def get_transform(self, img):
_, w = img.shape[:2]
do = self._rand_range() < self.prob
if do:
return HFlipTransform(w)
else:
return NoOpTransform()
def get_transform(self, img):
h, w = img.shape[:2]
do = self._rand_range() < self.prob
if do:
if self.horizontal:
return HFlipTransform(w)
elif self.vertical:
return VFlipTransform(h)
else:
return NoOpTransform()
def get_transform(self, img):
_, w = img.shape[:2]
do_horiz = self._rand_range() < self.prob/2
do_vert = self._rand_range() >= self.prob/2 and self._rand_range() < self.prob
if do_horiz:
return HFlipTransform(w)
elif do_vert:
return VFlipTransform(w)
else:
return NoOpTransform()
def get_transform(self, img):
h, w = img.shape[:2]
# do = self._rand_range() < self.prob # original implementation
do = False # No flip!
if do:
if self.horizontal:
return HFlipTransform(w)
elif self.vertical:
return VFlipTransform(h)
else:
return NoOpTransform()
def get_transform(self, img):
h, w = img.shape[:2]
do = self.do[self.idx]
# print('idx', self.idx)
# print('do', do)
# print('self.do', self.do)
self.idx += 1
if self.idx == self.ring:
self.idx = 0
self.do = [self._rand_range() < self.prob] * self.ring
if do:
if self.horizontal:
# print('HFlipTransform')
return HFlipTransform(w)
# print([HFlipTransform(w)]*self.ring)
# yield from [HFlipTransform(w)]*self.ring
elif self.vertical:
return VFlipTransform(h)
else:
# print('NoOpTransform')
return NoOpTransform()
def get_transform(self, img):
h, w = img.shape[:2]
if self.horizontal:
return HFlipTransform(w)
elif self.vertical:
return VFlipTransform(h)
Apply the resizing transform on rotated boxes. For details of how these (approximation)
formulas are derived, please refer to :meth:`RotatedBoxes.scale`.
Args:
rotated_boxes (ndarray): Nx5 floating point array of
(x_center, y_center, width, height, angle_degrees) format
in absolute coordinates.
"""
scale_factor_x = transform.new_w * 1.0 / transform.w
scale_factor_y = transform.new_h * 1.0 / transform.h
rotated_boxes[:, 0] *= scale_factor_x
rotated_boxes[:, 1] *= scale_factor_y
theta = rotated_boxes[:, 4] * np.pi / 180.0
c = np.cos(theta)
s = np.sin(theta)
rotated_boxes[:, 2] *= np.sqrt(
np.square(scale_factor_x * c) + np.square(scale_factor_y * s))
rotated_boxes[:, 3] *= np.sqrt(
np.square(scale_factor_x * s) + np.square(scale_factor_y * c))
rotated_boxes[:, 4] = np.arctan2(scale_factor_x * s,
scale_factor_y * c) * 180 / np.pi
return rotated_boxes
HFlipTransform.register_type("rotated_box", HFlip_rotated_box)
ResizeTransform.register_type("rotated_box", Resize_rotated_box)
# not necessary any more with latest fvcore
NoOpTransform.register_type("rotated_box", lambda t, x: x)
