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+import random
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+
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+import fitz
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+import cv2
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+from paddleocr import PPStructure
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+from PIL import Image
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+from loguru import logger
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+import numpy as np
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+
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+def region_to_bbox(region):
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+ x0 = region[0][0]
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+ y0 = region[0][1]
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+ x1 = region[2][0]
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+ y1 = region[2][1]
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+ return [x0, y0, x1, y1]
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+
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+
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+def dict_compare(d1, d2):
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+ return d1.items() == d2.items()
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+
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+
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+def remove_duplicates_dicts(lst):
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+ unique_dicts = []
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+ for dict_item in lst:
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+ if not any(dict_compare(dict_item, existing_dict) for existing_dict in unique_dicts):
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+ unique_dicts.append(dict_item)
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+ return unique_dicts
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+def doc_analyze(pdf_bytes: bytes, ocr: bool = False, show_log: bool = False):
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+ ocr_engine = PPStructure(table=False, ocr=ocr, show_log=show_log)
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+
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+ imgs = []
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+ with fitz.open("pdf", pdf_bytes) as doc:
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+ for index in range(0, doc.page_count):
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+ page = doc[index]
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+ dpi = 200
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+ mat = fitz.Matrix(dpi / 72, dpi / 72)
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+ pm = page.get_pixmap(matrix=mat, alpha=False)
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+
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+ # if width or height > 2000 pixels, don't enlarge the image
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+ # if pm.width > 2000 or pm.height > 2000:
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+ # pm = page.get_pixmap(matrix=fitz.Matrix(1, 1), alpha=False)
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+
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+ img = Image.frombytes("RGB", [pm.width, pm.height], pm.samples)
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+ img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
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+ img_dict = {
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+ "img": img,
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+ "width": pm.width,
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+ "height": pm.height
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+ }
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+ imgs.append(img_dict)
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+
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+ model_json = []
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+ for index, img_dict in enumerate(imgs):
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+ img = img_dict['img']
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+ page_width = img_dict['width']
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+ page_height = img_dict['height']
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+ result = ocr_engine(img)
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+ spans = []
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+ need_remove = []
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+ for line in result:
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+ line.pop('img')
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+ '''
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+ 为paddle输出适配type no.
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+ title: 0 # 标题
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+ text: 1 # 文本
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+ header: 2 # abandon
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+ footer: 2 # abandon
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+ reference: 1 # 文本 or abandon
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+ equation: 8 # 行间公式 block
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+ equation: 14 # 行间公式 text
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+ figure: 3 # 图片
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+ figure_caption: 4 # 图片描述
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+ table: 5 # 表格
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+ table_caption: 6 # 表格描述
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+ '''
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+ if line['type'] == 'title':
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+ line['category_id'] = 0
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+ elif line['type'] in ['text', 'reference']:
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+ line['category_id'] = 1
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+ elif line['type'] == 'figure':
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+ line['category_id'] = 3
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+ elif line['type'] == 'figure_caption':
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+ line['category_id'] = 4
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+ elif line['type'] == 'table':
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+ line['category_id'] = 5
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+ elif line['type'] == 'table_caption':
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+ line['category_id'] = 6
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+ elif line['type'] == 'equation':
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+ line['category_id'] = 8
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+ elif line['type'] in ['header', 'footer']:
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+ line['category_id'] = 2
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+ else:
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+ logger.warning(f"unknown type: {line['type']}")
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+ line['score'] = 0.5 + random.random() * 0.5
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+
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+ res = line.pop('res', None)
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+ if res is not None and len(res) > 0:
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+ for span in res:
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+ new_span = {'category_id': 15,
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+ 'bbox': region_to_bbox(span['text_region']),
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+ 'score': span['confidence'],
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+ 'text': span['text']
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+ }
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+ spans.append(new_span)
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+
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+ if len(spans) > 0:
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+ result.extend(spans)
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+
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+ result = remove_duplicates_dicts(result)
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+
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+ page_info = {
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+ "page_no": index,
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+ "height": page_height,
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+ "width": page_width
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+ }
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+ page_dict = {
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+ "layout_dets": result,
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+ "page_info": page_info
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+ }
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+
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+ model_json.append(page_dict)
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+
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+ return model_json
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