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plot.py
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import matplotlib.pyplot as plt
import numpy as np
import torch
import glob
from ImageLoader.ImageLoader3D import ImageLoader3D
from ModelArchitecture.Transformations import *
from torch.utils.data import DataLoader
from tqdm import tqdm
from ModelArchitecture.Encoders import ClassificationModel
from sklearn.manifold import TSNE
from sklearn.decomposition import PCA
from einops import rearrange, reduce
from skimage.measure import label,regionprops
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import mplcursors
path = './results/Recon_4_11_23/HalfUNet_ssim + l1_loss.npy'
def plot_loss_curves(path):
x = np.load(path)
plt.title(' Recon Loss')
plt.ylabel('Loss')
plt.xlabel('Epochs')
plt.plot(x[0,:])
plt.plot(x[1,:])
#plt.yticks(np.arange(min(x[1,:]), max(x[1,:])+1, 0.5))
#plt.xticks(np.arange(0,len(x[1,:]), 1))
#plt.grid(True)
plt.legend(['train','val'])
plt.savefig('./plots/' + path[10:-4] + '.png')
plt.show()
torch.manual_seed(6)
"""
wmh_indexes = np.load('./wmh_indexes_new.npy', allow_pickle=True).item()
real_abnormal = ImageLoader3D(wmh_indexes['train_names_flair'],wmh_indexes['train_names_seg'],image_size=(128,128,128),type_of_imgs='nifty',data='WMH',no_crop=False)
real_abnormal_test = ImageLoader3D(wmh_indexes['test_names_flair'],wmh_indexes['test_names_seg'],image_size=(128,128,128),type_of_imgs='nifty',data='wmh',no_crop=False)
train_names_flair = sorted(glob.glob(('./simulation_data/26_02_24/'+'wmh'+'/TrainSet/*[0-9]_FLAIR.nii.gz')))[:42]
train_names_seg = sorted(glob.glob(('./simulation_data/26_02_24/'+'wmh'+'/TrainSet/*[0-9]_manualmask.nii.gz')))[:42]
sim_abnormal = ImageLoader3D(train_names_flair,train_names_seg,type_of_imgs='nifty', transform = None)
real_ab_list = []
real_n_list = []
real_ab_test_list = []
real_n_test_list = []
# sim_n_list = []
sim_ab_list = []
sim_n_list = []
# rearrange('(h h2) (w w2) (d d2) c-> (b h w d) (c h2 w2 d2)',h2=16,w2=16,d2=16)
# reduce(real_abnormal[i]['gt'][:,:,:,1],'(h h2) (w w2) (d d2) -> (h w d)','mean',h2=16,w2=16,d2=16) >0.1
with tqdm(range(len(real_abnormal_test))) as pbar:
for i in pbar:
# real_ab_list.append(real_abnormal[i]['input'].reshape(-1))
labels = label(real_abnormal_test[i]['gt'][:,:,:,0])
regions = regionprops(labels)
ab_patches = []
for region in regions:
cent = (int(region.centroid[0]),int(region.centroid[1]),int(region.centroid[2]))
#print(cent)
padx1 = cent[0]-16
if(padx1<0):
padx1 = -padx1
else:
padx1 = 0
padx2 = cent[0]+16-127
if(padx2<0):
padx2 = 0
pady1 = cent[1]-16
if(pady1<0):
pady1 = -pady1
else:
pady1 = 0
pady2 = cent[1]+16-127
if(pady2<0):
pady2 = 0
padz1 = cent[2]-16
if(padz1<0):
padz1 = -padz1
else:
padz1 = 0
padz2 = cent[2]+16-127
if(padz2<0):
padz2 = 0
