本文整理汇总了Python中object_detection.core.preprocessor.random_crop_to_aspect_ratio方法的典型用法代码示例。如果您正苦于以下问题：Python preprocessor.random_crop_to_aspect_ratio方法的具体用法？Python preprocessor.random_crop_to_aspect_ratio怎么用？Python preprocessor.random_crop_to_aspect_ratio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.random_crop_to_aspect_ratio方法的6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: testRandomCropToAspectRatio
# 需要导入模块: from object_detection.core import preprocessor [as 别名]
# 或者: from object_detection.core.preprocessor import random_crop_to_aspect_ratio [as 别名]
def testRandomCropToAspectRatio(self):
preprocessing_options = [(preprocessor.normalize_image, {
'original_minval': 0,
'original_maxval': 255,
'target_minval': 0,
'target_maxval': 1
})]
images = self.createTestImages()
boxes = self.createTestBoxes()
labels = self.createTestLabels()
tensor_dict = {
fields.InputDataFields.image: images,
fields.InputDataFields.groundtruth_boxes: boxes,
fields.InputDataFields.groundtruth_classes: labels
}
tensor_dict = preprocessor.preprocess(tensor_dict, preprocessing_options)
images = tensor_dict[fields.InputDataFields.image]
preprocessing_options = [(preprocessor.random_crop_to_aspect_ratio, {
'aspect_ratio': 2.0
})]
cropped_tensor_dict = preprocessor.preprocess(tensor_dict,
preprocessing_options)
cropped_images = cropped_tensor_dict[fields.InputDataFields.image]
cropped_boxes = cropped_tensor_dict[
fields.InputDataFields.groundtruth_boxes]
boxes_shape = tf.shape(boxes)
cropped_boxes_shape = tf.shape(cropped_boxes)
images_shape = tf.shape(images)
cropped_images_shape = tf.shape(cropped_images)
with self.test_session() as sess:
(boxes_shape_, cropped_boxes_shape_, images_shape_,
cropped_images_shape_) = sess.run([
boxes_shape, cropped_boxes_shape, images_shape, cropped_images_shape
])
self.assertAllEqual(boxes_shape_, cropped_boxes_shape_)
self.assertEqual(images_shape_[1], cropped_images_shape_[1] * 2)
self.assertEqual(images_shape_[2], cropped_images_shape_[2]) 示例2: test_build_random_crop_to_aspect_ratio
# 需要导入模块: from object_detection.core import preprocessor [as 别名]
# 或者: from object_detection.core.preprocessor import random_crop_to_aspect_ratio [as 别名]
def test_build_random_crop_to_aspect_ratio(self):
preprocessor_text_proto = """
random_crop_to_aspect_ratio {
aspect_ratio: 0.85
overlap_thresh: 0.35
}
"""
preprocessor_proto = preprocessor_pb2.PreprocessingStep()
text_format.Merge(preprocessor_text_proto, preprocessor_proto)
function, args = preprocessor_builder.build(preprocessor_proto)
self.assertEqual(function, preprocessor.random_crop_to_aspect_ratio)
self.assert_dictionary_close(args, {'aspect_ratio': 0.85,
'overlap_thresh': 0.35}) 示例3: testRandomCropToAspectRatioWithCache
# 需要导入模块: from object_detection.core import preprocessor [as 别名]
# 或者: from object_detection.core.preprocessor import random_crop_to_aspect_ratio [as 别名]
def testRandomCropToAspectRatioWithCache(self):
preprocess_options = [(preprocessor.random_crop_to_aspect_ratio, {})]
self._testPreprocessorCache(preprocess_options,
test_boxes=True,
test_masks=False,
test_keypoints=False) 示例4: testRandomCropToAspectRatio
# 需要导入模块: from object_detection.core import preprocessor [as 别名]
# 或者: from object_detection.core.preprocessor import random_crop_to_aspect_ratio [as 别名]
def testRandomCropToAspectRatio(self):
images = self.createTestImages()
boxes = self.createTestBoxes()
