challenge/2019_COCO_DensePose/densepose_cocoeval.py [734:1024]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - m = gind # if match made store id of match for both dt and gt if m == -1: continue dtIg[tind, dind] = gtIg[m] dtm[tind, dind] = gt[m]['id'] gtm[tind, m] = d['id'] if p.iouType == 'uv': if not len(ioubs)==0: for dind, d in enumerate(dt): # information about best match so far (m=-1 -> unmatched) if dtm[tind, dind] == 0: ioub = 0.8 m = -1 for gind, g in enumerate(gt): # if this gt already matched, and not a crowd, continue if gtm[tind,gind]>0 and not iscrowd[gind]: continue # continue to next gt unless better match made if ioubs[dind,gind] < ioub: continue # if match successful and best so far, store appropriately ioub = ioubs[dind,gind] m = gind # if match made store id of match for both dt and gt if m > -1: dtIg[:, dind] = gtIg[m] if gtIg[m]: dtm[tind, dind] = gt[m]['id'] gtm[tind, m] = d['id'] # set unmatched detections outside of area range to ignore a = np.array([d['area']aRng[1] for d in dt]).reshape((1, len(dt))) dtIg = np.logical_or(dtIg, np.logical_and(dtm==0, np.repeat(a,T,0))) # store results for given image and category #print('Done with the function', len(self.ious[imgId, catId])) return { 'image_id': imgId, 'category_id': catId, 'aRng': aRng, '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, } 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 print('Categories:', p.catIds) _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] 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 = [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') 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) #print('DTIG', np.sum(np.logical_not(dtIg)), len(dtIg)) #print('GTIG', np.sum(np.logical_not(gtIg)), len(gtIg)) 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) #print('TP_SUM', tp_sum, 'FP_SUM', fp_sum) 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, side='left') try: for ri, pi in enumerate(inds): q[ri] = pr[pi] except: pass precision[t,:,k,a,m] = np.array(q) print('Final', np.max(precision), np.min(precision)) 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={:0.2f}s).'.format( toc-tic)) def summarize(self): ''' Compute and display summary metrics for evaluation results. Note this functin can *only* be applied on the default parameter setting ''' def _summarize( ap=1, iouThr=None, areaRng='all', maxDets=100 ): p = self.params iStr = ' {:<18} {} @[ {}={:<9} | area={:>6s} | maxDets={:>3d} ] = {:0.3f}' titleStr = 'Average Precision' if ap == 1 else 'Average Recall' typeStr = '(AP)' if ap==1 else '(AR)' measure = 'IoU' if self.params.iouType == 'keypoints': measure = 'OKS' elif self.params.iouType =='uv': measure = 'OGPS' iouStr = '{:0.2f}:{:0.2f}'.format(p.iouThrs[0], p.iouThrs[-1]) \ if iouThr is None else '{:0.2f}'.format(iouThr) aind = [i for i, aRng in enumerate(p.areaRngLbl) if aRng == areaRng] mind = [i for i, mDet in enumerate(p.maxDets) if mDet == maxDets] if ap == 1: # dimension of precision: [TxRxKxAxM] s = self.eval['precision'] # IoU if iouThr is not None: t = np.where(np.abs(iouThr - p.iouThrs)<0.001)[0] s = s[t] s = s[:,:,:,aind,mind] else: # dimension of recall: [TxKxAxM] s = self.eval['recall'] if iouThr is not None: t = np.where(iouThr == p.iouThrs)[0] s = s[t] s = s[:,:,aind,mind] if len(s[s>-1])==0: mean_s = -1 else: mean_s = np.mean(s[s>-1]) print(iStr.format(titleStr, typeStr, measure, iouStr, areaRng, maxDets, mean_s)) return mean_s def _summarizeDets(): stats = np.zeros((12,)) stats[0] = _summarize(1) stats[1] = _summarize(1, iouThr=.5, maxDets=self.params.maxDets[2]) stats[2] = _summarize(1, iouThr=.75, maxDets=self.params.maxDets[2]) stats[3] = _summarize(1, areaRng='small', maxDets=self.params.maxDets[2]) stats[4] = _summarize(1, areaRng='medium', maxDets=self.params.maxDets[2]) stats[5] = _summarize(1, areaRng='large', maxDets=self.params.maxDets[2]) stats[6] = _summarize(0, maxDets=self.params.maxDets[0]) stats[7] = _summarize(0, maxDets=self.params.maxDets[1]) stats[8] = _summarize(0, maxDets=self.params.maxDets[2]) stats[9] = _summarize(0, areaRng='small', maxDets=self.params.maxDets[2]) stats[10] = _summarize(0, areaRng='medium', maxDets=self.params.maxDets[2]) stats[11] = _summarize(0, areaRng='large', maxDets=self.params.maxDets[2]) return stats def _summarizeKps(): stats = np.zeros((10,)) stats[0] = _summarize(1, maxDets=20) stats[1] = _summarize(1, maxDets=20, iouThr=.5) stats[2] = _summarize(1, maxDets=20, iouThr=.75) stats[3] = _summarize(1, maxDets=20, areaRng='medium') stats[4] = _summarize(1, maxDets=20, areaRng='large') stats[5] = _summarize(0, maxDets=20) stats[6] = _summarize(0, maxDets=20, iouThr=.5) stats[7] = _summarize(0, maxDets=20, iouThr=.75) stats[8] = _summarize(0, maxDets=20, areaRng='medium') stats[9] = _summarize(0, maxDets=20, areaRng='large') return stats def _summarizeUvs(): stats = np.zeros((18,)) stats[0] = _summarize(1, maxDets=self.params.maxDets[0]) stats[1] = _summarize(1, maxDets=self.params.maxDets[0], iouThr=.5) stats[2] = _summarize(1, maxDets=self.params.maxDets[0], iouThr=.55) stats[3] = _summarize(1, maxDets=self.params.maxDets[0], iouThr=.60) stats[4] = _summarize(1, maxDets=self.params.maxDets[0], iouThr=.65) stats[5] = _summarize(1, maxDets=self.params.maxDets[0], iouThr=.70) stats[6] = _summarize(1, maxDets=self.params.maxDets[0], iouThr=.75) stats[7] = _summarize(1, maxDets=self.params.maxDets[0], iouThr=.80) stats[8] = _summarize(1, maxDets=self.params.maxDets[0], iouThr=.85) stats[9] = _summarize(1, maxDets=self.params.maxDets[0], iouThr=.90) stats[10] = _summarize(1, maxDets=self.params.maxDets[0], iouThr=.95) stats[11] = _summarize(1, maxDets=self.params.maxDets[0], areaRng='medium') stats[12] = _summarize(1, maxDets=self.params.maxDets[0], areaRng='large') stats[13] = _summarize(0, maxDets=self.params.maxDets[0]) stats[14] = _summarize(0, maxDets=self.params.maxDets[0], iouThr=.5) stats[15] = _summarize(0, maxDets=self.params.maxDets[0], iouThr=.75) stats[16] = _summarize(0, maxDets=self.params.maxDets[0], areaRng='medium') stats[17] = _summarize(0, maxDets=self.params.maxDets[0], areaRng='large') return stats if not self.eval: raise Exception('Please run accumulate() first') iouType = self.params.iouType if iouType in ['segm','bbox']: summarize = _summarizeDets elif iouType in ['keypoints']: summarize = _summarizeKps elif iouType in ['uv']: summarize = _summarizeUvs self.stats = summarize() def __str__(self): self.summarize() # ================ functions for dense pose ============================== def findAllClosestVerts(self, gt, U_points, V_points, Index_points): # I_gt = np.array(gt['dp_I']) U_gt = np.array(gt['dp_U']) V_gt = np.array(gt['dp_V']) # #print(I_gt) # ClosestVerts = np.ones(Index_points.shape)*-1 for i in np.arange(24): # if sum(Index_points == (i+1))>0: UVs = np.array( [U_points[Index_points == (i+1)],V_points[Index_points == (i+1)]]) Current_Part_UVs = self.Part_UVs[i] Current_Part_ClosestVertInds = self.Part_ClosestVertInds[i] D = ssd.cdist( Current_Part_UVs.transpose(), UVs.transpose()).squeeze() ClosestVerts[Index_points == (i+1)] = Current_Part_ClosestVertInds[ np.argmin(D,axis=0) ] # ClosestVertsGT = np.ones(Index_points.shape)*-1 for i in np.arange(24): if sum(I_gt==(i+1))>0: UVs = np.array([ U_gt[I_gt==(i+1)], V_gt[I_gt==(i+1)] ]) Current_Part_UVs = self.Part_UVs[i] Current_Part_ClosestVertInds = self.Part_ClosestVertInds[i] D = ssd.cdist( Current_Part_UVs.transpose(), UVs.transpose()).squeeze() ClosestVertsGT[I_gt==(i+1)] = Current_Part_ClosestVertInds[ np.argmin(D,axis=0) ] # return ClosestVerts, ClosestVertsGT - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - detectron/datasets/densepose_cocoeval.py [490:780]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - m = gind # if match made store id of match for both dt and gt if m == -1: continue dtIg[tind, dind] = gtIg[m] dtm[tind, dind] = gt[m]['id'] gtm[tind, m] = d['id'] if p.iouType == 'uv': if not len(ioubs)==0: for dind, d in enumerate(dt): # information about best match so far (m=-1 -> unmatched) if dtm[tind, dind] == 0: ioub = 0.8 m = -1 for gind, g in enumerate(gt): # if this gt already matched, and not a crowd, continue if gtm[tind,gind]>0 and not iscrowd[gind]: continue # continue to next gt unless better match made if ioubs[dind,gind] < ioub: continue # if match successful and best so far, store appropriately ioub = ioubs[dind,gind] m = gind # if match made store id of match for both dt and gt if m > -1: dtIg[:, dind] = gtIg[m] if gtIg[m]: dtm[tind, dind] = gt[m]['id'] gtm[tind, m] = d['id'] # set unmatched detections outside of area range to ignore a = np.array([d['area']aRng[1] for d in dt]).reshape((1, len(dt))) dtIg = np.logical_or(dtIg, np.logical_and(dtm==0, np.repeat(a,T,0))) # store results for given image and category #print('Done with the function', len(self.ious[imgId, catId])) return { 'image_id': imgId, 'category_id': catId, 'aRng': aRng, '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, } 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 print('Categories:', p.catIds) _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] 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 = [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') 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) #print('DTIG', np.sum(np.logical_not(dtIg)), len(dtIg)) #print('GTIG', np.sum(np.logical_not(gtIg)), len(gtIg)) 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) #print('TP_SUM', tp_sum, 'FP_SUM', fp_sum) 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, side='left') try: for ri, pi in enumerate(inds): q[ri] = pr[pi] except: pass precision[t,:,k,a,m] = np.array(q) print('Final', np.max(precision), np.min(precision)) 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={:0.2f}s).'.format( toc-tic)) def summarize(self): ''' Compute and display summary metrics for evaluation results. Note this functin can *only* be applied on the default parameter setting ''' def _summarize( ap=1, iouThr=None, areaRng='all', maxDets=100 ): p = self.params iStr = ' {:<18} {} @[ {}={:<9} | area={:>6s} | maxDets={:>3d} ] = {:0.3f}' titleStr = 'Average Precision' if ap == 1 else 'Average Recall' typeStr = '(AP)' if ap==1 else '(AR)' measure = 'IoU' if self.params.iouType == 'keypoints': measure = 'OKS' elif self.params.iouType =='uv': measure = 'OGPS' iouStr = '{:0.2f}:{:0.2f}'.format(p.iouThrs[0], p.iouThrs[-1]) \ if iouThr is None else '{:0.2f}'.format(iouThr) aind = [i for i, aRng in enumerate(p.areaRngLbl) if aRng == areaRng] mind = [i for i, mDet in enumerate(p.maxDets) if mDet == maxDets] if ap == 1: # dimension of precision: [TxRxKxAxM] s = self.eval['precision'] # IoU if iouThr is not None: t = np.where(np.abs(iouThr - p.iouThrs)<0.001)[0] s = s[t] s = s[:,:,:,aind,mind] else: # dimension of recall: [TxKxAxM] s = self.eval['recall'] if iouThr is not None: t = np.where(iouThr == p.iouThrs)[0] s = s[t] s = s[:,:,aind,mind] if len(s[s>-1])==0: mean_s = -1 else: mean_s = np.mean(s[s>-1]) print(iStr.format(titleStr, typeStr, measure, iouStr, areaRng, maxDets, mean_s)) return mean_s def _summarizeDets(): stats = np.zeros((12,)) stats[0] = _summarize(1) stats[1] = _summarize(1, iouThr=.5, maxDets=self.params.maxDets[2]) stats[2] = _summarize(1, iouThr=.75, maxDets=self.params.maxDets[2]) stats[3] = _summarize(1, areaRng='small', maxDets=self.params.maxDets[2]) stats[4] = _summarize(1, areaRng='medium', maxDets=self.params.maxDets[2]) stats[5] = _summarize(1, areaRng='large', maxDets=self.params.maxDets[2]) stats[6] = _summarize(0, maxDets=self.params.maxDets[0]) stats[7] = _summarize(0, maxDets=self.params.maxDets[1]) stats[8] = _summarize(0, maxDets=self.params.maxDets[2]) stats[9] = _summarize(0, areaRng='small', maxDets=self.params.maxDets[2]) stats[10] = _summarize(0, areaRng='medium', maxDets=self.params.maxDets[2]) stats[11] = _summarize(0, areaRng='large', maxDets=self.params.maxDets[2]) return stats def _summarizeKps(): stats = np.zeros((10,)) stats[0] = _summarize(1, maxDets=20) stats[1] = _summarize(1, maxDets=20, iouThr=.5) stats[2] = _summarize(1, maxDets=20, iouThr=.75) stats[3] = _summarize(1, maxDets=20, areaRng='medium') stats[4] = _summarize(1, maxDets=20, areaRng='large') stats[5] = _summarize(0, maxDets=20) stats[6] = _summarize(0, maxDets=20, iouThr=.5) stats[7] = _summarize(0, maxDets=20, iouThr=.75) stats[8] = _summarize(0, maxDets=20, areaRng='medium') stats[9] = _summarize(0, maxDets=20, areaRng='large') return stats def _summarizeUvs(): stats = np.zeros((18,)) stats[0] = _summarize(1, maxDets=self.params.maxDets[0]) stats[1] = _summarize(1, maxDets=self.params.maxDets[0], iouThr=.5) stats[2] = _summarize(1, maxDets=self.params.maxDets[0], iouThr=.55) stats[3] = _summarize(1, maxDets=self.params.maxDets[0], iouThr=.60) stats[4] = _summarize(1, maxDets=self.params.maxDets[0], iouThr=.65) stats[5] = _summarize(1, maxDets=self.params.maxDets[0], iouThr=.70) stats[6] = _summarize(1, maxDets=self.params.maxDets[0], iouThr=.75) stats[7] = _summarize(1, maxDets=self.params.maxDets[0], iouThr=.80) stats[8] = _summarize(1, maxDets=self.params.maxDets[0], iouThr=.85) stats[9] = _summarize(1, maxDets=self.params.maxDets[0], iouThr=.90) stats[10] = _summarize(1, maxDets=self.params.maxDets[0], iouThr=.95) stats[11] = _summarize(1, maxDets=self.params.maxDets[0], areaRng='medium') stats[12] = _summarize(1, maxDets=self.params.maxDets[0], areaRng='large') stats[13] = _summarize(0, maxDets=self.params.maxDets[0]) stats[14] = _summarize(0, maxDets=self.params.maxDets[0], iouThr=.5) stats[15] = _summarize(0, maxDets=self.params.maxDets[0], iouThr=.75) stats[16] = _summarize(0, maxDets=self.params.maxDets[0], areaRng='medium') stats[17] = _summarize(0, maxDets=self.params.maxDets[0], areaRng='large') return stats if not self.eval: raise Exception('Please run accumulate() first') iouType = self.params.iouType if iouType in ['segm','bbox']: summarize = _summarizeDets elif iouType in ['keypoints']: summarize = _summarizeKps elif iouType in ['uv']: summarize = _summarizeUvs self.stats = summarize() def __str__(self): self.summarize() # ================ functions for dense pose ============================== def findAllClosestVerts(self, gt, U_points, V_points, Index_points): # I_gt = np.array(gt['dp_I']) U_gt = np.array(gt['dp_U']) V_gt = np.array(gt['dp_V']) # #print(I_gt) # ClosestVerts = np.ones(Index_points.shape)*-1 for i in np.arange(24): # if sum(Index_points == (i+1))>0: UVs = np.array( [U_points[Index_points == (i+1)],V_points[Index_points == (i+1)]]) Current_Part_UVs = self.Part_UVs[i] Current_Part_ClosestVertInds = self.Part_ClosestVertInds[i] D = ssd.cdist( Current_Part_UVs.transpose(), UVs.transpose()).squeeze() ClosestVerts[Index_points == (i+1)] = Current_Part_ClosestVertInds[ np.argmin(D,axis=0) ] # ClosestVertsGT = np.ones(Index_points.shape)*-1 for i in np.arange(24): if sum(I_gt==(i+1))>0: UVs = np.array([ U_gt[I_gt==(i+1)], V_gt[I_gt==(i+1)] ]) Current_Part_UVs = self.Part_UVs[i] Current_Part_ClosestVertInds = self.Part_ClosestVertInds[i] D = ssd.cdist( Current_Part_UVs.transpose(), UVs.transpose()).squeeze() ClosestVertsGT[I_gt==(i+1)] = Current_Part_ClosestVertInds[ np.argmin(D,axis=0) ] # return ClosestVerts, ClosestVertsGT - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -