本文整理汇总了Python中object_detection.core.preprocessor._strict_random_crop_image方法的典型用法代码示例。如果您正苦于以下问题：Python preprocessor._strict_random_crop_image方法的具体用法？Python preprocessor._strict_random_crop_image怎么用？Python preprocessor._strict_random_crop_image使用的例子？那么恭喜您, 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._strict_random_crop_image方法的8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: testStrictRandomCropImageWithMasks
# 需要导入模块: from object_detection.core import preprocessor [as 别名]
# 或者: from object_detection.core.preprocessor import _strict_random_crop_image [as 别名]
def testStrictRandomCropImageWithMasks(self):
image = self.createColorfulTestImage()[0]
boxes = self.createTestBoxes()
labels = self.createTestLabels()
masks = tf.random_uniform([2, 200, 400], dtype=tf.float32)
with mock.patch.object(
tf.image,
'sample_distorted_bounding_box'
) as mock_sample_distorted_bounding_box:
mock_sample_distorted_bounding_box.return_value = (
tf.constant([6, 143, 0], dtype=tf.int32),
tf.constant([190, 237, -1], dtype=tf.int32),
tf.constant([[[0.03, 0.3575, 0.98, 0.95]]], dtype=tf.float32))
(new_image, new_boxes, new_labels,
new_masks) = preprocessor._strict_random_crop_image(
image, boxes, labels, masks=masks)
with self.test_session() as sess:
new_image, new_boxes, new_labels, new_masks = sess.run([
new_image, new_boxes, new_labels, new_masks])
expected_boxes = np.array([
[0.0, 0.0, 0.75789469, 1.0],
[0.23157893, 0.24050637, 0.75789469, 1.0],
], dtype=np.float32)
self.assertAllEqual(new_image.shape, [190, 237, 3])
self.assertAllEqual(new_masks.shape, [2, 190, 237])
self.assertAllClose(
new_boxes.flatten(), expected_boxes.flatten()) 示例2: testStrictRandomCropImageWithKeypoints
# 需要导入模块: from object_detection.core import preprocessor [as 别名]
# 或者: from object_detection.core.preprocessor import _strict_random_crop_image [as 别名]
def testStrictRandomCropImageWithKeypoints(self):
image = self.createColorfulTestImage()[0]
boxes = self.createTestBoxes()
labels = self.createTestLabels()
keypoints = self.createTestKeypoints()
with mock.patch.object(
tf.image,
'sample_distorted_bounding_box'
) as mock_sample_distorted_bounding_box:
mock_sample_distorted_bounding_box.return_value = (
tf.constant([6, 143, 0], dtype=tf.int32),
tf.constant([190, 237, -1], dtype=tf.int32),
tf.constant([[[0.03, 0.3575, 0.98, 0.95]]], dtype=tf.float32))
(new_image, new_boxes, new_labels,
new_keypoints) = preprocessor._strict_random_crop_image(
image, boxes, labels, keypoints=keypoints)
with self.test_session() as sess:
new_image, new_boxes, new_labels, new_keypoints = sess.run([
new_image, new_boxes, new_labels, new_keypoints])
expected_boxes = np.array([
[0.0, 0.0, 0.75789469, 1.0],
[0.23157893, 0.24050637, 0.75789469, 1.0],
], dtype=np.float32)
expected_keypoints = np.array([
[[np.nan, np.nan],
[np.nan, np.nan],
[np.nan, np.nan]],
[[0.38947368, 0.07173],
[0.49473682, 0.24050637],
[0.60000002, 0.40928277]]
], dtype=np.float32)
self.assertAllEqual(new_image.shape, [190, 237, 3])
self.assertAllClose(
new_boxes.flatten(), expected_boxes.flatten())
self.assertAllClose(
new_keypoints.flatten(), expected_keypoints.flatten()) 示例3: testStrictRandomCropImageWithGroundtruthWeights
# 需要导入模块: from object_detection.core import preprocessor [as 别名]
# 或者: from object_detection.core.preprocessor import _strict_random_crop_image [as 别名]
def testStrictRandomCropImageWithGroundtruthWeights(self):
image = self.createColorfulTestImage()[0]
boxes = self.createTestBoxes()
labels = self.createTestLabels()
weights = self.createTestGroundtruthWeights()
with mock.patch.object(
tf.image,
'sample_distorted_bounding_box'
) as mock_sample_distorted_bounding_box:
mock_sample_distorted_bounding_box.return_value = (
tf.constant([6, 143, 0], dtype=tf.int32),
tf.constant([190, 237, -1], dtype=tf.int32),
