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import nibabel | |
import numpy as np | |
import glob | |
import platform | |
directory_split = "\\" if platform.system() == "Windows" else "/" | |
def convert_folder(source_folder, destination_folder): | |
for seg in glob.glob(source_folder + "*.nii*"): | |
name = seg.split(directory_split)[-1] | |
convert_file(seg, destination_folder + name) | |
def convert_file(source_file, destination_file): | |
nifti = read_nifti(source_file) | |
img = nifti.get_fdata() | |
segbox = generate_segmentbox(np.array(img)) | |
store_nifti(nifti, segbox, destination_file) | |
def read_nifti(nifti_file): | |
return nibabel.load(nifti_file) | |
def store_nifti(original, new_img, destination): | |
headers = original.header | |
save = nibabel.Nifti1Image(new_img, np.eye(4), headers) | |
nibabel.save(save, destination) | |
def generate_segmentbox(image): | |
image = np.swapaxes(image, 0, 2) | |
for i, layer in enumerate(image): | |
if(np.amax(layer) < 1): | |
continue | |
y = np.any(layer, axis = 1) | |
x = np.any(layer, axis = 0) | |
y_min, y_max = np.argmax(y) + 1, layer.shape[0] - np.argmax(np.flipud(y)) | |
x_min, x_max = np.argmax(x) + 1, layer.shape[1] - np.argmax(np.flipud(x)) | |
layer[y_min - 1:y_max, x_min - 1:x_max] = 1 | |
image = np.swapaxes(image, 0, 2) | |
return image | |
if __name__ == "__main__": | |
path = "D:\\Datasets\\Temp\\Labels\\" | |
convert_folder(path, "D:\\Datasets\\Temp\\Boxes\\") |