dataset.py

import os
import sys
import re
import six
import math
import lmdb
import torch
import copy
import random
import pickle

from augmentation.weather import Fog, Snow, Frost
from augmentation.warp import Curve, Distort, Stretch
from augmentation.geometry import Rotate, Perspective, Shrink, TranslateX, TranslateY
from augmentation.pattern import VGrid, HGrid, Grid, RectGrid, EllipseGrid
from augmentation.noise import GaussianNoise, ShotNoise, ImpulseNoise, SpeckleNoise
from augmentation.blur import GaussianBlur, DefocusBlur, MotionBlur, GlassBlur, ZoomBlur
from augmentation.camera import Contrast, Brightness, JpegCompression, Pixelate
from augmentation.weather import Fog, Snow, Frost, Rain, Shadow
from augmentation.process import Posterize, Solarize, Invert, Equalize, AutoContrast, Sharpness, Color

from natsort import natsorted
from PIL import Image
import PIL.ImageOps
import numpy as np
from torch.utils.data import Dataset, ConcatDataset, Subset
from torch._utils import _accumulate
import torchvision.transforms as transforms
import torchvision.transforms.functional as TF
import random


class Batch_Balanced_Dataset(object):

    def __init__(self, opt):
        """
        Modulate the data ratio in the batch.
        For example, when select_data is "MJ-ST" and batch_ratio is "0.5-0.5",
        the 50% of the batch is filled with MJ and the other 50% of the batch is filled with ST.
        """
        if not os.path.exists(f'./saved_models/{opt.exp_name}/'):
            os.makedirs(f'./saved_models/{opt.exp_name}/')
        log = open(f'./saved_models/{opt.exp_name}/log_dataset.txt', 'a')
        dashed_line = '-' * 80
        print(dashed_line)
        log.write(dashed_line + '\n')
        print(f'dataset_root: {opt.train_data}\nopt.select_data: {opt.select_data}\nopt.batch_ratio: {opt.batch_ratio}')
        log.write(f'dataset_root: {opt.train_data}\nopt.select_data: {opt.select_data}\nopt.batch_ratio: {opt.batch_ratio}\n')
        assert len(opt.select_data) == len(opt.batch_ratio)

        _AlignCollate = AlignCollate(imgH=opt.imgH, imgW=opt.imgW, keep_ratio_with_pad=opt.PAD, opt=opt)
        self.data_loader_list = []
        self.dataloader_iter_list = []
        batch_size_list = []
        Total_batch_size = 0
        notSelectiveVal = True
        if opt.selective_sample_str != '':
            notSelectiveVal = False
        for selected_d, batch_ratio_d in zip(opt.select_data, opt.batch_ratio):
            _batch_size = max(round(opt.batch_size * float(batch_ratio_d)), 1)
            print(dashed_line)
            log.write(dashed_line + '\n')
            _dataset, _dataset_log = hierarchical_dataset(root=opt.train_data, opt=opt, notSelective=notSelectiveVal, select_data=[selected_d])
            total_number_dataset = len(_dataset)
            log.write(_dataset_log)

            """
            The total number of data can be modified with opt.total_data_usage_ratio.
            ex) opt.total_data_usage_ratio = 1 indicates 100% usage, and 0.2 indicates 20% usage.
            See 4.2 section in our paper.
            """
            number_dataset = int(total_number_dataset * float(opt.total_data_usage_ratio))
            dataset_split = [number_dataset, total_number_dataset - number_dataset]
            indices = range(total_number_dataset)
            _dataset, _ = [Subset(_dataset, indices[offset - length:offset])
                           for offset, length in zip(_accumulate(dataset_split), dataset_split)]
            selected_d_log = f'num total samples of {selected_d}: {total_number_dataset} x {opt.total_data_usage_ratio} (total_data_usage_ratio) = {len(_dataset)}\n'
            selected_d_log += f'num samples of {selected_d} per batch: {opt.batch_size} x {float(batch_ratio_d)} (batch_ratio) = {_batch_size}'
            print(selected_d_log)
            log.write(selected_d_log + '\n')
            batch_size_list.append(str(_batch_size))
            Total_batch_size += _batch_size

            _data_loader = torch.utils.data.DataLoader(
                _dataset, batch_size=_batch_size,
                shuffle=True,
                num_workers=int(opt.workers),
                collate_fn=_AlignCollate, pin_memory=True)
            self.data_loader_list.append(_data_loader)
            self.dataloader_iter_list.append(iter(_data_loader))

