本文整理汇总了Python中object_detection.core.model.DetectionModel方法的典型用法代码示例。如果您正苦于以下问题：Python model.DetectionModel方法的具体用法？Python model.DetectionModel怎么用？Python model.DetectionModel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.model的用法示例。
在下文中一共展示了model.DetectionModel方法的4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: postprocess
# 需要导入模块: from object_detection.core import model [as 别名]
# 或者: from object_detection.core.model import DetectionModel [as 别名]
def postprocess(self, prediction_dict, **params):
"""Convert predicted output tensors to final detections. Unused.
Args:
prediction_dict: a dictionary holding prediction tensors.
**params: Additional keyword arguments for specific implementations of
DetectionModel.
Returns:
detections: a dictionary with empty fields.
"""
return {
'detection_boxes': None,
'detection_scores': None,
'detection_classes': None,
'num_detections': None
} 示例2: postprocess
# 需要导入模块: from object_detection.core import model [as 别名]
# 或者: from object_detection.core.model import DetectionModel [as 别名]
def postprocess(self, prediction_dict, true_image_shapes, **params):
"""Convert predicted output tensors to final detections. Unused.
Args:
prediction_dict: a dictionary holding prediction tensors.
true_image_shapes: int32 tensor of shape [batch, 3] where each row is
of the form [height, width, channels] indicating the shapes
of true images in the resized images, as resized images can be padded
with zeros.
**params: Additional keyword arguments for specific implementations of
DetectionModel.
Returns:
detections: a dictionary with empty fields.
"""
return {
'detection_boxes': None,
'detection_scores': None,
'detection_classes': None,
'num_detections': None
} 示例3: postprocess
# 需要导入模块: from object_detection.core import model [as 别名]
# 或者: from object_detection.core.model import DetectionModel [as 别名]
def postprocess(self, prediction_dict, true_image_shapes, **params):
"""Convert predicted output tensors to final detections. Unused.
Args:
prediction_dict: a dictionary holding prediction tensors.
true_image_shapes: int32 tensor of shape [batch, 3] where each row is
of the form [height, width, channels] indicating the shapes
of true images in the resized images, as resized images can be padded
with zeros.
**params: Additional keyword arguments for specific implementations of
DetectionModel.
Returns:
detections: a dictionary with empty fields.
"""
return {
'detection_boxes': None,
'detection_scores': None,
'detection_classes': None,
'num_detections': None
} 示例4: _format_groundtruth_data
# 需要导入模块: from object_detection.core import model [as 别名]
# 或者: from object_detection.core.model import DetectionModel [as 别名]
def _format_groundtruth_data(self, image_shape):
"""Helper function for preparing groundtruth data for target assignment.
In order to be consistent with the model.DetectionModel interface,
groundtruth boxes are specified in normalized coordinates and classes are
specified as label indices with no assumed background category. To prepare
for target assignment, we:
1) convert boxes to absolute coordinates,
2) add a background class at class index 0
Args:
image_shape: A 1-D int32 tensor of shape [4] representing the shape of the
input image batch.
Returns:
groundtruth_boxlists: A list of BoxLists containing (absolute) coordinates
of the groundtruth boxes.
groundtruth_classes_with_background_list: A list of 2-D one-hot
(or k-hot) tensors of shape [num_boxes, num_classes+1] containing the
class targets with the 0th index assumed to map to the background class.
"""
groundtruth_boxlists = [
box_list_ops.to_absolute_coordinates(
box_list.BoxList(boxes), image_shape[1], image_shape[2])
for boxes in self.groundtruth_lists(fields.BoxListFields.boxes)]
groundtruth_classes_with_background_list = [
tf.to_float(
tf.pad(one_hot_encoding, [[0, 0], [1, 0]], mode='CONSTANT'))
for one_hot_encoding in self.groundtruth_lists(
fields.BoxListFields.classes)]
return groundtruth_boxlists, groundtruth_classes_with_background_list