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