label_ops.py

# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
# 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
from __future__ import unicode_literals

import copy
import numpy as np
import string
from shapely.geometry import LineString, Point, Polygon
import json
import copy
from ppocr.utils.logging import get_logger


class ClsLabelEncode(object):
    def __init__(self, label_list, **kwargs):
        self.label_list = label_list

    def __call__(self, data):
        label = data['label']
        if label not in self.label_list:
            return None
        label = self.label_list.index(label)
        data['label'] = label
        return data


class DetLabelEncode(object):
    def __init__(self, **kwargs):
        pass

    def __call__(self, data):
        label = data['label']
        label = json.loads(label)
        nBox = len(label)
        boxes, txts, txt_tags = [], [], []
        for bno in range(0, nBox):
            box = label[bno]['points']
            txt = label[bno]['transcription']
            boxes.append(box)
            txts.append(txt)
            if txt in ['*', '###']:
                txt_tags.append(True)
            else:
                txt_tags.append(False)
        if len(boxes) == 0:
            return None
        boxes = self.expand_points_num(boxes)
        boxes = np.array(boxes, dtype=np.float32)
        txt_tags = np.array(txt_tags, dtype=np.bool)

        data['polys'] = boxes
        data['texts'] = txts
        data['ignore_tags'] = txt_tags
        return data

    def order_points_clockwise(self, pts):
        rect = np.zeros((4, 2), dtype="float32")
        s = pts.sum(axis=1)
        rect[0] = pts[np.argmin(s)]
        rect[2] = pts[np.argmax(s)]
        tmp = np.delete(pts, (np.argmin(s), np.argmax(s)), axis=0)
        diff = np.diff(np.array(tmp), axis=1)
        rect[1] = tmp[np.argmin(diff)]
        rect[3] = tmp[np.argmax(diff)]
        return rect

    def expand_points_num(self, boxes):
        max_points_num = 0
        for box in boxes:
            if len(box) > max_points_num:
                max_points_num = len(box)
        ex_boxes = []
        for box in boxes:
            ex_box = box + [box[-1]] * (max_points_num - len(box))
            ex_boxes.append(ex_box)
        return ex_boxes


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

    def __init__(self,
                 max_text_length,
                 character_dict_path=None,
                 use_space_char=False):

        self.max_text_len = max_text_length
        self.beg_str = "sos"
        self.end_str = "eos"
        self.lower = False

        if character_dict_path is None:
            logger = get_logger()
            logger.warning(
                "The character_dict_path is None, model can only recognize number and lower letters"
            )
            self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
            dict_character = list(self.character_str)
            self.lower = True
        else:
            self.character_str = []
            with open(character_dict_path, "rb") as fin:
                lines = fin.readlines()
                for line in lines:
                    line = line.decode('utf-8').strip("\n").strip("\r\n")
                    self.character_str.append(line)
            if use_space_char:
                self.character_str.append(" ")
            dict_character = list(self.character_str)
        dict_character = self.add_special_char(dict_character)
        self.dict = {}
        for i, char in enumerate(dict_character):
            self.dict[char] = i
        self.character = dict_character

    def add_special_char(self, 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 or len(text) > self.max_text_len:
            return None
        if self.lower:
            text = text.lower()
        text_list = []
        for char in text:
            if char not in self.dict:
                # logger = get_logger()
                # logger.warning('{} is not in dict'.format(char))
                continue
            text_list.append(self.dict[char])
        if len(text_list) == 0:
            return None
        return text_list


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

    def __init__(self,
                 max_text_length,
                 character_dict_path=None,
                 use_space_char=False,
                 **kwargs):

        super(NRTRLabelEncode, self).__init__(
            max_text_length, character_dict_path, use_space_char)

    def __call__(self, data):
        text = data['label']
        text = self.encode(text)
        if text is None:
            return None
        if len(text) >= self.max_text_len - 1:
            return None
        data['length'] = np.array(len(text))
        text.insert(0, 2)
        text.append(3)
        text = text + [0] * (self.max_text_len - len(text))
        data['label'] = np.array(text)
        return data

    def add_special_char(self, dict_character):
        dict_character = ['blank', '<unk>', '<s>', '</s>'] + dict_character
        return dict_character