pad_shape = ((padx1,padx2),(pady1,pady2),(padz1,padz2))
new_cent = list(cent)
new_cent[0]+=padx1-padx2
new_cent[1]+=pady1-pady2
new_cent[2]+=padz1-padz2
image = np.pad(real_abnormal_test[i]['input'][:,:,:,0],pad_shape)
ab_patch = image[new_cent[0]-16:new_cent[0]+16,new_cent[1]-16:new_cent[1]+16,new_cent[2]-16:new_cent[2]+16]
ab_patches.append(ab_patch.reshape(-1))
real_ab_test_list.append(ab_patch.reshape(-1))
#print(ab_patch.shape,cent,pad_shape,new_cent)
n_patch_mask = reduce(real_abnormal_test[i]['gt'][:,:,:,0],'(h h2) (w w2) (d d2) -> (h w d)','mean',h2=32,w2=32,d2=32) <=0
patched_image = rearrange(real_abnormal_test[i]['input'],'(h h2) (w w2) (d d2) c-> (h w d) (c h2 w2 d2)',h2=32,w2=32,d2=32)
n_patches = patched_image[n_patch_mask,:]
real_n_test_list.append(n_patches)
pbar.update(0)
real_ab_test_list = np.stack(real_ab_test_list)
real_n_test_list = np.concatenate(real_n_test_list)
np.save('Patchestrain_real_ab_test_wmh_26_02_24_32.npy',real_ab_test_list)
np.save('Patchestrain_real_n_test_wmh_26_02_24_32.npy',real_n_test_list)
with tqdm(range(len(real_abnormal))) as pbar:
for i in pbar:
# real_ab_list.append(real_abnormal[i]['input'].reshape(-1))
labels = label(real_abnormal[i]['gt'][:,:,:,0])
regions = regionprops(labels)
ab_patches = []
for region in regions:
cent = (int(region.centroid[0]),int(region.centroid[1]),int(region.centroid[2]))
#print(cent)
padx1 = cent[0]-16
if(padx1<0):
padx1 = -padx1
else:
padx1 = 0
padx2 = cent[0]+16-127
if(padx2<0):
padx2 = 0
pady1 = cent[1]-16
if(pady1<0):
pady1 = -pady1
else:
pady1 = 0
pady2 = cent[1]+16-127
if(pady2<0):
pady2 = 0
padz1 = cent[2]-16
if(padz1<0):
padz1 = -padz1
else:
padz1 = 0
padz2 = cent[2]+16-127
if(padz2<0):
padz2 = 0
pad_shape = ((padx1,padx2),(pady1,pady2),(padz1,padz2))
new_cent = list(cent)
new_cent[0]+=padx1-padx2
new_cent[1]+=pady1-pady2
new_cent[2]+=padz1-padz2
image = np.pad(real_abnormal[i]['input'][:,:,:,0],pad_shape)
ab_patch = image[new_cent[0]-16:new_cent[0]+16,new_cent[1]-16:new_cent[1]+16,new_cent[2]-16:new_cent[2]+16]
ab_patches.append(ab_patch.reshape(-1))
real_ab_list.append(ab_patch.reshape(-1))
# print(ab_patch.shape)
n_patch_mask = reduce(real_abnormal[i]['gt'][:,:,:,0],'(h h2) (w w2) (d d2) -> (h w d)','mean',h2=32,w2=32,d2=32) <=0
patched_image = rearrange(real_abnormal[i]['input'],'(h h2) (w w2) (d d2) c-> (h w d) (c h2 w2 d2)',h2=32,w2=32,d2=32)
n_patches = patched_image[n_patch_mask,:]
real_n_list.append(n_patches)
pbar.update(0)
real_ab_list = np.stack(real_ab_list)
real_n_list = np.concatenate(real_n_list)
np.save('Patchestrain_real_ab_wmh_26_02_24_32.npy',real_ab_list)
np.save('Patchestrain_real_n_wmh_26_02_24_32.npy',real_n_list)
with tqdm(range(len(sim_abnormal))) as pbar:
for i in pbar:
# real_ab_list.append(real_abnormal[i]['input'].reshape(-1))
labels = label(sim_abnormal[i]['gt'][:,:,:,0])
