Image/Manga Translator

Translate texts in manga/images.
中文说明 | Change Log
Join us on discord https://discord.gg/Ak8APNy4vb

Some manga/images will never be translated, therefore this project is born.
Primarily designed for translating Japanese text, but also supports Chinese, English and Korean.
Supports inpainting and text rendering.
Successor to https://github.com/PatchyVideo/MMDOCR-HighPerformance
Also check out GUI implementation: https://github.com/dmMaze/BallonsTranslator

This is a hobby project, you are welcome to contribute!
Currently this only a simple demo, many imperfections exist, we need your support to make this project better! Samples are found under here.

Support Us

GPU server is not cheap, please consider to donate to us.

Ko-fi: https://ko-fi.com/voilelabs
Patreon: https://www.patreon.com/voilelabs
爱发电: https://afdian.net/@voilelabs

Online Demo

Official Demo (by zyddnys): https://touhou.ai/imgtrans/
Browser Userscript (by QiroNT): https://greasyfork.org/scripts/437569

Note this may not work sometimes due to stupid google gcp kept restarting my instance. In that case you can wait for me to restart the service, which may take up to 24 hrs.
Note this online demo is using the current main branch version.

Installation

# First, you need to have Python(>=3.8) installed on your system
# The latest version often does not work with some pytorch libraries yet
$ python --version
Python 3.10.6

# Clone this repo
$ git clone https://github.com/zyddnys/manga-image-translator.git

# Install the dependencies
$ pip install -r requirements.txt

$ pip install git+https://github.com/lucasb-eyer/pydensecrf.git

The models will be downloaded into ./models at runtime.

If you are on windows

Before you start the pip install, first install Microsoft C++ Build Tools (Download, Instructions) as some pip dependencies will not compile without it. (See #114).

To use cuda on windows install the correct pytorch version as instructed on https://pytorch.org/.
Add --upgrade --force-reinstall to the pip command to overwrite the currently installed pytorch version.

Also, if you have trouble installing pydensecrf with the command above you can install the pre-compiled wheels with pip install https://www.lfd.uci.edu/~gohlke/pythonlibs/#_pydensecrf.

Usage

Demo mode (default)

# saves singular image into /result folder for demonstration purposes
# `--use-cuda` is optional, if you have a compatible NVIDIA GPU, you can use it.
# use `--use-cuda-limited` to defer vram expensive language translations to the cpu
# use `--inpainter=none` to disable inpainting.
# use `--translator=<translator>` to specify a translator.
# use `--translator=none` if you only want to use inpainting (blank bubbles)
# use `--target-lang <language_code>` to specify a target language.
# if you are not getting good result for small images you can try specifying an upscaler to enlarge and enhance the image to improve detection
# replace <path_to_image_file> with the path to the image file.
$ python -m manga_translator -v --use-cuda --translator=google -l ENG -i <path_to_image_file>
# result can be found in `result/`.

Batch mode

# same options as above.
# use `--mode batch` to enable batch translation.
# replace <path_to_image_folder> with the path to the image folder.
$ python -m manga_translator -v --mode batch --use-cuda --translator=google -l ENG -i <path_to_image_folder>
# results can be found in `<path_to_image_folder>-translated/`.

Web Mode

# same options as above.
# use `--mode web` to start a web server.
$ python -m manga_translator -v --mode web --use-cuda
# the demo will be serving on http://127.0.0.1:5003

Manual translation

Manual translation replaces machine translation with human translators. Basic manual translation demo can be found at http://127.0.0.1:5003/manual when using web mode.

API

Two modes of translation service are provided by the demo: synchronous mode and asynchronous mode.
In synchronous mode your HTTP POST request will finish once the translation task is finished.
In asynchronous mode your HTTP POST request will respond with a task_id immediately, you can use this task_id to poll for translation task state.

Synchronous mode

POST a form request with form data file:<content-of-image> to http://127.0.0.1:5003/run
Wait for response
Use the resultant task_id to find translation result in result/ directory, e.g. using Nginx to expose result/

Asynchronous mode

POST a form request with form data file:<content-of-image> to http://127.0.0.1:5003/submit
Acquire translation task_id
Poll for translation task state by posting JSON {"taskid": <task-id>} to http://127.0.0.1:5003/task-state
Translation is finished when the resultant state is either finished, error or error-lang
Find translation result in result/ directory, e.g. using Nginx to expose result/

Manual translation

POST a form request with form data file:<content-of-image> to http://127.0.0.1:5003/manual-translate and wait for response.

