Ejemplo n.º 1
0
    def myBuildDBNtest(self):
        #提取所有帧中,原始的骨架点,得到一个矩阵,Skeleton_matrix ,同时返回骨架是否归0化
        #Skeleton_matrix, valid_skel = Extract_feature_UNnormalized(smp, used_joints, 1, smp.getNumFrames())
        time_tic = time.time()  
        import cPickle
        Skeleton_matrix=cPickle.load(open("testSkeleton_matrix","rb"))
        #print Skeleton_matrix
 
        Feature = Extract_feature_Realtime(Skeleton_matrix, self.njoints)

        Feature_normalized = normalize(Feature, self.Mean1, self.Std1)
       
        '''
        ##########################
        ### model 1  第一种网络构架模式  #
        ##########################
        dbn = GRBM_DBN(numpy_rng=numpy_rng, n_ins=528,
        hidden_layers_sizes=[1000, 1000, 500],
        n_outs=201)
        dbn.load('dbn_2014-05-23-20-07-28.npy')#预先训练好的构架
        #这里就是theano的奇葩函数构架
        validate_model = theano.function(inputs=[],
            outputs=dbn.logLayer.p_y_given_x,#输出是逻辑回归层的输出
            givens={ dbn.x: shared_x})   '''

        observ_likelihood_1 = self.validate_model(Feature_normalized)#调用函数得到结果



        ##########################
        # viterbi path decoding
        #####################
        observ_likelihood_1=observ_likelihood_1[0:50,:]
        #这里自己改了,只有第一个网络结构的,
        log_observ_likelihood = log(observ_likelihood_1.T) 
        #这里是一个矩阵, 行是样本,列是概率   [1884个样本, 201列] 用T转置了
        print "处理时间 %d sec" % int(time.time() - time_tic)
        time_tic = time.time()


        #下面就是vibiter算法了
        print("\t Viterbi path decoding " )
        # do it in log space avoid numeric underflow
        [path, predecessor_state_index, global_score] =viterbi_path_log(
            self.Prior,  self.Transition_matrix, log_observ_likelihood   )

        label=viterbi_endframe(path,5,30)
        # Some gestures are not within the vocabulary
        #[pred_label, begin_frame, end_frame, Individual_score, frame_length] = viterbi_colab_clean(
        #    path, global_score, threshold=-100, mini_frame=19)
 
        print "标记是:"
        print label
        print "viterbi处理时间 %d sec" % int(time.time() - time_tic)
Ejemplo n.º 2
0
    def myViterbi(self,log_observ_likelihood):  
        time_tic = time.time()

        #下面就是vibiter算法了
        print("\t Viterbi path decoding " )
        # do it in log space avoid numeric underflow
        [path, predecessor_state_index, global_score] =viterbi_path_log(
            self.Prior,  self.Transition_matrix, log_observ_likelihood   )

        label=viterbi_endframe(path,5,30)
        # Some gestures are not within the vocabulary
        #[pred_label, begin_frame, end_frame, Individual_score, frame_length] = viterbi_colab_clean(
        #    path, global_score, threshold=-100, mini_frame=19)
 
        print "标记是:"
        print label
        print "viterbi处理时间 %d sec" % int(time.time() - time_tic)
        return label
        data = numpy.reshape(data, (4 * 90 * 90, data.shape[-1]))
        data = numpy.array(data - data_mean[:, numpy.newaxis],
                           dtype=numpy.single)
        ftrs = model.do_write_features(data)

        ##########################
        # viterbi path decoding
        #####################
        log_observ_likelihood = log(ftrs.T)
        log_observ_likelihood[-1, 0:5] = 0
        log_observ_likelihood[-1, -5:] = 0

        print("\t Viterbi path decoding ")
        # do it in log space avoid numeric underflow
        [path, predecessor_state_index,
         global_score] = viterbi_path_log(log(Prior), log(Transition_matrix),
                                          log_observ_likelihood)
        #[path, predecessor_state_index, global_score] =  viterbi_path(Prior, Transition_matrix, observ_likelihood)

