def __init__(self, addr, port, window_size=25):
self._lock = threading.Lock()
self._skeleton = {}
# create ZMQ socket for receiving skeleton data
self.context = zmq.Context()
self.sub_skel = self.context.socket(zmq.SUB)
self.sub_skel.connect('tcp://{}:{}'.format(addr, port))
self.sub_skel.setsockopt(zmq.SUBSCRIBE, '')
# create thread that receive datas
t = threading.Thread(target=self.get_skeleton)
t.daemon = True
t.start()
# create reba assessment object
self.reba = Assessment(window_size)
def __init__(self, addr, port, window_size=25):
self._lock = threading.Lock()
self._skeleton = {}
# create ZMQ socket for receiving skeleton data
self.context = zmq.Context()
self.sub_skel = self.context.socket(zmq.SUB)
self.sub_skel.connect('tcp://{}:{}'.format(addr, port))
self.sub_skel.setsockopt(zmq.SUBSCRIBE, '')
# create thread that receive datas
t = threading.Thread(target=self.get_skeleton)
t.daemon = True
t.start()
# create reba assessment object
self.reba = Assessment(window_size)
import json
from pause.assessment import Assessment
from pause import skeleton_to_joints as skel
import sys
if __name__ == '__main__':
name = sys.argv[1]
filename = '/tmp/'+ name + '.json'
with open(filename) as data_file:
skel_data = json.load(data_file)
# convert skeleton to joints
joints_data = skel.convert_skel_to_joints(skel_data)
print joints_data
# assess the posture
reba = Assessment(25)
score = reba.assess(joints_data)
print 'name : {}, score = {}'.format(name,score)
import json
from pause.assessment import Assessment
from pause import skeleton_to_joints as skel
import sys
if __name__ == '__main__':
name = sys.argv[1]
filename = '/tmp/' + name + '.json'
with open(filename) as data_file:
skel_data = json.load(data_file)
# convert skeleton to joints
joints_data = skel.convert_skel_to_joints(skel_data)
print joints_data
# assess the posture
reba = Assessment(25)
score = reba.assess(joints_data)
print 'name : {}, score = {}'.format(name, score)
class KinectBridge(object):
def __init__(self, addr, port, window_size=25):
self._lock = threading.Lock()
self._skeleton = {}
# create ZMQ socket for receiving skeleton data
self.context = zmq.Context()
self.sub_skel = self.context.socket(zmq.SUB)
self.sub_skel.connect('tcp://{}:{}'.format(addr, port))
self.sub_skel.setsockopt(zmq.SUBSCRIBE, '')
# create thread that receive datas
t = threading.Thread(target=self.get_skeleton)
t.daemon = True
t.start()
# create reba assessment object
self.reba = Assessment(window_size)
def remove_user(self, user_index):
with self._lock:
del self._skeleton[user_index]
def remove_all_users(self):
with self._lock:
self._skeleton = {}
@property
def tracked_skeleton(self):
with self._lock:
return self._skeleton
@tracked_skeleton.setter
def tracked_skeleton(self, skeleton):
with self._lock:
self._skeleton[skeleton.user_id] = skeleton
def get_skeleton(self):
while True:
md = self.sub_skel.recv_json()
msg = self.sub_skel.recv()
skel_array = numpy.fromstring(msg, dtype=float, sep=",")
skel_array = skel_array.reshape(md['shape'])
# receive all the joints
nb_joints = md['shape'][0]
joints = {}
for i in range(nb_joints):
x, y, z, w = skel_array[i][0:4]
position = [x / w, y / w, z / w]
xp, yp = skel_array[i][4:6]
pixel_coord = [xp, yp]
joints[skeleton_joints[i]] = {}
joints[skeleton_joints[i]]['position'] = position
joints[skeleton_joints[i]]['pixel_coordinates'] = pixel_coord
# create the skeleton object
self.tracked_skeleton = Skeleton(md['timestamp'], md['user_index'],
joints)
def show_reba_score(self):
print 'score = {}'.format(self.reba.reba_log['reba'])
def process_skeleton(self, skeleton):
# first convert the skeleton data to the joints angles
joints_data = skel.convert_skel_to_joints(skeleton.joints)
print joints_data
