本文整理汇总了Python中object_detection.eval_util.result_dict_for_batched_example方法的典型用法代码示例。如果您正苦于以下问题：Python eval_util.result_dict_for_batched_example方法的具体用法？Python eval_util.result_dict_for_batched_example怎么用？Python eval_util.result_dict_for_batched_example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.eval_util的用法示例。
在下文中一共展示了eval_util.result_dict_for_batched_example方法的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: _make_evaluation_dict
# 需要导入模块: from object_detection import eval_util [as 别名]
# 或者: from object_detection.eval_util import result_dict_for_batched_example [as 别名]
def _make_evaluation_dict(self,
resized_groundtruth_masks=False,
batch_size=1,
max_gt_boxes=None,
scale_to_absolute=False):
input_data_fields = fields.InputDataFields
detection_fields = fields.DetectionResultFields
image = tf.zeros(shape=[batch_size, 20, 20, 3], dtype=tf.uint8)
if batch_size == 1:
key = tf.constant('image1')
else:
key = tf.constant([str(range(batch_size))])
detection_boxes = tf.tile(tf.constant([[[0., 0., 1., 1.]]]),
multiples=[batch_size, 1, 1])
detection_scores = tf.tile(tf.constant([[0.8]]), multiples=[batch_size, 1])
detection_classes = tf.tile(tf.constant([[0]]), multiples=[batch_size, 1])
detection_masks = tf.tile(tf.ones(shape=[1, 1, 20, 20], dtype=tf.float32),
multiples=[batch_size, 1, 1, 1])
num_detections = tf.ones([batch_size])
groundtruth_boxes = tf.constant([[0., 0., 1., 1.]])
groundtruth_classes = tf.constant([1])
groundtruth_instance_masks = tf.ones(shape=[1, 20, 20], dtype=tf.uint8)
if resized_groundtruth_masks:
groundtruth_instance_masks = tf.ones(shape=[1, 10, 10], dtype=tf.uint8)
if batch_size > 1:
groundtruth_boxes = tf.tile(tf.expand_dims(groundtruth_boxes, 0),
multiples=[batch_size, 1, 1])
groundtruth_classes = tf.tile(tf.expand_dims(groundtruth_classes, 0),
multiples=[batch_size, 1])
groundtruth_instance_masks = tf.tile(
tf.expand_dims(groundtruth_instance_masks, 0),
multiples=[batch_size, 1, 1, 1])
detections = {
detection_fields.detection_boxes: detection_boxes,
detection_fields.detection_scores: detection_scores,
detection_fields.detection_classes: detection_classes,
detection_fields.detection_masks: detection_masks,
detection_fields.num_detections: num_detections
}
groundtruth = {
input_data_fields.groundtruth_boxes: groundtruth_boxes,
input_data_fields.groundtruth_classes: groundtruth_classes,
input_data_fields.groundtruth_instance_masks: groundtruth_instance_masks
}
if batch_size > 1:
return eval_util.result_dict_for_batched_example(
image, key, detections, groundtruth,
scale_to_absolute=scale_to_absolute,
max_gt_boxes=max_gt_boxes)
else:
return eval_util.result_dict_for_single_example(
image, key, detections, groundtruth,
scale_to_absolute=scale_to_absolute) 示例2: _make_evaluation_dict
# 需要导入模块: from object_detection import eval_util [as 别名]
# 或者: from object_detection.eval_util import result_dict_for_batched_example [as 别名]
def _make_evaluation_dict(self,
resized_groundtruth_masks=False,
batch_size=1,
max_gt_boxes=None,
scale_to_absolute=False):
input_data_fields = fields.InputDataFields
detection_fields = fields.DetectionResultFields
image = tf.zeros(shape=[batch_size, 20, 20, 3], dtype=tf.uint8)
if batch_size == 1:
key = tf.constant('image1')
else:
key = tf.constant([str(i) for i in range(batch_size)])
detection_boxes = tf.tile(tf.constant([[[0., 0., 1., 1.]]]),
multiples=[batch_size, 1, 1])
detection_scores = tf.tile(tf.constant([[0.8]]), multiples=[batch_size, 1])
detection_classes = tf.tile(tf.constant([[0]]), multiples=[batch_size, 1])
detection_masks = tf.tile(tf.ones(shape=[1, 1, 20, 20], dtype=tf.float32),
multiples=[batch_size, 1, 1, 1])
num_detections = tf.ones([batch_size])
groundtruth_boxes = tf.constant([[0., 0., 1., 1.]])
