def load_train_data(count=2000, start=10000):
"""Loads training and/or evaluation data"""
print("Loading data...")
biffolder = '..\\..\\data\\bifs'
nobiffolder = '..\\..\\data\\nobifs'
# 4 dimensional data
data = np.zeros([2 * count, 17, 17, 17], dtype=np.float32)
# print(np.shape(data))
# First load the bif data.
for i in utility.my_range(start, start + count, 1):
currentfile = biffolder + '\\cropped' + str(i) + ".nii.gz"
data[i - start, :, :, :] = np.array(get_itk_array(currentfile))
print("Loaded bif data.")
# Now load the no bif data.
for i in utility.my_range(start, start + count, 1):
currentfile = nobiffolder + '\\cropped' + str(i) + ".nii.gz"
data[i + count - start, :, :, :] = np.array(get_itk_array(currentfile))
print("Loaded no bif data.")
print("All data loaded.")
return data
def load_train_data_folder(folder_name, data_type=0, number_files=0):
# Read info file.
with open(folder_name + "info.txt") as f:
content = f.readlines()
content = [x.strip() for x in content]
num_files = int(content[4])
print("Total Number of Files: ", num_files)
print("Loading training data...")
if number_files != 0:
num_files = number_files
print("Loading only the first ", num_files, " of data.")
if data_type == 0:
data = np.zeros([num_files, 64, 64, 64], dtype='float32')
else:
data = np.zeros([num_files, 64, 64, 64], dtype='int32')
for i in utility.my_range(0, num_files, 1):
data_file_name = folder_name + "cropped" + str(i) + ".nii.gz"
# 4 dimensional data
# type == 0 is data, 1 is labeled
if data_type == 0:
data[i] = np.asarray(get_itk_array(data_file_name),
dtype='float32')
else:
data[i] = np.asarray(get_itk_array(data_file_name), dtype='int32')
print("All data loaded.")
return data
def load_train_data(filename, type=0):
"""Loads training and/or evaluation data"""
print("Loading training data...")
# 4 dimensional data
# type == 0 is data, 1 is labeled
if type == 0:
data = np.asarray(get_itk_array(filename), dtype='float32')
else:
data = np.asarray(get_itk_array(filename), dtype='int32')
print("All data loaded.")
return data
def load_test_data(filename):
"""Loads testing data"""
print("Started loading test data.")
data = np.asarray(get_itk_array(filename), dtype='float32')
print("Loaded test data.")
return data
def load_test_data(filename):
"""Loads testing data"""
print("Started loading test data.")
data = np.zeros([1, 64, 64, 64], dtype=np.float32)
data[0] = np.array(get_itk_array(filename))
print("Loaded test data.")
return data
def load_train_data(filename, type=0):
"""Loads training and/or evaluation data"""
print("Loading training data...")
# 4 dimensional data
# type == 0 is data, 1 is labels
if type == 0:
data = np.zeros([1, 64, 64, 64], dtype=np.float32)
else:
data = np.zeros([1, 64, 64, 64], dtype=np.int32)
data[0] = get_itk_array(filename)
print("All data loaded.")
return data
def load_test_data(filename, blocksize=8):
"""Loads testing data"""
print("Started loading.")
inputimage = np.array(get_itk_array(filename))
inputsize = np.shape(inputimage)
xsize = inputsize[0]
ysize = inputsize[1]
zsize = inputsize[2]
count = 128 * 128 * 128
data = np.zeros([count, 17, 17, 17], dtype=np.float32)
index = 0
for i in utility.my_range(blocksize, blocksize + 128, 1):
# print("Step", i)
for j in utility.my_range(blocksize, blocksize + 128, 1):
for k in utility.my_range(blocksize, blocksize + 128, 1):
data[index, :, :, :] = inputimage[(i -
blocksize):(i + blocksize +
1),
(j -
blocksize):(j + blocksize +
1),
(k -
blocksize):(k + blocksize +
1)]
index = index + 1
print("Loaded data.")
return data
def load_test_data_folder(folder_name):
# Read info file.
with open(folder_name + "info.txt") as f:
content = f.readlines()
content = [x.strip() for x in content]
num_files = int(content[4])
print("Total Number of Files: ", num_files)
print("Loading testing data...")
