content: # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
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  # ==============================================================================
  
  """Contains classes specifying naming conventions used for object detection.
  
  
  Specifies:
    InputDataFields: standard fields used by reader/preprocessor/batcher.
    DetectionResultFields: standard fields returned by object detector.
    BoxListFields: standard field used by BoxList
    TfExampleFields: standard fields for tf-example dataset format (go/tf-example).
  """
  
  
  class InputDataFields(object):
    """Names for the input tensors.
  
    Holds the standard dataset field names to use for identifying input tensors. This
    should be used by the decoder to identify keys for the returned tensor_dict
    containing input tensors. And it should be used by the model to identify the
    tensors it needs.
  
    Attributes:
      image: image.
      original_image: image in the original input size.
      key: unique key corresponding to image.
      source_id: source of the original image.
      filename: original filename of the dataset (without common path).
      groundtruth_image_classes: image-level class labels.
      groundtruth_boxes: coordinates of the ground truth boxes in the image.
      groundtruth_classes: box-level class labels.
      groundtruth_label_types: box-level label types (e.g. explicit negative).
      groundtruth_is_crowd: [DEPRECATED, use groundtruth_group_of instead]
        is the groundtruth a single object or a crowd.
      groundtruth_area: area of a groundtruth segment.
      groundtruth_difficult: is a `difficult` object
      groundtruth_group_of: is a `group_of` objects, e.g. multiple objects of the
        same class, forming a connected group, where instances are heavily
        occluding each other.
      proposal_boxes: coordinates of object proposal boxes.
      proposal_objectness: objectness score of each proposal.
      groundtruth_instance_masks: ground truth instance masks.
      groundtruth_instance_boundaries: ground truth instance boundaries.
      groundtruth_instance_classes: instance mask-level class labels.
      groundtruth_keypoints: ground truth keypoints.
      groundtruth_keypoint_visibilities: ground truth keypoint visibilities.
      groundtruth_label_scores: groundtruth label scores.
      groundtruth_weights: groundtruth weight factor for bounding boxes.
      num_groundtruth_boxes: number of groundtruth boxes.
      true_image_shapes: true shapes of images in the resized images, as resized
        images can be padded with zeros.
    """
    image = 'image'
    original_image = 'original_image'
    key = 'key'
    source_id = 'source_id'
    filename = 'filename'
    groundtruth_image_classes = 'groundtruth_image_classes'
    groundtruth_boxes = 'groundtruth_boxes'
    groundtruth_classes = 'groundtruth_classes'
    groundtruth_label_types = 'groundtruth_label_types'
    groundtruth_is_crowd = 'groundtruth_is_crowd'
    groundtruth_area = 'groundtruth_area'
    groundtruth_difficult = 'groundtruth_difficult'
    groundtruth_group_of = 'groundtruth_group_of'
    proposal_boxes = 'proposal_boxes'
    proposal_objectness = 'proposal_objectness'
    groundtruth_instance_masks = 'groundtruth_instance_masks'
    groundtruth_instance_boundaries = 'groundtruth_instance_boundaries'
    groundtruth_instance_classes = 'groundtruth_instance_classes'
    groundtruth_keypoints = 'groundtruth_keypoints'
    groundtruth_keypoint_visibilities = 'groundtruth_keypoint_visibilities'
    groundtruth_label_scores = 'groundtruth_label_scores'
    groundtruth_weights = 'groundtruth_weights'
    num_groundtruth_boxes = 'num_groundtruth_boxes'
    true_image_shape = 'true_image_shape'
  
  
  class DetectionResultFields(object):
    """Naming conventions for storing the output of the detector.
  
    Attributes:
      source_id: source of the original image.
      key: unique key corresponding to image.
      detection_boxes: coordinates of the detection boxes in the image.
      detection_scores: detection scores for the detection boxes in the image.
      detection_classes: detection-level class labels.
      detection_masks: contains a segmentation mask for each detection box.
      detection_boundaries: contains an object boundary for each detection box.
      detection_keypoints: contains detection keypoints for each detection box.
      num_detections: number of detections in the batch.
    """
  
    source_id = 'source_id'
    key = 'key'
    detection_boxes = 'detection_boxes'
    detection_scores = 'detection_scores'
    detection_classes = 'detection_classes'
    detection_masks = 'detection_masks'
    detection_boundaries = 'detection_boundaries'
    detection_keypoints = 'detection_keypoints'
    num_detections = 'num_detections'
  
  
  class BoxListFields(object):
    """Naming conventions for BoxLists.
  
    Attributes:
      boxes: bounding box coordinates.
      classes: classes per bounding box.
      scores: scores per bounding box.
      weights: sample weights per bounding box.
      objectness: objectness score per bounding box.
      masks: masks per bounding box.
      boundaries: boundaries per bounding box.
      keypoints: keypoints per bounding box.
      keypoint_heatmaps: keypoint heatmaps per bounding box.
    """
    boxes = 'boxes'
    classes = 'classes'
    scores = 'scores'
    weights = 'weights'
    objectness = 'objectness'
    masks = 'masks'
    boundaries = 'boundaries'
    keypoints = 'keypoints'
    keypoint_heatmaps = 'keypoint_heatmaps'
  
  
  class TfExampleFields(object):
    """TF-example proto feature names for object detection.
  
    Holds the standard feature names to load from an Example proto for object
    detection.
  
    Attributes:
      image_encoded: JPEG encoded string
      image_format: image format, e.g. "JPEG"
      filename: filename
      channels: number of channels of image
      colorspace: colorspace, e.g. "RGB"
      height: height of image in pixels, e.g. 462
      width: width of image in pixels, e.g. 581
      source_id: original source of the image
      object_class_text: labels in text format, e.g. ["person", "cat"]
      object_class_label: labels in numbers, e.g. [16, 8]
      object_bbox_xmin: xmin coordinates of groundtruth box, e.g. 10, 30
      object_bbox_xmax: xmax coordinates of groundtruth box, e.g. 50, 40
      object_bbox_ymin: ymin coordinates of groundtruth box, e.g. 40, 50
      object_bbox_ymax: ymax coordinates of groundtruth box, e.g. 80, 70
      object_view: viewpoint of object, e.g. ["frontal", "left"]
      object_truncated: is object truncated, e.g. [true, false]
      object_occluded: is object occluded, e.g. [true, false]
      object_difficult: is object difficult, e.g. [true, false]
      object_group_of: is object a single object or a group of objects
      object_depiction: is object a depiction
      object_is_crowd: [DEPRECATED, use object_group_of instead]
        is the object a single object or a crowd
      object_segment_area: the area of the segment.
      object_weight: a weight factor for the object's bounding box.
      instance_masks: instance segmentation masks.
      instance_boundaries: instance boundaries.
      instance_classes: Classes for each instance segmentation mask.
      detection_class_label: class label in numbers.
      detection_bbox_ymin: ymin coordinates of a detection box.
      detection_bbox_xmin: xmin coordinates of a detection box.
      detection_bbox_ymax: ymax coordinates of a detection box.
      detection_bbox_xmax: xmax coordinates of a detection box.
      detection_score: detection score for the class label and box.
    """
    image_encoded = 'image/encoded'
    image_format = 'image/format'  # format is reserved keyword
    filename = 'image/filename'
    channels = 'image/channels'
    colorspace = 'image/colorspace'
    height = 'image/height'
    width = 'image/width'
    source_id = 'image/source_id'
    object_class_text = 'image/object/class/text'
    object_class_label = 'image/object/class/label'
    object_bbox_ymin = 'image/object/bbox/ymin'
    object_bbox_xmin = 'image/object/bbox/xmin'
    object_bbox_ymax = 'image/object/bbox/ymax'
    object_bbox_xmax = 'image/object/bbox/xmax'
    object_view = 'image/object/view'
    object_truncated = 'image/object/truncated'
    object_occluded = 'image/object/occluded'
    object_difficult = 'image/object/difficult'
    object_group_of = 'image/object/group_of'
    object_depiction = 'image/object/depiction'
    object_is_crowd = 'image/object/is_crowd'
    object_segment_area = 'image/object/segment/area'
    object_weight = 'image/object/weight'
    instance_masks = 'image/segmentation/object'
    instance_boundaries = 'image/boundaries/object'
    instance_classes = 'image/segmentation/object/class'
    detection_class_label = 'image/detection/label'
    detection_bbox_ymin = 'image/detection/bbox/ymin'
    detection_bbox_xmin = 'image/detection/bbox/xmin'
    detection_bbox_ymax = 'image/detection/bbox/ymax'
    detection_bbox_xmax = 'image/detection/bbox/xmax'
    detection_score = 'image/detection/score'
  