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kaldi_data_generator.py

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    Martin Maarand authored
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    kaldi_data_generator.py 23.39 KiB
    #!/usr/bin/env python3
    # -*- coding: utf-8 -*-
    
    import argparse
    import os
    import random
    from enum import Enum
    from pathlib import Path
    
    import cv2
    import numpy as np
    import tqdm
    from apistar.exceptions import ErrorResponse
    from arkindex import ArkindexClient, options_from_env
    
    from kaldi_data_generator.image_utils import (
        determine_rotate_angle,
        download_image,
        extract_min_area_rect_image,
        extract_polygon_image,
        rotate,
        trim,
    )
    from kaldi_data_generator.utils import logger, write_file
    
    api_client = ArkindexClient(**options_from_env())
    
    SEED = 42
    random.seed(SEED)
    MANUAL = "manual"
    TEXT_LINE = "text_line"
    WHITE = 255
    
    
    class Extraction(Enum):
        boundingRect: int = 0
        polygon: int = 1
        # minimum containing rectangle with an angle (cv2.min_area_rect)
        min_area_rect: int = 2
        deskew_polygon: int = 3
        deskew_min_area_rect: int = 4
    
    
    class HTRDataGenerator:
        def __init__(
            self,
            module,
            dataset_name="foo",
            out_dir_base="/tmp/kaldi_data",
            grayscale=True,
            extraction=Extraction.boundingRect,
            accepted_slugs=None,
            accepted_classes=None,
            filter_printed=False,
            skip_vertical_lines=False,
            accepted_worker_version_ids=None,
            transcription_type=TEXT_LINE,
            max_deskew_angle=45,
        ):
    
            self.module = module
            self.out_dir_base = out_dir_base
            self.dataset_name = dataset_name
            self.grayscale = grayscale
            self.extraction_mode = extraction
            self.accepted_slugs = accepted_slugs
            self.should_filter_by_slug = bool(self.accepted_slugs)
            self.accepted_classes = accepted_classes
            self.should_filter_by_class = bool(self.accepted_classes)
            self.accepted_worker_version_ids = accepted_worker_version_ids
            self.should_filter_by_worker = bool(self.accepted_worker_version_ids)
            self.should_filter_printed = filter_printed
            self.transcription_type = transcription_type
            self.skip_vertical_lines = skip_vertical_lines
            self.skipped_pages_count = 0
            self.skipped_vertical_lines_count = 0
            self.accepted_lines_count = 0
            self.max_deskew_angle = max_deskew_angle
    
            if MANUAL in self.accepted_worker_version_ids:
                self.accepted_worker_version_ids[
                    self.accepted_worker_version_ids.index(MANUAL)
                ] = None
    
            if self.module == "kraken":
                self.out_line_dir = out_dir_base
                os.makedirs(self.out_line_dir, exist_ok=True)
            else:
                self.out_line_text_dir = os.path.join(
                    self.out_dir_base, "Transcriptions", self.dataset_name
                )
                os.makedirs(self.out_line_text_dir, exist_ok=True)
                self.out_line_img_dir = os.path.join(
                    self.out_dir_base, "Lines", self.dataset_name
                )
                os.makedirs(self.out_line_img_dir, exist_ok=True)
    
        def get_image(self, image_url: str, page_id: str) -> "np.ndarray":
            out_full_img_dir = os.path.join(self.out_dir_base, "full", page_id)
            os.makedirs(out_full_img_dir, exist_ok=True)
            out_full_img_path = os.path.join(out_full_img_dir, "full.jpg")
            if self.grayscale:
                download_image(image_url).convert("L").save(
                    out_full_img_path, format="jpeg"
                )
                img = cv2.imread(out_full_img_path, cv2.IMREAD_GRAYSCALE)
            else:
                download_image(image_url).save(out_full_img_path, format="jpeg")
                img = cv2.imread(out_full_img_path)
            return img
    
        def get_accepted_zones(self, page_id: str):
            try:
                accepted_zones = []
                for elt in api_client.paginate(
                    "ListElementChildren", id=page_id, with_best_classes=True
                ):
                    printed = True
                    for classification in elt["best_classes"]:
                        if classification["ml_class"]["name"] == "handwritten":
                            printed = False
                    for classification in elt["best_classes"]:
                        if classification["ml_class"]["name"] in self.accepted_classes:
                            if self.should_filter_printed:
                                if not printed:
                                    accepted_zones.append(elt["zone"]["id"])
                            else:
                                accepted_zones.append(elt["zone"]["id"])
                logger.info(
                    "Number of accepted zone for page {} : {}".format(
                        page_id, len(accepted_zones)
                    )
                )
                return accepted_zones
            except ErrorResponse as e:
                logger.info(
                    f"ListTranscriptions failed {e.status_code} - {e.title} - {e.content} - {page_id}"
                )
                raise e
    
