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src/bbox_expander.py

@@ -61,7 +61,7 @@ def expand_bbox(points, ratio, width, height):
 
 
 def do_expansion(anno_path, ratio, suffix):
-    ufo = json.load(open(anno_path, "r"))
+    ufo = json.load(open(anno_path))
 
     for image_name in tqdm(ufo["images"]):
         image = ufo["images"][image_name]

src/bbox_shaker.py

@@ -80,7 +80,7 @@ def shake_bbox(points, width, height, ratio=0.125):
 
 
 def do_shaking(anno_path, ratio, suffix):
-    ufo = json.load(open(anno_path, "r"))
+    ufo = json.load(open(anno_path))
 
     for image_name in tqdm(ufo["images"]):
         image = ufo["images"][image_name]

src/bbox_transformer.py

@@ -44,10 +44,10 @@ def rotate_bbox(bbox, theta, anchor=None):
 def calc_error_from_rect(bbox):
     """Calculate the difference between the vertices orientation and default orientation
 
-    Default orientation is 
-        x1y1 : left-top, 
-        x2y2 : right-top, 
-        x3y3 : right-bot, 
+    Default orientation is
+        x1y1 : left-top,
+        x2y2 : right-top,
+        x3y3 : right-bot,
         x4y4 : left-bot.
     """
     x_min, y_min = np.min(bbox, axis=0)

src/convert_mlt.py

@@ -2,29 +2,24 @@
 import os
 import os.path as osp
 from glob import glob
-from PIL import Image
 
 import numpy as np
+from PIL import Image
+from torch.utils.data import ConcatDataset, DataLoader, Dataset
 from tqdm import tqdm
 
-from torch.utils.data import DataLoader, ConcatDataset, Dataset
-
-
-SRC_DATASET_DIR = '/data/datasets/ICDAR17_MLT'  # FIXME
-DST_DATASET_DIR = '/data/datasets/ICDAR17_Korean'  # FIXME
+SRC_DATASET_DIR = "/data/datasets/ICDAR17_MLT"  # FIXME
+DST_DATASET_DIR = "/data/datasets/ICDAR17_Korean"  # FIXME
 
 NUM_WORKERS = 32  # FIXME
 
-IMAGE_EXTENSIONS = {'.gif', '.jpg', '.png'}
+IMAGE_EXTENSIONS = {".gif", ".jpg", ".png"}
+
+LANGUAGE_MAP = {"Korean": "ko", "Latin": "en", "Symbols": None}
 
-LANGUAGE_MAP = {
-    'Korean': 'ko',
-    'Latin': 'en',
-    'Symbols': None
-}
 
 def get_language_token(x):
-    return LANGUAGE_MAP.get(x, 'others')
+    return LANGUAGE_MAP.get(x, "others")
 
 
 def maybe_mkdir(x):
@@ -34,25 +29,31 @@ def maybe_mkdir(x):
 
 class MLT17Dataset(Dataset):
     def __init__(self, image_dir, label_dir, copy_images_to=None):
-        image_paths = {x for x in glob(osp.join(image_dir, '*')) if osp.splitext(x)[1] in
-                       IMAGE_EXTENSIONS}
-        label_paths = set(glob(osp.join(label_dir, '*.txt')))
+        image_paths = {
+            x
+            for x in glob(osp.join(image_dir, "*"))
+            if osp.splitext(x)[1] in IMAGE_EXTENSIONS
+        }
+        label_paths = set(glob(osp.join(label_dir, "*.txt")))
         assert len(image_paths) == len(label_paths)
 
         sample_ids, samples_info = list(), dict()
         for image_path in image_paths:
             sample_id = osp.splitext(osp.basename(image_path))[0]
 
-            label_path = osp.join(label_dir, 'gt_{}.txt'.format(sample_id))
+            label_path = osp.join(label_dir, f"gt_{sample_id}.txt")
             assert label_path in label_paths
 
             words_info, extra_info = self.parse_label_file(label_path)
-            if 'ko' not in extra_info['languages'] or extra_info['languages'].difference({'ko', 'en'}):
+            if "ko" not in extra_info["languages"] or extra_info[
+                "languages"
+            ].difference({"ko", "en"}):
                 continue
 
             sample_ids.append(sample_id)
-            samples_info[sample_id] = dict(image_path=image_path, label_path=label_path,
-                                           words_info=words_info)
+            samples_info[sample_id] = dict(
+                image_path=image_path, label_path=label_path, words_info=words_info
+            )
 
         self.sample_ids, self.samples_info = sample_ids, samples_info
 
@@ -64,18 +65,26 @@ def __len__(self):
     def __getitem__(self, idx):
         sample_info = self.samples_info[self.sample_ids[idx]]
 
