def testNoFlipWhenProbIsZero(self):
numpy_image = np.dstack([[[5., 6.], [9., 0.]], [[4., 3.], [3., 5.]]])
image = tf.convert_to_tensor(numpy_image)
with self.test_session():
actual, is_flipped = preprocess_utils.flip_dim([image],
prob=0,
dim=0)
self.assertAllEqual(numpy_image, actual.eval())
self.assertAllEqual(False, is_flipped.eval())
actual, is_flipped = preprocess_utils.flip_dim([image],
prob=0,
dim=1)
self.assertAllEqual(numpy_image, actual.eval())
self.assertAllEqual(False, is_flipped.eval())
actual, is_flipped = preprocess_utils.flip_dim([image],
prob=0,
dim=2)
self.assertAllEqual(numpy_image, actual.eval())
self.assertAllEqual(False, is_flipped.eval())
def testFlipWhenProbIsOne(self):
numpy_image = np.dstack([[[5., 6.], [9., 0.]], [[4., 3.], [3., 5.]]])
dim0_flipped = np.dstack([[[9., 0.], [5., 6.]], [[3., 5.], [4., 3.]]])
dim1_flipped = np.dstack([[[6., 5.], [0., 9.]], [[3., 4.], [5., 3.]]])
dim2_flipped = np.dstack([[[4., 3.], [3., 5.]], [[5., 6.], [9., 0.]]])
image = tf.convert_to_tensor(numpy_image)
with self.test_session():
actual, is_flipped = preprocess_utils.flip_dim([image],
prob=1,
dim=0)
self.assertAllEqual(dim0_flipped, actual.eval())
self.assertAllEqual(True, is_flipped.eval())
actual, is_flipped = preprocess_utils.flip_dim([image],
prob=1,
dim=1)
self.assertAllEqual(dim1_flipped, actual.eval())
self.assertAllEqual(True, is_flipped.eval())
actual, is_flipped = preprocess_utils.flip_dim([image],
prob=1,
dim=2)
self.assertAllEqual(dim2_flipped, actual.eval())
self.assertAllEqual(True, is_flipped.eval())
def testReturnRandomFlipsOnMultipleEvals(self):
numpy_image = np.dstack([[[5., 6.], [9., 0.]], [[4., 3.], [3., 5.]]])
dim1_flipped = np.dstack([[[6., 5.], [0., 9.]], [[3., 4.], [5., 3.]]])
image = tf.convert_to_tensor(numpy_image)
tf.set_random_seed(53)
with self.test_session() as sess:
actual, is_flipped = preprocess_utils.flip_dim([image],
prob=0.5,
dim=1)
actual_image, actual_is_flipped = sess.run([actual, is_flipped])
self.assertAllEqual(numpy_image, actual_image)
self.assertEqual(False, actual_is_flipped)
actual_image, actual_is_flipped = sess.run([actual, is_flipped])
self.assertAllEqual(dim1_flipped, actual_image)
self.assertEqual(True, actual_is_flipped)
def testFlipMultipleImagesConsistentlyWhenProbIsOne(self):
numpy_image = np.dstack([[[5., 6.], [9., 0.]], [[4., 3.], [3., 5.]]])
numpy_label = np.dstack([[[0., 1.], [2., 3.]]])
image_dim1_flipped = np.dstack([[[6., 5.], [0., 9.]],
[[3., 4.], [5., 3.]]])
label_dim1_flipped = np.dstack([[[1., 0.], [3., 2.]]])
image = tf.convert_to_tensor(numpy_image)
label = tf.convert_to_tensor(numpy_label)
with self.test_session() as sess:
image, label, is_flipped = preprocess_utils.flip_dim(
[image, label], prob=1, dim=1)
actual_image, actual_label = sess.run([image, label])
self.assertAllEqual(image_dim1_flipped, actual_image)
self.assertAllEqual(label_dim1_flipped, actual_label)
self.assertEqual(True, is_flipped.eval())
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