Tesseract_OCR_SampleData.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "mount_file_id": "1ec580XK_R6FrMiG-WazL6qJipKAz9WbZ",
      "authorship_tag": "ABX9TyPLT7awbumLOPgFKklOdIwm",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/oyoungsun/OCRproject/blob/main/Tesseract_OCR_SampleData.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tkCjt5kGTFy4",
        "outputId": "34494a05-c136-41b4-efe7-be3687c2967c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "The following additional packages will be installed:\n",
            "  tesseract-ocr-eng tesseract-ocr-osd\n",
            "The following NEW packages will be installed:\n",
            "  tesseract-ocr tesseract-ocr-eng tesseract-ocr-osd\n",
            "0 upgraded, 3 newly installed, 0 to remove and 23 not upgraded.\n",
            "Need to get 4,850 kB of archives.\n",
            "After this operation, 16.3 MB of additional disk space will be used.\n",
            "Get:1 http://archive.ubuntu.com/ubuntu focal/universe amd64 tesseract-ocr-eng all 1:4.00~git30-7274cfa-1 [1,598 kB]\n",
            "Get:2 http://archive.ubuntu.com/ubuntu focal/universe amd64 tesseract-ocr-osd all 1:4.00~git30-7274cfa-1 [2,990 kB]\n",
            "Get:3 http://archive.ubuntu.com/ubuntu focal/universe amd64 tesseract-ocr amd64 4.1.1-2build2 [262 kB]\n",
            "Fetched 4,850 kB in 3s (1,784 kB/s)\n",
            "debconf: unable to initialize frontend: Dialog\n",
            "debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 76, <> line 3.)\n",
            "debconf: falling back to frontend: Readline\n",
            "debconf: unable to initialize frontend: Readline\n",
            "debconf: (This frontend requires a controlling tty.)\n",
            "debconf: falling back to frontend: Teletype\n",
            "dpkg-preconfigure: unable to re-open stdin: \n",
            "Selecting previously unselected package tesseract-ocr-eng.\n",
            "(Reading database ... 128288 files and directories currently installed.)\n",
            "Preparing to unpack .../tesseract-ocr-eng_1%3a4.00~git30-7274cfa-1_all.deb ...\n",
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            "Selecting previously unselected package tesseract-ocr-osd.\n",
            "Preparing to unpack .../tesseract-ocr-osd_1%3a4.00~git30-7274cfa-1_all.deb ...\n",
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            "Selecting previously unselected package tesseract-ocr.\n",
            "Preparing to unpack .../tesseract-ocr_4.1.1-2build2_amd64.deb ...\n",
            "Unpacking tesseract-ocr (4.1.1-2build2) ...\n",
            "Setting up tesseract-ocr-eng (1:4.00~git30-7274cfa-1) ...\n",
            "Setting up tesseract-ocr-osd (1:4.00~git30-7274cfa-1) ...\n",
            "Setting up tesseract-ocr (4.1.1-2build2) ...\n",
            "Processing triggers for man-db (2.9.1-1) ...\n",
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "The following additional packages will be installed:\n",
            "  libarchive-dev libleptonica-dev\n",
            "The following NEW packages will be installed:\n",
            "  libarchive-dev libleptonica-dev libtesseract-dev\n",
            "0 upgraded, 3 newly installed, 0 to remove and 23 not upgraded.\n",
            "Need to get 3,343 kB of archives.\n",
            "After this operation, 15.7 MB of additional disk space will be used.\n",
            "Get:1 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libarchive-dev amd64 3.4.0-2ubuntu1.2 [491 kB]\n",
            "Get:2 http://archive.ubuntu.com/ubuntu focal/universe amd64 libleptonica-dev amd64 1.79.0-1 [1,389 kB]\n",
            "Get:3 http://archive.ubuntu.com/ubuntu focal/universe amd64 libtesseract-dev amd64 4.1.1-2build2 [1,463 kB]\n",
            "Fetched 3,343 kB in 3s (1,178 kB/s)\n",
            "debconf: unable to initialize frontend: Dialog\n",
            "debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 76, <> line 3.)\n",
            "debconf: falling back to frontend: Readline\n",
            "debconf: unable to initialize frontend: Readline\n",
            "debconf: (This frontend requires a controlling tty.)\n",
            "debconf: falling back to frontend: Teletype\n",
            "dpkg-preconfigure: unable to re-open stdin: \n",
            "Selecting previously unselected package libarchive-dev:amd64.\n",
            "(Reading database ... 128335 files and directories currently installed.)\n",
            "Preparing to unpack .../libarchive-dev_3.4.0-2ubuntu1.2_amd64.deb ...\n",
            "Unpacking libarchive-dev:amd64 (3.4.0-2ubuntu1.2) ...\n",
            "Selecting previously unselected package libleptonica-dev:amd64.\n",
            "Preparing to unpack .../libleptonica-dev_1.79.0-1_amd64.deb ...\n",
            "Unpacking libleptonica-dev:amd64 (1.79.0-1) ...\n",
            "Selecting previously unselected package libtesseract-dev:amd64.\n",
            "Preparing to unpack .../libtesseract-dev_4.1.1-2build2_amd64.deb ...\n",
            "Unpacking libtesseract-dev:amd64 (4.1.1-2build2) ...\n",
            "Setting up libleptonica-dev:amd64 (1.79.0-1) ...\n",
            "Setting up libarchive-dev:amd64 (3.4.0-2ubuntu1.2) ...\n",
            "Setting up libtesseract-dev:amd64 (4.1.1-2build2) ...\n",
            "Processing triggers for man-db (2.9.1-1) ...\n"
          ]
        }
      ],
      "source": [
        "!sudo apt install tesseract-ocr\n",