        def get_transcriptions(self, page_id: str, accepted_zones):
            count = 0
            count_skipped = 0
            lines = []
            try:
                for res in api_client.paginate(
                    "ListTranscriptions", id=page_id, recursive=True
                ):
                    if (
                        self.should_filter_by_slug
                        and res["source"]["slug"] not in self.accepted_slugs
                    ):
                        continue
                    if (
                        self.should_filter_by_worker
                        and res["worker_version_id"] not in self.accepted_worker_version_ids
                    ):
                        continue
                    if (
                        self.should_filter_by_class
                        and res["element"]["zone"]["id"] not in accepted_zones
                    ):
                        continue
                    if res["element"]["type"] != self.transcription_type:
                        continue
    
                    text = res["text"]
                    if not text or not text.strip():
                        continue
    
                    if "zone" in res:
                        polygon = res["zone"]["polygon"]
                    elif "element" in res:
                        polygon = res["element"]["zone"]["polygon"]
                    else:
                        raise ValueError(f"Data problem with polygon :: {res}")
    
                    polygon = np.asarray(polygon).clip(0)
                    [x, y, w, h] = cv2.boundingRect(polygon)
                    if self.skip_vertical_lines:
                        if h > w:
                            count_skipped += 1
                            continue
                    lines.append(((x, y, w, h), polygon, text))
                    count += 1
                return (lines, count, count_skipped)
    
            except ErrorResponse as e:
                logger.info(
                    f"ListTranscriptions failed {e.status_code} - {e.title} - {e.content} - {page_id}"
                )
                raise e
    
        def _save_line_image(self, page_id, i, line_img, manifest_fp=None):
            if self.module == "kraken":
                cv2.imwrite(f"{self.out_line_dir}/{page_id}_{i}.png", line_img)
                manifest_fp.write(f"{page_id}_{i}.png\n")
            else:
                cv2.imwrite(f"{self.out_line_img_dir}/{page_id}_{i}.jpg", line_img)
    
        def extract_lines(self, page_id: str, image_data: dict):
            if self.should_filter_by_class:
                accepted_zones = self.get_accepted_zones(page_id)
            else:
                accepted_zones = []
            lines, count, count_skipped = self.get_transcriptions(page_id, accepted_zones)
    
            if count == 0:
                self.skipped_pages_count += 1
                logger.info(f"Page {page_id} skipped, because it has no lines")
                return
    
            logger.debug(f"Total num of lines {count + count_skipped}")
            logger.debug(f"Num of accepted lines {count}")
            logger.debug(f"Num of skipped lines {count_skipped}")
    
            self.skipped_vertical_lines_count += count_skipped
            self.accepted_lines_count += count
    
            full_image_url = image_data["s3_url"]
            if full_image_url is None:
                full_image_url = image_data["url"] + "/full/full/0/default.jpg"
    
            img = self.get_image(full_image_url, page_id=page_id)
    
            # sort vertically then horizontally
            sorted_lines = sorted(lines, key=lambda key: (key[0][1], key[0][0]))
    
            if self.module == "kraken":
                manifest_fp = open(f"{self.out_line_dir}/manifest.txt", "a")
                # append to file, not re-write it
            else:
                # not needed for kaldi
                manifest_fp = None
    
            if self.extraction_mode == Extraction.boundingRect:
                for i, ((x, y, w, h), polygon, text) in enumerate(sorted_lines):
                    cropped = img[y : y + h, x : x + w].copy()
                    self._save_line_image(page_id, i, cropped, manifest_fp)
    
            elif self.extraction_mode == Extraction.polygon:
                for i, (rect, polygon, text) in enumerate(sorted_lines):
                    polygon_img = extract_polygon_image(img, polygon=polygon, rect=rect)
                    self._save_line_image(page_id, i, polygon_img, manifest_fp)
    
            elif self.extraction_mode == Extraction.min_area_rect:
                for i, (rect, polygon, text) in enumerate(sorted_lines):
                    min_rect_img = extract_min_area_rect_image(
                        img, polygon=polygon, rect=rect
                    )
    
                    self._save_line_image(page_id, i, min_rect_img, manifest_fp)
    
            elif self.extraction_mode == Extraction.deskew_polygon:
                for i, (rect, polygon, text) in enumerate(sorted_lines):
                    # get angle from min area rect
                    rotate_angle = determine_rotate_angle(polygon)
    
                    if abs(rotate_angle) > self.max_deskew_angle:
                        logger.warning(
                            f"Deskew angle ({rotate_angle}) over the limit ({self.max_deskew_angle}), won't rotate"
                        )
                        rotate_angle = 0
    