        Total_batch_size_log = f'{dashed_line}\n'
        batch_size_sum = '+'.join(batch_size_list)
        Total_batch_size_log += f'Total_batch_size: {batch_size_sum} = {Total_batch_size}\n'
        Total_batch_size_log += f'{dashed_line}'
        opt.batch_size = Total_batch_size

        print(Total_batch_size_log)
        log.write(Total_batch_size_log + '\n')
        log.close()

    def get_batch(self):
        balanced_batch_images = []
        balanced_batch_texts = []

        for i, data_loader_iter in enumerate(self.dataloader_iter_list):
            try:
                image, text = data_loader_iter.next()
                balanced_batch_images.append(image)
                balanced_batch_texts += text
            except StopIteration:
                self.dataloader_iter_list[i] = iter(self.data_loader_list[i])
                image, text = self.dataloader_iter_list[i].next()
                balanced_batch_images.append(image)
                balanced_batch_texts += text
            except ValueError:
                pass

        balanced_batch_images = torch.cat(balanced_batch_images, 0)

        return balanced_batch_images, balanced_batch_texts

### notSelective - when False, LMDB dataset loader goes to the routine of randomly
### sampling indices to match --selective_sample_str, else it will no execute the code in the while loop
### and just do the normal VITSTR code
def hierarchical_dataset(root, opt, notSelective=True, select_data='/', segmRootDir=None, maxImages=None):
    """ select_data='/' contains all sub-directory of root directory """
    dataset_list = []
    dataset_log = f'dataset_root:    {root}\t dataset: {select_data[0]}'
    print(dataset_log)
    dataset_log += '\n'
    for dirpath, dirnames, filenames in os.walk(root+'/'):
        if not dirnames:
            select_flag = False
            for selected_d in select_data:
                if selected_d in dirpath:
                    select_flag = True
                    break

            if select_flag:
                if segmRootDir is None:
                    dataset = LmdbDataset(dirpath, opt, notSelective, maxImages=maxImages)
                else:
                    dataset = LMDBSegmentationDataset(dirpath, opt, notSelective, segmRootDir=segmRootDir, maxImages=maxImages)
                sub_dataset_log = f'sub-directory:\t/{os.path.relpath(dirpath, root)}\t num samples: {len(dataset)}'
                print(sub_dataset_log)
                dataset_log += f'{sub_dataset_log}\n'
                dataset_list.append(dataset)

    concatenated_dataset = ConcatDataset(dataset_list)

    return concatenated_dataset, dataset_log

class ValidDataset(Dataset):
    ### validPklData - pickle containing mapping of validIdx to original train/test idx
    ### knnDataRoot - root dir to open pickle file for knn, with forward slash
    ### knnCount - max number of knn from 0-knnCount, not necessarily the same number as
    ### inside the pickle knns
    ### typeSet - if 'train' or 'test'
    ### offsetStartIdx - start index of dataset to sample (0 to N-1), where N is size of valid test set
    ### offsetEndIdx - end index of dataset to sample (0 to N-1), where N is size of valid test set
    ### actual size of this dataset will be offsetStartIdx - offsetEndIdx
    def __init__(self, validPklData, lmdbDataset, typeSet, knnDataRoot, knnCount=None, offsetStartIdx=None, offsetEndIdx=None):
        self.validPklData = validPklData
        self.lmdbDataset = lmdbDataset
        self.typeSet = typeSet
        self.knnCount = knnCount
        self.totalValidImgs = len(validPklData)
        self.knnDataRoot = knnDataRoot
        ### this function is only for the test dataloader, remember to set batch size to one
        self.currentIdx = None
        self.knnPklData = None
        self.offsetStartIdx = None
        if offsetStartIdx is not None:
            self.totalValidImgs = offsetEndIdx - offsetStartIdx
            self.offsetStartIdx = offsetStartIdx
    ### this function is purposely created for the trainset dataloader
    ### call this function to load new pickle file for knn for training set
    ### be sure to call this function before looping over the dataloader again
    ### This function also applies offsetting for the test index num i
    def setCurrentTestNumKNN(self, testValidIdx):
        knnPklFile = self.knnDataRoot + "test" + str(testValidIdx + self.offsetStartIdx) + "knn.pkl"
        with open(knnPklFile, 'rb') as f:
            ### this data is a list of indices with index 0 nearest to the textValidIdx
            ### according to FAISS KNN
            self.knnPklData = pickle.load(f)
        self.totalValidImgs = self.knnCount
    ### index should be the same number thrown by __getitem__ function
    ### this function will only work properly if the batch size of testdataloader is equal to one
    def getValidPklIdx(self):
        return self.currentIdx
    def __len__(self):
        return self.totalValidImgs
    def __getitem__(self, index):
        if self.typeSet == 'train':
            data, label = self.lmdbDataset[self.validPklData[self.knnPklData[index]]]
        elif self.typeSet == 'test':
            if self.offsetStartIdx is not None:
                index = index + self.offsetStartIdx
            self.currentIdx = index
            data, label = self.lmdbDataset[self.validPklData[index]]
        else:
            assert(False)
        return data, label
class NShotDataset(Dataset):
    ### infPKLFile - the influence file containing the validTrainIdx list
    def __init__(self, infPKLData, validTrainPklData, lmdbDataset):
        self.infPKLData = infPKLData
        self.totalDataImg = len(infPKLData)
        self.validTrainPklData = validTrainPklData
        self.lmdbDataset = lmdbDataset
    def __len__(self):
        return self.totalDataImg
    def __getitem__(self, index):
        data, label = self.lmdbDataset[self.validTrainPklData[self.infPKLData[index]]]
        return data, label
class LmdbDataset(Dataset):

