本文整理汇总了Python中deeplab.common.LABELS_CLASS属性的典型用法代码示例。如果您正苦于以下问题：Python common.LABELS_CLASS属性的具体用法？Python common.LABELS_CLASS怎么用？Python common.LABELS_CLASS使用的例子？那么恭喜您, 这里精选的属性代码示例或许可以为您提供帮助。您也可以进一步了解该属性所在类deeplab.common的用法示例。
在下文中一共展示了common.LABELS_CLASS属性的3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: _get_data
# 需要导入模块: from deeplab import common [as 别名]
# 或者: from deeplab.common import LABELS_CLASS [as 别名]
def _get_data(data_provider, dataset_split):
"""Gets data from data provider.
Args:
data_provider: An object of slim.data_provider.
dataset_split: Dataset split.
Returns:
image: Image Tensor.
label: Label Tensor storing segmentation annotations.
image_name: Image name.
height: Image height.
width: Image width.
Raises:
ValueError: Failed to find label.
"""
if common.LABELS_CLASS not in data_provider.list_items():
raise ValueError('Failed to find labels.')
image, height, width = data_provider.get(
[common.IMAGE, common.HEIGHT, common.WIDTH])
# Some datasets do not contain image_name.
if common.IMAGE_NAME in data_provider.list_items():
image_name, = data_provider.get([common.IMAGE_NAME])
else:
image_name = tf.constant('')
label = None
if dataset_split != common.TEST_SET:
label, = data_provider.get([common.LABELS_CLASS])
return image, label, image_name, height, width 示例2: _preprocess_image
# 需要导入模块: from deeplab import common [as 别名]
# 或者: from deeplab.common import LABELS_CLASS [as 别名]
def _preprocess_image(self, sample):
"""Preprocesses the image and label.
Args:
sample: A sample containing image and label.
Returns:
sample: Sample with preprocessed image and label.
Raises:
ValueError: Ground truth label not provided during training.
"""
image = sample[common.IMAGE]
label = sample[common.LABELS_CLASS]
original_image, image, label = input_preprocess.preprocess_image_and_label(
image=image,
label=label,
crop_height=self.crop_size[0],
crop_width=self.crop_size[1],
min_resize_value=self.min_resize_value,
max_resize_value=self.max_resize_value,
resize_factor=self.resize_factor,
min_scale_factor=self.min_scale_factor,
max_scale_factor=self.max_scale_factor,
scale_factor_step_size=self.scale_factor_step_size,
ignore_label=self.ignore_label,
is_training=self.is_training,
model_variant=self.model_variant)
sample[common.IMAGE] = image
if not self.is_training:
# Original image is only used during visualization.
sample[common.ORIGINAL_IMAGE] = original_image
if label is not None:
sample[common.LABEL] = label
# Remove common.LABEL_CLASS key in the sample since it is only used to
# derive label and not used in training and evaluation.
sample.pop(common.LABELS_CLASS, None)
return sample 示例3: _preprocess_image
# 需要导入模块: from deeplab import common [as 别名]
# 或者: from deeplab.common import LABELS_CLASS [as 别名]
def _preprocess_image(self, sample):
"""Preprocesses the image and label.
Args:
sample: A sample containing image and label.
Returns:
sample: Sample with preprocessed image and label.
Raises:
ValueError: Ground truth label not provided during training.
"""
image = sample[common.IMAGE]
label = sample[common.LABELS_CLASS]
# print(self.crop_size)
original_image, image, label = input_preprocess.preprocess_image_and_label(
image=image,
label=label,
crop_height=self.crop_size[0],
crop_width=self.crop_size[1],
min_resize_value=self.min_resize_value,
max_resize_value=self.max_resize_value,
resize_factor=self.resize_factor,
min_scale_factor=self.min_scale_factor,
max_scale_factor=self.max_scale_factor,
scale_factor_step_size=self.scale_factor_step_size,
ignore_label=self.ignore_label,
is_training=self.is_training,
model_variant=self.model_variant)
sample[common.IMAGE] = image
if not self.is_training:
# Original image is only used during visualization.
sample[common.ORIGINAL_IMAGE] = original_image
if label is not None:
sample[common.LABEL] = label
# Remove common.LABEL_CLASS key in the sample since it is only used to
# derive label and not used in training and evaluation.
sample.pop(common.LABELS_CLASS, None)
return sample