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import glob
import json
import torch
import pandas as pd
import numpy as np
from PIL import Image
from torch.utils.data import Dataset
import torchvision.transforms as transforms
from functools import partial
import torch.nn.functional as F
import nibabel as nib
import tqdm
import copy
def resize_array(array, current_spacing):
"""
Resize the array to match the target spacing.
Args:
array (torch.Tensor): Input array to be resized.
current_spacing (tuple): Current voxel spacing (z_spacing, xy_spacing, xy_spacing).
target_spacing (tuple): Target voxel spacing (target_z_spacing, target_x_spacing, target_y_spacing).
Returns:
np.ndarray: Resized array.
"""
# Calculate new dimensions
original_shape = array.shape[2:]
new_shape = [original_shape[0], 256, 256]
scaling_factors = [new_shape[i] / original_shape[i] for i in range(len(original_shape))]
resized_spacing = [current_spacing[i] / scaling_factors[i] for i in range(len(original_shape))]
# Resize the array
resized_array = F.interpolate(array, size=new_shape, mode='trilinear', align_corners=False).cpu().numpy()
# breakpoint()
return resized_array, resized_spacing
def resize_mask(array, current_spacing):
"""
Resize the array to match the target spacing.
Args:
array (torch.Tensor): Input array to be resized.
current_spacing (tuple): Current voxel spacing (z_spacing, xy_spacing, xy_spacing).
target_spacing (tuple): Target voxel spacing (target_z_spacing, target_x_spacing, target_y_spacing).
Returns:
np.ndarray: Resized array.
"""
# Calculate new dimensions
original_shape = array.shape[2:]
new_shape = [original_shape[0], 256, 256]
resized_array = F.interpolate(array, size=new_shape, mode='nearest').cpu().numpy()
# breakpoint()
return resized_array
class CTReportDataset(Dataset):
def __init__(self, data_folder, csv_file, min_slices=20, resize_dim=500, force_num_frames=True):
self.data_folder = data_folder
self.min_slices = min_slices
# self.accession_to_text = self.load_accession_text(csv_file)
self.paths=[]
self.samples = self.prepare_samples()
percent = 80
num_files = int((len(self.samples) * percent) / 100)
#num_files = 2286
self.samples = self.samples[:num_files]
print(len(self.samples))
self.count = 0
# breakpoint()
#self.resize_dim = resize_dim
#self.resize_transform = transforms.Resize((resize_dim, resize_dim))
self.transform = transforms.Compose([
transforms.Resize((resize_dim,resize_dim)),
transforms.ToTensor()
])
self.nii_to_tensor = partial(self.nii_img_to_tensor, transform = self.transform)
self.sample_length=64
def load_accession_text(self, csv_file):
df = pd.read_csv(csv_file)
accession_to_text = {}
for index, row in df.iterrows():
# breakpoint()
accession_to_text[row['VolumeName']] = row["Findings_EN"],row['Impressions_EN']
return accession_to_text
def prepare_samples(self):
samples = []
import json
with open('/sd/shuhan/CT-RATE/single_disease_mask_json/train_single_prompt_opacity.json', 'r') as f:
items = [json.loads(line) for line in f]
# 2. 提取所有 volume_path
effusion_mask_paths = [item['disease_mask']
for item in items
if 'disease_mask' in item]
organ_mask_paths = [item['organ_mask']
for item in items
if 'organ_mask' in item]
disease_findings_list = [item['disease_findings']
for item in items
if 'disease_findings' in item]
disease_mask_channels = [item['disease_mask_channel']
for item in items
if 'disease_mask_channel' in item]
disease_labels = [item['disease_label']
for item in items
if 'disease_label' in item]
disease_classes = [item['disease_class']
for item in items
if 'disease_class' in item]
for (organ_mask_file, effusion_mask_file, disease_findings, disease_mask_channel, disease_label, disease_class) in tqdm.tqdm(zip(organ_mask_paths, effusion_mask_paths, disease_findings_list, disease_mask_channels, disease_labels, disease_classes)):
