save_path= os.path.join(data, file)
print file
time_start = time()
# we load precomputed feature set or recompute the whole feature set
if os.path.isfile(save_path):
print "loading exiting file"
data_dic = cPickle.load(open(save_path,'rb'))
video = data_dic["video"]
Feature_gesture = data_dic["Feature_gesture"]
assert video.shape[0] == Feature_gesture.shape[0]
else:
print("\t Processing file " + file)
# Create the object to access the sample
sample = GestureSample(os.path.join(data,file))
print "finish loading samples"
video, Feature_gesture = sample.get_test_data_wudi_lio(used_joints)
assert video.shape[0] == Feature_gesture.shape[0]# -*- coding: utf-8 -*-
print "finish preprocessing"
out_file = open(save_path, 'wb')
cPickle.dump({"video":video, "Feature_gesture":Feature_gesture}, out_file, protocol=cPickle.HIGHEST_PROTOCOL)
out_file.close()
print "start computing likelihood"
observ_likelihood = numpy.empty(shape=(video.shape[0],20*STATE_NO+1)) # 20 classed * 5 states + 1 ergodic state
for batchnumber in xrange(video.shape[0]/batch.micro):
video_temp = video[batch.micro*batchnumber:batch.micro*(batchnumber+1),:]
skel_temp = Feature_gesture[batch.micro*batchnumber:batch.micro*(batchnumber+1),:]
x_.set_value(normalize(video_temp, Mean_CNN, Std_CNN).astype("float32"),borrow=True)
for file_count, file in enumerate(samples):
condition = (file_count > -1)
if condition: #wudi only used first 650 for validation !!! Lio be careful!
save_path = os.path.join(save_dst, file)
if os.path.isfile(save_path):
print "loading exiting file"
data_dic = cPickle.load(open(save_path, 'rb'))
video = data_dic["video"]
Feature_gesture = data_dic["Feature_gesture"]
assert video.shape[0] == Feature_gesture.shape[0]
else:
print("\t Processing file " + file)
# Create the object to access the sample
sample = GestureSample(os.path.join(data, file))
print "finish loading samples"
video, Feature_gesture = sample.get_test_data_wudi_lio(used_joints)
assert video.shape[0] == Feature_gesture.shape[
0] # -*- coding: utf-8 -*-
print "finish preprocessing"
out_file = open(save_path, 'wb')
cPickle.dump({
"video": video,
"Feature_gesture": Feature_gesture
},
out_file,
protocol=cPickle.HIGHEST_PROTOCOL)
out_file.close()
samples=glob("*") # because wudi unzipped all the files already!
elif pc=="lio":
samples=glob("*.zip")
print len(samples), "samples found"
for file_count, file in enumerate(samples):
condition = (file_count > 650)
if condition: #wudi only used first 650 for validation !!! Lio be careful!
print("\t Processing file " + file)
# Create the object to access the sample
sample = GestureSample(os.path.join(data,file))
if not load_flag:
video = sample.get_test_data_wudi_lio()
save_path= os.path.join(save_dst, file)
out_file = open(save_path, 'wb')
cPickle.dump(video, out_file, protocol=cPickle.HIGHEST_PROTOCOL)
out_file.close()
else:
save_path= os.path.join(data,file,'test')
video = cPickle.load(open(save_path,"rb"))
print video.shape
print "start computing likelihood"
observ_likelihood = numpy.empty(shape=(video.shape[0],20*5+1)) # 20 classed * 5 states + 1 ergodic state
for batchnumber in xrange(video.shape[0]/batch.micro):
video_temp = video[batch.micro*batchnumber:batch.micro*(batchnumber+1),:]
x_.set_value(video_temp.astype("float32"),borrow=True)
y_pred, p_y_given_x = evalu_model()
