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functional.py
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functional.py
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import cv2
import math
import numpy as np
import skimage.transform
from scipy.ndimage.filters import gaussian_filter
from ..bbox_utils import denormalize_bbox, normalize_bbox
from ..functional import angle_2pi_range, preserve_channel_dim, _maybe_process_in_chunks, preserve_shape, clipped
from typing import Union, List, Sequence, Tuple, Optional
def bbox_rot90(bbox, factor, rows, cols): # skipcq: PYL-W0613
"""Rotates a bounding box by 90 degrees CCW (see np.rot90)
Args:
bbox (tuple): A bounding box tuple (x_min, y_min, x_max, y_max).
factor (int): Number of CCW rotations. Must be in set {0, 1, 2, 3} See np.rot90.
rows (int): Image rows.
cols (int): Image cols.
Returns:
tuple: A bounding box tuple (x_min, y_min, x_max, y_max).
"""
if factor not in {0, 1, 2, 3}:
raise ValueError("Parameter n must be in set {0, 1, 2, 3}")
x_min, y_min, x_max, y_max = bbox[:4]
if factor == 1:
bbox = y_min, 1 - x_max, y_max, 1 - x_min
elif factor == 2:
bbox = 1 - x_max, 1 - y_max, 1 - x_min, 1 - y_min
elif factor == 3:
bbox = 1 - y_max, x_min, 1 - y_min, x_max
return bbox
@angle_2pi_range
def keypoint_rot90(keypoint, factor, rows, cols, **params):
"""Rotates a keypoint by 90 degrees CCW (see np.rot90)
Args:
keypoint (tuple): A keypoint `(x, y, angle, scale)`.
factor (int): Number of CCW rotations. Must be in range [0;3] See np.rot90.
rows (int): Image height.
cols (int): Image width.
Returns:
tuple: A keypoint `(x, y, angle, scale)`.
Raises:
ValueError: if factor not in set {0, 1, 2, 3}
"""
x, y, angle, scale = keypoint[:4]
if factor not in {0, 1, 2, 3}:
raise ValueError("Parameter n must be in set {0, 1, 2, 3}")
if factor == 1:
x, y, angle = y, (cols - 1) - x, angle - math.pi / 2
elif factor == 2:
x, y, angle = (cols - 1) - x, (rows - 1) - y, angle - math.pi
elif factor == 3:
x, y, angle = (rows - 1) - y, x, angle + math.pi / 2
return x, y, angle, scale
@preserve_channel_dim
def rotate(img, angle, interpolation=cv2.INTER_LINEAR, border_mode=cv2.BORDER_REFLECT_101, value=None):
height, width = img.shape[:2]
matrix = cv2.getRotationMatrix2D((width / 2, height / 2), angle, 1.0)
warp_fn = _maybe_process_in_chunks(
cv2.warpAffine, M=matrix, dsize=(width, height), flags=interpolation, borderMode=border_mode, borderValue=value
)
return warp_fn(img)
def bbox_rotate(bbox, angle, rows, cols):
"""Rotates a bounding box by angle degrees.
Args:
bbox (tuple): A bounding box `(x_min, y_min, x_max, y_max)`.
angle (int): Angle of rotation in degrees.
rows (int): Image rows.
cols (int): Image cols.
Returns:
A bounding box `(x_min, y_min, x_max, y_max)`.
"""
x_min, y_min, x_max, y_max = bbox[:4]
scale = cols / float(rows)
x = np.array([x_min, x_max, x_max, x_min]) - 0.5
y = np.array([y_min, y_min, y_max, y_max]) - 0.5
angle = np.deg2rad(angle)
x_t = (np.cos(angle) * x * scale + np.sin(angle) * y) / scale
y_t = -np.sin(angle) * x * scale + np.cos(angle) * y
x_t = x_t + 0.5
y_t = y_t + 0.5
x_min, x_max = min(x_t), max(x_t)
y_min, y_max = min(y_t), max(y_t)
return x_min, y_min, x_max, y_max
@angle_2pi_range
def keypoint_rotate(keypoint, angle, rows, cols, **params):
"""Rotate a keypoint by angle.
