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dataset

AnomalyDatasetArguments dataclass

Anomaly dataset arguments.

Parameters:

  • train_samples (int) –

    number of train samples

  • val_samples (Tuple[int, int]) –

    number of validation samples (good, bad)

  • test_samples (Tuple[int, int]) –

    number of test samples (good, bad)

ClassificationDatasetArguments dataclass

Classification dataset arguments.

Parameters:

  • samples (List[int]) –

    number of samples per class

  • classes (Optional[List[str]]) –

    class names, if set it must be the same length as samples

  • val_size (Optional[float]) –

    validation set size

  • test_size (Optional[float]) –

    test set size

ClassificationMultilabelDatasetArguments dataclass

Classification dataset arguments.

Parameters:

  • samples (List[int]) –

    number of samples per class

  • classes (Optional[List[str]]) –

    class names, if set it must be the same length as samples

  • val_size (Optional[float]) –

    validation set size

  • test_size (Optional[float]) –

    test set size

  • percentage_other_classes (Optional[float]) –

    probability of adding other classes to the labels of each sample

ClassificationPatchDatasetArguments dataclass

Classification patch dataset arguments.

Parameters:

SegmentationDatasetArguments dataclass

Segmentation dataset arguments.

Parameters:

  • train_samples (List[int]) –

    List of samples per class in train set, element at index 0 are good samples

  • val_samples (Optional[List[int]]) –

    List of samples per class in validation set, same as above.

  • test_samples (Optional[List[int]]) –

    List of samples per class in test set, same as above.

  • classes (Optional[List[str]]) –

    Optional list of class names, must be equal to len(train_samples) - 1

anomaly_dataset(tmp_path, dataset_arguments)

Fixture used to dinamically generate anomaly dataset. By default images are random grayscales with size 10x10.

Parameters:

Returns:

Source code in quadra/utils/tests/fixtures/dataset/anomaly.py 
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@pytest.fixture
def anomaly_dataset(tmp_path: Path, dataset_arguments: AnomalyDatasetArguments) -> Tuple[str, AnomalyDatasetArguments]:
    """Fixture used to dinamically generate anomaly dataset. By default images are random grayscales with size 10x10.

    Args:
        tmp_path: path to temporary directory
        dataset_arguments: dataset arguments

    Returns:
        path to anomaly dataset
    """
    yield _build_anomaly_dataset(tmp_path, dataset_arguments)
    if tmp_path.exists():
        shutil.rmtree(tmp_path)

base_anomaly_dataset(tmp_path, request)

Generate base anomaly dataset with the following parameters
  • train_samples: 10
  • val_samples: (10, 10)
  • test_samples: (10, 10).

Parameters:

  • tmp_path (Path) –

    Path to temporary directory

  • request (Any) –

    Pytest SubRequest object

Yields:

Source code in quadra/utils/tests/fixtures/dataset/anomaly.py 
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@pytest.fixture(
    params=[AnomalyDatasetArguments(**{"train_samples": 10, "val_samples": (10, 10), "test_samples": (10, 10)})]
)
def base_anomaly_dataset(tmp_path: Path, request: Any) -> Tuple[str, AnomalyDatasetArguments]:
    """Generate base anomaly dataset with the following parameters:
        - train_samples: 10
        - val_samples: (10, 10)
        - test_samples: (10, 10).

    Args:
        tmp_path: Path to temporary directory
        request: Pytest SubRequest object

    Yields:
        Path to anomaly dataset and dataset arguments
    """
    yield _build_anomaly_dataset(tmp_path, request.param)
    if tmp_path.exists():
        shutil.rmtree(tmp_path)

base_binary_segmentation_dataset(tmp_path, request)

Generate a base binary segmentation dataset with the following structure
  • 10 good and 10 bad samples in train set
  • 10 good and 10 bad samples in validation set
  • 10 good and 10 bad samples in test set
  • 2 classes: good and bad.

