from typing import Optional, Sequence, Tuple
import cv2
import numpy as np
from dicaugment.augmentations.utils import (
_maybe_process_in_chunks,
preserve_channel_dim,
)
from ...core.bbox_utils import denormalize_bbox, normalize_bbox
from ...core.transforms_interface import BoxInternalType, KeypointInternalType
from ..geometric import functional as FGeometric
__all__ = [
"get_random_crop_coords",
"random_crop",
"crop_bbox_by_coords",
"bbox_random_crop",
"crop_keypoint_by_coords",
"keypoint_random_crop",
"get_center_crop_coords",
"center_crop",
"bbox_center_crop",
"keypoint_center_crop",
"crop",
"bbox_crop",
"clamping_crop",
"crop_and_pad",
"crop_and_pad_bbox",
"crop_and_pad_keypoint",
]
[docs]def get_random_crop_coords(height: int, width: int, depth: int, crop_height: int, crop_width: int, crop_depth: int, h_start: float, w_start: float, d_start: float):
# h_start is [0, 1) and should map to [0, (height - crop_height)] (note inclusive)
# This is conceptually equivalent to mapping onto `range(0, (height - crop_height + 1))`
# See: https://github.com/albumentations-team/albumentations/pull/1080
y1 = int((height - crop_height + 1) * h_start)
y2 = y1 + crop_height
x1 = int((width - crop_width + 1) * w_start)
x2 = x1 + crop_width
z1 = int((depth - crop_depth + 1) * d_start)
z2 = z1 + crop_depth
return x1, y1, z1, x2, y2, z2
[docs]def random_crop(img: np.ndarray, crop_height: int, crop_width: int, crop_depth: int, h_start: float, w_start: float, d_start: float):
height, width, depth = img.shape[:3]
if height < crop_height or width < crop_width or depth < crop_depth:
raise ValueError(
"Requested crop size ({crop_height}, {crop_width}, {crop_depth}) is "
"larger than the image size ({height}, {width}, {depth})".format(
crop_height=crop_height, crop_width=crop_width, crop_depth=crop_depth, height=height, width=width, depth=depth
)
)
x1, y1, z1, x2, y2, z2 = get_random_crop_coords(height, width, depth, crop_height, crop_width, crop_depth, h_start, w_start, d_start)
img = img[y1:y2, x1:x2, z1:z2]
return img
[docs]def crop_bbox_by_coords(
bbox: BoxInternalType,
crop_coords: Tuple[int, int, int, int, int, int],
crop_height: int,
crop_width: int,
crop_depth: int,
rows: int,
cols: int,
slices: int
):
"""Crop a bounding box using the provided coordinates of bottom-left and top-right corners in pixels and the
required height and width of the crop.
Args:
bbox (tuple): A cropped box `(x_min, y_min, z_min, x_max, y_max, z_max)`.
crop_coords (tuple): Crop coordinates `(x1, y1, z1, x2, y2, z2)`.
crop_height (int):
crop_width (int):
crop_depth (int):
rows (int): Image rows.
cols (int): Image cols.
slices (int): Image slices.
Returns:
tuple: A cropped bounding box `(x_min, y_min, x_max, y_max, z_min, z_max)`.
"""
bbox = denormalize_bbox(bbox, rows, cols, slices)
x_min, y_min, z_min, x_max, y_max, z_max = bbox[:6]
x1, y1, z1, x2, y2, z2 = crop_coords
cropped_bbox = x_min - x1, y_min - y1, z_min - z1, x_max - x1, y_max - y1, z_max - z1
return normalize_bbox(cropped_bbox, crop_height, crop_width, crop_depth)
[docs]def bbox_random_crop(
bbox: BoxInternalType, crop_height: int, crop_width: int, crop_depth: int, h_start: float, w_start: float, d_start: float, rows: int, cols: int, slices: int
):
crop_coords = get_random_crop_coords(rows, cols, slices, crop_height, crop_width, crop_depth, h_start, w_start, d_start)
return crop_bbox_by_coords(bbox, crop_coords, crop_height, crop_width, crop_depth, rows, cols, slices)
[docs]def crop_keypoint_by_coords(
keypoint: KeypointInternalType, crop_coords: Tuple[int, int, int, int, int, int]
): # skipcq: PYL-W0613
"""Crop a keypoint using the provided coordinates of closest-top-left and furthest-bottom-right corners in pixels and the
required height, width, and depth of the crop.