Args:
keypoint (tuple): A keypoint `(x, y, angle, scale)`.
angle (float): Rotation angle.
rows (int): Image height.
cols (int): Image width.
Returns:
tuple: A keypoint `(x, y, angle, scale)`.
"""
matrix = cv2.getRotationMatrix2D(((cols - 1) * 0.5, (rows - 1) * 0.5), angle, 1.0)
x, y, a, s = keypoint[:4]
x, y = cv2.transform(np.array([[[x, y]]]), matrix).squeeze()
return x, y, a + math.radians(angle), s
@preserve_channel_dim
def shift_scale_rotate(
img, angle, scale, dx, dy, interpolation=cv2.INTER_LINEAR, border_mode=cv2.BORDER_REFLECT_101, value=None
):
height, width = img.shape[:2]
center = (width / 2, height / 2)
matrix = cv2.getRotationMatrix2D(center, angle, scale)
matrix[0, 2] += dx * width
matrix[1, 2] += dy * height
warp_affine_fn = _maybe_process_in_chunks(
cv2.warpAffine, M=matrix, dsize=(width, height), flags=interpolation, borderMode=border_mode, borderValue=value
)
return warp_affine_fn(img)
@angle_2pi_range
def keypoint_shift_scale_rotate(keypoint, angle, scale, dx, dy, rows, cols, **params):
(
x,
y,
a,
s,
) = keypoint[:4]
height, width = rows, cols
center = (width / 2, height / 2)
matrix = cv2.getRotationMatrix2D(center, angle, scale)
matrix[0, 2] += dx * width
matrix[1, 2] += dy * height
x, y = cv2.transform(np.array([[[x, y]]]), matrix).squeeze()
angle = a + math.radians(angle)
scale = s * scale
return x, y, angle, scale
def bbox_shift_scale_rotate(bbox, angle, scale, dx, dy, rows, cols, **kwargs): # skipcq: PYL-W0613
x_min, y_min, x_max, y_max = bbox[:4]
height, width = rows, cols
center = (width / 2, height / 2)
matrix = cv2.getRotationMatrix2D(center, angle, scale)
matrix[0, 2] += dx * width
matrix[1, 2] += dy * height
x = np.array([x_min, x_max, x_max, x_min])
y = np.array([y_min, y_min, y_max, y_max])
ones = np.ones(shape=(len(x)))
points_ones = np.vstack([x, y, ones]).transpose()
points_ones[:, 0] *= width
points_ones[:, 1] *= height
tr_points = matrix.dot(points_ones.T).T
tr_points[:, 0] /= width
tr_points[:, 1] /= height
x_min, x_max = min(tr_points[:, 0]), max(tr_points[:, 0])
y_min, y_max = min(tr_points[:, 1]), max(tr_points[:, 1])
return x_min, y_min, x_max, y_max
@preserve_shape
def elastic_transform(
img,
alpha,
sigma,
alpha_affine,
interpolation=cv2.INTER_LINEAR,
border_mode=cv2.BORDER_REFLECT_101,
value=None,
random_state=None,
approximate=False,
):
"""Elastic deformation of images as described in [Simard2003]_ (with modifications).
Based on https://gist.github.com/erniejunior/601cdf56d2b424757de5
.. [Simard2003] Simard, Steinkraus and Platt, "Best Practices for
Convolutional Neural Networks applied to Visual Document Analysis", in
Proc. of the International Conference on Document Analysis and
Recognition, 2003.
