本文整理匯總了Python中object_detection.core.preprocessor.resize_image方法的典型用法代碼示例。如果您正苦於以下問題:Python preprocessor.resize_image方法的具體用法?Python preprocessor.resize_image怎麽用?Python preprocessor.resize_image使用的例子?那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類object_detection.core.preprocessor
的用法示例。
在下文中一共展示了preprocessor.resize_image方法的11個代碼示例,這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚,您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: testResizeImageWithMasks
# 需要導入模塊: from object_detection.core import preprocessor [as 別名]
# 或者: from object_detection.core.preprocessor import resize_image [as 別名]
def testResizeImageWithMasks(self):
"""Tests image resizing, checking output sizes."""
in_image_shape_list = [[60, 40, 3], [15, 30, 3]]
in_masks_shape_list = [[15, 60, 40], [10, 15, 30]]
height = 50
width = 100
expected_image_shape_list = [[50, 100, 3], [50, 100, 3]]
expected_masks_shape_list = [[15, 50, 100], [10, 50, 100]]
for (in_image_shape, expected_image_shape, in_masks_shape,
expected_mask_shape) in zip(in_image_shape_list,
expected_image_shape_list,
in_masks_shape_list,
expected_masks_shape_list):
in_image = tf.random_uniform(in_image_shape)
in_masks = tf.random_uniform(in_masks_shape)
out_image, out_masks = preprocessor.resize_image(
in_image, in_masks, new_height=height, new_width=width)
out_image_shape = tf.shape(out_image)
out_masks_shape = tf.shape(out_masks)
with self.test_session() as sess:
out_image_shape, out_masks_shape = sess.run(
[out_image_shape, out_masks_shape])
self.assertAllEqual(out_image_shape, expected_image_shape)
self.assertAllEqual(out_masks_shape, expected_mask_shape)
示例2: testResizeImageWithNoInstanceMask
# 需要導入模塊: from object_detection.core import preprocessor [as 別名]
# 或者: from object_detection.core.preprocessor import resize_image [as 別名]
def testResizeImageWithNoInstanceMask(self):
"""Tests image resizing, checking output sizes."""
in_image_shape_list = [[60, 40, 3], [15, 30, 3]]
in_masks_shape_list = [[0, 60, 40], [0, 15, 30]]
height = 50
width = 100
expected_image_shape_list = [[50, 100, 3], [50, 100, 3]]
expected_masks_shape_list = [[0, 50, 100], [0, 50, 100]]
for (in_image_shape, expected_image_shape, in_masks_shape,
expected_mask_shape) in zip(in_image_shape_list,
expected_image_shape_list,
in_masks_shape_list,
expected_masks_shape_list):
in_image = tf.random_uniform(in_image_shape)
in_masks = tf.random_uniform(in_masks_shape)
out_image, out_masks = preprocessor.resize_image(
in_image, in_masks, new_height=height, new_width=width)
out_image_shape = tf.shape(out_image)
out_masks_shape = tf.shape(out_masks)
with self.test_session() as sess:
out_image_shape, out_masks_shape = sess.run(
[out_image_shape, out_masks_shape])
self.assertAllEqual(out_image_shape, expected_image_shape)
self.assertAllEqual(out_masks_shape, expected_mask_shape)
示例3: build
# 需要導入模塊: from object_detection.core import preprocessor [as 別名]
# 或者: from object_detection.core.preprocessor import resize_image [as 別名]
def build(image_resizer_config):
"""Builds callable for image resizing operations.
Args:
image_resizer_config: image_resizer.proto object containing parameters for
an image resizing operation.
Returns:
image_resizer_fn: Callable for image resizing. This callable always takes
a rank-3 image tensor (corresponding to a single image) and returns a
rank-3 image tensor, possibly with new spatial dimensions.
Raises:
ValueError: if `image_resizer_config` is of incorrect type.
ValueError: if `image_resizer_config.image_resizer_oneof` is of expected
type.
ValueError: if min_dimension > max_dimension when keep_aspect_ratio_resizer
is used.
