n",
        "\r\n",
        "train_image_generator = zip(train_frame_generator, train_mask_generator)\r\n",
        "val_image_generator = zip(val_frame_generator, val_mask_generator)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y_wEoY1W513l"
      },
      "source": [
        "# U-Net Model for Lip Segmentation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_X5bJy3l50zA"
      },
      "source": [
        "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Conv2DTranspose, Dropout, Input, concatenate\r\n",
        "from tensorflow.keras.models import Model\r\n"
      ],
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FD9grIqu3Rdd"
      },
      "source": [
        "def U_NetModel(input_shape):\r\n",
        "\r\n",
        "    X_input = Input(input_shape)\r\n",
        "\r\n",
        "    ######## Down-Sampling Part\r\n",
        "    d1 = Conv2D(filters = 16, kernel_size = (3,3), \r\n",
        "                name = \"downsamping-1a\", padding = \"same\", \r\n",
        "                kernel_initializer= \"he_normal\", activation = \"relu\")(X_input)\r\n",
        "    d1 = Dropout(0.1)(d1)\r\n",
        "    d1 = Conv2D(filters = 16, kernel_size = (3,3), \r\n",
        "                name = \"downsampling-1b\", padding = \"same\", \r\n",
        "                kernel_initializer= \"he_normal\", activation = \"relu\")(d1)\r\n",
        "    p1 = MaxPooling2D(pool_size= (2,2))(d1)\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "    d2 = Conv2D(filters = 32, kernel_size = (3,3), \r\n",
        "                name = \"downsamping-2a\", padding = \"same\", \r\n",
        "                kernel_initializer= \"he_normal\", activation = \"relu\")(p1)\r\n",
        "    d2 = Dropout(0.1)(d2)\r\n",
        "    d2 = Conv2D(filters = 32, kernel_size = (3,3), \r\n",
        "                name = \"downsampling-2b\", padding = \"same\", \r\n",
        "                kernel_initializer= \"he_normal\", activation = \"relu\")(d2)\r\n",
        "    p2 = MaxPooling2D(pool_size= (2,2))(d2)\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "    d3 = Conv2D(filters = 64, kernel_size = (3,3), \r\n",
        "                name = \"downsamping-3a\", padding = \"same\", \r\n",
        "                kernel_initializer= \"he_normal\", activation = \"relu\")(p2)\r\n",
        "    d3 = Dropout(0.1)(d3)\r\n",
        "    d3 = Conv2D(filters = 64, kernel_size = (3,3), \r\n",
        "                name = \"downsampling-3b\", padding = \"same\", \r\n",
        "                kernel_initializer= \"he_normal\", activation = \"relu\")(d3)\r\n",
        "    p3 = MaxPooling2D(pool_size= (2,2))(d3)\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "    d4 = Conv2D(filters = 128, kernel_size = (3,3), \r\n",
        "                name = \"downsamping-4a\", padding = \"same\", \r\n",
        "                kernel_initializer= \"he_normal\", activation = \"relu\")(p3)\r\n",
        "    d4 = Dropout(0.1)(d4)\r\n",
        "    d4 = Conv2D(filters = 128, kernel_size = (3,3), \r\n",
        "                name = \"downsampling-4b\", padding = \"same\", \r\n",
        "                kernel_initializer= \"he_normal\", activation = \"relu\")(d4)\r\n",
        "    p4 = MaxPooling2D(pool_size= (2,2))(d4)\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "    d5 = Conv2D(filters = 256, kernel_size = (3,3), \r\n",
        "                name = \"downsamping-5a\", padding = \"same\", \r\n",
        "                kernel_initializer= \"he_normal\", activation = \"relu\")(p4)\r\n",
        "    d5 = Dropout(0.1)(d5)\r\n",
        "    d5 = Conv2D(filters = 256, kernel_size = (3,3), \r\n",
        "                name = \"downsampling-5b\", padding = \"same\", \r\n",
        "                kernel_initializer= \"he_normal\", activation = \"relu\")(d5)\r\n",
        "    d5 = Dropout(0.1)(d5)\r\n",
        "    d5 = Conv2D(filters = 256, kernel_size = (3,3), \r\n",
        "                name = \"downsampling-5c\", padding = \"same\", \r\n",
        "                kernel_initializer= \"he_normal\", activation = \"relu\")(d5)\r\n",
        "    \r\n",
        "\r\n",
        "\r\n",
        "    u6 = Conv2DTranspose(filters = 128, kernel_size = (2,2), \r\n",
        "                         strides = (2,2), padding = \"same\",\r\n",
        "                         name = \"upsampling-6a\")(d5)\r\n",