"""
if random_state is None:
random_state = np.random.RandomState(1234)
height, width = img.shape[:2]
# Random affine
center_square = np.float32((height, width)) // 2
square_size = min((height, width)) // 3
alpha = float(alpha)
sigma = float(sigma)
alpha_affine = float(alpha_affine)
pts1 = np.float32(
[
center_square + square_size,
[center_square[0] + square_size, center_square[1] - square_size],
center_square - square_size,
]
)
pts2 = pts1 + random_state.uniform(-alpha_affine, alpha_affine, size=pts1.shape).astype(np.float32)
matrix = cv2.getAffineTransform(pts1, pts2)
warp_fn = _maybe_process_in_chunks(
cv2.warpAffine, M=matrix, dsize=(width, height), flags=interpolation, borderMode=border_mode, borderValue=value
)
img = warp_fn(img)
if approximate:
# Approximate computation smooth displacement map with a large enough kernel.
# On large images (512+) this is approximately 2X times faster
dx = random_state.rand(height, width).astype(np.float32) * 2 - 1
cv2.GaussianBlur(dx, (17, 17), sigma, dst=dx)
dx *= alpha
dy = random_state.rand(height, width).astype(np.float32) * 2 - 1
cv2.GaussianBlur(dy, (17, 17), sigma, dst=dy)
dy *= alpha
else:
dx = np.float32(gaussian_filter((random_state.rand(height, width) * 2 - 1), sigma) * alpha)
dy = np.float32(gaussian_filter((random_state.rand(height, width) * 2 - 1), sigma) * alpha)
x, y = np.meshgrid(np.arange(width), np.arange(height))
map_x = np.float32(x + dx)
map_y = np.float32(y + dy)
remap_fn = _maybe_process_in_chunks(
cv2.remap, map1=map_x, map2=map_y, interpolation=interpolation, borderMode=border_mode, borderValue=value
)
return remap_fn(img)
@preserve_channel_dim
def resize(img, height, width, interpolation=cv2.INTER_LINEAR):
img_height, img_width = img.shape[:2]
if height == img_height and width == img_width:
return img
resize_fn = _maybe_process_in_chunks(cv2.resize, dsize=(width, height), interpolation=interpolation)
return resize_fn(img)
@preserve_channel_dim
def scale(img, scale, interpolation=cv2.INTER_LINEAR):
height, width = img.shape[:2]
new_height, new_width = int(height * scale), int(width * scale)
return resize(img, new_height, new_width, interpolation)
def keypoint_scale(keypoint: Sequence[float], scale_x: float, scale_y: float):
"""Scales a keypoint by scale_x and scale_y.
Args:
keypoint (tuple): A keypoint `(x, y, angle, scale)`.
scale_x: Scale coefficient x-axis.
scale_y: Scale coefficient y-axis.
Returns:
A keypoint `(x, y, angle, scale)`.
"""
x, y, angle, scale = keypoint[:4]
return x * scale_x, y * scale_y, angle, scale * max(scale_x, scale_y)
def py3round(number):
"""Unified rounding in all python versions."""