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

    def __init__(self,
                 max_text_length,
                 character_dict_path=None,
                 use_space_char=False,
                 **kwargs):
        super(CTCLabelEncode, self).__init__(
            max_text_length, character_dict_path, use_space_char)

    def __call__(self, data):
        text = data['label']
        text = self.encode(text)
        if text is None:
            return None
        data['length'] = np.array(len(text))
        text = text + [0] * (self.max_text_len - len(text))
        data['label'] = np.array(text)

        label = [0] * len(self.character)
        for x in text:
            label[x] += 1
        data['label_ace'] = np.array(label)
        return data

    def add_special_char(self, dict_character):
        dict_character = ['blank'] + dict_character
        return dict_character


class E2ELabelEncodeTest(BaseRecLabelEncode):
    def __init__(self,
                 max_text_length,
                 character_dict_path=None,
                 use_space_char=False,
                 **kwargs):
        super(E2ELabelEncodeTest, self).__init__(
            max_text_length, character_dict_path, use_space_char)

    def __call__(self, data):
        import json
        padnum = len(self.dict)
        label = data['label']
        label = json.loads(label)
        nBox = len(label)
        boxes, txts, txt_tags = [], [], []
        for bno in range(0, nBox):
            box = label[bno]['points']
            txt = label[bno]['transcription']
            boxes.append(box)
            txts.append(txt)
            if txt in ['*', '###']:
                txt_tags.append(True)
            else:
                txt_tags.append(False)
        boxes = np.array(boxes, dtype=np.float32)
        txt_tags = np.array(txt_tags, dtype=np.bool)
        data['polys'] = boxes
        data['ignore_tags'] = txt_tags
        temp_texts = []
        for text in txts:
            text = text.lower()
            text = self.encode(text)
            if text is None:
                return None
            text = text + [padnum] * (self.max_text_len - len(text)
                                      )  # use 36 to pad
            temp_texts.append(text)
        data['texts'] = np.array(temp_texts)
        return data


class E2ELabelEncodeTrain(object):
    def __init__(self, **kwargs):
        pass

    def __call__(self, data):
        import json
        label = data['label']
        label = json.loads(label)
        nBox = len(label)
        boxes, txts, txt_tags = [], [], []
        for bno in range(0, nBox):
            box = label[bno]['points']
            txt = label[bno]['transcription']
            boxes.append(box)
            txts.append(txt)
            if txt in ['*', '###']:
                txt_tags.append(True)
            else:
                txt_tags.append(False)
        boxes = np.array(boxes, dtype=np.float32)
        txt_tags = np.array(txt_tags, dtype=np.bool)

        data['polys'] = boxes
        data['texts'] = txts
        data['ignore_tags'] = txt_tags
        return data


class KieLabelEncode(object):
    def __init__(self, character_dict_path, norm=10, directed=False, **kwargs):
        super(KieLabelEncode, self).__init__()
        self.dict = dict({'': 0})
        with open(character_dict_path, 'r', encoding='utf-8') as fr:
            idx = 1
            for line in fr:
                char = line.strip()
                self.dict[char] = idx
                idx += 1
        self.norm = norm
        self.directed = directed

    def compute_relation(self, boxes):
        """Compute relation between every two boxes."""
        x1s, y1s = boxes[:, 0:1], boxes[:, 1:2]
        x2s, y2s = boxes[:, 4:5], boxes[:, 5:6]
        ws, hs = x2s - x1s + 1, np.maximum(y2s - y1s + 1, 1)
        dxs = (x1s[:, 0][None] - x1s) / self.norm
        dys = (y1s[:, 0][None] - y1s) / self.norm
        xhhs, xwhs = hs[:, 0][None] / hs, ws[:, 0][None] / hs
        whs = ws / hs + np.zeros_like(xhhs)
        relations = np.stack([dxs, dys, whs, xhhs, xwhs], -1)
        bboxes = np.concatenate([x1s, y1s, x2s, y2s], -1).astype(np.float32)
        return relations, bboxes

