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from add_text import add_text |
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from detect_bubbles import detect_bubbles |
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from process_bubble import process_bubble |
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from translator import MangaTranslator |
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from ultralytics import YOLO |
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from manga_ocr import MangaOcr |
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from PIL import Image |
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import gradio as gr |
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import numpy as np |
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import cv2 |
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MODEL = "model.pt" |
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EXAMPLE_LIST = [["examples/0.png"], |
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["examples/ex0.png"]] |
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TITLE = "Manga Translator" |
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DESCRIPTION = "Translate text in manga bubbles!" |
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def predict(img, translation_method, font): |
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results = detect_bubbles(MODEL, img) |
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manga_translator = MangaTranslator() |
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mocr = MangaOcr() |
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image = np.array(img) |
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for result in results: |
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x1, y1, x2, y2, score, class_id = result |
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detected_image = image[int(y1):int(y2), int(x1):int(x2)] |
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im = Image.fromarray(np.uint8((detected_image)*255)) |
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text = mocr(im) |
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detected_image, cont = process_bubble(detected_image) |
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text_translated = manga_translator.translate(text, |
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method=translation_method) |
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image_with_text = add_text(detected_image, text_translated, font, cont) |
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return image |
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demo = gr.Interface(fn=predict, |
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inputs=["image", |
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gr.Dropdown([("Google", "google"), |
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("Helsinki-NLP's opus-mt-ja-en model", |
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"hf")], |
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label="Translation Method", |
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value="google"), |
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gr.Dropdown([("animeace_i", ("fonts/animeace_i.ttf")), |
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("mangati", "fonts/mangati.ttf"), |
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("ariali", "fonts/ariali.ttf")], |
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label="Text Font", |
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value="fonts/animeace_i.ttf") |
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], |
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outputs=[gr.Image()], |
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examples=EXAMPLE_LIST, |
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title=TITLE, |
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description=DESCRIPTION) |
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demo.launch(debug=False, |
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share=False) |
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