Merge pull request #2625 from opendatalab/release-2.0.0

Release 2.0.0

magic_pdf/model/sub_modules/ocr/paddleocr2pytorch/pytorchocr/utils/resources/models_config.yml → mineru/model/ocr/paddleocr2pytorch/pytorchocr/utils/resources/models_config.yml

 lang:
   ch_lite:
-    det: ch_PP-OCRv3_det_infer.pth
+    det: ch_PP-OCRv5_det_infer.pth
     rec: ch_PP-OCRv5_rec_infer.pth
     dict: ppocrv5_dict.txt
   ch_lite_v4:
-    det: ch_PP-OCRv3_det_infer.pth
+    det: ch_PP-OCRv5_det_infer.pth
     rec: ch_PP-OCRv4_rec_infer.pth
     dict: ppocr_keys_v1.txt
   ch_server:
-    det: ch_PP-OCRv3_det_infer.pth
+    det: ch_PP-OCRv5_det_infer.pth
     rec: ch_PP-OCRv5_rec_server_infer.pth
     dict: ppocrv5_dict.txt
   ch_server_v4:
-    det: ch_PP-OCRv3_det_infer.pth
+    det: ch_PP-OCRv5_det_infer.pth
     rec: ch_PP-OCRv4_rec_server_infer.pth
     dict: ppocr_keys_v1.txt
   ch:
-    det: ch_PP-OCRv3_det_infer.pth
+    det: ch_PP-OCRv5_det_infer.pth
     rec: ch_PP-OCRv4_rec_server_doc_infer.pth
     dict: ppocrv4_doc_dict.txt
   en:
@@ -28,12 +28,12 @@ lang:
     rec: korean_PP-OCRv3_rec_infer.pth
     dict: korean_dict.txt
   japan:
-    det: Multilingual_PP-OCRv3_det_infer.pth
-    rec: japan_PP-OCRv3_rec_infer.pth
+    det: ch_PP-OCRv5_det_infer.pth
+    rec: ch_PP-OCRv5_rec_server_infer.pth
     dict: japan_dict.txt
   chinese_cht:
-    det: Multilingual_PP-OCRv3_det_infer.pth
-    rec: chinese_cht_PP-OCRv3_rec_infer.pth
+    det: ch_PP-OCRv5_det_infer.pth
+    rec: ch_PP-OCRv5_rec_server_infer.pth
     dict: chinese_cht_dict.txt
   ta:
     det: Multilingual_PP-OCRv3_det_infer.pth

mineru/model/ocr/paddleocr2pytorch/tools/infer/__init__.py

+# Copyright (c) Opendatalab. All rights reserved.

magic_pdf/model/sub_modules/ocr/paddleocr2pytorch/tools/infer/predict_det.py → mineru/model/ocr/paddleocr2pytorch/tools/infer/predict_det.py

@@ -117,6 +117,128 @@ class TextDetector(BaseOCRV20):
         self.net.eval()
         self.net.to(self.device)
 
+    def _batch_process_same_size(self, img_list):
+        """
+            对相同尺寸的图像进行批处理
+
+            Args:
+                img_list: 相同尺寸的图像列表
+
+            Returns:
+                batch_results: 批处理结果列表
+                total_elapse: 总耗时
+            """
+        starttime = time.time()
+
+        # 预处理所有图像
+        batch_data = []
+        batch_shapes = []
+        ori_imgs = []
+
+        for img in img_list:
+            ori_im = img.copy()
+            ori_imgs.append(ori_im)
+
+            data = {'image': img}
+            data = transform(data, self.preprocess_op)
+            if data is None:
+                # 如果预处理失败，返回空结果
+                return [(None, 0) for _ in img_list], 0
+
+            img_processed, shape_list = data
+            batch_data.append(img_processed)
+            batch_shapes.append(shape_list)
+
+        # 堆叠成批处理张量
+        try:
+            batch_tensor = np.stack(batch_data, axis=0)
+            batch_shapes = np.stack(batch_shapes, axis=0)
+        except Exception as e:
+            # 如果堆叠失败，回退到逐个处理
+            batch_results = []
+            for img in img_list:
+                dt_boxes, elapse = self.__call__(img)
+                batch_results.append((dt_boxes, elapse))
+            return batch_results, time.time() - starttime
+
+        # 批处理推理
+        with torch.no_grad():
+            inp = torch.from_numpy(batch_tensor)
+            inp = inp.to(self.device)
+            outputs = self.net(inp)
+
+        # 处理输出
+        preds = {}
+        if self.det_algorithm == "EAST":
+            preds['f_geo'] = outputs['f_geo'].cpu().numpy()
+            preds['f_score'] = outputs['f_score'].cpu().numpy()
+        elif self.det_algorithm == 'SAST':
+            preds['f_border'] = outputs['f_border'].cpu().numpy()
+            preds['f_score'] = outputs['f_score'].cpu().numpy()
+            preds['f_tco'] = outputs['f_tco'].cpu().numpy()
+            preds['f_tvo'] = outputs['f_tvo'].cpu().numpy()
+        elif self.det_algorithm in ['DB', 'PSE', 'DB++']:
+            preds['maps'] = outputs['maps'].cpu().numpy()
+        elif self.det_algorithm == 'FCE':
+            for i, (k, output) in enumerate(outputs.items()):
+                preds['level_{}'.format(i)] = output.cpu().numpy()
+        else:
+            raise NotImplementedError
+
+        # 后处理每个图像的结果
+        batch_results = []
+        total_elapse = time.time() - starttime
+
+        for i in range(len(img_list)):
+            # 提取单个图像的预测结果
+            single_preds = {}
+            for key, value in preds.items():
+                if isinstance(value, np.ndarray):
+                    single_preds[key] = value[i:i + 1]  # 保持批次维度
+                else:
+                    single_preds[key] = value
+
+            # 后处理
+            post_result = self.postprocess_op(single_preds, batch_shapes[i:i + 1])
+            dt_boxes = post_result[0]['points']
+
+            # 过滤和裁剪检测框
+            if (self.det_algorithm == "SAST" and
+                self.det_sast_polygon) or (self.det_algorithm in ["PSE", "FCE"] and
+                                           self.postprocess_op.box_type == 'poly'):
+                dt_boxes = self.filter_tag_det_res_only_clip(dt_boxes, ori_imgs[i].shape)
+            else:
+                dt_boxes = self.filter_tag_det_res(dt_boxes, ori_imgs[i].shape)
+
+            batch_results.append((dt_boxes, total_elapse / len(img_list)))
+
+        return batch_results, total_elapse
+
+    def batch_predict(self, img_list, max_batch_size=8):
+        """
+        批处理预测方法，支持多张图像同时检测
+
+        Args:
+            img_list: 图像列表
+            max_batch_size: 最大批处理大小
+
+        Returns:
+            batch_results: 批处理结果列表，每个元素为(dt_boxes, elapse)
+        """
+        if not img_list:
+            return []
+
+        batch_results = []
+
+        # 分批处理
+        for i in range(0, len(img_list), max_batch_size):
+            batch_imgs = img_list[i:i + max_batch_size]
+            # assert尺寸一致
+            batch_dt_boxes, batch_elapse = self._batch_process_same_size(batch_imgs)
+            batch_results.extend(batch_dt_boxes)
+
+        return batch_results
+
     def order_points_clockwise(self, pts):
         """
         reference from: https://github.com/jrosebr1/imutils/blob/master/imutils/perspective.py

mineru/model/reading_order/__init__.py

+# Copyright (c) Opendatalab. All rights reserved.

mineru/model/table/__init__.py

+# Copyright (c) Opendatalab. All rights reserved.