"""
if not isinstance(image_resizer_config, image_resizer_pb2.ImageResizer):
raise ValueError('image_resizer_config not of type '
'image_resizer_pb2.ImageResizer.')
if image_resizer_config.WhichOneof(
'image_resizer_oneof') == 'keep_aspect_ratio_resizer':
keep_aspect_ratio_config = image_resizer_config.keep_aspect_ratio_resizer
if not (keep_aspect_ratio_config.min_dimension
<= keep_aspect_ratio_config.max_dimension):
raise ValueError('min_dimension > max_dimension')
return functools.partial(
preprocessor.resize_to_range,
min_dimension=keep_aspect_ratio_config.min_dimension,
max_dimension=keep_aspect_ratio_config.max_dimension)
if image_resizer_config.WhichOneof(
'image_resizer_oneof') == 'fixed_shape_resizer':
fixed_shape_resizer_config = image_resizer_config.fixed_shape_resizer
return functools.partial(preprocessor.resize_image,
new_height=fixed_shape_resizer_config.height,
new_width=fixed_shape_resizer_config.width)
raise ValueError('Invalid image resizer option.')
示例4: test_build_resize_image
# 需要導入模塊: from object_detection.core import preprocessor [as 別名]
# 或者: from object_detection.core.preprocessor import resize_image [as 別名]
def test_build_resize_image(self):
preprocessor_text_proto = """
resize_image {
new_height: 75
new_width: 100
method: BICUBIC
}
"""
preprocessor_proto = preprocessor_pb2.PreprocessingStep()
text_format.Merge(preprocessor_text_proto, preprocessor_proto)
function, args = preprocessor_builder.build(preprocessor_proto)
self.assertEqual(function, preprocessor.resize_image)
self.assertEqual(args, {'new_height': 75,
'new_width': 100,
'method': tf.image.ResizeMethod.BICUBIC})
示例5: test_apply_image_and_box_augmentation
# 需要導入模塊: from object_detection.core import preprocessor [as 別名]
# 或者: from object_detection.core.preprocessor import resize_image [as 別名]
def test_apply_image_and_box_augmentation(self):
data_augmentation_options = [
(preprocessor.resize_image, {
'new_height': 20,
'new_width': 20,
'method': tf.image.ResizeMethod.NEAREST_NEIGHBOR
}),
(preprocessor.scale_boxes_to_pixel_coordinates, {}),
]
data_augmentation_fn = functools.partial(
inputs.augment_input_data,
data_augmentation_options=data_augmentation_options)
tensor_dict = {
fields.InputDataFields.image:
tf.constant(np.random.rand(10, 10, 3).astype(np.float32)),
fields.InputDataFields.groundtruth_boxes:
tf.constant(np.array([[.5, .5, 1., 1.]], np.float32))
}
augmented_tensor_dict = data_augmentation_fn(tensor_dict=tensor_dict)
with self.test_session() as sess:
augmented_tensor_dict_out = sess.run(augmented_tensor_dict)
self.assertAllEqual(
augmented_tensor_dict_out[fields.InputDataFields.image].shape,
[20, 20, 3]
)
self.assertAllClose(
augmented_tensor_dict_out[fields.InputDataFields.groundtruth_boxes],
[[10, 10, 20, 20]]
)
示例6: test_include_masks_in_data_augmentation
# 需要導入模塊: from object_detection.core import preprocessor [as 別名]
# 或者: from object_detection.core.preprocessor import resize_image [as 別名]
def test_include_masks_in_data_augmentation(self):
data_augmentation_options = [
(preprocessor.resize_image, {
'new_height': 20,
'new_width': 20,
'method': tf.image.ResizeMethod.NEAREST_NEIGHBOR
})
]
data_augmentation_fn = functools.partial(
inputs.augment_input_data,
data_augmentation_options=data_augmentation_options)
tensor_dict = {
fields.InputDataFields.image:
tf.constant(np.random.rand(10, 10, 3).astype(np.float32)),