        "    u6 = concatenate([u6, d4])\r\n",
        "    c6 = Conv2D(filters = 128, kernel_size = (2,2),\r\n",
        "                activation = \"relu\", padding = 'same',\r\n",
        "                kernel_initializer = 'he_normal', name = \"upsampling-6b\")(u6)\r\n",
        "    c6 = Dropout(0.2)(c6)\r\n",
        "    c6 = Conv2D(filters = 128, kernel_size = (2,2),\r\n",
        "                activation = \"relu\", padding = 'same',\r\n",
        "                kernel_initializer = 'he_normal', name = \"upsampling-6c\")(c6)\r\n",
        "\r\n",
        "    \r\n",
        "    \r\n",
        "    \r\n",
        "    u7 = Conv2DTranspose(filters = 64, kernel_size = (2,2), \r\n",
        "                         strides = (2,2), padding = \"same\",\r\n",
        "                         name = \"upsampling-7a\")(c6)\r\n",
        "    u7 = concatenate([u7, d3])\r\n",
        "    c7 = Conv2D(filters = 64, kernel_size = (2,2),\r\n",
        "                activation = \"relu\", padding = 'same',\r\n",
        "                kernel_initializer = 'he_normal', name = \"upsampling-7b\")(u7)\r\n",
        "    c7 = Dropout(0.2)(c7)\r\n",
        "    c7 = Conv2D(filters = 64, kernel_size = (2,2),\r\n",
        "                activation = \"relu\", padding = 'same',\r\n",
        "                kernel_initializer = 'he_normal', name = \"upsampling-7c\")(c7)\r\n",
        "\r\n",
        "    \r\n",
        "\r\n",
        "    \r\n",
        "    \r\n",
        "    u8 = Conv2DTranspose(filters = 32, kernel_size = (2,2), \r\n",
        "                         strides = (2,2), padding = \"same\",\r\n",
        "                         name = \"upsampling-8a\")(c7)\r\n",
        "    u8 = concatenate([u8, d2])\r\n",
        "    c8 = Conv2D(filters = 32, kernel_size = (2,2),\r\n",
        "                activation = \"relu\", padding = 'same',\r\n",
        "                kernel_initializer = 'he_normal', name = \"upsampling-8b\")(u8)\r\n",
        "    c8 = Dropout(0.2)(c8)\r\n",
        "    c8 = Conv2D(filters = 32, kernel_size = (2,2),\r\n",
        "                activation = \"relu\", padding = 'same',\r\n",
        "                kernel_initializer = 'he_normal', name = \"upsampling-8c\")(c8)\r\n",
        "\r\n",
        "    \r\n",
        "\r\n",
        "    \r\n",
        "    \r\n",
        "    u9 = Conv2DTranspose(filters = 16, kernel_size = (2,2), \r\n",
        "                         strides = (2,2), padding = \"same\",\r\n",
        "                         name = \"upsampling-9a\")(c8)\r\n",
        "    u9 = concatenate([u9, d1])\r\n",
        "    c9 = Conv2D(filters = 16, kernel_size = (2,2),\r\n",
        "                activation = \"relu\", padding = 'same',\r\n",
        "                kernel_initializer = 'he_normal', name = \"upsampling-9b\")(u9)\r\n",
        "    c9 = Dropout(0.2)(c9)\r\n",
        "    c9 = Conv2D(filters = 16, kernel_size = (2,2),\r\n",
        "                activation = \"relu\", padding = 'same',\r\n",
        "                kernel_initializer = 'he_normal', name = \"upsampling-9c\")(c9)\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "    output = Conv2D(filters = 1, kernel_size = (1,1), name = \"output\", activation = \"sigmoid\")(c9)\r\n",
        "\r\n",
        "    model = Model(inputs = [X_input], outputs = [output], name = \"U_NET_MODEL\")\r\n",
        "    \r\n",
        "    return model"
      ],
      "execution_count": 102,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VVo0NKFh5_Mb",
        "outputId": "53ce4f41-7514-4e42-ffe0-df96648b3ad1"
      },
      "source": [
        "# Free up RAM in case the model definition cells were run multiple times\r\n",
        "tf.keras.backend.clear_session()\r\n",
        "\r\n",
        "\r\n",
        "model = U_NetModel((512, 512,3))\r\n",
