# 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 __dir__ = os.path.dirname(__file__) sys.path.append(__dir__) sys.path.append(os.path.join(__dir__, '../..')) import utility from ppocr.utils.utility import initial_logger logger = initial_logger() from ppocr.utils.utility import get_image_file_list import cv2 import copy import numpy as np import math import time from ppocr.utils.character import CharacterOps class TextRecognizer(object): def __init__(self, args): self.predictor, self.input_tensor, self.output_tensors =\ utility.create_predictor(args, mode="rec") image_shape = [int(v) for v in args.rec_image_shape.split(",")] self.rec_image_shape = image_shape self.character_type = args.rec_char_type self.rec_batch_num = args.rec_batch_num self.rec_algorithm = args.rec_algorithm char_ops_params = {} char_ops_params["character_type"] = args.rec_char_type char_ops_params["character_dict_path"] = args.rec_char_dict_path if self.rec_algorithm != "RARE": char_ops_params['loss_type'] = 'ctc' self.loss_type = 'ctc' else: char_ops_params['loss_type'] = 'attention' self.loss_type = 'attention' self.char_ops = CharacterOps(char_ops_params) def resize_norm_img(self, img, max_wh_ratio): imgC, imgH, imgW = self.rec_image_shape if self.character_type == "ch": imgW = int(32 * max_wh_ratio) h = img.shape[0] w = img.shape[1] ratio = w / float(h) if math.ceil(imgH * ratio) > imgW: resized_w = imgW else: resized_w = int(math.ceil(imgH * ratio)) resized_image = cv2.resize(img, (resized_w, imgH)) resized_image = resized_image.astype('float32') resized_image = resized_image.transpose((2, 0, 1)) / 255 resized_image -= 0.5 resized_image /= 0.5 padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32) padding_im[:, :, 0:resized_w] = resized_image return padding_im def __call__(self, img_list): img_num = len(img_list) rec_res = [] batch_num = self.rec_batch_num predict_time = 0 for beg_img_no in range(0, img_num, batch_num): end_img_no = min(img_num, beg_img_no + batch_num) norm_img_batch = [] max_wh_ratio = 0 for ino in range(beg_img_no, end_img_no): h, w = img_list[ino].shape[0:2] wh_ratio = w * 1.0 / h max_wh_ratio = max(max_wh_ratio, wh_ratio) for ino in range(beg_img_no, end_img_no): norm_img = self.resize_norm_img(img_list[ino], max_wh_ratio) norm_img = norm_img[np.newaxis, :] norm_img_batch.append(norm_img) norm_img_batch = np.concatenate(norm_img_batch) norm_img_batch = norm_img_batch.copy() starttime = time.time() self.input_tensor.copy_from_cpu(norm_img_batch) self.predictor.zero_copy_run() if self.loss_type == "ctc": rec_idx_batch = self.output_tensors[0].copy_to_cpu() rec_idx_lod = self.output_tensors[0].lod()[0] predict_batch = self.output_tensors[1].copy_to_cpu() predict_lod = self.output_tensors[1].lod()[0] elapse = time.time() - starttime predict_time += elapse for rno in range(len(rec_idx_lod) - 1): beg = rec_idx_lod[rno] end = rec_idx_lod[rno + 1] rec_idx_tmp = rec_idx_batch[beg:end, 0] preds_text = self.char_ops.decode(rec_idx_tmp) beg = predict_lod[rno] end = predict_lod[rno + 1] probs = predict_batch[beg:end, :] ind = np.argmax(probs, axis=1) blank = probs.shape[1] valid_ind = np.where(ind != (blank - 1))[0] score = np.mean(probs[valid_ind, ind[valid_ind]]) rec_res.append([preds_text, score]) else: rec_idx_batch = self.output_tensors[0].copy_to_cpu() predict_batch = self.output_tensors[1].copy_to_cpu() elapse = time.time() - starttime predict_time += elapse for rno in range(len(rec_idx_batch)): end_pos = np.where(rec_idx_batch[rno, :] == 1)[0] if len(end_pos) <= 1: preds = rec_idx_batch[rno, 1:] score = np.mean(predict_batch[rno, 1:]) else: preds = rec_idx_batch[rno, 1:end_pos[1]] score = np.mean(predict_batch[rno, 1:end_pos[1]]) preds_text = self.char_ops.decode(preds) rec_res.append([preds_text, score]) return rec_res, predict_time if __name__ == "__main__": args = utility.parse_args() image_file_list = get_image_file_list(args.image_dir) text_recognizer = TextRecognizer(args) valid_image_file_list = [] img_list = [] for image_file in image_file_list: img = cv2.imread(image_file) if img is None: logger.info("error in loading image:{}".format(image_file)) continue valid_image_file_list.append(image_file) img_list.append(img) try: rec_res, predict_time = text_recognizer(img_list) except Exception as e: print(e) logger.info( "ERROR!!!! \n" "Please read the FAQ：https://github.com/PaddlePaddle/PaddleOCR#faq \n" "If your model has tps module: " "TPS does not support variable shape.\n" "Please set --rec_image_shape='3,32,100' and --rec_char_type='en' ") exit() for ino in range(len(img_list)): print("Predicts of %s:%s" % (valid_image_file_list[ino], rec_res[ino])) print("Total predict time for %d images:%.3f" % (len(img_list), predict_time))