    def __init__(self, root, opt, notSelective, maxImages=None):

        self.root = root
        self.opt = opt
        if self.opt.eval == False:
            self.currentInfluenceLS = copy.deepcopy(self.opt.influence_idx)
            random.shuffle(self.currentInfluenceLS)
        self.notSelective = notSelective
        self.selective_sample_ls = set([])
        self.env = lmdb.open(root, max_readers=32, readonly=True, lock=False, readahead=False, meminit=False)
        if not self.env:
            print('cannot create lmdb from %s' % (root))
            sys.exit(0)

        with self.env.begin(write=False) as txn:
            nSamples = int(txn.get('num-samples'.encode()))
            if maxImages is not None:
                nSamples = min(nSamples, maxImages)
            self.nSamples = nSamples

            if self.opt.data_filtering_off:
                # for fast check or benchmark evaluation with no filtering
                self.filtered_index_list = [index + 1 for index in range(self.nSamples)]
            else:
                """ Filtering part
                If you want to evaluate IC15-2077 & CUTE datasets which have special character labels,
                use --data_filtering_off and only evaluate on alphabets and digits.
                see https://github.com/clovaai/deep-text-recognition-benchmark/blob/6593928855fb7abb999a99f428b3e4477d4ae356/dataset.py#L190-L192

                And if you want to evaluate them with the model trained with --sensitive option,
                use --sensitive and --data_filtering_off,
                see https://github.com/clovaai/deep-text-recognition-benchmark/blob/dff844874dbe9e0ec8c5a52a7bd08c7f20afe704/test.py#L137-L144
                """
                self.filtered_index_list = []
                for index in range(self.nSamples):
                    index += 1  # lmdb starts with 1
                    label_key = 'label-%09d'.encode() % index
                    label = txn.get(label_key).decode('utf-8')

                    if len(label) > self.opt.batch_max_length:
                        # print(f'The length of the label is longer than max_length: length
                        # {len(label)}, {label} in dataset {self.root}')
                        continue

                    # By default, images containing characters which are not in opt.character are filtered.
                    # You can add [UNK] token to `opt.character` in utils.py instead of this filtering.
                    out_of_char = f'[^{self.opt.character}]'
                    if re.search(out_of_char, label.lower()):
                        continue

                    self.filtered_index_list.append(index)

                self.nSamples = len(self.filtered_index_list)

    def __len__(self):
        return self.nSamples

    def __getitem__(self, index):
        assert index <= len(self), 'index range error'

        ### Used for influence function training
        if self.opt.eval == False:
            index = self.currentInfluenceLS.pop(len(self.currentInfluenceLS)-1)
            if len(self.currentInfluenceLS) <= 0:
                self.currentInfluenceLS = copy.deepcopy(self.opt.influence_idx)
                random.shuffle(self.currentInfluenceLS)

        while True:
            index = self.filtered_index_list[index]

            if self.opt.max_selective_list != -1:
                if len(self.selective_sample_ls) >= self.opt.max_selective_list:
                    self.selective_sample_ls.clear()

            with self.env.begin(write=False) as txn:
                label_key = 'label-%09d'.encode() % index
                label = txn.get(label_key).decode('utf-8') ### label - raw utf8 string output
                if self.opt.selective_sample_str != '' and not self.notSelective:
                    if self.opt.ignore_case_sensitivity:
                        if label.lower() != self.opt.selective_sample_str.lower():
                            ### Reloop
                            self.selective_sample_ls.add(index)
                            while True:
                                index = random.randint(0, len(self)-1)
                                if index not in self.selective_sample_ls: break
                            continue
                    else:
                        if label != self.opt.selective_sample_str:
                            ### Reloop
                            self.selective_sample_ls.add(index)
                            while True:
                                index = random.randint(0, len(self)-1)
                                if index not in self.selective_sample_ls: break
                            continue
                img_key = 'image-%09d'.encode() % index
                imgbuf = txn.get(img_key)

