birdfsd_yolov5.preprocessing package

Submodules

birdfsd_yolov5.preprocessing.json2yolov5 module

exception birdfsd_yolov5.preprocessing.json2yolov5.FailedToParseImageURL[source]

Bases: Exception

class birdfsd_yolov5.preprocessing.json2yolov5.JSON2YOLO(projects: str, output_dir: str = 'dataset-YOLO', only_tar_file: bool = False, enable_s3: bool = True, copy_data_from: Optional[str] = None, filter_underrepresented_cls: bool = False, filter_cls_with_instances_under: Optional[int] = None, get_tasks_with_api: bool = False, force_update: bool = False)[source]

Bases: object

Converts the output of a Label-studio project to a YOLO dataset.

The output is a folder with the following structure:

dataset-YOLO ├── classes.txt ├── dataset_config.yml ├── images │ ├── train │ └── val └── labels ….├── train ….└── val

The output will also be stored in a tarball with the same name as the output folder.

The tasks that failed to export for any reason, will be logged at the end of the run.

static bbox_ls_to_yolo(x: float, y: float, width: float, height: float) → tuple[source]

From label-studio’s xywh to yolov5’s xywh.

Converts a bounding box from the format used by the labelme tool to the format used by the yolo tool.

Parameters

  • x – The x coordinate of the top left corner of the bounding box.

  • y – The y coordinate of the top left corner of the bounding box.

  • width – The width of the bounding box.

  • height – The height of the bounding box.

Returns

A tuple containing the x, y, width and height of the bounding box in the format used by the yolov5.

Return type

tuple

convert_to_yolo(task: dict) → Optional[list][source]

Convert the task to YOLO format.

Parameters

task (dict) – The task to be converted.

Returns

The labels in the task.

Return type

list

Raises
get_data(excluded_labels) → list[source]

This function is used to get data from the database.

Returns

A list of data.

Return type

list

plot_results(results: list) → None[source]

Plots the results of the classification.

Parameters

results (list) – The results of the classification.

Returns

None

run() → None[source]

Runs the preprocessing pipeline.

This method is used to run main preprocessing pipeline and convert the data to the yolov5 format.

Returns

None

Raises

BucketDoesNotExist – If the dataset S3 bucket does not exist.

split_data() → None[source]

Split the data into train and validation sets.

Returns

None

Module contents