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from mydataloader.basics import get_transforms, get_file_list, load_volumes, crop_volumes
from torch.utils.data import DataLoader
import torch
import os
from monai.transforms.utils import allow_missing_keys_mode
import matplotlib.pyplot as plt
import nibabel as nib
import numpy as np
if __name__ == "__main__":
dataset_path=r"E:\Projects\yang_proj\Task1\pelvis" #"E:\Projects\yang_proj\Task1\pelvis" "D:\Projects\data\Task1\pelvis"
'''
file_path = os.path.join(dataset_path, "1PC098", "ct.nii.gz")
ctimg = nib.load(file_path)
ctimg = ctimg.get_fdata()
mr_file_path = os.path.join(dataset_path, "1PC098", "mr.nii.gz")
mrimg = nib.load(mr_file_path)
mrimg = mrimg.get_fdata()
print("orig data shape:",ctimg.shape)
'''
#print the min and max value of the original data
normalize='minmax'
pad='minimum'
train_number=1
val_number=1
train_batch_size=8
val_batch_size=1
saved_name_train='./train_ds_2d.csv'
saved_name_val='./val_ds_2d.csv'
resized_size=(512,512,None)
div_size=(16,16,None)
center_crop=0
ifcheck_volume=False
ifcheck_sclices=False
save_folder = f'./logs/test_{normalize}'
os.makedirs(save_folder,exist_ok=True)
# volume-level transforms for both image and label
train_transforms = get_transforms(normalize,pad,resized_size,div_size)
train_ds, val_ds = get_file_list(dataset_path,
train_number,
val_number,
source='ct',
target='mr',)
train_crop_ds, val_crop_ds = crop_volumes(train_ds, val_ds,center_crop)
without_transforms_dataloader=DataLoader(train_crop_ds, batch_size=1)
ct_data_list=[]
mri_data_list=[]
mean_list_ct=[]
std_list_ct=[]
mean_list_mri=[]
std_list_mri=[]
ct_shape_list=[]
mri_shape_list=[]
untransformed_CT_min_list=[]
untransformed_CT_max_list=[]
untransformed_MRI_min_list=[]
untransformed_MRI_max_list=[]
# calculate the mean and std of the original data
for idx, checkdata in enumerate(without_transforms_dataloader):
untransformed_CT=checkdata['image']
untransformed_MRI=checkdata['label']
mean_ct=torch.mean(untransformed_CT.float())
std_ct=torch.std(untransformed_CT.float())
mean_list_ct.append(mean_ct)
std_list_ct.append(std_ct)
mean_mri=torch.mean(untransformed_MRI.float())
std_mri=torch.std(untransformed_MRI.float())
mean_list_mri.append(mean_mri)
std_list_mri.append(std_mri)
ct_shape_list.append(untransformed_CT.shape)
mri_shape_list.append(untransformed_MRI.shape)
untransformed_CT_min_list.append(torch.min(untransformed_CT))
untransformed_CT_max_list.append(torch.max(untransformed_CT))
untransformed_MRI_min_list.append(torch.min(untransformed_MRI))
untransformed_MRI_max_list.append(torch.max(untransformed_MRI))
ct_data_list.append(untransformed_CT)
mri_data_list.append(untransformed_MRI)
train_ds, val_ds = load_volumes(train_transforms,
train_crop_ds, val_crop_ds,
train_ds,
val_ds,
saved_name_train,
saved_name_val,
ifsave=False,
ifcheck=ifcheck_volume)
loader = DataLoader(train_ds, batch_size=1)
for idx, checkdata in enumerate(loader):
#transformed_CT=checkdata['image']
transformed_CT=checkdata['image']
transformed_MRI=checkdata['label']
dict = {"image": transformed_CT[0,:,:,:,:], "label": transformed_MRI[0,:,:,:,:]}
with allow_missing_keys_mode(train_transforms):
reversed_dict=train_transforms.inverse(dict)
reversed_ct=reversed_dict["image"]
reversed_mri=reversed_dict["label"]
print(f"{idx} original CT data shape:",ct_shape_list[idx])
print(f"{idx} transformed CT data shape:", transformed_CT.shape)
print (f"{idx} reversed CT shape:",reversed_ct.shape)
print(f"{idx} original MRI data shape:",mri_shape_list[idx])
print(f"{idx} transformed MRI data shape:", transformed_MRI.shape)
print(f"{idx} transformed MRI data shape:", transformed_MRI.shape)
reversed_ct = reversed_ct.squeeze().permute(1,0,2) #[452, 315, 104] -> [315, 452, 104]
transformed_CT = transformed_CT.squeeze().squeeze().permute(1,0,2) #[452, 315, 104] -> [315, 452, 104]
reversed_mri = reversed_mri.squeeze().permute(1,0,2) #[452, 315, 104] -> [315, 452, 104]
