def bilinear_resample(x,
warp,
batch_shape=None,
input_image_dims=None,
_=None):
if batch_shape is None:
if not _tf.executing_eagerly():
raise Exception(
'batch_shape must be provided when in tensorflow graph mode')
batch_shape = _ivy.shape(x)[:-3]
if input_image_dims is None:
if not _tf.executing_eagerly():
raise Exception(
'input_image_dims must be provided when in tensorflow graph mode'
)
input_image_dims = _ivy.shape(x)[-3:-1]
batch_shape = list(batch_shape)
input_image_dims = list(input_image_dims)
batch_shape_product = _reduce(_mul, batch_shape, 1)
warp_flat = _tf.reshape(warp, [batch_shape_product] + [-1, 2])
mat_flat = _tf.reshape(x, [batch_shape_product] + input_image_dims + [-1])
global _tfa
if _tfa is None:
try:
import tensorflow_addons as _tfa
except:
raise Exception(
'Unable to import tensorflow_addons, verify this is correctly installed.'
)
interpolated_flat = _tfa.image.interpolate_bilinear(mat_flat,
warp_flat,
indexing='xy')
return _tf.reshape(interpolated_flat,
batch_shape + input_image_dims + [-1])
def stack_images(images,
desired_aspect_ratio=(1, 1),
batch_shape=None,
image_dims=None):
num_images = len(images)
if num_images == 0:
raise Exception('At least 1 image must be provided')
if batch_shape is None:
batch_shape = _ivy.shape(images[0])[:-3]
if image_dims is None:
image_dims = _ivy.shape(images[0])[-3:-1]
num_batch_dims = len(batch_shape)
if num_images == 1:
return images[0]
img_ratio = image_dims[0] / image_dims[1]
desired_img_ratio = desired_aspect_ratio[0] / desired_aspect_ratio[1]
stack_ratio = img_ratio * desired_img_ratio
stack_height = (num_images / stack_ratio)**0.5
stack_height_int = math.ceil(stack_height)
stack_width_int = math.ceil(num_images / stack_height)
image_rows = list()
for i in range(stack_width_int):
images_to_concat = images[i * stack_height_int:(i + 1) *
stack_height_int]
images_to_concat += [_ivy.zeros_like(images[0])
] * (stack_height_int - len(images_to_concat))
image_rows.append(_ivy.concatenate(images_to_concat, num_batch_dims))
return _ivy.concatenate(image_rows, num_batch_dims + 1)
def gradient_image(x, batch_shape=None, image_dims=None, dev=None):
x_shape = _ivy.shape(x)
if batch_shape is None:
if not _tf.executing_eagerly():
raise Exception(
'batch_shape must be provided when in tensorflow graph mode')
batch_shape = x_shape[:-3]
if image_dims is None:
if not _tf.executing_eagerly():
raise Exception(
'image_dims must be provided when in tensorflow graph mode')
image_dims = x_shape[-3:-1]
if dev is None:
dev = _ivy.get_device(x)
# to list
batch_shape = list(batch_shape)
image_dims = list(image_dims)
num_dims = x_shape[-1]
# BS x H-1 x W x D
dy = x[..., 1:, :, :] - x[..., :-1, :, :]
# BS x H x W-1 x D
dx = x[..., :, 1:, :] - x[..., :, :-1, :]
# BS x H x W x D
dy = _ivy.concatenate(
(dy, _ivy.zeros(batch_shape + [1, image_dims[1], num_dims], dev=dev)),
-3)
dx = _ivy.concatenate(
(dx, _ivy.zeros(batch_shape + [image_dims[0], 1, num_dims], dev=dev)),
-2)
# BS x H x W x D, BS x H x W x D
return dy, dx
def bilinear_resample(x, warp):
batch_shape = _ivy.shape(x)[:-3]
input_image_dims = _ivy.shape(x)[-3:-1]
num_feats = x.shape[-1]
batch_shape = list(batch_shape)
input_image_dims = list(input_image_dims)
batch_shape_product = _reduce(_mul, batch_shape, 1)
warp_flat = _tf.reshape(warp, [batch_shape_product] + [-1, 2])
mat_flat = _tf.reshape(x, [batch_shape_product] + input_image_dims + [-1])
global _tfa
if _tfa is None:
try:
import tensorflow_addons as _tfa
except:
raise Exception('Unable to import tensorflow_addons, verify this is correctly installed.')
ret = _tfa.image.interpolate_bilinear(mat_flat, warp_flat, indexing='xy')
return _tf.reshape(ret, batch_shape + [-1, num_feats])
def gradient_image(x):
x_shape = _ivy.shape(x)
batch_shape = x_shape[:-3]
image_dims = x_shape[-3:-1]
dev_str = _ivy.dev_str(x)
# to list
batch_shape = list(batch_shape)
image_dims = list(image_dims)
num_dims = x_shape[-1]
# BS x H-1 x W x D
dy = x[..., 1:, :, :] - x[..., :-1, :, :]
# BS x H x W-1 x D
dx = x[..., :, 1:, :] - x[..., :, :-1, :]
# BS x H x W x D
dy = _ivy.concatenate((dy, _ivy.zeros(batch_shape + [1, image_dims[1], num_dims], dev_str=dev_str)), -3)
dx = _ivy.concatenate((dx, _ivy.zeros(batch_shape + [image_dims[0], 1, num_dims], dev_str=dev_str)), -2)
# BS x H x W x D, BS x H x W x D
return dy, dx