You will obtain a JSON response like this:

{
  "task_id": "12c779c9431f954971cae720eb104499",
  "status": "pending",
  "trans_result": [
    {
      "s": "☆上司来ちゃった……",
      "t": ""
    }
  ]
}

Fill in translated texts:

{
  "task_id": "12c779c9431f954971cae720eb104499",
  "status": "pending",
  "trans_result": [
    {
      "s": "☆上司来ちゃった……",
      "t": "☆Boss is here..."
    }
  ]
}

Post translated JSON to http://127.0.0.1:5003/post-manual-result and wait for response.
Then you can find the translation result in result/ directory, e.g. using Nginx to expose result/.

Translators Reference

Name	API Key	Offline	Note
google
youdao	✔️		Requires `YOUDAO_APP_KEY` and `YOUDAO_SECRET_KEY`
baidu	✔️		Requires `BAIDU_APP_ID` and `BAIDU_SECRET_KEY`
deepl	✔️		Requires `DEEPL_AUTH_KEY`
gpt3	✔️		Implements text-davinci-003. Requires `OPENAI_API_KEY`
gpt3.5	✔️		Implements gpt-3.5-turbo. Requires `OPENAI_API_KEY`
gpt4	✔️		Implements gpt-4. Requires `OPENAI_API_KEY`
papago
offline		✔️	Chooses most suitable offline translator for language
sugoi		✔️	Sugoi V4.0 Models (recommended for JPN->ENG)
m2m100		✔️	Supports every language
m2m100_big		✔️
none		✔️	Translate to empty texts
original		✔️	Keep original texts

API Key: Whether the translator requires an API key to be set as environment variable. For this you can create a .env file in the project root directory containing your api keys like so:

OPENAI_API_KEY=sk-xxxxxxx...
DEEPL_AUTH_KEY=xxxxxxxx...

Offline: Whether the translator can be used offline.
Sugoi is created by mingshiba, please support him in https://www.patreon.com/mingshiba

GPT Config Reference

Used by the --gpt-config argument.

# The prompt being feed into GPT before the text to translate.
# Use {to_lang} to indicate where the target language name should be inserted.
# Note: ChatGPT models don't use this prompt.
prompt_template: >
  Please help me to translate the following text from a manga to {to_lang}
  (if it's already in {to_lang} or looks like gibberish you have to output it as it is instead):\n

# What sampling temperature to use, between 0 and 2.
# Higher values like 0.8 will make the output more random,
# while lower values like 0.2 will make it more focused and deterministic.
temperature: 0.5

# An alternative to sampling with temperature, called nucleus sampling,
# where the model considers the results of the tokens with top_p probability mass.
# So 0.1 means only the tokens comprising the top 10% probability mass are considered.
top_p: 1

# The prompt being feed into ChatGPT before the text to translate.
# Use {to_lang} to indicate where the target language name should be inserted.
# Tokens used in this example: 62+
chat_system_template: >
  You are a professional translation engine,
  please translate the text into a colloquial, elegant and fluent content,
  without referencing machine translations.
  You must only translate the text content, never interpret it.
  If there's any issue in the text, output the text as is.

  Translate to {to_lang}.

# Samples being feed into ChatGPT to show an example conversation.
# In a [prompt, response] format, keyed by the target language name.
#
# Generally, samples should include some examples of translation preferences, and ideally
# some names of characters it's likely to encounter. Like the "ちゃん" -> "酱" preference,
# and the "ぼっちちゃん" -> "小孤独" name as shown below.
#
# If you'd like to disable this feature, just set this to an empty list.
chat_sample:
  Simplified Chinese: # Tokens used in this example: 161 + 171
    - <|1|>二人のちゅーを 目撃した ぼっちちゃん
      <|2|>ふたりさん
      <|3|>大好きなお友達には あいさつ代わりに ちゅーするんだって
      <|4|>アイス あげた
      <|5|>喜多ちゃんとは どどど どういった ご関係なのでしようか...
      <|6|>テレビで見た！
    - <|1|>小孤独目击了两人的接吻
      <|2|>二里酱
      <|3|>我听说人们会把亲吻作为与喜爱的朋友打招呼的方式
      <|4|>我给了她冰激凌
      <|5|>喜多酱和你是怎么样的关系啊...
      <|6|>我在电视上看到的！

# Overwrite configs for a specific model.
# For now the list is: gpt3, gpt35, gpt4
gpt35:
  temperature: 0.3

Language Code Reference

Used by the --target-lang or -l argument.