regions = regionprops(labels)
ab_patches = []
for region in regions:
cent = (int(region.centroid[0]),int(region.centroid[1]),int(region.centroid[2]))
#print(cent)
padx1 = cent[0]-16
if(padx1<0):
padx1 = -padx1
else:
padx1 = 0
padx2 = cent[0]+16-127
if(padx2<0):
padx2 = 0
pady1 = cent[1]-16
if(pady1<0):
pady1 = -pady1
else:
pady1 = 0
pady2 = cent[1]+16-127
if(pady2<0):
pady2 = 0
padz1 = cent[2]-16
if(padz1<0):
padz1 = -padz1
else:
padz1 = 0
padz2 = cent[2]+16-127
if(padz2<0):
padz2 = 0
pad_shape = ((padx1,padx2),(pady1,pady2),(padz1,padz2))
new_cent = list(cent)
new_cent[0]+=padx1-padx2
new_cent[1]+=pady1-pady2
new_cent[2]+=padz1-padz2
image = np.pad(sim_abnormal[i]['input'][:,:,:,0],pad_shape)
ab_patch = image[new_cent[0]-16:new_cent[0]+16,new_cent[1]-16:new_cent[1]+16,new_cent[2]-16:new_cent[2]+16]
ab_patches.append(ab_patch.reshape(-1))
sim_ab_list.append(ab_patch.reshape(-1))
# print(ab_patch.shape)
n_patch_mask = reduce(sim_abnormal[i]['gt'][:,:,:,0],'(h h2) (w w2) (d d2) -> (h w d)','mean',h2=32,w2=32,d2=32) <=0
patched_image = rearrange(sim_abnormal[i]['input'],'(h h2) (w w2) (d d2) c-> (h w d) (c h2 w2 d2)',h2=32,w2=32,d2=32)
n_patches = patched_image[n_patch_mask,:]
sim_n_list.append(n_patches)
pbar.update(0)
sim_ab_list = np.stack(sim_ab_list)
sim_n_list = np.concatenate(sim_n_list)
np.save('Patchestest_sim_ab_wmh_26_02_24_32.npy',sim_ab_list)
np.save('Patchestest_sim_n_wmh_26_02_24_32.npy',sim_n_list)
# print('Sizes are ',real_ab_test_list.shape, real_n_test_list.shape ,real_ab_list.shape, real_n_list.shape,sim_ab_list.shape,sim_n_list.shape)
"""
######################################################################################
combined_brats_samples = 250
brats_indexes = np.load('./brats_2021_42_training_indexes.npy', allow_pickle=True).item()
real_abnormal = ImageLoader3D(brats_indexes['train_names_flair'],brats_indexes['train_names_seg'],image_size=(128,128,128),type_of_imgs='nifty',data='brats',no_crop=False)
real_abnormal_test = ImageLoader3D(brats_indexes['test_names_flair'][:combined_brats_samples],brats_indexes['test_names_seg'][:combined_brats_samples],image_size=(128,128,128),type_of_imgs='nifty',data='brats',no_crop=False)
train_names_flair = sorted(glob.glob(('./simulation_data/26_02_24/'+'brats'+'/TrainSet/*[0-9]_FLAIR.nii.gz')))[:42]
train_names_seg = sorted(glob.glob(('./simulation_data/26_02_24/'+'brats'+'/TrainSet/*[0-9]_manualmask.nii.gz')))[:42]
sim_abnormal = ImageLoader3D(train_names_flair,train_names_seg,type_of_imgs='nifty', transform = None)
real_ab_list = []
real_n_list = []
real_ab_test_list = []
real_n_test_list = []
# sim_n_list = []
sim_ab_list = []
sim_n_list = []
# rearrange('(h h2) (w w2) (d d2) c-> (b h w d) (c h2 w2 d2)',h2=16,w2=16,d2=16)
# reduce(real_abnormal[i]['gt'][:,:,:,1],'(h h2) (w w2) (d d2) -> (h w d)','mean',h2=16,w2=16,d2=16) >0.1
with tqdm(range(len(real_abnormal_test))) as pbar:
