#!/usr/bin/env python
import sys

# parse aguments
#   image info file
#   threshold 1
#   threshold 2
#   threshold 3
#   threshold 4
#   median filter
#   filter nsigma
info = "/sls/X04SA/data/x04sa/ES3/pixel/images/tmp/spec_image_info.dat"
thres1 = 40
thres2 = 500
thres3 = 8000
thres4 = 10000

filter_median = True
filter_nsigma = 30.0

# 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))