import time
import cv2
from loguru import logger
from magic_pdf.config.constants import MODEL_NAME
from magic_pdf.model.sub_modules.model_init import AtomModelSingleton
from magic_pdf.model.sub_modules.model_utils import (
clean_vram, crop_img, get_res_list_from_layout_res)
from magic_pdf.model.sub_modules.ocr.paddleocr2pytorch.ocr_utils import (
get_adjusted_mfdetrec_res, get_ocr_result_list)
YOLO_LAYOUT_BASE_BATCH_SIZE = 1
MFD_BASE_BATCH_SIZE = 1
MFR_BASE_BATCH_SIZE = 16
class BatchAnalyze:
def __init__(self, model_manager, batch_ratio: int, show_log, layout_model, formula_enable, table_enable):
self.model_manager = model_manager
self.batch_ratio = batch_ratio
self.show_log = show_log
self.layout_model = layout_model
self.formula_enable = formula_enable
self.table_enable = table_enable
def __call__(self, images_with_extra_info: list) -> list:
if len(images_with_extra_info) == 0:
return []
images_layout_res = []
layout_start_time = time.time()
self.model = self.model_manager.get_model(
ocr=True,
show_log=self.show_log,
lang = None,
layout_model = self.layout_model,
formula_enable = self.formula_enable,
table_enable = self.table_enable,
)
images = [image for image, _, _ in images_with_extra_info]
if self.model.layout_model_name == MODEL_NAME.LAYOUTLMv3:
# layoutlmv3
for image in images:
layout_res = self.model.layout_model(image, ignore_catids=[])
images_layout_res.append(layout_res)
elif self.model.layout_model_name == MODEL_NAME.DocLayout_YOLO:
# doclayout_yolo
layout_images = []
for image_index, image in enumerate(images):
layout_images.append(image)
images_layout_res += self.model.layout_model.batch_predict(
# layout_images, self.batch_ratio * YOLO_LAYOUT_BASE_BATCH_SIZE
layout_images, YOLO_LAYOUT_BASE_BATCH_SIZE
)
# logger.info(
# f'layout time: {round(time.time() - layout_start_time, 2)}, image num: {len(images)}'
# )
if self.model.apply_formula:
# 公式检测
mfd_start_time = time.time()
images_mfd_res = self.model.mfd_model.batch_predict(
# images, self.batch_ratio * MFD_BASE_BATCH_SIZE
images, MFD_BASE_BATCH_SIZE
)
# logger.info(
# f'mfd time: {round(time.time() - mfd_start_time, 2)}, image num: {len(images)}'
# )
# 公式识别
mfr_start_time = time.time()
images_formula_list = self.model.mfr_model.batch_predict(
images_mfd_res,
images,
batch_size=self.batch_ratio * MFR_BASE_BATCH_SIZE,
)
mfr_count = 0
for image_index in range(len(images)):
images_layout_res[image_index] += images_formula_list[image_index]
mfr_count += len(images_formula_list[image_index])
# logger.info(
# f'mfr time: {round(time.time() - mfr_start_time, 2)}, image num: {mfr_count}'
# )
# 清理显存
# clean_vram(self.model.device, vram_threshold=8)
ocr_res_list_all_page = []
table_res_list_all_page = []
for index in range(len(images)):
_, ocr_enable, _lang = images_with_extra_info[index]
layout_res = images_layout_res[index]
np_array_img = images[index]
ocr_res_list, table_res_list, single_page_mfdetrec_res = (
get_res_list_from_layout_res(layout_res)
)
ocr_res_list_all_page.append({'ocr_res_list':ocr_res_list,
'lang':_lang,
'ocr_enable':ocr_enable,
'np_array_img':np_array_img,
'single_page_mfdetrec_res':single_page_mfdetrec_res,
'layout_res':layout_res,
})
for table_res in table_res_list:
table_img, _ = crop_img(table_res, np_array_img)
table_res_list_all_page.append({'table_res':table_res,
'lang':_lang,
'table_img':table_img,
})
# 文本框检测
det_start = time.time()
det_count = 0
# for ocr_res_list_dict in ocr_res_list_all_page:
for ocr_res_list_dict in ocr_res_list_all_page:
# Process each area that requires OCR processing
_lang = ocr_res_list_dict['lang']
# Get OCR results for this language's images
atom_model_manager = AtomModelSingleton()
ocr_model = atom_model_manager.get_atom_model(
atom_model_name='ocr',
ocr_show_log=False,
det_db_box_thresh=0.3,
lang=_lang
