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"""IAM Synthetic Paragraphs Dataset class."""
import random
from typing import Any, List, Sequence, Tuple
from loguru import logger as log
import numpy as np
from PIL import Image
from text_recognizer.data.base_data_module import BaseDataModule, load_and_print_info
from text_recognizer.data.base_dataset import (
BaseDataset,
convert_strings_to_labels,
)
from text_recognizer.data.iam import IAM
from text_recognizer.data.iam_lines import (
line_crops_and_labels,
load_line_crops_and_labels,
save_images_and_labels,
)
from text_recognizer.data.iam_paragraphs import (
get_dataset_properties,
IAMParagraphs,
IMAGE_SCALE_FACTOR,
NEW_LINE_TOKEN,
resize_image,
)
from text_recognizer.data.mappings.emnist import EmnistMapping
from text_recognizer.data.transforms.load_transform import load_transform_from_file
PROCESSED_DATA_DIRNAME = (
BaseDataModule.data_dirname() / "processed" / "iam_synthetic_paragraphs"
)
class IAMSyntheticParagraphs(IAMParagraphs):
"""IAM Handwriting database of synthetic paragraphs."""
def __init__(self) -> None:
super().__init__()
def prepare_data(self) -> None:
"""Prepare IAM lines to be used to generate paragraphs."""
if PROCESSED_DATA_DIRNAME.exists():
return
log.info("Preparing IAM lines for synthetic paragraphs dataset.")
log.info("Cropping IAM line regions and loading labels.")
iam = IAM(mapping=EmnistMapping(extra_symbols=(NEW_LINE_TOKEN,)))
iam.prepare_data()
crops_train, labels_train = line_crops_and_labels(iam, "train")
crops_test, labels_test = line_crops_and_labels(iam, "test")
crops_train = [resize_image(crop, IMAGE_SCALE_FACTOR) for crop in crops_train]
crops_test = [resize_image(crop, IMAGE_SCALE_FACTOR) for crop in crops_test]
log.info(f"Saving images and labels at {PROCESSED_DATA_DIRNAME}")
save_images_and_labels(
crops_train, labels_train, "train", PROCESSED_DATA_DIRNAME
)
save_images_and_labels(crops_test, labels_test, "test", PROCESSED_DATA_DIRNAME)
def setup(self, stage: str = None) -> None:
"""Loading synthetic dataset."""
log.info(f"IAM Synthetic dataset steup for stage {stage}...")
if stage == "fit" or stage is None:
line_crops, line_labels = load_line_crops_and_labels(
"train", PROCESSED_DATA_DIRNAME
)
data, paragraphs_labels = generate_synthetic_paragraphs(
line_crops=line_crops, line_labels=line_labels
)
targets = convert_strings_to_labels(
strings=paragraphs_labels,
mapping=self.mapping.inverse_mapping,
length=self.output_dims[0],
)
self.data_train = BaseDataset(
data,
targets,
transform=self.transform,
target_transform=self.target_transform,
)
def __repr__(self) -> str:
"""Return information about the dataset."""
basic = (
"IAM Synthetic Paragraphs Dataset\n" # pylint: disable=no-member
f"Num classes: {len(self.mapping)}\n"
f"Input dims : {self.dims}\n"
f"Output dims: {self.output_dims}\n"
)
if self.data_train is None:
return basic
x, y = next(iter(self.train_dataloader()))
x = x[0] if isinstance(x, list) else x
data = (
f"Train/val/test sizes: {len(self.data_train)}, 0, 0\n"
f"Train Batch x stats: {(x.shape, x.dtype, x.min(), x.mean(), x.std(), x.max())}\n"
f"Train Batch y stats: {(y.shape, y.dtype, y.min(), y.max())}\n"
)
return basic + data
def generate_synthetic_paragraphs(
line_crops: List[Image.Image], line_labels: List[str], max_batch_size: int = 9
) -> Tuple[List[Image.Image], List[str]]:
"""Generate synthetic paragraphs from randomly joining different subsets."""
paragraphs_properties = get_dataset_properties()
indices = list(range(len(line_labels)))
if max_batch_size >= paragraphs_properties["num_lines"]["max"]:
raise ValueError("max_batch_size greater or equalt to max num lines.")
batched_indices_list = [[index] for index in indices]
batched_indices_list.extend(
generate_random_batches(
values=indices, min_batch_size=2, max_batch_size=max_batch_size // 2
)
)
batched_indices_list.extend(
generate_random_batches(
values=indices, min_batch_size=2, max_batch_size=max_batch_size
)
)
batched_indices_list.extend(
generate_random_batches(
values=indices,
min_batch_size=max_batch_size // 2 + 1,
max_batch_size=max_batch_size,
)
)
paragraphs_crops, paragraphs_labels = [], []
for paragraph_indices in batched_indices_list:
paragraph_label = NEW_LINE_TOKEN.join(
[line_labels[i] for i in paragraph_indices]
)
if len(paragraph_label) > paragraphs_properties["label_length"]["max"]:
log.info(
(
"Label longer than longest label in original IAM paragraph dataset"
" - hence dropping."
)
)
continue
paragraph_crop = join_line_crops_to_form_paragraph(
[line_crops[i] for i in paragraph_indices]
)
max_paragraph_shape = paragraphs_properties["crop_shape"]["max"]
if (
paragraph_crop.height > max_paragraph_shape[0]
or paragraph_crop.width > max_paragraph_shape[1]
):
log.info(
(
"Crop larger than largest crop in original IAM paragraphs dataset"
" - hence dropping"
)
)
continue
paragraphs_crops.append(paragraph_crop)
paragraphs_labels.append(paragraph_label)
if len(paragraphs_crops) != len(paragraphs_labels):
raise ValueError("Number of crops does not match number of labels.")
return paragraphs_crops, paragraphs_labels
def join_line_crops_to_form_paragraph(line_crops: Sequence[Image.Image]) -> Image.Image:
"""Horizontally stack line crops and return a single image forming a paragraph."""
crop_shapes = np.array([line.size[::-1] for line in line_crops])
paragraph_height = crop_shapes[:, 0].sum()
paragraph_width = crop_shapes[:, 1].max()
paragraph_image = Image.new(
mode="L", size=(paragraph_width, paragraph_height), color=0
)
current_height = 0
for line_crop in line_crops:
paragraph_image.paste(line_crop, box=(0, current_height))
current_height += line_crop.height
return paragraph_image
def generate_random_batches(
values: List[Any], min_batch_size: int, max_batch_size: int
) -> List[List[Any]]:
"""Generate random batches of elements in values without replacement."""
shuffled_values = values.copy()
random.shuffle(shuffled_values)
start_index = 0
grouped_values_list = []
while start_index < len(shuffled_values):
num_values = random.randint(min_batch_size, max_batch_size)
grouped_values_list.append(
shuffled_values[start_index : start_index + num_values]
)
start_index += num_values
if sum([len(grp) for grp in grouped_values_list]) != len(values):
raise ValueError("Length of groups does not match length of values.")
return grouped_values_list
def create_synthetic_iam_paragraphs() -> None:
"""Creates and prints IAM Synthetic Paragraphs dataset."""
transform = load_transform_from_file("transform/paragraphs.yaml")
test_transform = load_transform_from_file("test_transform/paragraphs.yaml")
load_and_print_info(
IAMSyntheticParagraphs(transform=transform, test_transform=test_transform)
)
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