labels = self.createTestLabels()
weights = self.createTestGroundtruthWeights()
tensor_dict = {
fields.InputDataFields.image: images,
fields.InputDataFields.groundtruth_boxes: boxes,
fields.InputDataFields.groundtruth_classes: labels,
fields.InputDataFields.groundtruth_weights: weights,
}
tensor_dict = preprocessor.preprocess(tensor_dict, [])
images = tensor_dict[fields.InputDataFields.image]
preprocessing_options = [(preprocessor.random_crop_to_aspect_ratio, {
'aspect_ratio': 2.0
})]
cropped_tensor_dict = preprocessor.preprocess(tensor_dict,
preprocessing_options)
cropped_images = cropped_tensor_dict[fields.InputDataFields.image]
cropped_boxes = cropped_tensor_dict[
fields.InputDataFields.groundtruth_boxes]
boxes_shape = tf.shape(boxes)
cropped_boxes_shape = tf.shape(cropped_boxes)
images_shape = tf.shape(images)
cropped_images_shape = tf.shape(cropped_images)
with self.test_session() as sess:
(boxes_shape_, cropped_boxes_shape_, images_shape_,
cropped_images_shape_) = sess.run([
boxes_shape, cropped_boxes_shape, images_shape, cropped_images_shape
])
self.assertAllEqual(boxes_shape_, cropped_boxes_shape_)
self.assertEqual(images_shape_[1], cropped_images_shape_[1] * 2)
self.assertEqual(images_shape_[2], cropped_images_shape_[2]) 示例5: test_build_random_crop_to_aspect_ratio
# 需要导入模块: from object_detection.core import preprocessor [as 别名]
# 或者: from object_detection.core.preprocessor import random_crop_to_aspect_ratio [as 别名]
def test_build_random_crop_to_aspect_ratio(self):
preprocessor_text_proto = """
random_crop_to_aspect_ratio {
aspect_ratio: 0.85
overlap_thresh: 0.35
clip_boxes: False
}
"""
preprocessor_proto = preprocessor_pb2.PreprocessingStep()
text_format.Merge(preprocessor_text_proto, preprocessor_proto)
function, args = preprocessor_builder.build(preprocessor_proto)
self.assertEqual(function, preprocessor.random_crop_to_aspect_ratio)
self.assert_dictionary_close(args, {'aspect_ratio': 0.85,
'overlap_thresh': 0.35,
'clip_boxes': False}) 示例6: testRandomCropToAspectRatio
# 需要导入模块: from object_detection.core import preprocessor [as 别名]
# 或者: from object_detection.core.preprocessor import random_crop_to_aspect_ratio [as 别名]
def testRandomCropToAspectRatio(self):
images = self.createTestImages()
boxes = self.createTestBoxes()
labels = self.createTestLabels()
tensor_dict = {
fields.InputDataFields.image: images,
fields.InputDataFields.groundtruth_boxes: boxes,
fields.InputDataFields.groundtruth_classes: labels,
}
tensor_dict = preprocessor.preprocess(tensor_dict, [])
images = tensor_dict[fields.InputDataFields.image]
preprocessing_options = [(preprocessor.random_crop_to_aspect_ratio, {
'aspect_ratio': 2.0
})]
cropped_tensor_dict = preprocessor.preprocess(tensor_dict,
preprocessing_options)
cropped_images = cropped_tensor_dict[fields.InputDataFields.image]
cropped_boxes = cropped_tensor_dict[
fields.InputDataFields.groundtruth_boxes]
boxes_shape = tf.shape(boxes)
cropped_boxes_shape = tf.shape(cropped_boxes)
images_shape = tf.shape(images)
cropped_images_shape = tf.shape(cropped_images)
with self.test_session() as sess:
(boxes_shape_, cropped_boxes_shape_, images_shape_,
cropped_images_shape_) = sess.run([
boxes_shape, cropped_boxes_shape, images_shape, cropped_images_shape
])
self.assertAllEqual(boxes_shape_, cropped_boxes_shape_)
self.assertEqual(images_shape_[1], cropped_images_shape_[1] * 2)
self.assertEqual(images_shape_[2], cropped_images_shape_[2])