def transform(self, Xb, yb):
Xb, yb = super(RandomFlipBatchIteratorMixin, self).transform(Xb, yb)
Xb_flipped = Xb.copy()
yb_flipped = yb.copy()
if self.flip_horizontal_p > 0:
horizontal_flip_idx = get_random_idx(Xb, self.flip_horizontal_p)
Xb_flipped[horizontal_flip_idx] = Xb_flipped[horizontal_flip_idx, :, :, ::-1]
yb_flipped[horizontal_flip_idx, 0] = 1 - yb_flipped[horizontal_flip_idx, 0] - yb_flipped[horizontal_flip_idx, 2]
if self.flip_vertical_p > 0:
vertical_flip_idx = get_random_idx(Xb, self.flip_vertical_p)
Xb_flipped[vertical_flip_idx] = Xb_flipped[vertical_flip_idx, :, ::-1, :]
yb_flipped[vertical_flip_idx, 1] = 1 - yb_flipped[vertical_flip_idx, 1] - yb_flipped[vertical_flip_idx, 3]
return Xb_flipped, yb_flipped
def transform(self, Xb, yb):
Xb, yb = super(RandomFlipBatchIteratorMixin, self).transform(Xb, yb)
Xb_flipped = Xb.copy()
yb_flipped = yb.copy()
if self.flip_horizontal_p > 0:
horizontal_flip_idx = get_random_idx(Xb, self.flip_horizontal_p)
Xb_flipped[horizontal_flip_idx] = Xb_flipped[
horizontal_flip_idx, :, :, ::-1]
yb_flipped[horizontal_flip_idx,
0] = 1 - yb_flipped[horizontal_flip_idx,
0] - yb_flipped[horizontal_flip_idx,
2]
if self.flip_vertical_p > 0:
vertical_flip_idx = get_random_idx(Xb, self.flip_vertical_p)
Xb_flipped[vertical_flip_idx] = Xb_flipped[
vertical_flip_idx, :, ::-1, :]
yb_flipped[vertical_flip_idx,
1] = 1 - yb_flipped[vertical_flip_idx,
1] - yb_flipped[vertical_flip_idx,
3]
return Xb_flipped, yb_flipped
def transform(self, Xb, yb):
Xb, yb = super(AffineTransformPtsBatchIteratorMixin,
self).transform(Xb, yb)
# Skip if affine_p is 0. Setting affine_p may be useful for quickly
# disabling affine transformation
if self.affine_p == 0:
return Xb, yb
image_height = Xb.shape[2]
image_width = Xb.shape[3]
assert image_height == image_width
idx = get_random_idx(Xb, self.affine_p)
Xb_transformed = Xb.copy()
yb_transformed = yb.copy()
for i in idx:
scale = np.random.choice(self.affine_scale_choices)
rotation = np.random.choice(self.affine_rotation_choices)
shear = np.random.choice(self.affine_shear_choices)
translation_y = np.random.choice(self.affine_translation_choices)
translation_x = np.random.choice(self.affine_translation_choices)
transform_kwargs = dict(
scale=scale, rotation=rotation,
shear=shear,
translation_y=translation_y,
translation_x=translation_x,
return_tform=True
)
img_transformed, tform = im_affine_transform(
Xb[i], **transform_kwargs
)
Xb_transformed[i] = img_transformed
pts = yb_transformed[i].reshape(-1, 2) * image_height
pts = tform.inverse(pts).ravel()
yb_transformed[i] = pts / image_height
return Xb_transformed, yb_transformed
def transform(self, Xb, yb):
Xb, yb = super(AffineTransformPtsBatchIteratorMixin,
self).transform(Xb, yb)
# Skip if affine_p is 0. Setting affine_p may be useful for quickly
# disabling affine transformation
if self.affine_p == 0:
return Xb, yb
image_height = Xb.shape[2]
image_width = Xb.shape[3]
assert image_height == image_width
idx = get_random_idx(Xb, self.affine_p)
Xb_transformed = Xb.copy()
yb_transformed = yb.copy()
for i in idx:
scale = np.random.choice(self.affine_scale_choices)
rotation = np.random.choice(self.affine_rotation_choices)
shear = np.random.choice(self.affine_shear_choices)
translation_y = np.random.choice(self.affine_translation_choices)
translation_x = np.random.choice(self.affine_translation_choices)
transform_kwargs = dict(scale=scale,
rotation=rotation,
shear=shear,
translation_y=translation_y,
translation_x=translation_x,
return_tform=True)
img_transformed, tform = im_affine_transform(
Xb[i], **transform_kwargs)
Xb_transformed[i] = img_transformed
pts = yb_transformed[i].reshape(-1, 2) * image_height
pts = tform.inverse(pts).ravel()
yb_transformed[i] = pts / image_height
return Xb_transformed, yb_transformed
def transform(self, Xb, yb):
Xb, yb = super(AffineTransformBBoxBatchIteratorMixin,
self).transform(Xb, yb)
# Skip if affine_p is 0. Setting affine_p may be useful for quickly
# disabling affine transformation
if self.affine_p == 0:
return Xb, yb
image_height = Xb.shape[2]
image_width = Xb.shape[3]
assert image_height == image_width
idx = get_random_idx(Xb, self.affine_p)
Xb_transformed = Xb.copy()
yb_transformed = yb.copy()
for i in idx:
scale = np.random.choice(self.affine_scale_choices)
rotation = np.random.choice(self.affine_rotation_choices)
shear = np.random.choice(self.affine_shear_choices)
translation_y = np.random.choice(self.affine_translation_choices)
translation_x = np.random.choice(self.affine_translation_choices)
transform_kwargs = dict(scale=scale,
rotation=rotation,
shear=shear,
translation_y=translation_y,
translation_x=translation_x)
img_transformed = im_affine_transform(Xb[i], **transform_kwargs)
bbox_transformed = get_transformed_bbox(yb[i] * image_width,
image_width, image_height,
**transform_kwargs)
Xb_transformed[i] = img_transformed
yb_transformed[i] = np.array(bbox_transformed).astype(
np.float32) / image_width
return Xb_transformed, yb_transformed
def transform(self, Xb, yb):
Xb, yb = super(AffineTransformBBoxBatchIteratorMixin,
self).transform(Xb, yb)
# Skip if affine_p is 0. Setting affine_p may be useful for quickly
# disabling affine transformation
if self.affine_p == 0:
return Xb, yb
image_height = Xb.shape[2]
image_width = Xb.shape[3]
assert image_height == image_width
idx = get_random_idx(Xb, self.affine_p)
Xb_transformed = Xb.copy()
yb_transformed = yb.copy()
for i in idx:
scale = np.random.choice(self.affine_scale_choices)
rotation = np.random.choice(self.affine_rotation_choices)
shear = np.random.choice(self.affine_shear_choices)
translation_y = np.random.choice(self.affine_translation_choices)
translation_x = np.random.choice(self.affine_translation_choices)
transform_kwargs = dict(
scale=scale, rotation=rotation,
shear=shear,
translation_y=translation_y,
translation_x=translation_x
)
img_transformed = im_affine_transform(
Xb[i], **transform_kwargs)
bbox_transformed = get_transformed_bbox(
yb[i] * image_width, image_width, image_height, **transform_kwargs)
Xb_transformed[i] = img_transformed
yb_transformed[i] = np.array(bbox_transformed).astype(np.float32) / image_width
return Xb_transformed, yb_transformed