if abs(round(number) - number) == 0.5:
return int(2.0 * round(number / 2.0))
return int(round(number))
def _func_max_size(img, max_size, interpolation, func):
height, width = img.shape[:2]
scale = max_size / float(func(width, height))
if scale != 1.0:
new_height, new_width = tuple(py3round(dim * scale) for dim in (height, width))
img = resize(img, height=new_height, width=new_width, interpolation=interpolation)
return img
@preserve_channel_dim
def longest_max_size(img, max_size, interpolation):
return _func_max_size(img, max_size, interpolation, max)
@preserve_channel_dim
def smallest_max_size(img, max_size, interpolation):
return _func_max_size(img, max_size, interpolation, min)
@preserve_channel_dim
def perspective(
img: np.ndarray,
matrix: np.ndarray,
max_width: int,
max_height: int,
border_val: Union[int, float, List[int], List[float], np.ndarray],
border_mode: int,
keep_size: bool,
interpolation: int,
):
h, w = img.shape[:2]
perspective_func = _maybe_process_in_chunks(
cv2.warpPerspective,
M=matrix,
dsize=(max_width, max_height),
borderMode=border_mode,
borderValue=border_val,
flags=interpolation,
)
warped = perspective_func(img)
if keep_size:
return resize(warped, h, w, interpolation=interpolation)
return warped
def perspective_bbox(
bbox: Sequence[float],
height: int,
width: int,
matrix: np.ndarray,
max_width: int,
max_height: int,
keep_size: bool,
):
x1, y1, x2, y2 = denormalize_bbox(bbox, height, width)
points = np.array([[x1, y1], [x2, y1], [x2, y2], [x1, y2]], dtype=np.float32)
x1, y1, x2, y2 = float("inf"), float("inf"), 0, 0
for pt in points:
pt = perspective_keypoint(pt.tolist() + [0, 0], height, width, matrix, max_width, max_height, keep_size)
x, y = pt[:2]
x = np.clip(x, 0, width if keep_size else max_width)
y = np.clip(y, 0, height if keep_size else max_height)
x1 = min(x1, x)
x2 = max(x2, x)
y1 = min(y1, y)
y2 = max(y2, y)
x = np.clip([x1, x2], 0, width if keep_size else max_width)
y = np.clip([y1, y2], 0, height if keep_size else max_height)
return normalize_bbox(
(x[0], y[0], x[1], y[1]), height if keep_size else max_height, width if keep_size else max_width
)
def rotation2DMatrixToEulerAngles(matrix: np.ndarray):
return np.arctan2(matrix[1, 0], matrix[0, 0])
@angle_2pi_range
def perspective_keypoint(
keypoint: Union[List[int], List[float]],
height: int,
width: int,
matrix: np.ndarray,
max_width: int,
max_height: int,
keep_size: bool,
):
x, y, angle, scale = keypoint
keypoint_vector = np.array([x, y], dtype=np.float32).reshape([1, 1, 2])
x, y = cv2.perspectiveTransform(keypoint_vector, matrix)[0, 0]
angle += rotation2DMatrixToEulerAngles(matrix[:2, :2])
scale_x = np.sign(matrix[0, 0]) * np.sqrt(matrix[0, 0] ** 2 + matrix[0, 1] ** 2)
scale_y = np.sign(matrix[1, 1]) * np.sqrt(matrix[1, 0] ** 2 + matrix[1, 1] ** 2)
scale *= max(scale_x, scale_y)
if keep_size:
scale_x = width / max_width
scale_y = height / max_height
return keypoint_scale((x, y, angle, scale), scale_x, scale_y)
return x, y, angle, scale
def _is_identity_matrix(matrix: skimage.transform.ProjectiveTransform) -> bool:
return np.allclose(matrix.params, np.eye(3, dtype=np.float32))
@preserve_channel_dim
def warp_affine(
image: np.ndarray,
matrix: skimage.transform.ProjectiveTransform,
interpolation: int,
cval: Union[int, float, Sequence[int], Sequence[float]],
mode: int,
output_shape: Sequence[int],
) -> np.ndarray:
if _is_identity_matrix(matrix):
return image
dsize = int(np.round(output_shape[1])), int(np.round(output_shape[0]))
warp_fn = _maybe_process_in_chunks(
