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docs: fixed warnings and improved formatting #207

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Nov 8, 2024
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docs: fixed warnings and improved formatting #207

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9268747
fixed warnings
kozlov721 Nov 8, 2024
78ba320
formatting
kozlov721 Nov 8, 2024
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2 changes: 1 addition & 1 deletion luxonis_ml/data/__init__.py
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Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
import pkg_resources

from ..guard_extras import guard_missing_extra
from luxonis_ml.guard_extras import guard_missing_extra

with guard_missing_extra("data"):
from .augmentations import Augmentations
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8 changes: 4 additions & 4 deletions luxonis_ml/data/augmentations/batch_compose.py
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Expand Up @@ -42,8 +42,8 @@ def __init__(

@param transforms: List of transformations to compose
@type transforms: TransformsSeqType
@param bboxparams: Parameters for bounding boxes transforms. Defaults to None.
@type bboxparams: Optional[Union[dict, BboxParams]]
@param bbox_params: Parameters for bounding boxes transforms. Defaults to None.
@type bbox_params: Optional[Union[dict, BboxParams]]
@param keypoint_params: Parameters for keypoint transforms. Defaults to None.
@type keypoint_params: Optional[Union[dict, KeypointParams]]
@param additional_targets: Dict with keys - new target name, values - old target
Expand Down Expand Up @@ -283,8 +283,8 @@ def __init__(

@param transforms: List of transformations to compose
@type transforms: TransformsSeqType
@param bboxparams: Parameters for bounding boxes transforms. Defaults to None.
@type bboxparams: Optional[Union[dict, BboxParams]]
@param bbox_params: Parameters for bounding boxes transforms. Defaults to None.
@type bbox_params: Optional[Union[dict, BboxParams]]
@param keypoint_params: Parameters for keypoint transforms. Defaults to None.
@type keypoint_params: Optional[Union[dict, KeypointParams]]
@param additional_targets: Dict with keys - new target name, values - old target
Expand Down
100 changes: 50 additions & 50 deletions luxonis_ml/data/augmentations/custom/letterbox_resize.py
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Expand Up @@ -23,23 +23,23 @@ def __init__(
"""Augmentation to apply letterbox resizing to images. Also
transforms masks, bboxes and keypoints to correct shape.

@param height: Desired height of the output.
@type height: int
@param width: Desired width of the output.
@param height: Desired height of the output.
@type width: int
@param interpolation: Cv2 flag to specify interpolation used
when resizing. Defaults to cv2.INTER_LINEAR.
@param width: Desired width of the output.
@type interpolation: int, optional
@param border_value: Padding value for images. Defaults to 0.
@param interpolation: cv2 flag to specify interpolation used
when resizing. Defaults to C{cv2.INTER_LINEAR}.
@type border_value: int, optional
@param mask_value: Padding value for masks. Defaults to 0.
@param border_value: Padding value for images. Defaults to C{0}.
@type mask_value: int, optional
@param always_apply: Whether to always apply the transform.
Defaults to False.
@param mask_value: Padding value for masks. Defaults to C{0}.
@type always_apply: bool, optional
@param p: Probability of applying the transform. Defaults to
1.0.
@param always_apply: Whether to always apply the transform.
Defaults to C{False}.
@type p: float, optional
@param p: Probability of applying the transform. Defaults to
C{1.0}.
"""

super().__init__(always_apply, p)
Expand Down Expand Up @@ -108,20 +108,20 @@ def apply(
) -> np.ndarray:
"""Applies the letterbox augmentation to an image.

@param img: Input image to which resize is applied.
@type img: np.ndarray
@param pad_top: Number of pixels to pad at the top.
@param img: Input image to which resize is applied.
@type pad_top: int
@param pad_bottom: Number of pixels to pad at the bottom.
@param pad_top: Number of pixels to pad at the top.
@type pad_bottom: int
@param pad_left: Number of pixels to pad on the left.
@param pad_bottom: Number of pixels to pad at the bottom.
@type pad_left: int
@param pad_right: Number of pixels to pad on the right.
@param pad_left: Number of pixels to pad on the left.
@type pad_right: int
@param params: Additional parameters for the padding operation.
@type params: Any
@return: Image with applied letterbox resize.
@param pad_right: Number of pixels to pad on the right.
@type kwargs: Any
@param kwargs: Additional parameters for the padding operation.
@rtype: np.ndarray
@return: Image with applied letterbox resize.
"""

resized_img = cv2.resize(
Expand Down Expand Up @@ -155,20 +155,20 @@ def apply_to_mask(
) -> np.ndarray:
"""Applies letterbox augmentation to the input mask.

