Python eval_util.result_dict_for_batched_example方法代碼示例

本文整理匯總了Python中object_detection.eval_util.result_dict_for_batched_example方法的典型用法代碼示例。如果您正苦於以下問題：Python eval_util.result_dict_for_batched_example方法的具體用法？Python eval_util.result_dict_for_batched_example怎麽用？Python eval_util.result_dict_for_batched_example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.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: _make_evaluation_dict

# 需要導入模塊: from object_detection import eval_util [as 別名]
# 或者: from object_detection.eval_util import result_dict_for_batched_example [as 別名]
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)

開發者ID:ahmetozlu，項目名稱:vehicle_counting_tensorflow，代碼行數:58，代碼來源:eval_util_test.py

示例2: _make_evaluation_dict

# 需要導入模塊: from object_detection import eval_util [as 別名]
# 或者: from object_detection.eval_util import result_dict_for_batched_example [as 別名]
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)

開發者ID:IBM，項目名稱:MAX-Object-Detector，代碼行數:58，代碼來源:eval_util_test.py

示例3: _make_evaluation_dict

# 需要導入模塊: from object_detection import eval_util [as 別名]
# 或者: from object_detection.eval_util import result_dict_for_batched_example [as 別名]
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)

開發者ID:tensorflow，項目名稱:models，代碼行數:63，代碼來源:eval_util_test.py

注：本文中的object_detection.eval_util.result_dict_for_batched_example方法示例由純淨天空整理自Github/MSDocs等開源代碼及文檔管理平台，相關代碼片段篩選自各路編程大神貢獻的開源項目，源碼版權歸原作者所有，傳播和使用請參考對應項目的License；未經允許，請勿轉載。