        "model.summary()"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"U_NET_MODEL\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            [(None, 512, 512, 3) 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "downsamping-1a (Conv2D)         (None, 512, 512, 16) 448         input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dropout (Dropout)               (None, 512, 512, 16) 0           downsamping-1a[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "downsampling-1b (Conv2D)        (None, 512, 512, 16) 2320        dropout[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D)    (None, 256, 256, 16) 0           downsampling-1b[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "downsamping-2a (Conv2D)         (None, 256, 256, 32) 4640        max_pooling2d[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_1 (Dropout)             (None, 256, 256, 32) 0           downsamping-2a[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "downsampling-2b (Conv2D)        (None, 256, 256, 32) 9248        dropout_1[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2D)  (None, 128, 128, 32) 0           downsampling-2b[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "downsamping-3a (Conv2D)         (None, 128, 128, 64) 18496       max_pooling2d_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "dropout_2 (Dropout)             (None, 128, 128, 64) 0           downsamping-3a[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "downsampling-3b (Conv2D)        (None, 128, 128, 64) 36928       dropout_2[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2D)  (None, 64, 64, 64)   0           downsampling-3b[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "downsamping-4a (Conv2D)         (None, 64, 64, 128)  73856       max_pooling2d_2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "dropout_3 (Dropout)             (None, 64, 64, 128)  0           downsamping-4a[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "downsampling-4b (Conv2D)        (None, 64, 64, 128)  147584      dropout_3[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2D)  (None, 32, 32, 128)  0           downsampling-4b[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "downsamping-5a (Conv2D)         (None, 32, 32, 256)  295168      max_pooling2d_3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "dropout_4 (Dropout)             (None, 32, 32, 256)  0           downsamping-5a[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "downsampling-5b (Conv2D)        (None, 32, 32, 256)  590080      dropout_4[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dropout_5 (Dropout)             (None, 32, 32, 256)  0           downsampling-5b[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "downsampling-5c (Conv2D)        (None, 32, 32, 256)  590080      dropout_5[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "upsampling-6a (Conv2DTranspose) (None, 64, 64, 128)  131200      downsampling-5c[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "concatenate (Concatenate)       (None, 64, 64, 256)  0           upsampling-6a[0][0]              \n",
            "                                                                 downsampling-4b[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "upsampling-6b (Conv2D)          (None, 64, 64, 128)  131200      concatenate[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "dropout_6 (Dropout)             (None, 64, 64, 128)  0           upsampling-6b[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "upsampling-6c (Conv2D)          (None, 64, 64, 128)  65664       dropout_6[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "upsampling-7a (Conv2DTranspose) (None, 128, 128, 64) 32832       upsampling-6c[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_1 (Concatenate)     (None, 128, 128, 128 0           upsampling-7a[0][0]              \n",
            "                                                                 downsampling-3b[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "upsampling-7b (Conv2D)          (None, 128, 128, 64) 32832       concatenate_1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_7 (Dropout)             (None, 128, 128, 64) 0           upsampling-7b[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "upsampling-7c (Conv2D)          (None, 128, 128, 64) 16448       dropout_7[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "upsampling-8a (Conv2DTranspose) (None, 256, 256, 32) 8224        upsampling-7c[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_2 (Concatenate)     (None, 256, 256, 64) 0           upsampling-8a[0][0]              \n",