        "!sudo apt install libtesseract-dev"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pytesseract"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IaYwIkKqTUdS",
        "outputId": "923e425c-1a1b-41fc-aa2b-4e06b2913653"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting pytesseract\n",
            "  Downloading pytesseract-0.3.10-py3-none-any.whl (14 kB)\n",
            "Requirement already satisfied: Pillow>=8.0.0 in /usr/local/lib/python3.9/dist-packages (from pytesseract) (8.4.0)\n",
            "Requirement already satisfied: packaging>=21.3 in /usr/local/lib/python3.9/dist-packages (from pytesseract) (23.0)\n",
            "Installing collected packages: pytesseract\n",
            "Successfully installed pytesseract-0.3.10\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#한국어 검출 lib\n",
        "!sudo apt-get install tesseract-ocr-kor"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wdnOeOxsedzn",
        "outputId": "6ee308cf-a013-4d10-b113-7e06d5894ac6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "The following NEW packages will be installed:\n",
            "  tesseract-ocr-kor\n",
            "0 upgraded, 1 newly installed, 0 to remove and 23 not upgraded.\n",
            "Need to get 1,051 kB of archives.\n",
            "After this operation, 1,693 kB of additional disk space will be used.\n",
            "Get:1 http://archive.ubuntu.com/ubuntu focal/universe amd64 tesseract-ocr-kor all 1:4.00~git30-7274cfa-1 [1,051 kB]\n",
            "Fetched 1,051 kB in 2s (679 kB/s)\n",
            "debconf: unable to initialize frontend: Dialog\n",
            "debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 76, <> line 1.)\n",
            "debconf: falling back to frontend: Readline\n",
            "debconf: unable to initialize frontend: Readline\n",
            "debconf: (This frontend requires a controlling tty.)\n",
            "debconf: falling back to frontend: Teletype\n",
            "dpkg-preconfigure: unable to re-open stdin: \n",
            "Selecting previously unselected package tesseract-ocr-kor.\n",
            "(Reading database ... 128466 files and directories currently installed.)\n",
            "Preparing to unpack .../tesseract-ocr-kor_1%3a4.00~git30-7274cfa-1_all.deb ...\n",
            "Unpacking tesseract-ocr-kor (1:4.00~git30-7274cfa-1) ...\n",
            "Setting up tesseract-ocr-kor (1:4.00~git30-7274cfa-1) ...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **import**"
      ],
      "metadata": {
        "id": "qRZaE3SrUWO4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "from PIL import Image\n",
        "from google.colab.patches import cv2_imshow\n",
        "# from pytesseract import Output\n",
        "# import pytesseract\n",
        "import argparse\n",
        "import imutils\n",
        "import cv2"
      ],
      "metadata": {
        "id": "5QQwVRsPUHIA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#지원하는 언어 리스트\n",
        "print(pytesseract.get_languages())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FOzCWpJvU7IW",
        "outputId": "fcba30c3-7373-4fa3-e020-c4dcc948e52e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['eng', 'kor', 'osd']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **코드 실행부**"
      ],
      "metadata": {
        "id": "lQ_KBnL9USIw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 데이터 파일 불러오기\n"
      ],
      "metadata": {
        "id": "0sk-iVzjJoXj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#드라이브 마운트\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qDZ_IDQkWNvh",
        "outputId": "9b211086-21d9-472b-d6ac-64f68712c1ac"
      },
      "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": [
        "#image 불러오기 test\n",
        "path = '/00000019.png'\n",
        "image = cv2.imread(path, cv2.IMREAD_COLOR)\n",
        "path = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
        "cv2_imshow(image)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 65
        },
        "id": "uKvuY-dRaGk-",
        "outputId": "947dbbb7-2f04-4e1f-9fa4-96ea020e5581"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=3743x188 at 0x7FC1B67DFE50>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#imgage 흑백으로 불러와서 텍스트 kor로 추출\n",
        "handpath = '/content/drive/MyDrive/Colab Notebooks/New_sample/원천데이터/01_handwriting_sentence_images/1_sentence/00000019.png'\n",
        "\n",
        "img = cv2.imread(handpath)\n",
        "gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
        "\n",
        "# pytesseract_to_string 함수로 텍스트 추출\n",
        "#custom_config = r'--psm 4'\n",
        "#print(pytesseract.image_to_string(img, config=custom_config, lang='kor'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 267
        },
        "id": "K3CqlZgjUKpw",
        "outputId": "74eb7b93-afbe-41e4-bed4-60ef2752c73c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "error",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31merror\u001b[0m                                     Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-22-e5caa891834e>\u001b[0m in \u001b[0;36m<cell line: 5>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhandpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mgray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcvtColor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCOLOR_BGR2GRAY\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      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;31m# pytesseract_to_string 함수로 텍스트 추출\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31merror\u001b[0m: OpenCV(4.7.0) /io/opencv/modules/imgproc/src/color.cpp:182: error: (-215:Assertion failed) !_src.empty() in function 'cvtColor'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "한국어 인식은..매우매우 떨어짐..\n",