data = np.zeros([num_files, 64, 64, 64], dtype='float32')
for i in utility.my_range(0, num_files, 1):
data_file_name = folder_name + "cropped" + str(i) + ".nii.gz"
data[i] = np.asarray(get_itk_array(data_file_name), dtype='float32')
print("All data loaded.")
return data
import numpy as np
from itkutilities import get_itk_array, write_itk_imageArray
import utility
if len(sys.argv) != 7:
print("Usage: " + sys.argv[0] + " <rawData> <bifPointsData> <segData> <workFolder> <blockSize> <stepSize>")
sys.exit(1)
datafilename = sys.argv[1]
biffilename = sys.argv[2]
segfilename = sys.argv[3]
workfolder = sys.argv[4]
blocksize = int(sys.argv[5])
stepsize = int(sys.argv[6])
inputimg = get_itk_array(datafilename)
bifimg = get_itk_array(biffilename)
segimg = get_itk_array(segfilename)
inputSize = np.shape(bifimg)
xSize = inputSize[0]
ySize = inputSize[1]
zSize = inputSize[2]
file = open(workfolder + "info.txt", "w")
file.write(str(blocksize) + "\n")
file.write(str(xSize) + "\n")
file.write(str(ySize) + "\n")
file.write(str(zSize) + "\n")
print(blocksize, xSize, ySize, zSize)
from itkutilities import get_itk_array, write_itk_imageArray
import numpy as np
import sys
if __name__ == '__main__':
if len(sys.argv) != 9:
print(
"Usage: " + sys.argv[0] +
" <inputImage> <outputImage> <startX> <startY> <startZ> <sizeX> <sizeY> <sizeZ>"
)
sys.exit(1)
inputimg = get_itk_array(sys.argv[1])
outfile = sys.argv[2]
startX = int(sys.argv[3])
startY = int(sys.argv[4])
startZ = int(sys.argv[5])
sizeX = int(sys.argv[6])
sizeY = int(sys.argv[7])
sizeZ = int(sys.argv[8])
endX = startX + sizeX
endY = startY + sizeY
endZ = startZ + sizeZ
print(np.shape(inputimg))
print(startX, startY, startZ)
print(endX, endY, endZ)
# Join the small files into one big file.
index = 0
for i in utility.my_range(0, xSize, stepsize):
for j in utility.my_range(0, ySize, stepsize):
for k in utility.my_range(0, zSize, stepsize):
print("Step at: (", index, ")", startX, startY, startZ)
endX = startX + stepsize
endY = startY + stepsize
endZ = startZ + stepsize
currentfilename = splitfolder + "/cropped" + str(index) + ".nii.gz"
currentfile = np.array(get_itk_array(currentfilename),
dtype='uint8')
joinedfile[startX:endX, startY:endY, startZ:endZ] = currentfile
# Update indices
startZ = startZ + stepsize
index = index + 1
startY = startY + stepsize
startZ = 0
startX = startX + stepsize
startY = 0
# Write the file on to disk.
print("Writing File to Disk...")
