Merge branch 'opendatalab:dev' into dev

magic_pdf/model/sub_modules/ocr/paddleocr/ppocr_291_mod.py

+import copy
+import time
+
+
+import cv2
+import numpy as np
+from paddleocr import PaddleOCR
+from paddleocr.paddleocr import check_img, logger
+from paddleocr.ppocr.utils.utility import alpha_to_color, binarize_img
+from paddleocr.tools.infer.predict_system import sorted_boxes
+from paddleocr.tools.infer.utility import slice_generator, merge_fragmented, get_rotate_crop_image, \
+    get_minarea_rect_crop
+
+from magic_pdf.model.sub_modules.ocr.paddleocr.ocr_utils import update_det_boxes
+
+
+class ModifiedPaddleOCR(PaddleOCR):
+
+    def ocr(
+        self,
+        img,
+        det=True,
+        rec=True,
+        cls=True,
+        bin=False,
+        inv=False,
+        alpha_color=(255, 255, 255),
+        slice={},
+        mfd_res=None,
+    ):
+        """
+        OCR with PaddleOCR
+
+        Args:
+            img: Image for OCR. It can be an ndarray, img_path, or a list of ndarrays.
+            det: Use text detection or not. If False, only text recognition will be executed. Default is True.
+            rec: Use text recognition or not. If False, only text detection will be executed. Default is True.
+            cls: Use angle classifier or not. Default is True. If True, the text with a rotation of 180 degrees can be recognized. If no text is rotated by 180 degrees, use cls=False to get better performance.
+            bin: Binarize image to black and white. Default is False.
+            inv: Invert image colors. Default is False.
+            alpha_color: Set RGB color Tuple for transparent parts replacement. Default is pure white.
+            slice: Use sliding window inference for large images. Both det and rec must be True. Requires int values for slice["horizontal_stride"], slice["vertical_stride"], slice["merge_x_thres"], slice["merge_y_thres"] (See doc/doc_en/slice_en.md). Default is {}.
+
+        Returns:
+            If both det and rec are True, returns a list of OCR results for each image. Each OCR result is a list of bounding boxes and recognized text for each detected text region.
+            If det is True and rec is False, returns a list of detected bounding boxes for each image.
+            If det is False and rec is True, returns a list of recognized text for each image.
+            If both det and rec are False, returns a list of angle classification results for each image.
+
+        Raises:
+            AssertionError: If the input image is not of type ndarray, list, str, or bytes.
+            SystemExit: If det is True and the input is a list of images.
+
+        Note:
+            - If the angle classifier is not initialized (use_angle_cls=False), it will not be used during the forward process.
+            - For PDF files, if the input is a list of images and the page_num is specified, only the first page_num images will be processed.
+            - The preprocess_image function is used to preprocess the input image by applying alpha color replacement, inversion, and binarization if specified.
+        """
+        assert isinstance(img, (np.ndarray, list, str, bytes))
+        if isinstance(img, list) and det == True:
+            logger.error("When input a list of images, det must be false")
+            exit(0)
+        if cls == True and self.use_angle_cls == False:
+            logger.warning(
+                "Since the angle classifier is not initialized, it will not be used during the forward process"
+            )
+
+        img, flag_gif, flag_pdf = check_img(img, alpha_color)
+        # for infer pdf file
+        if isinstance(img, list) and flag_pdf:
+            if self.page_num > len(img) or self.page_num == 0:
+                imgs = img
+            else:
+                imgs = img[: self.page_num]
+        else:
+            imgs = [img]
+
+        def preprocess_image(_image):
+            _image = alpha_to_color(_image, alpha_color)
+            if inv:
+                _image = cv2.bitwise_not(_image)
+            if bin:
+                _image = binarize_img(_image)
+            return _image
+
+        if det and rec:
+            ocr_res = []
+            for img in imgs:
+                img = preprocess_image(img)
+                dt_boxes, rec_res, _ = self.__call__(img, cls, slice, mfd_res=mfd_res)
+                if not dt_boxes and not rec_res:
+                    ocr_res.append(None)
+                    continue
+                tmp_res = [[box.tolist(), res] for box, res in zip(dt_boxes, rec_res)]
+                ocr_res.append(tmp_res)
+            return ocr_res
+        elif det and not rec:
+            ocr_res = []
+            for img in imgs:
+                img = preprocess_image(img)
+                dt_boxes, elapse = self.text_detector(img)
+                if dt_boxes.size == 0:
+                    ocr_res.append(None)
+                    continue
+                tmp_res = [box.tolist() for box in dt_boxes]
+                ocr_res.append(tmp_res)
+            return ocr_res
+        else:
+            ocr_res = []
+            cls_res = []
+            for img in imgs:
