def _prepare()

in mask2former_video/data_video/datasets/ytvis_api/ytvoseval.py [0:0]

• 39 lines of code
• 19 McCabe index (conditional complexity)

    def _prepare(self):
        '''
        Prepare ._gts and ._dts for evaluation based on params
        :return: None
        '''
        def _toMask(anns, coco):
            # modify ann['segmentation'] by reference
            for ann in anns:
                for i, a in enumerate(ann['segmentations']):
                    if a:
                        rle = coco.annToRLE(ann, i)
                        ann['segmentations'][i] = rle
                l = [a for a in ann['areas'] if a]
                if len(l)==0:
                  ann['avg_area'] = 0
                else:
                  ann['avg_area'] = np.array(l).mean() 
        p = self.params
        if p.useCats:
            gts=self.cocoGt.loadAnns(self.cocoGt.getAnnIds(vidIds=p.vidIds, catIds=p.catIds))
            dts=self.cocoDt.loadAnns(self.cocoDt.getAnnIds(vidIds=p.vidIds, catIds=p.catIds))
        else:
            gts=self.cocoGt.loadAnns(self.cocoGt.getAnnIds(vidIds=p.vidIds))
            dts=self.cocoDt.loadAnns(self.cocoDt.getAnnIds(vidIds=p.vidIds))

        # convert ground truth to mask if iouType == 'segm'
        if p.iouType == 'segm':
            _toMask(gts, self.cocoGt)
            _toMask(dts, self.cocoDt)
        # set ignore flag
        for gt in gts:
            gt['ignore'] = gt['ignore'] if 'ignore' in gt else 0
            gt['ignore'] = 'iscrowd' in gt and gt['iscrowd']
            if p.iouType == 'keypoints':
                gt['ignore'] = (gt['num_keypoints'] == 0) or gt['ignore']
        self._gts = defaultdict(list)       # gt for evaluation
        self._dts = defaultdict(list)       # dt for evaluation
        for gt in gts:
            self._gts[gt['video_id'], gt['category_id']].append(gt)
        for dt in dts:
            self._dts[dt['video_id'], dt['category_id']].append(dt)
        self.evalVids = defaultdict(list)   # per-image per-category evaluation results
        self.eval     = {}                  # accumulated evaluation results