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import glob
import platform
import nibabel
import os
import numpy as np
import torch
from monai.transforms import Compose, Spacingd, ToTensord, LoadImaged, AddChanneld, ThresholdIntensityd, LoadImage, ScaleIntensityRanged, NormalizeIntensityd
from monai.data import NiftiSaver
from segment_to_segmentbox import generate_segmentbox
import pickle
from Logger.loggingservice import Logger
logger = Logger("http://82.194.207.154:5000/api/log", "XqnJHdalUd")
directory_split = "\\" if platform.system() == "Windows" else "/"
voxel_normalizer = Compose(
[
LoadImaged(keys=['image', 'boxes', 'label']),
ThresholdIntensityd(keys=['image'], above = False, threshold = 1000, cval = 1000),
ThresholdIntensityd(keys=['image'], above = True, threshold = -1024, cval = -1024),
AddChanneld(keys=['image', 'boxes', 'label']),
Spacingd(keys=['image', 'boxes', 'label'], pixdim=(0.7, 0.7, 0.7)),
#ScaleIntensityRanged(keys=['image'], a_min=-1024, a_max=400, b_min=-1, b_max=1),
NormalizeIntensityd(keys=['image']),
ThresholdIntensityd(keys=['boxes'], above = False, threshold = 0.5, cval = 1),
ThresholdIntensityd(keys=['boxes'], above = True, threshold = 0.5, cval = 0),
ToTensord(keys=['image', 'boxes', 'label'])
]
)
# voxel_normalizer = Compose(
# [
# LoadImaged(keys=['label']),
# AddChanneld(keys=['label']),
# Spacingd(keys=['label'], pixdim=(0.7, 0.7, 0.7)),
# ToTensord(keys=['label'])
# ]
# )
def crop(image, center, dimensions):
#holder = np.zeros(shape=dimensions)
holder = np.full(dimensions, -1).astype(np.float32)
crop = image[(center[0]-int(dimensions[0]/2)):center[0]+int(dimensions[0]/2),
(center[1]-int(dimensions[1]/2)):center[1]+int(dimensions[1]/2),
(center[2]-int(dimensions[2]/2)):center[2]+int(dimensions[2]/2)]
#return crop
holder[:crop.shape[0], :crop.shape[1], : crop.shape[2]] = crop
return holder
def find_center(box_array):
z_min = -1
z_max = 0
y_min = float('inf')
y_max = 0
x_min = float('inf')
x_max = 0
box_array = box_array.swapaxes(0, 2)
for i, layer in enumerate(box_array):
if(np.amax(layer) < 1):
continue
if(z_min == -1):
z_min = i
z_max = i
y = np.any(layer, axis = 1)
x = np.any(layer, axis = 0)
y_minl, y_maxl = np.argmax(y) + 1, layer.shape[0] - np.argmax(np.flipud(y))
x_minl, x_maxl = np.argmax(x) + 1, layer.shape[1] - np.argmax(np.flipud(x))
if(y_minl < y_min):
y_min = y_minl
if(x_minl < x_min):
x_min = x_minl
if(y_maxl > y_max):
y_max = y_maxl
if(x_maxl > x_max):
x_max = x_maxl
return x_min + int((x_max - x_min)/2), y_min + int((y_max - y_min)/2), z_min + int((z_max - z_min)/2)
def crop_and_store(root_folder, save_folder):
#os.mkdir(f"{save_folder}{directory_split}Images")
#os.mkdir(f"{save_folder}{directory_split}Boxes")
#os.mkdir(f"{save_folder}{directory_split}Labels")
image_directory = f'{root_folder}{directory_split}Images{directory_split}'
box_directory = f'{root_folder}{directory_split}Boxes{directory_split}'
label_directory = f'{root_folder}{directory_split}Labels{directory_split}'
crop_corner_dict = {}
for box_file in glob.glob(f'{box_directory}*'):
file_name = box_file.split(directory_split)[-1]
print(f"Processing {file_name}")
data = [{'image' : f"{image_directory}{file_name}", 'boxes' : f"{box_directory}{file_name}", 'label': f"{label_directory}{file_name}"}]
#imloader = LoadImage()
#org_box_shape = imloader(data[0]['boxes'])[0].shape
data = voxel_normalizer(data)
box = nibabel.load(f"{box_directory}{file_name}")
new_box_arr = torch.nn.functional.interpolate(torch.Tensor(box.get_fdata()).unsqueeze(0).unsqueeze(0),
data[0]['image'].numpy().shape[1:], mode='trilinear', align_corners = False).squeeze(0).squeeze(0)
