def grayscale_image_resizer(image, masks=None):
"""Convert to grayscale before applying image_resizer_fn.
Args:
image: A 3D tensor of shape [height, width, 3]
masks: (optional) rank 3 float32 tensor with shape [num_instances, height,
width] containing instance masks.
Returns:
Note that the position of the resized_image_shape changes based on whether
masks are present.
resized_image: A 3D tensor of shape [new_height, new_width, 1],
where the image has been resized (with bilinear interpolation) so that
min(new_height, new_width) == min_dimension or
max(new_height, new_width) == max_dimension.
resized_masks: If masks is not None, also outputs masks. A 3D tensor of
shape [num_instances, new_height, new_width].
resized_image_shape: A 1D tensor of shape [3] containing shape of the
resized image.
"""
# image_resizer_fn returns [resized_image, resized_image_shape] if
# mask==None, otherwise it returns
# [resized_image, resized_mask, resized_image_shape]. In either case, we
# only deal with first and last element of the returned list.
retval = image_resizer_fn(image, masks)
resized_image = retval[0]
resized_image_shape = retval[-1]
retval[0] = preprocessor.rgb_to_gray(resized_image)
retval[-1] = tf.concat([resized_image_shape[:-1], [1]], 0)
return retval
def grayscale_image_resizer(image, masks=None):
"""Convert to grayscale before applying image_resizer_fn.
Args:
image: A 3D tensor of shape [height, width, 3]
masks: (optional) rank 3 float32 tensor with shape [num_instances, height,
width] containing instance masks.
Returns:
Note that the position of the resized_image_shape changes based on whether
masks are present.
resized_image: A 3D tensor of shape [new_height, new_width, 1],
where the image has been resized (with bilinear interpolation) so that
min(new_height, new_width) == min_dimension or
max(new_height, new_width) == max_dimension.
resized_masks: If masks is not None, also outputs masks. A 3D tensor of
shape [num_instances, new_height, new_width].
resized_image_shape: A 1D tensor of shape [3] containing shape of the
resized image.
"""
# image_resizer_fn returns [resized_image, resized_image_shape] if
# mask==None, otherwise it returns
# [resized_image, resized_mask, resized_image_shape]. In either case, we
# only deal with first and last element of the returned list.
retval = image_resizer_fn(image, masks)
resized_image = retval[0]
resized_image_shape = retval[-1]
retval[0] = preprocessor.rgb_to_gray(resized_image)
retval[-1] = tf.concat([resized_image_shape[:-1], [1]], 0)
return retval
def grayscale_image_resizer(image, masks=None):
# image_resizer_fn returns [resized_image, resized_image_shape] if
# mask==None, otherwise it returns
# [resized_image, resized_mask, resized_image_shape]. In either case, we
# only deal with first and last element of the returned list.
retval = image_resizer_fn(image, masks)
resized_image = retval[0]
resized_image_shape = retval[-1]
retval[0] = preprocessor.rgb_to_gray(resized_image)
retval[-1] = tf.concat([resized_image_shape[:-1], [1]], 0)
return retval
def grayscale_image_resizer(image): [resized_image, resized_image_shape] = image_resizer_fn(image) grayscale_image = preprocessor.rgb_to_gray(resized_image) grayscale_image_shape = tf.concat([resized_image_shape[:-1], [1]], 0) return [grayscale_image, grayscale_image_shape]
def grayscale_image_resizer(image): [resized_image, resized_image_shape] = image_resizer_fn(image) grayscale_image = preprocessor.rgb_to_gray(resized_image) grayscale_image_shape = tf.concat([resized_image_shape[:-1], [1]], 0) return [grayscale_image, grayscale_image_shape]