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()
for gesture in gesturesList:
# Get the gesture ID, and start and end frames for the gesture
gestureID, startFrame, endFrame = gesture
frames_count = numpy.array(range(startFrame, endFrame + 1))
pred_label_temp = (
(gestureID - 1) * 10 + 5) * numpy.ones(len(frames_count))
plt.plot(frames_count,
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
frames_count = numpy.array(range(startFrame, endFrame+1))
pred_label_temp = ((gestureID-1) *10 +5) * numpy.ones(len(frames_count))
plt.plot(frames_count, pred_label_temp, color='r', linewidth=5.0)
# plot clean path
for i in range(len(begin_frame)):