new_box_arr[new_box_arr < 0.5] = 0
new_box_arr[new_box_arr >= 0.5] = 1
#new_box_arr =
#new_box_arr = data[0]['boxes'].squeeze()
try:
center = find_center(np.copy(new_box_arr.numpy()))
except:
print(f"Couldnt calculate center of {file_name}")
continue
centered_image = crop(data[0]['image'].squeeze(0).numpy(), center, (128, 128, 128))
#centered_box = crop(data[0]['boxes'].squeeze(0).numpy(), center, (128, 128, 128))
centered_box = crop(new_box_arr, center, (128, 128, 128))
centered_label = crop(data[0]['label'].squeeze(0).numpy(), center, (128, 128, 128))
centered_label[centered_label >= 0.5] = 1
centered_label[centered_label < 0.5] = 0
#centered_box = generate_segmentbox(np.copy(centered_label))
#centered_box[centered_box < 0.5] = 0
#centered_box[centered_box > 0.5] = 1
affine = data[0]['image_meta_dict']['affine']
box_nifti = nibabel.Nifti1Image(centered_box, affine)
image_nifti = nibabel.Nifti1Image(centered_image, affine)
label_nifti = nibabel.Nifti1Image(centered_label, affine)
nibabel.save(box_nifti, f"{save_folder}{directory_split}Boxes{directory_split}{file_name}")
nibabel.save(image_nifti, f"{save_folder}{directory_split}Images{directory_split}{file_name}")
nibabel.save(label_nifti, f"{save_folder}{directory_split}Labels{directory_split}{file_name}")
org_box_shape = box.get_fdata().shape
crop_corner_dict[file_name] = (center, data[0]['image'].squeeze(0).numpy().shape, org_box_shape)
print(f"Saving {file_name}")
pickle.dump(crop_corner_dict, open( f"{save_folder}{directory_split}crop_reference.bin", "wb" ))
def inject_image(holder, image, corner):
x_max = min(corner[0]+image.shape[0], holder.shape[0])
y_max = min(corner[1]+image.shape[1], holder.shape[1])
z_max = min(corner[2]+image.shape[2], holder.shape[2])
x_imslice = corner[0]+image.shape[0] - x_max
y_imslice = corner[1]+image.shape[1] - y_max
z_imslice = corner[2]+image.shape[2] - z_max
holder[corner[0]:x_max, corner[1]:y_max, corner[2]: z_max] = image[:image.shape[0] - x_imslice, :image.shape[1] - y_imslice, :image.shape[2] - z_imslice]
return holder
def calculate_corner(imputation_shape, center_in_holder):
x = center_in_holder[0] - imputation_shape[0]/2
y = center_in_holder[1] - imputation_shape[1]/2
z = center_in_holder[2] - imputation_shape[2]/2
return (int(x), int(y), int(z))
def load_and_relocate(reference_file, segment_folder, store_folder):
ref_dict = pickle.load(open(reference_file, 'rb'))
logger.LogInfo("Starting load and relocate", [])
for image_file in glob.glob(f"{segment_folder}{directory_split}*.nii.gz"):
try:
file_name = image_file.split(directory_split)[-1]
references = ref_dict[file_name[4:]]
holder = np.zeros(shape=references[1])
nib = nibabel.load(image_file)
image = nib.get_fdata()
corner = calculate_corner(image.shape, references[0])
injected = inject_image(holder, image, corner)
injected = torch.nn.functional.interpolate(torch.Tensor(injected).unsqueeze(0).unsqueeze(0), size=references[-1], mode='trilinear').squeeze(0).squeeze(0)
injected[injected >= 0.5] = 1
injected[injected < 0.5] = 0
injected = injected.numpy()
nifti = nibabel.Nifti1Image(injected, nib.affine)
nibabel.save(nifti, f"{save_folder}{directory_split}{file_name}")
print(file_name)
except:
logger.LogWarning(f"Failed relocate", [image_file])
if __name__ == "__main__":
crop_and_store("D:\\Datasets\\Temp\\", "D:\\Datasets\\Temp\\Save\\")
#ref = "/home/tumor/data/LungDx-Tumor/crop_reference.bin"
#segment_folder = "/home/tumor/data/LungDx-Tumor/Labels/"
#save_folder = "/home/tumor/data/LungDx-Full/Labels/"
#load_and_relocate(ref, segment_folder, save_folder)
#logger.LogMilestone("Finished relocating", [])
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