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
# the following 3 functions to read LUNA files are from: https://www.kaggle.com/arnavkj95/data-science-bowl-2017/candidate-generation-and-luna16-preprocessing/notebook
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