cv2.warpAffine, M=matrix.params[:2], dsize=dsize, flags=interpolation, borderMode=mode, borderValue=cval
)
tmp = warp_fn(image)
return tmp
@angle_2pi_range
def keypoint_affine(
keypoint: Sequence[float],
matrix: skimage.transform.ProjectiveTransform,
scale: dict,
) -> Sequence[float]:
if _is_identity_matrix(matrix):
return keypoint
x, y, a, s = keypoint[:4]
x, y = skimage.transform.matrix_transform(np.array([[x, y]]), matrix.params).ravel()
a += rotation2DMatrixToEulerAngles(matrix.params[:2])
s *= np.max([scale["x"], scale["y"]])
return x, y, a, s
def bbox_affine(
bbox: Sequence[float],
matrix: skimage.transform.ProjectiveTransform,
rows: int,
cols: int,
output_shape: Sequence[int],
) -> Sequence[float]:
if _is_identity_matrix(matrix):
return bbox
x_min, y_min, x_max, y_max = denormalize_bbox(bbox, rows, cols)
points = np.array(
[
[x_min, y_min],
[x_max, y_min],
[x_max, y_max],
[x_min, y_max],
]
)
points = skimage.transform.matrix_transform(points, matrix.params)
points[:, 0] = np.clip(points[:, 0], 0, output_shape[1])
points[:, 1] = np.clip(points[:, 1], 0, output_shape[0])
x_min = np.min(points[:, 0])
x_max = np.max(points[:, 0])
y_min = np.min(points[:, 1])
y_max = np.max(points[:, 1])
return normalize_bbox((x_min, y_min, x_max, y_max), output_shape[0], output_shape[1])
@preserve_channel_dim
def safe_rotate(
img: np.ndarray,
angle: int = 0,
interpolation: int = cv2.INTER_LINEAR,
value: int = None,
border_mode: int = cv2.BORDER_REFLECT_101,
):
old_rows, old_cols = img.shape[:2]
# getRotationMatrix2D needs coordinates in reverse order (width, height) compared to shape
image_center = (old_cols / 2, old_rows / 2)
# Rows and columns of the rotated image (not cropped)
new_rows, new_cols = safe_rotate_enlarged_img_size(angle=angle, rows=old_rows, cols=old_cols)
# Rotation Matrix
rotation_mat = cv2.getRotationMatrix2D(image_center, angle, 1.0)
# Shift the image to create padding
rotation_mat[0, 2] += new_cols / 2 - image_center[0]
rotation_mat[1, 2] += new_rows / 2 - image_center[1]
# CV2 Transformation function
warp_affine_fn = _maybe_process_in_chunks(
cv2.warpAffine,
M=rotation_mat,
dsize=(new_cols, new_rows),
flags=interpolation,
borderMode=border_mode,
borderValue=value,
)
# rotate image with the new bounds
rotated_img = warp_affine_fn(img)
# Resize image back to the original size
resized_img = resize(img=rotated_img, height=old_rows, width=old_cols, interpolation=interpolation)
return resized_img
def bbox_safe_rotate(bbox, angle, rows, cols):
old_rows = rows
old_cols = cols
# Rows and columns of the rotated image (not cropped)
new_rows, new_cols = safe_rotate_enlarged_img_size(angle=angle, rows=old_rows, cols=old_cols)
col_diff = int(np.ceil(abs(new_cols - old_cols) / 2))
row_diff = int(np.ceil(abs(new_rows - old_rows) / 2))
# Normalize shifts
norm_col_shift = col_diff / new_cols
norm_row_shift = row_diff / new_rows
# shift bbox
shifted_bbox = (
bbox[0] + norm_col_shift,
bbox[1] + norm_row_shift,
bbox[2] + norm_col_shift,
bbox[3] + norm_row_shift,
)
rotated_bbox = bbox_rotate(bbox=shifted_bbox, angle=angle, rows=new_rows, cols=new_cols)
# Bounding boxes are scale invariant, so this does not need to be rescaled to the old size
return rotated_bbox
def keypoint_safe_rotate(keypoint, angle, rows, cols):
old_rows = rows
old_cols = cols
# Rows and columns of the rotated image (not cropped)
new_rows, new_cols = safe_rotate_enlarged_img_size(angle=angle, rows=old_rows, cols=old_cols)
col_diff = int(np.ceil(abs(new_cols - old_cols) / 2))
row_diff = int(np.ceil(abs(new_rows - old_rows) / 2))
