val_utils/trackeval/datasets/burst.py

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()