                buf = six.BytesIO()
                buf.write(imgbuf)
                buf.seek(0)
                try:
                    if self.opt.rgb:
                        img = Image.open(buf).convert('RGB')  # for color image
                    else:
                        img = Image.open(buf).convert('L')

                except IOError:
                    print(f'Corrupted image for {index}')
                    # make dummy image and dummy label for corrupted image.
                    if self.opt.rgb:
                        img = Image.new('RGB', (self.opt.imgW, self.opt.imgH))
                    else:
                        img = Image.new('L', (self.opt.imgW, self.opt.imgH))
                    label = '[dummy_label]'

                if not self.opt.sensitive:
                    label = label.lower()

                # We only train and evaluate on alphanumerics (or pre-defined character set in train.py)
                out_of_char = f'[^{self.opt.character}]'
                label = re.sub(out_of_char, '', label)
                break
        return (img, label)


class RawDataset(Dataset):

    def __init__(self, root, opt):
        self.opt = opt
        self.image_path_list = []
        for dirpath, dirnames, filenames in os.walk(root):
            for name in filenames:
                _, ext = os.path.splitext(name)
                ext = ext.lower()
                if ext == '.jpg' or ext == '.jpeg' or ext == '.png':
                    self.image_path_list.append(os.path.join(dirpath, name))

        self.image_path_list = natsorted(self.image_path_list)
        self.nSamples = len(self.image_path_list)

    def __len__(self):
        return self.nSamples

    def __getitem__(self, index):

        try:
            if self.opt.rgb:
                img = Image.open(self.image_path_list[index]).convert('RGB')  # for color image
            else:
                img = Image.open(self.image_path_list[index]).convert('L')

        except IOError:
            print(f'Corrupted image for {index}')
            # make dummy image and dummy label for corrupted image.
            if self.opt.rgb:
                img = Image.new('RGB', (self.opt.imgW, self.opt.imgH))
            else:
                img = Image.new('L', (self.opt.imgW, self.opt.imgH))

        return (img, self.image_path_list[index])


def isless(prob=0.5):
    return np.random.uniform(0,1) < prob

class DataAugment(object):
    '''
    Supports with and without data augmentation
    '''
    def __init__(self, opt):
        self.opt = opt

        if not opt.eval:
            self.process = [Posterize(), Solarize(), Invert(), Equalize(), AutoContrast(), Sharpness(), Color()]
            self.camera = [Contrast(), Brightness(), JpegCompression(), Pixelate()]

            self.pattern = [VGrid(), HGrid(), Grid(), RectGrid(), EllipseGrid()]

            self.noise = [GaussianNoise(), ShotNoise(), ImpulseNoise(), SpeckleNoise()]
            self.blur = [GaussianBlur(), DefocusBlur(), MotionBlur(), GlassBlur(), ZoomBlur()]
            self.weather = [Fog(), Snow(), Frost(), Rain(), Shadow()]

            self.noises = [self.blur, self.noise, self.weather]
            self.processes = [self.camera, self.process]

            self.warp = [Curve(), Distort(), Stretch()]
            self.geometry = [Rotate(), Perspective(), Shrink()]

            self.isbaseline_aug = False
            # rand augment
            if self.opt.isrand_aug:
                self.augs = [self.process, self.camera, self.noise, self.blur, self.weather, self.pattern, self.warp, self.geometry]
            # semantic augment
            elif self.opt.issemantic_aug:
                self.geometry = [Rotate(), Perspective(), Shrink()]
                self.noise = [GaussianNoise()]
                self.blur = [MotionBlur()]
                self.augs = [self.noise, self.blur, self.geometry]
                self.isbaseline_aug = True
            # pp-ocr augment
            elif self.opt.islearning_aug:
                self.geometry = [Rotate(), Perspective()]
                self.noise = [GaussianNoise()]
                self.blur = [MotionBlur()]
                self.warp = [Distort()]
                self.augs = [self.warp, self.noise, self.blur, self.geometry]
                self.isbaseline_aug = True
            # scatter augment
            elif self.opt.isscatter_aug:
                self.geometry = [Shrink()]
                self.warp = [Distort()]
                self.augs = [self.warp, self.geometry]
                self.baseline_aug = True
            # rotation augment
            elif self.opt.isrotation_aug:
                self.geometry = [Rotate()]
                self.augs = [self.geometry]
                self.isbaseline_aug = True

