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labelme2yolo
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fix error output caused by circle shape (#40) 

* show program name and version info within argparse

* lint code

* fix error output caused by circle shape
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Wang Xin 2024-02-01 16:56:40 +08:00 committed by GitHub
parent 7946c199cf
commit efca771a3f
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4 changed files with 48 additions and 43 deletions
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6
src/labelme2yolo/__about__.py
View File

@ -1,7 +1,7 @@
# SPDX-FileCopyrightText: 2023-present Wang Xin <xinwang614@gmail.com>
#
# SPDX-License-Identifier: MIT
'''
"""
about version
'''
__version__ = '0.1.4'
"""
__version__ = "0.1.4"

4
src/labelme2yolo/__main__.py
View File

@ -1,9 +1,9 @@
# SPDX-FileCopyrightText: 2022-present Wang Xin <xinwang614@gmail.com>
#
# SPDX-License-Identifier: MIT
'''
"""
main
'''
"""
import sys
if __name__ == "__main__":

14
src/labelme2yolo/cli/__init__.py
View File

@ -5,15 +5,18 @@
cli init
"""
import argparse
from labelme2yolo.__about__ import __version__
from labelme2yolo.l2y import Labelme2YOLO
def run():
'''
"""
run cli
'''
"""
parser = argparse.ArgumentParser("labelme2yolo")
parser.add_argument(
"-v", "--version", action="version", version="%(prog)s " + __version__
)
parser.add_argument(
"--json_dir", type=str, help="Please input the path of the labelme json files."
)
@ -22,7 +25,7 @@ def run():
type=float,
nargs="?",
default=0.2,
help="Please input the validation dataset size, for example 0.1.",
help="Please input the validation dataset size, for example 0.2.",
)
parser.add_argument(
"--test_size",
@ -60,8 +63,7 @@ def run():
parser.print_help()
return 0
convertor = Labelme2YOLO(
args.json_dir, args.output_format, args.label_list)
convertor = Labelme2YOLO(args.json_dir, args.output_format, args.label_list)
if args.json_name is None:
convertor.convert(val_size=args.val_size, test_size=args.test_size)

67
src/labelme2yolo/l2y.py
View File

@ -35,7 +35,7 @@ def train_test_split(dataset_index, test_size=0.2):
"""Split dataset into train set and test set with test_size"""
test_size = min(max(0.0, test_size), 1.0)
total_size = len(dataset_index)
train_size = int(math.ceil(total_size * (1.0 - test_size)))
train_size = int(round(total_size * (1.0 - test_size)))
random.shuffle(dataset_index)
train_index = dataset_index[:train_size]
test_index = dataset_index[train_size:]
@ -169,22 +169,19 @@ class Labelme2YOLO:
self._label_list.append(label)
self._label_id_map[label] = len(self._label_id_map)
def _make_train_val_dir(self):
def _make_train_val_dir(self, create_test_dir=False):
self._label_dir_path = os.path.join(self._json_dir, "YOLODataset/labels/")
self._image_dir_path = os.path.join(self._json_dir, "YOLODataset/images/")
for yolo_path in (
os.path.join(self._label_dir_path + "train/"),
os.path.join(self._label_dir_path + "val/"),
os.path.join(self._label_dir_path + "test/"),
os.path.join(self._image_dir_path + "train/"),
os.path.join(self._image_dir_path + "val/"),
os.path.join(self._image_dir_path + "test/"),
):
if os.path.exists(yolo_path):
shutil.rmtree(yolo_path)
for yolo_path in [self._label_dir_path, self._image_dir_path]:
shutil.rmtree(yolo_path, ignore_errors=True)
os.makedirs(yolo_path)
parts = ["train", "val", "test"] if create_test_dir else ["train", "val"]
image_dirs = [os.path.join(self._image_dir_path, part) for part in parts]
label_dirs = [os.path.join(self._label_dir_path, part) for part in parts]
dirs = image_dirs + label_dirs
for yolo_path in dirs:
os.makedirs(yolo_path, exist_ok=True)
def _get_dataset_part_json_names(self, dataset_part: str):
"""Get json names in dataset_part folder"""
@ -196,7 +193,7 @@ class Labelme2YOLO:
json_names.append(sample_name + ".json")
return json_names
def _train_test_split(self, json_names, val_size, test_size):
def _train_test_split(self, json_names, val_size, test_size=None):
"""Split json names to train, val, test"""
total_size = len(json_names)
dataset_index = list(range(total_size))
@ -204,7 +201,7 @@ class Labelme2YOLO:
test_ids = []
if test_size is None:
test_size = 0.0
if test_size > 1e-8:
if test_size > 0.0:
train_ids, test_ids = train_test_split(
train_ids, test_size=test_size / (1 - val_size)
)
@ -220,19 +217,19 @@ class Labelme2YOLO:
os.path.join(self._json_dir, "**", "*.json"), recursive=True
)
json_names = sorted(json_names)
train_json_names, val_json_names, test_json_names = self._train_test_split(
json_names, val_size, test_size
)
self._make_train_val_dir()
self._make_train_val_dir(test_size > 0.0)
# convert labelme object to yolo format object, and save them to files
# also get image from labelme json file and save them under images folder
dirs = ("train/", "val/", "test/")
dirs = ("train", "val", "test")
names = (train_json_names, val_json_names, test_json_names)
for target_dir, json_names in zip(dirs, names):
target_part = target_dir.replace("/", "")
logger.info("Converting %s set ...", target_part)
logger.info("Converting %s set ...", target_dir)
for json_name in tqdm.tqdm(json_names):
self.covert_json_to_text(target_dir, json_name)
@ -291,13 +288,12 @@ class Labelme2YOLO:
(obj_center_x - shape["points"][1][0]) ** 2
+ (obj_center_y - shape["points"][1][1]) ** 2
)
obj_w = 2 * radius
obj_h = 2 * radius
yolo_center_x = round(float(obj_center_x / img_w), 6)
yolo_center_y = round(float(obj_center_y / img_h), 6)
yolo_w = round(float(obj_w / img_w), 6)
yolo_h = round(float(obj_h / img_h), 6)
num_points = 36
points = np.zeros(2 * num_points)
for i in range(num_points):
angle = 2.0 * math.pi * i / num_points
points[2 * i] = (obj_center_x + radius * math.cos(angle)) / img_w
points[2 * i + 1] = (obj_center_y + radius * math.sin(angle)) / img_h
if shape["label"]:
label = shape["label"]
@ -305,7 +301,7 @@ class Labelme2YOLO:
self._update_id_map(label)
label_id = self._label_id_map[shape["label"]]
return label_id, yolo_center_x, yolo_center_y, yolo_w, yolo_h
return label_id, points.tolist()
return None
@ -344,10 +340,17 @@ class Labelme2YOLO:
names_str += f'"{label}", '
names_str = names_str.rstrip(", ")
content = (
f"train: {train_dir}\nval: {val_dir}\ntest: {test_dir}\n"
f"nc: {len(self._label_id_map)}\n"
f"names: [{names_str}]"
)
if os.path.exists(test_dir):
content = (
f"train: {train_dir}\nval: {val_dir}\ntest: {test_dir}\n"
f"nc: {len(self._label_id_map)}\n"
f"names: [{names_str}]"
)
else:
content = (
f"train: {train_dir}\nval: {val_dir}\n"
f"nc: {len(self._label_id_map)}\n"
f"names: [{names_str}]"
)
yaml_file.write(content)