Update utils/bubble_utils.py

utils/bubble_utils.py

@@ -1,11 +1,6 @@
 import cv2
 import numpy as np
 from PIL import ImageDraw
-import os
-
-FONT_PATH = os.path.abspath(
-    os.path.join(os.path.dirname(__file__), "..", "NotoSansSC-Regular.ttf")
-)
 
 def detect_speech_bubbles(pil_img):
     """
@@ -43,47 +38,3 @@ def detect_speech_bubbles(pil_img):
         bubbles.append(polygon)
 
     return bubbles
-
-def bubble_pipeline_single(file_obj):
-    filename, image, chunks = load_and_split_image(file_obj, num_chunks=1)
-    chunk = chunks[0]
-
-    # 1. Detect bubbles, not OCR boxes
-    bubble_polygons = detect_speech_bubbles(chunk)
-
-    # 2. OCR for text only (no polygon)
-    translations = extract_and_translate_chunk(chunk)
-
-    translated_image = chunk.copy()
-
-    # 3. Assign each OCR text item to nearest bubble
-    for t in translations:
-        text = t["translated"]
-        text_center = np.mean(t["polygon"], axis=0)
-
-        # Find nearest bubble
-        dists = [np.linalg.norm(text_center - np.mean(b, axis=0)) for b in bubble_polygons]
-        bubble = bubble_polygons[int(np.argmin(dists))]
-
-        # Slightly shrink inside the bubble
-        bubble_inner = shrink_or_expand_polygon(bubble, 0.9)
-
-        # Fill bubble area
-        draw = ImageDraw.Draw(translated_image)
-        draw.polygon(bubble_inner, fill=(255,255,255))
-
-        # Draw translated text
-        draw_text_center(
-            translated_image,
-            bubble_inner,
-            text,
-            font_path=FONT_PATH
-        )
-
-    original_html = encode_image_to_html(chunk)
-    translated_html = encode_image_to_html(translated_image)
-
-    # Optional table
-    table_data = [[t["original"], t["translated"]] for t in translations]
-
-    return filename, original_html, translated_html, table_data, [translations]