# continue;
if file_count<800:
print("\t Processing file " + file)
# Create the object to access the sample
smp=GestureSample(os.path.join(data,file))
# ###############################################
# USE Ground Truth information to learn the model
# ###############################################
# Get the list of actions for this frame
gesturesList=smp.getGestures()
frame_num = smp.getNumFrames()
Feature_Array = np.zeros(shape = (frame_num , (njoints*(njoints-1)/2 + njoints**2)*3),dtype=np.float32)
# Target = np.zeros( shape=(frame_num, target_category), dtype=np.uint8)
#feature generate
Skeleton_matrix, valid_skel = Extract_feature_UNnormalized(smp,used_joints, 1, frame_num)
Feature_Array = Extract_feature_Realtime(Skeleton_matrix, njoints)
Feature_all.append(Feature_Array)
#target generate
labels = np.zeros(frame_num, np.uint8)
for row in gesturesList:
labels[int(row[1])-1:int(row[2])-1] = int(row[0])
Target_all.append(labels)
del smp
# save the skeleton file:
import cPickle as pickle
# continue;
time_tic = time.time()
if not file_count < 650:
print("\t Processing file " + file)
# Create the object to access the sample
smp = GestureSample(os.path.join(data_path, file))
# ###############################################
# USE Ground Truth information to learn the model
# ###############################################
# Get the list of actions for this frame
gesturesList = smp.getGestures()
###########################################################
# we check whether it's left dominant or right dominanant
# if right dominant, we correct them to left dominant
##########################################################
Skeleton_matrix, valid_skel = Extract_feature_UNnormalized(
smp, used_joints, 1, smp.getNumFrames())
Feature = Extract_feature_Realtime(Skeleton_matrix, njoints)
Feature_normalized = normalize(Feature, Mean1, Std1)
### Feed into DBN
shared_x = theano.shared(numpy.asarray(Feature_normalized,
dtype=theano.config.floatX),
borrow=True)
numpy_rng = numpy.random.RandomState(123)
### model 1
##########################
dbn = GRBM_DBN(numpy_rng=numpy_rng,
n_ins=528,
# continue;
if file_count<650:
print("\t Processing file " + file)
# Create the object to access the sample
smp=GestureSample(os.path.join(data,file))
# ###############################################
# USE Ground Truth information to learn the model
# ###############################################
# Get the list of actions for this frame
gesturesList=smp.getGestures()
# Iterate for each action in this sample
for gesture in gesturesList:
# Get the gesture ID, and start and end frames for the gesture
gestureID,startFrame,endFrame=gesture
Skeleton_matrix, valid_skel = Extract_feature_UNnormalized(smp, used_joints, startFrame, endFrame)
# to see we actually detect a skeleton:
if not valid_skel:
print "No detected Skeleton: ", gestureID
else:
### extract the features according to the CVPR2014 paper
Feature = Extract_feature_Realtime(Skeleton_matrix, njoints)
Target = numpy.zeros( shape=(Feature.shape[0], STATE_NO*20+1))
fr_no = Feature.shape[0]
for i in range(STATE_NO): #HMM states force alignment
begin_fr = numpy.round(fr_no* i /STATE_NO) + 1
end_fr = numpy.round( fr_no*(i+1) /STATE_NO)
#print "begin: %d, end: %d"%(begin_fr-1, end_fr)
seg_length=end_fr-begin_fr + 1
targets = numpy.zeros( shape =(STATE_NO*20+1,1))
targets[ i + STATE_NO*(gestureID-1)] = 1
gesturesList=smp.getGestures()
# Iterate for each action in this sample
# Then we also choose 5 frame before and after the ground true data:
seg_length = 5
for gesture in gesturesList:
# Get the gesture ID, and start and end frames for the gesture
gestureID,startFrame,endFrame=gesture
# This part is to extract action data
Skeleton_matrix = numpy.zeros(shape=(5, len(used_joints)*3))
HipCentre_matrix = numpy.zeros(shape=(5, 3))
frame_num = 0
## extract first 5 frames
if startFrame-seg_length > 0:
Skeleton_matrix, valid_skel = Extract_feature_UNnormalized(smp, used_joints, startFrame-seg_length+1, startFrame)
if not valid_skel:
print "No detected Skeleton: ", gestureID
else:
Feature = Extract_feature_Realtime(Skeleton_matrix, njoints)
begin_frame = count
end_frame = count+seg_length-1
Feature_all[begin_frame:end_frame,:] = Feature#前5帧的特征矩阵
Targets[begin_frame:end_frame, -1] = 1#最后一位是1，
count=count+seg_length-1
## extract last 5 frames
if endFrame+seg_length < smp.getNumFrames():
Skeleton_matrix, valid_skel = Extract_feature_UNnormalized(smp, used_joints, endFrame, endFrame+seg_length-1)
if not valid_skel:
print "No detected Skeleton: ", gestureID
def generate_eigenjoint(feature_name='sk_eigenjoint_nor_528',
labels_name='labels_raw'):
# Data folder (Training data)
print("Extracting the training files")
data = os.path.join("E:\\program\\Chalearn\\rawdata\\train\\")
target_dir = 'E:\\program\\Chalearn\\Chalearn_LSTM\\target\\'
# Get the list of training samples
samples = os.listdir(data)
output_dir = 'E:\\program\\Chalearn\\Chalearn_LSTM\\feature\\' + feature_name
if not os.path.exists(output_dir):
os.makedirs(output_dir)
used_joints = [
'ElbowLeft', 'WristLeft', 'ShoulderLeft', 'HandLeft', 'ElbowRight',
'WristRight', 'ShoulderRight', 'HandRight', 'Head', 'Spine',
'HipCenter'
]
njoints = len(used_joints)
f = open('SK_normalization.pkl', 'r')
normal_params = pickle.load(f)
f.close()
Mean = normal_params['Mean1']
Std = normal_params['Std1']
count = 0
# target_category = 21
Target_all = []
#Feature_all = numpy.zeros(shape=(400000, (njoints*(njoints-1)/2 + njoints**2)*3),dtype=numpy.float32)
for file_count, file in enumerate(samples):
if int(file[-8:-4]) != 417 and int(file[-8:-4]) != 675:
print("\t Processing file " + file)
# Create the object to access the sample
smp = GestureSample(os.path.join(data, file))
# ###############################################
# USE Ground Truth information to learn the model
# ###############################################
# Get the list of actions for this frame
gesturesList = smp.getGestures()
frame_num = smp.getNumFrames()
Feature_Array = np.zeros(
shape=(frame_num,
(njoints * (njoints - 1) / 2 + njoints**2) * 3),
dtype=np.float32)
# Target = np.zeros( shape=(frame_num, target_category), dtype=np.uint8)
#feature generate
Skeleton_matrix, valid_skel = Extract_feature_UNnormalized(
smp, used_joints, 1, frame_num)
Feature_Array = Extract_feature_Realtime(Skeleton_matrix, njoints)
Feature_Array = normalize(Feature_Array, Mean, Std)
add_ = Feature_Array[-1].reshape((1, Feature_Array.shape[1]))
Feature_Array = np.concatenate((Feature_Array, add_), axis=0)
#save sample sk features
output_name = '%04d.npy' % count
count += 1
np.save(os.path.join(output_dir, output_name), Feature_Array)
#target generate
labels = np.zeros(frame_num, np.uint8)
for row in gesturesList:
labels[int(row[1]) - 1:int(row[2]) - 1] = int(row[0])
Target_all.append(labels)
del smp
np.save(target_dir + '%s.npy' % labels_name, Target_all)