        "\n",
        "## 이미지 TIF 파일 형식자 바꿔주기"
      ],
      "metadata": {
        "id": "PvHEN2AKK0uW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pillow\n",
        "!pip install image"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WHiqvUImNhvJ",
        "outputId": "05653cb0-436e-4a25-ecf4-7f2986bf3783"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: pillow in /usr/local/lib/python3.9/dist-packages (8.4.0)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: image in /usr/local/lib/python3.9/dist-packages (1.5.33)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.9/dist-packages (from image) (1.16.0)\n",
            "Requirement already satisfied: django in /usr/local/lib/python3.9/dist-packages (from image) (4.1.7)\n",
            "Requirement already satisfied: pillow in /usr/local/lib/python3.9/dist-packages (from image) (8.4.0)\n",
            "Requirement already satisfied: asgiref<4,>=3.5.2 in /usr/local/lib/python3.9/dist-packages (from django->image) (3.6.0)\n",
            "Requirement already satisfied: sqlparse>=0.2.2 in /usr/local/lib/python3.9/dist-packages (from django->image) (0.4.3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# # TEST -*- coding: utf-8 -*-\n",
        "# from PIL import Image\n",
        "\n",
        "# # 2. 이미지 파일 변환(에러) #\n",
        "# handpath = '/content/drive/MyDrive/Colab Notebooks/New_sample/원천데이터/01_handwriting_sentence_images/1_sentence/00000019.png'\n",
        "# savepath = '/content/drive/MyDrive/Colab Notebooks/New_sample/원천데이터/01_handwriting_sentence_images/1_sentence_tif'\n",
        "# try:\n",
        "#     im = Image.open(handpath)\n",
        "#     im.save(savepath+'/mykor.hand.exp1.tif', 'tiff')\n",
        "# except OSError as e:\n",
        "#     print(e)\n"
      ],
      "metadata": {
        "id": "5RqyWaj4OTZu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "img  = savepath = '/content/drive/MyDrive/Colab Notebooks/New_sample/원천데이터/01_handwriting_sentence_images/1_sentence_tif/mykor.hand.exp1.tif'\n",
        "img\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "EgPzXpM6TbW2",
        "outputId": "fcc70c2f-5660-4b24-ba73-72c5abd29d22"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/content/drive/MyDrive/Colab Notebooks/New_sample/원천데이터/01_handwriting_sentence_images/1_sentence_tif/mykor.hand.exp1.tif'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tesseract img mykor.hand.exp1 -l kor batch.nochop makebox"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138
        },
        "id": "GFKr010RKeXh",
        "outputId": "f374ac73-1221-49af-a408-7c0eed0997d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "SyntaxError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-51-2700814999d0>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    tesseract img mykor.hand.exp1 -l kor batch.nochop makebox\u001b[0m\n\u001b[0m              ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text2image --text=example.txt --outputbase=mylang.NanumGothic.exp0 --font='NanumGothic' --fonts_dir=."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138
        },
        "id": "yiR0TcIYTSsm",
        "outputId": "34261634-1c72-410e-977a-fcf7ecf72ab4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "SyntaxError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-60-9a98ba60d51e>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    text2image --text=example.txt --outputbase=mylang.NanumGothic.exp0 --font='NanumGothic' --fonts_dir=.\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m cannot assign to operator\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "baDeZlkcUJ5g"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "네이버 나눔손글씨 글꼴 학습시키기"
      ],
      "metadata": {
        "id": "hmH6IfEtcBf6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!cd '/content/drive/MyDrive/Colab Notebooks/"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RwahT_8PdQwW",
        "outputId": "edc0e676-bbee-4d4e-fbb9-fb35dbb98b93"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/bin/bash: -c: line 0: unexpected EOF while looking for matching `''\n",
            "/bin/bash: -c: line 1: syntax error: unexpected end of file\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pwd"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "2l4PIm03eDTZ",
        "outputId": "df062a42-4904-491b-d4a6-3cbbdd6d50a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/content/ocr_kor/data'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/parksunwoo/ocr_kor"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hkvqgkp8cEwI",
        "outputId": "f1e9818a-7ef0-4db9-9a92-a7afe9324bf4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'ocr_kor'...\n",
            "remote: Enumerating objects: 525, done.\u001b[K\n",
            "remote: Counting objects: 100% (65/65), done.\u001b[K\n",
            "remote: Compressing objects: 100% (46/46), done.\u001b[K\n",
            "remote: Total 525 (delta 20), reused 60 (delta 18), pack-reused 460\u001b[K\n",
            "Receiving objects: 100% (525/525), 215.91 MiB | 19.36 MiB/s, done.\n",