@param img: Input mask to which resize is applied.
@type img: np.ndarray
@param pad_top: Number of pixels to pad at the top.
@param img: Input mask to which resize is applied.
@type pad_top: int
@param pad_bottom: Number of pixels to pad at the bottom.
@param pad_top: Number of pixels to pad at the top.
@type pad_bottom: int
@param pad_left: Number of pixels to pad on the left.
@param pad_bottom: Number of pixels to pad at the bottom.
@type pad_left: int
@param pad_right: Number of pixels to pad on the right.
@param pad_left: Number of pixels to pad on the left.
@type pad_right: int
@param params: Additional parameters for the padding operation.
@param pad_right: Number of pixels to pad on the right.
@type params: Any
@return: Mask with applied letterbox resize.
@param params: Additional parameters for the padding operation.
@rtype: np.ndarray
@return: Mask with applied letterbox resize.
"""

resized_img = cv2.resize(
Expand Down Expand Up @@ -202,20 +202,20 @@ def apply_to_bbox(
) -> BoxType:
"""Applies letterbox augmentation to the bounding box.

@param img: Bounding box to which resize is applied.
@type img: BoxType
@param pad_top: Number of pixels to pad at the top.
@type bbox: BoxType
@param bbox: Bounding box to which resize is applied.
@type pad_top: int
@param pad_bottom: Number of pixels to pad at the bottom.
@param pad_top: Number of pixels to pad at the top.
@type pad_bottom: int
@param pad_left: Number of pixels to pad on the left.
@param pad_bottom: Number of pixels to pad at the bottom.
@type pad_left: int
@param pad_right: Number of pixels to pad on the right.
@param pad_left: Number of pixels to pad on the left.
@type pad_right: int
@param params: Additional parameters for the padding operation.
@param pad_right: Number of pixels to pad on the right.
@type params: Any
@return: Bounding box with applied letterbox resize.
@param params: Additional parameters for the padding operation.
@rtype: BoxType
@return: Bounding box with applied letterbox resize.
"""

x_min, y_min, x_max, y_max = denormalize_bbox(
Expand Down Expand Up @@ -245,29 +245,29 @@ def apply_to_keypoint(
pad_bottom: int,
pad_left: int,
pad_right: int,
**params,
**kwargs,
) -> KeypointType:
"""Applies letterbox augmentation to the keypoint.

@param img: Keypoint to which resize is applied.
@type img: KeypointType
@param pad_top: Number of pixels to pad at the top.
@type keypoint: KeypointType
@param keypoint: Keypoint to which resize is applied.
@type pad_top: int
@param pad_bottom: Number of pixels to pad at the bottom.
@param pad_top: Number of pixels to pad at the top.
@type pad_bottom: int
@param pad_left: Number of pixels to pad on the left.
@param pad_bottom: Number of pixels to pad at the bottom.
@type pad_left: int
@param pad_right: Number of pixels to pad on the right.
@param pad_left: Number of pixels to pad on the left.
@type pad_right: int
@param params: Additional parameters for the padding operation.
@type params: Any
@return: Keypoint with applied letterbox resize.
@param pad_right: Number of pixels to pad on the right.
@type kwargs: Any
@param kwargs: Additional parameters for the padding operation.
@rtype: KeypointType
@return: Keypoint with applied letterbox resize.
"""

x, y, angle, scale = keypoint[:4]
scale_x = (self.width - pad_left - pad_right) / params["cols"]
scale_y = (self.height - pad_top - pad_bottom) / params["rows"]
scale_x = (self.width - pad_left - pad_right) / kwargs["cols"]
scale_y = (self.height - pad_top - pad_bottom) / kwargs["rows"]
new_x = (x * scale_x) + pad_left
new_y = (y * scale_y) + pad_top
# if keypoint is in the padding then set coordinates to -1
Expand All @@ -286,8 +286,8 @@ def apply_to_keypoint(
def get_transform_init_args_names(self) -> Tuple[str, ...]:
"""Gets the default arguments for the letterbox augmentation.

@return: The string keywords of the arguments.
@rtype: Tuple[str, ...]
@return: The string keywords of the arguments.
"""

return (
Expand All @@ -303,14 +303,14 @@ def _out_of_bounds(
) -> bool:
""" "Check if the given value is outside the specified limits.

@param value: The value to be checked.
@type value: float
@param min_limit: Minimum limit.
@param value: The value to be checked.
@type min_limit: float
@param max_limit: Maximum limit.
@param min_limit: Minimum limit.
@type max_limit: float
@param max_limit: Maximum limit.
@rtype: bool
@return: True if the value is outside the specified limits,
False otherwise.
@rtype: bool
"""
return value < min_limit or value > max_limit
66 changes: 33 additions & 33 deletions luxonis_ml/data/augmentations/custom/mixup.py
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Original file line number Diff line number Diff line change
Expand Up @@ -23,18 +23,18 @@ def __init__(
annotations into one. If images are not of same size then second
one is first resized to match the first one.