# if effusion_mask_file=='effusion_mask/train_fixed/train_288_b_1.nii.gz':
# continue
# breakpoint()
# for patient_folder in tqdm.tqdm(glob.glob(os.path.join(self.data_folder, '*'))):
# for accession_folder in glob.glob(os.path.join(patient_folder, '*')):
# for nii_file in glob.glob(os.path.join(accession_folder, '*.nii.gz')):
accession_number = organ_mask_file.split("/")[-1]
seg_file = '/sd/shuhan/CT-RATE/'+effusion_mask_file
nii_file = '/sd/shuhan/CT-RATE/'+organ_mask_file
# breakpoint()
#accession_number = accession_number.replace(".npz", ".nii.gz")
# if accession_number not in self.accession_to_text:
# continue
impression_text = disease_findings
# if impression_text == "Not given.":
# impression_text=""
# input_text_concat = ""
# for text in impression_text:
# input_text_concat = input_text_concat + str(text)
# input_text_concat = impression_text[0]
# input_text = f'{impression_text}'
samples.append((nii_file, seg_file, impression_text, disease_mask_channel, disease_label, disease_class))
self.paths.append(nii_file)
return samples
def __len__(self):
return len(self.samples)
def nii_img_to_tensor(self, path, seg_file, disease_mask_channel, disease_label, disease_class, transform):
nii_img = nib.load(str(path))
img_data = nii_img.get_fdata()
df = pd.read_csv("/sd/shuhan/CT-RATE/metadata/all_metadata.csv") #select the metadata
file_name = path.split("/")[-1]
row = df[df['VolumeName'] == file_name]
slope = float(row["RescaleSlope"].iloc[0])
intercept = float(row["RescaleIntercept"].iloc[0])
xy_spacing = float(row["XYSpacing"].iloc[0][1:][:-2].split(",")[0])
z_spacing = float(row["ZSpacing"].iloc[0])
nii_seg = nib.load(str(seg_file))
# breakpoint()
# xy_spacing = nib.affines.voxel_sizes(img.affine)
# z_spacing = nib.affines.voxel_sizes(img.affine)
mask_data = nii_seg.get_fdata()[int(disease_mask_channel)]
current = (z_spacing, xy_spacing, xy_spacing)
# breakpoint()
# img_data = slope * img_data + intercept
img_data = img_data.transpose(2, 0, 1)
tensor = torch.tensor(img_data)
tensor = tensor.unsqueeze(0).unsqueeze(0)
img_data, target_spacing = resize_array(tensor, current)
img_data = img_data[0][0]
mask_data = mask_data.transpose(2, 0, 1)
tensor = torch.tensor(mask_data)
tensor = tensor.unsqueeze(0).unsqueeze(0)
mask_data = resize_mask(tensor, current)
mask_data = mask_data[0][0]
# breakpoint()
assert mask_data.shape == img_data.shape
start_id = np.random.randint(0, img_data.shape[0]-1)
img_data = img_data[start_id]
mask_data = mask_data[start_id]
mask_all = np.zeros_like(img_data)
mask_all[mask_data>0] = 280
mask_data = (((mask_all ) / 300)).astype(np.float32) * 2 -1
img_data = (((img_data ) / 300)).astype(np.float32) * 2 -1
img_data = torch.tensor(img_data)
mask_data = torch.tensor(mask_data)
img_data = img_data.unsqueeze(0)
mask_data = mask_data.unsqueeze(0)
img_data=img_data.repeat(3,1,1)
mask_data=mask_data.repeat(3,1,1)
# example = {}
# example['name'] = file_name
# example['volume_data'] = tensor
# # example['organ_mask'] = volume_seg
# example['spacing'] = target_spacing
return img_data, mask_data, target_spacing, file_name
# return example
def __getitem__(self, index):
nii_file, seg_file, input_text, disease_mask_channel, disease_label, disease_class = self.samples[index]
video_tensor, volume_seg, spacing, file_name = self.nii_to_tensor(nii_file, seg_file, disease_mask_channel, disease_label, disease_class)
input_text = str(input_text)
input_text = input_text.replace('"', '')
input_text = input_text.replace('\'', '')
input_text = input_text.replace('(', '')
input_text = input_text.replace(')', '')
return dict(name=file_name, edited=torch.cat([video_tensor.float(), volume_seg.float()],dim=-1), edit=dict(c_concat=torch.cat([video_tensor.float(), torch.ones_like(video_tensor).detach()*-1],dim=-1), c_crossattn=input_text))
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