    def pad_text_indices(self, text_inds):
        """Pad text index to same length."""
        max_len = 300
        recoder_len = max([len(text_ind) for text_ind in text_inds])
        padded_text_inds = -np.ones((len(text_inds), max_len), np.int32)
        for idx, text_ind in enumerate(text_inds):
            padded_text_inds[idx, :len(text_ind)] = np.array(text_ind)
        return padded_text_inds, recoder_len

    def list_to_numpy(self, ann_infos):
        """Convert bboxes, relations, texts and labels to ndarray."""
        boxes, text_inds = ann_infos['points'], ann_infos['text_inds']
        boxes = np.array(boxes, np.int32)
        relations, bboxes = self.compute_relation(boxes)

        labels = ann_infos.get('labels', None)
        if labels is not None:
            labels = np.array(labels, np.int32)
            edges = ann_infos.get('edges', None)
            if edges is not None:
                labels = labels[:, None]
                edges = np.array(edges)
                edges = (edges[:, None] == edges[None, :]).astype(np.int32)
                if self.directed:
                    edges = (edges & labels == 1).astype(np.int32)
                np.fill_diagonal(edges, -1)
                labels = np.concatenate([labels, edges], -1)
        padded_text_inds, recoder_len = self.pad_text_indices(text_inds)
        max_num = 300
        temp_bboxes = np.zeros([max_num, 4])
        h, _ = bboxes.shape
        temp_bboxes[:h, :] = bboxes

        temp_relations = np.zeros([max_num, max_num, 5])
        temp_relations[:h, :h, :] = relations

        temp_padded_text_inds = np.zeros([max_num, max_num])
        temp_padded_text_inds[:h, :] = padded_text_inds

        temp_labels = np.zeros([max_num, max_num])
        temp_labels[:h, :h + 1] = labels

        tag = np.array([h, recoder_len])
        return dict(
            image=ann_infos['image'],
            points=temp_bboxes,
            relations=temp_relations,
            texts=temp_padded_text_inds,
            labels=temp_labels,
            tag=tag)

    def convert_canonical(self, points_x, points_y):

        assert len(points_x) == 4
        assert len(points_y) == 4

        points = [Point(points_x[i], points_y[i]) for i in range(4)]

        polygon = Polygon([(p.x, p.y) for p in points])
        min_x, min_y, _, _ = polygon.bounds
        points_to_lefttop = [
            LineString([points[i], Point(min_x, min_y)]) for i in range(4)
        ]
        distances = np.array([line.length for line in points_to_lefttop])
        sort_dist_idx = np.argsort(distances)
        lefttop_idx = sort_dist_idx[0]

        if lefttop_idx == 0:
            point_orders = [0, 1, 2, 3]
        elif lefttop_idx == 1:
            point_orders = [1, 2, 3, 0]
        elif lefttop_idx == 2:
            point_orders = [2, 3, 0, 1]
        else:
            point_orders = [3, 0, 1, 2]

        sorted_points_x = [points_x[i] for i in point_orders]
        sorted_points_y = [points_y[j] for j in point_orders]

        return sorted_points_x, sorted_points_y

    def sort_vertex(self, points_x, points_y):

        assert len(points_x) == 4
        assert len(points_y) == 4

        x = np.array(points_x)
        y = np.array(points_y)
        center_x = np.sum(x) * 0.25
        center_y = np.sum(y) * 0.25

        x_arr = np.array(x - center_x)
        y_arr = np.array(y - center_y)

        angle = np.arctan2(y_arr, x_arr) * 180.0 / np.pi
        sort_idx = np.argsort(angle)

        sorted_points_x, sorted_points_y = [], []
        for i in range(4):
            sorted_points_x.append(points_x[sort_idx[i]])
            sorted_points_y.append(points_y[sort_idx[i]])

        return self.convert_canonical(sorted_points_x, sorted_points_y)

