NgZGddeZGddeZGddeZdS)cbeZdZdZdZdZdZdZdZdZ dZ d Z d Z d Z d Zd ZdZdZdZdZdZdZdZdZdS)InputDataFieldsaSNames for the input tensors. Holds the standard data 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. image_additional_channels: additional channels. key: unique key corresponding to image. filename: original filename of the dataset (without common path). gt_image_classes: image-level class labels. gt_image_confidences: image-level class confidences. gt_labeled_classes: image-level annotation that indicates the classes for which an image has been labeled. gt_boxes: coordinates of the ground truth boxes in the image. gt_classes: box-level class labels. gt_confidences: box-level class confidences. The shape should be the same as the shape of gt_classes. gt_label_types: box-level label types (e.g. explicit negative). gt_is_crowd: [DEPRECATED, use gt_group_of instead] is the groundtruth a single object or a crowd. gt_area: area of a groundtruth segment. gt_difficult: is a `difficult` object gt_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. gt_instance_masks: ground truth instance masks. gt_instance_boundaries: ground truth instance boundaries. gt_instance_classes: instance mask-level class labels. gt_label_weights: groundtruth label weights. gt_weights: groundtruth weight factor for bounding boxes. image_height: height of images, used to decode image_width: width of images, used to decode imageimage_idfilenamebboxcls confidences label_typesimg_clsimg_confidences labeled_clsis_crowdarea difficultgroup_ofinstance_masksinstance_boundariesinstance_classes img_height img_widthimg_sizeN)__name__ __module__ __qualname____doc__rkeyrgt_boxes gt_classesgt_confidencesgt_label_typesgt_image_classesgt_image_confidencesgt_labeled_classes gt_is_crowdgt_area gt_difficult gt_group_ofgt_instance_masksgt_instance_boundariesgt_instance_classes image_height image_width image_sizeT/var/www/html/ai-engine/env/lib/python3.11/site-packages/effdet/evaluation/fields.pyrrs!!D E CHHJ"N"N ,&KGLK(2,LKJJJr/rc&eZdZdZdZdZdZdZdZdS)DetectionResultFieldsapNaming 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_multiclass_scores: class score distribution (including background) for detection boxes in the image including background class. detection_classes: detection-level class labels. detection_masks: contains a segmentation mask for each detection box. rrscorermasksN) rrrrrdetection_boxesdetection_scoresdetection_classesdetection_masksr.r/r0r2r2;s5   COOOOr/r2cFeZdZdZdZdZdZdZdZdZ dZ d Z d Z d Z d Zd ZdZdS) BoxListFieldsaNaming 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. is_crowd: is_crowd annotation per bounding box. boxesclassesscoresweightsr objectnessr4 boundaries keypointskeypoint_visibilitieskeypoint_heatmapsrrN)rrrrr;r<r=r>r r?r4r@rArBrCrrr.r/r0r:r:Os]   EG FGKJ EJI3+HHHHr/r:N)objectrr2r:r.r/r0rEs66666f666rF(Fr/