Python object_detection.utils.static_shape.get_height() Examples
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Example #1
| Source File: static_shape_test.py From Live-feed-object-device-identification-using-Tensorflow-and-OpenCV with Apache License 2.0 | 5 votes |
|
def test_return_correct_height(self):
tensor_shape = tf.TensorShape(dims=[32, 299, 384, 3])
self.assertEqual(299, static_shape.get_height(tensor_shape))
Example #2
| Source File: static_shape_test.py From MBMD with MIT License | 5 votes |
|
def test_die_on_tensor_shape_with_rank_three(self):
tensor_shape = tf.TensorShape(dims=[32, 299, 384])
with self.assertRaises(ValueError):
static_shape.get_batch_size(tensor_shape)
static_shape.get_height(tensor_shape)
static_shape.get_width(tensor_shape)
static_shape.get_depth(tensor_shape)
Example #3
| Source File: static_shape_test.py From Elphas with Apache License 2.0 | 5 votes |
|
def test_return_correct_height(self):
tensor_shape = tf.TensorShape(dims=[32, 299, 384, 3])
self.assertEqual(299, static_shape.get_height(tensor_shape))
Example #4
| Source File: static_shape_test.py From MBMD with MIT License | 5 votes |
|
def test_return_correct_height(self):
tensor_shape = tf.TensorShape(dims=[32, 299, 384, 3])
self.assertEqual(299, static_shape.get_height(tensor_shape))
Example #5
| Source File: static_shape_test.py From object_detection_with_tensorflow with MIT License | 5 votes |
|
def test_die_on_tensor_shape_with_rank_three(self):
tensor_shape = tf.TensorShape(dims=[32, 299, 384])
with self.assertRaises(ValueError):
static_shape.get_batch_size(tensor_shape)
static_shape.get_height(tensor_shape)
static_shape.get_width(tensor_shape)
static_shape.get_depth(tensor_shape)
Example #6
| Source File: static_shape_test.py From monopsr with MIT License | 5 votes |
|
def test_return_correct_height(self):
tensor_shape = tf.TensorShape(dims=[32, 299, 384, 3])
self.assertEqual(299, static_shape.get_height(tensor_shape))
Example #7
| Source File: static_shape_test.py From monopsr with MIT License | 5 votes |
|
def test_die_on_tensor_shape_with_rank_three(self):
tensor_shape = tf.TensorShape(dims=[32, 299, 384])
with self.assertRaises(ValueError):
static_shape.get_batch_size(tensor_shape)
static_shape.get_height(tensor_shape)
static_shape.get_width(tensor_shape)
static_shape.get_depth(tensor_shape)
Example #8
| Source File: shape_utils.py From monopsr with MIT License | 5 votes |
|
def check_min_image_dim(min_dim, image_tensor):
"""Checks that the image width/height are greater than some number.
This function is used to check that the width and height of an image are above
a certain value. If the image shape is static, this function will perform the
check at graph construction time. Otherwise, if the image shape varies, an
Assertion control dependency will be added to the graph.
Args:
min_dim: The minimum number of pixels along the width and height of the
image.
image_tensor: The image tensor to check size for.
Returns:
If `image_tensor` has dynamic size, return `image_tensor` with a Assert
control dependency. Otherwise returns image_tensor.
Raises:
ValueError: if `image_tensor`'s' width or height is smaller than `min_dim`.
"""
image_shape = image_tensor.get_shape()
image_height = static_shape.get_height(image_shape)
image_width = static_shape.get_width(image_shape)
if image_height is None or image_width is None:
shape_assert = tf.Assert(
tf.logical_and(tf.greater_equal(tf.shape(image_tensor)[1], min_dim),
tf.greater_equal(tf.shape(image_tensor)[2], min_dim)),
['image size must be >= {} in both height and width.'.format(min_dim)])
with tf.control_dependencies([shape_assert]):
return tf.identity(image_tensor)
if image_height < min_dim or image_width < min_dim:
raise ValueError(
'image size must be >= %d in both height and width; image dim = %d,%d' %
(min_dim, image_height, image_width))
return image_tensor
Example #9
| Source File: static_shape_test.py From AniSeg with Apache License 2.0 | 5 votes |
|
def test_return_correct_height(self):
tensor_shape = tf.TensorShape(dims=[32, 299, 384, 3])
self.assertEqual(299, static_shape.get_height(tensor_shape))
Example #10
| Source File: static_shape_test.py From AniSeg with Apache License 2.0 | 5 votes |
|
def test_die_on_tensor_shape_with_rank_three(self):
tensor_shape = tf.TensorShape(dims=[32, 299, 384])
with self.assertRaises(ValueError):
static_shape.get_batch_size(tensor_shape)
static_shape.get_height(tensor_shape)
static_shape.get_width(tensor_shape)
static_shape.get_depth(tensor_shape)
Example #11
| Source File: shape_utils.py From AniSeg with Apache License 2.0 | 5 votes |
|
def check_min_image_dim(min_dim, image_tensor):
"""Checks that the image width/height are greater than some number.
