unet_d8g_222f.py 文件源码

def eliminate_incorrectly_segmented(scans, masks):

        skip = dim // 2  # To Change see below ...
        sxm =   scans *   masks

        near_air_thresh = (-900 - MIN_BOUND) / (MAX_BOUND - MIN_BOUND) - PIXEL_MEAN  # version 3  # -750 gives one more (for 0_3, d4, -600 give 15 more than -900
        #near_air_thresh  #0.08628  for -840 # 0.067     # for -867; 0.1148 for -800
        cnt = 0        
        for i in range(sxm.shape[0]):
             #sx = sxm[i,skip]
             sx = sxm[i]
             mx = masks[i]
             if np.sum(mx) > 0:     # only check non-blanks ...(keep blanks)
                 sx_max = np.max(sx)
                 if (sx_max) <= near_air_thresh:
                     cnt += 1
                     print ("Entry, count # and max: ", i, cnt, sx_max)
                     print (stats.describe(sx, axis=None))
                     #plt.imshow(sx, cmap='gray')
                     plt.imshow(sx[0,skip], cmap='gray')    # selecting the mid entry
                     plt.show()

        s_eliminate = np.max(sxm, axis=(1,2,3,4)) <= near_air_thresh  # 3d
        s_preserve = np.max(sxm, axis=(1,2,3,4)) > near_air_thresh    #3d

        s_eliminate_sum = sum(s_eliminate)
        s_preserve_sum = sum(s_preserve) 
        print ("Eliminate, preserve =", s_eliminate_sum, s_preserve_sum)


        masks = masks[s_preserve]
        scans = scans[s_preserve]
        del(sxm)

        return scans, masks