        self.scale = False if opt.Transformer else True

    def __call__(self, img):
        '''
            Must call img.copy() if pattern, Rain or Shadow is used
        '''
        img = img.resize((self.opt.imgW, self.opt.imgH), Image.BICUBIC)

        if self.opt.eval or isless(self.opt.intact_prob):
            pass
        elif self.opt.isshap_aug:
            img = self.shap_aug(img)
        elif self.opt.isrand_aug or self.isbaseline_aug:
            img = self.rand_aug(img)
        # individual augment can also be selected
        elif self.opt.issel_aug:
            img = self.sel_aug(img)

        img = transforms.ToTensor()(img)
        if self.scale:
            img.sub_(0.5).div_(0.5)
        return img


    def rand_aug(self, img):
        augs = np.random.choice(self.augs, self.opt.augs_num, replace=False)
        for aug in augs:
            index = np.random.randint(0, len(aug))
            op = aug[index]
            mag = np.random.randint(0, 3) if self.opt.augs_mag is None else self.opt.augs_mag
            if type(op).__name__ == "Rain"  or type(op).__name__ == "Grid":
                img = op(img.copy(), mag=mag)
            else:
                img = op(img, mag=mag)

        return img

    def shap_aug(self, img):
        weatherProb = 0.094624746
        warpProb = 0.204524008
        geometryProb = 0.332274202
        noiseProb = 0.477033377
        cameraProb = 0.57329097
        patternProb = 0.743824929
        processProb = 0.845809948
        blurProb = 0.946237465
        noCorruptProb = 1

        prob = 1.
        iscurve = False

        corrProb = random.uniform(0, 1)
        if corrProb >= 0 and corrProb < weatherProb:
            mag = np.random.randint(self.opt.min_rand, self.opt.max_rand)
            index = np.random.randint(0, len(self.weather))
            op = self.weather[index]
            if type(op).__name__ == "Rain": #or "Grid" in type(op).__name__ :
                img = op(img.copy(), mag=mag, prob=prob)
            else:
                img = op(img, mag=mag, prob=prob)
        elif corrProb >= weatherProb and corrProb < warpProb:
            mag = np.random.randint(self.opt.min_rand, self.opt.max_rand)
            index = np.random.randint(0, len(self.warp))
            op = self.warp[index]
            if type(op).__name__ == "Curve":
                iscurve = True
            img = op(img, mag=mag, prob=prob)
        elif corrProb >= warpProb and corrProb < geometryProb:
            mag = np.random.randint(self.opt.min_rand, self.opt.max_rand)
            index = np.random.randint(0, len(self.geometry))
            op = self.geometry[index]
            if type(op).__name__ == "Rotate":
                img = op(img, iscurve=iscurve, mag=mag, prob=prob)
            else:
                img = op(img, mag=mag, prob=prob)
        elif corrProb >= geometryProb and corrProb < noiseProb:
            mag = np.random.randint(self.opt.min_rand, self.opt.max_rand)
            index = np.random.randint(0, len(self.noise))
            op = self.noise[index]
            img = op(img, mag=mag, prob=prob)
        elif corrProb >= noiseProb and corrProb < cameraProb:
            mag = np.random.randint(self.opt.min_rand, self.opt.max_rand)
            index = np.random.randint(0, len(self.camera))
            op = self.camera[index]
            img = op(img, mag=mag, prob=prob)
        elif corrProb >= cameraProb and corrProb < patternProb:
            mag = np.random.randint(self.opt.min_rand, self.opt.max_rand)
            index = np.random.randint(0, len(self.pattern))
            op = self.pattern[index]
            img = op(img.copy(), mag=mag, prob=prob)
        elif corrProb >= patternProb and corrProb < processProb:
            mag = np.random.randint(self.opt.min_rand, self.opt.max_rand)
            index = np.random.randint(0, len(self.process))
            op = self.process[index]
            img = op(img, mag=mag, prob=prob)
        elif corrProb >= processProb and corrProb < blurProb:
            mag = np.random.randint(self.opt.min_rand, self.opt.max_rand)
            index = np.random.randint(0, len(self.blur))
            op = self.blur[index]
            img = op(img, mag=mag, prob=prob)
        elif corrProb >= blurProb and corrProb <= noCorruptProb:
            pass

        return img

    def sel_aug(self, img):

        prob = 1.

