#!/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 TranscriptionData, logger, write_file # api_client = ArkindexClient(**options_from_env()) # api_client = None SEED = 42 random.seed(SEED) MANUAL = "manual" TEXT_LINE = "text_line" WHITE = 255 ROTATION_CLASSES_TO_ANGLES = { "rotate_0": 0, "rotate_left_90": 90, "rotate_180": 180, "rotate_right_90": -90, } def create_api_client(): logger.info("Creating API client") return ArkindexClient(**options_from_env()) 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, format, 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, should_rotate=False, api_client=None, ): self.format = format 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 self.should_rotate = should_rotate self.api_client = api_client if MANUAL in self.accepted_worker_version_ids: self.accepted_worker_version_ids[ self.accepted_worker_version_ids.index(MANUAL) ] = None if self.format == "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 self.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 self.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}") trans_data = TranscriptionData( element_id=res["element"]["id"], polygon=polygon, text=text, ) if self.skip_vertical_lines: rect = trans_data.rect if rect.height > rect.width: count_skipped += 1 continue lines.append(trans_data) count += 1 if self.should_rotate: classes_by_elem = self.get_children_classes(page_id) for trans in lines: rotation_classes = [ c for c in classes_by_elem[trans.element_id] if c in ROTATION_CLASSES_TO_ANGLES ] if len(rotation_classes) > 0: if len(rotation_classes) > 1: logger.warning( f"Several rotation classes = {len(rotation_classes)} - {trans.element_id}" ) trans.rotation_class = rotation_classes[0] else: logger.warning(f"No rotation classes on {trans.element_id}") 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 get_children_classes(self, page_id): return { elem["id"]: [ best_class["ml_class"]["name"] for best_class in elem["best_classes"] if best_class["state"] != "rejected" ] for elem in self.api_client.paginate( "ListElementChildren", id=page_id, recursive=True, type=TEXT_LINE, with_best_classes=True, ) } def _save_line_image( self, page_id, i, line_img, manifest_fp=None, trans: TranscriptionData = None ): if self.should_rotate: if trans.rotation_class: rotate_angle = ROTATION_CLASSES_TO_ANGLES[trans.rotation_class] line_img = self.rotate_and_trim(line_img, rotate_angle) if self.format == "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.rect.y, key.rect.x)) if self.format == "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, trans in enumerate(sorted_lines): (x, y, w, h) = trans.rect cropped = img[y : y + h, x : x + w].copy() self._save_line_image(page_id, i, cropped, manifest_fp, trans) elif self.extraction_mode == Extraction.polygon: for i, trans in enumerate(sorted_lines): polygon_img = extract_polygon_image( img, polygon=trans.polygon, rect=trans.rect ) self._save_line_image(page_id, i, polygon_img, manifest_fp, trans) elif self.extraction_mode == Extraction.min_area_rect: for i, trans in enumerate(sorted_lines): min_rect_img = extract_min_area_rect_image( img, polygon=trans.polygon, rect=trans.rect ) self._save_line_image(page_id, i, min_rect_img, manifest_fp, trans) elif self.extraction_mode == Extraction.deskew_polygon: for i, trans in enumerate(sorted_lines): # get angle from min area rect rotate_angle = determine_rotate_angle(trans.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=trans.polygon, rect=trans.rect ) trimmed_img = self.rotate_and_trim(polygon_img, rotate_angle) self._save_line_image(page_id, i, trimmed_img, manifest_fp, trans) elif self.extraction_mode == Extraction.deskew_min_area_rect: for i, trans in enumerate(sorted_lines): # get angle from min area rect rotate_angle = determine_rotate_angle(trans.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=trans.polygon, rect=trans.rect ) trimmed_img = self.rotate_and_trim(min_rect_img, rotate_angle) self._save_line_image(page_id, i, trimmed_img, manifest_fp, trans) else: raise ValueError(f"Unsupported extraction mode: {self.extraction_mode}") if self.format == "kraken": manifest_fp.close() for i, trans in enumerate(sorted_lines): if self.format == "kraken": write_file(f"{self.out_line_dir}/{page_id}_{i}.gt.txt", trans.text) else: write_file(f"{self.out_line_text_dir}/{page_id}_{i}.txt", trans.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 = self.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 self.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 self.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 self.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( "--should_rotate", action="store_true", default=False, help="Use text line rotation class to rotate lines if possible", ) 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") api_client = create_api_client() if not args.split_only: data_generator = HTRDataGenerator( format=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, should_rotate=args.should_rotate, api_client=api_client, ) # 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()