def testDieOnTargetSizeGreaterThanImageSize(self):
image = np.dstack([[[5, 6], [9, 0]], [[4, 3], [3, 5]]])
with self.test_session():
image_placeholder = tf.placeholder(tf.float32)
padded_image = preprocess_utils.pad_to_bounding_box(
image_placeholder, 0, 0, 2, 1, 255)
with self.assertRaisesWithPredicateMatch(
errors.InvalidArgumentError,
'target_width must be >= width'):
padded_image.eval(feed_dict={image_placeholder: image})
padded_image = preprocess_utils.pad_to_bounding_box(
image_placeholder, 0, 0, 1, 2, 255)
with self.assertRaisesWithPredicateMatch(
errors.InvalidArgumentError,
'target_height must be >= height'):
padded_image.eval(feed_dict={image_placeholder: image})
def testDieIfImageTensorRankIsNotThree(self):
image = np.vstack([[5, 6], [9, 0]])
with self.test_session():
image_placeholder = tf.placeholder(tf.float32)
padded_image = preprocess_utils.pad_to_bounding_box(
image_placeholder, 0, 0, 2, 2, 255)
with self.assertRaisesWithPredicateMatch(
errors.InvalidArgumentError, 'Wrong image tensor rank'):
padded_image.eval(feed_dict={image_placeholder: image})
def testReturnOriginalImageWhenTargetSizeIsEqualToImageSize(self):
image = np.dstack([[[5, 6], [9, 0]], [[4, 3], [3, 5]]])
with self.test_session():
image_placeholder = tf.placeholder(tf.float32)
padded_image = preprocess_utils.pad_to_bounding_box(
image_placeholder, 0, 0, 2, 2, 255)
self.assertAllClose(
padded_image.eval(feed_dict={image_placeholder: image}), image)
def testDieIfTargetSizeNotPossibleWithGivenOffset(self):
image = np.dstack([[[5, 6], [9, 0]], [[4, 3], [3, 5]]])
with self.test_session():
image_placeholder = tf.placeholder(tf.float32)
padded_image = preprocess_utils.pad_to_bounding_box(
image_placeholder, 3, 0, 4, 4, 255)
with self.assertRaisesWithPredicateMatch(
errors.InvalidArgumentError,
'target size not possible with the given target offsets'):
padded_image.eval(feed_dict={image_placeholder: image})
def preprocess_image_and_label_yjy(image,
label,
crop_height,
crop_width,
min_scale_factor=1.,
max_scale_factor=1.,
is_training=True):
if is_training and label is None:
raise ValueError('During training, label must be provided.')
# Keep reference to original image and label.
original_image = image
original_label = label
processed_image = tf.cast(image, tf.float32)
# randomly rotate
# todo: rotate factor should be determined by command line
rotate_factor = preprocess_utils.get_rotate_scale(min_rotate_factor=0,
max_rotate_factor=359,
step_size=90)
processed_image, label = preprocess_utils.randomly_rotate_image_and_label(
processed_image, label, rotate_factor)
processed_image.set_shape([None, None, 3])
# randomly scale
scale_factor = preprocess_utils.get_random_scale(min_scale_factor,
max_scale_factor, 0)
processed_image, label = preprocess_utils.randomly_scale_image_and_label(
processed_image, label, scale_factor)
processed_image.set_shape([None, None, 3])
# Pad image to have dimensions >= [crop_height, crop_width]
image_shape = tf.shape(processed_image)
image_height = image_shape[0]
image_width = image_shape[1]
target_height = image_height + tf.maximum(crop_height - image_height, 0)
target_width = image_width + tf.maximum(crop_width - image_width, 0)
