def evaluateImg()

in evaluation/tiny_benchmark/third/Cityscapes/evaluation/eval_script/origin_eval_MR_multisetup.py [0:0]

• 77 lines of code
• 45 McCabe index (conditional complexity)

    def evaluateImg(self, imgId, catId, hRng, vRng, maxDet):
        '''
        perform evaluation for single category and image
        :return: dict (single image results)
        '''
        p = self.params
        if p.useCats:
            gt = self._gts[imgId,catId]
            dt = self._dts[imgId,catId]
        else:
            gt = [_ for cId in p.catIds for _ in self._gts[imgId,cId]]
            dt = [_ for cId in p.catIds for _ in self._dts[imgId,cId]]
        if len(gt) == 0 and len(dt) ==0:
            return None

        for g in gt:
            if g['ignore']:
                g['_ignore'] = 1
            else:
                g['_ignore'] = 0
        # sort dt highest score first, sort gt ignore last
        gtind = np.argsort([g['_ignore'] for g in gt], kind='mergesort')
        gt = [gt[i] for i in gtind]
        dtind = np.argsort([-d['score'] for d in dt], kind='mergesort')
        dt = [dt[i] for i in dtind[0:maxDet]]
        # exclude dt out of height range
        dt = [d for d in dt if d['height'] >= hRng[0] / self.params.expFilter and d['height'] < hRng[1] * self.params.expFilter]
        dtind = np.array([int(d['id'] - dt[0]['id']) for d in dt])

        # load computed ious
        if len(dtind) > 0:
            ious = self.computeIoU(gt,dt)
        else:
            ious = []

        T = len(p.iouThrs)
        G = len(gt)
        D = len(dt)
        gtm  = np.zeros((T,G))
        dtm  = np.zeros((T,D))
        gtIg = np.array([g['_ignore'] for g in gt])
        dtIg = np.zeros((T,D))
        if not len(ious)==0:
            for tind, t in enumerate(p.iouThrs):
                for dind, d in enumerate(dt):
                    # information about best match so far (m=-1 -> unmatched)
                    iou = min([t,1-1e-10])
                    bstOa = iou
                    bstg = -2
                    bstm = -2
                    for gind, g in enumerate(gt):
                        m = gtm[tind,gind]
                        # if this gt already matched, and not a crowd, continue
                        if m>0:
                            continue
                        # if dt matched to reg gt, and on ignore gt, stop
                        if bstm!=-2 and gtIg[gind] == 1:
                            break
                        # continue to next gt unless better match made
                        if ious[dind,gind] < bstOa:
                            continue
                        # if match successful and best so far, store appropriately
                        bstOa=ious[dind,gind]
                        bstg = gind
                        if gtIg[gind] == 0:
                            bstm = 1
                        else:
                            bstm = -1

                    # if match made store id of match for both dt and gt
                    if bstg ==-2:
                        continue
                    dtIg[tind,dind] = gtIg[bstg]
                    dtm[tind,dind]  = gt[bstg]['id']
                    if bstm == 1:
                        gtm[tind,bstg]     = d['id']

        # store results for given image and category
        return {
                'image_id':     imgId,
                'category_id':  catId,
                'hRng':         hRng,
                'vRng':         vRng,
                'maxDet':       maxDet,
                'dtIds':        [d['id'] for d in dt],
                'gtIds':        [g['id'] for g in gt],
                'dtMatches':    dtm,
                'gtMatches':    gtm,
                'dtScores':     [d['score'] for d in dt],
                'gtIgnore':     gtIg,
                'dtIgnore':     dtIg,
            }