luxonis_ml.data.parsers.CreateMLParser

class documentation

class CreateMLParser(BaseParser):

Constructor: CreateMLParser(dataset, dataset_type, task_name, full_warnings)

Parse a directory with CreateML annotations into LDF.

Expected format:

dataset_dir/
├── train/
│   ├── img1.jpg
│   ├── img2.jpg
│   └── ...
│   └── _annotations.createml.json
├── valid/
└── test/

This is one of the formats that Roboflow can generate.

Static Method	`validate_split`	Validate whether a split directory has the expected format.
Method	`from_dir`	Parse all data in a source dataset directory.
Method	`from_split`	Parse CreateML annotations into LDF records.

Inherited from BaseParser:

Class Method	`discover_dir_splits`	Return present and valid split directories keyed by their canonical split names.
Class Method	`validate`	Validate whether the dataset directory has the expected format.
Method	`__init__`	Create a parser for a target dataset.
Method	`get_parser_issue_messages`	Return parser issue messages collected during the last parse.
Method	`parse_dir`	Parse an entire dataset directory into the target dataset.
Method	`parse_split`	Parse one split subdirectory into the target dataset.
Method	`reset_parser_issue_messages`	Clear collected parser issue messages.
Static Method	`_apply_counts_to_pool`	Distribute images across splits based on requested counts.
Static Method	`_apply_counts_to_splits`	Apply count-based split requests to existing splits.
Static Method	`_canonicalize_split_name`	All current parsers use `train` and `test` split names whereas validation splits can vary in name between `val` `valid` and `validation`.
Static Method	`_compare_stem_files`	Compare sets of files by stem.
Static Method	`_get_added_images`	Return unique images yielded by a dataset generator.
Static Method	`_list_images`	List OpenCV-supported images in a directory.
Static Method	`_sample_from_splits`	Sample from each original split independently.
Method	`_get_parser_issue_messages`	Return parser issue messages collected during the last parse.
Method	`_log_skipped_annotation_summary`	Undocumented
Method	`_parse_available_splits`	Undocumented
Method	`_parse_split`	Parse data in one split subdirectory.
Method	`_remove_unsplit_records`	Remove records that are not assigned to any split.
Method	`_reset_parser_issue_messages`	Clear collected parser issue messages and warning counters.
Method	`_warn_skipped_annotation`	Undocumented
Method	`_wrap_generator`	Add configured task names to generated records.
Constant	`_CANONICAL_SPLIT_NAMES`	Undocumented
Constant	`_SKIPPED_WARNING_LIMIT`	Undocumented
Constant	`_SPLIT_NAMES`	Undocumented
Instance Variable	`_dataset`	Undocumented
Instance Variable	`_dataset_type`	Undocumented
Instance Variable	`_full_warnings`	Undocumented
Instance Variable	`_logged_skipped_annotation_warnings`	Undocumented
Instance Variable	`_parser_issue_messages`	Undocumented
Instance Variable	`_seen_parser_issue_messages`	Undocumented
Instance Variable	`_skipped_annotation_counts_by_reason`	Undocumented
Instance Variable	`_suppressed_skipped_annotation_warnings`	Undocumented
Instance Variable	`_task_name`	Undocumented

@staticmethod

def validate_split(split_path: Path) -> dict[str, Any] | None: ¶

overrides luxonis_ml.data.parsers.BaseParser.validate_split

Validate whether a split directory has the expected format.

Parameters
split_path:`Path`	Path to a split directory.

Returns
`dict[str, Any] \| None`	Keyword arguments for `from_split`, or `None` if the split is not in the expected format.

def from_dir(self, dataset_dir: Path) -> tuple[list[Path], list[Path], list[Path]]: ¶

overrides luxonis_ml.data.parsers.BaseParser.from_dir

Parse all data in a source dataset directory.

Parameters
dataset_dir:`Path`	Source dataset directory.
**kwargs	Additional parser-specific arguments.

Returns
`tuple[list[Path], list[Path], list[Path]]`	Added images for the train, validation, and test splits.

def from_split(self, image_dir: Path, annotation_path: Path) -> ParserOutput: ¶

overrides luxonis_ml.data.parsers.BaseParser.from_split

Parse CreateML annotations into LDF records.

Annotations include classification and object detection.

Parameters
image_dir:`Path`	Directory with images.
annotation_path:`Path`	Annotation JSON file.

Returns
`ParserOutput`	Parser output containing annotation records, skeleton metadata, and added images.