fields.InputDataFields.groundtruth_instance_masks:
tf.constant(np.zeros([2, 10, 10], np.uint8))
}
augmented_tensor_dict = data_augmentation_fn(tensor_dict=tensor_dict)
with self.test_session() as sess:
augmented_tensor_dict_out = sess.run(augmented_tensor_dict)
self.assertAllEqual(
augmented_tensor_dict_out[fields.InputDataFields.image].shape,
[20, 20, 3])
self.assertAllEqual(augmented_tensor_dict_out[
fields.InputDataFields.groundtruth_instance_masks].shape, [2, 20, 20])
示例7: test_include_keypoints_in_data_augmentation
# 需要導入模塊: from object_detection.core import preprocessor [as 別名]
# 或者: from object_detection.core.preprocessor import resize_image [as 別名]
def test_include_keypoints_in_data_augmentation(self):
data_augmentation_options = [
(preprocessor.resize_image, {
'new_height': 20,
'new_width': 20,
'method': tf.image.ResizeMethod.NEAREST_NEIGHBOR
}),
(preprocessor.scale_boxes_to_pixel_coordinates, {}),
]
data_augmentation_fn = functools.partial(
inputs.augment_input_data,
data_augmentation_options=data_augmentation_options)
tensor_dict = {
fields.InputDataFields.image:
tf.constant(np.random.rand(10, 10, 3).astype(np.float32)),
fields.InputDataFields.groundtruth_boxes:
tf.constant(np.array([[.5, .5, 1., 1.]], np.float32)),
fields.InputDataFields.groundtruth_keypoints:
tf.constant(np.array([[[0.5, 1.0], [0.5, 0.5]]], np.float32))
}
augmented_tensor_dict = data_augmentation_fn(tensor_dict=tensor_dict)
with self.test_session() as sess:
augmented_tensor_dict_out = sess.run(augmented_tensor_dict)
self.assertAllEqual(
augmented_tensor_dict_out[fields.InputDataFields.image].shape,
[20, 20, 3]
)
self.assertAllClose(
augmented_tensor_dict_out[fields.InputDataFields.groundtruth_boxes],
[[10, 10, 20, 20]]
)
self.assertAllClose(
augmented_tensor_dict_out[fields.InputDataFields.groundtruth_keypoints],
[[[10, 20], [10, 10]]]
)
示例8: testResizeImageWithMasks
# 需要導入模塊: from object_detection.core import preprocessor [as 別名]
# 或者: from object_detection.core.preprocessor import resize_image [as 別名]
def testResizeImageWithMasks(self):
"""Tests image resizing, checking output sizes."""
in_image_shape_list = [[60, 40, 3], [15, 30, 3]]
in_masks_shape_list = [[15, 60, 40], [10, 15, 30]]
height = 50
width = 100
expected_image_shape_list = [[50, 100, 3], [50, 100, 3]]
expected_masks_shape_list = [[15, 50, 100], [10, 50, 100]]
for (in_image_shape, expected_image_shape, in_masks_shape,
expected_mask_shape) in zip(in_image_shape_list,
expected_image_shape_list,
in_masks_shape_list,
expected_masks_shape_list):
in_image = tf.random_uniform(in_image_shape)
in_masks = tf.random_uniform(in_masks_shape)
out_image, out_masks, _ = preprocessor.resize_image(
in_image, in_masks, new_height=height, new_width=width)
out_image_shape = tf.shape(out_image)
out_masks_shape = tf.shape(out_masks)
with self.test_session() as sess:
out_image_shape, out_masks_shape = sess.run(
[out_image_shape, out_masks_shape])
self.assertAllEqual(out_image_shape, expected_image_shape)
self.assertAllEqual(out_masks_shape, expected_mask_shape)
示例9: testResizeImageWithNoInstanceMask
# 需要導入模塊: from object_detection.core import preprocessor [as 別名]
# 或者: from object_detection.core.preprocessor import resize_image [as 別名]
def testResizeImageWithNoInstanceMask(self):
"""Tests image resizing, checking output sizes."""
