本文整理匯總了Python中object_detection.core.model.DetectionModel方法的典型用法代碼示例。如果您正苦於以下問題：Python model.DetectionModel方法的具體用法？Python model.DetectionModel怎麽用？Python model.DetectionModel使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類object_detection.core.model的用法示例。
在下文中一共展示了model.DetectionModel方法的4個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: postprocess
# 需要導入模塊: from object_detection.core import model [as 別名]
# 或者: from object_detection.core.model import DetectionModel [as 別名]
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
# 需要導入模塊: from object_detection.core import model [as 別名]
# 或者: from object_detection.core.model import DetectionModel [as 別名]
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
# 需要導入模塊: from object_detection.core import model [as 別名]
# 或者: from object_detection.core.model import DetectionModel [as 別名]
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
# 需要導入模塊: from object_detection.core import model [as 別名]
# 或者: from object_detection.core.model import DetectionModel [as 別名]
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