    def __call__(self, data):
        import json
        label = data['label']
        annotations = json.loads(label)
        boxes, texts, text_inds, labels, edges = [], [], [], [], []
        for ann in annotations:
            box = ann['points']
            x_list = [box[i][0] for i in range(4)]
            y_list = [box[i][1] for i in range(4)]
            sorted_x_list, sorted_y_list = self.sort_vertex(x_list, y_list)
            sorted_box = []
            for x, y in zip(sorted_x_list, sorted_y_list):
                sorted_box.append(x)
                sorted_box.append(y)
            boxes.append(sorted_box)
            text = ann['transcription']
            texts.append(ann['transcription'])
            text_ind = [self.dict[c] for c in text if c in self.dict]
            text_inds.append(text_ind)
            if 'label' in ann.keys():
                labels.append(ann['label'])
            elif 'key_cls' in ann.keys():
                labels.append(ann['key_cls'])
            else:
                raise ValueError(
                    "Cannot found 'key_cls' in ann.keys(), please check your training annotation."
                )
            edges.append(ann.get('edge', 0))
        ann_infos = dict(
            image=data['image'],
            points=boxes,
            texts=texts,
            text_inds=text_inds,
            edges=edges,
            labels=labels)

        return self.list_to_numpy(ann_infos)


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

    def __init__(self,
                 max_text_length,
                 character_dict_path=None,
                 use_space_char=False,
                 **kwargs):
        super(AttnLabelEncode, self).__init__(
            max_text_length, character_dict_path, use_space_char)

    def add_special_char(self, dict_character):
        self.beg_str = "sos"
        self.end_str = "eos"
        dict_character = [self.beg_str] + dict_character + [self.end_str]
        return dict_character

    def __call__(self, data):
        text = data['label']
        text = 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 = [0] + text + [len(self.character) - 1] + [0] * (self.max_text_len
                                                               - len(text) - 2)
        data['label'] = np.array(text)
        return data

    def get_ignored_tokens(self):
        beg_idx = self.get_beg_end_flag_idx("beg")
        end_idx = self.get_beg_end_flag_idx("end")
        return [beg_idx, end_idx]

    def get_beg_end_flag_idx(self, beg_or_end):
        if beg_or_end == "beg":
            idx = np.array(self.dict[self.beg_str])
        elif beg_or_end == "end":
            idx = np.array(self.dict[self.end_str])
        else:
            assert False, "Unsupport type %s in get_beg_end_flag_idx" \
                          % beg_or_end
        return idx


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

    def __init__(self,
                 max_text_length,
                 character_dict_path=None,
                 use_space_char=False,
                 **kwargs):
        super(SEEDLabelEncode, self).__init__(
            max_text_length, character_dict_path, use_space_char)

    def add_special_char(self, dict_character):
        self.padding = "padding"
        self.end_str = "eos"
        self.unknown = "unknown"
        dict_character = dict_character + [
            self.end_str, self.padding, self.unknown
        ]
        return dict_character

    def __call__(self, data):
        text = data['label']
        text = self.encode(text)
        if text is None:
            return None
        if len(text) >= self.max_text_len:
            return None
        data['length'] = np.array(len(text)) + 1  # conclude eos
        text = text + [len(self.character) - 3] + [len(self.character) - 2] * (
            self.max_text_len - len(text) - 1)
        data['label'] = np.array(text)
        return data


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

    def __init__(self,
                 max_text_length=25,
                 character_dict_path=None,
                 use_space_char=False,
                 **kwargs):
        super(SRNLabelEncode, self).__init__(
            max_text_length, character_dict_path, use_space_char)

    def add_special_char(self, dict_character):
        dict_character = dict_character + [self.beg_str, self.end_str]
        return dict_character

    def __call__(self, data):
        text = data['label']
        text = self.encode(text)
        char_num = len(self.character)
        if text is None:
            return None
        if len(text) > self.max_text_len:
            return None
        data['length'] = np.array(len(text))
        text = text + [char_num - 1] * (self.max_text_len - len(text))
        data['label'] = np.array(text)
        return data

    def get_ignored_tokens(self):
        beg_idx = self.get_beg_end_flag_idx("beg")
        end_idx = self.get_beg_end_flag_idx("end")
        return [beg_idx, end_idx]

