luxonis_ml.data.augmentations.custom.LetterboxResize

class documentation

class LetterboxResize(A.DualTransform):

Constructor: LetterboxResize(height, width, interpolation, image_fill_value, ...)

Augmentation that resizes an image with padding to maintain the aspect ratio.

Static Method	`compute_padding`	Compute the padding required to resize an image to the letterbox format.
Method	`__init__`	Create a `LetterboxResize` augmentation.
Method	`apply`	Apply the letterbox augmentation to an image.
Method	`apply_to_bboxes`	Apply letterbox augmentation to the bounding box.
Method	`apply_to_keypoints`	Apply letterbox augmentation to the keypoint.
Method	`apply_to_mask`	Apply letterbox augmentation to the input mask.
Method	`get_params_dependent_on_data`	Return parameters dependent on input.
Instance Variable	`height`	The desired height of the output image.
Instance Variable	`width`	The desired width of the output image.
Property	`targets`	Define the targets the augmentation will be applied to.
Method	`_apply_to_image_data`	Undocumented
Instance Variable	`_image_fill_value`	Undocumented
Instance Variable	`_interpolation`	Undocumented
Instance Variable	`_mask_fill_value`	Undocumented

@staticmethod

def compute_padding(orig_height: int, orig_width: int, out_height: int, out_width: int) -> tuple[int, int, int, int]: ¶

Compute the padding required to resize an image to the letterbox format.

Parameters
orig_height:`int`	Original height of the image.
orig_width:`int`	Original width of the image.
out_height:`int`	Desired height of the output.
out_width:`int`	Desired width of the output.

Returns
`tuple[int, int, int, int]`	Padding values for the top, bottom, left and right sides of the image.

def __init__(self, height: int, width: int, interpolation: int = cv2.INTER_LINEAR, image_fill_value: Color = 'black', mask_fill_value: int = 0, p: float = 1.0): ¶

Create a LetterboxResize augmentation.

Parameters
height:`int`	The desired height of the output image
width:`int`	The desired width of the output image
interpolation:`int`	`cv2` flag to specify interpolation used when resizing. Defaults to `cv2.INTER_LINEAR`.
image_fill_value:`Color`	Padding value for images. Can be a string color name or an RGB tuple. Defaults to `"black"`.
mask_fill_value:`int`	Padding value for masks. Must be an integer representing a class label. Defaults to `0` (background).
p:`float`	The probability of applying the transform. Defaults to `1.0`.

@override

def apply(self, img: np.ndarray, pad_top: int, pad_bottom: int, pad_left: int, pad_right: int, **_) -> np.ndarray: ¶

Apply the letterbox augmentation to an image.

Parameters
img:`np.ndarray`	The input image of shape (H, W, …) to which the letterbox resize will be applied.
pad_top:`int`	The number of pixels to pad at the top of the image.
pad_bottom:`int`	The number of pixels to pad at the bottom of the image.
pad_left:`int`	The number of pixels to pad on the left side of the image.
pad_right:`int`	The number of pixels to pad on the right side of the image.
**_	Undocumented

Returns
`np.ndarray`	Resized and padded image.

@override

def apply_to_bboxes(self, bbox: np.ndarray, pad_top: int, pad_bottom: int, pad_left: int, pad_right: int, **_) -> np.ndarray: ¶

Apply letterbox augmentation to the bounding box.

Parameters
bbox:`np.ndarray`	The input bounding boxes of shape (N, 4 + ) to which the letterbox resize will be applied. Individual bounding boxes should be in the format (x_min, y_min, x_max, y_max, …) and normalized to the range [0, 1]. The trailing dimensions (if any) are not modified by the augmentation.
pad_top:`int`	The number of pixels to pad at the top of the image.
pad_bottom:`int`	The number of pixels to pad at the bottom of the image.
pad_left:`int`	The number of pixels to pad on the left side of the image.
pad_right:`int`	The number of pixels to pad on the right side of the image.
**_	Undocumented

Returns
`np.ndarray`	Transformed bounding boxes in the same format and normalization as the input.

@override

def apply_to_keypoints(self, keypoint: np.ndarray, pad_top: int, pad_bottom: int, pad_left: int, pad_right: int, orig_height: int, orig_width: int, **_) -> np.ndarray: ¶

Apply letterbox augmentation to the keypoint.

Parameters
keypoint:`np.ndarray`	The input keypoints of shape (N, 2 + ) to which the letterbox resize will be applied. Individual keypoints should be in the format (x, y, …) and normalized to the range [0, 1].
pad_top:`int`	The number of pixels to pad at the top of the image.
pad_bottom:`int`	The number of pixels to pad at the bottom of the image.
pad_left:`int`	The number of pixels to pad on the left side of the image.
pad_right:`int`	The number of pixels to pad on the right side of the image.
orig_height:`int`	Original height of the image before resizing.
orig_width:`int`	Original width of the image before resizing.
**_	Undocumented

Returns
`np.ndarray`	Transformed keypoints in the same format and normalization as the input. Keypoints that fall outside the image boundaries after transformation will have their coordinates set to − 1.

@override

def apply_to_mask(self, mask: np.ndarray, pad_top: int, pad_bottom: int, pad_left: int, pad_right: int, **_) -> np.ndarray: ¶

Apply letterbox augmentation to the input mask.

Parameters
mask:`np.ndarray`	The input mask of shape (H, W, …) to which the letterbox resize will be applied.
pad_top:`int`	The number of pixels to pad at the top of the mask.
pad_bottom:`int`	The number of pixels to pad at the bottom of the mask.
pad_left:`int`	The number of pixels to pad on the left side of the mask.
pad_right:`int`	The number of pixels to pad on the right side of the mask.
**_	Undocumented

Returns
`np.ndarray`	Resized and padded mask.

@override

def get_params_dependent_on_data(self, params: dict[str, Any], data: dict[str, Any]) -> dict[str, Any]: ¶

Return parameters dependent on input.

Parameters
params:`dict[str, Any]`	The existing augmentation parameters dictionary.
data:`dict[str, Any]`	The dictionary with input data.

Returns
`dict[str, Any]`	A dictionary with extra parameters required for the augmentation, such as padding values and original image dimensions.

height = ¶

The desired height of the output image.

width = ¶

The desired width of the output image.

@property
@override

targets: dict[str, Any] = ¶

Define the targets the augmentation will be applied to.

def _apply_to_image_data(self, img: np.ndarray, pad_top: int, pad_bottom: int, pad_left: int, pad_right: int, interpolation: int, fill_value: RGB) -> np.ndarray: ¶

Undocumented

_image_fill_value = ¶

Undocumented

_interpolation = ¶

Undocumented

_mask_fill_value = ¶

Undocumented