114 lines
3.4 KiB
Python
114 lines
3.4 KiB
Python
###################################################################################################
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# Deployment specific global definitions - executed after startup.py
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###################################################################################################
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from ch.psi.pshell.serial import TcpDevice
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from ch.psi.pshell.modbus import ModbusTCP
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run("RobotSC")
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#run("RobotModbus")
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add_device(img.getContrast(), force = True)
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def detect_pucks(ip):
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"""
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"""
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aux = grayscale(ip, in_place=False)
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threshold(aux,0,50)
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binary_fill_holes(aux)
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return analyse_particles(aux, 10000,50000,
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fill_holes = False, exclude_edges = True,print_table=True,
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output_image = "outlines", minCirc = 0.4, maxCirc = 1.0)
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def detect_samples(ip):
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"""
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"""
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aux = grayscale(ip, in_place=False)
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invert(aux)
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subtract_background(aux)
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auto_threshold(aux)
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binary_open(aux)
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return analyse_particles(aux, 250,1000,
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fill_holes = False, exclude_edges = True,print_table=True,
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output_image = "outlines", minCirc = 0.7, maxCirc = 1.0)
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r,g,b = [0]*256,[0]*256,[0]*256
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b[0]=0xFF
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b[1]=0xFF ; g[1] = 0x80; r[1] = 0x80
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outline_lut1 = (r,g,b)
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r,g,b = [0]*256,[0]*256,[0]*256
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g[0]=0x80;r[0]=0x80;
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g[1]=0xFF ; r[1] = 0x80; b[1] = 0x80
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outline_lut2 = (r,g,b)
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from mathutils import estimate_peak_indexes, fit_gaussians, create_fit_point_list, Gaussian
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import java.awt.Color as Color
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import mathutils
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mathutils.MAX_ITERATIONS = 100000
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def fit(ydata, xdata = None, draw_plot = True):
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if xdata is None:
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xdata = frange(0, len(ydata), 1)
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max_y= max(ydata)
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index_max = ydata.index(max_y)
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max_x= xdata[index_max]
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print "Max index:" + str(index_max),
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print " x:" + str(max_x),
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print " y:" + str(max_y)
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if draw_plot:
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plots = plot([ydata],["data"],[xdata], title="Fit" )
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p = None if plots is None else plots[0]
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gaussians = fit_gaussians(ydata, xdata, [index_max,])
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if gaussians[0] is None:
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if draw_plot and (p is not None):
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p.addMarker(max_x, None, "Max="+str(round(max_x,4)), Color.GRAY)
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print "Fitting error"
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return (None, None, None)
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(norm, mean, sigma) = gaussians[0]
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if draw_plot:
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fitted_gaussian_function = Gaussian(norm, mean, sigma)
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scale_x = [float(min(xdata)), float(max(xdata)) ]
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points = max((len(xdata)+1), 100)
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resolution = (scale_x[1]-scale_x[0]) / points
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fit_y = []
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fit_x = frange(scale_x[0],scale_x[1],resolution, True)
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for x in fit_x:
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fit_y.append(fitted_gaussian_function.value(x))
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#Server
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if p is None:
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plot([ydata,fit_y],["data","fit"],[xdata,fit_x], title="Fit")
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draw_plot = False
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else:
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p.addSeries(LinePlotSeries("fit"))
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p.getSeries(1).setData(fit_x, fit_y)
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if abs(mean - xdata[index_max]) < abs((scale_x[0] + scale_x[1])/2):
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if draw_plot:
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p.addMarker(mean, None, "Mean="+str(round(mean,4)), Color.MAGENTA.darker())
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print "Mean -> " + str(mean)
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return (norm, mean, sigma)
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else:
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if draw_plot:
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p.addMarker(max_x, None, "Max="+str(round(max_x,4)), Color.GRAY)
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print "Invalid gaussian fit: " + str(mean)
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return (None, None, None)
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context = get_context() |