            "Resolving deltas: 100% (55/55), done.\n",
            "Updating files: 100% (271/271), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content/ocr_kor/data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cdqdNkZuc8kI",
        "outputId": "36362940-7325-43e6-9813-51329ddc6d1e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/ocr_kor/data\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!./create_gt_file.sh basic | tee -a gt_basic.txt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7HrqXDjddpv_",
        "outputId": "58670a1c-eb65-438a-ee90-3189776e244f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "basic\n",
            "generator/TextRecognitionDataGenerator/out/basic/저녁때 기업인 식생활 만나다 기업_0.jpg\t저녁때 기업인 식생활 만나다 기업\n",
            "generator/TextRecognitionDataGenerator/out/basic/중반 용서하다 치우다 깊숙이 화요일 그만큼 가요 쪽 혹시 자라나다_0.jpg\t중반 용서하다 치우다 깊숙이 화요일 그만큼 가요 쪽 혹시 자라나다\n",
            "generator/TextRecognitionDataGenerator/out/basic/치즈 기록하다 전화기 문학 목요일_0.jpg\t치즈 기록하다 전화기 문학 목요일\n",
            "generator/TextRecognitionDataGenerator/out/basic/매너 미디어 타다 보장 전선_0.jpg\t매너 미디어 타다 보장 전선\n",
            "generator/TextRecognitionDataGenerator/out/basic/농민 특성 지우개 철도 전설 벽 향하다 아스팔트 모두 존중하다_0.jpg\t농민 특성 지우개 철도 전설 벽 향하다 아스팔트 모두 존중하다\n",
            "generator/TextRecognitionDataGenerator/out/basic/유형 남성 시설 걱정 발전하다_0.jpg\t유형 남성 시설 걱정 발전하다\n",
            "generator/TextRecognitionDataGenerator/out/basic/차 탓 운전 발전되다 서른_0.jpg\t차 탓 운전 발전되다 서른\n",
            "generator/TextRecognitionDataGenerator/out/basic/발달하다 주다 자기 짓다 전_1.jpg\t발달하다 주다 자기 짓다 전\n",
            "generator/TextRecognitionDataGenerator/out/basic/국제화 아쉬움 넘치다 뜨다 낡다_1.jpg\t국제화 아쉬움 넘치다 뜨다 낡다\n",
            "generator/TextRecognitionDataGenerator/out/basic/가스 까맣다 나빠지다 생선 출판 흐르다 비롯되다 인격 자랑스럽다 저렇게_1.jpg\t가스 까맣다 나빠지다 생선 출판 흐르다 비롯되다 인격 자랑스럽다 저렇게\n",
            "generator/TextRecognitionDataGenerator/out/basic/지도 씨름 나이 눈썹 결혼 조깅 궁금하다 대중 뚜껑 충돌_1.jpg\t지도 씨름 나이 눈썹 결혼 조깅 궁금하다 대중 뚜껑 충돌\n",
            "generator/TextRecognitionDataGenerator/out/basic/변명 인간 어서 뒷산 행운 꼴 거 종일 타입 얘_1.jpg\t변명 인간 어서 뒷산 행운 꼴 거 종일 타입 얘\n",
            "generator/TextRecognitionDataGenerator/out/basic/무기 턱 남대문 보완하다 바깥_1.jpg\t무기 턱 남대문 보완하다 바깥\n",
            "generator/TextRecognitionDataGenerator/out/basic/멋 굶다 올 글자 관심_1.jpg\t멋 굶다 올 글자 관심\n",
            "generator/TextRecognitionDataGenerator/out/basic/기초하다 냉면 속도 헤아리다 연속_2.jpg\t기초하다 냉면 속도 헤아리다 연속\n",
            "generator/TextRecognitionDataGenerator/out/basic/살피다 따라가다 수많다 비우다 증상_2.jpg\t살피다 따라가다 수많다 비우다 증상\n",
            "generator/TextRecognitionDataGenerator/out/basic/피부 하드웨어 시외버스 인체 근육 중국집 보수적 엉터리 어때 판매하다_2.jpg\t피부 하드웨어 시외버스 인체 근육 중국집 보수적 엉터리 어때 판매하다\n",
            "generator/TextRecognitionDataGenerator/out/basic/확장 옳다 출구 속도 얼마나_2.jpg\t확장 옳다 출구 속도 얼마나\n",
            "generator/TextRecognitionDataGenerator/out/basic/경향 동서 무엇 만 감사하다 불확실하다 때때로 공항버스 시원하다 피해_2.jpg\t경향 동서 무엇 만 감사하다 불확실하다 때때로 공항버스 시원하다 피해\n",
            "generator/TextRecognitionDataGenerator/out/basic/부드럽다 떨리다 중국집 입사 의하다_3.jpg\t부드럽다 떨리다 중국집 입사 의하다\n",
            "generator/TextRecognitionDataGenerator/out/basic/예선 현지 억 대단히 통합_3.jpg\t예선 현지 억 대단히 통합\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd .."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZfcQ40wHfy1E",
        "outputId": "77ac4dfc-a5f6-423c-bb25-7ccf0afa2720"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/ocr_kor\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install fire\n",
        "!pip install lmdb"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0YAR5Ihof-Nl",
        "outputId": "249c307a-c168-4726-a4bf-47da07dfb84b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: fire in /usr/local/lib/python3.9/dist-packages (0.5.0)\n",
            "Requirement already satisfied: termcolor in /usr/local/lib/python3.9/dist-packages (from fire) (2.2.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.9/dist-packages (from fire) (1.16.0)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting lmdb\n",
            "  Downloading lmdb-1.4.0-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (305 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m305.9/305.9 KB\u001b[0m \u001b[31m5.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: lmdb\n",
            "Successfully installed lmdb-1.4.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "MZEG7c0Yf27h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!python3 data/create_lmdb_dataset.py --inputPath data/generator/TextRecognitionDataGenerator/ --gtFile data/gt_basic.txt --outputPath data/data_lmdb_release/training;"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PeKso-ZDetZ2",
        "outputId": "55f299cd-bd35-4f46-df3c-c96647a2f0de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "data/generator/TextRecognitionDataGenerator/out/basic/조 응 네 제일 배 진행자 과 엄마 집단적 서울역_1.jpg does not exist\n",
            "data/generator/TextRecognitionDataGenerator/out/basic/석 댐 햄 미처 이래 거리 울리다 위쪽 발걸음 신입생_1.jpg does not exist\n",
            "data/generator/TextRecognitionDataGenerator/out/basic/뺨 전 첫 소비 지능 원래 관심사 더더욱 미스 출판사_0.jpg does not exist\n",
            "data/generator/TextRecognitionDataGenerator/out/basic/몸 곰 씨 마침 요즈음 형 외로움 잘하다 심리 교사_1.jpg does not exist\n",
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            "data/generator/TextRecognitionDataGenerator/out/basic/개 보도 짜증 짓 뿌리다 합격하다 흙 변하다 자유 동_1.jpg does not exist\n",
