def testReturnCorrectCropOfSingleImage(self):
np.random.seed(0)
height, width = 10, 20
image = np.random.randint(0, 256, size=(height, width, 3))
crop_height, crop_width = 2, 4
image_placeholder = tf.placeholder(tf.int32, shape=(None, None, 3))
[cropped] = preprocess_utils.random_crop([image_placeholder],
crop_height,
crop_width)
with self.test_session():
cropped_image = cropped.eval(feed_dict={image_placeholder: image})
# Ensure we can find the cropped image in the original:
is_found = False
for x in range(0, width - crop_width + 1):
for y in range(0, height - crop_height + 1):
if np.isclose(image[y:y+crop_height, x:x+crop_width, :],
cropped_image).all():
is_found = True
break
self.assertTrue(is_found)
def testDieOnRandomCropWhenImagesWithDifferentWidth(self):
crop_height, crop_width = 2, 3
image1 = tf.placeholder(tf.float32, name='image1', shape=(None, None, 3))
image2 = tf.placeholder(tf.float32, name='image2', shape=(None, None, 1))
cropped = preprocess_utils.random_crop(
[image1, image2], crop_height, crop_width)
with self.test_session() as sess:
with self.assertRaises(errors.InvalidArgumentError):
sess.run(cropped, feed_dict={image1: np.random.rand(4, 5, 3),
image2: np.random.rand(4, 6, 1)})
def testDieOnRandomCropWhenCropSizeIsGreaterThanImage(self):
crop_height, crop_width = 5, 9
image1 = tf.placeholder(tf.float32, name='image1', shape=(None, None, 3))
image2 = tf.placeholder(tf.float32, name='image2', shape=(None, None, 1))
cropped = preprocess_utils.random_crop(
[image1, image2], crop_height, crop_width)
with self.test_session() as sess:
with self.assertRaisesWithPredicateMatch(
errors.InvalidArgumentError,
'Crop size greater than the image size.'):
sess.run(cropped, feed_dict={image1: np.random.rand(4, 5, 3),
image2: np.random.rand(4, 5, 1)})
def testReturnDifferentCropAreasOnTwoEvals(self):
tf.set_random_seed(0)
crop_height, crop_width = 2, 3
image = np.random.randint(0, 256, size=(100, 200, 3))
image_placeholder = tf.placeholder(tf.int32, shape=(None, None, 3))
[cropped] = preprocess_utils.random_crop(
[image_placeholder], crop_height, crop_width)
with self.test_session():
crop0 = cropped.eval(feed_dict={image_placeholder: image})
crop1 = cropped.eval(feed_dict={image_placeholder: image})
self.assertFalse(np.isclose(crop0, crop1).all())
def testRandomCropMaintainsNumberOfChannels(self):
np.random.seed(0)
crop_height, crop_width = 10, 20
image = np.random.randint(0, 256, size=(100, 200, 3))
tf.set_random_seed(37)
image_placeholder = tf.placeholder(tf.int32, shape=(None, None, 3))
[cropped] = preprocess_utils.random_crop(
[image_placeholder], crop_height, crop_width)
with self.test_session():
cropped_image = cropped.eval(feed_dict={image_placeholder: image})
self.assertTupleEqual(cropped_image.shape, (crop_height, crop_width, 3))
def testReturnConsistenCropsOfImagesInTheList(self):
tf.set_random_seed(0)
height, width = 10, 20
crop_height, crop_width = 2, 3
labels = np.linspace(0, height * width-1, height * width)
labels = labels.reshape((height, width, 1))
image = np.tile(labels, (1, 1, 3))
image_placeholder = tf.placeholder(tf.int32, shape=(None, None, 3))
label_placeholder = tf.placeholder(tf.int32, shape=(None, None, 1))
[cropped_image, cropped_label] = preprocess_utils.random_crop(
[image_placeholder, label_placeholder], crop_height, crop_width)
with self.test_session() as sess:
cropped_image, cropped_labels = sess.run([cropped_image, cropped_label],
feed_dict={
image_placeholder: image,
label_placeholder: labels})
for i in range(3):
self.assertAllEqual(cropped_image[:, :, i], cropped_labels.squeeze())
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