)
for res in ocr_res_list_dict['ocr_res_list']:
new_image, useful_list = crop_img(
res, ocr_res_list_dict['np_array_img'], crop_paste_x=50, crop_paste_y=50
)
adjusted_mfdetrec_res = get_adjusted_mfdetrec_res(
ocr_res_list_dict['single_page_mfdetrec_res'], useful_list
)
# OCR-det
new_image = cv2.cvtColor(new_image, cv2.COLOR_RGB2BGR)
ocr_res = ocr_model.ocr(
new_image, mfd_res=adjusted_mfdetrec_res, rec=False
)[0]
# Integration results
if ocr_res:
ocr_result_list = get_ocr_result_list(ocr_res, useful_list, ocr_res_list_dict['ocr_enable'], new_image, _lang)
ocr_res_list_dict['layout_res'].extend(ocr_result_list)
# det_count += len(ocr_res_list_dict['ocr_res_list'])
# logger.info(f'ocr-det time: {round(time.time()-det_start, 2)}, image num: {det_count}')
# 表格识别 table recognition
if self.model.apply_table:
table_start = time.time()
# for table_res_list_dict in table_res_list_all_page:
for table_res_dict in table_res_list_all_page:
_lang = table_res_dict['lang']
atom_model_manager = AtomModelSingleton()
table_model = atom_model_manager.get_atom_model(
atom_model_name='table',
table_model_name='rapid_table',
table_model_path='',
table_max_time=400,
device='cpu',
lang=_lang,
table_sub_model_name='slanet_plus'
)
html_code, table_cell_bboxes, logic_points, elapse = table_model.predict(table_res_dict['table_img'])
# 判断是否返回正常
if html_code:
expected_ending = html_code.strip().endswith(
'</html>'
) or html_code.strip().endswith('</table>')
if expected_ending:
table_res_dict['table_res']['html'] = html_code
else:
logger.warning(
'table recognition processing fails, not found expected HTML table end'
)
else:
logger.warning(
'table recognition processing fails, not get html return'
)
# logger.info(f'table time: {round(time.time() - table_start, 2)}, image num: {len(table_res_list_all_page)}')
# Create dictionaries to store items by language
need_ocr_lists_by_lang = {} # Dict of lists for each language
img_crop_lists_by_lang = {} # Dict of lists for each language
for layout_res in images_layout_res:
for layout_res_item in layout_res:
if layout_res_item['category_id'] in [15]:
if 'np_img' in layout_res_item and 'lang' in layout_res_item:
lang = layout_res_item['lang']
# Initialize lists for this language if not exist
if lang not in need_ocr_lists_by_lang:
need_ocr_lists_by_lang[lang] = []
img_crop_lists_by_lang[lang] = []
# Add to the appropriate language-specific lists
need_ocr_lists_by_lang[lang].append(layout_res_item)
img_crop_lists_by_lang[lang].append(layout_res_item['np_img'])
# Remove the fields after adding to lists
layout_res_item.pop('np_img')
layout_res_item.pop('lang')
if len(img_crop_lists_by_lang) > 0:
# Process OCR by language
rec_time = 0
rec_start = time.time()
total_processed = 0
# Process each language separately
for lang, img_crop_list in img_crop_lists_by_lang.items():
if len(img_crop_list) > 0:
# Get OCR results for this language's images
atom_model_manager = AtomModelSingleton()
ocr_model = atom_model_manager.get_atom_model(
atom_model_name='ocr',
ocr_show_log=False,
det_db_box_thresh=0.3,
lang=lang
)
ocr_res_list = ocr_model.ocr(img_crop_list, det=False, tqdm_enable=True)[0]
# Verify we have matching counts
assert len(ocr_res_list) == len(
need_ocr_lists_by_lang[lang]), f'ocr_res_list: {len(ocr_res_list)}, need_ocr_list: {len(need_ocr_lists_by_lang[lang])} for lang: {lang}'
# Process OCR results for this language
for index, layout_res_item in enumerate(need_ocr_lists_by_lang[lang]):
ocr_text, ocr_score = ocr_res_list[index]
layout_res_item['text'] = ocr_text
layout_res_item['score'] = float(f"{ocr_score:.3f}")
total_processed += len(img_crop_list)
rec_time += time.time() - rec_start
# logger.info(f'ocr-rec time: {round(rec_time, 2)}, total images processed: {total_processed}')
return images_layout_res