def loadRes()

in mask2former_video/data_video/datasets/ytvis_api/ytvos.py [0:0]
41 lines of code
17 McCabe index (conditional complexity)

    def loadRes(self, resFile):
        """
        Load result file and return a result api object.
        :param   resFile (str)     : file name of result file
        :return: res (obj)         : result api object
        """
        res = YTVOS()
        res.dataset['videos'] = [img for img in self.dataset['videos']]

        print('Loading and preparing results...')
        tic = time.time()
        if type(resFile) == str or (PYTHON_VERSION == 2 and type(resFile) == unicode):
            anns = json.load(open(resFile))
        elif type(resFile) == np.ndarray:
            anns = self.loadNumpyAnnotations(resFile)
        else:
            anns = resFile
        assert type(anns) == list, 'results in not an array of objects'
        annsVidIds = [ann['video_id'] for ann in anns]
        assert set(annsVidIds) == (set(annsVidIds) & set(self.getVidIds())), \
               'Results do not correspond to current coco set'
        if 'segmentations' in anns[0]:
            res.dataset['categories'] = copy.deepcopy(self.dataset['categories'])
            for id, ann in enumerate(anns):
                ann['areas'] = []
                if not 'bboxes' in ann:
                    ann['bboxes'] = []
                for seg in ann['segmentations']:
                    # now only support compressed RLE format as segmentation results
                    if seg:
                        ann['areas'].append(maskUtils.area(seg))
                        if len(ann['bboxes']) < len(ann['areas']):
                            ann['bboxes'].append(maskUtils.toBbox(seg))
                    else:
                        ann['areas'].append(None)
                        if len(ann['bboxes']) < len(ann['areas']):
                            ann['bboxes'].append(None)
                ann['id'] = id+1
                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() 
                ann['iscrowd'] = 0
        print('DONE (t={:0.2f}s)'.format(time.time()- tic))

        res.dataset['annotations'] = anns
        res.createIndex()
        return res