for i in pbar:
# real_ab_list.append(real_abnormal[i]['input'].reshape(-1))
labels = label(real_abnormal_test[i]['gt'][:,:,:,0])
regions = regionprops(labels)
ab_patches = []
for region in regions:
cent = (int(region.centroid[0]),int(region.centroid[1]),int(region.centroid[2]))
#print(cent)
padx1 = cent[0]-16
if(padx1<0):
padx1 = -padx1
else:
padx1 = 0
padx2 = cent[0]+16-127
if(padx2<0):
padx2 = 0
pady1 = cent[1]-16
if(pady1<0):
pady1 = -pady1
else:
pady1 = 0
pady2 = cent[1]+16-127
if(pady2<0):
pady2 = 0
padz1 = cent[2]-16
if(padz1<0):
padz1 = -padz1
else:
padz1 = 0
padz2 = cent[2]+16-127
if(padz2<0):
padz2 = 0
pad_shape = ((padx1,padx2),(pady1,pady2),(padz1,padz2))
new_cent = list(cent)
new_cent[0]+=padx1-padx2
new_cent[1]+=pady1-pady2
new_cent[2]+=padz1-padz2
image = np.pad(real_abnormal_test[i]['input'][:,:,:,0],pad_shape)
ab_patch = image[new_cent[0]-16:new_cent[0]+16,new_cent[1]-16:new_cent[1]+16,new_cent[2]-16:new_cent[2]+16]
ab_patches.append(ab_patch.reshape(-1))
real_ab_test_list.append(ab_patch.reshape(-1))
# print(ab_patch.shape)
n_patch_mask = reduce(real_abnormal_test[i]['gt'][:,:,:,0],'(h h2) (w w2) (d d2) -> (h w d)','mean',h2=32,w2=32,d2=32) <=0
patched_image = rearrange(real_abnormal_test[i]['input'],'(h h2) (w w2) (d d2) c-> (h w d) (c h2 w2 d2)',h2=32,w2=32,d2=32)
n_patches = patched_image[n_patch_mask,:]
real_n_test_list.append(n_patches)
pbar.update(0)
real_ab_test_list = np.stack(real_ab_test_list)
real_n_test_list = np.concatenate(real_n_test_list)
np.save('Patchestest_real_ab_test_brats_26_02_24_32.npy',real_ab_test_list)
np.save('Patchestest_real_n_test_brats_26_02_24_32.npy',real_n_test_list)
with tqdm(range(len(real_abnormal))) as pbar:
for i in pbar:
# real_ab_list.append(real_abnormal[i]['input'].reshape(-1))
labels = label(real_abnormal[i]['gt'][:,:,:,0])
regions = regionprops(labels)
ab_patches = []
for region in regions:
cent = (int(region.centroid[0]),int(region.centroid[1]),int(region.centroid[2]))
#print(cent)
padx1 = cent[0]-16
if(padx1<0):
padx1 = -padx1
else:
padx1 = 0
padx2 = cent[0]+16-127
if(padx2<0):
padx2 = 0
pady1 = cent[1]-16
if(pady1<0):
pady1 = -pady1
else:
pady1 = 0
pady2 = cent[1]+16-127
if(pady2<0):
pady2 = 0
padz1 = cent[2]-16
if(padz1<0):
padz1 = -padz1
else:
padz1 = 0
padz2 = cent[2]+16-127
if(padz2<0):
padz2 = 0
pad_shape = ((padx1,padx2),(pady1,pady2),(padz1,padz2))
new_cent = list(cent)
new_cent[0]+=padx1-padx2
new_cent[1]+=pady1-pady2
new_cent[2]+=padz1-padz2
image = np.pad(real_abnormal[i]['input'][:,:,:,0],pad_shape)
ab_patch = image[new_cent[0]-16:new_cent[0]+16,new_cent[1]-16:new_cent[1]+16,new_cent[2]-16:new_cent[2]+16]
ab_patches.append(ab_patch.reshape(-1))
real_ab_list.append(ab_patch.reshape(-1))