        # Some gestures are not within the vocabulary
        [pred_label, begin_frame, end_frame, Individual_score,
         frame_length] = viterbi_colab_clean(path,
                                             global_score,
                                             threshold=-100,
                                             mini_frame=19)

        #begin_frame = begin_frame + 1
        end_frame = end_frame + 5  # note here 3DCNN should add 5 frames because we used 4 frames
        ### plot the path and prediction
        if False:
            im = imdisplay(global_score)
            plt.imshow(im, cmap='gray')
Ejemplo n.º 4
0
        #####################
        if Flag_multiview:
            accumulate_prob = numpy.zeros((ftrs.shape[0]/5, ftrs.shape[1]))
            for i in range(5):
                accumulate_prob += ftrs[i*ftrs.shape[0]/5:(i+1)*ftrs.shape[0]/5,:]

            accumulate_prob = accumulate_prob/5.0
            log_observ_likelihood = log(accumulate_prob.T + numpy.finfo(numpy.float32).eps) 
        else:
            log_observ_likelihood = log(ftrs.T + numpy.finfo(numpy.float32).eps) 
        log_observ_likelihood[-1, 0:5] = 0
        log_observ_likelihood[-1, -5:] = 0

        print("\t Viterbi path decoding " )
        # do it in log space avoid numeric underflow
        [path, predecessor_state_index, global_score] = viterbi_path_log(log(Prior), log(Transition_matrix), log_observ_likelihood)
        #[path, predecessor_state_index, global_score] =  viterbi_path(Prior, Transition_matrix, observ_likelihood)
        
        # Some gestures are not within the vocabulary
        [pred_label, begin_frame, end_frame, Individual_score, frame_length] = viterbi_colab_clean(path, global_score, threshold=-100, mini_frame=19)

        #begin_frame = begin_frame + 1
        end_frame = end_frame + 5 # note here 3DCNN should add 5 frames because we used 4 frames
        ### plot the path and prediction
        if True:
            im  = imdisplay(global_score)
            plt.clf()
            plt.imshow(im, cmap='gray')
            plt.plot(range(global_score.shape[-1]), path, color='c',linewidth=2.0)
            plt.xlim((0, global_score.shape[-1]))
            # plot ground truth

            time_tic = time.time() 
            load_path_cropped= os.path.join(outPred_cropped,file)
            load_path_depth= os.path.join(outPred_depth,file)

            dic_cropped= cPickle.load( open(load_path_cropped, "rb" ) )
            dic_depth = cPickle.load( open(load_path_depth, "rb" ) )

            log_observ_likelihood_cropped= dic_cropped['log_observ_likelihood']
            log_observ_likelihood_depth = dic_depth['log_observ_likelihood']

            log_observ_likelihood = log_observ_likelihood_cropped + log_observ_likelihood_depth 
            print "Viterbi path decoding " + file
            # do it in log space avoid numeric underflow
            [path, predecessor_state_index, global_score] = viterbi_path_log(log(Prior), log(Transition_matrix), log_observ_likelihood)
            #[path, predecessor_state_index, global_score] =  viterbi_path(Prior, Transition_matrix, observ_likelihood)
        
            # Some gestures are not within the vocabulary
            [pred_label, begin_frame, end_frame, Individual_score, frame_length] = viterbi_colab_clean(path, global_score, threshold=-100, mini_frame=19)
            ### In theory we need add frame, but it seems that the groutnd truth is about 3 frames more, a bit random
            end_frame = end_frame + 3

            print "Elapsed time %d sec" % int(time.time() - time_tic)
            prediction=[]
            for i in range(len(begin_frame)):
                prediction.append([ pred_label[i], begin_frame[i], end_frame[i]] )

            if True:
                import matplotlib.pyplot as plt
                im  = imdisplay(global_score)