# Pad image with mean pixel value.
mean_pixel = tf.reshape([0., 0., 0.], [1, 1, 3]) # [127.5, 127.5, 127.5]
processed_image = preprocess_utils.pad_to_bounding_box(
processed_image, 0, 0, target_height, target_width, mean_pixel)
# crop to [crop_height,crop_width]
processed_image, label = preprocess_utils.random_crop(
processed_image, label, crop_height, crop_width)
if label is not None:
label.set_shape([6])
if original_label is not None:
original_label.set_shape([6])
return original_image, processed_image, original_label, label
def testReturnPaddedImageWithNonZeroPadValue(self):
for dtype in [np.int32, np.int64, np.float32, np.float64]:
image = np.dstack([[[5, 6], [9, 0]], [[4, 3], [3,
5]]]).astype(dtype)
expected_image = np.dstack([[[255, 255, 255, 255, 255],
[255, 255, 255, 255, 255],
[255, 5, 6, 255, 255],
[255, 9, 0, 255, 255],
[255, 255, 255, 255, 255]],
[[255, 255, 255, 255, 255],
[255, 255, 255, 255, 255],
[255, 4, 3, 255, 255],
[255, 3, 5, 255, 255],
[255, 255, 255, 255,
255]]]).astype(dtype)
with self.test_session():
image_placeholder = tf.placeholder(tf.float32)
padded_image = preprocess_utils.pad_to_bounding_box(
image_placeholder, 2, 1, 5, 5, 255)
self.assertAllClose(
padded_image.eval(feed_dict={image_placeholder: image}),
expected_image)
def preprocess_image_and_label(image,
label,
crop_height,
crop_width,
min_resize_value=None,
max_resize_value=None,
resize_factor=None,
min_scale_factor=1.,
max_scale_factor=1.,
scale_factor_step_size=0,
ignore_label=255,
is_training=True,
model_variant=None):
"""Preprocesses the image and label.
Args:
image: Input image.
label: Ground truth annotation label.
crop_height: The height value used to crop the image and label.
crop_width: The width value used to crop the image and label.
min_resize_value: Desired size of the smaller image side.
max_resize_value: Maximum allowed size of the larger image side.
resize_factor: Resized dimensions are multiple of factor plus one.
min_scale_factor: Minimum scale factor value.
max_scale_factor: Maximum scale factor value.
scale_factor_step_size: The step size from min scale factor to max scale
factor. The input is randomly scaled based on the value of
(min_scale_factor, max_scale_factor, scale_factor_step_size).
ignore_label: The label value which will be ignored for training and
evaluation.
is_training: If the preprocessing is used for training or not.
model_variant: Model variant (string) for choosing how to mean-subtract the
images. See feature_extractor.network_map for supported model variants.
Returns:
original_image: Original image (could be resized).
processed_image: Preprocessed image.
label: Preprocessed ground truth segmentation label.
Raises:
ValueError: Ground truth label not provided during training.
"""
if is_training and label is None:
raise ValueError('During training, label must be provided.')
if model_variant is None:
tf.logging.warning(
'Default mean-subtraction is performed. Please specify '
'a model_variant. See feature_extractor.network_map for '
'supported model variants.')
# Keep reference to original image.
original_image = image
processed_image = tf.cast(image, tf.float32)
if label is not None:
label = tf.cast(label, tf.int32)
# Resize image and label to the desired range.
if min_resize_value is not None or max_resize_value is not None:
[processed_image,
label] = (preprocess_utils.resize_to_range(image=processed_image,
label=label,
min_size=min_resize_value,
max_size=max_resize_value,
factor=resize_factor,
align_corners=True))
# The `original_image` becomes the resized image.
original_image = tf.identity(processed_image)
# Data augmentation by randomly scaling the inputs.
if is_training:
scale = preprocess_utils.get_random_scale(min_scale_factor,
max_scale_factor,
scale_factor_step_size)
processed_image, label = preprocess_utils.randomly_scale_image_and_label(
processed_image, label, scale)
processed_image.set_shape([None, None, 3])
# Pad image and label to have dimensions >= [crop_height, crop_width]
image_shape = tf.shape(processed_image)
image_height = image_shape[0]
image_width = image_shape[1]
target_height = image_height + tf.maximum(crop_height - image_height, 0)
target_width = image_width + tf.maximum(crop_width - image_width, 0)
# Pad image with mean pixel value.
mean_pixel = tf.reshape(feature_extractor.mean_pixel(model_variant),
[1, 1, 3])
processed_image = preprocess_utils.pad_to_bounding_box(
processed_image, 0, 0, target_height, target_width, mean_pixel)
if label is not None:
label = preprocess_utils.pad_to_bounding_box(label, 0, 0,
target_height,
target_width,
ignore_label)
