56 lines
1.5 KiB
Python
56 lines
1.5 KiB
Python
#!/usr/bin/env python3
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import sys
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sys.path.insert(0, '/Users/guillemhernandezsola/code/manga-translator')
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import cv2
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import json
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import numpy as np
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import importlib.util
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spec = importlib.util.spec_from_file_location("manga_translator", "/Users/guillemhernandezsola/code/manga-translator/manga-translator.py")
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mt = importlib.util.module_from_spec(spec)
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spec.loader.exec_module(mt)
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image_path = '004.png'
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detector = mt.MacVisionDetector(source_lang='en')
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raw = detector.read(image_path)
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image = cv2.imread(image_path)
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# Filter as the pipeline does
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filtered = []
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for bbox, text, conf in raw:
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t = mt.normalize_text(text)
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qb = mt.quad_bbox(bbox)
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if conf < 0.12:
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continue
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if len(t) < 1:
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continue
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if mt.is_noise_text(t):
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continue
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if mt.is_sound_effect(t):
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continue
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if mt.is_title_text(t):
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continue
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filtered.append((bbox, t, conf))
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print(f"Filtered {len(filtered)} detections")
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# Now run grouping
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bubbles, bubble_boxes, bubble_quads, bubble_indices = mt.group_tokens(
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filtered, image.shape, gap_px=18, bbox_padding=3
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)
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# Find which bubble contains box 5
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box5_region = (378, 570, 536, 753)
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print("\n=== BUBBLES ===")
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for bid, box in bubble_boxes.items():
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print(f"Bubble {bid}: {box}")
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print(f" Indices: {bubble_indices[bid]}")
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print(f" Detections:")
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for idx in bubble_indices[bid]:
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b = mt.quad_bbox(filtered[idx][0])
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print(f" {idx}: [{b[0]:3d},{b[1]:3d} -> {b[2]:3d},{b[3]:3d}] = {filtered[idx][1]}")
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