CHS: Chinese (Simplified)
CHT: Chinese (Traditional)
CSY: Czech
NLD: Dutch
ENG: English
FRA: French
DEU: German
HUN: Hungarian
ITA: Italian
JPN: Japanese
KOR: Korean
PLK: Polish
PTB: Portuguese (Brazil)
ROM: Romanian
RUS: Russian
ESP: Spanish
TRK: Turkish
UKR: Ukrainian
VIN: Vietnames

Options

-h, --help                                   show this help message and exit
-m, --mode {demo,batch,web,web_client,ws,api}
                                             Run demo in single image demo mode (demo), batch
                                             translation mode (batch), web service mode (web)
-i, --input INPUT [INPUT ...]                Path to an image file if using demo mode, or path to an
                                             image folder if using batch mode
-o, --dest DEST                              Path to the destination folder for translated images in
                                             batch mode
-l, --target-lang {CHS,CHT,CSY,NLD,ENG,FRA,DEU,HUN,ITA,JPN,KOR,PLK,PTB,ROM,RUS,ESP,TRK,UKR,VIN}
                                             Destination language
-v, --verbose                                Print debug info and save intermediate images in result
                                             folder
-f, --format {png,webp,jpg}                  Output format of the translation.
--detector {default,ctd,craft,none}          Text detector used for creating a text mask from an
                                             image, DO NOT use craft for manga, it's not designed
                                             for it
--ocr {32px,48px_ctc}                        Optical character recognition (OCR) model to use
--inpainter {default,lama_mpe,sd,none,original}
                                             Inpainting model to use
--upscaler {waifu2x,esrgan,4xultrasharp}     Upscaler to use. --upscale-ratio has to be set for it
                                             to take effect
--upscale-ratio {1,2,3,4,8,16,32}            Image upscale ratio applied before detection. Can
                                             improve text detection.
--colorizer {mc2}                            Colorization model to use.
--translator {google,youdao,baidu,deepl,papago,gpt3,gpt3.5,gpt4,none,original,offline,nllb,nllb_big,sugoi,jparacrawl,jparacrawl_big,m2m100,m2m100_big}
                                             Language translator to use
--translator-chain TRANSLATOR_CHAIN          Output of one translator goes in another. Example:
                                             --translator-chain "google:JPN;sugoi:ENG".
--selective-translation SELECTIVE_TRANSLATION
                                             Select a translator based on detected language in
                                             image. Note the first translation service acts as
                                             default if the language isnt defined. Example:
                                             --translator-chain "google:JPN;sugoi:ENG".
--use-cuda                                   Turn on/off cuda
--use-cuda-limited                           Turn on/off cuda (excluding offline translator)
--model-dir MODEL_DIR                        Model directory (by default ./models in project root)
--retries RETRIES                            Retry attempts on encountered error. -1 means infinite
                                             times.
--revert-upscaling                           Downscales the previously upscaled image after
                                             translation back to original size (Use with --upscale-
                                             ratio).
--detection-size DETECTION_SIZE              Size of image used for detection
--det-rotate                                 Rotate the image for detection. Might improve
                                             detection.
--det-auto-rotate                            Rotate the image for detection to prefer vertical
                                             textlines. Might improve detection.
--det-invert                                 Invert the image colors for detection. Might improve
                                             detection.
--det-gamma-correct                          Applies gamma correction for detection. Might improve
                                             detection.
--unclip-ratio UNCLIP_RATIO                  How much to extend text skeleton to form bounding box
--box-threshold BOX_THRESHOLD                Threshold for bbox generation
--text-threshold TEXT_THRESHOLD              Threshold for text detection
--inpainting-size INPAINTING_SIZE            Size of image used for inpainting (too large will
                                             result in OOM)
--colorization-size COLORIZATION_SIZE        Size of image used for colorization. Set to -1 to use
                                             full image size
--font-size FONT_SIZE                        Use fixed font size for rendering
--font-size-offset FONT_SIZE_OFFSET          Offset font size by a given amount, positive number
                                             increase font size and vice versa
--font-size-minimum FONT_SIZE_MINIMUM        Minimum output font size. Default is
                                             image_sides_sum/150
--force-horizontal                           Force text to be rendered horizontally
--force-vertical                             Force text to be rendered vertically
--align-left                                 Align rendered text left
--align-center                               Align rendered text centered
--align-right                                Align rendered text right
--uppercase                                  Change text to uppercase
--lowercase                                  Change text to lowercase
--manga2eng                                  Render english text translated from manga with some
                                             additional typesetting. Ignores some other argument
                                             options
--gpt-config GPT_CONFIG                      Path to GPT config file, more info in README
--mtpe                                       Turn on/off machine translation post editing (MTPE) on
                                             the command line (works only on linux right now)
--save-text                                  Save extracted text and translations into a text file.
--save-text-file SAVE_TEXT_FILE              Like --save-text but with a specified file path.
--filter-text FILTER_TEXT                    Filter regions by their text with a regex. Example
                                             usage: --text-filter ".*badtext.*"
--prep-manual                                Prepare for manual typesetting by outputting blank,
                                             inpainted images, plus copies of the original for
                                             reference
--font-path FONT_PATH                        Path to font file
--host HOST                                  Used by web module to decide which host to attach to
--port PORT                                  Used by web module to decide which port to attach to
--nonce NONCE                                Used by web module as secret for securing internal web
                                             server communication
--ws-url WS_URL                              Server URL for WebSocket mode
--save-quality SAVE_QUALITY                  Quality of saved JPEG image, range from 0 to 100 with
                                             100 being best
--ignore-bubble {Numbers from 1 to 50}       The threshold for ignoring text in non bubble areas, with
                                             valid values ranging from 1 to 50, does not ignore others.
                                             Recommendation 5 to 10. If it is too small, normal bubble
                                             areas may be ignored, and if it is too large, non bubble
                                             areas may be considered normal bubbles
                                             For example, --ignore-bubble 5

