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add_text.py

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+from PIL import Image, ImageDraw, ImageFont
+import numpy as np
+import textwrap
+import cv2
+
+
+def add_text(image, text, font_path, bubble_contour):
+    """
+    Add text inside a speech bubble contour.
+
+    Args:
+        image (numpy.ndarray): Processed bubble image (cv2 format - BGR).
+        text (str): Text to be placed inside the speech bubble.
+        font_path (str): Font path.
+        bubble_contour (numpy.ndarray): Contour of the detected speech bubble.
+
+    Returns:
+        numpy.ndarray: Image with text placed inside the speech bubble.
+    """
+    pil_image = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
+    draw = ImageDraw.Draw(pil_image)
+
+    x, y, w, h = cv2.boundingRect(bubble_contour)
+
+    line_height = 16
+    font_size = 14
+    wrapping_ratio = 0.075
+
+    wrapped_text = textwrap.fill(text, width=int(w * wrapping_ratio), 
+                                 break_long_words=True)
+    
+    font = ImageFont.truetype(font_path, size=font_size)
+
+    lines = wrapped_text.split('\n')
+    total_text_height = (len(lines)) * line_height
+
+    while total_text_height > h:
+        line_height -= 2
+        font_size -= 2
+        wrapping_ratio += 0.025
+
+        wrapped_text = textwrap.fill(text, width=int(w * wrapping_ratio), 
+                                 break_long_words=True)
+                                 
+        font = ImageFont.truetype(font_path, size=font_size)
+
+        lines = wrapped_text.split('\n')
+        total_text_height = (len(lines)) * line_height                         
+
+    # Vertical centering
+    text_y = y + (h - total_text_height) // 2
+
+    for line in lines:
+        text_length = draw.textlength(line, font=font)
+
+        # Horizontal centering
+        text_x = x + (w - text_length) // 2
+
+        draw.text((text_x, text_y), line, font=font, fill=(0, 0, 0))
+
+        text_y += line_height
+
+    image[:, :, :] = cv2.cvtColor(np.array(pil_image), cv2.COLOR_RGB2BGR)
+
+    return image

translator.py

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+from deep_translator import GoogleTranslator
+from transformers import pipeline
+
+
+class MangaTranslator:
+    def __init__(self):
+        self.target = "en"
+        self.source = "ja"
+
+    def translate(self, text, method="google"):
+        """
+        Translates the given text to the target language using the specified method.
+
+        Args:
+            text (str): The text to be translated.
+            method (str):'google' for Google Translator, 
+                         'hf' for Helsinki-NLP's opus-mt-ja-en model (HF pipeline)
+
+        Returns:
+            str: The translated text.
+        """
+        if method == "hf":
+            return self._translate_with_hf(self._preprocess_text(text))
+        elif method == "google":
+            return self._translate_with_google(self._preprocess_text(text))
+        else:
+            raise ValueError("Invalid translation method.")
+            
+    def _translate_with_google(self, text):
+        translator = GoogleTranslator(source=self.source, target=self.target)
+        translated_text = translator.translate(text)
+        return translated_text if translated_text != None else text
+
+    def _translate_with_hf(self, text):
+        pipe = pipeline("translation", model=f"Helsinki-NLP/opus-mt-ja-en")
+        translated_text = pipe(text)[0]["translation_text"]
+        return translated_text if translated_text != None else text
+
+    def _preprocess_text(self, text):
+        preprocessed_text = text.replace("．", ".")
+        return preprocessed_text