This function is used to check that the width and height of an image are above
a certain value. If the image shape is static, this function will perform the
check at graph construction time. Otherwise, if the image shape varies, an
Assertion control dependency will be added to the graph.
Args:
min_dim: The minimum number of pixels along the width and height of the
image.
image_tensor: The image tensor to check size for.
Returns:
If `image_tensor` has dynamic size, return `image_tensor` with a Assert
control dependency. Otherwise returns image_tensor.
Raises:
ValueError: if `image_tensor`'s' width or height is smaller than `min_dim`.
"""
image_shape = image_tensor.get_shape()
image_height = static_shape.get_height(image_shape)
image_width = static_shape.get_width(image_shape)
if image_height is None or image_width is None:
shape_assert = tf.Assert(
tf.logical_and(tf.greater_equal(tf.shape(image_tensor)[1], min_dim),
tf.greater_equal(tf.shape(image_tensor)[2], min_dim)),
['image size must be >= {} in both height and width.'.format(min_dim)])
with tf.control_dependencies([shape_assert]):
return tf.identity(image_tensor)
if image_height < min_dim or image_width < min_dim:
raise ValueError(
'image size must be >= %d in both height and width; image dim = %d,%d' %
(min_dim, image_height, image_width))
return image_tensor
Example #12
| Source File: static_shape_test.py From MAX-Object-Detector with Apache License 2.0 | 5 votes |
|
def test_return_correct_height(self):
tensor_shape = tf.TensorShape(dims=[32, 299, 384, 3])
self.assertEqual(299, static_shape.get_height(tensor_shape))
Example #13
| Source File: static_shape_test.py From object_detection_with_tensorflow with MIT License | 5 votes |
|
def test_return_correct_height(self):
tensor_shape = tf.TensorShape(dims=[32, 299, 384, 3])
self.assertEqual(299, static_shape.get_height(tensor_shape))
Example #14
| Source File: shape_utils.py From Live-feed-object-device-identification-using-Tensorflow-and-OpenCV with Apache License 2.0 | 5 votes |
|
def check_min_image_dim(min_dim, image_tensor):
"""Checks that the image width/height are greater than some number.
This function is used to check that the width and height of an image are above
a certain value. If the image shape is static, this function will perform the
check at graph construction time. Otherwise, if the image shape varies, an
Assertion control dependency will be added to the graph.
Args:
min_dim: The minimum number of pixels along the width and height of the
image.
image_tensor: The image tensor to check size for.
Returns:
If `image_tensor` has dynamic size, return `image_tensor` with a Assert
control dependency. Otherwise returns image_tensor.
Raises:
ValueError: if `image_tensor`'s' width or height is smaller than `min_dim`.
"""
image_shape = image_tensor.get_shape()
image_height = static_shape.get_height(image_shape)
image_width = static_shape.get_width(image_shape)
if image_height is None or image_width is None:
shape_assert = tf.Assert(
tf.logical_and(tf.greater_equal(tf.shape(image_tensor)[1], min_dim),
tf.greater_equal(tf.shape(image_tensor)[2], min_dim)),
['image size must be >= {} in both height and width.'.format(min_dim)])
with tf.control_dependencies([shape_assert]):
return tf.identity(image_tensor)
if image_height < min_dim or image_width < min_dim:
raise ValueError(
'image size must be >= %d in both height and width; image dim = %d,%d' %
(min_dim, image_height, image_width))
return image_tensor
Example #15
| Source File: static_shape_test.py From Live-feed-object-device-identification-using-Tensorflow-and-OpenCV with Apache License 2.0 | 5 votes |
|
def test_die_on_tensor_shape_with_rank_three(self):
tensor_shape = tf.TensorShape(dims=[32, 299, 384])
with self.assertRaises(ValueError):
static_shape.get_batch_size(tensor_shape)
static_shape.get_height(tensor_shape)
static_shape.get_width(tensor_shape)
static_shape.get_depth(tensor_shape)
Example #16
| Source File: static_shape_test.py From object_detection_kitti with Apache License 2.0 | 5 votes |
|
def test_return_correct_height(self):
tensor_shape = tf.TensorShape(dims=[32, 299, 384, 3])
self.assertEqual(299, static_shape.get_height(tensor_shape))
Example #17
| Source File: static_shape_test.py From hands-detection with MIT License | 5 votes |
|