Docker

Requirements:

Docker (version 19.03+ required for CUDA / GPU accelaration)
Docker Compose (Optional if you want to use files in the demo/doc folder)
Nvidia Container Runtime (Optional if you want to use CUDA)

This project has docker support under zyddnys/manga-image-translator:main image. This docker image contains all required dependencies / models for the project. It should be noted that this image is fairly large (~ 15GB).

Hosting the web server

The web server can be hosted using (For CPU)

docker run -p 5003:5003 -v result:/app/result --ipc=host --rm zyddnys/manga-image-translator:main -l ENG --manga2eng -v --mode web --host=0.0.0.0 --port=5003

docker-compose -f demo/doc/docker-compose-web-with-cpu.yml up

depending on which you prefer. The web server should start on port 5003 and images should become in the /result folder.

Using as CLI

To use docker with the CLI (I.e in batch mode)

docker run -v <targetFolder>:/app/<targetFolder> -v <targetFolder>-translated:/app/<targetFolder>-translated  --ipc=host --rm zyddnys/manga-image-translator:main --mode=batch -i=/app/<targetFolder> <cli flags>

Note: In the event you need to reference files on your host machine you will need to mount the associated files as volumes into the /app folder inside the container. Paths for the CLI will need to be the internal docker path /app/... instead of the paths on your host machine

Setting Translation Secrets

Some translation services require API keys to function to set these pass them as env vars into the docker container. For example:

docker run --env="DEEPL_AUTH_KEY=xxx" --ipc=host --rm zyddnys/manga-image-translator:main <cli flags>

Using with Nvidia GPU

To use with a supported GPU please first read the initial Docker section. There are some special dependencies you will need to use

To run the container with the following flags set:

docker run ... --gpus=all ... zyddnys/manga-image-translator:main ... --use-cuda

Or (For the web server + GPU)

docker-compose -f demo/doc/docker-compose-web-with-gpu.yml up

Building locally

To build the docker image locally you can run (You will require make on your machine)

make build-image

Then to test the built image run

make run-web-server

Next steps

A list of what needs to be done next, you're welcome to contribute.

Use diffusion model based inpainting to achieve near perfect result, but this could be much slower.
IMPORTANT!!!HELP NEEDED!!! The current text rendering engine is barely usable, we need your help to improve text rendering!
Text rendering area is determined by detected text lines, not speech bubbles.
This works for images without speech bubbles, but making it impossible to decide where to put translated English text. I have no idea how to solve this.
Ryota et al. proposed using multimodal machine translation, maybe we can add ViT features for building custom NMT models.
Make this project works for video(rewrite code in C++ and use GPU/other hardware NN accelerator).
Used for detecting hard subtitles in videos, generting ass file and remove them completetly.
~~Mask refinement based using non deep learning algorithms, I am currently testing out CRF based algorithm.~~
~~Angled text region merge is not currently supported~~
Create pip repository