    def get_beg_end_flag_idx(self, beg_or_end):
        if beg_or_end == "beg":
            idx = np.array(self.dict[self.beg_str])
        elif beg_or_end == "end":
            idx = np.array(self.dict[self.end_str])
        else:
            assert False, "Unsupport type %s in get_beg_end_flag_idx" \
                          % beg_or_end
        return idx


class TableLabelEncode(object):
    """ Convert between text-label and text-index """

    def __init__(self,
                 max_text_length,
                 max_elem_length,
                 max_cell_num,
                 character_dict_path,
                 span_weight=1.0,
                 **kwargs):
        self.max_text_length = max_text_length
        self.max_elem_length = max_elem_length
        self.max_cell_num = max_cell_num
        list_character, list_elem = self.load_char_elem_dict(
            character_dict_path)
        list_character = self.add_special_char(list_character)
        list_elem = self.add_special_char(list_elem)
        self.dict_character = {}
        for i, char in enumerate(list_character):
            self.dict_character[char] = i
        self.dict_elem = {}
        for i, elem in enumerate(list_elem):
            self.dict_elem[elem] = i
        self.span_weight = span_weight

    def load_char_elem_dict(self, character_dict_path):
        list_character = []
        list_elem = []
        with open(character_dict_path, "rb") as fin:
            lines = fin.readlines()
            substr = lines[0].decode('utf-8').strip("\r\n").split("\t")
            character_num = int(substr[0])
            elem_num = int(substr[1])
            for cno in range(1, 1 + character_num):
                character = lines[cno].decode('utf-8').strip("\r\n")
                list_character.append(character)
            for eno in range(1 + character_num, 1 + character_num + elem_num):
                elem = lines[eno].decode('utf-8').strip("\r\n")
                list_elem.append(elem)
        return list_character, list_elem

    def add_special_char(self, list_character):
        self.beg_str = "sos"
        self.end_str = "eos"
        list_character = [self.beg_str] + list_character + [self.end_str]
        return list_character

    def get_span_idx_list(self):
        span_idx_list = []
        for elem in self.dict_elem:
            if 'span' in elem:
                span_idx_list.append(self.dict_elem[elem])
        return span_idx_list

    def __call__(self, data):
        cells = data['cells']
        structure = data['structure']['tokens']
        structure = self.encode(structure, 'elem')
        if structure is None:
            return None
        elem_num = len(structure)
        structure = [0] + structure + [len(self.dict_elem) - 1]
        structure = structure + [0] * (self.max_elem_length + 2 - len(structure)
                                       )
        structure = np.array(structure)
        data['structure'] = structure
        elem_char_idx1 = self.dict_elem['<td>']
        elem_char_idx2 = self.dict_elem['<td']
        span_idx_list = self.get_span_idx_list()
        td_idx_list = np.logical_or(structure == elem_char_idx1,
                                    structure == elem_char_idx2)
        td_idx_list = np.where(td_idx_list)[0]

        structure_mask = np.ones(
            (self.max_elem_length + 2, 1), dtype=np.float32)
        bbox_list = np.zeros((self.max_elem_length + 2, 4), dtype=np.float32)
        bbox_list_mask = np.zeros(
            (self.max_elem_length + 2, 1), dtype=np.float32)
        img_height, img_width, img_ch = data['image'].shape
        if len(span_idx_list) > 0:
            span_weight = len(td_idx_list) * 1.0 / len(span_idx_list)
            span_weight = min(max(span_weight, 1.0), self.span_weight)
        for cno in range(len(cells)):
            if 'bbox' in cells[cno]:
                bbox = cells[cno]['bbox'].copy()
                bbox[0] = bbox[0] * 1.0 / img_width
                bbox[1] = bbox[1] * 1.0 / img_height
                bbox[2] = bbox[2] * 1.0 / img_width
                bbox[3] = bbox[3] * 1.0 / img_height
                td_idx = td_idx_list[cno]
                bbox_list[td_idx] = bbox
                bbox_list_mask[td_idx] = 1.0
                cand_span_idx = td_idx + 1
                if cand_span_idx < (self.max_elem_length + 2):
                    if structure[cand_span_idx] in span_idx_list:
                        structure_mask[cand_span_idx] = span_weight