# Randomly crop the image and label.
if is_training and label is not None:
processed_image, label = preprocess_utils.random_crop(
[processed_image, label], crop_height, crop_width)
processed_image.set_shape([crop_height, crop_width, 3])
if label is not None:
label.set_shape([crop_height, crop_width, 1])
if is_training:
# Randomly left-right flip the image and label.
processed_image, label, _ = preprocess_utils.flip_dim(
[processed_image, label], _PROB_OF_FLIP, dim=1)
return original_image, processed_image, label
def preprocess_image_and_label(image,
label,
crop_height,
crop_width,
min_resize_value=None,
max_resize_value=None,
resize_factor=None,
min_scale_factor=1.,
max_scale_factor=1.,
scale_factor_step_size=0,
ignore_label=255,
is_training=True,
model_variant=None):
"""Preprocesses the image and label.
Args:
image: Input image.
label: Ground truth annotation label.
crop_height: The height value used to crop the image and label.
crop_width: The width value used to crop the image and label.
min_resize_value: Desired size of the smaller image side.
max_resize_value: Maximum allowed size of the larger image side.
resize_factor: Resized dimensions are multiple of factor plus one.
min_scale_factor: Minimum scale factor value.
max_scale_factor: Maximum scale factor value.
scale_factor_step_size: The step size from min scale factor to max scale
factor. The input is randomly scaled based on the value of
(min_scale_factor, max_scale_factor, scale_factor_step_size).
ignore_label: The label value which will be ignored for training and
evaluation.
is_training: If the preprocessing is used for training or not.
model_variant: Model variant (string) for choosing how to mean-subtract the
images. See feature_extractor.network_map for supported model variants.
Returns:
original_image: Original image (could be resized).
processed_image: Preprocessed image.
label: Preprocessed ground truth segmentation label.
Raises:
ValueError: Ground truth label not provided during training.
"""
if is_training and label is None:
raise ValueError('During training, label must be provided.')
if model_variant is None:
tf.logging.warning('Default mean-subtraction is performed. Please specify '
'a model_variant. See feature_extractor.network_map for '
'supported model variants.')
# Keep reference to original image.
original_image = image
processed_image = tf.cast(image, tf.float32)
if label is not None:
label = tf.cast(label, tf.int32)
# Resize image and label to the desired range.
if min_resize_value is not None or max_resize_value is not None:
[processed_image, label] = (
preprocess_utils.resize_to_range(
image=processed_image,
label=label,
min_size=min_resize_value,
max_size=max_resize_value,
factor=resize_factor,
align_corners=True))
# The `original_image` becomes the resized image.
original_image = tf.identity(processed_image)
# Data augmentation by randomly scaling the inputs.
scale = preprocess_utils.get_random_scale(
min_scale_factor, max_scale_factor, scale_factor_step_size)
processed_image, label = preprocess_utils.randomly_scale_image_and_label(
processed_image, label, scale)
processed_image.set_shape([None, None, 3])
# Pad image and label to have dimensions >= [crop_height, crop_width]
image_shape = tf.shape(processed_image)
image_height = image_shape[0]
image_width = image_shape[1]
target_height = image_height + tf.maximum(crop_height - image_height, 0)
target_width = image_width + tf.maximum(crop_width - image_width, 0)
# Pad image with mean pixel value.
mean_pixel = tf.reshape(
feature_extractor.mean_pixel(model_variant), [1, 1, 3])
processed_image = preprocess_utils.pad_to_bounding_box(
processed_image, 0, 0, target_height, target_width, mean_pixel)
if label is not None:
label = preprocess_utils.pad_to_bounding_box(
label, 0, 0, target_height, target_width, ignore_label)
# Randomly crop the image and label.
if is_training and label is not None:
processed_image, label = preprocess_utils.random_crop(
[processed_image, label], crop_height, crop_width)
processed_image.set_shape([crop_height, crop_width, 3])
if label is not None:
label.set_shape([crop_height, crop_width, 1])
if is_training:
# Randomly left-right flip the image and label.
processed_image, label, _ = preprocess_utils.flip_dim(
[processed_image, label], _PROB_OF_FLIP, dim=1)
return original_image, processed_image, label
def preprocess_image_and_label_seq(
image,
label,
prior_segs,
crop_height,
crop_width,
channel,
seq_length,
label_for_each_frame,
pre_crop_height=None,
pre_crop_width=None,
num_class=None,
HU_window=None,
min_resize_value=None,
max_resize_value=None,
resize_factor=None,
min_scale_factor=1.,
max_scale_factor=1.,
scale_factor_step_size=0,
ignore_label=255,
rotate_angle=None,
is_training=True,
model_variant=None,
):
"""Preprocesses the image and label.