Parameters:

  • tmp_path (Path) –

    path to temporary directory

  • request (Any) –

    pytest request

Yields:

Source code in quadra/utils/tests/fixtures/dataset/segmentation.py 
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@pytest.fixture(
    params=[
        SegmentationDatasetArguments(
            **{"train_samples": [10, 10], "val_samples": [10, 10], "test_samples": [10, 10], "classes": ["bad"]}
        )
    ]
)
def base_binary_segmentation_dataset(
    tmp_path: Path, request: Any
) -> Tuple[str, SegmentationDatasetArguments, Dict[str, int]]:
    """Generate a base binary segmentation dataset with the following structure:
        - 10 good and 10 bad samples in train set
        - 10 good and 10 bad samples in validation set
        - 10 good and 10 bad samples in test set
        - 2 classes: good and bad.

    Args:
        tmp_path: path to temporary directory
        request: pytest request

    Yields:
        Tuple containing path to dataset, dataset arguments and class to index mapping
    """
    yield _build_segmentation_dataset(tmp_path, request.param)
    if tmp_path.exists():
        shutil.rmtree(tmp_path)

base_classification_dataset(tmp_path, request)

Generate base classification dataset with the following parameters
  • 10 samples per class
  • 2 classes (class_1 and class_2) By default generated images are grayscale and 10x10 pixels.

Parameters:

  • tmp_path (Path) –

    path to temporary directory

  • request (Any) –

    pytest request

Yields:

Source code in quadra/utils/tests/fixtures/dataset/classification.py 
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@pytest.fixture(
    params=[
        ClassificationDatasetArguments(
            **{"samples": [10, 10], "classes": ["class_1", "class_2"], "val_size": 0.1, "test_size": 0.1}
        )
    ]
)
def base_classification_dataset(tmp_path: Path, request: Any) -> Tuple[str, ClassificationDatasetArguments]:
    """Generate base classification dataset with the following parameters:
        - 10 samples per class
        - 2 classes (class_1 and class_2)
        By default generated images are grayscale and 10x10 pixels.

    Args:
        tmp_path: path to temporary directory
        request: pytest request

    Yields:
        Tuple containing path to created dataset and dataset arguments
    """
    yield _build_classification_dataset(tmp_path, request.param)
    if tmp_path.exists():
        shutil.rmtree(tmp_path)

base_multiclass_segmentation_dataset(tmp_path, request)

Generate a base binary segmentation dataset with the following structure
  • 10 good, 15 defect_1 and 20 defect_2 samples in train set
  • 5 good, 10 defect_1 and 15 defect_2 samples in validation set
  • 10 good, 10 defect_1 and 10 defect_2 samples in test set
  • 3 classes: good, defect_1 and defect_2.

Parameters:

  • tmp_path (Path) –

    path to temporary directory

  • request (Any) –

    pytest request

Yields:

Source code in quadra/utils/tests/fixtures/dataset/segmentation.py 
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@pytest.fixture(
    params=[
        SegmentationDatasetArguments(
            **{
                "train_samples": [10, 15, 20],
                "val_samples": [5, 10, 15],
                "test_samples": [10, 10, 10],
                "classes": ["defect_1", "defect_2"],
            }
        )
    ]
)
def base_multiclass_segmentation_dataset(
    tmp_path: Path, request: Any
) -> Tuple[str, SegmentationDatasetArguments, Dict[str, int]]:
    """Generate a base binary segmentation dataset with the following structure:
        - 10 good, 15 defect_1 and 20 defect_2 samples in train set
        - 5 good, 10 defect_1 and 15 defect_2 samples in validation set
        - 10 good, 10 defect_1 and 10 defect_2 samples in test set
        - 3 classes: good, defect_1 and defect_2.