def test_die_on_tensor_shape_with_rank_three(self):
tensor_shape = tf.TensorShape(dims=[32, 299, 384])
with self.assertRaises(ValueError):
static_shape.get_batch_size(tensor_shape)
static_shape.get_height(tensor_shape)
static_shape.get_width(tensor_shape)
static_shape.get_depth(tensor_shape)
Example #18
| Source File: static_shape_test.py From hands-detection with MIT License | 5 votes |
|
def test_return_correct_height(self):
tensor_shape = tf.TensorShape(dims=[32, 299, 384, 3])
self.assertEqual(299, static_shape.get_height(tensor_shape))
Example #19
| Source File: static_shape_test.py From moveo_ros with MIT License | 5 votes |
|
def test_die_on_tensor_shape_with_rank_three(self):
tensor_shape = tf.TensorShape(dims=[32, 299, 384])
with self.assertRaises(ValueError):
static_shape.get_batch_size(tensor_shape)
static_shape.get_height(tensor_shape)
static_shape.get_width(tensor_shape)
static_shape.get_depth(tensor_shape)
Example #20
| Source File: static_shape_test.py From moveo_ros with MIT License | 5 votes |
|
def test_return_correct_height(self):
tensor_shape = tf.TensorShape(dims=[32, 299, 384, 3])
self.assertEqual(299, static_shape.get_height(tensor_shape))
Example #21
| Source File: static_shape_test.py From BMW-TensorFlow-Training-GUI with Apache License 2.0 | 5 votes |
|
def test_die_on_tensor_shape_with_rank_three(self):
tensor_shape = tf.TensorShape(dims=[32, 299, 384])
with self.assertRaises(ValueError):
static_shape.get_batch_size(tensor_shape)
static_shape.get_height(tensor_shape)
static_shape.get_width(tensor_shape)
static_shape.get_depth(tensor_shape)
Example #22
| Source File: static_shape_test.py From BMW-TensorFlow-Training-GUI with Apache License 2.0 | 5 votes |
|
def test_return_correct_height(self):
tensor_shape = tf.TensorShape(dims=[32, 299, 384, 3])
self.assertEqual(299, static_shape.get_height(tensor_shape))
Example #23
| Source File: shape_utils.py From BMW-TensorFlow-Training-GUI with Apache License 2.0 | 5 votes |
|
def check_min_image_dim(min_dim, image_tensor):
"""Checks that the image width/height are greater than some number.
This function is used to check that the width and height of an image are above
a certain value. If the image shape is static, this function will perform the
check at graph construction time. Otherwise, if the image shape varies, an
Assertion control dependency will be added to the graph.
Args:
min_dim: The minimum number of pixels along the width and height of the
image.
image_tensor: The image tensor to check size for.
Returns:
If `image_tensor` has dynamic size, return `image_tensor` with a Assert
control dependency. Otherwise returns image_tensor.
Raises:
ValueError: if `image_tensor`'s' width or height is smaller than `min_dim`.
"""
image_shape = image_tensor.get_shape()
image_height = static_shape.get_height(image_shape)
image_width = static_shape.get_width(image_shape)
if image_height is None or image_width is None:
shape_assert = tf.Assert(
tf.logical_and(tf.greater_equal(tf.shape(image_tensor)[1], min_dim),
tf.greater_equal(tf.shape(image_tensor)[2], min_dim)),
['image size must be >= {} in both height and width.'.format(min_dim)])
with tf.control_dependencies([shape_assert]):
return tf.identity(image_tensor)
if image_height < min_dim or image_width < min_dim:
raise ValueError(
'image size must be >= %d in both height and width; image dim = %d,%d' %
(min_dim, image_height, image_width))
return image_tensor
Example #24
| Source File: shape_utils.py From camera-feed-object-detector-tf-serve with Apache License 2.0 | 5 votes |
|
def check_min_image_dim(min_dim, image_tensor):
"""Checks that the image width/height are greater than some number.
This function is used to check that the width and height of an image are above
a certain value. If the image shape is static, this function will perform the
check at graph construction time. Otherwise, if the image shape varies, an
Assertion control dependency will be added to the graph.
Args:
min_dim: The minimum number of pixels along the width and height of the
image.
image_tensor: The image tensor to check size for.
Returns:
If `image_tensor` has dynamic size, return `image_tensor` with a Assert
control dependency. Otherwise returns image_tensor.
Raises:
ValueError: if `image_tensor`'s' width or height is smaller than `min_dim`.
