def process_info(app_context, base_dir):
try:
files = get_files(app_context.application_context)
file_images = []
output = []
for index, file_new in enumerate(files):
start_time = time.time()
file = get_json(file_new['file']['name'], base_dir)[0]
file_properties = File(file)
ocr_level, lang = get_ocr_config(file_new,
file_properties.get_pages())
file = preprocess_file(file_properties, lang, ocr_level)
file['file'] = file_new['file']
file['config'] = file_new['config']
output.append(file)
output[index]['status'] = {
'code': 200,
'message': "tesseract ocr successful"
}
end_time = time.time()
extraction_time = (end_time - start_time) / len(
file_properties.get_pages())
log_info(
'tesseract ocr per page completed in {}'.format(
extraction_time), app_context.application_context)
app_context.application_context["outputs"] = output
log_info("successfully completed tesseract ocr", None)
except Exception as e:
log_exception("Error occured during tesseract ocr ",
app_context.application_context, e)
return None
return app_context.application_context
def process_input(app_context, base_dir):
try:
files = get_files(app_context.application_context)
output_files = []
langs = []
for index, file in enumerate(files):
file_output = {"status": {}}
file = get_json(base_dir, file['file']['name'])[0]
file_properties = File(file)
if "page_info" in file.keys():
page_paths = file_properties.get_pages()
else:
page_paths = doc_pre_processing(file['file']['name'],
config.BASE_DIR)
page_res = text_extraction(file_properties, page_paths, file)
output_files.append(page_res)
langs.append(file_properties.get_language())
app_context.application_context["outputs"] = output_files
log_info("successfully completed google vision ocr", None)
except Exception as e:
log_exception("Error occured during google vision ocr",
app_context.application_context, e)
return None, None
return app_context.application_context, langs
def get_segmented_regions(app_context,base_dir) :
try:
files = get_files(app_context.application_context)
output = []
for index,file in enumerate(files):
file = get_json(base_dir, file['file']['name'])
file_properties = File(file)
pages = file_properties.get_pages()
page_counts = len(pages)
start_time = time.time()
for page_index in range(page_counts):
print('processing for page : ', page_index)
# page_lines = file_properties.get_lines(page_index)
# page_regions = file_properties.get_regions(page_index)
# page_words = file_properties.get_words(page_index)
#font_meta = font_properties(file_properties.get_page(page_index))
font_meta = []
#page_regions = region_unifier.region_unifier(page_lines,page_regions)
#file_properties.set_regions(page_index, segment_regions(page_words,page_lines,page_regions))
file_properties.set_font_properties(page_index,font_meta)
output.append(file_properties.get_file())
output[index]['status']= {'message':"block-segmenter successful"}
end_time = time.time()
extraction_time = (end_time - start_time)/page_counts
log_info('block segmentation per page completed in {}'.format(extraction_time), app_context.application_context)
app_context.application_context["outputs"] =output
log_info("successfully completed block segmentation", None)
except Exception as e:
log_exception("Error occured during block segmentation ", app_context.application_context, e)
return None
return app_context.application_context
def get_layout(app_context):
try:
files = get_files(app_context.application_context)
#files = get_json(app_context.application_context)
#files = get_files(json_data)
file_images = []
output = []
for index, file_new in enumerate(files):
file = get_json(file_new['file']['name'])[0]
file_properties = File(file)
page_paths = file_properties.get_pages()
start_time = time.time()
for idx, page_path in enumerate(page_paths):
page_lines = file_properties.get_lines(idx)
page_words = file_properties.get_words(idx)
line_coords = get_coord(page_lines)
#page_path = '/'.join(page_path.split('/')[-4:])
page_path = 'upload/' + page_path.split('upload/')[-1]
#masked_image, table_and_lines = extract_table_line_regions(page_path)
#cell_regions = cell_layout(table_and_lines,page_path)
if torch.cuda.is_available():
torch.cuda.device(0)
print("*******cuda available")
torch.cuda.empty_cache()
time.sleep(1)
regions = primalaynet.predict_primanet(page_path, line_coords)
#regions += cell_regions
file['pages'][idx]["regions"] = regions
file['file'] = file_new['file']
file['config'] = file_new['config']
output.append(file)
output[index]['status'] = {}
output[index]['status']['message'] = "layout-detector successful"
end_time = time.time()
extraction_time = (end_time - start_time) / len(page_paths)
log_info(
'Layout detection per page completed in {}'.format(
extraction_time), app_context.application_context)
app_context.application_context["outputs"] = output
log_info("successfully completed layout detection", None)
except Exception as e:
log_exception("Error occured during prima layout detection ",
app_context.application_context, e)
return None
return app_context.application_context