openrec/preprocess/cppd_label_encode.py

import random

import numpy as np

from openrec.preprocess.ctc_label_encode import BaseRecLabelEncode


class CPPDLabelEncode(BaseRecLabelEncode):
    """Convert between text-label and text-index."""

    def __init__(
            self,
            max_text_length,
            character_dict_path=None,
            use_space_char=False,
            ch=False,
            #  ch_7000=7000,
            ignore_index=100,
            use_sos=False,
            pos_len=False,
            **kwargs):
        self.use_sos = use_sos
        super(CPPDLabelEncode,
              self).__init__(max_text_length, character_dict_path,
                             use_space_char)
        self.ch = ch
        self.ignore_index = ignore_index
        self.pos_len = pos_len

    def __call__(self, data):
        text = data['label']
        if self.ch:
            text, text_node_index, text_node_num = self.encodech(text)
            if text is None:
                return None
            if len(text) > self.max_text_len:
                return None
            data['length'] = np.array(len(text))
            # text.insert(0, 0)
            if self.pos_len:
                text_pos_node = [i_ for i_ in range(len(text), -1, -1)
                                 ] + [100] * (self.max_text_len - len(text))
            else:
                text_pos_node = [1] * (len(text) + 1) + [0] * (
                    self.max_text_len - len(text))

            text.append(0)
            text + [0] * (self.max_text_len - len(text))

            text = text + [self.ignore_index
                           ] * (self.max_text_len + 1 - len(text))

            data['label'] = np.array(text)
            data['label_node'] = np.array(text_node_num + text_pos_node)
            data['label_index'] = np.array(text_node_index)
            # data['label_ctc'] = np.array(ctc_text)
            return data
        else:
            text, text_char_node, ch_order = self.encode(text)

            if text is None:
                return None
            if len(text) > self.max_text_len:
                return None
            data['length'] = np.array(len(text))
            # text.insert(0, 0)
            if self.pos_len:
                text_pos_node = [i_ for i_ in range(len(text), -1, -1)
                                 ] + [100] * (self.max_text_len - len(text))
            else:
                text_pos_node = [1] * (len(text) + 1) + [0] * (
                    self.max_text_len - len(text))

            text.append(0)

            text = text + [self.ignore_index
                           ] * (self.max_text_len + 1 - len(text))
            data['label'] = np.array(text)
            data['label_node'] = np.array(text_char_node + text_pos_node)
            data['label_order'] = np.array(ch_order)

            return data

    def add_special_char(self, dict_character):
        if self.use_sos:
            dict_character = ['<s>', '</s>'] + dict_character
        else:
            dict_character = ['</s>'] + dict_character
        self.num_character = len(dict_character)

        return dict_character

    def encode(self, text):
        """convert text-label into text-index.
        input:
            text: text labels of each image. [batch_size]

        output:
            text: concatenated text index for CTCLoss.
                    [sum(text_lengths)] = [text_index_0 + text_index_1 + ... + text_index_(n - 1)]
            length: length of each text. [batch_size]
        """
        if len(text) == 0:
            return None, None, None
        if self.lower:
            text = text.lower()
        text_node = [0 for _ in range(self.num_character)]
        text_node[0] = 1
        text_list = []
        ch_order = []
        order = 1
        for char in text:
            if char not in self.dict:
                continue
            text_list.append(self.dict[char])
            text_node[self.dict[char]] += 1
            ch_order.append(
                [self.dict[char], text_node[self.dict[char]], order])
            order += 1

        no_ch_order = []
        for char in self.character:
            if char not in text:
                no_ch_order.append([self.dict[char], 1, 0])
        random.shuffle(no_ch_order)
        ch_order = ch_order + no_ch_order
        ch_order = ch_order[:self.max_text_len + 1]

        if len(text_list) == 0 or len(text_list) > self.max_text_len:
            return None, None, None
        return text_list, text_node, ch_order.sort()

    def encodech(self, text):
        """convert text-label into text-index.
        input:
            text: text labels of each image. [batch_size]

        output:
            text: concatenated text index for CTCLoss.
                    [sum(text_lengths)] = [text_index_0 + text_index_1 + ... + text_index_(n - 1)]
            length: length of each text. [batch_size]
        """
        if len(text) == 0:
            return None, None, None
        if self.lower:
            text = text.lower()
        text_node_dict = {}
        text_node_dict.update({0: 1})
        character_index = [_ for _ in range(self.num_character)]
        text_list = []
        for char in text:
            if char not in self.dict:
                continue
            i_c = self.dict[char]
            text_list.append(i_c)

            if i_c in text_node_dict.keys():
                text_node_dict[i_c] += 1
            else:
                text_node_dict.update({i_c: 1})
        for ic in list(text_node_dict.keys()):
            character_index.remove(ic)
        none_char_index = random.sample(character_index,
                                        37 - len(list(text_node_dict.keys())))
        for ic in none_char_index:
            text_node_dict[ic] = 0

        text_node_index = sorted(text_node_dict)

        text_node_num = [text_node_dict[k] for k in text_node_index]
        if len(text_list) == 0 or len(text_list) > self.max_text_len:
            return None, None, None
        return text_list, text_node_index, text_node_num