plt.plot(frames_count,
pred_label_temp,
color='#ffff00',
linewidth=2.0)
if False:
plt.show()
else:
from pylab import savefig
import matplotlib.pyplot as pl
save_dir = r'C:\Users\PC-User\Documents\GitHub\chalearn2014_wudi_lio\path_combined'
save_path = os.path.join(save_dir, file)
savefig(save_path + '.eps', format='eps', bbox_inches='tight')
#plt.show()
print "Elapsed time %d sec" % int(time.time() - time_tic)
pred = []
for i in range(len(begin_frame)):
pred.append([pred_label[i], begin_frame[i], end_frame[i]])
sample.exportPredictions(pred, outPred)
# ###############################################
## delete the sample
del sample
TruthDir = r'I:\Kaggle_multimodal\Test_label'
final_score = evalGesture(outPred, TruthDir)
print("The score for this prediction is " + "{:.12f}".format(final_score))
# combined: 0.804609104245
frames_count = numpy.array(range(begin_frame[i], end_frame[i]+1))
pred_label_temp = ((pred_label[i]-1) *STATE_NO +2) * numpy.ones(len(frames_count))
plt.plot(frames_count, pred_label_temp, color='#ffff00', linewidth=2.0)
if False:
plt.show()
else:
from pylab import savefig
import matplotlib.pyplot as pl
save_dir=r'C:\Users\PC-User\Documents\GitHub\chalearn2014_wudi_lio\path_combined'
save_path= os.path.join(save_dir,file)
savefig(save_path+'.eps', format='eps', bbox_inches='tight')
#plt.show()
print "Elapsed time %d sec" % int(time.time() - time_tic)
pred=[]
for i in range(len(begin_frame)):
pred.append([ pred_label[i], begin_frame[i], end_frame[i]] )
sample.exportPredictions(pred,outPred)
# ###############################################
## delete the sample
del sample
TruthDir=r'I:\Kaggle_multimodal\Test_label'
final_score = evalGesture(outPred,TruthDir)
print("The score for this prediction is " + "{:.12f}".format(final_score))
# combined: 0.804609104245
def predLabel(self, video_file, matrix_file, pred_file, filename):
# time_start = time.time()
print("\t Processing file " + filename)
sample_video = GestureSample(video_file)
observ_likelihood = cPickle.load(open(matrix_file, "rb"))
# print 'fininsh Loadinging obs_likelihodd'
# print observ_likelihood.shape
log_observ_likelihood = log(observ_likelihood.T +
numpy.finfo(numpy.float32).eps)
log_observ_likelihood[-1, 0:5] = 0
log_observ_likelihood[-1, -5:] = 0
#viterbi decoding
[path, predecessor_state_index,
global_score] = viterbi_path_log(log(Prior), log(Transition_matrix),
log_observ_likelihood)
[pred_label, begin_frame, end_frame, Individual_score,
frame_length] = viterbi_colab_states(path,
global_score,
state_no=5,
threshold=-5,
mini_frame=15)
#heuristically we need to add 1 more frame here
begin_frame += 1
end_frame += 5 # because we cut 4 frames as a cuboid so we need add extra 4 frames
#plotting
gesturesList = sample_video.getGestures()
import matplotlib.pyplot as plt
STATE_NO = 5
im = imdisplay(global_score)
plt.plot(range(global_score.shape[-1]),
path,
color='#39FF14',
linewidth=2.0)
plt.xlim((0, global_score.shape[-1]))
plt.ylim((101, 0))
plt.xlabel('Frames')
plt.ylabel('HMM states')
plt.title('Multi_model(DBN+3DCNN)')
# plot ground truth
for gesture in gesturesList:
gestureID, startFrame, endFrame = gesture
frames_count = numpy.array(range(startFrame, endFrame + 1))
pred_label_temp = (
(gestureID - 1) * STATE_NO + 2) * 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)):
rames_count = numpy.array(range(begin_frame[i], end_frame[i] + 1))
pred_label_temp = ((pred_label[i] - 1) * STATE_NO +
2) * numpy.ones(len(frames_count))
plt.plot(frames_count,
pred_label_temp,
color='#FFFF33',
linewidth=2.0)
plt.show()
pred = []
for i in range(len(begin_frame)):
pred.append([pred_label[i], begin_frame[i], end_frame[i]])
sample_video.exportPredictions(pred, pred_file)
