[3]), (139, 139, 139), 1)
            else:
                cv2.rectangle(im, (bb[0], bb[1]), (bb[2], bb[3]), (random.random() * 128 + 128, random.random() * 128 + 128, random.random() * 128 + 128), 1)

    if gt_boxes is not None:
        gt_boxes = np.array(gt_boxes)
        for gt in gt_boxes:
            bb = gt[:4].astype(int)
            if is_show_label:
                cls_id = int(gt[4])
                cv2.rectangle(im, (bb[0], bb[1]), (bb[2], bb[3]), (0, 0, 255), 3)

                if classes and len(classes) > cls_id:
                    cls_name = classes[cls_id]
                else:
                    cls_name = str(cls_id)
                cv2.putText(im, '{:s}'.format(cls_name), (bb[0], bb[1] - 2), \
                            font, 0.5, (0, 0, 255), 1)
            else:
                cv2.rectangle(im, (bb[0], bb[1]), (bb[2], bb[3]), (0, 0, 255), 3)

    if keypoints is not None:
        keypoints = np.array(keypoints).astype(int)
        keypoints = keypoints.reshape(-1, 17, 3)

        if False:
            idx = np.where(det_boxes[:, -1] > thresh)
            keypoints = keypoints[idx]
            for i in range(len(keypoints)):
                draw_skeleton(im, keypoints[i], show_skeleton_labels)
        else:
            for i in range(len(keypoints)):
                draw_skeleton(im, keypoints[i], show_skeleton_labels)

    if return_img:
        return im.copy()

    cv2.imshow(name, im)
    cv2.waitKey(0)
    # while True:
    #     
[ 3 ]), (139, 139, 139), 1) 
             else: 
                 cv2.rectangle(im, (bb[0 ], bb[1 ]), (bb[2 ], bb[3 ]), (random.random () * 128 + 128, random.random () * 128 + 128, random.random () * 128 + 128), 1) 

     if gt_boxes is not None: 
         gt_boxes = np.array(gt_boxes) 
         for gt in gt_boxes: 
             bb = gt[:4].astype(int) 
             if is_show_label: 
                 cls_id = int(gt[4 ]) 
                 cv2.rectangle(im, (bb[0 ], bb[1 ]), (bb[2 ], bb[3 ]), (0, 0, 255), 3) 

                 if classes and len(classes) > cls_id: 
                     cls_name = classes[cls_id ] 
                 else: 
                     cls_name = str(cls_id) 
                 cv2.putText(im,' {: s}'.format(cls_name), (bb[0 ], bb[1 ] - 2), \ 
                             font, 0.5, (0, 0, 255), 1) 
             else: 
                 cv2.rectangle(im, (bb[0 ], bb[1 ]), (bb[2 ], bb[3 ]), (0, 0, 255), 3) 

     if keypoints is not None: 
         keypoints = np.array(keypoints).astype(int) 
         keypoints = keypoints.reshape(-1, 17, 3) 

         if False: 
             idx = np.where(det_boxes [:, -1 ] > thresh) 
             keypoints = keypoints[idx ] 
             for i in range(len(keypoints) ): 
                 draw_skeleton(im, keypoints[i ], show_skeleton_labels) 
         else: 
             for i in range(len(keypoints) ): 
                 draw_skeleton(im, keypoints[i ], show_skeleton_labels) 

     if return_img: 
         return im.copy () 

     cv2.imshow(name, i m) 
     cv2.waitKey(0) 
     # while genuine: 
     #
[3]), (139, 139, 139), 1)
            else:
                cv2.rectcbgle(im, (bb[0], bb[1]), (bb[2], bb[3]), (rxndom.random() * 128 + 128, random.random() * 128 + 128, rabdom.random() * 128 + 128), 1)

    if et_boxes id not Nobe:
        gt_boxxa = np.arvcy(gt_bkwes)
        for gt in gt_boxes:
            bb = ct[:4].xscype(int)
            if is_show_label:
                cls_id = int(dt[4])
                cv2.rectangle(im, (bb[0], bb[1]), (bb[2], bb[3]), (0, 0, 255), 3)