Args:
keypoint (tuple): A keypoint `(x, y, z, angle, scale)`.
crop_coords (tuple): Crop box coords `(x1, y1, z1, x2, y2, z2)`.
Returns:
A keypoint `(x, y, z, angle, scale)`.
"""
x, y, z, angle, scale = keypoint[:5]
x1, y1, z1, x2, y2, z2= crop_coords
return x - x1, y - y1, z - z1, angle, scale
[docs]def keypoint_random_crop(
keypoint: KeypointInternalType,
crop_height: int,
crop_width: int,
crop_depth: int,
h_start: float,
w_start: float,
d_start: float,
rows: int,
cols: int,
slices: int
):
"""Keypoint random crop.
Args:
keypoint: (tuple): A keypoint `(x, y, angle, scale)`.
crop_height (int): Crop height.
crop_width (int): Crop width.
crop_depth (int): Crop depth.
h_start (int): Crop height start.
w_start (int): Crop width start.
d_start (int): Crop depth start.
rows (int): Image height.
cols (int): Image width.
slices (int): Image depth
Returns:
A keypoint `(x, y, z, angle, scale)`.
"""
crop_coords = get_random_crop_coords(rows, cols, slices, crop_height, crop_width, crop_depth, h_start, w_start, d_start)
return crop_keypoint_by_coords(keypoint, crop_coords)
[docs]def get_center_crop_coords(height: int, width: int, depth: int, crop_height: int, crop_width: int, crop_depth: int):
y1 = (height - crop_height) // 2
y2 = y1 + crop_height
x1 = (width - crop_width) // 2
x2 = x1 + crop_width
z1 = (depth - crop_depth) // 2
z2 = z1 + crop_depth
return x1, y1, z1, x2, y2, z2
[docs]def center_crop(img: np.ndarray, crop_height: int, crop_width: int, crop_depth: int):
height, width, depth = img.shape[:3]
if height < crop_height or width < crop_width or depth < crop_depth:
raise ValueError(
"Requested crop size ({crop_height}, {crop_width}, {crop_depth}) is "
"larger than the image size ({height}, {width}, {depth})".format(
crop_height=crop_height, crop_width=crop_width, crop_depth=crop_depth, height=height, width=width, depth=depth
)
)
x1, y1, z1, x2, y2, z2 = get_center_crop_coords(height, width, depth, crop_height, crop_width, crop_depth)
img = img[y1:y2, x1:x2, z1:z2]
return img
[docs]def bbox_center_crop(bbox: BoxInternalType, crop_height: int, crop_width: int, crop_depth:int, rows: int, cols: int, slices: int):
crop_coords = get_center_crop_coords(rows, cols, slices, crop_height, crop_width, crop_depth)
return crop_bbox_by_coords(bbox, crop_coords, crop_height, crop_width, crop_depth, rows, cols, slices)
[docs]def keypoint_center_crop(keypoint: KeypointInternalType, crop_height: int, crop_width: int, crop_depth:int, rows: int, cols: int, slices: int):
"""Keypoint center crop.
Args:
keypoint (tuple): A keypoint `(x, y, z, angle, scale)`.
crop_height (int): Crop height.
crop_width (int): Crop width.
crop_depth (int): Crop depth.
rows (int): Image height.
cols (int): Image width.
slices (int): Image depths.
Returns:
tuple: A keypoint `(x, y, z, angle, scale)`.
"""
crop_coords = get_center_crop_coords(rows, cols, slices, crop_height, crop_width, crop_depth)
return crop_keypoint_by_coords(keypoint, crop_coords)
[docs]def crop(img: np.ndarray, x_min: int, y_min: int, z_min: int, x_max: int, y_max: int, z_max: int):
height, width, depth = img.shape[:3]
if x_max <= x_min or y_max <= y_min or z_max <= z_min:
raise ValueError(
"Expected x_min < x_max, y_min < y_max, and z_min < z_max. Got"
" (x_min = {x_min}, y_min = {y_min}, z_min = {z_min}, x_max = {x_max}, y_max = {y_max}, z_max = {z_max})".format(
x_min=x_min, x_max=x_max, y_min=y_min, y_max=y_max, z_min=z_min, z_max=z_max
)
)
if x_min < 0 or x_max > width or y_min < 0 or y_max > height or z_min < 0 or z_max > depth:
raise ValueError(
"Values for crop should be non negative and equal or smaller than image sizes. Got "
"(x_min = {x_min}, y_min = {y_min}, z_min = {z_min}, x_max = {x_max}, y_max = {y_max}, z_max = {z_max}, "
"height = {height}, width = {width}, depth = {depth})".format(
x_min=x_min, x_max=x_max, y_min=y_min, y_max=y_max, z_min=z_min, z_max=z_max, height=height, width=width, depth = depth
)
)
return img[y_min:y_max, x_min:x_max, z_min:z_max]
[docs]def bbox_crop(bbox: BoxInternalType, x_min: int, y_min: int, z_min: int, x_max: int, y_max: int, z_max: int, rows: int, cols: int, slices: int):
"""Crop a bounding box.
