def accumulate()

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

• 47 lines of code
• 26 McCabe index (conditional complexity)

    def accumulate(self, p = None):
        '''
        Accumulate per image evaluation results and store the result in self.eval
        :param p: input params for evaluation
        :return: None
        '''
        print('Accumulating evaluation results...')
        tic = time.time()
        if not self.evalImgs:
            print('Please run evaluate() first')
        # allows input customized parameters
        if p is None:
            p = self.params
        p.catIds = p.catIds if p.useCats == 1 else [-1]
        T = len(p.iouThrs)
        R = len(p.fppiThrs)
        K = len(p.catIds) if p.useCats else 1
        M = len(p.maxDets)
        ys = -np.ones((T, R, K, M))  # -1 for the precision of absent categories

        # create dictionary for future indexing
        _pe = self._paramsEval
        catIds = _pe.catIds if _pe.useCats else [-1]
        setK = set(catIds)
        setM = set(_pe.maxDets)
        setI = set(_pe.imgIds)
        # get inds to evaluate
        k_list = [n for n, k in enumerate(p.catIds) if k in setK]

        m_list = [m for n, m in enumerate(p.maxDets) if m in setM]
        i_list = [n for n, i in enumerate(p.imgIds) if i in setI]
        I0 = len(_pe.imgIds)

        # retrieve E at each category, area range, and max number of detections
        add_gts_count = []  # add by hui
        for k, k0 in enumerate(k_list):
            Nk = k0*I0
            for m, maxDet in enumerate(m_list):
                E = [self.evalImgs[Nk + i] for i in i_list]
                E = [e for e in E if not e is None]
                if len(E) == 0:
                    continue

                dtScores = np.concatenate([e['dtScores'][0:maxDet] for e in E])

                # different sorting method generates slightly different results.
                # mergesort is used to be consistent as Matlab implementation.

                inds = np.argsort(-dtScores, kind='mergesort')
                score_inds = inds.copy()
                dtm = np.concatenate([e['dtMatches'][:, 0:maxDet] for e in E], axis=1)[:, inds]
                dtIg = np.concatenate([e['dtIgnore'][:, 0:maxDet] for e in E], axis=1)[:, inds]
                gtIg = np.concatenate([e['gtIgnore'] for e in E])
                npig = np.count_nonzero(gtIg == 0)
                if npig == 0:
                    continue
                add_gts_count.append(npig)  # add by hui
                tps = np.logical_and(dtm, np.logical_not(dtIg))
                fps = np.logical_and(np.logical_not(dtm), np.logical_not(dtIg))