training and synthetic data generation code

manga_ocr_dev/data/process_manga109s.py

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+import xml.etree.ElementTree as ET
+from pathlib import Path
+
+import cv2
+import pandas as pd
+from tqdm import tqdm
+
+from manga_ocr_dev.env import MANGA109_ROOT
+
+
+def get_books():
+    root = MANGA109_ROOT / 'Manga109s_released_2021_02_28'
+    books = (root / 'books.txt').read_text().splitlines()
+    books = pd.DataFrame({
+        'book': books,
+        'annotations': [str(root / 'annotations' / f'{book}.xml') for book in books],
+        'images': [str(root / 'images' / book) for book in books],
+    })
+
+    return books
+
+
+def export_frames():
+    books = get_books()
+
+    data = []
+    for book in tqdm(books.itertuples(), total=len(books)):
+        tree = ET.parse(book.annotations)
+        root = tree.getroot()
+        for page in root.findall('./pages/page'):
+            for frame in page.findall('./frame'):
+                row = {}
+                row['book'] = book.book
+                row['page_index'] = int(page.attrib['index'])
+                row['page_path'] = str(Path(book.images) / f'{row["page_index"]:03d}.jpg')
+                row['page_width'] = int(page.attrib['width'])
+                row['page_height'] = int(page.attrib['height'])
+                row['id'] = frame.attrib['id']
+                row['xmin'] = int(frame.attrib['xmin'])
+                row['ymin'] = int(frame.attrib['ymin'])
+                row['xmax'] = int(frame.attrib['xmax'])
+                row['ymax'] = int(frame.attrib['ymax'])
+                data.append(row)
+    data = pd.DataFrame(data)
+
+    data.page_path = data.page_path.apply(lambda x: '/'.join(Path(x).parts[-4:]))
+    data.to_csv(MANGA109_ROOT / 'frames.csv', index=False)
+
+
+def export_crops():
+    crops_root = MANGA109_ROOT / 'crops'
+    crops_root.mkdir(parents=True, exist_ok=True)
+    margin = 10
+
+    books = get_books()
+
+    data = []
+    for book in tqdm(books.itertuples(), total=len(books)):
+        tree = ET.parse(book.annotations)
+        root = tree.getroot()
+        for page in root.findall('./pages/page'):
+            for text in page.findall('./text'):
+                row = {}
+                row['book'] = book.book
+                row['page_index'] = int(page.attrib['index'])
+                row['page_path'] = str(Path(book.images) / f'{row["page_index"]:03d}.jpg')
+                row['page_width'] = int(page.attrib['width'])
+                row['page_height'] = int(page.attrib['height'])
+                row['id'] = text.attrib['id']
+                row['text'] = text.text
+                row['xmin'] = int(text.attrib['xmin'])
+                row['ymin'] = int(text.attrib['ymin'])
+                row['xmax'] = int(text.attrib['xmax'])
+                row['ymax'] = int(text.attrib['ymax'])
+                data.append(row)
+    data = pd.DataFrame(data)
+
+    n_test = int(0.1 * len(data))
+    data['split'] = 'train'
+    data.loc[data.sample(len(data)).iloc[:n_test].index, 'split'] = 'test'
+
+    data['crop_path'] = str(crops_root) + '\\' + data.id + '.png'
+
+    data.page_path = data.page_path.apply(lambda x: '/'.join(Path(x).parts[-4:]))
+    data.crop_path = data.crop_path.apply(lambda x: '/'.join(Path(x).parts[-2:]))
+    data.to_csv(MANGA109_ROOT / 'data.csv', index=False)
+
+    for page_path, boxes in tqdm(data.groupby('page_path'), total=data.page_path.nunique()):
+        img = cv2.imread(str(MANGA109_ROOT / page_path))
+
+        for box in boxes.itertuples():
+            xmin = max(box.xmin - margin, 0)
+            xmax = min(box.xmax + margin, img.shape[1])
+            ymin = max(box.ymin - margin, 0)
+            ymax = min(box.ymax + margin, img.shape[0])
+            crop = img[ymin:ymax, xmin:xmax]
+            out_path = (crops_root / box.id).with_suffix('.png')
+            cv2.imwrite(str(out_path), crop)
+
+
+if __name__ == '__main__':
+    export_frames()
+    export_crops()