        if self.opt.process:
            mag = np.random.randint(self.opt.min_rand, self.opt.max_rand)
            index = np.random.randint(0, len(self.process))
            op = self.process[index]
            img = op(img, mag=mag, prob=prob)

        if self.opt.noise:
            mag = np.random.randint(self.opt.min_rand, self.opt.max_rand)
            index = np.random.randint(0, len(self.noise))
            op = self.noise[index]
            img = op(img, mag=mag, prob=prob)

        if self.opt.blur:
            mag = np.random.randint(self.opt.min_rand, self.opt.max_rand)
            index = np.random.randint(0, len(self.blur))
            op = self.blur[index]
            img = op(img, mag=mag, prob=prob)

        if self.opt.weather:
            mag = np.random.randint(self.opt.min_rand, self.opt.max_rand)
            index = np.random.randint(0, len(self.weather))
            op = self.weather[index]
            if type(op).__name__ == "Rain": #or "Grid" in type(op).__name__ :
                img = op(img.copy(), mag=mag, prob=prob)
            else:
                img = op(img, mag=mag, prob=prob)

        if self.opt.camera:
            mag = np.random.randint(self.opt.min_rand, self.opt.max_rand)
            index = np.random.randint(0, len(self.camera))
            op = self.camera[index]
            img = op(img, mag=mag, prob=prob)

        if self.opt.pattern:
            mag = np.random.randint(self.opt.min_rand, self.opt.max_rand)
            index = np.random.randint(0, len(self.pattern))
            op = self.pattern[index]
            img = op(img.copy(), mag=mag, prob=prob)

        iscurve = False
        if self.opt.warp:
            mag = np.random.randint(self.opt.min_rand, self.opt.max_rand)
            index = np.random.randint(0, len(self.warp))
            op = self.warp[index]
            if type(op).__name__ == "Curve":
                iscurve = True
            img = op(img, mag=mag, prob=prob)

        if self.opt.geometry:
            mag = np.random.randint(self.opt.min_rand, self.opt.max_rand)
            index = np.random.randint(0, len(self.geometry))
            op = self.geometry[index]
            if type(op).__name__ == "Rotate":
                img = op(img, iscurve=iscurve, mag=mag, prob=prob)
            else:
                img = op(img, mag=mag, prob=prob)

        return img


class ResizeNormalize(object):

    def __init__(self, size, interpolation=Image.BICUBIC):
        self.size = size
        self.interpolation = interpolation
        self.toTensor = transforms.ToTensor()

    def __call__(self, img):
        img = img.resize(self.size, self.interpolation)
        img = self.toTensor(img)
        img.sub_(0.5).div_(0.5)
        return img


class NormalizePAD(object):

    def __init__(self, max_size, PAD_type='right'):
        self.toTensor = transforms.ToTensor()
        self.max_size = max_size
        self.max_width_half = math.floor(max_size[2] / 2)
        self.PAD_type = PAD_type

    def __call__(self, img):
        img = self.toTensor(img)
        img.sub_(0.5).div_(0.5)
        c, h, w = img.size()
        Pad_img = torch.FloatTensor(*self.max_size).fill_(0)
        Pad_img[:, :, :w] = img  # right pad
        if self.max_size[2] != w:  # add border Pad
            Pad_img[:, :, w:] = img[:, :, w - 1].unsqueeze(2).expand(c, h, self.max_size[2] - w)

        return Pad_img


class AlignCollate(object):

    def __init__(self, imgH=32, imgW=100, keep_ratio_with_pad=False, opt=None):
        self.imgH = imgH
        self.imgW = imgW
        self.keep_ratio_with_pad = keep_ratio_with_pad
        self.opt = opt

    def __call__(self, batch):
        # print("type batch: ", type(batch))
        # print("type batch[0]: ", type(batch[0]))
        batch = filter(lambda x: x is not None, batch)
        images, labels = zip(*batch)

        if self.keep_ratio_with_pad:  # same concept with 'Rosetta' paper
            resized_max_w = self.imgW
            input_channel = 3 if images[0].mode == 'RGB' else 1
            transform = NormalizePAD((input_channel, self.imgH, resized_max_w))

            resized_images = []
            for image in images:
                w, h = image.size
                ratio = w / float(h)
                if math.ceil(self.imgH * ratio) > self.imgW:
                    resized_w = self.imgW
                else:
                    resized_w = math.ceil(self.imgH * ratio)

                resized_image = image.resize((resized_w, self.imgH), Image.BICUBIC)
                resized_images.append(transform(resized_image))
                # resized_image.save('./image_test/%d_test.jpg' % w)

            image_tensors = torch.cat([t.unsqueeze(0) for t in resized_images], 0)

        else:
            transform = DataAugment(self.opt)
            #i = 0
            #for image in images:
            #    transform(image)
            #    if i == 1:
            #        exit(0)
            #    else:
            #        i = i + 1
            image_tensors = [transform(image) for image in images]
            image_tensors = torch.cat([t.unsqueeze(0) for t in image_tensors], 0)

