doctr/datasets/utils.py

# Copyright (C) 2021-2024, Mindee.

# This program is licensed under the Apache License 2.0.
# See LICENSE or go to <https://opensource.org/licenses/Apache-2.0> for full license details.

import string
import unicodedata
from collections.abc import Sequence
from functools import partial
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple, TypeVar, Union
from typing import Sequence as SequenceType

import numpy as np
from PIL import Image

from doctr.io.image import get_img_shape
from doctr.utils.geometry import convert_to_relative_coords, extract_crops, extract_rcrops

from .vocabs import VOCABS

__all__ = ["translate", "encode_string", "decode_sequence", "encode_sequences", "pre_transform_multiclass"]

ImageTensor = TypeVar("ImageTensor")


def translate(
    input_string: str,
    vocab_name: str,
    unknown_char: str = "■",
) -> str:
    """Translate a string input in a given vocabulary

    Args:
    ----
        input_string: input string to translate
        vocab_name: vocabulary to use (french, latin, ...)
        unknown_char: unknown character for non-translatable characters

    Returns:
    -------
        A string translated in a given vocab
    """
    if VOCABS.get(vocab_name) is None:
        raise KeyError("output vocabulary must be in vocabs dictionnary")

    translated = ""
    for char in input_string:
        if char not in VOCABS[vocab_name]:
            # we need to translate char into a vocab char
            if char in string.whitespace:
                # remove whitespaces
                continue
            # normalize character if it is not in vocab
            char = unicodedata.normalize("NFD", char).encode("ascii", "ignore").decode("ascii")
            if char == "" or char not in VOCABS[vocab_name]:
                # if normalization fails or char still not in vocab, return unknown character)
                char = unknown_char
        translated += char
    return translated


def encode_string(
    input_string: str,
    vocab: str,
) -> List[int]:
    """Given a predefined mapping, encode the string to a sequence of numbers

    Args:
    ----
        input_string: string to encode
        vocab: vocabulary (string), the encoding is given by the indexing of the character sequence

    Returns:
    -------
        A list encoding the input_string
    """
    try:
        return list(map(vocab.index, input_string))
    except ValueError:
        raise ValueError(
            f"some characters cannot be found in 'vocab'. \
                         Please check the input string {input_string} and the vocabulary {vocab}"
        )


def decode_sequence(
    input_seq: Union[np.ndarray, SequenceType[int]],
    mapping: str,
) -> str:
    """Given a predefined mapping, decode the sequence of numbers to a string

    Args:
    ----
        input_seq: array to decode
        mapping: vocabulary (string), the encoding is given by the indexing of the character sequence

    Returns:
    -------
        A string, decoded from input_seq
    """
    if not isinstance(input_seq, (Sequence, np.ndarray)):
        raise TypeError("Invalid sequence type")
    if isinstance(input_seq, np.ndarray) and (input_seq.dtype != np.int_ or input_seq.max() >= len(mapping)):
        raise AssertionError("Input must be an array of int, with max less than mapping size")

    return "".join(map(mapping.__getitem__, input_seq))


def encode_sequences(
    sequences: List[str],
    vocab: str,
    target_size: Optional[int] = None,
    eos: int = -1,
    sos: Optional[int] = None,
    pad: Optional[int] = None,
    dynamic_seq_length: bool = False,
) -> np.ndarray:
    """Encode character sequences using a given vocab as mapping

    Args:
    ----
        sequences: the list of character sequences of size N
        vocab: the ordered vocab to use for encoding
        target_size: maximum length of the encoded data
        eos: encoding of End Of String
        sos: optional encoding of Start Of String
        pad: optional encoding for padding. In case of padding, all sequences are followed by 1 EOS then PAD
        dynamic_seq_length: if `target_size` is specified, uses it as upper bound and enables dynamic sequence size

    Returns:
    -------
        the padded encoded data as a tensor
    """
    if 0 <= eos < len(vocab):
        raise ValueError("argument 'eos' needs to be outside of vocab possible indices")

    if not isinstance(target_size, int) or dynamic_seq_length:
        # Maximum string length + EOS
        max_length = max(len(w) for w in sequences) + 1
        if isinstance(sos, int):
            max_length += 1
        if isinstance(pad, int):
            max_length += 1
        target_size = max_length if not isinstance(target_size, int) else min(max_length, target_size)

    # Pad all sequences
    if isinstance(pad, int):  # pad with padding symbol
        if 0 <= pad < len(vocab):
            raise ValueError("argument 'pad' needs to be outside of vocab possible indices")
        # In that case, add EOS at the end of the word before padding
        default_symbol = pad
    else:  # pad with eos symbol
        default_symbol = eos
    encoded_data: np.ndarray = np.full([len(sequences), target_size], default_symbol, dtype=np.int32)

    # Encode the strings
    for idx, seq in enumerate(map(partial(encode_string, vocab=vocab), sequences)):
        if isinstance(pad, int):  # add eos at the end of the sequence
            seq.append(eos)
        encoded_data[idx, : min(len(seq), target_size)] = seq[: min(len(seq), target_size)]

    if isinstance(sos, int):  # place sos symbol at the beginning of each sequence
        if 0 <= sos < len(vocab):
            raise ValueError("argument 'sos' needs to be outside of vocab possible indices")
        encoded_data = np.roll(encoded_data, 1)
        encoded_data[:, 0] = sos

    return encoded_data


def convert_target_to_relative(img: ImageTensor, target: Dict[str, Any]) -> Tuple[ImageTensor, Dict[str, Any]]:
    target["boxes"] = convert_to_relative_coords(target["boxes"], get_img_shape(img))
    return img, target


def crop_bboxes_from_image(img_path: Union[str, Path], geoms: np.ndarray) -> List[np.ndarray]:
    """Crop a set of bounding boxes from an image

    Args:
    ----
        img_path: path to the image
        geoms: a array of polygons of shape (N, 4, 2) or of straight boxes of shape (N, 4)

    Returns:
    -------
        a list of cropped images
    """
    with Image.open(img_path) as pil_img:
        img: np.ndarray = np.array(pil_img.convert("RGB"))
    # Polygon
    if geoms.ndim == 3 and geoms.shape[1:] == (4, 2):
        return extract_rcrops(img, geoms.astype(dtype=int))
    if geoms.ndim == 2 and geoms.shape[1] == 4:
        return extract_crops(img, geoms.astype(dtype=int))
    raise ValueError("Invalid geometry format")


def pre_transform_multiclass(img, target: Tuple[np.ndarray, List]) -> Tuple[np.ndarray, Dict[str, List]]:
    """Converts multiclass target to relative coordinates.

    Args:
    ----
        img: Image
        target: tuple of target polygons and their classes names

    Returns:
    -------
        Image and dictionary of boxes, with class names as keys
    """
    boxes = convert_to_relative_coords(target[0], get_img_shape(img))
    boxes_classes = target[1]
    boxes_dict: Dict = {k: [] for k in sorted(set(boxes_classes))}
    for k, poly in zip(boxes_classes, boxes):
        boxes_dict[k].append(poly)
    boxes_dict = {k: np.stack(v, axis=0) for k, v in boxes_dict.items()}
    return img, boxes_dict