            "                                                                 downsampling-2b[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "upsampling-8b (Conv2D)          (None, 256, 256, 32) 8224        concatenate_2[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_8 (Dropout)             (None, 256, 256, 32) 0           upsampling-8b[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "upsampling-8c (Conv2D)          (None, 256, 256, 32) 4128        dropout_8[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "upsampling-9a (Conv2DTranspose) (None, 512, 512, 16) 2064        upsampling-8c[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_3 (Concatenate)     (None, 512, 512, 32) 0           upsampling-9a[0][0]              \n",
            "                                                                 downsampling-1b[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "upsampling-9b (Conv2D)          (None, 512, 512, 16) 2064        concatenate_3[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_9 (Dropout)             (None, 512, 512, 16) 0           upsampling-9b[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "upsampling-9c (Conv2D)          (None, 512, 512, 16) 1040        dropout_9[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "output (Conv2D)                 (None, 512, 512, 1)  17          upsampling-9c[0][0]              \n",
            "==================================================================================================\n",
            "Total params: 2,204,785\n",
            "Trainable params: 2,204,785\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T6mbYcpT6heK"
      },
      "source": [
        "# Mean-IOU Intersection Custom Metrics"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MlVRILXb6hHW"
      },
      "source": [
        "import keras.backend as K\r\n",
        "def castF(x):\r\n",
        "    return K.cast(x, K.floatx())\r\n",
        "\r\n",
        "def castB(x):\r\n",
        "    return K.cast(x, bool)\r\n",
        "\r\n",
        "def iou_loss_core(true,pred):  #this can be used as a loss if you make it negative\r\n",
        "    intersection = true * pred\r\n",
        "    notTrue = 1 - true\r\n",
        "    union = true + (notTrue * pred)\r\n",
        "\r\n",
        "    return (K.sum(intersection, axis=-1) + K.epsilon()) / (K.sum(union, axis=-1) + K.epsilon())\r\n",
        "\r\n",
        "def competitionMetric2(true, pred): #any shape can go - can't be a loss function\r\n",
        "\r\n",
        "    tresholds = [0.5 + (i*.05)  for i in range(10)]\r\n",
        "\r\n",
        "    #flattened images (batch, pixels)\r\n",
        "    true = K.batch_flatten(true)\r\n",
        "    pred = K.batch_flatten(pred)\r\n",
        "    pred = castF(K.greater(pred, 0.5))\r\n",
        "\r\n",
        "    #total white pixels - (batch,)\r\n",
        "    trueSum = K.sum(true, axis=-1)\r\n",
        "    predSum = K.sum(pred, axis=-1)\r\n",
        "\r\n",
        "    #has mask or not per image - (batch,)\r\n",
        "    true1 = castF(K.greater(trueSum, 1))    \r\n",
        "    pred1 = castF(K.greater(predSum, 1))\r\n",
        "\r\n",
        "    #to get images that have mask in both true and pred\r\n",
        "    truePositiveMask = castB(true1 * pred1)\r\n",
        "\r\n",
        "    #separating only the possible true positives to check iou\r\n",
        "    testTrue = tf.boolean_mask(true, truePositiveMask)\r\n",
        "    testPred = tf.boolean_mask(pred, truePositiveMask)\r\n",
        "\r\n",
        "    #getting iou and threshold comparisons\r\n",
        "    iou = iou_loss_core(testTrue,testPred) \r\n",
        "    truePositives = [castF(K.greater(iou, tres)) for tres in tresholds]\r\n",
        "\r\n",
        "    #mean of thressholds for true positives and total sum\r\n",
        "    truePositives = K.mean(K.stack(truePositives, axis=-1), axis=-1)\r\n",
        "    truePositives = K.sum(truePositives)\r\n",
        "\r\n",
        "    #to get images that don't have mask in both true and pred\r\n",
        "    trueNegatives = (1-true1) * (1 - pred1) # = 1 -true1 - pred1 + true1*pred1\r\n",
        "    trueNegatives = K.sum(trueNegatives) \r\n",
        "\r\n",
        "    return (truePositives + trueNegatives) / castF(K.shape(true)[0])"
      ],
      "execution_count": 104,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tWg73LgF60YM"
      },
      "source": [
        "model.compile(optimizer= tf.keras.optimizers.Adam(lr = 0.0001),\r\n",
        "              loss = 'binary_crossentropy',\r\n",
