def get_transform(self, img):
h, w = img.shape[:2]
do = self._rand_range() < self.prob
if do:
return Rotation90Transform(h, w)
else:
return NoOpTransform()
def get_transform(self, image):
h, w = image.shape[:2]
center = None
if self.is_range:
angle = np.random.uniform(self.angle[0], self.angle[1])
if self.center is not None:
center = (
np.random.uniform(self.center[0][0], self.center[1][0]),
np.random.uniform(self.center[0][1], self.center[1][1]),
)
else:
angle = np.random.choice(self.angle)
if self.center is not None:
center = np.random.choice(self.center)
if center is not None:
center = (w * center[0], h * center[1]
) # Convert to absolute coordinates
if angle % 360 == 0:
return NoOpTransform()
return RotationTransform(h,
w,
angle,
expand=self.expand,
center=center,
interp=self.interp)
def get_transform(self, image):
h, w = image.shape[:2]
trans_x = int(self.t_range * w)
trans_y = int(self.t_range * h)
trans_x = random.randint(-trans_x, trans_x + 1)
trans_y = random.randint(-trans_y, trans_y + 1)
if trans_x == trans_y == 0:
return NoOpTransform()
else:
return TranslationTransform(trans_x, trans_y)
def __call__(self, dataset_dict):
"""
Args:
dict: a dict in standard model input format. See tutorials for details.
Returns:
list[dict]:
a list of dicts, which contain augmented version of the input image.
The total number of dicts is ``len(min_sizes) * (2 if flip else 1)``.
Each dict has field "transforms" which is a TransformList,
containing the transforms that are used to generate this image.
"""
numpy_image = dataset_dict["image"].permute(1, 2, 0).numpy()
shape = numpy_image.shape
orig_shape = (dataset_dict["height"], dataset_dict["width"])
if shape[:2] != orig_shape:
# It transforms the "original" image in the dataset to the input image
pre_tfm = ResizeTransform(orig_shape[0], orig_shape[1], shape[0],
shape[1])
else:
pre_tfm = NoOpTransform()
# Create all combinations of augmentations to use
aug_candidates = [] # each element is a list[Augmentation]
for min_size in self.min_sizes:
resize = ResizeShortestEdge(min_size, self.max_size)
aug_candidates.append([resize]) # resize only
if self.flip:
flip = RandomFlip(prob=1.0)
aug_candidates.append([resize, flip]) # resize + flip
# Apply all the augmentations
ret = []
for aug in aug_candidates:
new_image, tfms = apply_augmentations(aug, np.copy(numpy_image))
torch_image = torch.from_numpy(
np.ascontiguousarray(new_image.transpose(2, 0, 1)))
dic = copy.deepcopy(dataset_dict)
dic["transforms"] = pre_tfm + tfms
dic["image"] = torch_image
if self.proposal_topk is not None:
image_shape = new_image.shape[:2] # h, w
transform_proposals(dic,
image_shape,
tfms,
proposal_topk=self.proposal_topk)
ret.append(dic)
return ret
def get_transform(self, image):
if self.direction == "":
return NoOpTransform()
direction = self.direction
do_horizontal = False
do_vertical = False
if direction == "horizontal" or direction == "both":
do_horizontal = (self._rand_range() < self.prob)
if direction == "vertical" or direction == "both":
do_vertical = (self._rand_range() < self.prob)
if do_horizontal:
direction = "horizontal"
if do_vertical:
direction = "vertical"
if do_horizontal and do_vertical:
direction = "both"
return FlipTransform(direction)
def __call__(self, dataset_dict):
"""
Args:
dict: a detection dataset dict in standard format
Returns:
list[dict]:
a list of dataset dicts, which contain augmented version of the input image.
The total number of dicts is ``len(min_sizes) * (2 if flip else 1)``.
Each dict has field "transforms" which is a TransformList,
containing the transforms that are used to generate this image.
"""
numpy_image = dataset_dict["image"].permute(1, 2, 0).numpy()
shape = numpy_image.shape
orig_shape = (dataset_dict["height"], dataset_dict["width"])
if shape[:2] != orig_shape:
# It transforms the "original" image in the dataset to the input image
pre_tfm = ResizeTransform(orig_shape[0], orig_shape[1], shape[0],
shape[1])
else:
pre_tfm = NoOpTransform()
# Create all combinations of augmentations to use
tfm_gen_candidates = [] # each element is a list[TransformGen]
for min_size in self.min_sizes:
resize = ResizeShortestEdge(min_size, self.max_size)
tfm_gen_candidates.append([resize]) # resize only
if self.flip:
flip = RandomFlip(prob=1.0)
tfm_gen_candidates.append([resize, flip]) # resize + flip
if self.vertical_flip:
flip = RandomFlip(prob=1.0, horizontal=False, vertical=True)
tfm_gen_candidates.append([resize, flip]) # resize + flip
tfm_gen_candidates.append([resize, Rotation90Gen(prob=1.0)])
# tfm_gen_candidates.append([resize, CLAHEGen(prob=1.0, img_format=self.image_format)])
tfm_gen_candidates.append([
resize,
Rotation90Gen(prob=1.0),
Rotation90Gen(prob=1.0),
Rotation90Gen(prob=1.0)
])
# Apply all the augmentations
ret = []
for tfm_gen in tfm_gen_candidates:
new_image, tfms = apply_transform_gens(tfm_gen,
np.copy(numpy_image))
torch_image = torch.from_numpy(
np.ascontiguousarray(new_image.transpose(2, 0, 1)))
dic = copy.deepcopy(dataset_dict)
dic["transforms"] = pre_tfm + tfms
dic["image"] = torch_image
# pop original image width and height to prevent rescale boxes in postprocess of detector
dic.pop("width")
dic.pop("height")
ret.append(dic)
return ret
def get_transform(self, img):
do = self._rand_range() < self.prob
if do:
return CLAHE(self.img_format)
else:
return NoOpTransform()