            "data/generator/TextRecognitionDataGenerator/out/basic/해 뽑다 지진 짓 칭찬하다 손질 장군 지난달 담그다 돌_0.jpg does not exist\n",
            "data/generator/TextRecognitionDataGenerator/out/basic/종 인분 뜻밖 듯 몰려오다 각자 다양하다 비교적 승리하다 웬_3.jpg does not exist\n",
            "data/generator/TextRecognitionDataGenerator/out/basic/식 식물 여건 선 생활하다 기숙사 식품 자살 다소 비_3.jpg does not exist\n",
            "Traceback (most recent call last):\n",
            "  File \"/content/ocr_kor/data/create_lmdb_dataset.py\", line 87, in <module>\n",
            "    fire.Fire(createDataset)\n",
            "  File \"/usr/local/lib/python3.9/dist-packages/fire/core.py\", line 141, in Fire\n",
            "    component_trace = _Fire(component, args, parsed_flag_args, context, name)\n",
            "  File \"/usr/local/lib/python3.9/dist-packages/fire/core.py\", line 475, in _Fire\n",
            "    component, remaining_args = _CallAndUpdateTrace(\n",
            "  File \"/usr/local/lib/python3.9/dist-packages/fire/core.py\", line 691, in _CallAndUpdateTrace\n",
            "    component = fn(*varargs, **kwargs)\n",
            "  File \"/content/ocr_kor/data/create_lmdb_dataset.py\", line 47, in createDataset\n",
            "    imagePath, label = datalist[i].strip('\\n').split('\\t')\n",
            "ValueError: not enough values to unpack (expected 2, got 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! CUDA_VISIBLE_DEVICES=0 python3 deep-text-recognition-benchmark/train.py --train_data data/data_lmdb_release/training --valid_data data/data_lmdb_release/validation --select_data basic-skew --batch_ratio 0.5-0.5 --Transformation TPS --FeatureExtraction VGG--SequenceModeling None --Prediction Attn --data_filtering_off  --batch_max_length 50 --workers 4;\n",
        "\n",
        "! CUDA_VISIBLE_DEVICES=0 python3 deep-text-recognition-benchmark/test.py --eval_data data/data_lmdb_release/evaluation --benchmark_all_eval --Transformation TPS --FeatureExtraction VGG --SequenceModeling None --Prediction Attn --saved_model saved_models/TPS-VGG-None-Attn-Seed1111/best_accuracy.pth --data_filtering_off --workers 4"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DU3GVz8Kflbw",
        "outputId": "5c5e8ccd-2701-46b9-dc78-781e47692b64"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "usage: train.py\n",
            "       [-h]\n",
            "       [--experiment_name EXPERIMENT_NAME]\n",
            "       [--train_data TRAIN_DATA]\n",
            "       [--valid_data VALID_DATA]\n",
            "       [--manualSeed MANUALSEED]\n",
            "       [--workers WORKERS]\n",
            "       [--batch_size BATCH_SIZE]\n",
            "       [--num_iter NUM_ITER]\n",
            "       [--valInterval VALINTERVAL]\n",
            "       [--continue_model CONTINUE_MODEL]\n",
            "       [--adam]\n",
            "       [--lr LR]\n",
            "       [--beta1 BETA1]\n",
            "       [--rho RHO]\n",
            "       [--eps EPS]\n",
            "       [--grad_clip GRAD_CLIP]\n",
            "       [--select_data SELECT_DATA]\n",
            "       [--batch_ratio BATCH_RATIO]\n",
            "       [--total_data_usage_ratio TOTAL_DATA_USAGE_RATIO]\n",
            "       [--batch_max_length BATCH_MAX_LENGTH]\n",
            "       [--imgH IMGH]\n",
            "       [--imgW IMGW]\n",
            "       [--rgb]\n",
            "       [--character CHARACTER]\n",
            "       [--sensitive]\n",
            "       [--PAD]\n",
            "       [--data_filtering_off]\n",
            "       --Transformation\n",
            "       TRANSFORMATION\n",
            "       --FeatureExtraction\n",
            "       FEATUREEXTRACTION\n",
            "       --SequenceModeling\n",
            "       SEQUENCEMODELING\n",
            "       --Prediction\n",
            "       PREDICTION\n",
            "       [--num_fiducial NUM_FIDUCIAL]\n",
            "       [--input_channel INPUT_CHANNEL]\n",
            "       [--output_channel OUTPUT_CHANNEL]\n",
            "       [--hidden_size HIDDEN_SIZE]\n",
            "       [--gpu_devices GPU_DEVICES [GPU_DEVICES ...]]\n",
            "       [--gpu GPU]\n",
            "       [--dist-url DIST_URL]\n",
            "       [--dist-backend DIST_BACKEND]\n",
            "       [--local_rank LOCAL_RANK]\n",
            "       [--world_size WORLD_SIZE]\n",
            "       [--distributed]\n",
            "train.py: error: the following arguments are required: --SequenceModeling\n",
            "/usr/local/lib/python3.9/dist-packages/torch/cuda/__init__.py:497: UserWarning: Can't initialize NVML\n",
            "  warnings.warn(\"Can't initialize NVML\")\n",
            "No SequenceModeling module specified\n",
            "model input parameters 32 100 20 1 512 256 974 50 TPS VGG None Attn\n",
            "loading pretrained model from saved_models/TPS-VGG-None-Attn-Seed1111/best_accuracy.pth\n",
            "Traceback (most recent call last):\n",
            "  File \"/content/ocr_kor/deep-text-recognition-benchmark/test.py\", line 236, in <module>\n",
            "    test(opt)\n",
            "  File \"/content/ocr_kor/deep-text-recognition-benchmark/test.py\", line 162, in test\n",
            "    model.load_state_dict(torch.load(opt.saved_model, map_location='cuda:0'))\n",
            "  File \"/usr/local/lib/python3.9/dist-packages/torch/serialization.py\", line 771, in load\n",
            "    with _open_file_like(f, 'rb') as opened_file:\n",
