80 lines
2.5 KiB
Python
80 lines
2.5 KiB
Python
###################################################################################################
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# Procedure to detect the cover orientation
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###################################################################################################
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#Parameters
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FRAMES_INTEGRATION = 3
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STEP_SIZE = 2
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POSITION_NAMES = [ 'A','B','C','D', 'E', 'F']
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POSITION_ANGLES = [330, 30, 90, 150, 210, 270]
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POSITION_TOLERANCE = 3
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MINIMUM_CONFIDENCE = 10
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DEBUG = True
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PLOT = True
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REFERENCE_IMG = "ref2"
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#Load reference image
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ref = load_image(str("{images}/cover/" + REFERENCE_IMG + ".png") , title="Line")
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#Pre-process camera image
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#ip = load_image("{images}/cover/Cover_000" + str(index) + ".png", title="Img")
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ip = integrate_frames(FRAMES_INTEGRATION)
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ip = grayscale(ip, True)
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smooth(ip)
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#bandpass_filter(ip, 30, 1000)
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edges(ip)
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auto_threshold(ip, method = "MaxEntropy")
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cx,cy = int(ip.width/2), int(ip.height/2)
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ip = sub_image(ip, cx-ref.width/2, cy-ref.height/2, ref.width, ref.height)
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#Show ROI of pre-processed image
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image_panel = show_panel(ip.bufferedImage)
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#Calculate correlation between image and reference, rotating the reference from 0 to 360
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import ch.psi.pshell.imaging.Utils.integrateVertically as integrateVertically
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ydata = []
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xdata = range (0,360,STEP_SIZE)
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for i in xdata:
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r = ref.duplicate()
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r.getProcessor().setBackgroundValue(0.0)
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r.getProcessor().rotate(float(i))
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op = op_fft(r, ip, "correlate")
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bi = op.getBufferedImage()
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p = integrateVertically(bi)
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ydata.append(sum(p))
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#image_panel = show_panel(op.bufferedImage)
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#time.sleep(1.0)
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#Calculate angle of the highest correlation, and confidence level
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peaks = estimate_peak_indexes(ydata, xdata, (min(ydata) + max(ydata))/2, 25.0)
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peaks_x = map(lambda x:xdata[x], peaks)
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peaks_y = map(lambda x:ydata[x], peaks)
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confidence = None if len(peaks_x)<2 else int(((float(peaks_y[0])/peaks_y[1])-1) * 1000)
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angle = (None if len(peaks_x)==0 else peaks_x[0])
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#From angle and confidence level estimate hexiposi position
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position = None
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if angle is not None:
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for i in range(len(POSITION_NAMES)):
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if abs(POSITION_ANGLES[i] - angle) <= POSITION_TOLERANCE:
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position = POSITION_NAMES[i]
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#Plot the correlations values agains angle
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if PLOT:
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p = plot(ydata, xdata=xdata)[0]
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#Output results
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if DEBUG:
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print "Peaks", peaks
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print "Peak indexes: " + str(peaks_x)
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print "Peak values: " + str(peaks_y)
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print "Angle: " , angle
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print "Position: " , position
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print "Confidence: " , confidence
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#Set return value
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set_return ([position, angle, confidence])
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