# 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 copy import numpy as np import json import time import logging from PIL import Image import tools.infer.utility as utility import tools.infer.predict_rec as predict_rec import tools.infer.predict_det as predict_det import tools.infer.predict_cls as predict_cls from ppocr.utils.utility import get_image_file_list, check_and_read from ppocr.utils.logging import get_logger from tools.infer.utility import draw_ocr_box_txt, get_rotate_crop_image logger = get_logger() class TextSystem(object): def __init__(self, args): if not args.show_log: logger.setLevel(logging.INFO) self.text_detector = predict_det.TextDetector(args) self.text_recognizer = predict_rec.TextRecognizer(args) self.use_angle_cls = args.use_angle_cls self.drop_score = args.drop_score if self.use_angle_cls: self.text_classifier = predict_cls.TextClassifier(args) self.args = args self.crop_image_res_index = 0 def draw_crop_rec_res(self, output_dir, img_crop_list, rec_res): os.makedirs(output_dir, exist_ok=True) bbox_num = len(img_crop_list) for bno in range(bbox_num): cv2.imwrite( os.path.join(output_dir, f"mg_crop_{bno+self.crop_image_res_index}.jpg"), img_crop_list[bno]) logger.debug(f"{bno}, {rec_res[bno]}") self.crop_image_res_index += bbox_num def __call__(self, img, cls=True): time_dict = {'det': 0, 'rec': 0, 'csl': 0, 'all': 0} start = time.time() ori_im = img.copy() dt_boxes, elapse = self.text_detector(img) time_dict['det'] = elapse logger.debug("dt_boxes num : {}, elapse : {}".format( len(dt_boxes), elapse)) if dt_boxes is None: return None, None img_crop_list = [] dt_boxes = sorted_boxes(dt_boxes) for bno in range(len(dt_boxes)): tmp_box = copy.deepcopy(dt_boxes[bno]) img_crop = get_rotate_crop_image(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 : {}, elapse : {}".format( len(img_crop_list), elapse)) rec_res, elapse = self.text_recognizer(img_crop_list) time_dict['rec'] = elapse logger.debug("rec_res num : {}, elapse : {}".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 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 def sorted_boxes(dt_boxes): """ Sort text boxes in order from top to bottom, left to right args: dt_boxes(array):detected text boxes with shape [4, 2] return: sorted boxes(array) with shape [4, 2] """ num_boxes = dt_boxes.shape[0] sorted_boxes = sorted(dt_boxes, key=lambda x: (x[0][1], x[0][0])) _boxes = list(sorted_boxes) for i in range(num_boxes - 1): for j in range(i, 0, -1): if abs(_boxes[j + 1][0][1] - _boxes[j][0][1]) < 10 and \ (_boxes[j + 1][0][0] < _boxes[j][0][0]): tmp = _boxes[j] _boxes[j] = _boxes[j + 1] _boxes[j + 1] = tmp else: break return _boxes def main(args): image_file_list = get_image_file_list(args.image_dir) image_file_list = image_file_list[args.process_id::args.total_process_num] text_sys = TextSystem(args) is_visualize = True font_path = args.vis_font_path drop_score = args.drop_score draw_img_save_dir = args.draw_img_save_dir os.makedirs(draw_img_save_dir, exist_ok=True) save_results = [] logger.info( "In PP-OCRv3, rec_image_shape parameter defaults to '3, 48, 320', " "if you are using recognition model with PP-OCRv2 or an older version, please set --rec_image_shape='3,32,320" ) # warm up 10 times if args.warmup: img = np.random.uniform(0, 255, [640, 640, 3]).astype(np.uint8) for i in range(10): res = text_sys(img) total_time = 0 cpu_mem, gpu_mem, gpu_util = 0, 0, 0 _st = time.time() count = 0 for idx, image_file in enumerate(image_file_list): img, flag_gif, flag_pdf = check_and_read(image_file) if not flag_gif and not flag_pdf: img = cv2.imread(image_file) if not flag_pdf: if img is None: logger.debug("error in loading image:{}".format(image_file)) continue imgs = [img] else: page_num = args.page_num if page_num > len(img) or page_num == 0: page_num = len(img) imgs = img[:page_num] for index, img in enumerate(imgs): starttime = time.time() dt_boxes, rec_res, time_dict = text_sys(img) elapse = time.time() - starttime total_time += elapse if len(imgs) > 1: logger.debug( str(idx) + '_' + str(index) + " Predict time of %s: %.3fs" % (image_file, elapse)) else: logger.debug( str(idx) + " Predict time of %s: %.3fs" % (image_file, elapse)) for text, score in rec_res: logger.debug("{}, {:.3f}".format(text, score)) res = [{ "transcription": rec_res[i][0], "points": np.array(dt_boxes[i]).astype(np.int32).tolist(), } for i in range(len(dt_boxes))] if len(imgs) > 1: save_pred = os.path.basename(image_file) + '_' + str( index) + "\t" + json.dumps( res, ensure_ascii=False) + "\n" else: save_pred = os.path.basename(image_file) + "\t" + json.dumps( res, ensure_ascii=False) + "\n" save_results.append(save_pred) if is_visualize: image = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) boxes = dt_boxes txts = [rec_res[i][0] for i in range(len(rec_res))] scores = [rec_res[i][1] for i in range(len(rec_res))] draw_img = draw_ocr_box_txt( image, boxes, txts, scores, drop_score=drop_score, font_path=font_path) if flag_gif: save_file = image_file[:-3] + "png" elif flag_pdf: save_file = image_file.replace('.pdf', '_' + str(index) + '.png') else: save_file = image_file cv2.imwrite( os.path.join(draw_img_save_dir, os.path.basename(save_file)), draw_img[:, :, ::-1]) logger.debug("The visualized image saved in {}".format( os.path.join(draw_img_save_dir, os.path.basename( save_file)))) logger.info("The predict total time is {}".format(time.time() - _st)) if args.benchmark: text_sys.text_detector.autolog.report() text_sys.text_recognizer.autolog.report() with open( os.path.join(draw_img_save_dir, "system_results.txt"), 'w', encoding='utf-8') as f: f.writelines(save_results) if __name__ == "__main__": args = utility.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)