        #else:
        #    transform = ResizeNormalize((self.imgW, self.imgH))
        #    image_tensors = [transform(image) for image in images]
        #    image_tensors = torch.cat([t.unsqueeze(0) for t in image_tensors], 0)

        return image_tensors, labels

class STRCharSegmDataset(Dataset):
    ### imgRoot - above the ./images folder
    ### minCharNum - set to 0 to deactivate. If greater than 0, this dataset will only output
    ### images >= minCharNum
    def __init__(self, annotFile, imgRoot, transforms, minCharNum=0,\
    charNum=-1, charToQuery=None):
        self.transforms = transforms
        self.minCharNum = minCharNum
        with open(annotFile) as file:
            self.lines = file.readlines()
        self.filteredLines = []
        for lineStr in self.lines:
            splitStr = lineStr.split()
            gtLabel = splitStr[-1]
            if self.minCharNum > 0 and len(gtLabel) >= self.minCharNum:
                if charNum != -1 and gtLabel[charNum] == charToQuery:
                    self.filteredLines.append(lineStr)
        self.totalItems = len(self.filteredLines)
        self.imgRoot = imgRoot

    def __len__(self):
        return self.totalItems

    def __getitem__(self, index):
        lineStr = self.filteredLines[index]
        splitStr = lineStr.split()
        imgFilename = splitStr[0]
        gtLabel = splitStr[-1]
        imgPIL = Image.open(os.path.join(self.imgRoot, imgFilename)).convert('L')
        imgPIL = self.transforms(imgPIL)
        return imgPIL, gtLabel

### Class simplifying the LMDB reader
class MyLMDBReader(Dataset):
    ### indexMap - pass here the file created that maps indices from
    ### limitedCharIdx ---> fullLMDBIdx
    ### Should be of format = "char1_N" assumed to be getting only labels
    ### where the first char is capital N. char1 is the first char.
    ### maxImages - set this to a number to reduce dataset size
    def __init__(self, root, opt, indexMap=None, charIdx=None, maxImages=None):
        self.root = root
        self.opt = opt
        self.env = lmdb.open(root, max_readers=32, readonly=True, lock=False, readahead=False, meminit=False)
        self.indexMapList = None
        if indexMap is not None:
            with open(indexMap, 'rb') as f:
                self.indexMapList = pickle.load(f)[charIdx] ### type list
                lesserSize = min(len(self.indexMapList), maxImages)
                self.indexMapList = self.indexMapList[:lesserSize]
        if not self.env:
            print('cannot create lmdb from %s' % (root))
            sys.exit(0)

        with self.env.begin(write=False) as txn:
            self.nSamples = int(txn.get('num-samples'.encode()))

            if self.opt.data_filtering_off:
                # for fast check or benchmark evaluation with no filtering
                self.filtered_index_list = [index + 1 for index in range(self.nSamples)]
            else:
                """ Filtering part
                If you want to evaluate IC15-2077 & CUTE datasets which have special character labels,
                use --data_filtering_off and only evaluate on alphabets and digits.
                see https://github.com/clovaai/deep-text-recognition-benchmark/blob/6593928855fb7abb999a99f428b3e4477d4ae356/dataset.py#L190-L192

                And if you want to evaluate them with the model trained with --sensitive option,
                use --sensitive and --data_filtering_off,
                see https://github.com/clovaai/deep-text-recognition-benchmark/blob/dff844874dbe9e0ec8c5a52a7bd08c7f20afe704/test.py#L137-L144
                """
                self.filtered_index_list = []
                for index in range(self.nSamples):
                    index += 1  # lmdb starts with 1
                    label_key = 'label-%09d'.encode() % index
                    label = txn.get(label_key).decode('utf-8')

                    if len(label) > self.opt.batch_max_length:
                        # print(f'The length of the label is longer than max_length: length
                        # {len(label)}, {label} in dataset {self.root}')
                        continue