    Args:
        tmp_path: path to temporary directory
        request: pytest request

    Yields:
        Tuple containing path to dataset, dataset arguments and class to index mapping
    """
    yield _build_segmentation_dataset(tmp_path, request.param)
    if tmp_path.exists():
        shutil.rmtree(tmp_path)

base_multilabel_classification_dataset(tmp_path, request)

Fixture to generate base multilabel classification dataset with the following parameters
  • 10 samples per class
  • 3 classes (class_1, class_2 and class_3)
  • 10% of samples in validation set
  • 10% of samples in test set
  • 30% of possibility to add each other class to the sample By default generated images are grayscale and 10x10 pixels.

Parameters:

  • tmp_path (Path) –

    path to temporary directory

  • request (Any) –

    pytest request

Yields:

Source code in quadra/utils/tests/fixtures/dataset/classification.py 
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@pytest.fixture(
    params=[
        ClassificationMultilabelDatasetArguments(
            **{
                "samples": [10, 10, 10],
                "classes": ["class_1", "class_2", "class_3"],
                "val_size": 0.1,
                "test_size": 0.1,
                "percentage_other_classes": 0.3,
            }
        )
    ]
)
def base_multilabel_classification_dataset(
    tmp_path: Path, request: Any
) -> Tuple[str, ClassificationMultilabelDatasetArguments]:
    """Fixture to generate base multilabel classification dataset with the following parameters:
        - 10 samples per class
        - 3 classes (class_1, class_2 and class_3)
        - 10% of samples in validation set
        - 10% of samples in test set
        - 30% of possibility to add each other class to the sample
        By default generated images are grayscale and 10x10 pixels.

    Args:
        tmp_path: path to temporary directory
        request: pytest request

    Yields:
        Tuple containing path to created dataset and dataset arguments
    """
    yield _build_multilabel_classification_dataset(tmp_path, request.param)
    if tmp_path.exists():
        shutil.rmtree(tmp_path)

base_patch_classification_dataset(tmp_path, request)

Generate a classification patch dataset with the following parameters
  • 3 classes named bg, a and b
  • 15, 20 and 25 samples for each class
  • patch size of 16x20
  • 60% overlap
  • 10% validation set
  • 10% test set.

Parameters:

  • tmp_path (Path) –

    path to temporary directory

  • request (Any) –

    pytest SubRequest object

Source code in quadra/utils/tests/fixtures/dataset/classification.py 
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@pytest.fixture(
    params=[
        ClassificationPatchDatasetArguments(
            **{
                "samples": [15, 20, 25],
                "classes": ["bg", "a", "b"],
                "patch_size": [16, 20],
                "overlap": 0.6,
                "val_size": 0.1,
                "test_size": 0.1,
            }
        )
    ]
)
def base_patch_classification_dataset(
    tmp_path: Path, request: Any
) -> Tuple[str, ClassificationDatasetArguments, Dict[str, int]]:
    """Generate a classification patch dataset with the following parameters:
        - 3 classes named bg, a and b
        - 15, 20 and 25 samples for each class
        - patch size of 16x20
        - 60% overlap
        - 10% validation set
        - 10% test set.

    Args:
        tmp_path: path to temporary directory
        request: pytest SubRequest object
    """
    yield _build_classification_patch_dataset(tmp_path, request.param)
    if tmp_path.exists():
        shutil.rmtree(tmp_path)

classification_dataset(tmp_path, dataset_arguments)

Generate classification dataset. If val_size or test_size are set, it will generate a train.txt, val.txt and test.txt file in the dataset directory. By default generated images are 10x10 pixels.

Parameters:

Yields:

Source code in quadra/utils/tests/fixtures/dataset/classification.py 
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@pytest.fixture
def classification_dataset(
    tmp_path: Path, dataset_arguments: ClassificationDatasetArguments
) -> Tuple[str, ClassificationDatasetArguments]:
    """Generate classification dataset. If val_size or test_size are set, it will generate a train.txt, val.txt and
        test.txt file in the dataset directory. By default generated images are 10x10 pixels.

