# Copyright (c) 2021 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. from __future__ import absolute_import from __future__ import division from __future__ import print_function import paddle import os import sys __dir__ = os.path.dirname(__file__) sys.path.append(__dir__) sys.path.append(os.path.join(__dir__, '..')) from extract_textpoint_slow import * from extract_textpoint_fast import generate_pivot_list_fast, restore_poly class PGNet_PostProcess(object): # two different post-process def __init__(self, character_dict_path, valid_set, score_thresh, outs_dict, shape_list): self.Lexicon_Table = get_dict(character_dict_path) self.valid_set = valid_set self.score_thresh = score_thresh self.outs_dict = outs_dict self.shape_list = shape_list def pg_postprocess_fast(self): p_score = self.outs_dict['f_score'] p_border = self.outs_dict['f_border'] p_char = self.outs_dict['f_char'] p_direction = self.outs_dict['f_direction'] if isinstance(p_score, paddle.Tensor): p_score = p_score[0].numpy() p_border = p_border[0].numpy() p_direction = p_direction[0].numpy() p_char = p_char[0].numpy() else: p_score = p_score[0] p_border = p_border[0] p_direction = p_direction[0] p_char = p_char[0] src_h, src_w, ratio_h, ratio_w = self.shape_list[0] instance_yxs_list, seq_strs = generate_pivot_list_fast( p_score, p_char, p_direction, self.Lexicon_Table, score_thresh=self.score_thresh) poly_list, keep_str_list = restore_poly(instance_yxs_list, seq_strs, p_border, ratio_w, ratio_h, src_w, src_h, self.valid_set) data = { 'points': poly_list, 'strs': keep_str_list, } return data def pg_postprocess_slow(self): p_score = self.outs_dict['f_score'] p_border = self.outs_dict['f_border'] p_char = self.outs_dict['f_char'] p_direction = self.outs_dict['f_direction'] if isinstance(p_score, paddle.Tensor): p_score = p_score[0].numpy() p_border = p_border[0].numpy() p_direction = p_direction[0].numpy() p_char = p_char[0].numpy() else: p_score = p_score[0] p_border = p_border[0] p_direction = p_direction[0] p_char = p_char[0] src_h, src_w, ratio_h, ratio_w = self.shape_list[0] is_curved = self.valid_set == "totaltext" instance_yxs_list = generate_pivot_list_slow( p_score, p_char, p_direction, score_thresh=self.score_thresh, is_backbone=True, is_curved=is_curved) p_char = paddle.to_tensor(np.expand_dims(p_char, axis=0)) char_seq_idx_set = [] for i in range(len(instance_yxs_list)): gather_info_lod = paddle.to_tensor(instance_yxs_list[i]) f_char_map = paddle.transpose(p_char, [0, 2, 3, 1]) feature_seq = paddle.gather_nd(f_char_map, gather_info_lod) feature_seq = np.expand_dims(feature_seq.numpy(), axis=0) feature_len = [len(feature_seq[0])] featyre_seq = paddle.to_tensor(feature_seq) feature_len = np.array([feature_len]).astype(np.int64) length = paddle.to_tensor(feature_len) seq_pred = paddle.fluid.layers.ctc_greedy_decoder( input=featyre_seq, blank=36, input_length=length) seq_pred_str = seq_pred[0].numpy().tolist()[0] seq_len = seq_pred[1].numpy()[0][0] temp_t = [] for c in seq_pred_str[:seq_len]: temp_t.append(c) char_seq_idx_set.append(temp_t) seq_strs = [] for char_idx_set in char_seq_idx_set: pr_str = ''.join([self.Lexicon_Table[pos] for pos in char_idx_set]) seq_strs.append(pr_str) poly_list = [] keep_str_list = [] all_point_list = [] all_point_pair_list = [] for yx_center_line, keep_str in zip(instance_yxs_list, seq_strs): if len(yx_center_line) == 1: yx_center_line.append(yx_center_line[-1]) offset_expand = 1.0 if self.valid_set == 'totaltext': offset_expand = 1.2 point_pair_list = [] for batch_id, y, x in yx_center_line: offset = p_border[:, y, x].reshape(2, 2) if offset_expand != 1.0: offset_length = np.linalg.norm( offset, axis=1, keepdims=True) expand_length = np.clip( offset_length * (offset_expand - 1), a_min=0.5, a_max=3.0) offset_detal = offset / offset_length * expand_length offset = offset + offset_detal ori_yx = np.array([y, x], dtype=np.float32) point_pair = (ori_yx + offset)[:, ::-1] * 4.0 / np.array( [ratio_w, ratio_h]).reshape(-1, 2) point_pair_list.append(point_pair) all_point_list.append([ int(round(x * 4.0 / ratio_w)), int(round(y * 4.0 / ratio_h)) ]) all_point_pair_list.append(point_pair.round().astype(np.int32) .tolist()) detected_poly, pair_length_info = point_pair2poly(point_pair_list) detected_poly = expand_poly_along_width( detected_poly, shrink_ratio_of_width=0.2) detected_poly[:, 0] = np.clip( detected_poly[:, 0], a_min=0, a_max=src_w) detected_poly[:, 1] = np.clip( detected_poly[:, 1], a_min=0, a_max=src_h) if len(keep_str) < 2: continue keep_str_list.append(keep_str) detected_poly = np.round(detected_poly).astype('int32') if self.valid_set == 'partvgg': middle_point = len(detected_poly) // 2 detected_poly = detected_poly[ [0, middle_point - 1, middle_point, -1], :] poly_list.append(detected_poly) elif self.valid_set == 'totaltext': poly_list.append(detected_poly) else: print('--> Not supported format.') exit(-1) data = { 'points': poly_list, 'strs': keep_str_list, } return data