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script/ms/image_get_int.py

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+#!/usr/bin/env python
+import sys
+
+# parse aguments
+#   image info file
+#   threshold 1
+#   threshold 2
+#   threshold 3
+#   threshold 4
+#   median filter
+#   filter nsigma
+info = sys.argv[1]
+thres1 = int(sys.argv[2])
+thres2 = int(sys.argv[3])
+thres3 = int(sys.argv[4])
+thres4 = int(sys.argv[5])
+
+filter_median = False
+if len(sys.argv) > 6 and sys.argv[6] == '-median':
+    filter_median = True
+
+filter_nsigma = 0.0
+if len(sys.argv) > 7:
+    filter_nsigma = float(sys.argv[7])
+
+# read image info text file
+f = open(info)
+
+fname = f.next().strip()
+timestamp = f.next().strip()
+header = f.next().strip()
+width, height, depth = [int(p) for p in f.next().strip().split()]
+x1,y1,x2,y2 = [int(p) for p in f.next().strip().split()]
+bx1,by1,bx2,by2 = [int(p) for p in f.next().strip().split()]
+
+f.close()
+
+# read actual image file
+import numpy
+img = numpy.fromfile(fname, dtype=numpy.uint32)
+img.shape = height, width
+
+# signal roi
+area_I  = ( x2 -  x1 + 1) * ( y2 -  y1 + 1)
+
+I_sum = img[y1:y2, x1:x2].sum()
+thresh1_count = len(numpy.where(img>thres1)[0]) 
+thresh2_count = len(numpy.where(img>thres2)[0]) 
+thresh3_count = len(numpy.where(img>thres3)[0]) 
+thresh4_count = len(numpy.where(img>thres4)[0]) 
+
+# background roi
+I_sum_bgr = img[by1:by2, bx1:bx2].sum()
+area_bgr= (bx2 - bx1 + 1) * (by2 - by1 + 1)
+
+set_return ((I_sum, area_I, thresh1_count, thresh2_count, thresh3_count, thresh4_count, I_sum_bgr, area_bgr))
+