# print(ab_patch.shape)
n_patch_mask = reduce(real_abnormal[i]['gt'][:,:,:,0],'(h h2) (w w2) (d d2) -> (h w d)','mean',h2=32,w2=32,d2=32) <=0
patched_image = rearrange(real_abnormal[i]['input'],'(h h2) (w w2) (d d2) c-> (h w d) (c h2 w2 d2)',h2=32,w2=32,d2=32)
n_patches = patched_image[n_patch_mask,:]
real_n_list.append(n_patches)
pbar.update(0)
real_ab_list = np.stack(real_ab_list)
real_n_list = np.concatenate(real_n_list)
np.save('Patchestest_real_ab_brats_26_02_24_32.npy',real_ab_list)
np.save('Patchestest_real_n_brats_26_02_24_32.npy',real_n_list)
with tqdm(range(len(sim_abnormal))) as pbar:
for i in pbar:
# real_ab_list.append(real_abnormal[i]['input'].reshape(-1))
labels = label(sim_abnormal[i]['gt'][:,:,:,0])
regions = regionprops(labels)
ab_patches = []
for region in regions:
cent = (int(region.centroid[0]),int(region.centroid[1]),int(region.centroid[2]))
#print(cent)
padx1 = cent[0]-16
if(padx1<0):
padx1 = -padx1
else:
padx1 = 0
padx2 = cent[0]+16-127
if(padx2<0):
padx2 = 0
pady1 = cent[1]-16
if(pady1<0):
pady1 = -pady1
else:
pady1 = 0
pady2 = cent[1]+16-127
if(pady2<0):
pady2 = 0
padz1 = cent[2]-16
if(padz1<0):
padz1 = -padz1
else:
padz1 = 0
padz2 = cent[2]+16-127
if(padz2<0):
padz2 = 0
pad_shape = ((padx1,padx2),(pady1,pady2),(padz1,padz2))
new_cent = list(cent)
new_cent[0]+=padx1-padx2
new_cent[1]+=pady1-pady2
new_cent[2]+=padz1-padz2
image = np.pad(sim_abnormal[i]['input'][:,:,:,0],pad_shape)
ab_patch = image[new_cent[0]-16:new_cent[0]+16,new_cent[1]-16:new_cent[1]+16,new_cent[2]-16:new_cent[2]+16]
ab_patches.append(ab_patch.reshape(-1))
sim_ab_list.append(ab_patch.reshape(-1))
# print(ab_patch.shape)
n_patch_mask = reduce(sim_abnormal[i]['gt'][:,:,:,0],'(h h2) (w w2) (d d2) -> (h w d)','mean',h2=32,w2=32,d2=32) <=0
patched_image = rearrange(sim_abnormal[i]['input'],'(h h2) (w w2) (d d2) c-> (h w d) (c h2 w2 d2)',h2=32,w2=32,d2=32)
n_patches = patched_image[n_patch_mask,:]
sim_n_list.append(n_patches)
pbar.update(0)
sim_ab_list = np.stack(sim_ab_list)
sim_n_list = np.concatenate(sim_n_list)
np.save('Patchestest_sim_ab_brats_26_02_24_32.npy',sim_ab_list)
np.save('Patchestest_sim_n_brats_26_02_24_32.npy',sim_n_list)
# print('Sizes are ',real_ab_test_list.shape, real_n_test_list.shape ,real_ab_list.shape, real_n_list.shape,sim_ab_list.shape,sim_n_list.shape)
"""
# test_real_ab = PCA(50).fit_transform(real_ab_list)
# test_real_n = PCA(50).fit_transform(real_n_list)
# test_real_ab_test = PCA(50).fit_transform(real_ab_test_list)
# test_real_n_test = PCA(50).fit_transform(real_n_test_list)
# test_sim_ab = PCA(50).fit_transform(sim_ab_list)
# test_sim_n = PCA(50).fit_transform(sim_n_list)
# np.random.shuffle(test_real_ab)
# np.random.shuffle(test_real_n)
# np.random.shuffle(test_real_ab_test)
# np.random.shuffle(test_real_n_test)
# np.random.shuffle(test_sim_ab)