in_image_shape_list = [[60, 40, 3], [15, 30, 3]]
in_masks_shape_list = [[0, 60, 40], [0, 15, 30]]
height = 50
width = 100
expected_image_shape_list = [[50, 100, 3], [50, 100, 3]]
expected_masks_shape_list = [[0, 50, 100], [0, 50, 100]]
for (in_image_shape, expected_image_shape, in_masks_shape,
expected_mask_shape) in zip(in_image_shape_list,
expected_image_shape_list,
in_masks_shape_list,
expected_masks_shape_list):
in_image = tf.random_uniform(in_image_shape)
in_masks = tf.random_uniform(in_masks_shape)
out_image, out_masks, _ = preprocessor.resize_image(
in_image, in_masks, new_height=height, new_width=width)
out_image_shape = tf.shape(out_image)
out_masks_shape = tf.shape(out_masks)
with self.test_session() as sess:
out_image_shape, out_masks_shape = sess.run(
[out_image_shape, out_masks_shape])
self.assertAllEqual(out_image_shape, expected_image_shape)
self.assertAllEqual(out_masks_shape, expected_mask_shape)
示例10: testResizeImageWithMasksTensorInputHeightAndWidth
# 需要導入模塊: from object_detection.core import preprocessor [as 別名]
# 或者: from object_detection.core.preprocessor import resize_image [as 別名]
def testResizeImageWithMasksTensorInputHeightAndWidth(self):
"""Tests image resizing, checking output sizes."""
in_image_shape_list = [[60, 40, 3], [15, 30, 3]]
in_masks_shape_list = [[15, 60, 40], [10, 15, 30]]
height = tf.constant(50, dtype=tf.int32)
width = tf.constant(100, dtype=tf.int32)
expected_image_shape_list = [[50, 100, 3], [50, 100, 3]]
expected_masks_shape_list = [[15, 50, 100], [10, 50, 100]]
for (in_image_shape, expected_image_shape, in_masks_shape,
expected_mask_shape) in zip(in_image_shape_list,
expected_image_shape_list,
in_masks_shape_list,
expected_masks_shape_list):
in_image = tf.random_uniform(in_image_shape)
in_masks = tf.random_uniform(in_masks_shape)
out_image, out_masks, _ = preprocessor.resize_image(
in_image, in_masks, new_height=height, new_width=width)
out_image_shape = tf.shape(out_image)
out_masks_shape = tf.shape(out_masks)
with self.test_session() as sess:
out_image_shape, out_masks_shape = sess.run(
[out_image_shape, out_masks_shape])
self.assertAllEqual(out_image_shape, expected_image_shape)
self.assertAllEqual(out_masks_shape, expected_mask_shape)
示例11: testResizeToRangeWithInstanceMasksTensorOfSizeZero
# 需要導入模塊: from object_detection.core import preprocessor [as 別名]
# 或者: from object_detection.core.preprocessor import resize_image [as 別名]
def testResizeToRangeWithInstanceMasksTensorOfSizeZero(self):
"""Tests image resizing, checking output sizes."""
in_image_shape_list = [[60, 40, 3], [15, 30, 3]]
in_masks_shape_list = [[0, 60, 40], [0, 15, 30]]
height = 50
width = 100
expected_image_shape_list = [[50, 100, 3], [50, 100, 3]]
expected_masks_shape_list = [[0, 50, 100], [0, 50, 100]]
for (in_image_shape, expected_image_shape, in_masks_shape,
expected_mask_shape) in zip(in_image_shape_list,
expected_image_shape_list,
in_masks_shape_list,
expected_masks_shape_list):
in_image = tf.random_uniform(in_image_shape)
in_masks = tf.random_uniform(in_masks_shape)
out_image, out_masks = preprocessor.resize_image(
in_image, in_masks, new_height=height, new_width=width)
out_image_shape = tf.shape(out_image)
out_masks_shape = tf.shape(out_masks)
with self.test_session() as sess:
out_image_shape, out_masks_shape = sess.run(
[out_image_shape, out_masks_shape])
self.assertAllEqual(out_image_shape, expected_image_shape)
self.assertAllEqual(out_masks_shape, expected_mask_shape)