@type alpha: Union[float, Tuple[float, float]]
@param alpha: Mixing coefficient, either a single float or a
tuple representing the range. Defaults to 0.5.
@type alpha: Union[float, Tuple[float, float]], optional
tuple representing the range. Defaults to C{0.5}.
@type out_batch_size: int
@param out_batch_size: Number of output images in the batch.
Defaults to 1.
@type out_batch_size: int, optional
Defaults to C{1}.
@type always_apply: bool
@param always_apply: Whether to always apply the transform.
Defaults to False.
@type always_apply: bool, optional
@param p: Probability of applying the transform. Defaults to
0.5.
Defaults to C{False}.
@type p: float, optional
@param p: Probability of applying the transform. Defaults to
C{0.5}.
"""
super().__init__(batch_size=2, always_apply=always_apply, p=p)

Expand All @@ -44,17 +44,17 @@ def __init__(
def get_transform_init_args_names(self) -> Tuple[str, ...]:
"""Gets the default arguments for the mixup augmentation.

@return: The string keywords of the arguments.
@rtype: Tuple[str, ...]
@return: The string keywords of the arguments.
"""
return ("alpha", "out_batch_size")

@property
def targets_as_params(self) -> List[str]:
"""List of augmentation targets.

@return: Output list of augmentation targets.
@rtype: List[str]
@return: Output list of augmentation targets.
"""
return ["image_batch"]

Expand All @@ -66,15 +66,15 @@ def apply_to_image_batch(
) -> List[np.ndarray]:
"""Applies the transformation to a batch of images.

@type image_batch: List[np.ndarray]
@param image_batch: Batch of input images to which the
transformation is applied.
@type image_batch: List[np.ndarray]
@param image_shapes: Shapes of the input images in the batch.
@type image_shapes: List[Tuple[int, int]]
@param params: Additional parameters for the transformation.
@param image_shapes: Shapes of the input images in the batch.
@type params: Any
@return: List of transformed images.
@param params: Additional parameters for the transformation.
@rtype: List[np.ndarray]
@return: List of transformed images.
"""
image1 = image_batch[0]
# resize second image to size of the first one
Expand All @@ -101,15 +101,15 @@ def apply_to_mask_batch(
) -> List[np.ndarray]:
"""Applies the transformation to a batch of masks.

@param image_batch: Batch of input masks to which the
@type mask_batch: List[np.ndarray]
@param mask_batch: Batch of input masks to which the
transformation is applied.
@type image_batch: List[np.ndarray]
@param image_shapes: Shapes of the input images in the batch.
@type image_shapes: List[Tuple[int, int]]
@param params: Additional parameters for the transformation.
@param image_shapes: Shapes of the input images in the batch.
@type params: Any
@return: List of transformed masks.
@param params: Additional parameters for the transformation.
@rtype: List[np.ndarray]
@return: List of transformed masks.
"""
mask1 = mask_batch[0]
mask2 = cv2.resize(
Expand All @@ -127,39 +127,39 @@ def apply_to_bboxes_batch(
self,
bboxes_batch: List[BoxType],
image_shapes: List[Tuple[int, int]],
**params,
**kwargs,
) -> List[BoxType]:
"""Applies the transformation to a batch of bboxes.

@param image_batch: Batch of input bboxes to which the
@type bboxes_batch: List[BoxType]
@param bboxes_batch: Batch of input bboxes to which the
transformation is applied.
@type image_batch: List[BoxType]
@param image_shapes: Shapes of the input images in the batch.
@type image_shapes: List[Tuple[int, int]]
@param params: Additional parameters for the transformation.
@type params: Any
@return: List of transformed bboxes.
@param image_shapes: Shapes of the input images in the batch.
@type kwargs: Any
@param kwargs: Additional parameters for the transformation.
@rtype: List[BoxType]
@return: List of transformed bboxes.
"""
return [bboxes_batch[0] + bboxes_batch[1]]

def apply_to_keypoints_batch(
self,
keypoints_batch: List[KeypointType],
image_shapes: List[Tuple[int, int]],
**params,
**kwargs,
) -> List[KeypointType]:
"""Applies the transformation to a batch of keypoints.

@param image_batch: Batch of input keypoints to which the
@type keypoints_batch: List[BoxType]
@param keypoints_batch: Batch of input keypoints to which the
transformation is applied.
@type image_batch: List[BoxType]
@param image_shapes: Shapes of the input images in the batch.
@type image_shapes: List[Tuple[int, int]]
@param params: Additional parameters for the transformation.
@type params: Any
@return: List of transformed keypoints.
@param image_shapes: Shapes of the input images in the batch.
@type kwargs: Any
@param kwargs: Additional parameters for the transformation.
@rtype: List[BoxType]
@return: List of transformed keypoints.
"""
scaled_kpts2 = []
scale_x = image_shapes[0][1] / image_shapes[1][1]
Expand Down
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