        "              metrics= [competitionMetric2])"
      ],
      "execution_count": 105,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        },
        "id": "jUwCLzxZ62jy",
        "outputId": "4efbbc23-e88c-4dd3-e828-e94d627604d9"
      },
      "source": [
        "checkpoint_path = os.path.join(os.getcwd(), 'weights','cp.ckpt')\r\n",
        "checkpoint_dir = os.path.dirname(checkpoint_path)\r\n",
        "#os.mkdir(checkpoint_dir)\r\n",
        "\r\n",
        "callbacks = tf.keras.callbacks.ModelCheckpoint(checkpoint_path, \r\n",
        "                                                save_best_only= False, \r\n",
        "                                                save_weights_only= True,\r\n",
        "                                                verbose = 1,\r\n",
        "                                                monitor = 'val_competitionMetric2',\r\n",
        "                                                mode = 'max')\r\n",
        "                                                \r\n",
        "NO_OF_TRAINING_IMAGES = len(os.listdir(dir_train_frame))\r\n",
        "NO_OF_VAL_IMAGES = len(os.listdir(dir_val_frame))\r\n",
        "num_epochs = 3\r\n",
        "\r\n",
        "\r\n",
        "def scheduler(epoch ,lr):\r\n",
        "    if epoch > num_epochs * 0.85:\r\n",
        "        lr = 0.00001\r\n",
        "    elif epoch > num_epochs * 0.30:\r\n",
        "        lr = 0.0001\r\n",
        "    else:\r\n",
        "        lr = 0.001\r\n",
        "    print(f\"Learning Rate : {lr}\")\r\n",
        "    return lr\r\n",
        "\r\n",
        "from keras.callbacks import LearningRateScheduler \r\n",
        "learning_rate = LearningRateScheduler(scheduler)\r\n",
        "\r\n",
        "from keras.callbacks import EarlyStopping\r\n",
        "earlystopping = EarlyStopping(monitor = 'val_competitionMetric2', verbose = 1,\r\n",
        "                              min_delta = 0.01, patience = 3, mode = 'max')\r\n",
        "\r\n",
        "#latest = tf.train.latest_checkpoint(checkpoint_dir)\r\n",
        "# model.load_weights(latest)\r\n",
        "\r\n",
        "history = model.fit(\r\n",
        "        train_gen,\r\n",
        "        epochs = num_epochs,\r\n",
        "        validation_data= val_gen,\r\n",
        "        callbacks = [callbacks, learning_rate, earlystopping],\r\n",
        "        verbose = 1)\r\n",
        "\r\n",
        "\r\n"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/3\n",
            "Learning Rate : 0.001\n",
            "883/883 [==============================] - 789s 891ms/step - loss: 0.0049 - competitionMetric2: 0.9350 - val_loss: 5.0383e-09 - val_competitionMetric2: 1.0000\n",
            "\n",
            "Epoch 00001: saving model to /content/weights/cp.ckpt\n",
            "Epoch 2/3\n",
            "Learning Rate : 0.0001\n",
            "256/883 [=======>......................] - ETA: 8:36 - loss: 3.8750e-08 - competitionMetric2: 1.0000"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-106-8692dbf3deca>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     40\u001b[0m         \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mval_gen\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m         \u001b[0mcallbacks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlearning_rate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mearlystopping\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 42\u001b[0;31m         verbose = 1)\n\u001b[0m\u001b[1;32m     43\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     44\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1098\u001b[0m                 _r=1):\n\u001b[1;32m   1099\u001b[0m               \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1100\u001b[0;31m               \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1101\u001b[0m               \u001b[0;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1102\u001b[0m                 \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    826\u001b[0m     \u001b[0mtracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    827\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mtrace\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTrace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_name\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtm\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 