Merge branch 'opendatalab:dev' into dev

magic_pdf/model/batch_analyze.py

@@ -181,17 +181,16 @@ class BatchAnalyze:
 
                 # 对每个分辨率组进行批处理
                 for group_key, group_crops in tqdm(resolution_groups.items(), desc=f"OCR-det {lang}"):
-                    raw_images = [crop_info[0] for crop_info in group_crops]
-
                     # 计算目标尺寸（组内最大尺寸，向上取整到32的倍数）
-                    max_h = max(img.shape[0] for img in raw_images)
-                    max_w = max(img.shape[1] for img in raw_images)
+                    max_h = max(crop_info[0].shape[0] for crop_info in group_crops)
+                    max_w = max(crop_info[0].shape[1] for crop_info in group_crops)
                     target_h = ((max_h + 32 - 1) // 32) * 32
                     target_w = ((max_w + 32 - 1) // 32) * 32
 
                     # 对所有图像进行padding到统一尺寸
                     batch_images = []
-                    for img in raw_images:
+                    for crop_info in group_crops:
+                        img = crop_info[0]
                         h, w = img.shape[:2]
                         # 创建目标尺寸的白色背景
                         padded_img = np.ones((target_h, target_w, 3), dtype=np.uint8) * 255
@@ -208,9 +207,32 @@ class BatchAnalyze:
                     for i, (crop_info, (dt_boxes, elapse)) in enumerate(zip(group_crops, batch_results)):
                         new_image, useful_list, ocr_res_list_dict, res, adjusted_mfdetrec_res, _lang = crop_info
 
-                        if dt_boxes is not None:
-                            # 构造OCR结果格式 - 每个box应该是4个点的列表
-                            ocr_res = [box.tolist() for box in dt_boxes]
+                        if dt_boxes is not None and len(dt_boxes) > 0:
+                            # 直接应用原始OCR流程中的关键处理步骤
+                            from magic_pdf.model.sub_modules.ocr.paddleocr2pytorch.ocr_utils import (
+                                merge_det_boxes, update_det_boxes, sorted_boxes
+                            )
+
+                            # 1. 排序检测框
+                            if len(dt_boxes) > 0:
+                                dt_boxes_sorted = sorted_boxes(dt_boxes)
+                            else:
+                                dt_boxes_sorted = []
+
+                            # 2. 合并相邻检测框
+                            if dt_boxes_sorted:
+                                dt_boxes_merged = merge_det_boxes(dt_boxes_sorted)
+                            else:
+                                dt_boxes_merged = []
+
+                            # 3. 根据公式位置更新检测框（关键步骤！）
+                            if dt_boxes_merged and adjusted_mfdetrec_res:
+                                dt_boxes_final = update_det_boxes(dt_boxes_merged, adjusted_mfdetrec_res)
+                            else:
+                                dt_boxes_final = dt_boxes_merged
+
+                            # 构造OCR结果格式
+                            ocr_res = [box.tolist() if hasattr(box, 'tolist') else box for box in dt_boxes_final]
 
                             if ocr_res:
                                 ocr_result_list = get_ocr_result_list(