            "  File \"/usr/local/lib/python3.9/dist-packages/torch/serialization.py\", line 270, in _open_file_like\n",
            "    return _open_file(name_or_buffer, mode)\n",
            "  File \"/usr/local/lib/python3.9/dist-packages/torch/serialization.py\", line 251, in __init__\n",
            "    super(_open_file, self).__init__(open(name, mode))\n",
            "FileNotFoundError: [Errno 2] No such file or directory: 'saved_models/TPS-VGG-None-Attn-Seed1111/best_accuracy.pth'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!cd /ocr_kor\n",
        "!pwd"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KvAQN8uTgbnk",
        "outputId": "aecab44f-413d-4b32-9a17-33a92d744e12"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/bin/bash: line 0: cd: /ocr_kor: No such file or directory\n",
            "/content\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pytorch"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b-iynMBYg89X",
        "outputId": "2a138df6-615d-4cd4-cf1a-5a10ef065d8a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting pytorch\n",
            "  Downloading pytorch-1.0.2.tar.gz (689 bytes)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Building wheels for collected packages: pytorch\n",
            "  \u001b[1;31merror\u001b[0m: \u001b[1msubprocess-exited-with-error\u001b[0m\n",
            "  \n",
            "  \u001b[31m×\u001b[0m \u001b[32mpython setup.py bdist_wheel\u001b[0m did not run successfully.\n",
            "  \u001b[31m│\u001b[0m exit code: \u001b[1;36m1\u001b[0m\n",
            "  \u001b[31m╰─>\u001b[0m See above for output.\n",
            "  \n",
            "  \u001b[1;35mnote\u001b[0m: This error originates from a subprocess, and is likely not a problem with pip.\n",
            "  Building wheel for pytorch (setup.py) ... \u001b[?25lerror\n",
            "\u001b[31m  ERROR: Failed building wheel for pytorch\u001b[0m\u001b[31m\n",
            "\u001b[0m\u001b[?25h  Running setup.py clean for pytorch\n",
            "Failed to build pytorch\n",
            "Installing collected packages: pytorch\n",
            "  \u001b[1;31merror\u001b[0m: \u001b[1msubprocess-exited-with-error\u001b[0m\n",
            "  \n",
            "  \u001b[31m×\u001b[0m \u001b[32mRunning setup.py install for pytorch\u001b[0m did not run successfully.\n",
            "  \u001b[31m│\u001b[0m exit code: \u001b[1;36m1\u001b[0m\n",
            "  \u001b[31m╰─>\u001b[0m See above for output.\n",
            "  \n",
            "  \u001b[1;35mnote\u001b[0m: This error originates from a subprocess, and is likely not a problem with pip.\n",
            "  Running setup.py install for pytorch ... \u001b[?25l\u001b[?25herror\n",
            "\u001b[1;31merror\u001b[0m: \u001b[1mlegacy-install-failure\u001b[0m\n",
            "\n",
            "\u001b[31m×\u001b[0m Encountered error while trying to install package.\n",
            "\u001b[31m╰─>\u001b[0m pytorch\n",
            "\n",
            "\u001b[1;35mnote\u001b[0m: This is an issue with the package mentioned above, not pip.\n",
            "\u001b[1;36mhint\u001b[0m: See above for output from the failure.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip3 install torch\n",
        "!pip3 install torchvision"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WZ9Zxoeyhyu1",
        "outputId": "ef7037a6-9129-4537-9f47-209453b25bf3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.9/dist-packages (1.13.1+cu116)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.9/dist-packages (from torch) (4.5.0)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.9/dist-packages (0.14.1+cu116)\n",
            "Requirement already satisfied: torch==1.13.1 in /usr/local/lib/python3.9/dist-packages (from torchvision) (1.13.1+cu116)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.9/dist-packages (from torchvision) (1.22.4)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.9/dist-packages (from torchvision) (4.5.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.9/dist-packages (from torchvision) (2.27.1)\n",
            "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.9/dist-packages (from torchvision) (8.4.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.9/dist-packages (from requests->torchvision) (3.4)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.9/dist-packages (from requests->torchvision) (2.0.12)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.9/dist-packages (from requests->torchvision) (2022.12.7)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.9/dist-packages (from requests->torchvision) (1.26.15)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!ls"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZUtihrSAiLCY",
        "outputId": "7f5873b3-f651-4958-ac7e-a6f5b3f6e44d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "best_accuracy.pth  sample_data\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = 10\n",
        "torch.save(model, '/model.pth')"
      ],
      "metadata": {
        "id": "hq1Q39Poimf7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "\n",
        "model = torch.load(\"/content/best_accuracy.pth\")"
      ],
      "metadata": {
        "id": "UTHbZfQ_iEw5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install CTCLabelConverter\n",
        "!pip install AttnLabelConverter\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qa7ewUQrlonM",
