utils.py

#!/usr/bin/env python3
# -*- coding:utf-8 -*-
# Author: kerlomz <kerlomz@gmail.com>
import io

import PIL.Image
import cv2
import numpy as np
import tensorflow as tf

from config import *
from pretreatment import preprocessing

PATH_MAP = {
    RunMode.Trains: TRAINS_PATH,
    RunMode.Test: TEST_PATH
}

REGEX_MAP = {
    RunMode.Trains: TRAINS_REGEX,
    RunMode.Test: TEST_REGEX
}


def encode_maps():
    return {char: i for i, char in enumerate(GEN_CHAR_SET, 0)}


# Training is not useful for decoding
# Here is for debugging, positioning error source use
# def decode_maps():
#     return {i: char for i, char in enumerate(GEN_CHAR_SET, 0)}


class DataIterator:
    def __init__(self, mode: RunMode):
        self.mode = mode
        self.data_dir = PATH_MAP[mode]
        self.image = []
        self.image_path = []
        self.label_list = []
        self.image_batch = []
        self.label_batch = []
        self._label_batch = []
        self._size = 0

    @staticmethod
    def _encoder(code):
        if isinstance(code, bytes):
            code = code.decode('utf8')

        for k, v in CHAR_REPLACE.items():
            if not k or not v:
                break
            code.replace(k, v)
        code = code.lower() if 'LOWER' in CHAR_SET or not CASE_SENSITIVE else code
        code = code.upper() if 'UPPER' in CHAR_SET else code
        try:
            return [SPACE_INDEX if code == SPACE_TOKEN else encode_maps()[c] for c in list(code)]
        except KeyError as e:
            exception(
                'The sample label {} contains invalid charset: {}.'.format(
                    code, e.args[0]
                ), ConfigException.SAMPLE_LABEL_ERROR
            )

    def read_sample_from_files(self, data_set=None):
        if data_set:
            self.image_path = data_set
            try:
                self.label_list = [
                    self._encoder(re.search(REGEX_MAP[self.mode], i.split(PATH_SPLIT)[-1]).group()) for i in data_set
                ]
            except AttributeError as e:
                regex_not_found = "group" in e.args[0]
                if regex_not_found:
                    exception(
                        "Configured {} is '{}', it may be wrong and unable to get label properly.".format(
                            "TrainRegex" if self.mode == RunMode.Trains else "TestRegex",
                            TRAINS_REGEX if self.mode == RunMode.Trains else TEST_REGEX
                        ),
                        ConfigException.GET_LABEL_REGEX_ERROR
                    )
        else:
            for root, sub_folder, file_list in os.walk(self.data_dir):
                for file_path in file_list:
                    image_name = os.path.join(root, file_path)
                    if file_path in IGNORE_FILES:
                        continue
                    self.image_path.append(image_name)
                    # Get the label from the file name based on the regular expression.
                    code = re.search(
                        REGEX_MAP[self.mode], image_name.split(PATH_SPLIT)[-1]
                    )
                    if not code:
                        exception(
                            "Configured {} is '{}', it may be wrong and unable to get label properly.".format(
                                "TrainRegex" if self.mode == RunMode.Trains else "TestRegex",
                                TRAINS_REGEX if self.mode == RunMode.Trains else TEST_REGEX
                            ),
                            ConfigException.GET_LABEL_REGEX_ERROR
                        )
                    code = code.group()
                    # The manual verification code platform is not case sensitive,
                    # - it will affect the accuracy of the training set.
                    # Here is a case conversion based on the selected character set.
                    self.label_list.append(self._encoder(code))
        self._size = len(self.label_list)

    def read_sample_from_tfrecords(self, path):
        filename_queue = tf.train.string_input_producer([path])
        reader = tf.TFRecordReader()

        self._size = len([_ for _ in tf.python_io.tf_record_iterator(path)])

        _, serialized_example = reader.read(filename_queue)
        features = tf.parse_single_example(
            serialized_example,
            features={
                'label': tf.FixedLenFeature([], tf.string),
                'image': tf.FixedLenFeature([], tf.string),
            }
        )
        image = tf.cast(features['image'], tf.string)
        label = tf.cast(features['label'], tf.string)

        min_after_dequeue = 1000
        batch = BATCH_SIZE if self.mode == RunMode.Trains else TEST_BATCH_SIZE
        capacity = min_after_dequeue + 3 * batch
        self.image_batch, self.label_batch = tf.train.shuffle_batch(
            [image, label],
            batch_size=batch,
            capacity=capacity,
            num_threads=64,
            min_after_dequeue=min_after_dequeue
        )