Args:
image: Input image.
label: Ground truth annotation label.
crop_height: The height value used to crop the image and label.
crop_width: The width value used to crop the image and label.
min_resize_value: Desired size of the smaller image side.
max_resize_value: Maximum allowed size of the larger image side.
resize_factor: Resized dimensions are multiple of factor plus one.
min_scale_factor: Minimum scale factor value.
max_scale_factor: Maximum scale factor value.
scale_factor_step_size: The step size from min scale factor to max scale
factor. The input is randomly scaled based on the value of
(min_scale_factor, max_scale_factor, scale_factor_step_size).
ignore_label: The label value which will be ignored for training and
evaluation.
is_training: If the preprocessing is used for training or not.
model_variant: Model variant (string) for choosing how to mean-subtract the
images. See feature_extractor.network_map for supported model variants.
Returns:
original_image: Original image (could be resized).
processed_image: Preprocessed image.
label: Preprocessed ground truth segmentation label.
Raises:
ValueError: Ground truth label not provided during training.
"""
if is_training and label is None:
raise ValueError('During training, label must be provided.')
# if (prior_num_slice is not None) != (prior_imgs is not None or prior_segs is not None):
# raise ValueError('prior_num_slice should exist when import prior and vice versa')
if model_variant is None:
tf.logging.warning(
'Default mean-subtraction is performed. Please specify '
'a model_variant. See feature_extractor.network_map for '
'supported model variants.')
# Keep reference to original image.
original_image = image
original_label = label
# sample prior if exist
# TODO: sample problem (consider z gt)
# data type and value convert
if HU_window is not None:
image = preprocess_utils.HU_to_pixelvalue(image, HU_window)
processed_image = tf.cast(image, tf.float32)
if label is not None:
label = tf.cast(label, tf.int32)
if pre_crop_height is not None and pre_crop_width is not None:
if is_training and label is not None:
if prior_segs is not None:
processed_image, label, prior_segs = preprocess_utils.random_crop(
[processed_image, label, prior_segs], pre_crop_height,
pre_crop_width)
else:
processed_image, label = preprocess_utils.random_crop(
[processed_image, label], pre_crop_height, pre_crop_width)
# Resize image and label to the desired range.
# TODO: interface for this func.
if min_resize_value or max_resize_value:
[processed_image,
label] = (preprocess_utils.resize_to_range(image=processed_image,
label=label,
min_size=min_resize_value,
max_size=max_resize_value,
factor=resize_factor,
align_corners=True))
# The `original_image` becomes the resized image.
original_image = tf.identity(processed_image)
if prior_segs is not None:
# prior_segs = tf.cast(prior_segs, tf.int32)
prior_segs, _ = (preprocess_utils.resize_to_range(
image=prior_segs,
min_size=min_resize_value,
max_size=max_resize_value,
factor=resize_factor,
align_corners=True))
# Data augmentation by randomly scaling the inputs.
if is_training:
scale = preprocess_utils.get_random_scale(min_scale_factor,
max_scale_factor,
scale_factor_step_size)
processed_image, label = preprocess_utils.randomly_scale_image_and_label(
processed_image, label, scale)
processed_image.set_shape([None, None, seq_length * channel])
if prior_segs is not None:
prior_segs = preprocess_utils.scale_image_data(prior_segs, scale)
# Pad image and label to have dimensions >= [crop_height, crop_width]
image_shape = tf.shape(processed_image)
# image_shape = processed_image.get_shape().as_list()
image_height = image_shape[0]
image_width = image_shape[1]
target_height = image_height + tf.maximum(crop_height - image_height, 0)
target_width = image_width + tf.maximum(crop_width - image_width, 0)