                    # get polygon image
                    polygon_img = extract_polygon_image(img, polygon=polygon, rect=rect)
    
                    trimmed_img = self.rotate_and_trim(polygon_img, rotate_angle)
    
                    self._save_line_image(page_id, i, trimmed_img, manifest_fp)
    
            elif self.extraction_mode == Extraction.deskew_min_area_rect:
                for i, (rect, polygon, text) in enumerate(sorted_lines):
                    # get angle from min area rect
                    rotate_angle = determine_rotate_angle(polygon)
    
                    if abs(rotate_angle) > self.max_deskew_angle:
                        logger.warning(
                            f"Deskew angle ({rotate_angle}) over the limit ({self.max_deskew_angle}), won't rotate"
                        )
                        rotate_angle = 0
    
                    min_rect_img = extract_min_area_rect_image(
                        img, polygon=polygon, rect=rect
                    )
    
                    trimmed_img = self.rotate_and_trim(min_rect_img, rotate_angle)
    
                    self._save_line_image(page_id, i, trimmed_img, manifest_fp)
            else:
                raise ValueError(f"Unsupported extraction mode: {self.extraction_mode}")
    
            if self.module == "kraken":
                manifest_fp.close()
    
            for i, (rect, polygon, text) in enumerate(sorted_lines):
                if self.module == "kraken":
                    write_file(f"{self.out_line_dir}/{page_id}_{i}.gt.txt", text)
                else:
                    write_file(f"{self.out_line_text_dir}/{page_id}_{i}.txt", text)
    
        def rotate_and_trim(self, img, rotate_angle):
            """
            Rotate image by given an angle and trim extra whitespace left after rotating
            """
            if self.grayscale:
                background = WHITE
            else:
                background = (WHITE, WHITE, WHITE)
    
            # rotate polygon image
            deskewed_img = rotate(img, rotate_angle, background)
            # trim extra whitespace left after rotating
            trimmed_img = trim(deskewed_img, background)
            trimmed_img = np.array(trimmed_img)
    
            return trimmed_img
    
        def run_pages(self, pages: list):
            if all(isinstance(n, str) for n in pages):
                for page in pages:
                    elt = api_client.request("RetrieveElement", id=page)
                    page_id = elt["id"]
                    image_data = elt["zone"]["image"]
                    logger.debug(f"Page {page_id}")
                    self.extract_lines(page_id, image_data)
            else:
                for page in tqdm.tqdm(pages):
                    page_id = page["id"]
                    image_data = page["zone"]["image"]
                    logger.debug(f"Page {page_id}")
                    self.extract_lines(page_id, image_data)
    
        def run_volumes(self, volume_ids: list):
            for volume_id in tqdm.tqdm(volume_ids):
                logger.info(f"Volume {volume_id}")
                pages = [
                    page
                    for page in api_client.paginate(
                        "ListElementChildren", id=volume_id, recursive=True, type="page"
                    )
                ]
                self.run_pages(pages)
    
        def run_folders(self, element_ids: list, volume_type: str):
            for elem_id in tqdm.tqdm(element_ids):
                logger.info(f"Folder {elem_id}")
                vol_ids = [
                    page["id"]
                    for page in api_client.paginate(
                        "ListElementChildren", id=elem_id, recursive=True, type=volume_type
                    )
                ]
                self.run_volumes(vol_ids)
    
        def run_corpora(self, corpus_ids: list, volume_type: str):
            for corpus_id in tqdm.tqdm(corpus_ids):
                logger.info(f"Corpus {corpus_id}")
                vol_ids = [
                    vol["id"]
                    for vol in api_client.paginate(
                        "ListElements", corpus=corpus_id, type=volume_type
                    )
                ]
                self.run_volumes(vol_ids)
    
    
    class Split(Enum):
        Train: int = 0
        Test: int = 1
        Validation: int = 2
    