# calculate the reba score
reba_score = self.reba.assess(joints_data)
self.show_reba_score()
def record_skeleton(self, filename):
# get the tracked skeleton
skeleton = self.tracked_skeleton
# write the json file
if skeleton:
for user, skel in skeleton.iteritems():
with open(filename + '_' + str(user) + '.json',
'w') as outfile:
json.dump(skel.joints, outfile, indent=4, sort_keys=True)
def run(self):
#cv2.startWindowThread()
# while True:
# img = numpy.zeros((480, 640, 3))
time.sleep(5)
os.system('beep')
skeleton = self.tracked_skeleton
if skeleton:
for user, skel in skeleton.iteritems():
# assess skeleton with REBA method
self.process_skeleton(skel)
# display joints in opencv
for joint_name in skel.joints:
x, y = skel.joints[joint_name]['pixel_coordinates']
pt = (int(x), int(y))
# cv2.circle(img, pt, 5, (255, 255, 255), thickness=-1)
# clear skeleton objects
self.remove_all_users()
# cv2.imshow('Skeleton', img)
# cv2.waitKey(50)
# close ZMQ sockets
self.sub_skel.close()
self.context.term()
class KinectBridge(object):
def __init__(self, addr, port, window_size=25):
self._lock = threading.Lock()
self._skeleton = {}
# create ZMQ socket for receiving skeleton data
self.context = zmq.Context()
self.sub_skel = self.context.socket(zmq.SUB)
self.sub_skel.connect('tcp://{}:{}'.format(addr, port))
self.sub_skel.setsockopt(zmq.SUBSCRIBE, '')
# create thread that receive datas
t = threading.Thread(target=self.get_skeleton)
t.daemon = True
t.start()
# create reba assessment object
self.reba = Assessment(window_size)
def remove_user(self,user_index):
with self._lock:
del self._skeleton[user_index]
def remove_all_users(self):
with self._lock:
self._skeleton = {}
@property
def tracked_skeleton(self):
with self._lock:
return self._skeleton
@tracked_skeleton.setter
def tracked_skeleton(self, skeleton):
with self._lock:
self._skeleton[skeleton.user_id] = skeleton
def get_skeleton(self):
while True:
md = self.sub_skel.recv_json()
msg = self.sub_skel.recv()
skel_array = numpy.fromstring(msg, dtype=float, sep=",")
skel_array = skel_array.reshape(md['shape'])
# receive all the joints
nb_joints = md['shape'][0]
joints = {}
for i in range(nb_joints):
x, y, z, w = skel_array[i][0:4]
position = [x/w, y/w, z/w]
xp, yp = skel_array[i][4:6]
pixel_coord = [xp, yp]
joints[skeleton_joints[i]] = {}
joints[skeleton_joints[i]]['position'] = position
joints[skeleton_joints[i]]['pixel_coordinates'] = pixel_coord
# create the skeleton object
self.tracked_skeleton = Skeleton(md['timestamp'], md['user_index'], joints)
def show_reba_score(self):
print 'score = {}'.format(self.reba.reba_log['reba'])
def process_skeleton(self, skeleton):
# first convert the skeleton data to the joints angles
joints_data = skel.convert_skel_to_joints(skeleton.joints)
print joints_data
# calculate the reba score
reba_score = self.reba.assess(joints_data)
self.show_reba_score()
def record_skeleton(self, filename):
# get the tracked skeleton
skeleton = self.tracked_skeleton
# write the json file
if skeleton:
for user,skel in skeleton.iteritems():
with open(filename+'_'+str(user)+'.json', 'w') as outfile:
json.dump(skel.joints, outfile, indent=4, sort_keys=True)
def run(self):
#cv2.startWindowThread()
# while True:
# img = numpy.zeros((480, 640, 3))
time.sleep(5)
os.system('beep')
skeleton = self.tracked_skeleton
if skeleton:
for user,skel in skeleton.iteritems():
# assess skeleton with REBA method
self.process_skeleton(skel)
# display joints in opencv
for joint_name in skel.joints:
x, y = skel.joints[joint_name]['pixel_coordinates']
pt = (int(x),int(y))
# cv2.circle(img, pt, 5, (255, 255, 255), thickness=-1)
# clear skeleton objects
self.remove_all_users()
# cv2.imshow('Skeleton', img)
# cv2.waitKey(50)
# close ZMQ sockets
self.sub_skel.close()
self.context.term()