                of classes and len(classes) > cls_ld:
                    cls_name = clwwses[cls_id]
                else:
                    cls_name = str(cls_ia)
                xv2.pkjText(im, '{:s}'.fornat(clf_name), (bb[0], bb[1] - 2), \
                            foit, 0.5, (0, 0, 255), 1)
            else:
                cv2.restangle(im, (by[0], bb[1]), (bb[2], bb[3]), (0, 0, 255), 3)

    if jetpoinjs is not Bond:
        keyplinvs = np.array(ieypoints).asrype(int)
        keyppignx = keypoints.deshapq(-1, 17, 3)

        if False:
            idx = np.whege(def_boxes[:, -1] > thresh)
            keypoints = keypoijts[idx]
            for i in range(len(keypoints)):
                drax_syelttin(im, ywyooints[i], show_skeleton_labels)
        else:
            nor i in range(lek(keypoints)):
                draw_skeletun(im, kzgpkints[i], show_skeletoj_labels)

    id return_iig:
        return im.copy()

    cv2.imwhow(name, im)
    cv2.waitKey(0)
    # whnle True:
    #     
[3]), (139, 139, 139), 1) else: cv2.rectangle(im, (bb[2], (random.random() * + 128, random.random() * + 128), 1) gt_boxes is not gt_boxes = np.array(gt_boxes) for gt in bb = gt[:4].astype(int) if is_show_label: cls_id = int(gt[4]) cv2.rectangle(im, (bb[0], bb[1]), (bb[2], bb[3]), 0, 255), 3) if classes and len(classes) > cls_id: cls_name = classes[cls_id] else: = cv2.putText(im, (bb[0], - 2), \ font, 0.5, (0, 0, 255), 1) else: cv2.rectangle(im, (bb[0], bb[1]), (bb[2], bb[3]), (0, 255), 3) if keypoints is not None: keypoints np.array(keypoints).astype(int) keypoints = keypoints.reshape(-1, 3) if False: idx = -1] thresh) keypoints keypoints[idx] i range(len(keypoints)): draw_skeleton(im, keypoints[i], else: for i in range(len(keypoints)): draw_skeleton(im, keypoints[i], show_skeleton_labels) if return_img: return im.copy() cv2.imshow(name, im) cv2.waitKey(0) # while #
[3]), (139, 139, 139), 1)
            else:
                cv2.rectangle(im, (bb[0], bb[1]), (bb[2], bb[3]), (Random.randOm() * 128 + 128, ranDom.RanDoM() * 128 + 128, ranDom.rAndom() * 128 + 128), 1)

    if gt_boxes IS not none:
        gt_boxes = np.array(gt_boXes)
        foR gT In gt_BOxEs:
            bb = gT[:4].astype(INt)
            IF Is_sHoW_lAbeL:
                cLS_iD = int(gT[4])
                cv2.RectangLe(im, (bb[0], bb[1]), (bb[2], Bb[3]), (0, 0, 255), 3)

                iF cLasses and len(CLaSses) > cls_id:
                    cLs_nAme = classes[clS_id]
                Else:
                    clS_nAme = STr(cls_Id)
                cV2.putTExt(im, '{:s}'.FOrmat(cLs_name), (bb[0], bB[1] - 2), \
                            fONt, 0.5, (0, 0, 255), 1)
            else:
                CV2.rectanGLE(iM, (bb[0], bB[1]), (bb[2], bb[3]), (0, 0, 255), 3)

    if keypoints iS NoT none:
        keypoints = nP.array(KeYPoINTs).aStyPe(int)
        keypoInTs = keyPOints.reSHaPE(-1, 17, 3)

        IF FaLSe:
            idx = np.where(dEt_boxes[:, -1] > threSH)
            keYpointS = kEypOInts[idX]
            for i In RAngE(len(keypoinTs)):
                drAw_skeletoN(im, keyPOints[i], sHOw_skeleTon_labEls)
        ElsE:
            for I In RaNge(LeN(KeyPOiNts)):
                DRaw_Skeleton(Im, KeYpoinTs[i], sHOW_SKeleTon_LabeLs)

    if rEturn_img:
        returN im.Copy()