# Pad image with mean pixel value.
# TODO: check padding value
# mean_pixel = tf.reshape(
# features_extractor.mean_pixel(model_variant), [1, 1, 3])
mean_pixel = 0.0
processed_image = preprocess_utils.pad_to_bounding_box(
processed_image, 0, 0, target_height, target_width, mean_pixel)
if label is not None:
label = preprocess_utils.pad_to_bounding_box(label, 0, 0,
target_height,
target_width, 0)
if prior_segs is not None:
prior_segs = preprocess_utils.pad_to_bounding_box(
prior_segs, 0, 0, target_height, target_width, 0)
# Randomly crop the image and label.
# TODO: Do it in the right way
# TODO: offset_height, offset_width for input
# processed_image, label = preprocess_utils.random_crop(
# [processed_image, label], crop_height, crop_width)
if is_training and label is not None:
if prior_segs is not None:
processed_image, label, prior_segs = preprocess_utils.random_crop(
[processed_image, label, prior_segs], crop_height, crop_width)
else:
processed_image, label = preprocess_utils.random_crop(
[processed_image, label], crop_height, crop_width)
processed_image.set_shape([crop_height, crop_width, seq_length * channel])
if label is not None:
label.set_shape([crop_height, crop_width, seq_length * channel])
# if label_for_each_frame:
# label.set_shape([crop_height, crop_width, seq_length*channel])
# else:
# label.set_shape([crop_height, crop_width, 1])
if prior_segs is not None:
prior_segs = tf.squeeze(tf.image.resize_bilinear(
tf.expand_dims(prior_segs, axis=0), [crop_height, crop_width]),
axis=0)
# prior_segs.set_shape([crop_height,crop_width,num_class])
if is_training:
# Randomly left-right flip the image and label.
if prior_segs is not None:
processed_image, label, prior_segs, _ = preprocess_utils.flip_dim(
[processed_image, label, prior_segs], _PROB_OF_FLIP, dim=1)
else:
processed_image, label, _ = preprocess_utils.flip_dim(
[processed_image, label], _PROB_OF_FLIP, dim=1)
# TODO: coplete random rotate method
# Randomly rotate the image and label.
if rotate_angle is not None:
pass
# processed_image, label, _ = preprocess_utils.random_rotate([processed_image, label], _PROB_OF_ROT, rotate_angle)
return original_image, processed_image, label, original_label, prior_segs
def preprocess_image_and_label(image,
label,
crop_height,
crop_width,
min_resize_value=None,
max_resize_value=None,
resize_factor=None,
min_scale_factor=1.,
max_scale_factor=1.,
scale_factor_step_size=0,
ignore_label=255,
is_training=True,
model_variant=None,
strong_weak=False,
output_valid=False):
"""Preprocesses the image and label.
Args:
image: Input image.
label: Ground truth annotation label.
crop_height: The height value used to crop the image and label.
crop_width: The width value used to crop the image and label.
min_resize_value: Desired size of the smaller image side.
max_resize_value: Maximum allowed size of the larger image side.
resize_factor: Resized dimensions are multiple of factor plus one.
min_scale_factor: Minimum scale factor value.
max_scale_factor: Maximum scale factor value.
scale_factor_step_size: The step size from min scale factor to max scale
factor. The input is randomly scaled based on the value of
(min_scale_factor, max_scale_factor, scale_factor_step_size).
ignore_label: The label value which will be ignored for training and
evaluation.
is_training: If the preprocessing is used for training or not.
model_variant: Model variant (string) for choosing how to mean-subtract the
images. See feature_extractor.network_map for supported model variants.
strong_weak: Generate a pair of (strong, weak) augmented images for
consistency. Also, need to get the valid region exluding padding.
output_valid: Output valid region excluding padding or not.
Returns:
original_image: Original image (could be resized).
processed_image: Preprocessed image.
label: Preprocessed ground truth segmentation label.
Raises:
ValueError: Ground truth label not provided during training.
"""
if is_training and label is None:
raise ValueError('During training, label must be provided.')
if model_variant is None:
tf.logging.warning(
'Default mean-subtraction is performed. Please specify '
'a model_variant. See feature_extractor.network_map for '
'supported model variants.')