    @property
    def size(self):
        return self._size

    def labels(self, index):
        if (TRAINS_USE_TFRECORDS and self.mode == RunMode.Trains) or (TEST_USE_TFRECORDS and self.mode == RunMode.Test):
            return self.label_list
        else:
            return [self.label_list[i] for i in index]

    @staticmethod
    def _image(path_or_bytes):

        # im = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
        # The OpenCV cannot handle gif format images, it will return None.
        # if im is None:
        path_or_stream = io.BytesIO(path_or_bytes) if isinstance(path_or_bytes, bytes) else path_or_bytes
        pil_image = PIL.Image.open(path_or_stream)
        rgb = pil_image.split()
        size = pil_image.size

        if len(rgb) > 3 and REPLACE_TRANSPARENT:
            background = PIL.Image.new('RGB', pil_image.size, (255, 255, 255))
            background.paste(pil_image, (0, 0, size[0], size[1]), pil_image)
            pil_image = background

        pil_image = pil_image.convert('L')
        im = np.array(pil_image)
        im = preprocessing(im, BINARYZATION, SMOOTH, BLUR).astype(np.float32)
        im = cv2.resize(im, (RESIZE[0], RESIZE[1]))
        im = im.swapaxes(0, 1)
        return np.array(im[:, :, np.newaxis] / 255.)

    @staticmethod
    def _get_input_lens(sequences):
        lengths = np.asarray([len(_) for _ in sequences], dtype=np.int64)
        return sequences, lengths

    def generate_batch_by_files(self, index=None):
        if index:
            image_batch = [self._image(self.image_path[i]) for i in index]
            label_batch = [self.label_list[i] for i in index]
        else:
            image_batch = [self._image(i) for i in self.image_path]
            label_batch = self.label_list
        return self._generate_batch(image_batch, label_batch)

    def _generate_batch(self, image_batch, label_batch):
        batch_inputs, batch_seq_len = self._get_input_lens(np.array(image_batch))
        batch_labels = sparse_tuple_from_label(label_batch)
        self._label_batch = batch_labels
        return batch_inputs, batch_seq_len, batch_labels

    def generate_batch_by_tfrecords(self, sess):
        _image, _label = sess.run([self.image_batch, self.label_batch])
        image_batch, label_batch = [], []
        for (i1, i2) in zip(_image, _label):
            try:
                image_batch.append(self._image(i1))
                label_batch.append(self._encoder(i2))
            except OSError:
                continue
        self._label_batch = label_batch
        self.label_list = label_batch
        return self._generate_batch(image_batch, label_batch)


def accuracy_calculation(original_seq, decoded_seq, ignore_value=-1):
    original_seq_len = len(original_seq)
    decoded_seq_len = len(decoded_seq)
    if original_seq_len != decoded_seq_len:
        print(original_seq)
        print('original lengths {} is different from the decoded_seq {}, please check again'.format(
            original_seq_len,
            decoded_seq_len
        ))
        return 0
    count = 0
    # Here is for debugging, positioning error source use
    # error_sample = []
    for i, origin_label in enumerate(original_seq):
        decoded_label = [j for j in decoded_seq[i] if j != ignore_value]
        if i < 5:
            print(i, len(origin_label), len(decoded_label), origin_label, decoded_label)
        if origin_label == decoded_label:
            count += 1
    # Training is not useful for decoding
    # Here is for debugging, positioning error source use
    #     if origin_label != decoded_label and len(error_sample) < 500:
    #         error_sample.append({
    #             "origin": "".join([decode_maps()[i] for i in origin_label]),
    #             "decode": "".join([decode_maps()[i] for i in decoded_label])
    #         })
    # print(error_sample)
    return count * 1.0 / len(original_seq)


# Convert a sequence list to a sparse matrix
def sparse_tuple_from_label(sequences, dtype=np.int32):
    indices = []
    values = []
    for n, seq in enumerate(sequences):
        indices.extend(zip([n] * len(seq), range(0, len(seq), 1)))
        values.extend(seq)

    indices = np.asarray(indices, dtype=np.int64)
    values = np.asarray(values, dtype=dtype)
    shape = np.asarray([len(sequences), np.asarray(indices).max(0)[1] + 1], dtype=np.int64)
    return indices, values, shape