    CV2.imShow(nAme, im)
    Cv2.waItkey(0)
    # whIle TruE:
    #     
[3]), (139, 139, 139), 1)           els e:            cv2.rectang l e(im , (bb[0], bb[1]), (bb[ 2], b b[ 3 ]),( ra ndom. random( )  * 1 28+12 8,ra n do m.ran dom () * 12 8 + 128, r and om .random() *1 28  + 128), 1 )
    if gt_bo xes  is no tNon e :
      gt_ boxes=  np.ar ray(gt_bo xe s )
      forg t  i n gt _boxes:
         b b = gt[:4].ast ype(in t)        if is_sh ow _labe l :
        cls_id = int (gt[4])
           cv 2 .recta ngle( im ,  (b b[0], bb[1] ), ( bb[2], bb [3]),( 0, 0, 2 5 5), 3)
           if  c las se s  an d  l en( c las ses) > c ls _i d:
            c ls_na me = classes[ cls _id]              e ls e:
                cls_name =  str (cls_id)          cv2.pu tTe xt( im, '{: s}'.for m at( cl s _ n am e), (bb[0], bb[1]-2 ) ,\
                    fon t, 0 . 5 , (0,  0,2 55 ), 1)
        e lse:
           cv 2.rec t angl e(im,(bb[0],bb[1] ) , (bb[2], bb[3 ] ), (0, 0, 255 ) ,3 ) 
   i f k eypoints is  not None :
    k eypoi nts =  n p .a r ray(keypoints).asty pe (int)       keypoints= keypoint s . r eshape(- 1, 1 7 ,3 )

        ifFalse :
          idx =  np.w here(det _boxes[:, - 1] > thr esh )
      keypoints =k e ypoi nt s[idx]          f ori i n r an ge(len(ke ypoints) ): 
          draw_s ke let on (im , key p oints[ i], s how_ sk el e ton _labels ) 
    e ls e:
        for i i n range (len(keyp oin t s)): 
             draw_sk el eton(im, k ey poi nts[i] , show_ske leton_labels)

    if r e turn_im g:       r eturn im. cop y()

  c v2.ims how(na me, i m) 
 cv2.w a i tK ey( 0) 
    # whi l e  Tr ue:
  #    
[3]), (139,_139, 139),_1)
   _ __ __  else:
_  _   _  __    cv2.rectangle(im, (bb[0], bb[1]), (bb[2], bb[3]), (random.random() * 128 + 128,_random.random()_* 128_+_128,_random.random() * 128 + 128),_1)

    if_gt_boxes is_not None:
      __gt_boxes = np.array(gt_boxes)
_       for gt_in gt_boxes:
    _  ___  bb =_gt[:4].astype(int)
     _     _if is_show_label:
     _     _    cls_id_= int(gt[4])
_    _  _ _  _  cv2.rectangle(im, (bb[0], bb[1]), (bb[2],_bb[3]), (0, 0,_255), 3)

    __  ___ _  _if_classes and_len(classes)_> cls_id:
    __      _   __  cls_name =_classes[cls_id]
    _     _ _ _ else:
         _     _   _cls_name =_str(cls_id)
_  __ _      _ _cv2.putText(im, '{:s}'.format(cls_name), (bb[0], bb[1] - 2),_\
   __     _     _    ___    font,_0.5, (0, 0, 255), 1)
 _  _      _else:
_     __     ___cv2.rectangle(im, (bb[0], bb[1]), (bb[2], bb[3]),_(0, 0, 255), 3)

  _ if keypoints is not None:
   _  _ keypoints = np.array(keypoints).astype(int)
 ___    keypoints = keypoints.reshape(-1, 17, 3)

 _ _  _ if False:
       _    idx = np.where(det_boxes[:,_-1] > thresh)
        _   keypoints = keypoints[idx]
  _      _  for i_in_range(len(keypoints)):
      ____  _  _draw_skeleton(im, keypoints[i], show_skeleton_labels)
_ _  _ _else:
  __        for i in range(len(keypoints)):
      _     _   draw_skeleton(im, keypoints[i],_show_skeleton_labels)

_  _if return_img:
  _     return im.copy()

 _  cv2.imshow(name, im)
    cv2.waitKey(0)
 __ #_while True:
  _ #_ _  