tf.constant([[[0.03, 0.3575, 0.98, 0.95]]], dtype=tf.float32))
new_image, new_boxes, new_labels, new_groundtruth_weights = (
preprocessor._strict_random_crop_image(
image, boxes, labels, weights))
with self.test_session() as sess:
new_image, new_boxes, new_labels, new_groundtruth_weights = (
sess.run(
[new_image, new_boxes, new_labels, new_groundtruth_weights])
)
expected_boxes = np.array(
[[0.0, 0.0, 0.75789469, 1.0],
[0.23157893, 0.24050637, 0.75789469, 1.0]], dtype=np.float32)
self.assertAllEqual(new_image.shape, [190, 237, 3])
self.assertAllEqual(new_groundtruth_weights, [1.0, 0.5])
self.assertAllClose(
new_boxes.flatten(), expected_boxes.flatten()) 示例4: testStrictRandomCropImageWithMasks
# 需要导入模块: from object_detection.core import preprocessor [as 别名]
# 或者: from object_detection.core.preprocessor import _strict_random_crop_image [as 别名]
def testStrictRandomCropImageWithMasks(self):
image = self.createColorfulTestImage()[0]
boxes = self.createTestBoxes()
labels = self.createTestLabels()
weights = self.createTestGroundtruthWeights()
masks = tf.random_uniform([2, 200, 400], dtype=tf.float32)
with mock.patch.object(
tf.image,
'sample_distorted_bounding_box'
) as mock_sample_distorted_bounding_box:
mock_sample_distorted_bounding_box.return_value = (
tf.constant([6, 143, 0], dtype=tf.int32),
tf.constant([190, 237, -1], dtype=tf.int32),
tf.constant([[[0.03, 0.3575, 0.98, 0.95]]], dtype=tf.float32))
new_image, new_boxes, new_labels, new_weights, new_masks = (
preprocessor._strict_random_crop_image(
image, boxes, labels, weights, masks=masks))
with self.test_session() as sess:
new_image, new_boxes, new_labels, new_weights, new_masks = sess.run(
[new_image, new_boxes, new_labels, new_weights, new_masks])
expected_boxes = np.array(
[[0.0, 0.0, 0.75789469, 1.0],
[0.23157893, 0.24050637, 0.75789469, 1.0]], dtype=np.float32)
self.assertAllEqual(new_image.shape, [190, 237, 3])
self.assertAllEqual(new_masks.shape, [2, 190, 237])
self.assertAllClose(
new_boxes.flatten(), expected_boxes.flatten()) 示例5: testStrictRandomCropImageWithKeypoints
# 需要导入模块: from object_detection.core import preprocessor [as 别名]
# 或者: from object_detection.core.preprocessor import _strict_random_crop_image [as 别名]
def testStrictRandomCropImageWithKeypoints(self):
image = self.createColorfulTestImage()[0]
boxes = self.createTestBoxes()
labels = self.createTestLabels()
weights = self.createTestGroundtruthWeights()
keypoints = self.createTestKeypoints()
with mock.patch.object(
tf.image,
'sample_distorted_bounding_box'
) as mock_sample_distorted_bounding_box:
mock_sample_distorted_bounding_box.return_value = (
tf.constant([6, 143, 0], dtype=tf.int32),
tf.constant([190, 237, -1], dtype=tf.int32),
tf.constant([[[0.03, 0.3575, 0.98, 0.95]]], dtype=tf.float32))
new_image, new_boxes, new_labels, new_weights, new_keypoints = (
preprocessor._strict_random_crop_image(
image, boxes, labels, weights, keypoints=keypoints))
with self.test_session() as sess:
new_image, new_boxes, new_labels, new_weights, new_keypoints = sess.run(
[new_image, new_boxes, new_labels, new_weights, new_keypoints])
expected_boxes = np.array([
[0.0, 0.0, 0.75789469, 1.0],
[0.23157893, 0.24050637, 0.75789469, 1.0],], dtype=np.float32)
expected_keypoints = np.array([
[[np.nan, np.nan],
[np.nan, np.nan],
[np.nan, np.nan]],
[[0.38947368, 0.07173],
[0.49473682, 0.24050637],
[0.60000002, 0.40928277]]
], dtype=np.float32)
self.assertAllEqual(new_image.shape, [190, 237, 3])
self.assertAllClose(
new_boxes.flatten(), expected_boxes.flatten())
self.assertAllClose(
new_keypoints.flatten(), expected_keypoints.flatten()) 示例6: testStrictRandomCropImageWithLabelScores
# 需要导入模块: from object_detection.core import preprocessor [as 别名]
# 或者: from object_detection.core.preprocessor import _strict_random_crop_image [as 别名]
def testStrictRandomCropImageWithLabelScores(self):
