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
gesturesList=smp.getGestures()
for gesture in gesturesList:
# Get the gesture ID, and start and end frames for the gesture
gestureID,startFrame,endFrame=gesture
#####################
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')
plt.plot(range(global_score.shape[-1]),
path,
color='c',
linewidth=2.0)
plt.xlim((0, global_score.shape[-1]))
# plot ground truth
gesturesList = smp.getGestures()
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)
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]))