        "outputId": "653cca0e-2875-4bcd-f907-039da0c8f4f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "\u001b[31mERROR: Could not find a version that satisfies the requirement CTCLabelConverter (from versions: none)\u001b[0m\u001b[31m\n",
            "\u001b[0m\u001b[31mERROR: No matching distribution found for CTCLabelConverter\u001b[0m\u001b[31m\n",
            "\u001b[0mLooking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "\u001b[31mERROR: Could not find a version that satisfies the requirement AttnLabelConverter (from versions: none)\u001b[0m\u001b[31m\n",
            "\u001b[0m\u001b[31mERROR: No matching distribution found for AttnLabelConverter\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import string\n",
        "import argparse\n",
        "\n",
        "import torch\n",
        "import torch.backends.cudnn as cudnn\n",
        "import torch.utils.data\n",
        "\n",
        "from utils import CTCLabelConverter, AttnLabelConverter\n",
        "from dataset import RawDataset, AlignCollate\n",
        "from model import Model\n",
        "\n",
        "\n",
        "def demo(opt):\n",
        "    \"\"\" model configuration \"\"\"\n",
        "    if 'CTC' in opt.Prediction:\n",
        "        converter = CTCLabelConverter(opt.character)\n",
        "    else:\n",
        "        converter = AttnLabelConverter(opt.character)\n",
        "    opt.num_class = len(converter.character)\n",
        "\n",
        "    if opt.rgb:\n",
        "        opt.input_channel = 3\n",
        "    model = Model(opt)\n",
        "    print('model input parameters', opt.imgH, opt.imgW, opt.num_fiducial, opt.input_channel, opt.output_channel,\n",
        "          opt.hidden_size, opt.num_class, opt.batch_max_length, opt.Transformation, opt.FeatureExtraction,\n",
        "          opt.SequenceModeling, opt.Prediction)\n",
        "\n",
        "    model = torch.nn.DataParallel(model)\n",
        "    if torch.cuda.is_available():\n",
        "        model = model.cuda()\n",
        "\n",
        "    # load model\n",
        "    print('loading pretrained model from %s' % opt.saved_model)\n",
        "    model.load_state_dict(torch.load(opt.saved_model))\n",
        "\n",
        "    # prepare data. two demo images from https://github.com/bgshih/crnn#run-demo\n",
        "    AlignCollate_demo = AlignCollate(imgH=opt.imgH, imgW=opt.imgW, keep_ratio_with_pad=opt.PAD)\n",
        "    demo_data = RawDataset(root=opt.image_folder, opt=opt)  # use RawDataset\n",
        "    demo_loader = torch.utils.data.DataLoader(\n",
        "        demo_data, batch_size=opt.batch_size,\n",
        "        shuffle=False,\n",
        "        num_workers=int(opt.workers),\n",
        "        collate_fn=AlignCollate_demo, pin_memory=True)\n",
        "\n",
        "    # predict\n",
        "    model.eval()\n",
        "    for image_tensors, image_path_list in demo_loader:\n",
        "        batch_size = image_tensors.size(0)\n",
        "        with torch.no_grad():\n",
        "            image = image_tensors.cuda()\n",
        "            # For max length prediction\n",
        "            length_for_pred = torch.cuda.IntTensor([opt.batch_max_length] * batch_size)\n",
        "            text_for_pred = torch.cuda.LongTensor(batch_size, opt.batch_max_length + 1).fill_(0)\n",
        "\n",
        "        if 'CTC' in opt.Prediction:\n",
        "            preds = model(image, text_for_pred).log_softmax(2)\n",
        "\n",
        "            # Select max probabilty (greedy decoding) then decode index to character\n",
        "            preds_size = torch.IntTensor([preds.size(1)] * batch_size)\n",
        "            _, preds_index = preds.permute(1, 0, 2).max(2)\n",
        "            preds_index = preds_index.transpose(1, 0).contiguous().view(-1)\n",
        "            preds_str = converter.decode(preds_index.data, preds_size.data)\n",
        "\n",
        "        else:\n",
        "            preds = model(image, text_for_pred, is_train=False)\n",
        "\n",
        "            # select max probabilty (greedy decoding) then decode index to character\n",
        "            _, preds_index = preds.max(2)\n",
        "            preds_str = converter.decode(preds_index, length_for_pred)\n",
        "\n",
        "        print('-' * 80)\n",
        "        print('image_path\\tpredicted_labels')\n",
        "        print('-' * 80)\n",
        "        for img_name, pred in zip(image_path_list, preds_str):\n",
        "            if 'Attn' in opt.Prediction:\n",
        "                pred = pred[:pred.find('[s]')]  # prune after \"end of sentence\" token ([s])\n",
        "\n",
        "            print(f'{img_name}\\t{pred}')\n",
        "\n",
        "\n",
        "if __name__ == '__main__':\n",
        "    parser = argparse.ArgumentParser()\n",
        "    parser.add_argument('--image_folder', required=True, help='path to image_folder which contains text images')\n",
        "    parser.add_argument('--workers', type=int, help='number of data loading workers', default=4)\n",
        "    parser.add_argument('--batch_size', type=int, default=192, help='input batch size')\n",
        "    parser.add_argument('--saved_model', required=True, help=\"path to saved_model to evaluation\")\n",
        "    \"\"\" Data processing \"\"\"\n",
        "    parser.add_argument('--batch_max_length', type=int, default=50, help='maximum-label-length')\n",
