classification

ClassificationDataset(samples, targets, class_to_idx=None, resize=None, roi=None, transform=None, rgb=True, channel=3, random_padding=False, circular_crop=False)

Bases: ImageClassificationListDataset

Custom Classification Dataset.

Parameters:

• samples (list[str]) –

  List of paths to images

• targets (list[str | int]) –

  List of targets

• class_to_idx (dict | None, default: None ) –

  Defaults to None.

• resize (int | None, default: None ) –

  Resize image to this size. Defaults to None.

• roi (tuple[int, int, int, int] | None, default: None ) –

  Region of interest. Defaults to None.

• transform (Callable | None, default: None ) –

  transform function. Defaults to None.

• rgb (bool, default: True ) –

  Use RGB space

• channel (int, default: 3 ) –

  Number of channels. Defaults to 3.

• random_padding (bool, default: False ) –

  Random padding. Defaults to False.

• circular_crop (bool, default: False ) –

  Circular crop. Defaults to False.

Source code in quadra/datasets/classification.py

def __init__(
    self,
    samples: list[str],
    targets: list[str | int],
    class_to_idx: dict | None = None,
    resize: int | None = None,
    roi: tuple[int, int, int, int] | None = None,
    transform: Callable | None = None,
    rgb: bool = True,
    channel: int = 3,
    random_padding: bool = False,
    circular_crop: bool = False,
):
    super().__init__(samples, targets, class_to_idx, resize, roi, transform, rgb, channel)
    if transform is None:
        self.transform = None

    self.random_padding = random_padding
    self.circular_crop = circular_crop

ImageClassificationListDataset(samples, targets, class_to_idx=None, resize=None, roi=None, transform=None, rgb=True, channel=3, allow_missing_label=False)

Bases: Dataset

Standard classification dataset.

Parameters:

• samples (list[str]) –

  List of paths to images to be read

• targets (list[str | int]) –

  List of labels, one for every image in samples

• class_to_idx (dict | None, default: None ) –

  mapping from classes to unique indexes. Defaults to None.

• resize (int | None, default: None ) –

  Integer specifying the size of a first optional resize keeping the aspect ratio: the smaller side of the image will be resized to resize, while the longer will be resized keeping the aspect ratio. Defaults to None.

• roi (tuple[int, int, int, int] | None, default: None ) –

  Optional ROI, with (x_upper_left, y_upper_left, x_bottom_right, y_bottom_right). Defaults to None.

• transform (Callable | None, default: None ) –

  Optional Albumentations transform. Defaults to None.

• rgb (bool, default: True ) –

  if False, image will be converted in grayscale

• channel (int, default: 3 ) –

  1 or 3. If rgb is True, then channel will be set at 3.

• allow_missing_label (bool | None, default: False ) –

  If set to false warn the user if the dataset contains missing labels

Source code in quadra/datasets/classification.py

def __init__(
    self,
    samples: list[str],
    targets: list[str | int],
    class_to_idx: dict | None = None,
    resize: int | None = None,
    roi: tuple[int, int, int, int] | None = None,
    transform: Callable | None = None,
    rgb: bool = True,
    channel: int = 3,
    allow_missing_label: bool | None = False,
):
    super().__init__()
    assert len(samples) == len(
        targets
    ), f"Samples ({len(samples)}) and targets ({len(targets)}) must have the same length"
    # Setting the ROI
    self.roi = roi

    # Keep-Aspect-Ratio resize
    self.resize = resize

    if not allow_missing_label and None in targets:
        warnings.warn(
            (
                "Dataset contains empty targets but allow_missing_label is set to False, "
                "be careful because None labels will not work inside Dataloaders"
            ),
            UserWarning,
            stacklevel=2,
        )

    targets = [-1 if target is None else target for target in targets]
    # Data
    self.x = np.array(samples)
    self.y = np.array(targets)

    if class_to_idx is None:
        unique_targets = np.unique(targets)
        class_to_idx = {c: i for i, c in enumerate(unique_targets)}

    self.class_to_idx = class_to_idx
    self.idx_to_class = {v: k for k, v in class_to_idx.items()}
    self.samples = [
        (path, self.class_to_idx[self.y[i]] if (self.y[i] != -1 and self.y[i] != "-1") else -1)
        for i, path in enumerate(self.x)
    ]

    self.rgb = rgb
    self.channel = 3 if rgb else channel

    self.transform = transform

MultilabelClassificationDataset(samples, targets, class_to_idx=None, transform=None, rgb=True)

Bases: Dataset

Custom MultilabelClassification Dataset.

Parameters:

• samples (list[str]) –

  list of paths to images.

• targets (ndarray) –

  array of multiple targets per sample. The array must be a one-hot enoding. It must have a shape of (n_samples, n_targets).

• class_to_idx (dict | None, default: None ) –

  Defaults to None.

• transform (Callable | None, default: None ) –

  transform function. Defaults to None.

• rgb (bool, default: True ) –

  Use RGB space

Source code in quadra/datasets/classification.py

def __init__(
    self,
    samples: list[str],
    targets: np.ndarray,
    class_to_idx: dict | None = None,
    transform: Callable | None = None,
    rgb: bool = True,
):
    super().__init__()
    assert len(samples) == len(
        targets
    ), f"Samples ({len(samples)}) and targets ({len(targets)}) must have the same length"

    # Data
    self.x = samples
    self.y = targets

    # Class to idx and the other way around
    if class_to_idx is None:
        unique_targets = targets.shape[1]
        class_to_idx = {c: i for i, c in enumerate(range(unique_targets))}
    self.class_to_idx = class_to_idx
    self.idx_to_class = {v: k for k, v in class_to_idx.items()}
    self.samples = list(zip(self.x, self.y))
    self.rgb = rgb
    self.transform = transform