#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import argparse
import os
import random
from enum import Enum
from io import BytesIO
from pathlib import Path
import cv2
import numpy as np
import requests
from PIL import Image
from apistar.exceptions import ErrorResponse
from arkindex import ArkindexClient, options_from_env
api_client = ArkindexClient(**options_from_env())
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))
print('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 KaldiDataGenerator:
def __init__(self, dataset_name='foo', out_dir_base='/tmp/kaldi_data', grayscale=True,
extraction=Extraction.boundingRect):
self.out_dir_base = out_dir_base
self.dataset_name = dataset_name
self.grayscale = grayscale
self.extraction_mode = extraction
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, page_id):
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 extract_lines(self, page_id):
count = 0
line_bounding_rects = []
line_polygons = []
line_transcriptions = []
try:
for res in api_client.paginate('ListTranscriptions', id=page_id, type='line'):
text = res['text']
if not text or not text.strip():
continue
line_transcriptions.append(text)
polygon = np.asarray(res['zone']['polygon']).clip(0)
line_polygons.append(polygon)
[x, y, w, h] = cv2.boundingRect(polygon)
line_bounding_rects.append([x, y, w, h])
count += 1
except ErrorResponse as e:
print("ListTranscriptions failed", e.status_code, e.title, e.content, page_id)
raise e
print("Num of lines", count)
full_image_url = res['zone']['image']['s3_url']
img = self.get_image(full_image_url, page_id=page_id)
if self.extraction_mode == Extraction.boundingRect:
for i, [x, y, w, h] in enumerate(line_bounding_rects):
cropped = img[y:y + h, x:x + w].copy()
cv2.imwrite(f'{self.out_line_img_dir}/{page_id}_{i}.jpg', cropped)
elif self.extraction_mode == Extraction.polygon:
for i, (polygon, rect) in enumerate(zip(line_polygons, line_bounding_rects)):
polygon_img = self.extract_polygon_image(img, polygon=polygon, rect=rect)
cv2.imwrite(f'{self.out_line_img_dir}/{page_id}_{i}.jpg', polygon_img)
else:
raise ValueError("Unsupported extraction mode")
for i, text in enumerate(line_transcriptions):
write_file(f"{self.out_line_text_dir}/{page_id}_{i}.txt", text)
@staticmethod
def extract_polygon_image(img, polygon, rect):
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, page_ids):
for page_id in page_ids:
print("Page", page_id)
self.extract_lines(page_id)
def run_volumes(self, volume_ids):
for volume_id in volume_ids:
print("Vol", volume_id)
page_ids = [page['id'] for page in api_client.paginate('ListElementChildren', id=volume_id)]
self.run_pages(page_ids)
class Split(Enum):
Train: int = 0
Test: int = 1
Validation: int = 2
class KaldiPartitionSplitter:
def __init__(self, out_dir_base='/tmp/kaldi_data', split_train_ratio=0.8, split_test_ratio=0.1):
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
def page_level_split(self, line_ids):
page_ids = list({'_'.join(line_id.split('_')[:-1]) for line_id in line_ids})
random.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 create_partitions(self):
print("Creating partitions")
lines_path = Path(f'{self.out_dir_base}/Lines')
line_ids = [str(file.relative_to(lines_path).with_suffix('')) for file in lines_path.glob('**/*.jpg')]
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:
print(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 training data from annotations from Arkindex",
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('-n', '--dataset_name', type=str, required=True,
help='Name of the dataset being created for kaldi '
'(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 train (between 0 and 1 - train_ratio)')
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]}')
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('--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')
return parser
def main():
args = create_parser().parse_args()
print("ARGS", args, '\n')
kaldi_data_generator = KaldiDataGenerator(dataset_name=args.dataset_name,
out_dir_base=args.out_dir,
grayscale=args.grayscale,
extraction=args.extraction_mode)
kaldi_partitioner = KaldiPartitionSplitter(out_dir_base=args.out_dir,
split_train_ratio=args.train_ratio,
split_test_ratio=args.test_ratio)
# extract all the lines and transcriptions
if args.pages:
kaldi_data_generator.run_pages(args.pages)
if args.volumes:
kaldi_data_generator.run_volumes(args.volumes)
print()
# create partitions from all the extracted data
kaldi_partitioner.create_partitions()
print("DONE")
if __name__ == '__main__':
main()