本文整理汇总了Python中object_detection.utils.ops.batch_position_sensitive_crop_regions方法的典型用法代码示例。如果您正苦于以下问题:Python ops.batch_position_sensitive_crop_regions方法的具体用法?Python ops.batch_position_sensitive_crop_regions怎么用?Python ops.batch_position_sensitive_crop_regions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.utils.ops
的用法示例。
在下文中一共展示了ops.batch_position_sensitive_crop_regions方法的7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: test_position_sensitive_with_single_bin
# 需要导入模块: from object_detection.utils import ops [as 别名]
# 或者: from object_detection.utils.ops import batch_position_sensitive_crop_regions [as 别名]
def test_position_sensitive_with_single_bin(self):
num_spatial_bins = [1, 1]
image_shape = [2, 3, 3, 4]
crop_size = [2, 2]
image = tf.random_uniform(image_shape)
boxes = tf.random_uniform((2, 3, 4))
box_ind = tf.constant([0, 0, 0, 1, 1, 1], dtype=tf.int32)
# When a single bin is used, position-sensitive crop and pool should be
# the same as non-position sensitive crop and pool.
crop = tf.image.crop_and_resize(image, tf.reshape(boxes, [-1, 4]), box_ind,
crop_size)
crop_and_pool = tf.reduce_mean(crop, [1, 2], keepdims=True)
crop_and_pool = tf.reshape(crop_and_pool, [2, 3, 1, 1, 4])
ps_crop_and_pool = ops.batch_position_sensitive_crop_regions(
image, boxes, crop_size, num_spatial_bins, global_pool=True)
with self.test_session() as sess:
expected_output, output = sess.run((crop_and_pool, ps_crop_and_pool))
self.assertAllClose(output, expected_output)
示例2: test_position_sensitive_with_global_pool_false_and_single_bin
# 需要导入模块: from object_detection.utils import ops [as 别名]
# 或者: from object_detection.utils.ops import batch_position_sensitive_crop_regions [as 别名]
def test_position_sensitive_with_global_pool_false_and_single_bin(self):
num_spatial_bins = [1, 1]
image_shape = [2, 3, 3, 4]
crop_size = [1, 1]
images = tf.random_uniform(image_shape)
boxes = tf.random_uniform((2, 3, 4))
# box_ind = tf.constant([0, 0, 0, 1, 1, 1], dtype=tf.int32)
# Since single_bin is used and crop_size = [1, 1] (i.e., no crop resize),
# the outputs are the same whatever the global_pool value is.
ps_crop_and_pool = ops.batch_position_sensitive_crop_regions(
images, boxes, crop_size, num_spatial_bins, global_pool=True)
ps_crop = ops.batch_position_sensitive_crop_regions(
images, boxes, crop_size, num_spatial_bins, global_pool=False)
with self.test_session() as sess:
pooled_output, unpooled_output = sess.run((ps_crop_and_pool, ps_crop))
self.assertAllClose(pooled_output, unpooled_output)
示例3: test_position_sensitive_with_single_bin
# 需要导入模块: from object_detection.utils import ops [as 别名]
# 或者: from object_detection.utils.ops import batch_position_sensitive_crop_regions [as 别名]
def test_position_sensitive_with_single_bin(self):
num_spatial_bins = [1, 1]
image_shape = [2, 3, 3, 4]
crop_size = [2, 2]
def graph_fn():
image = tf.random_uniform(image_shape)
boxes = tf.random_uniform((2, 3, 4))
box_ind = tf.constant([0, 0, 0, 1, 1, 1], dtype=tf.int32)
# When a single bin is used, position-sensitive crop and pool should be
# the same as non-position sensitive crop and pool.
crop = tf.image.crop_and_resize(image,
tf.reshape(boxes, [-1, 4]), box_ind,
crop_size)
crop_and_pool = tf.reduce_mean(crop, [1, 2], keepdims=True)
crop_and_pool = tf.reshape(crop_and_pool, [2, 3, 1, 1, 4])
ps_crop_and_pool = ops.batch_position_sensitive_crop_regions(
image, boxes, crop_size, num_spatial_bins, global_pool=True)
return crop_and_pool, ps_crop_and_pool
# Crop and resize is not supported on TPUs.
expected_output, output = self.execute_cpu(graph_fn, [])
self.assertAllClose(output, expected_output)
示例4: test_position_sensitive_with_global_pool_false_and_single_bin
# 需要导入模块: from object_detection.utils import ops [as 别名]
# 或者: from object_detection.utils.ops import batch_position_sensitive_crop_regions [as 别名]
def test_position_sensitive_with_global_pool_false_and_single_bin(self):
num_spatial_bins = [1, 1]
image_shape = [2, 3, 3, 4]
crop_size = [1, 1]
def graph_fn():
images = tf.random_uniform(image_shape)
boxes = tf.random_uniform((2, 3, 4))
# box_ind = tf.constant([0, 0, 0, 1, 1, 1], dtype=tf.int32)
# Since single_bin is used and crop_size = [1, 1] (i.e., no crop resize),
# the outputs are the same whatever the global_pool value is.
ps_crop_and_pool = ops.batch_position_sensitive_crop_regions(
images, boxes, crop_size, num_spatial_bins, global_pool=True)
ps_crop = ops.batch_position_sensitive_crop_regions(
images, boxes, crop_size, num_spatial_bins, global_pool=False)
return ps_crop_and_pool, ps_crop
pooled_output, unpooled_output = self.execute(graph_fn, [])
self.assertAllClose(pooled_output, unpooled_output)
