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@@ -185,11 +185,11 @@ class CellFusionEngine:
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fused_cells = self._nms_filter(fused_cells, self.iou_nms_threshold)
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# Phase 4: OCR 补偿（可选）
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- if self.enable_ocr_compensation and ocr_boxes:
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- fused_cells, cell_labels, ocr_comp_count = self._compensate_with_ocr(
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- fused_cells, cell_labels, ocr_boxes, (w, h)
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- )
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- fusion_stats['ocr_compensated_count'] = ocr_comp_count
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+ # if self.enable_ocr_compensation and ocr_boxes:
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+ # fused_cells, cell_labels, ocr_comp_count = self._compensate_with_ocr(
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+ # fused_cells, cell_labels, ocr_boxes, (w, h)
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+ # )
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+ # fusion_stats['ocr_compensated_count'] = ocr_comp_count
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fusion_stats['fused_count'] = len(fused_cells)
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@@ -306,7 +306,14 @@ class CellFusionEngine:
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# 如果覆盖率>40%，说明这是一个真实的合并单元格
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# 降低阈值从0.5到0.4，因为合并单元格可能包含很多空白区域
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if coverage > 0.4:
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- fused_cells.append(rtdetr_cell)
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+ # 认定为合并单元格，取bounding与RT-DETR的最大范围
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+ fused_cell = [
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+ min(bounding_x1, rtdetr_cell[0]),
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+ min(bounding_y1, rtdetr_cell[1]),
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+ max(bounding_x2, rtdetr_cell[2]),
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+ max(bounding_y2, rtdetr_cell[3])
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+ ]
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+ fused_cells.append(fused_cell)
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cell_labels.append('merged_span') # 标记为合并单元格
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rtdetr_matched[rt_idx] = True
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# 标记所有被包含的UNet单元格
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