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visualize_rmn.py
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visualize_rmn.py
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import nibabel as nib
import numpy as np
import matplotlib.pyplot as plt
import keyboard
######################## V1) cu nilabel ######################
'''
def wait_till_a_pressed():
while True:
if keyboard.is_pressed("a"):
break
#
scan = nib.load(r'E:\an_4_LICENTA\Workspace\Dataset\Experiment\00000119_brain_flair.nii').get_fdata()
#
#scan = nib.load(r'E:\an_4_LICENTA\Workspace\Dataset\Experiment\00000119_brain_t1.nii').get_fdata()
#
#scan = nib.load(r'E:\an_4_LICENTA\Workspace\Dataset\Experiment\00000119_brain_t1ce.nii').get_fdata()
#
#scan = nib.load(r'E:\an_4_LICENTA\Workspace\Dataset\Experiment\00000119_brain_t2.nii').get_fdata()
#
#scan = nib.load(r'E:\an_4_LICENTA\Workspace\Dataset\Experiment\00000119_final_seg.nii').get_fdata()
#print(scan.shape)
i = 0
test = scan[:,:,i]
plt.imshow(test)
plt.show()
while True:
i += 5
wait_till_a_pressed()
plt.close()
test = scan[:, :, i]
plt.imshow(test)
plt.show()
#print(i)
'''
######################## V2) cu nilearn ######################
'''
import nilearn
from nilearn.plotting import plot_anat, show
nilearn.plotting.plot_glass_brain(r'E:\an_4_LICENTA\Workspace\Dataset\Experiment\00000057_brain_flair.nii')
nilearn.plotting.show()
'''
######################## V3) cu nilabel ######################
def wait_till_a_pressed():
while True:
if keyboard.is_pressed("a"):
break
def img_is_color(img):
if len(img.shape) == 3:
# Check the color channels to see if they're all the same.
c1, c2, c3 = img[:, : , 0], img[:, :, 1], img[:, :, 2]
if (c1 == c2).all() and (c2 == c3).all():
return True
return False
def show_image_list(list_images, list_titles=None, list_cmaps=None, grid=True, num_cols=2, figsize=(20, 10),
title_fontsize=30):
assert isinstance(list_images, list)
assert len(list_images) > 0
assert isinstance(list_images[0], np.ndarray)
if list_titles is not None:
assert isinstance(list_titles, list)
assert len(list_images) == len(list_titles), '%d imgs != %d titles' % (len(list_images), len(list_titles))
if list_cmaps is not None:
assert isinstance(list_cmaps, list)
assert len(list_images) == len(list_cmaps), '%d imgs != %d cmaps' % (len(list_images), len(list_cmaps))
num_images = len(list_images)
num_cols = min(num_images, num_cols)
num_rows = int(num_images / num_cols) + (1 if num_images % num_cols != 0 else 0)
# Create a grid of subplots.
fig, axes = plt.subplots(num_rows, num_cols, figsize=figsize)
# Create list of axes for easy iteration.
if isinstance(axes, np.ndarray):
list_axes = list(axes.flat)
else:
list_axes = [axes]
for i in range(num_images):
img = list_images[i]
title = list_titles[i] if list_titles is not None else 'Image %d' % (i)
cmap = list_cmaps[i] if list_cmaps is not None else (None if img_is_color(img) else 'gray')
list_axes[i].imshow(img, cmap=cmap)
list_axes[i].set_title(title, fontsize=title_fontsize)
list_axes[i].grid(grid)
for i in range(num_images, len(list_axes)):
list_axes[i].set_visible(False)
fig.tight_layout()
_ = plt.show()
#
scan1 = nib.load(r'E:\an_4_LICENTA\Workspace\Dataset\Experiment\BraTS2021_00000\BraTS2021_00000_t1.nii').get_fdata()
# FLAIR = Fluid-Attenuated Inversion Recovery
scan2 = nib.load(r'E:\an_4_LICENTA\Workspace\Dataset\Experiment\BraTS2021_00000\BraTS2021_00000_boolseg.nii').get_fdata()
#
scan3 = nib.load(r'E:\an_4_LICENTA\Workspace\Dataset\Experiment\BraTS2021_00000\BraTS2021_00000_t2.nii').get_fdata()
#
scan4 = nib.load(r'E:\an_4_LICENTA\Workspace\Dataset\Experiment\BraTS2021_00000\BraTS2021_00000_t1ce.nii').get_fdata()
# Ground Truth
gt = nib.load(r'E:\an_4_LICENTA\Workspace\Dataset\Experiment\BraTS2021_00000\BraTS2021_00000_seg.nii').get_fdata()
# BINARY GT - !!!! JUST IF IT EXISTS !!!!
#scan2 = nib.load(r'E:\an_4_LICENTA\Workspace\Dataset\Experiment\BraTS2021_00060\BraTS2021_00060_boolseg.nii').get_fdata()
#print(scan1.shape)
i = 0
test = gt[:,:,i]
plt.imshow(test)
plt.show()
while True:
wait_till_a_pressed()
plt.close()
list_images = [scan1[:,:,i], scan2[:,:,i], scan3[:,:,i], scan4[:,:,i], gt[:,:,i]]
# #print number of aparitions o avalue in gt
unique_gt, counts_gt = np.unique(gt[:,:,i], return_counts=True)
d = dict(zip(unique_gt, counts_gt))
#print('GT-ul are urmatoarele valori: ')
#print(d)
unique_scan1, counts_scan1 = np.unique(scan1[:, :, i], return_counts=True)
d = dict(zip(unique_scan1, counts_scan1))
#print('scan1-ul are urmatoarele valori: ')
#print(d)
show_image_list(list_images, figsize=(10, 10))
#print(i)
i += 1