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Fixed bug with validate_split in yolov8 parser #368

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Sep 8, 2025
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Fixed bug with validate_split in yolov8 parser #368

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122 changes: 66 additions & 56 deletions luxonis_ml/data/parsers/yolov8_parser.py
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Original file line number Diff line number Diff line change
Expand Up @@ -94,61 +94,61 @@ def fit_boundingbox(self, points: np.ndarray) -> dict[str, float]:
def _detect_dataset_dir_format(
dataset_dir: Path,
) -> tuple[Format | None, list[str]]:
"""Checks if dataset directory structure is in FiftyOne or
"""Checks if dataset directory structure is in Ultralytics or
Roboflow format."""
split_folders = ["train", "valid"] # test folder is optional
non_split_folders = ["images", "labels"]
roboflow_folders = ["train", "valid"] # test folder is optional
ultralytics_folders = ["images", "labels"]

existing = [d.name for d in dataset_dir.iterdir() if d.is_dir()]

if all(folder in existing for folder in split_folders):
if all(folder in existing for folder in roboflow_folders):
return Format.ROBOFLOW, existing
if all(folder in existing for folder in non_split_folders):
if all(folder in existing for folder in ultralytics_folders):
return Format.ULTRALYTICS, existing
return None, []

@staticmethod
def validate_split(
split_path: Path, dir_format: Format
) -> dict[str, Any] | None:
if dir_format is Format.ROBOFLOW:
images_path = split_path / "images"
label_path = split_path / "labels"
elif dir_format is Format.ULTRALYTICS:
images_path = split_path.parent.parent / "images" / split_path.name
label_path = split_path.parent.parent / "labels" / split_path.name
else:
def validate_split(split_path: Path) -> dict[str, Any] | None:
if not split_path.exists():
return None

if not images_path.exists():
return None
if not label_path.exists():
candidates = [
(
split_path / "images",
split_path / "labels",
split_path.parent,
), # ROBOFLOW
(
split_path.parent.parent / "images" / split_path.name,
split_path.parent.parent / "labels" / split_path.name,
split_path.parent.parent,
), # ULTRALYTICS
]

images_path = labels_path = yaml_root = None
for img_dir, lbl_dir, yroot in candidates:
if img_dir.exists() and lbl_dir.exists():
images_path, labels_path, yaml_root = img_dir, lbl_dir, yroot
break

if images_path is None or labels_path is None or yaml_root is None:
return None

labels = label_path.glob("*.txt")
label_files = list(labels_path.glob("*.txt"))
images = BaseParser._list_images(images_path)
if not BaseParser._compare_stem_files(images, labels):
if not BaseParser._compare_stem_files(images, label_files):
return None

if dir_format is Format.ROBOFLOW:
yaml_file_location = split_path.parent
elif dir_format is Format.ULTRALYTICS:
yaml_file_location = split_path.parent.parent

yaml_file = next(
(
f
for ext in ("*.yaml", "*.yml")
for f in yaml_file_location.glob(ext)
),
(f for ext in ("*.yaml", "*.yml") for f in yaml_root.glob(ext)),
None,
)
if not yaml_file:
if yaml_file is None:
return None

return {
"image_dir": images_path,
"annotation_dir": label_path,
"annotation_dir": labels_path,
"classes_path": yaml_file,
}

Expand All @@ -173,22 +173,20 @@ def validate(cls, dataset_dir: Path) -> bool:
]
if "train" not in splits or len(splits) < 2:
return False
return all(
cls.validate_split(dataset_dir / s, dir_format) for s in splits
)
return all(cls.validate_split(dataset_dir / s) for s in splits)

if dir_format is Format.ULTRALYTICS:
non_split_folders = ["images", "labels"]
folders = [d.name for d in dataset_dir.iterdir() if d.is_dir()]
if not all(f in non_split_folders for f in folders):
if not all(f in folders for f in non_split_folders):
return False

subfolders = [
d.name
for d in (dataset_dir / "images").iterdir()
if d.is_dir()
]
return all(
cls.validate_split(dataset_dir / "images" / split, dir_format)
cls.validate_split(dataset_dir / "images" / split)
for split in subfolders
)

Expand All @@ -206,30 +204,42 @@ def from_dir(
classes_path = dataset_dir / yaml_file.name
dir_format, splits = self._detect_dataset_dir_format(dataset_dir)
added_train_imgs = self._parse_split(
image_dir=dataset_dir / "images" / "train"
if dir_format is Format.ULTRALYTICS
else dataset_dir / "train" / "images",
annotation_dir=dataset_dir / "labels" / "train"
if dir_format is Format.ULTRALYTICS
else dataset_dir / "train" / "labels",
image_dir=(
dataset_dir / "images" / "train"
if dir_format is Format.ULTRALYTICS
else dataset_dir / "train" / "images"
),
annotation_dir=(
dataset_dir / "labels" / "train"
if dir_format is Format.ULTRALYTICS
else dataset_dir / "train" / "labels"
),
classes_path=classes_path,
)
added_val_imgs = self._parse_split(
image_dir=dataset_dir / "images" / "val"
if dir_format is Format.ULTRALYTICS
else dataset_dir / "valid" / "images",
annotation_dir=dataset_dir / "labels" / "val"
if dir_format is Format.ULTRALYTICS
else dataset_dir / "valid" / "labels",
image_dir=(
dataset_dir / "images" / "val"
if dir_format is Format.ULTRALYTICS
else dataset_dir / "valid" / "images"
),
annotation_dir=(
dataset_dir / "labels" / "val"
if dir_format is Format.ULTRALYTICS
else dataset_dir / "valid" / "labels"
),
classes_path=classes_path,
)
added_test_imgs = self._parse_split(
image_dir=dataset_dir / "images" / "test"
if dir_format is Format.ULTRALYTICS
else dataset_dir / "test" / "images",
annotation_dir=dataset_dir / "labels" / "test"
if dir_format is Format.ULTRALYTICS
else dataset_dir / "test" / "labels",
image_dir=(
dataset_dir / "images" / "test"
if dir_format is Format.ULTRALYTICS
else dataset_dir / "test" / "images"
),
annotation_dir=(
dataset_dir / "labels" / "test"
if dir_format is Format.ULTRALYTICS
else dataset_dir / "test" / "labels"
),
classes_path=classes_path,
)

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