本文整理汇总了Python中object_detection.core.preprocessor.resize_to_min_dimension方法的典型用法代码示例。如果您正苦于以下问题：Python preprocessor.resize_to_min_dimension方法的具体用法？Python preprocessor.resize_to_min_dimension怎么用？Python preprocessor.resize_to_min_dimension使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类object_detection.core.preprocessor的用法示例。
在下文中一共展示了preprocessor.resize_to_min_dimension方法的5个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: testResizeToMinDimensionWithInstanceMasksTensorOfSizeZero
# 需要导入模块: from object_detection.core import preprocessor [as 别名]
# 或者: from object_detection.core.preprocessor import resize_to_min_dimension [as 别名]
def testResizeToMinDimensionWithInstanceMasksTensorOfSizeZero(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]]
min_dim = 50
expected_image_shape_list = [[75, 50, 3], [50, 100, 3]]
expected_masks_shape_list = [[0, 75, 50], [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_to_min_dimension(
in_image, in_masks, min_dimension=min_dim)
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: testResizeToMinDimensionWithInstanceMasksTensorOfSizeZero
# 需要导入模块: from object_detection.core import preprocessor [as 别名]
# 或者: from object_detection.core.preprocessor import resize_to_min_dimension [as 别名]
def testResizeToMinDimensionWithInstanceMasksTensorOfSizeZero(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]]
min_dim = 50
expected_image_shape_list = [[75, 50, 3], [50, 100, 3]]
expected_masks_shape_list = [[0, 75, 50], [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_to_min_dimension(
in_image, in_masks, min_dimension=min_dim)
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: testResizeToMinDimensionTensorShapes
# 需要导入模块: from object_detection.core import preprocessor [as 别名]
# 或者: from object_detection.core.preprocessor import resize_to_min_dimension [as 别名]
def testResizeToMinDimensionTensorShapes(self):
in_image_shape_list = [[60, 55, 3], [15, 30, 3]]
in_masks_shape_list = [[15, 60, 55], [10, 15, 30]]
min_dim = 50
expected_image_shape_list = [[60, 55, 3], [50, 100, 3]]
expected_masks_shape_list = [[15, 60, 55], [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.placeholder(tf.float32, shape=(None, None, 3))
in_masks = tf.placeholder(tf.float32, shape=(None, None, None))
in_masks = tf.random_uniform(in_masks_shape)
out_image, out_masks, _ = preprocessor.resize_to_min_dimension(
in_image, in_masks, min_dimension=min_dim)
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],
feed_dict={
in_image: np.random.randn(*in_image_shape),
in_masks: np.random.randn(*in_masks_shape)
})
self.assertAllEqual(out_image_shape, expected_image_shape)
self.assertAllEqual(out_masks_shape, expected_mask_shape) 示例4: testResizeToMinDimensionRaisesErrorOn4DImage
# 需要导入模块: from object_detection.core import preprocessor [as 别名]
# 或者: from object_detection.core.preprocessor import resize_to_min_dimension [as 别名]
def testResizeToMinDimensionRaisesErrorOn4DImage(self):
image = tf.random_uniform([1, 200, 300, 3])
with self.assertRaises(ValueError):
preprocessor.resize_to_min_dimension(image, 500) 示例5: testResizeToMinDimensionTensorShapes
# 需要导入模块: from object_detection.core import preprocessor [as 别名]
# 或者: from object_detection.core.preprocessor import resize_to_min_dimension [as 别名]
def testResizeToMinDimensionTensorShapes(self):
in_image_shape_list = [[60, 55, 3], [15, 30, 3]]
in_masks_shape_list = [[15, 60, 55], [10, 15, 30]]
min_dim = 50
expected_image_shape_list = [[60, 55, 3], [50, 100, 3]]
expected_masks_shape_list = [[15, 60, 55], [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.placeholder(tf.float32, shape=(None, None, 3))
in_masks = tf.placeholder(tf.float32, shape=(None, None, None))
in_masks = tf.random_uniform(in_masks_shape)
out_image, out_masks = preprocessor.resize_to_min_dimension(
in_image, in_masks, min_dimension=min_dim)
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],
feed_dict={
in_image: np.random.randn(*in_image_shape),
in_masks: np.random.randn(*in_masks_shape)
})
self.assertAllEqual(out_image_shape, expected_image_shape)
self.assertAllEqual(out_masks_shape, expected_mask_shape)