        data['bbox_list'] = bbox_list
        data['bbox_list_mask'] = bbox_list_mask
        data['structure_mask'] = structure_mask
        char_beg_idx = self.get_beg_end_flag_idx('beg', 'char')
        char_end_idx = self.get_beg_end_flag_idx('end', 'char')
        elem_beg_idx = self.get_beg_end_flag_idx('beg', 'elem')
        elem_end_idx = self.get_beg_end_flag_idx('end', 'elem')
        data['sp_tokens'] = np.array([
            char_beg_idx, char_end_idx, elem_beg_idx, elem_end_idx,
            elem_char_idx1, elem_char_idx2, self.max_text_length,
            self.max_elem_length, self.max_cell_num, elem_num
        ])
        return data

    def encode(self, text, char_or_elem):
        """convert text-label into text-index.
        """
        if char_or_elem == "char":
            max_len = self.max_text_length
            current_dict = self.dict_character
        else:
            max_len = self.max_elem_length
            current_dict = self.dict_elem
        if len(text) > max_len:
            return None
        if len(text) == 0:
            if char_or_elem == "char":
                return [self.dict_character['space']]
            else:
                return None
        text_list = []
        for char in text:
            if char not in current_dict:
                return None
            text_list.append(current_dict[char])
        if len(text_list) == 0:
            if char_or_elem == "char":
                return [self.dict_character['space']]
            else:
                return None
        return text_list

    def get_ignored_tokens(self, char_or_elem):
        beg_idx = self.get_beg_end_flag_idx("beg", char_or_elem)
        end_idx = self.get_beg_end_flag_idx("end", char_or_elem)
        return [beg_idx, end_idx]

    def get_beg_end_flag_idx(self, beg_or_end, char_or_elem):
        if char_or_elem == "char":
            if beg_or_end == "beg":
                idx = np.array(self.dict_character[self.beg_str])
            elif beg_or_end == "end":
                idx = np.array(self.dict_character[self.end_str])
            else:
                assert False, "Unsupport type %s in get_beg_end_flag_idx of char" \
                              % beg_or_end
        elif char_or_elem == "elem":
            if beg_or_end == "beg":
                idx = np.array(self.dict_elem[self.beg_str])
            elif beg_or_end == "end":
                idx = np.array(self.dict_elem[self.end_str])
            else:
                assert False, "Unsupport type %s in get_beg_end_flag_idx of elem" \
                              % beg_or_end
        else:
            assert False, "Unsupport type %s in char_or_elem" \
                % char_or_elem
        return idx


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

    def __init__(self,
                 max_text_length,
                 character_dict_path=None,
                 use_space_char=False,
                 **kwargs):
        super(SARLabelEncode, self).__init__(
            max_text_length, character_dict_path, use_space_char)

    def add_special_char(self, dict_character):
        beg_end_str = "<BOS/EOS>"
        unknown_str = "<UKN>"
        padding_str = "<PAD>"
        dict_character = dict_character + [unknown_str]
        self.unknown_idx = len(dict_character) - 1
        dict_character = dict_character + [beg_end_str]
        self.start_idx = len(dict_character) - 1
        self.end_idx = len(dict_character) - 1
        dict_character = dict_character + [padding_str]
        self.padding_idx = len(dict_character) - 1

        return dict_character

    def __call__(self, data):
        text = data['label']
        text = self.encode(text)
        if text is None:
            return None
        if len(text) >= self.max_text_len - 1:
            return None
        data['length'] = np.array(len(text))
        target = [self.start_idx] + text + [self.end_idx]
        padded_text = [self.padding_idx for _ in range(self.max_text_len)]

        padded_text[:len(target)] = target
        data['label'] = np.array(padded_text)
        return data

    def get_ignored_tokens(self):
        return [self.padding_idx]


class PRENLabelEncode(BaseRecLabelEncode):
    def __init__(self,
                 max_text_length,
                 character_dict_path,
                 use_space_char=False,
                 **kwargs):
        super(PRENLabelEncode, self).__init__(
            max_text_length, character_dict_path, use_space_char)

