Script execution

script/CoverDetection.py

@@ -56,53 +56,26 @@ renderer = show_panel(ip.bufferedImage)
 
 #renderer = show_panel(op.bufferedImage)
 #line.show()
-vals = []
-for i in range (180):
+ydata = []
+xdata = range (0,180,2)
+for i in xdata:
     l = line.duplicate()
     l.getProcessor().setBackgroundValue(0.0)
     l.getProcessor().rotate(float(i))
     op = op_fft(ip, l, "correlate") 
     bi = op.getBufferedImage()
     p = integrateVertically(bi)
-    vals.append(sum(p))
+    ydata.append(sum(p))
     
     #renderer = show_panel(op.bufferedImage)
     #time.sleep(0.001)
-p = plot(vals)[0]
+p = plot(ydata)[0]
 
 
+peaks = estimate_peak_indexes(ydata, xdata,  (min(ydata) + max(ydata))/2,  25.0)
+print "Peak indexes: " + str(map(lambda x:xdata[x], peaks))
+print "Peak values:  " + str(map(lambda x:ydata[x], peaks))
 
-threshold = (min(vals) + max(vals))/2
-min_peak_distance = 10.0
-xdata=Arr.indexesInt(len(vals))
-
-peaks = estimate_peak_indexes(vals, xdata, threshold, min_peak_distance)
-print "Peak indexes: " + str(peaks)
-print "Peak x: " + str(map(lambda x:xdata[x], peaks))
-print "Peak y: " + str(map(lambda x:vals[x], peaks))
-
-gaussians = fit_gaussians(vals, xdata, peaks)
 
 for i in range(len(peaks)):
-    peak = peaks[i]
-    (normalization, mean_val, sigma) = gaussians[i]
-    if abs(mean_val - xdata[peak]) < min_peak_distance:
-        print "Peak -> " +  str(mean_val)
-        p.addMarker(mean_val, None, "N="+str(round(normalization,2)), Color(210,0,0))
-    else:
-        print "Invalid gaussian fit: " +  str(mean_val)
-
-
-#image_fft(ip)
-
-
-
-"""
-(r,output) =  analyse_particles(ip, 200000,500000, 
-        fill_holes = True, exclude_edges = False, print_table=True,
-        output_image = "outlines", minCirc = 0.3
-        , maxCirc = 1.0)
-
-
-show_panel(output.bufferedImage)
-"""
+    p.addMarker(peaks[i], None, "N="+str(round(peaks[i],2)), Color(80,0,80))