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superpixel_sizes.py
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superpixel_sizes.py
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import os
import glob
import numpy as np
import SimpleITK as sitk
import matplotlib.pyplot as plt
from skimage.segmentation import slic as skimage_slic
# img_bname = f'~/medical-data/ABD-CT/ABD-CT-Preprocessed/superpix-MIDDLE_*.nii.gz'
# img_bname = f'~/medical-data/ABD-CT/ABD-CT-Preprocessed/supervox_*.nii.gz'
img_bname = f'~/medical-data/ABD-CT/ABD-CT-Preprocessed/supervox_NEW_*.nii.gz'
imgs = glob.glob(img_bname)
imgs = sorted(imgs, key = lambda x: int(x.split('_')[-1].split('.nii.gz')[0]) )
# imgs = ['~/medical-data/ABD-CT/ABD-CT-Preprocessed/image_0.nii.gz']
# msks = ['~/medical-data/ABD-CT/ABD-CT-Preprocessed/fgmask_0.nii.gz']
def supervox(img, **kwargs):
c = skimage_slic(np.moveaxis(img, 0, -1),
n_segments=120,
compactness=0.01, #should be low
max_num_iter=10,
sigma=2,
spacing=None,
multichannel=False, # Fixed
convert2lab=None,
enforce_connectivity=True, # Fixed
min_size_factor=0.2, #0.5
max_size_factor=5,
slic_zero=False,
start_label=1, # Fixed
mask=None, # Fixed
channel_axis=- 1)
return np.moveaxis(c, -1, 0)
num_superpixels = []
for path in imgs:
vol = sitk.ReadImage(path)
vol = sitk.GetArrayFromImage(vol)
for idx in range(vol.shape[0]):
slc = vol[idx, :, :]
count = len(np.unique(slc)) - 1
num_superpixels.append(count)
num_superpixels = sorted(num_superpixels)
plt.hist(num_superpixels, bins=40, edgecolor='black')
plt.savefig('hist.png')
size_superpixels = []
for path in imgs:
vol = sitk.ReadImage(path)
vol = sitk.GetArrayFromImage(vol)
for idx in range(vol.shape[0]):
slc = vol[idx, :, :]
for sp in np.unique(slc):
if sp == 0:
continue
sze = (slc == sp).sum()
size_superpixels.append(sze)
size_superpixels = sorted(size_superpixels)
plt.hist(size_superpixels, bins=40, edgecolor='black')
plt.savefig('tmp_plots/size_hist2.png')
# size_superpixels = []
# for path, fgpath in zip(imgs, msks):
# scan = sitk.ReadImage(path)
# scan = sitk.GetArrayFromImage(scan)
# scan_msk = sitk.ReadImage(fgpath)
# scan_msk = sitk.GetArrayFromImage(scan_msk)
# vol = supervox(scan)
# vol = vol * scan_msk.astype(np.int32)
# # for idx in range(20):
# # slc = vol[idx, :, :]
# # plt.imshow(slc)
# # plt.savefig(f'tmp_plots/{idx}.png')
# for idx in range(vol.shape[0]):
# slc = vol[idx, :, :]
# for sp in np.unique(slc):
# if sp == 0:
# continue
# sze = (slc == sp).sum()
# size_superpixels.append(sze)
# size_superpixels = sorted(size_superpixels)
# plt.hist(size_superpixels, bins=40, edgecolor='black')
# plt.savefig('tmp_plots/size_hist2.png')