        @property
        def short_name(self) -> str:
            if self == self.Validation:
                return "val"
            return self.name.lower()
    
    
    class KaldiPartitionSplitter:
        def __init__(
            self,
            out_dir_base="/tmp/kaldi_data",
            split_train_ratio=0.8,
            split_test_ratio=0.1,
            use_existing_split=False,
        ):
            self.out_dir_base = out_dir_base
            self.split_train_ratio = split_train_ratio
            self.split_test_ratio = split_test_ratio
            self.split_val_ratio = 1 - self.split_train_ratio - self.split_test_ratio
            self.use_existing_split = use_existing_split
    
        def page_level_split(self, line_ids: list) -> dict:
            # need to sort again, because `set` will lose the order
            page_ids = sorted({"_".join(line_id.split("_")[:-1]) for line_id in line_ids})
            random.Random(SEED).shuffle(page_ids)
            page_count = len(page_ids)
    
            train_page_ids = page_ids[: round(page_count * self.split_train_ratio)]
            page_ids = page_ids[round(page_count * self.split_train_ratio) :]
    
            test_page_ids = page_ids[: round(page_count * self.split_test_ratio)]
            page_ids = page_ids[round(page_count * self.split_test_ratio) :]
    
            val_page_ids = page_ids
    
            page_dict = {page_id: Split.Train.value for page_id in train_page_ids}
            page_dict.update({page_id: Split.Test.value for page_id in test_page_ids})
            page_dict.update({page_id: Split.Validation.value for page_id in val_page_ids})
            return page_dict
    
        def existing_split(self, line_ids: list) -> list:
            split_dict = {split.short_name: [] for split in Split}
            for line_id in line_ids:
                split_prefix = line_id.split("/")[0].lower()
                split_dict[split_prefix].append(line_id)
            splits = [split_dict[split.short_name] for split in Split]
            return splits
    
        def create_partitions(self):
            logger.info("Creating partitions")
            lines_path = Path(f"{self.out_dir_base}/Lines")
            line_ids = [
                str(file.relative_to(lines_path).with_suffix(""))
                for file in sorted(lines_path.glob("**/*.jpg"))
            ]
    
            if self.use_existing_split:
                logger.info("Using existing split")
                datasets = self.existing_split(line_ids)
            else:
                page_dict = self.page_level_split(line_ids)
                datasets = [[] for _ in range(3)]
                for line_id in line_ids:
                    page_id = "_".join(line_id.split("_")[:-1])
                    split_id = page_dict[page_id]
                    datasets[split_id].append(line_id)
    
            partitions_dir = os.path.join(self.out_dir_base, "Partitions")
            os.makedirs(partitions_dir, exist_ok=True)
            for i, dataset in enumerate(datasets):
                if not dataset:
                    logger.info(f"Partition {Split(i).name} is empty! Skipping..")
                    continue
                file_name = f"{partitions_dir}/{Split(i).name}Lines.lst"
                write_file(file_name, "\n".join(dataset) + "\n")
    
    
    def create_parser():
        parser = argparse.ArgumentParser(
            description="Script to generate Kaldi or kraken training data from annotations from Arkindex",
            formatter_class=argparse.ArgumentDefaultsHelpFormatter,
        )
        parser.add_argument(
            "-f",
            "--format",
            type=str,
            help="is the data generated going to be used for kaldi or kraken",
        )
        parser.add_argument(
            "-n",
            "--dataset_name",
            type=str,
            help="Name of the dataset being created for kaldi or kraken "
            "(useful for distinguishing different datasets when in Lines or Transcriptions directory)",
        )
        parser.add_argument(
            "-o", "--out_dir", type=str, required=True, help="output directory"
        )
        parser.add_argument(
            "--train_ratio",
            type=float,
            default=0.8,
            help="Ratio of pages to be used in train (between 0 and 1)",
        )
        parser.add_argument(
            "--test_ratio",
            type=float,
            default=0.1,
            help="Ratio of pages to be used in test (between 0 and 1 - train_ratio)",
        )
        parser.add_argument(
            "--use_existing_split",
            action="store_true",
            default=False,
            help="Use an existing split instead of random. "
            "Expecting line_ids to be prefixed with (train, val and test)",
        )
        parser.add_argument(
            "--split_only",
            "--no_download",
            action="store_true",
            default=False,
            help="Create the split from already downloaded lines, don't download the lines",
        )
        parser.add_argument(
            "--no_split",
            action="store_true",
            default=False,
            help="No splitting of the data to be done just download the line in the right format",
        )
    
        parser.add_argument(
            "-e",
            "--extraction_mode",
            type=lambda x: Extraction[x],
            default=Extraction.boundingRect,
            help=f"Mode for extracting the line images: {[e.name for e in Extraction]}",
        )
    