# Shift keypoint
shifted_keypoint = (keypoint[0] + col_diff, keypoint[1] + row_diff, keypoint[2], keypoint[3])
# Rotate keypoint
rotated_keypoint = keypoint_rotate(shifted_keypoint, angle, rows=new_rows, cols=new_cols)
# Scale the keypoint
return keypoint_scale(rotated_keypoint, old_cols / new_cols, old_rows / new_rows)
def safe_rotate_enlarged_img_size(angle: float, rows: int, cols: int):
deg_angle = abs(angle)
# The rotation angle
angle = np.deg2rad(deg_angle % 90)
# The width of the frame to contain the rotated image
r_cols = cols * np.cos(angle) + rows * np.sin(angle)
# The height of the frame to contain the rotated image
r_rows = cols * np.sin(angle) + rows * np.cos(angle)
# The above calculations work as is for 0<90 degrees, and for 90<180 the cols and rows are flipped
if deg_angle > 90:
return int(r_cols), int(r_rows)
else:
return int(r_rows), int(r_cols)
@clipped
def piecewise_affine(
img: np.ndarray,
matrix: skimage.transform.PiecewiseAffineTransform,
interpolation: int,
mode: str,
cval: float,
) -> np.ndarray:
return skimage.transform.warp(
img, matrix, order=interpolation, mode=mode, cval=cval, preserve_range=True, output_shape=img.shape
)
def to_distance_maps(
keypoints: Sequence[Sequence[float]], height: int, width: int, inverted: bool = False
) -> np.ndarray:
"""Generate a ``(H,W,N)`` array of distance maps for ``N`` keypoints.
The ``n``-th distance map contains at every location ``(y, x)`` the
euclidean distance to the ``n``-th keypoint.
This function can be used as a helper when augmenting keypoints with a
method that only supports the augmentation of images.
Args:
keypoint (sequence of float): keypoint coordinates
height (int): image height
width (int): image width
inverted (bool): If ``True``, inverted distance maps are returned where each
distance value d is replaced by ``d/(d+1)``, i.e. the distance
maps have values in the range ``(0.0, 1.0]`` with ``1.0`` denoting
exactly the position of the respective keypoint.
Returns:
(H,W,N) ndarray
A ``float32`` array containing ``N`` distance maps for ``N``
keypoints. Each location ``(y, x, n)`` in the array denotes the
euclidean distance at ``(y, x)`` to the ``n``-th keypoint.
If `inverted` is ``True``, the distance ``d`` is replaced
by ``d/(d+1)``. The height and width of the array match the
height and width in ``KeypointsOnImage.shape``.
"""
distance_maps = np.zeros((height, width, len(keypoints)), dtype=np.float32)
yy = np.arange(0, height)
xx = np.arange(0, width)
grid_xx, grid_yy = np.meshgrid(xx, yy)
for i, (x, y) in enumerate(keypoints):
distance_maps[:, :, i] = (grid_xx - x) ** 2 + (grid_yy - y) ** 2
distance_maps = np.sqrt(distance_maps)
if inverted:
return 1 / (distance_maps + 1)
return distance_maps
def from_distance_maps(
distance_maps: np.ndarray,
inverted: bool,
if_not_found_coords: Optional[Union[Sequence[int], dict]],
threshold: Optional[float] = None,
) -> List[Tuple[float, float]]:
"""Convert outputs of ``to_distance_maps()`` to ``KeypointsOnImage``.
This is the inverse of `to_distance_maps`.
Args:
distance_maps (np.ndarray): The distance maps. ``N`` is the number of keypoints.
inverted (bool): Whether the given distance maps were generated in inverted mode
(i.e. :func:`KeypointsOnImage.to_distance_maps` was called with ``inverted=True``) or in non-inverted mode.
if_not_found_coords (tuple, list, dict or None, optional):
Coordinates to use for keypoints that cannot be found in `distance_maps`.
* If this is a ``list``/``tuple``, it must contain two ``int`` values.
* If it is a ``dict``, it must contain the keys ``x`` and ``y`` with each containing one ``int`` value.
