Files
mxsc/script/local.py
2017-01-26 16:18:21 +01:00

176 lines
5.4 KiB
Python

###################################################################################################
# Deployment specific global definitions - executed after startup.py
###################################################################################################
from ch.psi.pshell.serial import TcpDevice
from ch.psi.pshell.modbus import ModbusTCP
"""
class RobotTCP(TcpDevice):
def __init__(self, name, server):
TcpDevice.__init__(self, name, server)
def execute(self, system, command, *argv):
#print "Done"
cmd = str(system) + "," + str(command)
for arg in argv:
cmd = cmd + "," + str(arg)
cmd = cmd + "\n"
ret = ""
try:
ret = robot.write(cmd)
finally:
self.getLogger().info(cmd + " ret = " + str(ret))
return ret
def mount(self, puck, sample):
return self.execute('1', '1', puck, sample)
add_device(RobotTCP("robot_tcp", "127.0.0.1:3333"), force = True)
"""
class RobotModbus(DeviceBase):
def __init__(self, name):
DeviceBase.__init__(self, name)
robot_req.write(0)
def execute(self, command, *argv):
#if robot_req.read() != 0:
# raise Exception("Ongoing command")
#if robot_ack.read() != 0:
# raise Exception("Robot is not ready")
robot_cmd.write(command)
args = [0,0,0,0,0,0]
index = 0
for arg in argv:
args[index] = arg
index = index + 1
robot_args.write(to_array(args, 'i'))
try:
self.request()
err = robot_ack.take()
if err == 1:
ret = robot_ret.read()
return ret
if err == 2:
raise Exception("Invalid command: " + str(command))
raise Exception("Unknown error: " + str(err))
finally:
self.cancel_request()
def request(self):
robot_req.write(1)
#while robot_ack.read() == 0:
# time.sleep(0.001)
def cancel_request(self):
robot_req.write(0)
#while robot_ack.read() != 0:
# time.sleep(0.001)
def mount(self, puck, sample):
return self.execute('1', '1', puck, sample)
add_device(RobotModbus("robot"), force = True)
add_device(img.getContrast(), force = True)
def detect_pucks(ip):
"""
"""
aux = grayscale(ip, in_place=False)
threshold(aux,0,50)
binary_fill_holes(aux)
return analyse_particles(aux, 10000,50000,
fill_holes = False, exclude_edges = True,print_table=True,
output_image = "outlines", minCirc = 0.4, maxCirc = 1.0)
def detect_samples(ip):
"""
"""
aux = grayscale(ip, in_place=False)
invert(aux)
subtract_background(aux)
auto_threshold(aux)
binary_open(aux)
return analyse_particles(aux, 250,1000,
fill_holes = False, exclude_edges = True,print_table=True,
output_image = "outlines", minCirc = 0.7, maxCirc = 1.0)
r,g,b = [0]*256,[0]*256,[0]*256
b[0]=0xFF
b[1]=0xFF ; g[1] = 0x80; r[1] = 0x80
outline_lut1 = (r,g,b)
r,g,b = [0]*256,[0]*256,[0]*256
g[0]=0x80;r[0]=0x80;
g[1]=0xFF ; r[1] = 0x80; b[1] = 0x80
outline_lut2 = (r,g,b)
from mathutils import estimate_peak_indexes, fit_gaussians, create_fit_point_list, Gaussian
import java.awt.Color as Color
import mathutils
mathutils.MAX_ITERATIONS = 100000
def fit(ydata, xdata = None, draw_plot = True):
if xdata is None:
xdata = frange(0, len(ydata), 1)
max_y= max(ydata)
index_max = ydata.index(max_y)
max_x= xdata[index_max]
print "Max index:" + str(index_max),
print " x:" + str(max_x),
print " y:" + str(max_y)
if draw_plot:
plots = plot([ydata],["data"],[xdata], title="Fit" )
p = None if plots is None else plots[0]
gaussians = fit_gaussians(ydata, xdata, [index_max,])
if gaussians[0] is None:
if draw_plot and (p is not None):
p.addMarker(max_x, None, "Max="+str(round(max_x,4)), Color.GRAY)
print "Fitting error"
return (None, None, None)
(norm, mean, sigma) = gaussians[0]
if draw_plot:
fitted_gaussian_function = Gaussian(norm, mean, sigma)
scale_x = [float(min(xdata)), float(max(xdata)) ]
points = max((len(xdata)+1), 100)
resolution = (scale_x[1]-scale_x[0]) / points
fit_y = []
fit_x = frange(scale_x[0],scale_x[1],resolution, True)
for x in fit_x:
fit_y.append(fitted_gaussian_function.value(x))
#Server
if p is None:
plot([ydata,fit_y],["data","fit"],[xdata,fit_x], title="Fit")
draw_plot = False
else:
p.addSeries(LinePlotSeries("fit"))
p.getSeries(1).setData(fit_x, fit_y)
if abs(mean - xdata[index_max]) < abs((scale_x[0] + scale_x[1])/2):
if draw_plot:
p.addMarker(mean, None, "Mean="+str(round(mean,4)), Color.MAGENTA.darker())
print "Mean -> " + str(mean)
return (norm, mean, sigma)
else:
if draw_plot:
p.addMarker(max_x, None, "Max="+str(round(max_x,4)), Color.GRAY)
print "Invalid gaussian fit: " + str(mean)
return (None, None, None)