# np.random.shuffle(test_sim_n)
# np.save('test_real_ab_brats_26_02_24_32.npy',test_real_ab)
# np.save('test_real_n_brats_26_02_24_32.npy',test_real_n)
# np.save('test_real_ab_test_brats_26_02_24_32.npy',test_real_ab_test)
# np.save('test_real_n_test_brats_26_02_24_32.npy',test_real_n_test)
# np.save('test_sim_ab_brats_26_02_24_32.npy',test_sim_ab)
# np.save('test_sim_n_brats_26_02_24_32.npy',test_sim_n)
test_real_ab = np.load('train_real_ab_brats_26_02_24_32.npy')
test_real_n = np.load('train_real_n_brats_26_02_24_32.npy')
test_real_ab_test = np.load('train_real_ab_test_brats_26_02_24_32.npy')
test_real_n_test = np.load('train_real_n_test_brats_26_02_24_32.npy')
test_sim_ab = np.load('train_sim_ab_brats_26_02_24_32.npy')
test_sim_n = np.load('train_sim_n_brats_26_02_24_32.npy')
print('Sizes are ', test_real_ab.shape, test_real_n.shape,test_sim_ab.shape,test_sim_n.shape)
min_val = min(test_real_ab_test.shape[0],test_real_n_test.shape[0],test_real_ab.shape[0],test_real_n.shape[0],test_sim_ab.shape[0],test_sim_n.shape[0])
print(min_val)
test_real_ab=test_real_ab[:min_val]
test_real_n = test_real_n[:min_val]
test_real_ab_test=test_real_ab_test[:min_val]
test_real_n_test = test_real_n_test[:min_val]
test_sim_ab = test_sim_ab[:min_val]
test_sim_n = test_sim_n[:min_val]
metrics = ['euclidean',]# 'euclidean', 'canberra', 'hamming', 'sokalmichener', 'braycurtis', 'russellrao', 'dice', 'l2', 'yule', 'manhattan', 'nan_euclidean', 'jaccard', 'rogerstanimoto', 'minkowski', 'cityblock', 'correlation', 'sqeuclidean', 'mahalanobis', 'sokalsneath', 'cosine', 'l1', 'kulsinski', 'matching']
for perplex in [15,20,25]:
for i in metrics:
print(perplex,i)
test_tsne = TSNE(3,perplexity=perplex,n_iter=10000,metric=i).fit_transform(np.concatenate([test_real_ab,test_sim_ab,test_real_ab_test,test_real_n,test_sim_n,test_real_n_test]))
size_tsne = min_val
ax = plt.axes(projection ="3d")
ax.scatter3D(test_tsne[:size_tsne,0],test_tsne[:size_tsne,1],test_tsne[:size_tsne,2])
ax.scatter3D(test_tsne[size_tsne:size_tsne*2,0],test_tsne[size_tsne:size_tsne*2,1],test_tsne[size_tsne:size_tsne*2,2])
ax.scatter3D(test_tsne[size_tsne*2:size_tsne*3,0],test_tsne[size_tsne*2:size_tsne*3,1],test_tsne[size_tsne*2:size_tsne*3,2])
ax.scatter3D(test_tsne[size_tsne*3:size_tsne*4,0],test_tsne[size_tsne*3:size_tsne*4,1],test_tsne[size_tsne*3:size_tsne*4,2])
ax.scatter3D(test_tsne[size_tsne*4:size_tsne*5,0],test_tsne[size_tsne*4:size_tsne*5,1],test_tsne[size_tsne*4:size_tsne*5,2])
ax.scatter3D(test_tsne[size_tsne*5:,0],test_tsne[size_tsne*5:,1],test_tsne[size_tsne*5:,2])
plt.title('Perplexity '+str(perplex)+' '+i)
plt.legend(['Real Abnormal','Sim Abnormal','Real Abnormal Test','Real Normal','Real Normal Test','Sim Normal'])
plt.savefig('./images/2_03_24/3d/'+str(perplex)+'/tsne_3imgs_'+i+'.png')
plt.show()
plt.close()
"""