* If this is ``None``, then the keypoint will not be added.
threshold (float): The search for keypoints works by searching for the
argmin (non-inverted) or argmax (inverted) in each channel. This
parameters contains the maximum (non-inverted) or minimum (inverted) value to accept in order to view a hit
as a keypoint. Use ``None`` to use no min/max.
nb_channels (None, int): Number of channels of the image on which the keypoints are placed.
Some keypoint augmenters require that information. If set to ``None``, the keypoint's shape will be set
to ``(height, width)``, otherwise ``(height, width, nb_channels)``.
"""
if distance_maps.ndim != 3:
raise ValueError(
f"Expected three-dimensional input, "
f"got {distance_maps.ndim} dimensions and shape {distance_maps.shape}."
)
height, width, nb_keypoints = distance_maps.shape
drop_if_not_found = False
if if_not_found_coords is None:
drop_if_not_found = True
if_not_found_x = -1
if_not_found_y = -1
elif isinstance(if_not_found_coords, (tuple, list)):
if len(if_not_found_coords) != 2:
raise ValueError(
f"Expected tuple/list 'if_not_found_coords' to contain exactly two entries, "
f"got {len(if_not_found_coords)}."
)
if_not_found_x = if_not_found_coords[0]
if_not_found_y = if_not_found_coords[1]
elif isinstance(if_not_found_coords, dict):
if_not_found_x = if_not_found_coords["x"]
if_not_found_y = if_not_found_coords["y"]
else:
raise ValueError(
f"Expected if_not_found_coords to be None or tuple or list or dict, got {type(if_not_found_coords)}."
)
keypoints = []
for i in range(nb_keypoints):
if inverted:
hitidx_flat = np.argmax(distance_maps[..., i])
else:
hitidx_flat = np.argmin(distance_maps[..., i])
hitidx_ndim = np.unravel_index(hitidx_flat, (height, width))
if not inverted and threshold is not None:
found = distance_maps[hitidx_ndim[0], hitidx_ndim[1], i] < threshold
elif inverted and threshold is not None:
found = distance_maps[hitidx_ndim[0], hitidx_ndim[1], i] >= threshold
else:
found = True
if found:
keypoints.append((float(hitidx_ndim[1]), float(hitidx_ndim[0])))
else:
if not drop_if_not_found:
keypoints.append((if_not_found_x, if_not_found_y))
return keypoints
def keypoint_piecewise_affine(
keypoint: Sequence[float],
matrix: skimage.transform.PiecewiseAffineTransform,
h: int,
w: int,
keypoints_threshold: float,
) -> Tuple[float, float, float, float]:
x, y, a, s = keypoint
dist_maps = to_distance_maps([(x, y)], h, w, True)
dist_maps = piecewise_affine(dist_maps, matrix, 0, "constant", 0)
x, y = from_distance_maps(dist_maps, True, {"x": -1, "y": -1}, keypoints_threshold)[0]
return x, y, a, s
def bbox_piecewise_affine(
bbox: Sequence[float],
matrix: skimage.transform.PiecewiseAffineTransform,
h: int,
w: int,
keypoints_threshold: float,
) -> Tuple[float, float, float, float]:
x1, y1, x2, y2 = denormalize_bbox(tuple(bbox), h, w)
keypoints = [
(x1, y1),
(x2, y1),
(x2, y2),
(x1, y2),
]
dist_maps = to_distance_maps(keypoints, h, w, True)
dist_maps = piecewise_affine(dist_maps, matrix, 0, "constant", 0)
keypoints = from_distance_maps(dist_maps, True, {"x": -1, "y": -1}, keypoints_threshold)
keypoints = [i for i in keypoints if 0 <= i[0] < w and 0 <= i[1] < h]
keypoints_arr = np.array(keypoints)
x1 = keypoints_arr[:, 0].min()
y1 = keypoints_arr[:, 1].min()
x2 = keypoints_arr[:, 0].max()
y2 = keypoints_arr[:, 1].max()
return normalize_bbox((x1, y1, x2, y2), h, w)