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.recThrs)
        K           = len(p.catIds) if p.useCats else 1
        A           = len(p.areaRng)
        M           = len(p.maxDets)
        precision   = -np.ones((T,R,K,A,M)) # -1 for the precision of absent categories
        recall      = -np.ones((T,K,A,M))

        # create dictionary for future indexing
        _pe = self._paramsEval
        catIds = _pe.catIds if _pe.useCats else [-1]
        setK = set(catIds)
        setA = set(map(tuple, _pe.areaRng))
        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]
        a_list = [n for n, a in enumerate(map(lambda x: tuple(x), p.areaRng)) if a in setA]
        i_list = [n for n, i in enumerate(p.imgIds)  if i in setI]
        # K0 = len(_pe.catIds)
        I0 = len(_pe.imgIds)
        A0 = len(_pe.areaRng)
        # retrieve E at each category, area range, and max number of detections
        for k, k0 in enumerate(k_list):
            Nk = k0*A0*I0
            for a, a0 in enumerate(a_list):
                Na = a0*I0
                for m, maxDet in enumerate(m_list):
                    E = [self.evalImgs[Nk+Na+i] for i in i_list]
                    E = filter(None, E)
                    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')

                    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 = len([ig for ig in gtIg if ig == 0])
                    if npig == 0:
                        continue
                    tps = np.logical_and(               dtm,  np.logical_not(dtIg) )
                    fps = np.logical_and(np.logical_not(dtm), np.logical_not(dtIg) )

                    tp_sum = np.cumsum(tps, axis=1).astype(dtype=np.float)
                    fp_sum = np.cumsum(fps, axis=1).astype(dtype=np.float)
                    for t, (tp, fp) in enumerate(zip(tp_sum, fp_sum)):
                        tp = np.array(tp)
                        fp = np.array(fp)
                        nd = len(tp)
                        rc = tp / npig
                        pr = tp / (fp+tp+np.spacing(1))
                        q  = np.zeros((R,))

                        if nd:
                            recall[t,k,a,m] = rc[-1]
                        else:
                            recall[t,k,a,m] = 0

                        # numpy is slow without cython optimization for accessing elements
                        # use python array gets significant speed improvement
                        pr = pr.tolist(); q = q.tolist()

                        for i in range(nd-1, 0, -1):
                            if pr[i] > pr[i-1]:
                                pr[i-1] = pr[i]

                        inds = np.searchsorted(rc, p.recThrs)
                        try:
                            for ri, pi in enumerate(inds):
                                q[ri] = pr[pi]
                        except:
                            pass
                        precision[t,:,k,a,m] = np.array(q)
        self.eval = {
            'params': p,
            'counts': [T, R, K, A, M],
            'date': datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
            'precision': precision,
            'recall':   recall,
        }
        toc = time.time()
        print 'DONE (t=%0.2fs).'%( toc-tic )

        Accumulate per image evaluation results and store the result in self.eval
        :param p: input params for evaluation
        :return: None
        
lib/dataset/pycocotools/cocoeval.py