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Commit 4a76d32d authored by Martin's avatar Martin
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refactor, use class

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kaldi_data_generator.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import glob
import os
from pathlib import Path
import requests
import random
from io import BytesIO
from PIL import Image
from pathlib import Path
import cv2
import numpy as np
import random
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
......@@ -33,122 +30,141 @@ def download_image(url):
# 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]))
image.size[0],
image.size[1]))
return image
def write_file(file_name, content):
with open(file_name, 'w') as f:
f.write(content)
def get_image(image_url, grayscale, out_dir):
out_full_img_dir = os.path.join(out_dir, '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 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(page_id, grayscale=True, out_dir='/tmp'):
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 = res['zone']['polygon']
line_polygons.append(polygon)
[x, y, w, h] = cv2.boundingRect(np.asarray(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
full_image_url = res['zone']['image']['s3_url']
img = get_image(full_image_url, grayscale=grayscale, out_dir=out_dir)
out_line_img_dir = os.path.join(out_dir, 'Lines', page_id)
os.makedirs(out_line_img_dir, exist_ok=True)
for i, [x, y, w, h] in enumerate(line_bounding_rects):
croped = img[y:y + h, x:x + w].copy()
# cv2.imwrite(f'{out_line_img_dir}/{i}.jpg', croped)
cv2.imwrite(f'{out_line_img_dir}_{i}.jpg', croped)
out_line_text_dir = os.path.join(out_dir, 'Transcriptions', page_id)
os.makedirs(out_line_text_dir, exist_ok=True)
for i, text in enumerate(line_transcriptions):
write_file(f"{out_line_text_dir}_{i}.txt", text)
# write_file(f"{out_line_text_dir}/{i}.txt", text)
split_train_ratio = 0.8
split_test_ratio = 0.1
split_val_ratio = 1 - split_train_ratio - split_test_ratio
def page_level_split(line_ids):
# page_ids = list({'_'.join(line_id.split('_')[:-1]) for line_id in line_ids})
page_ids = list({line_id for line_id in line_ids})
random.shuffle(page_ids)
page_count = len(page_ids)
train_page_ids = page_ids[:round(page_count * split_train_ratio)]
page_ids = page_ids[round(page_count * split_train_ratio):]
test_page_ids = page_ids[:round(page_count * split_test_ratio)]
page_ids = page_ids[round(page_count * split_test_ratio):]
val_page_ids = page_ids
page_dict = {page_id: TRAIN for page_id in train_page_ids}
page_dict.update({page_id: TEST for page_id in test_page_ids})
page_dict.update({page_id: VAL for page_id in val_page_ids})
return (train_page_ids, val_page_ids, test_page_ids), page_dict
TRAIN,TEST,VAL = 0,1,2
out_file_dict = {0 : 'Train', 1 : 'Test', 2 : 'Validation'}
def create_partitions(line_ids, out_dir):
(train_page_ids, val_page_ids, test_page_ids), page_dict = page_level_split(line_ids)
datasets = [[] for i in range(3)]
for line_id in line_ids:
page_id = line_id
split_id = page_dict[page_id]
datasets[split_id].append(line_id)
partitions_dir = os.path.join(out_dir, 'Partitions')
os.makedirs(partitions_dir, exist_ok=True)
for i, dataset in enumerate(datasets):
file_name = f"{partitions_dir}/{out_file_dict[i]}Lines.lst"
with open(file_name, 'w') as f:
f.write('\n'.join(dataset) + '\n')
out_dir_base = '/tmp/foo2'
#page_id = 'bf23cc96-f6b2-4182-923e-6c163db37eba'
page_ids = ['bf23cc96-f6b2-4182-923e-6c163db37eba',
'7c51e648-370e-43b7-9340-3b1a17c13828',
'56521074-59f4-4173-bfc1-4b1384ff8139',]
for page_id in page_ids:
extract_lines(page_id, out_dir=out_dir_base)
lines_path = Path(f'{out_dir_base}/Lines')
line_ids = [str(file.relative_to(lines_path).with_suffix('')) for file in lines_path.glob('**/*.jpg')]
create_partitions(line_ids, out_dir=out_dir_base)
TRAIN, TEST, VAL = 0, 1, 2
out_file_dict = {0: 'Train', 1: 'Test', 2: 'Validation'}
class KaldiDataGenerator:
def __init__(self, dataset_name='foo', out_dir_base='/tmp/kaldi_data', split_train_ratio=0.8, split_test_ratio=0.1,
grayscale=True):
self.out_dir_base = out_dir_base
self.dataset_name = dataset_name
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.grayscale = grayscale
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 = res['zone']['polygon']
line_polygons.append(polygon)
[x, y, w, h] = cv2.boundingRect(np.asarray(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("C", count)
full_image_url = res['zone']['image']['s3_url']
img = self.get_image(full_image_url, page_id=page_id)
out_line_img_dir = os.path.join(self.out_dir_base, 'Lines', self.dataset_name, page_id)
os.makedirs(out_line_img_dir, exist_ok=True)
for i, [x, y, w, h] in enumerate(line_bounding_rects):
cropped = img[y:y + h, x:x + w].copy()
cv2.imwrite(f'{out_line_img_dir}_{i}.jpg', cropped)
out_line_text_dir = os.path.join(self.out_dir_base, 'Transcriptions', self.dataset_name, page_id)
os.makedirs(out_line_text_dir, exist_ok=True)
for i, text in enumerate(line_transcriptions):
write_file(f"{out_line_text_dir}_{i}.txt", text)
def page_level_split(self, line_ids):
page_ids = list({'_'.join(line_id.split('_')[:-1]) for line_id in line_ids})
# page_ids = list({line_id 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: TRAIN for page_id in train_page_ids}
page_dict.update({page_id: TEST for page_id in test_page_ids})
page_dict.update({page_id: VAL for page_id in val_page_ids})
return page_dict
def create_partitions(self):
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):
file_name = f"{partitions_dir}/{out_file_dict[i]}Lines.lst"
write_file(file_name, '\n'.join(dataset) + '\n')
def run_pages(self, page_ids):
for page_id in page_ids:
print("P", page_id)
self.extract_lines(page_id)
def run_volumes(self, volume_ids):
for volume_id in volume_ids:
print("V", volume_id)
page_ids = [page['id'] for page in api_client.paginate('ListElementChildren', id=volume_id)]
self.run_pages(page_ids)
example_page_ids = [
'bf23cc96-f6b2-4182-923e-6c163db37eba',
'7c51e648-370e-43b7-9340-3b1a17c13828',
'56521074-59f4-4173-bfc1-4b1384ff8139',
]
example_volume_ids = [
'8f4005e9-1921-47b0-be7b-e27c7fd29486',
]
kaldi_data_generator = KaldiDataGenerator()
# kaldi_data_generator.run_page(example_page_ids)
kaldi_data_generator.run_volumes(example_volume_ids)
kaldi_data_generator.create_partitions()
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