"""
image_shape = image_tensor.get_shape()
image_height = static_shape.get_height(image_shape)
image_width = static_shape.get_width(image_shape)
if image_height is None or image_width is None:
shape_assert = tf.Assert(
tf.logical_and(tf.greater_equal(tf.shape(image_tensor)[1], min_dim),
tf.greater_equal(tf.shape(image_tensor)[2], min_dim)),
['image size must be >= {} in both height and width.'.format(min_dim)])
with tf.control_dependencies([shape_assert]):
return tf.identity(image_tensor)
if image_height < min_dim or image_width < min_dim:
raise ValueError(
'image size must be >= %d in both height and width; image dim = %d,%d' %
(min_dim, image_height, image_width))
return image_tensor
Example #25
| Source File: shape_utils.py From ros_tensorflow with Apache License 2.0 | 5 votes |
|
def check_min_image_dim(min_dim, image_tensor):
"""Checks that the image width/height are greater than some number.
This function is used to check that the width and height of an image are above
a certain value. If the image shape is static, this function will perform the
check at graph construction time. Otherwise, if the image shape varies, an
Assertion control dependency will be added to the graph.
Args:
min_dim: The minimum number of pixels along the width and height of the
image.
image_tensor: The image tensor to check size for.
Returns:
If `image_tensor` has dynamic size, return `image_tensor` with a Assert
control dependency. Otherwise returns image_tensor.
Raises:
ValueError: if `image_tensor`'s' width or height is smaller than `min_dim`.
"""
image_shape = image_tensor.get_shape()
image_height = static_shape.get_height(image_shape)
image_width = static_shape.get_width(image_shape)
if image_height is None or image_width is None:
shape_assert = tf.Assert(
tf.logical_and(tf.greater_equal(tf.shape(image_tensor)[1], min_dim),
tf.greater_equal(tf.shape(image_tensor)[2], min_dim)),
['image size must be >= {} in both height and width.'.format(min_dim)])
with tf.control_dependencies([shape_assert]):
return tf.identity(image_tensor)
if image_height < min_dim or image_width < min_dim:
raise ValueError(
'image size must be >= %d in both height and width; image dim = %d,%d' %
(min_dim, image_height, image_width))
return image_tensor
Example #26
| Source File: static_shape_test.py From ros_tensorflow with Apache License 2.0 | 5 votes |
|
def test_die_on_tensor_shape_with_rank_three(self):
tensor_shape = tf.TensorShape(dims=[32, 299, 384])
with self.assertRaises(ValueError):
static_shape.get_batch_size(tensor_shape)
static_shape.get_height(tensor_shape)
static_shape.get_width(tensor_shape)
static_shape.get_depth(tensor_shape)
Example #27
| Source File: static_shape_test.py From ros_tensorflow with Apache License 2.0 | 5 votes |
|
def test_return_correct_height(self):
tensor_shape = tf.TensorShape(dims=[32, 299, 384, 3])
self.assertEqual(299, static_shape.get_height(tensor_shape))
Example #28
| Source File: shape_utils.py From Gun-Detector with Apache License 2.0 | 5 votes |
|
def check_min_image_dim(min_dim, image_tensor):
"""Checks that the image width/height are greater than some number.
This function is used to check that the width and height of an image are above
a certain value. If the image shape is static, this function will perform the
check at graph construction time. Otherwise, if the image shape varies, an
Assertion control dependency will be added to the graph.
Args:
min_dim: The minimum number of pixels along the width and height of the
image.
image_tensor: The image tensor to check size for.
Returns:
If `image_tensor` has dynamic size, return `image_tensor` with a Assert
control dependency. Otherwise returns image_tensor.
Raises:
ValueError: if `image_tensor`'s' width or height is smaller than `min_dim`.
"""
image_shape = image_tensor.get_shape()
image_height = static_shape.get_height(image_shape)
image_width = static_shape.get_width(image_shape)
if image_height is None or image_width is None:
shape_assert = tf.Assert(
tf.logical_and(tf.greater_equal(tf.shape(image_tensor)[1], min_dim),
tf.greater_equal(tf.shape(image_tensor)[2], min_dim)),
['image size must be >= {} in both height and width.'.format(min_dim)])
with tf.control_dependencies([shape_assert]):
return tf.identity(image_tensor)
if image_height < min_dim or image_width < min_dim:
raise ValueError(
'image size must be >= %d in both height and width; image dim = %d,%d' %
(min_dim, image_height, image_width))
return image_tensor
Example #29
| Source File: static_shape_test.py From Gun-Detector with Apache License 2.0 | 5 votes |
|
def test_die_on_tensor_shape_with_rank_three(self):
tensor_shape = tf.TensorShape(dims=[32, 299, 384])
with self.assertRaises(ValueError):
static_shape.get_batch_size(tensor_shape)
static_shape.get_height(tensor_shape)
static_shape.get_width(tensor_shape)
static_shape.get_depth(tensor_shape)
Example #30
| Source File: static_shape_test.py From Gun-Detector with Apache License 2.0 | 5 votes |
|
def test_return_correct_height(self):
tensor_shape = tf.TensorShape(dims=[32, 299, 384, 3])
self.assertEqual(299, static_shape.get_height(tensor_shape))