image = self.createColorfulTestImage()[0]
boxes = self.createTestBoxes()
labels = self.createTestLabels()
label_scores = self.createTestLabelScores()
with mock.patch.object(
tf.image,
'sample_distorted_bounding_box'
) as mock_sample_distorted_bounding_box:
mock_sample_distorted_bounding_box.return_value = (
tf.constant([6, 143, 0], dtype=tf.int32),
tf.constant([190, 237, -1], dtype=tf.int32),
tf.constant([[[0.03, 0.3575, 0.98, 0.95]]], dtype=tf.float32))
new_image, new_boxes, new_labels, new_label_scores = (
preprocessor._strict_random_crop_image(
image, boxes, labels, label_scores))
with self.test_session() as sess:
new_image, new_boxes, new_labels, new_label_scores = (
sess.run(
[new_image, new_boxes, new_labels, new_label_scores])
)
expected_boxes = np.array(
[[0.0, 0.0, 0.75789469, 1.0],
[0.23157893, 0.24050637, 0.75789469, 1.0]], dtype=np.float32)
self.assertAllEqual(new_image.shape, [190, 237, 3])
self.assertAllEqual(new_label_scores, [1.0, 0.5])
self.assertAllClose(
new_boxes.flatten(), expected_boxes.flatten()) 示例7: testStrictRandomCropImageWithMasks
# 需要导入模块: from object_detection.core import preprocessor [as 别名]
# 或者: from object_detection.core.preprocessor import _strict_random_crop_image [as 别名]
def testStrictRandomCropImageWithMasks(self):
image = self.createColorfulTestImage()[0]
boxes = self.createTestBoxes()
labels = self.createTestLabels()
masks = tf.random_uniform([2, 200, 400], dtype=tf.float32)
with mock.patch.object(
tf.image,
'sample_distorted_bounding_box'
) as mock_sample_distorted_bounding_box:
mock_sample_distorted_bounding_box.return_value = (
tf.constant([6, 143, 0], dtype=tf.int32),
tf.constant([190, 237, -1], dtype=tf.int32),
tf.constant([[[0.03, 0.3575, 0.98, 0.95]]], dtype=tf.float32))
new_image, new_boxes, new_labels, new_masks = (
preprocessor._strict_random_crop_image(
image, boxes, labels, masks=masks))
with self.test_session() as sess:
new_image, new_boxes, new_labels, new_masks = sess.run(
[new_image, new_boxes, new_labels, new_masks])
expected_boxes = np.array(
[[0.0, 0.0, 0.75789469, 1.0],
[0.23157893, 0.24050637, 0.75789469, 1.0]], dtype=np.float32)
self.assertAllEqual(new_image.shape, [190, 237, 3])
self.assertAllEqual(new_masks.shape, [2, 190, 237])
self.assertAllClose(
new_boxes.flatten(), expected_boxes.flatten()) 示例8: testStrictRandomCropImageWithKeypoints
# 需要导入模块: from object_detection.core import preprocessor [as 别名]
# 或者: from object_detection.core.preprocessor import _strict_random_crop_image [as 别名]
def testStrictRandomCropImageWithKeypoints(self):
image = self.createColorfulTestImage()[0]
boxes = self.createTestBoxes()
labels = self.createTestLabels()
keypoints = self.createTestKeypoints()
with mock.patch.object(
tf.image,
'sample_distorted_bounding_box'
) as mock_sample_distorted_bounding_box:
mock_sample_distorted_bounding_box.return_value = (
tf.constant([6, 143, 0], dtype=tf.int32),
tf.constant([190, 237, -1], dtype=tf.int32),
tf.constant([[[0.03, 0.3575, 0.98, 0.95]]], dtype=tf.float32))
new_image, new_boxes, new_labels, new_keypoints = (
preprocessor._strict_random_crop_image(
image, boxes, labels, keypoints=keypoints))
with self.test_session() as sess:
new_image, new_boxes, new_labels, new_keypoints = sess.run(
[new_image, new_boxes, new_labels, new_keypoints])
expected_boxes = np.array([
[0.0, 0.0, 0.75789469, 1.0],
[0.23157893, 0.24050637, 0.75789469, 1.0],], dtype=np.float32)
expected_keypoints = np.array([
[[np.nan, np.nan],
[np.nan, np.nan],
[np.nan, np.nan]],
[[0.38947368, 0.07173],
[0.49473682, 0.24050637],
[0.60000002, 0.40928277]]
], dtype=np.float32)
self.assertAllEqual(new_image.shape, [190, 237, 3])
self.assertAllClose(
new_boxes.flatten(), expected_boxes.flatten())
self.assertAllClose(
new_keypoints.flatten(), expected_keypoints.flatten())