828\u001b[0;31m       \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    829\u001b[0m       \u001b[0mcompiler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"xla\"\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_experimental_compile\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m\"nonXla\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    830\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    853\u001b[0m       \u001b[0;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    854\u001b[0m       \u001b[0;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 855\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    856\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    857\u001b[0m       \u001b[0;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2941\u001b[0m        filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[1;32m   2942\u001b[0m     return graph_function._call_flat(\n\u001b[0;32m-> 2943\u001b[0;31m         filtered_flat_args, captured_inputs=graph_function.captured_inputs)  # pylint: disable=protected-access\n\u001b[0m\u001b[1;32m   2944\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2945\u001b[0m   \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1917\u001b[0m       \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1918\u001b[0m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0;32m-> 1919\u001b[0;31m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0m\u001b[1;32m   1920\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n\u001b[1;32m   1921\u001b[0m         \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    558\u001b[0m               \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    559\u001b[0m               \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 560\u001b[0;31m               ctx=ctx)\n\u001b[0m\u001b[1;32m    561\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    562\u001b[0m           outputs = execute.execute_with_cancellation(\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0;32m---> 60\u001b[0;31m                                         inputs, attrs, num_outputs)\n\u001b[0m\u001b[1;32m     61\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zlunrvXsBtBZ"
      },
      "source": [
        "predict_dir = os.path.join(os.getcwd(), 'predict')\r\n",
        "os.mkdir(predict_dir)"
      ],
      "execution_count": 107,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9iIaBzgwBkcf"
      },
      "source": [
        "def predict_segmentation(input_img):\r\n",
        "    latest = tf.train.latest_checkpoint(checkpoint_dir)\r\n",
        "    model.load_weights(latest)\r\n",
        "    fig, ax = plt.subplots(2, figsize  = (10, 6))\r\n",
        "    img_train = mpimg.imread(input_img)\r\n",
        "    img = adjust_gamma(img_train)\r\n",
        "    img = differnce_of_gaussian_filter(img)\r\n",
        "    img_train = cv.resize(img_train, (512,512))\r\n",
        "    img = cv.resize(img, (512,512))\r\n",
        "    plt.imsave(os.path.join(predict_dir, 'img-1.jpg'), img_train)\r\n",
        "    ax[0].imshow(img_train)\r\n",
        "    img_train = img_train / 255.\r\n",
        "\r\n",
        "    img_train = np.expand_dims(img, 0)\r\n",
        "    predict = model.predict(img_train, verbose = 1)\r\n",
        "    prt = (predict > 0.4).astype(np.uint8)\r\n",
        "    plt.imsave(os.path.join(predict_dir,'mask-1.jpg'), np.squeeze(prt), cmap = 'gray')\r\n",
        "    ax[1].imshow(np.squeeze(prt), cmap = 'gray')\r\n",
        "    plt.show()\r\n"
      ],
      "execution_count": 114,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        },
        "id": "D7trNVMTCB5O",
        "outputId": "1ae3a88e-7e05-478e-ec2c-d9ceaf00af3f"
      },
      "source": [
        "predict_segmentation(os.path.join(dir_train_frame, '00000001.jpg'))"
      ],
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1/1 [==============================] - 0s 469ms/step\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4gRQ1Su_9-JT"
      },
      "source": [
        "import pandas as pd\r\n",
        "\r\n",
        "output_metadata = pd.read_csv(os.path.join(os.getcwd(), 'lip','list.csv'))\r\n",
        "output_metadata.head(10)\r\n",
        " \r\n",
        "new_name_l = []\r\n",
        "new_mask_l = []\r\n",
