class RealTimePlotModule():
"""
This module plots a bar chart with the probability distribution on the states.
Usage
python plot_probabilities.py
Input port: /processing/NamePort:o
"""
def __init__(self, list_port):
self.input_port = []
self.port = []
plt.ion()
self.fig = plt.figure()
self.ax = self.fig.add_subplot(111, projection='3d')
data = np.zeros((2, 66))
color = ['b', 'r']
self.lines = []
self.skeleton = []
self.skeleton = Skeleton('dhm66_ISB_Xsens.urdf')
for i, name_port in enumerate(list_port):
self.input_port.append(name_port)
self.port.append(yarp.BufferedPortBottle())
self.port[i].open('/plot_skeleton' + name_port)
yarp.Network.connect(name_port, self.port[i].getName())
self.lines = self.skeleton.visualise_from_joints(data,
color_list=color,
lines=[])
# self.skeleton[i].visualise_from_joints(data, color=color[i])
def update_plot(self):
color = ['b', 'r']
data_joint = []
for i, port in enumerate(self.port):
b_in = self.port[i].read()
data = b_in.toString().split(' ')
if len(data) == 67:
del data[0]
data = list(map(float, data))
data = np.asarray(data)
self.ax.cla()
plt.sca(self.ax)
data_joint.append(np.deg2rad(data))
# self.lines[i] = self.skeleton[i].visualise_from_joints(data, color=color[i], lines=self.lines[i])
self.skeleton.visualise_from_joints(data_joint,
color_list=color,
lines=self.lines)
self.fig.canvas.draw()
return
def close(self):
for i_port, port in zip(self.input_port, self.port):
yarp.Network.disconnect(i_port, port.getName())
port.close()
class SkeletonWidget(QtWidgets.QWidget):
def __init__(self, parent=None):
list_port = ['/processing/xsens/JointAngles:o', '/AE/reconstruct:o']
self.input_port = []
self.port = []
self.fig = plt.figure()
self.ax = self.fig.add_subplot(111, projection='3d')
data = np.zeros((1, 66))
color = ['b', 'r']
self.lines = []
self.skeleton = []
self.skeleton = Skeleton('dhm66_ISB_Xsens.urdf')
for i, name_port in enumerate(list_port):
self.input_port.append(name_port)
self.port.append(yarp.BufferedPortBottle())
self.port[i].open('/plot_skeleton' + name_port)
yarp.Network.connect(name_port, self.port[i].getName())
QtWidgets.QWidget.__init__(self, parent) # Inherit from QWidget
self.fig = Figure()
self.pltCanv = Canvas(self.fig)
self.pltCanv.setSizePolicy(QSizePolicy.Expanding,
QSizePolicy.Expanding)
self.pltCanv.setFocusPolicy(QtCore.Qt.ClickFocus)
self.pltCanv.setFocus()
self.pltCanv.updateGeometry()
self.ax = self.fig.add_subplot(111, projection='3d')
self.ax.mouse_init()
#=============================================
# Main plot widget layout
#=============================================
self.layVMainMpl = QVBoxLayout()
self.layVMainMpl.addWidget(self.pltCanv)
self.setLayout(self.layVMainMpl)
self.lines = []
self.skeleton = []
self.skeleton = Skeleton('dhm66_ISB_Xsens.urdf')
self.timer = self.pltCanv.new_timer(100,
[(self.update_canvas, (), {})])
self.timer.start()
def update_canvas(self):
color = ['b', 'r']
data_joint = []
for i, port in enumerate(self.port):
b_in = self.port[i].read()
data = b_in.toString().split(' ')
if len(data) == 67:
del data[0]
data = list(map(float, data))
data = np.asarray(data)
data_joint.append(np.deg2rad(data))
self.ax.clear()
self.lines = self.skeleton.visualise_from_joints(self.ax,
data_joint,
color_list=color,
lines=[])
self.pltCanv.figure.canvas.draw()
if x[0, i] < 0:
x[0, i] = 0.0
elif x[0, i] > 1.0:
x[0, i] = 1.0
# x[0, 0] = 0.5
decoded_data = autoencoder.decode_data(x)
if metric == 'posture':
whole_body = reduce_posture.reduce2complete(decoded_data[0])
posture.update_posture(whole_body)
# elseax1:
posture.update_posture(decoded_data[0])
ergo_score = tools.compute_sequence_ergo(ergo_assessment,
decoded_data[0], 0, ergo_name,
'')
posture.visualise_from_joints(ax1, [decoded_data[0]])
plt.sca(ax0)
ax0.scatter(x[0, 0] * len(X), x[0, 1] * len(X))
plt.show()
cid = fig.canvas.mpl_connect('button_press_event', onclick)
plt.sca(ax1)
posture.visualise_from_joints(ax1, [np.zeros((66))])
# pd.DataFrame(ergo_grid).to_csv(config_ergo + "_grid.csv", header=False, index=False)
plt.show()