    Args:
        tmp_path: path to temporary directory
        dataset_arguments: dataset arguments

    Yields:
        Tuple containing path to created dataset and dataset arguments
    """
    yield _build_classification_dataset(tmp_path, dataset_arguments)
    if tmp_path.exists():
        shutil.rmtree(tmp_path)

classification_patch_dataset(tmp_path, dataset_arguments)

Fixture to dinamically generate a classification patch dataset.

By default generated images are 224x224 pixels
and associated masks contains a 50x50 pixels square with the corresponding image class, so at the current stage
is not possible to have images with multiple annotations. The patch dataset will be generated using the standard
parameters of generate_patch_dataset function.

Parameters:

Yields:

Source code in quadra/utils/tests/fixtures/dataset/classification.py 
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@pytest.fixture
def classification_patch_dataset(
    tmp_path: Path, dataset_arguments: ClassificationDatasetArguments
) -> Tuple[str, ClassificationDatasetArguments, Dict[str, int]]:
    """Fixture to dinamically generate a classification patch dataset.

        By default generated images are 224x224 pixels
        and associated masks contains a 50x50 pixels square with the corresponding image class, so at the current stage
        is not possible to have images with multiple annotations. The patch dataset will be generated using the standard
        parameters of generate_patch_dataset function.

    Args:
        tmp_path: path to temporary directory
        dataset_arguments: dataset arguments

    Yields:
        Tuple containing path to created dataset, dataset arguments and class to index mapping
    """
    yield _build_classification_patch_dataset(tmp_path, dataset_arguments)
    if tmp_path.exists():
        shutil.rmtree(tmp_path)

multilabel_classification_dataset(tmp_path, dataset_arguments)

Fixture to dinamically generate a multilabel classification dataset. Generates a samples.txt file in the dataset directory containing the path to the image and the corresponding classes. If val_size or test_size are set, it will generate a train.txt, val.txt and test.txt file in the dataset directory. By default generated images are 10x10 pixels.

Parameters:

Returns:

Source code in quadra/utils/tests/fixtures/dataset/classification.py 
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@pytest.fixture
def multilabel_classification_dataset(
    tmp_path: Path, dataset_arguments: ClassificationMultilabelDatasetArguments
) -> Tuple[str, ClassificationMultilabelDatasetArguments]:
    """Fixture to dinamically generate a multilabel classification dataset.
        Generates a samples.txt file in the dataset directory containing the path to the image and the corresponding
        classes. If val_size or test_size are set, it will generate a train.txt, val.txt and test.txt file in the
        dataset directory. By default generated images are 10x10 pixels.

    Args:
        tmp_path: path to temporary directory
        dataset_arguments: dataset arguments

    Returns:
        Tuple containing path to created dataset and dataset arguments
    """
    yield _build_multilabel_classification_dataset(tmp_path, dataset_arguments)
    if tmp_path.exists():
        shutil.rmtree(tmp_path)

segmentation_dataset(tmp_path, dataset_arguments)

Fixture to dinamically generate a segmentation dataset. By default generated images are 224x224 pixels and associated masks contains a 50x50 pixels square with the corresponding image class, so at the current stage is not possible to have images with multiple annotations. Split files are saved as train.txt, val.txt and test.txt.

Parameters:

Yields:

Source code in quadra/utils/tests/fixtures/dataset/segmentation.py 
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@pytest.fixture
def segmentation_dataset(
    tmp_path: Path, dataset_arguments: SegmentationDatasetArguments
) -> Tuple[str, SegmentationDatasetArguments, Dict[str, int]]:
    """Fixture to dinamically generate a segmentation dataset. By default generated images are 224x224 pixels
        and associated masks contains a 50x50 pixels square with the corresponding image class, so at the current stage
        is not possible to have images with multiple annotations. Split files are saved as train.txt,
        val.txt and test.txt.

    Args:
        tmp_path: path to temporary directory
        dataset_arguments: dataset arguments

    Yields:
        Tuple containing path to dataset, dataset arguments and class to index mapping
    """
    yield _build_segmentation_dataset(tmp_path, dataset_arguments)
    if tmp_path.exists():
        shutil.rmtree(tmp_path)