94 lines
2.5 KiB
Python
94 lines
2.5 KiB
Python
"""
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OCR 结果后处理（纯逻辑，无 IO）。
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对 OCR 返回的原始结果做结构化解析、置信度过滤、阅读顺序修正。
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不依赖任何外部资源，可独立测试。
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"""
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from __future__ import annotations
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from typing import Any
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def filter_low_confidence(
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blocks: list[dict[str, Any]],
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threshold: float = 0.5,
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) -> list[dict[str, Any]]:
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"""过滤低置信度结果。
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Args:
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blocks: 原始 OCR 块列表。
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threshold: 置信度阈值（默认 0.5）。
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Returns:
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过滤后的块列表。
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"""
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return [b for b in blocks if b.get("confidence", 0) >= threshold]
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def merge_adjacent_lines(
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blocks: list[dict[str, Any]],
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y_tolerance: float = 10.0,
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) -> list[dict[str, Any]]:
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"""合并同一行的相邻块。
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Args:
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blocks: OCR 块列表（需含 bbox 坐标）。
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y_tolerance: Y 轴容差（px），同一行最大差异。
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Returns:
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合并后的块列表。
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"""
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if not blocks:
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return []
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sorted_blocks = sorted(blocks, key=lambda b: (_center_y(b), _center_x(b)))
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merged: list[dict[str, Any]] = []
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current = sorted_blocks[0]
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for block in sorted_blocks[1:]:
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if abs(_center_y(block) - _center_y(current)) <= y_tolerance:
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current["text"] += block["text"]
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current["confidence"] = max(current["confidence"], block["confidence"])
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else:
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merged.append(current)
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current = block
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merged.append(current)
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return merged
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def extract_text(
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blocks: list[dict[str, Any]],
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separator: str = "",
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) -> str:
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"""从 OCR 块中提取纯文本。
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Args:
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blocks: OCR 块列表。
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separator: 块之间的分隔符。
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Returns:
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拼接后的文本。
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"""
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return separator.join(b["text"] for b in blocks)
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def _center_y(block: dict[str, Any]) -> float:
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"""获取块的中心 Y 坐标。"""
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pts = block.get("bbox", {}).get("points", block.get("bbox", []))
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if pts and isinstance(pts, list) and len(pts) > 0:
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ys = [p[1] for p in pts if isinstance(p, (list, tuple)) and len(p) >= 2]
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return sum(ys) / len(ys) if ys else 0.0
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return 0.0
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def _center_x(block: dict[str, Any]) -> float:
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"""获取块的中心 X 坐标。"""
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pts = block.get("bbox", {}).get("points", block.get("bbox", []))
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if pts and isinstance(pts, list) and len(pts) > 0:
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xs = [p[0] for p in pts if isinstance(p, (list, tuple)) and len(p) >= 2]
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return sum(xs) / len(xs) if xs else 0.0
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return 0.0
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