groundtruth_classes = tf.constant([1])
groundtruth_instance_masks = tf.ones(shape=[1, 20, 20], dtype=tf.uint8)
if resized_groundtruth_masks:
groundtruth_instance_masks = tf.ones(shape=[1, 10, 10], dtype=tf.uint8)
if batch_size > 1:
groundtruth_boxes = tf.tile(tf.expand_dims(groundtruth_boxes, 0),
multiples=[batch_size, 1, 1])
groundtruth_classes = tf.tile(tf.expand_dims(groundtruth_classes, 0),
multiples=[batch_size, 1])
groundtruth_instance_masks = tf.tile(
tf.expand_dims(groundtruth_instance_masks, 0),
multiples=[batch_size, 1, 1, 1])
detections = {
detection_fields.detection_boxes: detection_boxes,
detection_fields.detection_scores: detection_scores,
detection_fields.detection_classes: detection_classes,
detection_fields.detection_masks: detection_masks,
detection_fields.num_detections: num_detections
}
groundtruth = {
input_data_fields.groundtruth_boxes: groundtruth_boxes,
input_data_fields.groundtruth_classes: groundtruth_classes,
input_data_fields.groundtruth_instance_masks: groundtruth_instance_masks
}
if batch_size > 1:
return eval_util.result_dict_for_batched_example(
image, key, detections, groundtruth,
scale_to_absolute=scale_to_absolute,
max_gt_boxes=max_gt_boxes)
else:
return eval_util.result_dict_for_single_example(
image, key, detections, groundtruth,
scale_to_absolute=scale_to_absolute) 示例3: _make_evaluation_dict
# 需要导入模块: from object_detection import eval_util [as 别名]
# 或者: from object_detection.eval_util import result_dict_for_batched_example [as 别名]
def _make_evaluation_dict(self,
resized_groundtruth_masks=False,
batch_size=1,
max_gt_boxes=None,
scale_to_absolute=False):
input_data_fields = fields.InputDataFields
detection_fields = fields.DetectionResultFields
image = tf.zeros(shape=[batch_size, 20, 20, 3], dtype=tf.uint8)
if batch_size == 1:
key = tf.constant('image1')
else:
key = tf.constant([str(i) for i in range(batch_size)])
detection_boxes = tf.tile(tf.constant([[[0., 0., 1., 1.]]]),
multiples=[batch_size, 1, 1])
detection_scores = tf.tile(tf.constant([[0.8]]), multiples=[batch_size, 1])
detection_classes = tf.tile(tf.constant([[0]]), multiples=[batch_size, 1])
detection_masks = tf.tile(tf.ones(shape=[1, 1, 20, 20], dtype=tf.float32),
multiples=[batch_size, 1, 1, 1])
num_detections = tf.ones([batch_size])
groundtruth_boxes = tf.constant([[0., 0., 1., 1.]])
groundtruth_classes = tf.constant([1])
groundtruth_instance_masks = tf.ones(shape=[1, 20, 20], dtype=tf.uint8)
groundtruth_keypoints = tf.constant([[0.0, 0.0], [0.5, 0.5], [1.0, 1.0]])
if resized_groundtruth_masks:
groundtruth_instance_masks = tf.ones(shape=[1, 10, 10], dtype=tf.uint8)
if batch_size > 1:
groundtruth_boxes = tf.tile(tf.expand_dims(groundtruth_boxes, 0),
multiples=[batch_size, 1, 1])
groundtruth_classes = tf.tile(tf.expand_dims(groundtruth_classes, 0),
multiples=[batch_size, 1])
groundtruth_instance_masks = tf.tile(
tf.expand_dims(groundtruth_instance_masks, 0),
multiples=[batch_size, 1, 1, 1])
groundtruth_keypoints = tf.tile(
tf.expand_dims(groundtruth_keypoints, 0),
multiples=[batch_size, 1, 1])
detections = {
detection_fields.detection_boxes: detection_boxes,
detection_fields.detection_scores: detection_scores,
detection_fields.detection_classes: detection_classes,
detection_fields.detection_masks: detection_masks,
detection_fields.num_detections: num_detections
}
groundtruth = {
input_data_fields.groundtruth_boxes: groundtruth_boxes,
input_data_fields.groundtruth_classes: groundtruth_classes,
input_data_fields.groundtruth_keypoints: groundtruth_keypoints,
input_data_fields.groundtruth_instance_masks: groundtruth_instance_masks
}
if batch_size > 1:
return eval_util.result_dict_for_batched_example(
image, key, detections, groundtruth,
scale_to_absolute=scale_to_absolute,
max_gt_boxes=max_gt_boxes)
else:
return eval_util.result_dict_for_single_example(
image, key, detections, groundtruth,
scale_to_absolute=scale_to_absolute)