# The following tests are only executed on CPU because the output
# shape is not constant.
示例5: test_position_sensitive_with_global_pool_false_and_known_boxes
# 需要导入模块: from object_detection.utils import ops [as 别名]
# 或者: from object_detection.utils.ops import batch_position_sensitive_crop_regions [as 别名]
def test_position_sensitive_with_global_pool_false_and_known_boxes(self):
num_spatial_bins = [2, 2]
image_shape = [2, 2, 2, 4]
crop_size = [2, 2]
images = tf.constant(range(1, 2 * 2 * 4 + 1) * 2, dtype=tf.float32,
shape=image_shape)
# First box contains whole image, and second box contains only first row.
boxes = tf.constant(np.array([[[0., 0., 1., 1.]],
[[0., 0., 0.5, 1.]]]), dtype=tf.float32)
# box_ind = tf.constant([0, 1], dtype=tf.int32)
expected_output = []
# Expected output, when the box containing whole image.
expected_output.append(
np.reshape(np.array([[4, 7],
[10, 13]]),
(1, 2, 2, 1))
)
# Expected output, when the box containing only first row.
expected_output.append(
np.reshape(np.array([[3, 6],
[7, 10]]),
(1, 2, 2, 1))
)
expected_output = np.stack(expected_output, axis=0)
ps_crop = ops.batch_position_sensitive_crop_regions(
images, boxes, crop_size, num_spatial_bins, global_pool=False)
with self.test_session() as sess:
output = sess.run(ps_crop)
self.assertAllEqual(output, expected_output)
示例6: test_position_sensitive_with_global_pool_false_and_known_boxes
# 需要导入模块: from object_detection.utils import ops [as 别名]
# 或者: from object_detection.utils.ops import batch_position_sensitive_crop_regions [as 别名]
def test_position_sensitive_with_global_pool_false_and_known_boxes(self):
num_spatial_bins = [2, 2]
image_shape = [2, 2, 2, 4]
crop_size = [2, 2]
images = tf.constant(
list(range(1, 2 * 2 * 4 + 1)) * 2, dtype=tf.float32, shape=image_shape)
# First box contains whole image, and second box contains only first row.
boxes = tf.constant(np.array([[[0., 0., 1., 1.]],
[[0., 0., 0.5, 1.]]]), dtype=tf.float32)
# box_ind = tf.constant([0, 1], dtype=tf.int32)
expected_output = []
# Expected output, when the box containing whole image.
expected_output.append(
np.reshape(np.array([[4, 7],
[10, 13]]),
(1, 2, 2, 1))
)
# Expected output, when the box containing only first row.
expected_output.append(
np.reshape(np.array([[3, 6],
[7, 10]]),
(1, 2, 2, 1))
)
expected_output = np.stack(expected_output, axis=0)
ps_crop = ops.batch_position_sensitive_crop_regions(
images, boxes, crop_size, num_spatial_bins, global_pool=False)
with self.test_session() as sess:
output = sess.run(ps_crop)
self.assertAllEqual(output, expected_output)
开发者ID:ShivangShekhar,项目名称:Live-feed-object-device-identification-using-Tensorflow-and-OpenCV,代码行数:38,代码来源:ops_test.py
示例7: test_position_sensitive_with_global_pool_false_and_known_boxes
# 需要导入模块: from object_detection.utils import ops [as 别名]
# 或者: from object_detection.utils.ops import batch_position_sensitive_crop_regions [as 别名]
def test_position_sensitive_with_global_pool_false_and_known_boxes(self):
num_spatial_bins = [2, 2]
image_shape = [2, 2, 2, 4]
crop_size = [2, 2]
# box_ind = tf.constant([0, 1], dtype=tf.int32)
expected_output = []
# Expected output, when the box containing whole image.
expected_output.append(
np.reshape(np.array([[4, 7],
[10, 13]]),
(1, 2, 2, 1))
)
# Expected output, when the box containing only first row.
expected_output.append(
np.reshape(np.array([[3, 6],
[7, 10]]),
(1, 2, 2, 1))
)
expected_output = np.stack(expected_output, axis=0)
def graph_fn():
images = tf.constant(
list(range(1, 2 * 2 * 4 + 1)) * 2, dtype=tf.float32,
shape=image_shape)
# First box contains whole image, and second box contains only first row.
boxes = tf.constant(np.array([[[0., 0., 1., 1.]],
[[0., 0., 0.5, 1.]]]), dtype=tf.float32)
ps_crop = ops.batch_position_sensitive_crop_regions(
images, boxes, crop_size, num_spatial_bins, global_pool=False)
return ps_crop
output = self.execute(graph_fn, [])
self.assertAllEqual(output, expected_output)