# 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(os.path.abspath(__file__)) sys.path.append(__dir__) sys.path.append(os.path.abspath(os.path.join(__dir__, '../..'))) import tools.infer.utility as utility from ppocr.utils.utility import initial_logger logger = initial_logger() from ppocr.utils.utility import get_image_file_list, check_and_read_gif import cv2 import copy import numpy as np import math import time import paddle.fluid as fluid 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") self.rec_image_shape = [int(v) for v in args.rec_image_shape.split(",")] self.character_type = args.rec_char_type self.rec_batch_num = args.rec_batch_num self.rec_algorithm = args.rec_algorithm self.text_len = args.max_text_length char_ops_params = { "character_type": args.rec_char_type, "character_dict_path": args.rec_char_dict_path, "use_space_char": args.use_space_char, "max_text_length": args.max_text_length } if self.rec_algorithm in ["CRNN", "Rosetta", "STAR-Net"]: char_ops_params['loss_type'] = 'ctc' self.loss_type = 'ctc' elif self.rec_algorithm == "RARE": char_ops_params['loss_type'] = 'attention' self.loss_type = 'attention' elif self.rec_algorithm == "SRN": char_ops_params['loss_type'] = 'srn' self.loss_type = 'srn' self.char_ops = CharacterOps(char_ops_params) def resize_norm_img(self, img, max_wh_ratio): imgC, imgH, imgW = self.rec_image_shape assert imgC == img.shape[2] if self.character_type == "ch": imgW = int((32 * max_wh_ratio)) h, w = img.shape[:2] 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 resize_norm_img_srn(self, img, image_shape): imgC, imgH, imgW = image_shape img_black = np.zeros((imgH, imgW)) im_hei = img.shape[0] im_wid = img.shape[1] if im_wid <= im_hei * 1: img_new = cv2.resize(img, (imgH * 1, imgH)) elif im_wid <= im_hei * 2: img_new = cv2.resize(img, (imgH * 2, imgH)) elif im_wid <= im_hei * 3: img_new = cv2.resize(img, (imgH * 3, imgH)) else: img_new = cv2.resize(img, (imgW, imgH)) img_np = np.asarray(img_new) img_np = cv2.cvtColor(img_np, cv2.COLOR_BGR2GRAY) img_black[:, 0:img_np.shape[1]] = img_np img_black = img_black[:, :, np.newaxis] row, col, c = img_black.shape c = 1 return np.reshape(img_black, (c, row, col)).astype(np.float32) def srn_other_inputs(self, image_shape, num_heads, max_text_length, char_num): imgC, imgH, imgW = image_shape feature_dim = int((imgH / 8) * (imgW / 8)) encoder_word_pos = np.array(range(0, feature_dim)).reshape( (feature_dim, 1)).astype('int64') gsrm_word_pos = np.array(range(0, max_text_length)).reshape( (max_text_length, 1)).astype('int64') gsrm_attn_bias_data = np.ones((1, max_text_length, max_text_length)) gsrm_slf_attn_bias1 = np.triu(gsrm_attn_bias_data, 1).reshape( [-1, 1, max_text_length, max_text_length]) gsrm_slf_attn_bias1 = np.tile( gsrm_slf_attn_bias1, [1, num_heads, 1, 1]).astype('float32') * [-1e9] gsrm_slf_attn_bias2 = np.tril(gsrm_attn_bias_data, -1).reshape( [-1, 1, max_text_length, max_text_length]) gsrm_slf_attn_bias2 = np.tile( gsrm_slf_attn_bias2, [1, num_heads, 1, 1]).astype('float32') * [-1e9] encoder_word_pos = encoder_word_pos[np.newaxis, :] gsrm_word_pos = gsrm_word_pos[np.newaxis, :] return [ encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1, gsrm_slf_attn_bias2 ] def process_image_srn(self, img, image_shape, num_heads, max_text_length, char_ops=None): norm_img = self.resize_norm_img_srn(img, image_shape) norm_img = norm_img[np.newaxis, :] char_num = char_ops.get_char_num() [encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1, gsrm_slf_attn_bias2] = \ self.srn_other_inputs(image_shape, num_heads, max_text_length, char_num) gsrm_slf_attn_bias1 = gsrm_slf_attn_bias1.astype(np.float32) gsrm_slf_attn_bias2 = gsrm_slf_attn_bias2.astype(np.float32) return (norm_img, encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1, gsrm_slf_attn_bias2) def __call__(self, img_list): img_num = len(img_list) # Calculate the aspect ratio of all text bars width_list = [] for img in img_list: width_list.append(img.shape[1] / float(img.shape[0])) # Sorting can speed up the recognition process indices = np.argsort(np.array(width_list)) #rec_res = [] rec_res = [['', 0.0]] * img_num 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] h, w = img_list[indices[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): if self.loss_type != "srn": norm_img = self.resize_norm_img(img_list[indices[ino]], max_wh_ratio) norm_img = norm_img[np.newaxis, :] norm_img_batch.append(norm_img) else: norm_img = self.process_image_srn(img_list[indices[ino]], self.rec_image_shape, 8, 25, self.char_ops) encoder_word_pos_list = [] gsrm_word_pos_list = [] gsrm_slf_attn_bias1_list = [] gsrm_slf_attn_bias2_list = [] encoder_word_pos_list.append(norm_img[1]) gsrm_word_pos_list.append(norm_img[2]) gsrm_slf_attn_bias1_list.append(norm_img[3]) gsrm_slf_attn_bias2_list.append(norm_img[4]) norm_img_batch.append(norm_img[0]) norm_img_batch = np.concatenate(norm_img_batch, axis=0) encoder_word_pos_list = np.concatenate(encoder_word_pos_list) gsrm_word_pos_list = np.concatenate(gsrm_word_pos_list) gsrm_slf_attn_bias1_list = np.concatenate(gsrm_slf_attn_bias1_list) gsrm_slf_attn_bias2_list = np.concatenate(gsrm_slf_attn_bias2_list) starttime = time.time() norm_img_batch = fluid.core.PaddleTensor(norm_img_batch) encoder_word_pos_list = fluid.core.PaddleTensor( encoder_word_pos_list) gsrm_word_pos_list = fluid.core.PaddleTensor(gsrm_word_pos_list) gsrm_slf_attn_bias1_list = fluid.core.PaddleTensor( gsrm_slf_attn_bias1_list) gsrm_slf_attn_bias2_list = fluid.core.PaddleTensor( gsrm_slf_attn_bias2_list) inputs = [ norm_img_batch, encoder_word_pos_list, gsrm_slf_attn_bias1_list, gsrm_slf_attn_bias2_list, gsrm_word_pos_list ] self.predictor.run(inputs) 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] if len(valid_ind) == 0: continue score = np.mean(probs[valid_ind, ind[valid_ind]]) # rec_res.append([preds_text, score]) rec_res[indices[beg_img_no + rno]] = [preds_text, score] elif self.loss_type == 'srn': rec_idx_batch = self.output_tensors[0].copy_to_cpu() probs = self.output_tensors[1].copy_to_cpu() char_num = self.char_ops.get_char_num() preds = rec_idx_batch.reshape(-1) elapse = time.time() - starttime predict_time += elapse total_preds = preds.copy() for ino in range(int(len(rec_idx_batch) / self.text_len)): preds = total_preds[ino * self.text_len:(ino + 1) * self.text_len] ind = np.argmax(probs, axis=1) valid_ind = np.where(preds != int(char_num - 1))[0] if len(valid_ind) == 0: continue score = np.mean(probs[valid_ind, ind[valid_ind]]) preds = preds[:valid_ind[-1] + 1] preds_text = self.char_ops.decode(preds) rec_res[indices[beg_img_no + ino]] = [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]) rec_res[indices[beg_img_no + rno]] = [preds_text, score] return rec_res, predict_time def main(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, flag = check_and_read_gif(image_file) if not flag: 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)) if __name__ == "__main__": main(utility.parse_args())