                    # By default, images containing characters which are not in opt.character are filtered.
                    # You can add [UNK] token to `opt.character` in utils.py instead of this filtering.
                    out_of_char = f'[^{self.opt.character}]'
                    if re.search(out_of_char, label.lower()):
                        continue

                    self.filtered_index_list.append(index)

                self.nSamples = len(self.filtered_index_list)

        if self.indexMapList is not None:
            self.nSamples = len(self.indexMapList)

    def __len__(self):
        return self.nSamples

    def __getitem__(self, index):
        ### Acquire mapped index of filtered char only dataset
        if self.indexMapList is not None:
            index = self.indexMapList[index]
        # assert index <= len(self), 'index range error'

        while True:
            index = self.filtered_index_list[index]

            with self.env.begin(write=False) as txn:
                label_key = 'label-%09d'.encode() % index
                label = txn.get(label_key).decode('utf-8') ### label - raw utf8 string output
                img_key = 'image-%09d'.encode() % index
                imgbuf = txn.get(img_key)

                buf = six.BytesIO()
                buf.write(imgbuf)
                buf.seek(0)
                try:
                    if self.opt.rgb:
                        img = Image.open(buf).convert('RGB')  # for color image
                    else:
                        img = Image.open(buf).convert('L')

                except IOError:
                    print(f'Corrupted image for {index}')
                    # make dummy image and dummy label for corrupted image.
                    if self.opt.rgb:
                        img = Image.new('RGB', (self.opt.imgW, self.opt.imgH))
                    else:
                        img = Image.new('L', (self.opt.imgW, self.opt.imgH))
                    label = '[dummy_label]'

                if not self.opt.sensitive:
                    label = label.lower()

                # We only train and evaluate on alphanumerics (or pre-defined character set in train.py)
                out_of_char = f'[^{self.opt.character}]'
                label = re.sub(out_of_char, '', label)
                break
        return (img, label)

class LMDBSegmentationDataset(LmdbDataset):
    ### segmRootDir - if not None,
    def __init__(self, root, opt, notSelective, segmRootDir, maxImages=None):
        super().__init__(root, opt, notSelective, maxImages=maxImages)
        self.segmRootDir = segmRootDir

    def __getitem__(self, index):
        originalIdx = index
        assert index <= len(self), 'index range error'

        ### Used for influence function training
        if self.opt.eval == False:
            index = self.currentInfluenceLS.pop(len(self.currentInfluenceLS)-1)
            if len(self.currentInfluenceLS) <= 0:
                self.currentInfluenceLS = copy.deepcopy(self.opt.influence_idx)
                random.shuffle(self.currentInfluenceLS)

        while True:
            index = self.filtered_index_list[index]

            if self.opt.max_selective_list != -1:
                if len(self.selective_sample_ls) >= self.opt.max_selective_list:
                    self.selective_sample_ls.clear()

            with self.env.begin(write=False) as txn:
                label_key = 'label-%09d'.encode() % index
                label = txn.get(label_key).decode('utf-8') ### label - raw utf8 string output
                if self.opt.selective_sample_str != '' and not self.notSelective:
                    if self.opt.ignore_case_sensitivity:
                        if label.lower() != self.opt.selective_sample_str.lower():
                            ### Reloop
                            self.selective_sample_ls.add(index)
                            while True:
                                index = random.randint(0, len(self)-1)
                                if index not in self.selective_sample_ls: break
                            continue
                    else:
                        if label != self.opt.selective_sample_str:
                            ### Reloop
                            self.selective_sample_ls.add(index)
                            while True:
                                index = random.randint(0, len(self)-1)
                                if index not in self.selective_sample_ls: break
                            continue
                img_key = 'image-%09d'.encode() % index
                imgbuf = txn.get(img_key)

                buf = six.BytesIO()
                buf.write(imgbuf)
                buf.seek(0)
                try:
                    if self.opt.rgb:
                        img = Image.open(buf).convert('RGB')  # for color image
                    else:
                        img = Image.open(buf).convert('L')

                except IOError:
                    print(f'Corrupted image for {index}')
                    # make dummy image and dummy label for corrupted image.
                    if self.opt.rgb:
                        img = Image.new('RGB', (self.opt.imgW, self.opt.imgH))
                    else:
                        img = Image.new('L', (self.opt.imgW, self.opt.imgH))
                    label = '[dummy_label]'

                if not self.opt.sensitive:
                    label = label.lower()

                # We only train and evaluate on alphanumerics (or pre-defined character set in train.py)
                out_of_char = f'[^{self.opt.character}]'
                label = re.sub(out_of_char, '', label)
                break

        ### Acquire segmentations
        with open(self.segmRootDir + "{}.pkl".format(originalIdx), 'rb') as f:
            segmData = pickle.load(f)
        label = (segmData, label)
        return (img, label)

def tensor2im(image_tensor, imtype=np.uint8):
    image_numpy = image_tensor.cpu().float().numpy()
    if image_numpy.shape[0] == 1:
        image_numpy = np.tile(image_numpy, (3, 1, 1))
    image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0
    return image_numpy.astype(imtype)


def save_image(image_numpy, image_path):
    image_pil = Image.fromarray(image_numpy)
    image_pil.save(image_path)