    def add_special_char(self, dict_character):
        padding_str = '<PAD>'  # 0 
        end_str = '<EOS>'  # 1
        unknown_str = '<UNK>'  # 2

        dict_character = [padding_str, end_str, unknown_str] + dict_character
        self.padding_idx = 0
        self.end_idx = 1
        self.unknown_idx = 2

        return dict_character

    def encode(self, text):
        if len(text) == 0 or len(text) >= self.max_text_len:
            return None
        if self.lower:
            text = text.lower()
        text_list = []
        for char in text:
            if char not in self.dict:
                text_list.append(self.unknown_idx)
            else:
                text_list.append(self.dict[char])
        text_list.append(self.end_idx)
        if len(text_list) < self.max_text_len:
            text_list += [self.padding_idx] * (
                self.max_text_len - len(text_list))
        return text_list

    def __call__(self, data):
        text = data['label']
        encoded_text = self.encode(text)
        if encoded_text is None:
            return None
        data['label'] = np.array(encoded_text)
        return data


class VQATokenLabelEncode(object):
    """
    Label encode for NLP VQA methods
    """

    def __init__(self,
                 class_path,
                 contains_re=False,
                 add_special_ids=False,
                 algorithm='LayoutXLM',
                 infer_mode=False,
                 ocr_engine=None,
                 **kwargs):
        super(VQATokenLabelEncode, self).__init__()
        from paddlenlp.transformers import LayoutXLMTokenizer, LayoutLMTokenizer, LayoutLMv2Tokenizer
        from ppocr.utils.utility import load_vqa_bio_label_maps
        tokenizer_dict = {
            'LayoutXLM': {
                'class': LayoutXLMTokenizer,
                'pretrained_model': 'layoutxlm-base-uncased'
            },
            'LayoutLM': {
                'class': LayoutLMTokenizer,
                'pretrained_model': 'layoutlm-base-uncased'
            },
            'LayoutLMv2': {
                'class': LayoutLMv2Tokenizer,
                'pretrained_model': 'layoutlmv2-base-uncased'
            }
        }
        self.contains_re = contains_re
        tokenizer_config = tokenizer_dict[algorithm]
        self.tokenizer = tokenizer_config['class'].from_pretrained(
            tokenizer_config['pretrained_model'])
        self.label2id_map, id2label_map = load_vqa_bio_label_maps(class_path)
        self.add_special_ids = add_special_ids
        self.infer_mode = infer_mode
        self.ocr_engine = ocr_engine

    def __call__(self, data):
        # load bbox and label info
        ocr_info = self._load_ocr_info(data)

        height, width, _ = data['image'].shape

        words_list = []
        bbox_list = []
        input_ids_list = []
        token_type_ids_list = []
        segment_offset_id = []
        gt_label_list = []

        entities = []

        # for re
        train_re = self.contains_re and not self.infer_mode
        if train_re:
            relations = []
            id2label = {}
            entity_id_to_index_map = {}
            empty_entity = set()

        data['ocr_info'] = copy.deepcopy(ocr_info)

        for info in ocr_info:
            if train_re:
                # for re
                if len(info["text"]) == 0:
                    empty_entity.add(info["id"])
                    continue
                id2label[info["id"]] = info["label"]
                relations.extend([tuple(sorted(l)) for l in info["linking"]])
            # smooth_box
            bbox = self._smooth_box(info["bbox"], height, width)

            text = info["text"]
            encode_res = self.tokenizer.encode(
                text, pad_to_max_seq_len=False, return_attention_mask=True)

            if not self.add_special_ids:
                # TODO: use tok.all_special_ids to remove
                encode_res["input_ids"] = encode_res["input_ids"][1:-1]
                encode_res["token_type_ids"] = encode_res["token_type_ids"][1:
                                                                            -1]
                encode_res["attention_mask"] = encode_res["attention_mask"][1:
                                                                            -1]
            # parse label
            if not self.infer_mode:
                label = info['label']
                gt_label = self._parse_label(label, encode_res)