        parser.add_argument(
            "--max_deskew_angle",
            type=int,
            default=45,
            help="Maximum angle by which deskewing is allowed to rotate the line image. "
            "If the angle determined by deskew tool is bigger than max "
            "then that line won't be deskewed/rotated.",
        )
    
        parser.add_argument(
            "--transcription_type",
            type=str,
            default="text_line",
            help="Which type of elements' transcriptions to use? (page, paragraph, text_line, etc)",
        )
    
        group = parser.add_mutually_exclusive_group(required=False)
        group.add_argument(
            "--grayscale", action="store_true", help="Convert images to grayscale"
        )
        group.add_argument("--color", action="store_false", help="Use color images")
        parser.set_defaults(grayscale=True)
    
        parser.add_argument(
            "--corpora",
            nargs="*",
            help="List of corpus ids to be used, separated by spaces",
        )
        parser.add_argument(
            "--folders",
            type=str,
            nargs="*",
            help="List of folder ids to be used, separated by spaces. "
            "Elements of `volume_type` will be searched recursively in these folders",
        )
        parser.add_argument(
            "--volumes",
            nargs="*",
            help="List of volume ids to be used, separated by spaces",
        )
        parser.add_argument(
            "--pages", nargs="*", help="List of page ids to be used, separated by spaces"
        )
        parser.add_argument(
            "-v",
            "--volume_type",
            type=str,
            default="volume",
            help="Volumes (1 level above page) may have a different name on corpora",
        )
        parser.add_argument(
            "--skip_vertical_lines",
            action="store_true",
            default=False,
            help="skips vertical lines when downloading",
        )
    
        parser.add_argument(
            "--accepted_slugs",
            nargs="*",
            help="List of accepted slugs for downloading transcriptions",
        )
    
        parser.add_argument(
            "--accepted_classes",
            nargs="*",
            help="List of accepted ml_class names. Filter lines by class of related elements",
        )
    
        parser.add_argument(
            "--accepted_worker_version_ids",
            nargs="*",
            default=[],
            help="List of accepted worker version ids. Filter lines by worker version ids of related elements"
            "Use `--accepted_worker_version_ids manual` to get only manual transcriptions",
        )
    
        parser.add_argument(
            "--filter_printed",
            action="store_true",
            help="Filter lines annotated as printed",
        )
        return parser
    
    
    def main():
        parser = create_parser()
        args = parser.parse_args()
    
        if not args.dataset_name and not args.split_only and not args.format == "kraken":
            parser.error("--dataset_name must be specified (unless --split-only)")
    
        logger.info(f"ARGS {args} \n")
    
        if not args.split_only:
            data_generator = HTRDataGenerator(
                module=args.format,
                dataset_name=args.dataset_name,
                out_dir_base=args.out_dir,
                grayscale=args.grayscale,
                extraction=args.extraction_mode,
                accepted_slugs=args.accepted_slugs,
                accepted_classes=args.accepted_classes,
                filter_printed=args.filter_printed,
                skip_vertical_lines=args.skip_vertical_lines,
                transcription_type=args.transcription_type,
                accepted_worker_version_ids=args.accepted_worker_version_ids,
                max_deskew_angle=args.max_deskew_angle,
            )
    
            # extract all the lines and transcriptions
            if args.pages:
                data_generator.run_pages(args.pages)
            if args.volumes:
                data_generator.run_volumes(args.volumes)
            if args.folders:
                data_generator.run_folders(args.folders, args.volume_type)
            if args.corpora:
                data_generator.run_corpora(args.corpora, args.volume_type)
            if data_generator.skipped_vertical_lines_count > 0:
                logger.info(
                    f"Number of skipped pages: {data_generator.skipped_pages_count}"
                )
                skipped_ratio = data_generator.skipped_vertical_lines_count / (
                    data_generator.skipped_vertical_lines_count
                    + data_generator.accepted_lines_count
                )
                logger.info(
                    f"Skipped {data_generator.skipped_vertical_lines_count} vertical lines ({skipped_ratio}/1.0)"
                )
        else:
            logger.info("Creating a split from already downloaded files")
        if not args.no_split:
            kaldi_partitioner = KaldiPartitionSplitter(
                out_dir_base=args.out_dir,
                split_train_ratio=args.train_ratio,
                split_test_ratio=args.test_ratio,
                use_existing_split=args.use_existing_split,
            )
    
            # create partitions from all the extracted data
            kaldi_partitioner.create_partitions()
        else:
            logger.info("No split to be done")
    
        logger.info("DONE")
    
    
    if __name__ == "__main__":
        main()