# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import sys import subprocess __dir__ = os.path.dirname(os.path.abspath(__file__)) sys.path.append(__dir__) sys.path.insert(0, os.path.abspath(os.path.join(__dir__, "../"))) os.environ["FLAGS_allocator_strategy"] = "auto_growth" import cv2 import json import numpy as np import time import logging from copy import deepcopy from paddle.utils import try_import from ppocr.utils.utility import get_image_file_list, check_and_read from ppocr.utils.logging import get_logger from ppocr.utils.visual import draw_ser_results, draw_re_results from tools.infer.predict_system import TextSystem from ppstructure.layout.predict_layout import LayoutPredictor from ppstructure.table.predict_table import TableSystem, to_excel from ppstructure.utility import parse_args, draw_structure_result, cal_ocr_word_box logger = get_logger() class StructureSystem(object): def __init__(self, args): self.mode = args.mode self.recovery = args.recovery self.image_orientation_predictor = None if args.image_orientation: import paddleclas self.image_orientation_predictor = paddleclas.PaddleClas( model_name="text_image_orientation" ) if self.mode == "structure": if not args.show_log: logger.setLevel(logging.INFO) if args.layout == False and args.ocr == True: args.ocr = False logger.warning( "When args.layout is false, args.ocr is automatically set to false" ) # init model self.layout_predictor = None self.text_system = None self.table_system = None if args.layout: self.layout_predictor = LayoutPredictor(args) if args.ocr: self.text_system = TextSystem(args) if args.table: if self.text_system is not None: self.table_system = TableSystem( args, self.text_system.text_detector, self.text_system.text_recognizer, ) else: self.table_system = TableSystem(args) elif self.mode == "kie": from ppstructure.kie.predict_kie_token_ser_re import SerRePredictor self.kie_predictor = SerRePredictor(args) self.return_word_box = args.return_word_box def __call__(self, img, return_ocr_result_in_table=False, img_idx=0): time_dict = { "image_orientation": 0, "layout": 0, "table": 0, "table_match": 0, "det": 0, "rec": 0, "kie": 0, "all": 0, } start = time.time() if self.image_orientation_predictor is not None: tic = time.time() cls_result = self.image_orientation_predictor.predict(input_data=img) cls_res = next(cls_result) angle = cls_res[0]["label_names"][0] cv_rotate_code = { "90": cv2.ROTATE_90_COUNTERCLOCKWISE, "180": cv2.ROTATE_180, "270": cv2.ROTATE_90_CLOCKWISE, } if angle in cv_rotate_code: img = cv2.rotate(img, cv_rotate_code[angle]) toc = time.time() time_dict["image_orientation"] = toc - tic if self.mode == "structure": ori_im = img.copy() if self.layout_predictor is not None: layout_res, elapse = self.layout_predictor(img) time_dict["layout"] += elapse else: h, w = ori_im.shape[:2] layout_res = [dict(bbox=None, label="table", score=0.0)] # As reported in issues such as #10270 and #11665, the old # implementation, which recognizes texts from the layout regions, # has problems with OCR recognition accuracy. # # To enhance the OCR recognition accuracy, we implement a patch fix # that first use text_system to detect and recognize all text information # and then filter out relevant texts according to the layout regions. text_res = None if self.text_system is not None: text_res, ocr_time_dict = self._predict_text(img) time_dict["det"] += ocr_time_dict["det"] time_dict["rec"] += ocr_time_dict["rec"] res_list = [] for region in layout_res: res = "" if region["bbox"] is not None: x1, y1, x2, y2 = region["bbox"] x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2) roi_img = ori_im[y1:y2, x1:x2, :] else: x1, y1, x2, y2 = 0, 0, w, h roi_img = ori_im bbox = [x1, y1, x2, y2] if region["label"] == "table": if self.table_system is not None: res, table_time_dict = self.table_system( roi_img, return_ocr_result_in_table ) time_dict["table"] += table_time_dict["table"] time_dict["table_match"] += table_time_dict["match"] time_dict["det"] += table_time_dict["det"] time_dict["rec"] += table_time_dict["rec"] else: if text_res is not None: # Filter the text results whose regions intersect with the current layout bbox. res = self._filter_text_res(text_res, bbox) res_list.append( { "type": region["label"].lower(), "bbox": bbox, "Crop_img": roi_img, "res": res, "img_idx": img_idx, "score": region["score"], } ) end = time.time() time_dict["all"] = end - start return res_list, time_dict elif self.mode == "kie": re_res, elapse = self.kie_predictor(img) time_dict["kie"] = elapse time_dict["all"] = elapse return re_res[0], time_dict return None, None def _predict_text(self, img): filter_boxes, filter_rec_res, ocr_time_dict = self.text_system(img) # remove style char, # when using the recognition model trained on the PubtabNet dataset, # it will recognize the text format in the table, such as style_token = [ "", "", "", "", "", "", "", "", "", "", "", "", "", "", ] res = [] for box, rec_res in zip(filter_boxes, filter_rec_res): rec_str, rec_conf = rec_res[0], rec_res[1] for token in style_token: if token in rec_str: rec_str = rec_str.replace(token, "") if self.return_word_box: word_box_content_list, word_box_list = cal_ocr_word_box( rec_str, box, rec_res[2] ) res.append( { "text": rec_str, "confidence": float(rec_conf), "text_region": box.tolist(), "text_word": word_box_content_list, "text_word_region": word_box_list, } ) else: res.append( { "text": rec_str, "confidence": float(rec_conf), "text_region": box.tolist(), } ) return res, ocr_time_dict def _filter_text_res(self, text_res, bbox): res = [] for r in text_res: box = r["text_region"] rect = box[0][0], box[0][1], box[2][0], box[2][1] if self._has_intersection(bbox, rect): res.append(r) return res def _has_intersection(self, rect1, rect2): x_min1, y_min1, x_max1, y_max1 = rect1 x_min2, y_min2, x_max2, y_max2 = rect2 if x_min1 > x_max2 or x_max1 < x_min2: return False if y_min1 > y_max2 or y_max1 < y_min2: return False return True def save_structure_res(res, save_folder, img_name, img_idx=0): excel_save_folder = os.path.join(save_folder, img_name) os.makedirs(excel_save_folder, exist_ok=True) res_cp = deepcopy(res) # save res with open( os.path.join(excel_save_folder, "res_{}.txt".format(img_idx)), "w", encoding="utf8", ) as f: for region in res_cp: roi_img = region.pop("Crop_img") f.write("{}\n".format(json.dumps(region))) if ( region["type"].lower() == "table" and len(region["res"]) > 0 and "html" in region["res"] ): excel_path = os.path.join( excel_save_folder, "{}_{}.xlsx".format(region["bbox"], img_idx) ) to_excel(region["res"]["html"], excel_path) elif region["type"].lower() == "figure": img_path = os.path.join( excel_save_folder, "{}_{}.jpg".format(region["bbox"], img_idx) ) cv2.imwrite(img_path, roi_img) def main(args): image_file_list = get_image_file_list(args.image_dir) image_file_list = image_file_list image_file_list = image_file_list[args.process_id :: args.total_process_num] if not args.use_pdf2docx_api: structure_sys = StructureSystem(args) save_folder = os.path.join(args.output, structure_sys.mode) os.makedirs(save_folder, exist_ok=True) img_num = len(image_file_list) for i, image_file in enumerate(image_file_list): logger.info("[{}/{}] {}".format(i, img_num, image_file)) img, flag_gif, flag_pdf = check_and_read(image_file) img_name = os.path.basename(image_file).split(".")[0] if args.recovery and args.use_pdf2docx_api and flag_pdf: try_import("pdf2docx") from pdf2docx.converter import Converter os.makedirs(args.output, exist_ok=True) docx_file = os.path.join(args.output, "{}_api.docx".format(img_name)) cv = Converter(image_file) cv.convert(docx_file) cv.close() logger.info("docx save to {}".format(docx_file)) continue if not flag_gif and not flag_pdf: img = cv2.imread(image_file) if not flag_pdf: if img is None: logger.error("error in loading image:{}".format(image_file)) continue imgs = [img] else: imgs = img all_res = [] for index, img in enumerate(imgs): res, time_dict = structure_sys(img, img_idx=index) img_save_path = os.path.join( save_folder, img_name, "show_{}.jpg".format(index) ) os.makedirs(os.path.join(save_folder, img_name), exist_ok=True) if structure_sys.mode == "structure" and res != []: draw_img = draw_structure_result(img, res, args.vis_font_path) save_structure_res(res, save_folder, img_name, index) elif structure_sys.mode == "kie": if structure_sys.kie_predictor.predictor is not None: draw_img = draw_re_results(img, res, font_path=args.vis_font_path) else: draw_img = draw_ser_results(img, res, font_path=args.vis_font_path) with open( os.path.join(save_folder, img_name, "res_{}_kie.txt".format(index)), "w", encoding="utf8", ) as f: res_str = "{}\t{}\n".format( image_file, json.dumps({"ocr_info": res}, ensure_ascii=False) ) f.write(res_str) if res != []: cv2.imwrite(img_save_path, draw_img) logger.info("result save to {}".format(img_save_path)) if args.recovery and res != []: from ppstructure.recovery.recovery_to_doc import ( sorted_layout_boxes, convert_info_docx, ) h, w, _ = img.shape res = sorted_layout_boxes(res, w) all_res += res if args.recovery and all_res != []: try: convert_info_docx(img, all_res, save_folder, img_name) except Exception as ex: logger.error( "error in layout recovery image:{}, err msg: {}".format( image_file, ex ) ) continue logger.info("Predict time : {:.3f}s".format(time_dict["all"])) if __name__ == "__main__": args = parse_args() if args.use_mp: p_list = [] total_process_num = args.total_process_num for process_id in range(total_process_num): cmd = ( [sys.executable, "-u"] + sys.argv + ["--process_id={}".format(process_id), "--use_mp={}".format(False)] ) p = subprocess.Popen(cmd, stdout=sys.stdout, stderr=sys.stdout) p_list.append(p) for p in p_list: p.wait() else: main(args)