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from add_text import add_text
from detect_bubbles import detect_bubbles
from process_bubble import process_bubble
from translator import MangaTranslator
from ultralytics import YOLO
from manga_ocr import MangaOcr
from PIL import Image
import gradio as gr
import numpy as np
import cv2
MODEL = "model.pt"
EXAMPLE_LIST = [["examples/0.png"],
["examples/ex0.png"]]
TITLE = "Manga Translator"
DESCRIPTION = "Translate text in manga bubbles!"
def predict(img, translation_method, font):
if translation_method == None:
translation_method = "google"
if font == None:
font = "fonts/animeace_i.ttf"
results = detect_bubbles(MODEL, img)
manga_translator = MangaTranslator()
mocr = MangaOcr()
image = np.array(img)
for result in results:
x1, y1, x2, y2, score, class_id = result
detected_image = image[int(y1):int(y2), int(x1):int(x2)]
im = Image.fromarray(np.uint8((detected_image)*255))
text = mocr(im)
detected_image, cont = process_bubble(detected_image)
text_translated = manga_translator.translate(text,
method=translation_method)
image_with_text = add_text(detected_image, text_translated, font, cont)
return image
demo = gr.Interface(fn=predict,
inputs=["image",
gr.Dropdown([("Google", "google"),
("Helsinki-NLP's opus-mt-ja-en model",
"hf"),
("Baidu", "baidu"),
("Bing", "bing")],],
label="Translation Method",
value="google"),
gr.Dropdown([("animeace_i", "fonts/animeace_i.ttf"),
("mangati", "fonts/mangati.ttf"),
("ariali", "fonts/ariali.ttf")],
label="Text Font",
value="fonts/animeace_i.ttf")
],
outputs=[gr.Image()],
examples=EXAMPLE_LIST,
title=TITLE,
description=DESCRIPTION)
demo.launch(debug=False,
share=False)
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