            # construct entities for re
            if train_re:
                if gt_label[0] != self.label2id_map["O"]:
                    entity_id_to_index_map[info["id"]] = len(entities)
                    label = label.upper()
                    entities.append({
                        "start": len(input_ids_list),
                        "end":
                        len(input_ids_list) + len(encode_res["input_ids"]),
                        "label": label.upper(),
                    })
            else:
                entities.append({
                    "start": len(input_ids_list),
                    "end": len(input_ids_list) + len(encode_res["input_ids"]),
                    "label": 'O',
                })
            input_ids_list.extend(encode_res["input_ids"])
            token_type_ids_list.extend(encode_res["token_type_ids"])
            bbox_list.extend([bbox] * len(encode_res["input_ids"]))
            words_list.append(text)
            segment_offset_id.append(len(input_ids_list))
            if not self.infer_mode:
                gt_label_list.extend(gt_label)

        data['input_ids'] = input_ids_list
        data['token_type_ids'] = token_type_ids_list
        data['bbox'] = bbox_list
        data['attention_mask'] = [1] * len(input_ids_list)
        data['labels'] = gt_label_list
        data['segment_offset_id'] = segment_offset_id
        data['tokenizer_params'] = dict(
            padding_side=self.tokenizer.padding_side,
            pad_token_type_id=self.tokenizer.pad_token_type_id,
            pad_token_id=self.tokenizer.pad_token_id)
        data['entities'] = entities

        if train_re:
            data['relations'] = relations
            data['id2label'] = id2label
            data['empty_entity'] = empty_entity
            data['entity_id_to_index_map'] = entity_id_to_index_map
        return data

    def _load_ocr_info(self, data):
        def trans_poly_to_bbox(poly):
            x1 = np.min([p[0] for p in poly])
            x2 = np.max([p[0] for p in poly])
            y1 = np.min([p[1] for p in poly])
            y2 = np.max([p[1] for p in poly])
            return [x1, y1, x2, y2]

        if self.infer_mode:
            ocr_result = self.ocr_engine.ocr(data['image'], cls=False)
            ocr_info = []
            for res in ocr_result:
                ocr_info.append({
                    "text": res[1][0],
                    "bbox": trans_poly_to_bbox(res[0]),
                    "poly": res[0],
                })
            return ocr_info
        else:
            info = data['label']
            # read text info
            info_dict = json.loads(info)
            return info_dict["ocr_info"]

    def _smooth_box(self, bbox, height, width):
        bbox[0] = int(bbox[0] * 1000.0 / width)
        bbox[2] = int(bbox[2] * 1000.0 / width)
        bbox[1] = int(bbox[1] * 1000.0 / height)
        bbox[3] = int(bbox[3] * 1000.0 / height)
        return bbox

    def _parse_label(self, label, encode_res):
        gt_label = []
        if label.lower() == "other":
            gt_label.extend([0] * len(encode_res["input_ids"]))
        else:
            gt_label.append(self.label2id_map[("b-" + label).upper()])
            gt_label.extend([self.label2id_map[("i-" + label).upper()]] *
                            (len(encode_res["input_ids"]) - 1))
        return gt_label


class MultiLabelEncode(BaseRecLabelEncode):
    def __init__(self,
                 max_text_length,
                 character_dict_path=None,
                 use_space_char=False,
                 **kwargs):
        super(MultiLabelEncode, self).__init__(
            max_text_length, character_dict_path, use_space_char)

        self.ctc_encode = CTCLabelEncode(max_text_length, character_dict_path,
                                         use_space_char, **kwargs)
        self.sar_encode = SARLabelEncode(max_text_length, character_dict_path,
                                         use_space_char, **kwargs)

    def __call__(self, data):

        data_ctc = copy.deepcopy(data)
        data_sar = copy.deepcopy(data)
        data_out = dict()
        data_out['img_path'] = data.get('img_path', None)
        data_out['image'] = data['image']
        ctc = self.ctc_encode.__call__(data_ctc)
        sar = self.sar_encode.__call__(data_sar)
        if ctc is None or sar is None:
            return None
        data_out['label_ctc'] = ctc['label']
        data_out['label_sar'] = sar['label']
        data_out['length'] = ctc['length']
        return data_out