print('dice:', f1[armax])
# print 'accuracy:',1-(np.sum(np.asarray(img1!=grdtruth,dtype=int)))
if __name__ == '__main__':
scores = [] #np.zeros(20)
labels = [] #np.zeros(20)
preds = [] #np.zeros(20)
masks = [] #np.zeros(20)
for num in range(int(sys.argv[1]), int(sys.argv[2])):
d = "%01d" % num
labels.append(get_itk_array('labels_srxray/' + d + '.nii.gz'))
preds.append(get_itk_array('confmaps_matthias/' + d + '.mhd'))
# pred = np.asarray(pred/128,dtype='int32')
# pred = np.flipud(np.fliplr(pred))
# print np.unique(label),np.unique(pred)
# print np.mean(label==pred)
#print f1_score(label.flatten(),pred.flatten())
#print precision_score(label.flatten(),pred.flatten())
#print recall_score(label.flatten(),pred.flatten())
#f1 = f1_score(label.flatten(),pred.flatten(),sample_weight=mask.flatten())
#precision = precision_score(label.flatten(),pred.flatten(),sample_weight=mask.flatten())
#recall = recall_score(label.flatten(),pred.flatten(),sample_weight=mask.flatten())
import os
import sys
import numpy as np
from itkutilities import get_itk_array, write_itk_imageArray
import utility
if len(sys.argv) != 4:
print("Usage: " + sys.argv[0] + " <testData> <workFolder> <stepsize>")
sys.exit(1)
datafilename = sys.argv[1]
workfolder = sys.argv[2]
stepsize = int(sys.argv[3])
inputimg = get_itk_array(datafilename)
# File will be split into (stepsize x stepsize x stepsize) chunks
startX = 0
startY = 0
startZ = 0
inputSize = np.shape(inputimg)
xSize = inputSize[0]
ySize = inputSize[1]
zSize = inputSize[2]
file = open(workfolder + "info.txt", "w")
file.write(str(stepsize) + "\n")
file.write(str(xSize) + "\n")
file.write(str(ySize) + "\n")
file.write(str(zSize) + "\n")
from itkutilities import get_itk_array, write_itk_imageArray
import numpy as np
import sys
if __name__ == '__main__':
if len(sys.argv) != 4:
print("Usage: " + sys.argv[0] +
" <bifurcationImage> <skeletonImage> <outputImage>")
sys.exit(1)
bifimg = get_itk_array(sys.argv[1])
skelimg = get_itk_array(sys.argv[2])
outputimg = sys.argv[3]
bifimg = np.multiply(bifimg, 2)
bifimg = np.maximum(skelimg, bifimg)
write_itk_imageArray(np.asarray(bifimg, dtype='uint8'), outputimg)
import vtk
import os
import sys
import numpy as np
from itkutilities import get_itk_array, write_itk_imageArray
import utility
if len(sys.argv) != 3:
print("Usage: " + sys.argv[0] + " <input> <output>")
sys.exit(1)
inputfile = sys.argv[1]
outputfile = sys.argv[2]
inputimage = np.array(get_itk_array(inputfile), dtype="uint8")
# print(np.shape(inputimage))
dims = np.shape(inputimage)
inputimg = np.zeros(dims + np.full(np.shape(dims), 2))
inputimg[1:dims[0] + 1, 1:dims[1] + 1, 1:dims[2] + 1] = inputimage
# print(inputimg)
dims = np.shape(inputimg)
ptindex = 0
Points = vtk.vtkPoints()
from itkutilities import get_itk_array, write_itk_imageArray
import numpy as np
import sys
if __name__ == '__main__':
# Read file
inputimg = np.array(get_itk_array(sys.argv[1]), dtype='uint16')
inputimg = (inputimg / 256).astype('uint8')
# Write to File
write_itk_imageArray(inputimg, sys.argv[2])
from itkutilities import get_itk_array, write_itk_imageArray
import numpy as np
import sys
if __name__ == '__main__':
if len(sys.argv) != 3:
print("Usage: " + sys.argv[0] + " <inputImage> <outputImage>")
sys.exit(1)
inputimg = np.array(get_itk_array(sys.argv[1]), dtype="uint8")
outputimg = sys.argv[2]
inputimg = np.subtract(inputimg, inputimg.min())
inputimg = np.multiply(inputimg, 1.0 / inputimg.max(), casting='unsafe')
write_itk_imageArray(np.asarray(inputimg), outputimg)
import sys
import numpy as np
from itkutilities import get_itk_array, write_itk_imageArray
import utility
if len(sys.argv) != 5:
print("Usage: " + sys.argv[0] +
" <testExecutable> <modelFile> <testData> <workFolder>")
sys.exit(1)
testfilename = sys.argv[1]
modelfilename = sys.argv[2]
datafilename = sys.argv[3]
workfolder = sys.argv[4]
inputimg = get_itk_array(datafilename)
# File will be split into (stepsize x stepsize x stepsize) chunks
stepsize = 200
startX = 0
startY = 0
startZ = 0
inputSize = np.shape(inputimg)
xSize = inputSize[0]
ySize = inputSize[1]
zSize = inputSize[2]
# Break here the file into smaller chunks.
index = 0