transformed_MRI = transformed_MRI.squeeze().squeeze().permute(1,0,2) #[452, 315, 104] -> [315, 452, 104]
if normalize == 'zscore':
# reverse the normalization using std and mean
reversed_ct = reversed_ct*std_list_ct[idx]+mean_list_ct[idx]
reversed_mri = reversed_mri*std_list_mri[idx]+mean_list_mri[idx]
elif normalize == 'minmax':
# reverse the normalization using min and max
reversed_ct = reversed_ct*(untransformed_CT_max_list[idx]-untransformed_CT_min_list[idx])+untransformed_CT_min_list[idx]
reversed_mri = reversed_mri*(untransformed_MRI_max_list[idx]-untransformed_MRI_min_list[idx])+untransformed_MRI_min_list[idx]
elif normalize == 'inputonly':
reversed_mri = reversed_mri*(untransformed_MRI_max_list[idx]-untransformed_MRI_min_list[idx])+untransformed_MRI_min_list[idx]
## compare the min and max value of the original and reversed data
print(f"{idx} untransformed ct data min and max: {untransformed_CT_min_list[idx]}, {untransformed_CT_max_list[idx]}")
print(f"{idx} transformed ct data min and max: {torch.min(transformed_CT)}, {torch.max(transformed_CT)}")
print(f"{idx} reversed ct min and max: {torch.min(reversed_ct)}, {torch.max(reversed_ct)}")
print(f"{idx} untransformed mri data min and max: {untransformed_MRI_min_list[idx]}, {untransformed_MRI_max_list[idx]}")
print(f"{idx} transformed mri data min and max: {torch.min(transformed_MRI)}, {torch.max(transformed_MRI)}")
print(f"{idx} reversed mri min and max: {torch.min(reversed_mri)}, {torch.max(reversed_mri)}")
# Save as png
for i in range(reversed_ct.shape[-1]):
if i>=50 and i<=51:
imgformat='png'
dpi=300
saved_name=os.path.join(save_folder,f"{i}.{imgformat}")
## save original image
img_ct = ct_data_list[idx][0,0,:,:,i]
fig_ct = plt.figure()
plt.gca().set_axis_off()
plt.subplots_adjust(top = 1, bottom = 0, right = 1, left = 0,
hspace = 0, wspace = 0)
plt.margins(0,0)
plt.imshow(img_ct, cmap='gray')
plt.savefig(saved_name.replace(f'.{imgformat}',f'_{idx}_original_ct.{imgformat}'), format=f'{imgformat}'
, bbox_inches='tight', pad_inches=0, dpi=dpi)
plt.close(fig_ct)
## save transformed image
img_ct_transformed = transformed_CT[:,:,i]
fig_ct_transformed = plt.figure()
plt.gca().set_axis_off()
plt.subplots_adjust(top = 1, bottom = 0, right = 1, left = 0,
hspace = 0, wspace = 0)
plt.margins(0,0)
plt.imshow(img_ct_transformed, cmap='gray')
plt.savefig(saved_name.replace(f'.{imgformat}',f'_{idx}_transformed_ct.{imgformat}'), format=f'{imgformat}'
, bbox_inches='tight', pad_inches=0, dpi=dpi)
plt.close(fig_ct_transformed)
## save reversed image
img_ct_reversed = reversed_ct[:,:,i]
fig_ct_reversed = plt.figure()
plt.gca().set_axis_off()
plt.subplots_adjust(top = 1, bottom = 0, right = 1, left = 0,
hspace = 0, wspace = 0)
plt.margins(0,0)
plt.imshow(img_ct_reversed, cmap='gray') #.squeeze()
plt.savefig(saved_name.replace(f'.{imgformat}',f'_{idx}_reversed_ct.{imgformat}'), format=f'{imgformat}'
, bbox_inches='tight', pad_inches=0, dpi=dpi)
plt.close(fig_ct_reversed)
## save original image for MRI
img_mri = mri_data_list[idx][0,0,:,:,i]
fig_mri = plt.figure()
plt.gca().set_axis_off()
plt.subplots_adjust(top = 1, bottom = 0, right = 1, left = 0,
hspace = 0, wspace = 0)
plt.margins(0,0)
plt.imshow(img_mri, cmap='gray')
plt.savefig(saved_name.replace(f'.{imgformat}',f'_{idx}_original_mri.{imgformat}'), format=f'{imgformat}'
, bbox_inches='tight', pad_inches=0, dpi=dpi)
plt.close(fig_mri)
## save transformed image
img_mri_transformed = transformed_MRI[:,:,i]
fig_mri_transformed = plt.figure()
plt.gca().set_axis_off()
plt.subplots_adjust(top = 1, bottom = 0, right = 1, left = 0,
hspace = 0, wspace = 0)
plt.margins(0,0)
plt.imshow(img_mri_transformed, cmap='gray')
plt.savefig(saved_name.replace(f'.{imgformat}',f'_{idx}_transformed_mri.{imgformat}'), format=f'{imgformat}'
, bbox_inches='tight', pad_inches=0, dpi=dpi)
plt.close(fig_mri_transformed)
## save reversed image
img_mri_reversed = reversed_mri[:,:,i]