-        image_fname = osp.basename(sample_info['image_path'])
-        image = Image.open(sample_info['image_path'])
+        image_fname = osp.basename(sample_info["image_path"])
+        image = Image.open(sample_info["image_path"])
         img_w, img_h = image.size
 
         if self.copy_images_to:
             maybe_mkdir(self.copy_images_to)
-            image.save(osp.join(self.copy_images_to, osp.basename(sample_info['image_path'])))
+            image.save(
+                osp.join(self.copy_images_to, osp.basename(sample_info["image_path"]))
+            )
 
-        license_tag = dict(usability=True, public=True, commercial=True, type='CC-BY-SA',
-                           holder=None)
-        sample_info_ufo = dict(img_h=img_h, img_w=img_w, words=sample_info['words_info'], tags=None,
-                               license_tag=license_tag)
+        license_tag = dict(
+            usability=True, public=True, commercial=True, type="CC-BY-SA", holder=None
+        )
+        sample_info_ufo = dict(
+            img_h=img_h,
+            img_w=img_w,
+            words=sample_info["words_info"],
+            tags=None,
+            license_tag=license_tag,
+        )
 
         return image_fname, sample_info_ufo
 
@@ -86,52 +95,62 @@ def rearrange_points(points):
                 points = np.roll(points, -start_idx, axis=0).tolist()
             return points
 
-        with open(label_path, encoding='utf-8') as f:
+        with open(label_path, encoding="utf-8") as f:
             lines = f.readlines()
 
         words_info, languages = dict(), set()
         for word_idx, line in enumerate(lines):
-            items = line.strip().split(',', 9)
+            items = line.strip().split(",", 9)
             language, transcription = items[8], items[9]
             points = np.array(items[:8], dtype=np.float32).reshape(4, 2).tolist()
             points = rearrange_points(points)
 
-            illegibility = transcription == '###'
-            orientation = 'Horizontal'
+            illegibility = transcription == "###"
+            orientation = "Horizontal"
             language = get_language_token(language)
             words_info[word_idx] = dict(
-                points=points, transcription=transcription, language=[language],
-                illegibility=illegibility, orientation=orientation, word_tags=None
+                points=points,
+                transcription=transcription,
+                language=[language],
+                illegibility=illegibility,
+                orientation=orientation,
+                word_tags=None,
             )
             languages.add(language)
 
         return words_info, dict(languages=languages)
 
 
 def main():
-    dst_image_dir = osp.join(DST_DATASET_DIR, 'images')
+    dst_image_dir = osp.join(DST_DATASET_DIR, "images")
     # dst_image_dir = None
 
-    mlt_train = MLT17Dataset(osp.join(SRC_DATASET_DIR, 'raw/ch8_training_images'),
-                             osp.join(SRC_DATASET_DIR, 'raw/ch8_training_gt'),
-                             copy_images_to=dst_image_dir)
-    mlt_valid = MLT17Dataset(osp.join(SRC_DATASET_DIR, 'raw/ch8_validation_images'),
-                             osp.join(SRC_DATASET_DIR, 'raw/ch8_validation_gt'),
-                             copy_images_to=dst_image_dir)
+    mlt_train = MLT17Dataset(
+        osp.join(SRC_DATASET_DIR, "raw/ch8_training_images"),
+        osp.join(SRC_DATASET_DIR, "raw/ch8_training_gt"),
+        copy_images_to=dst_image_dir,
+    )
+    mlt_valid = MLT17Dataset(
+        osp.join(SRC_DATASET_DIR, "raw/ch8_validation_images"),
+        osp.join(SRC_DATASET_DIR, "raw/ch8_validation_gt"),
+        copy_images_to=dst_image_dir,
+    )
     mlt_merged = ConcatDataset([mlt_train, mlt_valid])
 
     anno = dict(images=dict())
     with tqdm(total=len(mlt_merged)) as pbar:
-        for batch in DataLoader(mlt_merged, num_workers=NUM_WORKERS, collate_fn=lambda x: x):
+        for batch in DataLoader(
+            mlt_merged, num_workers=NUM_WORKERS, collate_fn=lambda x: x
+        ):
             image_fname, sample_info = batch[0]
-            anno['images'][image_fname] = sample_info
+            anno["images"][image_fname] = sample_info
             pbar.update(1)
 
-    ufo_dir = osp.join(DST_DATASET_DIR, 'ufo')
+    ufo_dir = osp.join(DST_DATASET_DIR, "ufo")
     maybe_mkdir(ufo_dir)
-    with open(osp.join(ufo_dir, 'train.json'), 'w') as f:
+    with open(osp.join(ufo_dir, "train.json"), "w") as f:
         json.dump(anno, f, indent=4)
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     main()