refactor(model): rename and restructure model modules

magic_pdf/model/sub_modules/table/rapidtable/rapid_table.py

+import numpy as np
+from rapid_table import RapidTable
+from rapidocr_paddle import RapidOCR
+
+
+class RapidTableModel(object):
+    def __init__(self):
+        self.table_model = RapidTable()
+        self.ocr_engine = RapidOCR(det_use_cuda=True, cls_use_cuda=True, rec_use_cuda=True)
+
+    def predict(self, image):
+        ocr_result, _ = self.ocr_engine(np.asarray(image))
+        html_code, table_cell_bboxes, elapse = self.table_model(np.asarray(image), ocr_result)
+        return html_code, table_cell_bboxes, elapse
\ No newline at end of file

magic_pdf/model/pek_sub_modules/structeqtable/StructTableModel.py → magic_pdf/model/sub_modules/table/structeqtable/struct_eqtable.py

-import re
-
 import torch
 from struct_eqtable import build_model
 
+from magic_pdf.model.sub_modules.table.table_utils import minify_html
+
 
 class StructTableModel:
     def __init__(self, model_path, max_new_tokens=1024, max_time=60):
@@ -31,15 +31,7 @@ class StructTableModel:
         )
 
         if output_format == "html":
-            results = [self.minify_html(html) for html in results]
+            results = [minify_html(html) for html in results]
 
         return results
 
-    def minify_html(self, html):
-        # 移除多余的空白字符
-        html = re.sub(r'\s+', ' ', html)
-        # 移除行尾的空白字符
-        html = re.sub(r'\s*>\s*', '>', html)
-        # 移除标签前的空白字符
-        html = re.sub(r'\s*<\s*', '<', html)
-        return html.strip()
\ No newline at end of file

magic_pdf/model/sub_modules/table/table_utils.py

+import re
+
+
+def minify_html(html):
+    # 移除多余的空白字符
+    html = re.sub(r'\s+', ' ', html)
+    # 移除行尾的空白字符
+    html = re.sub(r'\s*>\s*', '>', html)
+    # 移除标签前的空白字符
+    html = re.sub(r'\s*<\s*', '<', html)
+    return html.strip()
\ No newline at end of file

magic_pdf/model/ppTableModel.py → magic_pdf/model/sub_modules/table/tablemaster/tablemaster_paddle.py

@@ -7,7 +7,7 @@ from PIL import Image
 import numpy as np
 
 
-class ppTableModel(object):
+class TableMasterPaddleModel(object):
     """
         This class is responsible for converting image of table into HTML format using a pre-trained model.
 

magic_pdf/pdf_parse_union_core_v2.py

@@ -164,8 +164,8 @@ class ModelSingleton:
 
 
 def do_predict(boxes: List[List[int]], model) -> List[int]:
-    from magic_pdf.model.v3.helpers import (boxes2inputs, parse_logits,
-                                            prepare_inputs)
+    from magic_pdf.model.sub_modules.reading_oreder.layoutreader.helpers import (boxes2inputs, parse_logits,
+                                                                                 prepare_inputs)
 
     inputs = boxes2inputs(boxes)
     inputs = prepare_inputs(inputs, model)
@@ -206,7 +206,7 @@ def cal_block_index(fix_blocks, sorted_bboxes):
                 del block['real_lines']
 
         import numpy as np
-        from magic_pdf.model.v3.xycut import recursive_xy_cut
+        from magic_pdf.model.sub_modules.reading_oreder.layoutreader.xycut import recursive_xy_cut
 
         random_boxes = np.array(block_bboxes)
         np.random.shuffle(random_boxes)

setup.py

@@ -49,6 +49,7 @@ if __name__ == '__main__':
                      "doclayout_yolo==0.0.2",  # doclayout_yolo
                      "rapidocr-paddle",  # rapidocr-paddle
                      "rapid_table",  # rapid_table
+                     "PyYAML",  # yaml
                      "detectron2"
                      ],
         },

tests/test_table/test_tablemaster.py

@@ -2,7 +2,7 @@ import unittest
 from PIL import Image
 from lxml import etree
 
-from magic_pdf.model.ppTableModel import ppTableModel
+from magic_pdf.model.sub_modules.table.tablemaster.tablemaster_paddle import ppTableModel
 
 
 class TestppTableModel(unittest.TestCase):