fig_mri_reversed = plt.figure()
plt.gca().set_axis_off()
plt.subplots_adjust(top = 1, bottom = 0, right = 1, left = 0,
hspace = 0, wspace = 0)
plt.margins(0,0)
plt.imshow(img_mri_reversed, cmap='gray') #.squeeze()
plt.savefig(saved_name.replace(f'.{imgformat}',f'_{idx}_reversed_mri.{imgformat}'), format=f'{imgformat}'
, bbox_inches='tight', pad_inches=0, dpi=dpi)
plt.close(fig_mri_reversed)
# pixels' intensity histogram of ct images
# Flatten the 3D arrays to 1D arrays to calculate the histogram
flattened_volume1 = ct_data_list[idx].numpy().flatten()
flattened_volume2 = reversed_ct.numpy().flatten()
flattened_volume3 = transformed_CT.numpy().flatten()
print(flattened_volume1.shape)
print(flattened_volume2.shape)
# Set up the matplotlib figure and axes
fig, axs = plt.subplots(3, 1, figsize=(10, 8))
# Histogram settings
bins = 256 # Adjust the number of bins for the histogram as needed
hist_range1 = (np.min(flattened_volume1), 3000) # Range based on both volumes np.max(flattened_volume1)
hist_range2 = (np.min(flattened_volume2), 3000) # Range based on both volumes np.max(flattened_volume2)
hist_range3 = (np.min(flattened_volume3), np.max(flattened_volume3)) # Range based on both volumes np.max(flattened_volume2)
# Plot histogram for the first volume
axs[0].hist(flattened_volume1, bins=bins, range=hist_range1, color='blue', alpha=0.7)
axs[0].set_title('Histogram of Pixel Intensities for original ct image')
axs[0].set_xlabel('Pixel intensity')
axs[0].set_ylabel('Frequency')
# Plot histogram for the second volume
axs[1].hist(flattened_volume2, bins=bins, range=hist_range2, color='green', alpha=0.7)
axs[1].set_title('Histogram of Pixel Intensities for reversed ct image')
axs[1].set_xlabel('Pixel intensity')
axs[1].set_ylabel('Frequency')
# Plot histogram for the third volume
axs[2].hist(flattened_volume3, bins=bins, range=hist_range3, color='red', alpha=0.7)
axs[2].set_title('Histogram of Pixel Intensities for transformed ct image')
axs[2].set_xlabel('Pixel intensity')
axs[2].set_ylabel('Frequency')
# Adjust layout for better spacing
plt.tight_layout()
plt.savefig(os.path.join(save_folder,f'histograms_{idx}_ct.png'))
plt.close(fig)
# pixels' intensity histogram of mri images
# Flatten the 3D arrays to 1D arrays to calculate the histogram
flattened_volume1 = mri_data_list[idx].numpy().flatten()
flattened_volume2 = reversed_mri.numpy().flatten()
flattened_volume3 = transformed_MRI.numpy().flatten()
print(flattened_volume1.shape)
print(flattened_volume2.shape)
# Set up the matplotlib figure and axes
fig, axs = plt.subplots(3, 1, figsize=(10, 8))
# Histogram settings
bins = 256 # Adjust the number of bins for the histogram as needed
hist_range1 = (np.min(flattened_volume1), 3000) # Range based on both volumes np.max(flattened_volume1)
hist_range2 = (np.min(flattened_volume2), 3000) # Range based on both volumes np.max(flattened_volume2)
hist_range3 = (np.min(flattened_volume3), np.max(flattened_volume3)) # Range based on both volumes np.max(flattened_volume2)
# Plot histogram for the first volume
axs[0].hist(flattened_volume1, bins=bins, range=hist_range1, color='blue', alpha=0.7)
axs[0].set_title('Histogram of Pixel Intensities for original mri image')
axs[0].set_xlabel('Pixel intensity')
axs[0].set_ylabel('Frequency')
# Plot histogram for the second volume
axs[1].hist(flattened_volume2, bins=bins, range=hist_range2, color='green', alpha=0.7)
axs[1].set_title('Histogram of Pixel Intensities for reversed mri image')
axs[1].set_xlabel('Pixel intensity')
axs[1].set_ylabel('Frequency')
# Plot histogram for the third volume
axs[2].hist(flattened_volume3, bins=bins, range=hist_range3, color='red', alpha=0.7)
axs[2].set_title('Histogram of Pixel Intensities for transformed mri image')
axs[2].set_xlabel('Pixel intensity')
axs[2].set_ylabel('Frequency')
# Adjust layout for better spacing
plt.tight_layout()
plt.savefig(os.path.join(save_folder,f'histograms_{idx}_mri.png'))
plt.close(fig) |