        "    parser.add_argument('--imgH', type=int, default=32, help='the height of the input image')\n",
        "    parser.add_argument('--imgW', type=int, default=400, help='the width of the input image')\n",
        "    parser.add_argument('--rgb', action='store_true', help='use rgb input')\n",
        "    # parser.add_argument('--character', type=str, default='0123456789abcdefghijklmnopqrstuvwxyz', help='character label')\n",
        "    parser.add_argument('--character', type=str, default='가각간갇갈감갑값갓강갖같갚갛개객걀걔거걱건걷걸검겁것겉게겨격겪견결겹경곁계고곡곤곧골곰곱곳공과관광괜괴굉교구국군굳굴굵굶굽궁권귀귓규균귤그극근글긁금급긋긍기긴길김깅깊까깍깎깐깔깜깝깡깥깨꺼꺾껌껍껏껑께껴꼬꼭꼴꼼꼽꽂꽃꽉꽤꾸꾼꿀꿈뀌끄끈끊끌끓끔끗끝끼낌나낙낚난날낡남납낫낭낮낯낱낳내냄냇냉냐냥너넉넌널넓넘넣네넥넷녀녁년념녕노녹논놀놈농높놓놔뇌뇨누눈눕뉘뉴늄느늑는늘늙능늦늬니닐님다닥닦단닫달닭닮담답닷당닿대댁댐댓더덕던덜덟덤덥덧덩덮데델도독돈돌돕돗동돼되된두둑둘둠둡둥뒤뒷드득든듣들듬듭듯등디딩딪따딱딴딸땀땅때땜떠떡떤떨떻떼또똑뚜뚫뚱뛰뜨뜩뜯뜰뜻띄라락란람랍랑랗래랜램랫략량러럭런럴럼럽럿렁렇레렉렌려력련렬렵령례로록론롬롭롯료루룩룹룻뤄류륙률륭르른름릇릎리릭린림립릿링마막만많말맑맘맙맛망맞맡맣매맥맨맵맺머먹먼멀멈멋멍멎메멘멩며면멸명몇모목몬몰몸몹못몽묘무묵묶문묻물뭄뭇뭐뭘뭣므미민믿밀밉밌및밑바박밖반받발밝밟밤밥방밭배백뱀뱃뱉버번벌범법벗베벤벨벼벽변별볍병볕보복볶본볼봄봇봉뵈뵙부북분불붉붐붓붕붙뷰브븐블비빌빔빗빚빛빠빡빨빵빼뺏뺨뻐뻔뻗뼈뼉뽑뿌뿐쁘쁨사삭산살삶삼삿상새색샌생샤서석섞선설섬섭섯성세섹센셈셋셔션소속손솔솜솟송솥쇄쇠쇼수숙순숟술숨숫숭숲쉬쉰쉽슈스슨슬슴습슷승시식신싣실싫심십싯싱싶싸싹싼쌀쌍쌓써썩썰썹쎄쏘쏟쑤쓰쓴쓸씀씌씨씩씬씹씻아악안앉않알앓암압앗앙앞애액앨야약얀얄얇양얕얗얘어억언얹얻얼엄업없엇엉엊엌엎에엔엘여역연열엷염엽엿영옆예옛오옥온올옮옳옷옹와완왕왜왠외왼요욕용우욱운울움웃웅워원월웨웬위윗유육율으윽은을음응의이익인일읽잃임입잇있잊잎자작잔잖잘잠잡잣장잦재쟁쟤저적전절젊점접젓정젖제젠젯져조족존졸좀좁종좋좌죄주죽준줄줌줍중쥐즈즉즌즐즘증지직진질짐집짓징짙짚짜짝짧째쨌쩌쩍쩐쩔쩜쪽쫓쭈쭉찌찍찢차착찬찮찰참찻창찾채책챔챙처척천철첩첫청체쳐초촉촌촛총촬최추축춘출춤춥춧충취츠측츰층치칙친칠침칫칭카칸칼캄캐캠커컨컬컴컵컷케켓켜코콘콜콤콩쾌쿄쿠퀴크큰클큼키킬타탁탄탈탑탓탕태택탤터턱턴털텅테텍텔템토톤톨톱통퇴투툴툼퉁튀튜트특튼튿틀틈티틱팀팅파팎판팔팝패팩팬퍼퍽페펜펴편펼평폐포폭폰표푸푹풀품풍퓨프플픔피픽필핏핑하학한할함합항해핵핸햄햇행향허헌험헤헬혀현혈협형혜호혹혼홀홈홉홍화확환활황회획횟횡효후훈훌훔훨휘휴흉흐흑흔흘흙흡흥흩희흰히힘', help='character label')\n",
        "    parser.add_argument('--sensitive', action='store_true', help='for sensitive character mode')\n",
        "    parser.add_argument('--PAD', action='store_true', help='whether to keep ratio then pad for image resize')\n",
        "    \"\"\" Model Architecture \"\"\"\n",
        "    parser.add_argument('--Transformation', type=str, required=True, help='Transformation stage. None|TPS')\n",
        "    parser.add_argument('--FeatureExtraction', type=str, required=True, help='FeatureExtraction stage. VGG|RCNN|ResNet')\n",
        "    parser.add_argument('--SequenceModeling', type=str, required=True, help='SequenceModeling stage. None|BiLSTM')\n",
        "    parser.add_argument('--Prediction', type=str, required=True, help='Prediction stage. CTC|Attn')\n",
        "    parser.add_argument('--num_fiducial', type=int, default=20, help='number of fiducial points of TPS-STN')\n",
        "    parser.add_argument('--input_channel', type=int, default=1, help='the number of input channel of Feature extractor')\n",
        "    parser.add_argument('--output_channel', type=int, default=512,\n",
        "                        help='the number of output channel of Feature extractor')\n",
        "    parser.add_argument('--hidden_size', type=int, default=256, help='the size of the LSTM hidden state')\n",
        "\n",
        "    opt = parser.parse_args()\n",
        "\n",
        "    \"\"\" vocab / character number configuration \"\"\"\n",
        "    if opt.sensitive:\n",
        "        opt.character = string.printable[:-6]  # same with ASTER setting (use 94 char).\n",
        "\n",
        "    cudnn.benchmark = True\n",
        "    cudnn.deterministic = True\n",
        "    opt.num_gpu = torch.cuda.device_count()\n",
        "\n",
        "    demo(opt)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 396
        },
        "id": "D6BwSrlDiUff",
        "outputId": "55d33031-8292-4be8-ae7b-dc491096f880"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ModuleNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-38-3f8ec7649f69>\u001b[0m in \u001b[0;36m<cell line: 8>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\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[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mCTCLabelConverter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mAttnLabelConverter\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mdataset\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mRawDataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mAlignCollate\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mModel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'utils'",
            "",
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
          ],
          "errorDetails": {
            "actions": [
              {
                "action": "open_url",
                "actionText": "Open Examples",
                "url": "/notebooks/snippets/importing_libraries.ipynb"
              }
            ]
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! CUDA_VISIBLE_DEVICES=0 python3 deep-text-recognition-benchmark/demo.py \\\n",
        "                        --Transformation TPS --FeatureExtraction VGG --SequenceModeling BiLSTM --Prediction Attn \\\n",
        "                        --image_folder data/demo_image/ \\\n",
        "                        --saved_model deep-text-recognition-benchmark/saved_models/TPS-VGG-BiLSTM-Attn-Seed9998/best_accuracy.pth;        "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ym2PtiIUkW2Z",
        "outputId": "a4be5bc0-32e4-4d09-d87f-b7e8460f0f69"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "python3: can't open file '/content/deep-text-recognition-benchmark/demo.py': [Errno 2] No such file or directory\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "77xCboTukm0u"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}