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)
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')
#####################
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)