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import os
from .burst_helpers.burst_base import BURSTBase
from .burst_helpers.format_converter import GroundTruthBURSTFormatToTAOFormatConverter, PredictionBURSTFormatToTAOFormatConverter
from .. import utils
class BURST(BURSTBase):
"""Dataset class for TAO tracking"""
@staticmethod
def get_default_dataset_config():
tao_config = BURSTBase.get_default_dataset_config()
code_path = utils.get_code_path()
# e.g. 'data/gt/tsunami/exemplar_guided/'
tao_config['GT_FOLDER'] = os.path.join(
code_path, 'data/gt/burst/val/') # Location of GT data
# e.g. 'data/trackers/tsunami/exemplar_guided/mask_guided/validation/'
tao_config['TRACKERS_FOLDER'] = os.path.join(
code_path, 'data/trackers/burst/class-guided/') # Trackers location
# set to True or False
tao_config['EXEMPLAR_GUIDED'] = False
return tao_config
def _iou_type(self):
return 'mask'
def _box_or_mask_from_det(self, det):
return det['segmentation']
def _calculate_area_for_ann(self, ann):
import pycocotools.mask as cocomask
return cocomask.area(ann["segmentation"])
def _calculate_similarities(self, gt_dets_t, tracker_dets_t):
similarity_scores = self._calculate_mask_ious(gt_dets_t, tracker_dets_t, is_encoded=True, do_ioa=False)
return similarity_scores
def _is_exemplar_guided(self):
exemplar_guided = self.config['EXEMPLAR_GUIDED']
return exemplar_guided
def _postproc_ground_truth_data(self, data):
return GroundTruthBURSTFormatToTAOFormatConverter(data).convert()
def _postproc_prediction_data(self, data):
return PredictionBURSTFormatToTAOFormatConverter(
self.gt_data, data,
exemplar_guided=self._is_exemplar_guided()).convert()
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