+                if not isinstance(img, list):
+                    img = preprocess_image(img)
+                    img = [img]
+                if self.use_angle_cls and cls:
+                    img, cls_res_tmp, elapse = self.text_classifier(img)
+                    if not rec:
+                        cls_res.append(cls_res_tmp)
+                rec_res, elapse = self.text_recognizer(img)
+                ocr_res.append(rec_res)
+            if not rec:
+                return cls_res
+            return ocr_res
+
+    def __call__(self, img, cls=True, slice={}, mfd_res=None):
+        time_dict = {"det": 0, "rec": 0, "cls": 0, "all": 0}
+
+        if img is None:
+            logger.debug("no valid image provided")
+            return None, None, time_dict
+
+        start = time.time()
+        ori_im = img.copy()
+        if slice:
+            slice_gen = slice_generator(
+                img,
+                horizontal_stride=slice["horizontal_stride"],
+                vertical_stride=slice["vertical_stride"],
+            )
+            elapsed = []
+            dt_slice_boxes = []
+            for slice_crop, v_start, h_start in slice_gen:
+                dt_boxes, elapse = self.text_detector(slice_crop, use_slice=True)
+                if dt_boxes.size:
+                    dt_boxes[:, :, 0] += h_start
+                    dt_boxes[:, :, 1] += v_start
+                    dt_slice_boxes.append(dt_boxes)
+                    elapsed.append(elapse)
+            dt_boxes = np.concatenate(dt_slice_boxes)
+
+            dt_boxes = merge_fragmented(
+                boxes=dt_boxes,
+                x_threshold=slice["merge_x_thres"],
+                y_threshold=slice["merge_y_thres"],
+            )
+            elapse = sum(elapsed)
+        else:
+            dt_boxes, elapse = self.text_detector(img)
+
+        time_dict["det"] = elapse
+
+        if dt_boxes is None:
+            logger.debug("no dt_boxes found, elapsed : {}".format(elapse))
+            end = time.time()
+            time_dict["all"] = end - start
+            return None, None, time_dict
+        else:
+            logger.debug(
+                "dt_boxes num : {}, elapsed : {}".format(len(dt_boxes), elapse)
+            )
+        img_crop_list = []
+
+        dt_boxes = sorted_boxes(dt_boxes)
+
+        if mfd_res:
+            bef = time.time()
+            dt_boxes = update_det_boxes(dt_boxes, mfd_res)
+            aft = time.time()
+            logger.debug("split text box by formula, new dt_boxes num : {}, elapsed : {}".format(
+                len(dt_boxes), aft - bef))
+
+        for bno in range(len(dt_boxes)):
+            tmp_box = copy.deepcopy(dt_boxes[bno])
+            if self.args.det_box_type == "quad":
+                img_crop = get_rotate_crop_image(ori_im, tmp_box)
+            else:
+                img_crop = get_minarea_rect_crop(ori_im, tmp_box)
+            img_crop_list.append(img_crop)
+        if self.use_angle_cls and cls:
+            img_crop_list, angle_list, elapse = self.text_classifier(img_crop_list)
+            time_dict["cls"] = elapse
+            logger.debug(
+                "cls num  : {}, elapsed : {}".format(len(img_crop_list), elapse)
+            )
+        if len(img_crop_list) > 1000:
+            logger.debug(
+                f"rec crops num: {len(img_crop_list)}, time and memory cost may be large."
+            )
+
+        rec_res, elapse = self.text_recognizer(img_crop_list)
+        time_dict["rec"] = elapse
+        logger.debug("rec_res num  : {}, elapsed : {}".format(len(rec_res), elapse))
+        if self.args.save_crop_res:
+            self.draw_crop_rec_res(self.args.crop_res_save_dir, img_crop_list, rec_res)
+        filter_boxes, filter_rec_res = [], []
+        for box, rec_result in zip(dt_boxes, rec_res):
+            text, score = rec_result[0], rec_result[1]
+            if score >= self.drop_score:
+                filter_boxes.append(box)
+                filter_rec_res.append(rec_result)
+        end = time.time()
+        time_dict["all"] = end - start
+        return filter_boxes, filter_rec_res, time_dict

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

@@ -164,7 +164,7 @@ class ModelSingleton:
 
 
 def do_predict(boxes: List[List[int]], model) -> List[int]:
-    from magic_pdf.model.v3.helpers import (boxes2inputs, parse_logits,
+    from magic_pdf.model.sub_modules.reading_oreder.layoutreader.helpers import (boxes2inputs, parse_logits,
                                                                                  prepare_inputs)
 
     inputs = boxes2inputs(boxes)
@@ -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 TableMasterPaddleModel
 
 
 class TestppTableModel(unittest.TestCase):
@@ -11,7 +11,7 @@ class TestppTableModel(unittest.TestCase):
         # 修改table模型路径
         config = {"device": "cuda",
                   "model_dir": "/home/quyuan/.cache/modelscope/hub/opendatalab/PDF-Extract-Kit/models/TabRec/TableMaster"}
-        table_model = ppTableModel(config)
+        table_model = TableMasterPaddleModel(config)
         res = table_model.img2html(img)
         # 验证生成的 HTML 是否符合预期
         parser = etree.HTMLParser()