def computeIoU()

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

• 42 lines of code
• 29 McCabe index (conditional complexity)

    def computeIoU(self, vidId, catId):
        p = self.params
        if p.useCats:
            gt = self._gts[vidId,catId]
            dt = self._dts[vidId,catId]
        else:
            gt = [_ for cId in p.catIds for _ in self._gts[vidId,cId]]
            dt = [_ for cId in p.catIds for _ in self._dts[vidId,cId]]
        if len(gt) == 0 and len(dt) ==0:
            return []
        inds = np.argsort([-d['score'] for d in dt], kind='mergesort')
        dt = [dt[i] for i in inds]
        if len(dt) > p.maxDets[-1]:
            dt=dt[0:p.maxDets[-1]]

        if p.iouType == 'segm':
            g = [g['segmentations'] for g in gt]
            d = [d['segmentations'] for d in dt]
        elif p.iouType == 'bbox':
            g = [g['bboxes'] for g in gt]
            d = [d['bboxes'] for d in dt]
        else:
            raise Exception('unknown iouType for iou computation')

        # compute iou between each dt and gt region
        iscrowd = [int(o['iscrowd']) for o in gt]
        #ious = maskUtils.iou(d,g,iscrowd)
        def iou_seq(d_seq, g_seq):
            i = .0
            u = .0
            for d, g in zip(d_seq, g_seq):
                if d and g:
                    i += maskUtils.area(maskUtils.merge([d, g], True))
                    u += maskUtils.area(maskUtils.merge([d, g], False))
                elif not d and g:
                    u += maskUtils.area(g)
                elif d and not g:
                    u += maskUtils.area(d)
            if not u > .0:
                print("Mask sizes in video {} and category {} may not match!".format(vidId, catId))
            iou = i / u if u > .0 else .0
            return iou
        ious = np.zeros([len(d), len(g)])
        for i, j in np.ndindex(ious.shape):
            ious[i, j] = iou_seq(d[i], g[j])
        #print(vidId, catId, ious.shape, ious)
        return ious