frames_count = numpy.array(
range(begin_frame[i], end_frame[i] + 1))
pred_label_temp = ((pred_label[i] - 1) * 10 + 5) * numpy.ones(
len(frames_count))
plt.plot(frames_count,
pred_label_temp,
color='#ffff00',
linewidth=2.0)
plt.show()
else:
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]])
smp.exportPredictions(pred, outPred)
# ###############################################
## delete the sample
del smp
TruthDir = './training/gt/'
final_score = evalGesture(outPred, TruthDir)
print("The score for this prediction is " + "{:.12f}".format(final_score))
# Submision folder (output)
outSubmision = './training/submision/'
# Prepare submision file (only for validation and final evaluation data sets)
createSubmisionFile(outPred, outSubmision)
save_dir=r'.\ConvNet_3DCNN\training\Test_Depth_'+ CNN_NAME
if not os.path.exists(save_dir):
os.makedirs(save_dir)
save_path= os.path.join(save_dir,file)
out_file = open(save_path, 'wb')
dic = {'log_observ_likelihood':log_observ_likelihood}
cPickle.dump(dic, out_file, protocol=cPickle.HIGHEST_PROTOCOL)
out_file.close()
pred=[]
for i in range(len(begin_frame)):
pred.append([ pred_label[i], begin_frame[i], end_frame[i]] )
smp.exportPredictions(pred,outPred)
# ###############################################
## delete the sample
del smp
TruthDir=r'I:\Kaggle_multimodal\ChalearnLAP2104_EvaluateTrack3\input\ref//'
CNN_NAME = 'ConvNet__2014-05-28_01.59.00_150'
outPred='./ConvNet_3DCNN/training/Test_3DCNN_' + CNN_NAME
final_score = evalGesture(outPred,TruthDir)
print("The score for this prediction is " + "{:.12f}".format(final_score))
# Submision folder (output)
outSubmision='./training/submision/'
# Prepare submision file (only for validation and final evaluation data sets)