# Keep reference to original image.
original_image = image
processed_image = tf.cast(image, tf.float32)
if label is not None:
label = tf.cast(label, tf.int32)
# Resize image and label to the desired range.
if min_resize_value or max_resize_value:
[processed_image,
label] = (preprocess_utils.resize_to_range(image=processed_image,
label=label,
min_size=min_resize_value,
max_size=max_resize_value,
factor=resize_factor,
align_corners=True))
# The `original_image` becomes the resized image.
original_image = tf.identity(processed_image)
# Data augmentation by randomly scaling the inputs.
if is_training:
scale = preprocess_utils.get_random_scale(min_scale_factor,
max_scale_factor,
scale_factor_step_size)
processed_image, label = preprocess_utils.randomly_scale_image_and_label(
processed_image, label, scale)
processed_image.set_shape([None, None, 3])
# Pad image and label to have dimensions >= [crop_height, crop_width]
image_shape = tf.shape(processed_image)
image_height = image_shape[0]
image_width = image_shape[1]
target_height = image_height + tf.maximum(crop_height - image_height, 0)
target_width = image_width + tf.maximum(crop_width - image_width, 0)
if strong_weak:
# # Color distortion (operates in [0, 1])
strong = processed_image / 255.
strong = preprocess_utils.random_color_jitter(strong, _PROB_OF_JITTER)
strong = strong * 255.
# Pad image with mean pixel value.
mean_pixel = tf.reshape(feature_extractor.mean_pixel(model_variant),
[1, 1, 3])
processed_image = preprocess_utils.pad_to_bounding_box(
processed_image, 0, 0, target_height, target_width, mean_pixel)
if strong_weak:
strong = preprocess_utils.pad_to_bounding_box(strong, 0, 0,
target_height,
target_width, mean_pixel)
if label is not None:
label = preprocess_utils.pad_to_bounding_box(label, 0, 0,
target_height,
target_width,
ignore_label)
# Randomly crop the image and label.
if is_training and label is not None:
if not strong_weak:
processed_image, label = preprocess_utils.random_crop(
[processed_image, label], crop_height, crop_width)
else:
processed_image, label, strong = preprocess_utils.random_crop(
[processed_image, label, strong], crop_height, crop_width)
processed_image.set_shape([crop_height, crop_width, 3])
if label is not None:
label.set_shape([crop_height, crop_width, 1])
if not is_training and output_valid:
# Construct the valid mask excluding the boundary padding
xs, ys = tf.meshgrid(tf.range(0, crop_width), tf.range(0, crop_height))
valid_x = tf.cast(tf.less(xs, image_width), tf.int32)
valid_y = tf.cast(tf.less(ys, image_height), tf.int32)
valid = tf.reshape(valid_x * valid_y, [crop_height, crop_width, 1])
if is_training:
if strong_weak or output_valid:
# Construct the valid mask excluding the boundary padding
xs, ys = tf.meshgrid(tf.range(0, crop_width),
tf.range(0, crop_height))
valid_x = tf.cast(tf.less(xs, image_width), tf.int32)
valid_y = tf.cast(tf.less(ys, image_height), tf.int32)
valid = tf.reshape(valid_x * valid_y, [crop_height, crop_width, 1])
if strong_weak:
# Ignore CutOut region
strong, label, valid = preprocess_utils.cutout_with_mask(
strong,
label,
pad_size=50,
mean_pixel=mean_pixel,
ignore_label=ignore_label,
valid=valid)
# Randomly left-right flip the image and label.
processed_image, strong, valid, label, _ = preprocess_utils.flip_dim(
[processed_image, strong, valid, label], _PROB_OF_FLIP, dim=1)
return original_image, processed_image, label, strong, valid
# Randomly left-right flip the image and label.
if not output_valid:
processed_image, label, _ = preprocess_utils.flip_dim(
[processed_image, label], _PROB_OF_FLIP, dim=1)
else:
processed_image, valid, label, _ = preprocess_utils.flip_dim(
[processed_image, valid, label], _PROB_OF_FLIP, dim=1)
if not output_valid:
return original_image, processed_image, label
else:
return original_image, processed_image, label, valid
