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import gradio as gr
import subprocess as sp
from skimage.measure import marching_cubes
import nibabel as nib
from nibabel.processing import resample_to_output
def nifti_to_glb(path):
# load NIFTI into numpy array
image = nib.load(path)
resampled = resample_to_output(image, [1, 1, 1], order=1)
data = resampled.get_fdata().astype("uint8")
# extract surface
verts, faces, normals, values = marching_cubes(data, 0)
faces += 1
with open('prediction.obj', 'w') as thefile:
for item in verts:
thefile.write("v {0} {1} {2}\n".format(item[0],item[1],item[2]))
for item in normals:
thefile.write("vn {0} {1} {2}\n".format(item[0],item[1],item[2]))
for item in faces:
thefile.write("f {0}//{0} {1}//{1} {2}//{2}\n".format(item[0],item[1],item[2]))
def run_model(input_path):
from livermask.utils.run import run_analysis
run_analysis(cpu=False, extension='.nii', path=input_path, output='prediction', verbose=True, vessels=False, name="./model.h5")
#cmd_docker = ["python3", "-m", "livermask.livermask", "--input", input_path, "--output", "prediction", "--verbose"]
#sp.check_call(cmd_docker, shell=True) # @FIXME: shell=True here is not optimal -> starts a shell after calling script
#p = sp.Popen(cmd_docker, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
#stdout, stderr = p.communicate()
#print("stdout:", stdout)
#print("stderr:", stderr)
def load_mesh(mesh_file_name):
path = mesh_file_name.name
run_model(path)
nifti_to_glb("prediction-livermask.nii")
return "./prediction.obj"
if __name__ == "__main__":
print("Launching demo...")
demo = gr.Interface(
fn=load_mesh,
inputs=gr.UploadButton(label="Click to Upload a File", file_types=[".nii", ".nii.nz"], file_count="single"),
outputs=gr.Model3D(clear_color=[0.0, 0.0, 0.0, 0.0], label="3D Model"),
title="livermask: Automatic Liver Parenchyma segmentation in CT",
description="Using pretrained deep learning model trained on the LiTS17 dataset",
)
demo.launch(server_name="0.0.0.0", server_port=7860)
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