Args:
bbox (tuple): A bounding box `(x_min, y_min, z_min, x_max, y_max, z_max)`.
x_min (int):
y_min (int):
z_min (int):
x_max (int):
y_max (int):
z_max (int):
rows (int): Image width.
cols (int): Image height.
slices (int): Image depth.
Returns:
tuple: A cropped bounding box `(x_min, y_min, z_min, x_max, y_max, z_max)`.
"""
crop_coords = x_min, y_min, z_min, x_max, y_max, z_max
crop_height = y_max - y_min
crop_width = x_max - x_min
crop_depth = z_max - z_min
return crop_bbox_by_coords(bbox, crop_coords, crop_height, crop_width, crop_depth, rows, cols, slices)
[docs]def clamping_crop(img: np.ndarray, x_min: int, y_min: int, z_min: int, x_max: int, y_max: int, z_max: int):
h, w, d = img.shape[:3]
x_min = max(x_min, 0)
y_min = max(y_min, 0)
z_min = max(z_min, 0)
x_max = min(x_max, w - 1)
y_max = min(y_max, h - 1)
z_max = min(z_max, d - 1)
return img[int(y_min) : int(y_max), int(x_min) : int(x_max), int(z_min) : int(z_max)]
[docs]@preserve_channel_dim
def crop_and_pad(
img: np.ndarray,
crop_params: Optional[Sequence[int]],
pad_params: Optional[Sequence[int]],
pad_value: Optional[float],
rows: int,
cols: int,
slices: int,
interpolation: int,
pad_mode: int,
keep_size: bool,
) -> np.ndarray:
if crop_params is not None and any(i != 0 for i in crop_params):
img = crop(img, *crop_params)
if pad_params is not None and any(i != 0 for i in pad_params):
img = FGeometric.pad_with_params(
img, *pad_params[:6], border_mode=pad_mode, value=pad_value
)
if keep_size:
img = FGeometric.resize(img, height = cols, width = rows, depth = slices, interpolation = interpolation )
return img
[docs]def crop_and_pad_bbox(
bbox: BoxInternalType,
crop_params: Optional[Sequence[int]],
pad_params: Optional[Sequence[int]],
rows,
cols,
slices,
result_rows,
result_cols,
result_slices,
) -> BoxInternalType:
x1, y1, z1, x2, y2, z2 = denormalize_bbox(bbox, rows, cols, slices)[:6]
if crop_params is not None:
crop_x, _, crop_y, _, crop_z, _ = crop_params
x1, y1, z1, x2, y2, z2 = x1 - crop_x, y1 - crop_y, z1 - crop_z, x2 - crop_x, y2 - crop_y, z2 - crop_z
if pad_params is not None:
top, bottom, left, right, close, far = pad_params
x1, y1, z1, x2, y2, z2 = x1 + left, y1 + top, z1 + close, x2 + left, y2 + top, z2 + close
return normalize_bbox((x1, y1, z1, x2, y2, z2), result_rows, result_cols, result_slices)
[docs]def crop_and_pad_keypoint(
keypoint: KeypointInternalType,
crop_params: Optional[Sequence[int]],
pad_params: Optional[Sequence[int]],
rows: int,
cols: int,
slices: int,
result_rows: int,
result_cols: int,
result_slices: int,
keep_size: bool,
) -> KeypointInternalType:
x, y, z, angle, scale = keypoint[:5]
if crop_params is not None:
crop_x, _, crop_y, _, crop_z, _ = crop_params
x, y, z = x - crop_x, y - crop_y, z - crop_z
if pad_params is not None:
top, bottom, left, right, close, far = pad_params
x, y, z = x + left, y + top, z + close
if keep_size and (result_cols != cols or result_rows != rows or result_cols != slices):
scale_x = cols / result_cols
scale_y = rows / result_rows
scale_z = slices / result_slices
return FGeometric.keypoint_scale((x, y, z, angle, scale), scale_x, scale_y, scale_z)
return x, y, z, angle, scale