#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import argparse
import logging
import os
import random
from enum import Enum
from io import BytesIO
from pathlib import Path
from typing import Tuple
import cv2
import numpy as np
import requests
import tqdm
from apistar.exceptions import ErrorResponse
from arkindex import ArkindexClient, options_from_env
from PIL import Image
Box = Tuple[int, int, int, int]
logging.basicConfig(
level=logging.INFO, format="%(asctime)s %(levelname)s/%(name)s: %(message)s"
)
logger = logging.getLogger(os.path.basename(__file__))
api_client = ArkindexClient(**options_from_env())
SEED = 42
random.seed(SEED)
MANUAL = "manual"
TEXT_LINE = "text_line"
def download_image(url):
"""
Download an image and open it with Pillow
"""
assert url.startswith("http"), "Image URL must be HTTP(S)"
# Download the image
# Cannot use stream=True as urllib's responses do not support the seek(int) method,
# which is explicitly required by Image.open on file-like objects
resp = requests.get(url)
resp.raise_for_status()
# Preprocess the image and prepare it for classification
image = Image.open(BytesIO(resp.content))
logger.debug(
"Downloaded image {} - size={}x{}".format(url, image.size[0], image.size[1])
)
return image
def write_file(file_name, content):
with open(file_name, "w") as f:
f.write(content)
class Extraction(Enum):
boundingRect: int = 0
polygon: int = 1
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,
):
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
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 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":
f = open(f"{self.out_line_dir}/manifest.txt", "a")
# append to file, not re-write it
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()
if self.module == "kraken":
cv2.imwrite(f"{self.out_line_dir}/{page_id}_{i}.png", cropped)
f.write(f"{page_id}_{i}.png\n")
else:
cv2.imwrite(f"{self.out_line_img_dir}/{page_id}_{i}.jpg", cropped)
elif self.extraction_mode == Extraction.polygon:
for i, (rect, polygon, text) in enumerate(sorted_lines):
polygon_img = self.extract_polygon_image(
img, polygon=polygon, rect=rect
)
if self.module == "kraken":
cv2.imwrite(f"{self.out_line_dir}/{page_id}_{i}.png", polygon_img)
f.write(f"{page_id}_{i}.png\n")
else:
cv2.imwrite(
f"{self.out_line_img_dir}/{page_id}_{i}.jpg", polygon_img
)
else:
raise ValueError("Unsupported extraction mode")
if self.module == "kraken":
f.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)
@staticmethod
def extract_polygon_image(
img: "np.ndarray", polygon: "np.ndarray", rect: Box
) -> "np.ndarray":
pts = polygon.copy()
[x, y, w, h] = rect
cropped = img[y : y + h, x : x + w].copy()
pts = pts - pts.min(axis=0)
mask = np.zeros(cropped.shape[:2], np.uint8)
cv2.drawContours(mask, [pts], -1, (255, 255, 255), -1, cv2.LINE_AA)
dst = cv2.bitwise_and(cropped, cropped, mask=mask)
bg = np.ones_like(cropped, np.uint8) * 255
cv2.bitwise_not(bg, bg, mask=mask)
dst2 = bg + dst
return dst2
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(
"--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,
)
# 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()