        "for row in output_metadata.itertuples():\r\n",
        "  new_name = re.split('(\\d+)',row.filename)\r\n",
        "  new_mask = re.split('(\\d+)',row.mask)\r\n",
        "  new_name_l.append(new_name[1] + new_name[2])\r\n",
        "  new_mask_l.append(new_mask[1] + new_mask[2])\r\n",
        "\r\n",
        "output_metadata.drop(labels = ['filename', 'mask', 'width', 'height', 'class'], axis = 1, inplace = True)\r\n",
        "output_metadata['file_name'] = new_name_l\r\n",
        "output_metadata['mask'] = new_mask_l\r\n",
        "\r\n",
        "\r\n",
        "output_metadata.set_index(output_metadata['mask'], inplace = True)\r\n",
        "output_metadata.drop(labels = ['mask'], axis = 1, inplace = True)\r\n",
        "output_metadata.head()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HnoLr2h9sxJ6"
      },
      "source": [
        "\r\n",
        "*   width -----------------> (512/ 1280) * value\r\n",
        "\r\n",
        "*   height ----------------> (512/720) * value\r\n",
        "\r\n",
        "\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hBPVljAwnSr6"
      },
      "source": [
        "output_metadata['xmin'] *= (512/ 1280) \r\n",
        "output_metadata['xmax'] *= (512 / 1280)\r\n",
        "output_metadata['ymin'] *= (512 / 720)\r\n",
        "output_metadata['ymax'] *= (512 / 720)\r\n",
        "\r\n",
        "output_metadata.xmin = output_metadata.xmin.astype(int)\r\n",
        "output_metadata.xmax =  output_metadata.xmax.astype(int)\r\n",
        "output_metadata.ymin =  output_metadata.ymin.astype(int)\r\n",
        "output_metadata.ymax =  output_metadata.ymax.astype(int)\r\n",
        "\r\n",
        "output_metadata.head()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "El4jBwaBuFyJ",
        "outputId": "b8c62f0e-8ffd-4d5f-88df-66f40711cb08"
      },
      "source": [
        "from skimage.feature import hog\r\n",
        "from skimage import exposure\r\n",
        "image = mpimg.imread(os.path.join(dir_train_frame, '00000001.jpg'))\r\n",
        "image = cv.resize(image, (512,512))\r\n",
        "\r\n",
        "image = image\r\n",
        "\r\n",
        "\r\n",
        "fd, hog_image = hog(image, orientations=9, pixels_per_cell=(8, 8),\r\n",
        "                    cells_per_block=(2, 2), visualize=True, multichannel=True)\r\n",
        "\r\n",
        "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4), sharex=True, sharey=True)\r\n",
        "\r\n",
        "ax1.axis('off')\r\n",
        "ax1.imshow(image, cmap=plt.cm.gray)\r\n",
        "ax1.set_title('Input image')\r\n",
        "\r\n",
        "# Rescale histogram for better display\r\n",
        "hog_image_rescaled = exposure.rescale_intensity(hog_image, in_range=(0, 10))\r\n",
        "\r\n",
        "print(image.shape)\r\n",
        "print(hog_image_rescaled.shape)\r\n",
        "\r\n",
        "ax2.axis('off')\r\n",
        "ax2.imshow(hog_image_rescaled, cmap=plt.cm.gray)\r\n",
        "ax2.set_title('Histogram of Oriented Gradients')\r\n",
        "plt.show()"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(512, 512, 3)\n",
            "(512, 512)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "ETPNZMmzwLyg",
        "outputId": "33712b84-7d0b-43ec-e665-ceac59e46fcd"
      },
      "source": [
        "def adjust_gamma(image, gamma = 0.2):\r\n",
        "    table = np.array([\r\n",
        "                    ((i / 255.0) ** gamma) * 255 for i in np.arange(0,256)\r\n",
        "                    ]).astype('uint8')\r\n",
        "\r\n",
        "    return cv.LUT(image, table)\r\n",
        "\r\n",
        "def differnce_of_gaussian_filter(img, kernel_1 = (3,3), kernel_2 = (5,5), sigmoid_1 = 1.0, sigmoid_2 = 2.0):\r\n",
        "    low_sigma = cv.GaussianBlur(img, kernel_1, sigmoid_1)\r\n",
        "    high_sigma = cv.GaussianBlur(img, kernel_2, sigmoid_2)\r\n",
        "\r\n",
        "    return low_sigma - high_sigma\r\n",
        "\r\n",
        "img = adjust_gamma(image)\r\n",
        "img = differnce_of_gaussian_filter(img)\r\n",
        "img = img / 255.0\r\n",
        "plt.imshow(img)\r\n",
        "print(img.shape)\r\n",
        "print(type(image))"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(512, 512, 3)\n",
            "<class 'numpy.ndarray'>\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nH97T2Ev83_B"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}