import numpy
from scipy import signal, ndimage

def img_get_int(fname,  thres1, thres2, thres3, thres4, header, width, height, depth, x1,y1,x2,y2, bx1,by1,bx2,by2 , filter_median = False, filter_nsigma = 0):
    # read actual image file    
    img = numpy.fromfile(fname, dtype=numpy.uint32)
    img.shape = height, width
    if filter_nsigma>0:
        img = ndimage.gaussian_filter(img, filter_nsigma)
    elif filter_median:
        #img = signal.medfilt2d(img.astype('d'), kernel_size=3)
        img = ndimage.median_filter(img, size=3)
    # 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)
    
    return (I_sum, area_I, thresh1_count, thresh2_count, thresh3_count, thresh4_count, I_sum_bgr, area_bgr)


def img_read(fname, header, width, height, depth):
    img = numpy.fromfile(fname, dtype=numpy.uint32)
    img.shape = height, width
    #return img.flatten().astype(int)
    return img.astype(int)
    