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from numpy import load
from lungtumormask.dataprocessing import preprocess, post_process
from lungtumormask.network import UNet_double
import torch as T
import nibabel
def load_model():
if T.cuda.is_available():
gpu_device = T.device('cuda')
else:
gpu_device = T.device('cpu')
model = UNet_double(3, 1, 1, tuple([64, 128, 256, 512, 1024]), tuple([2 for i in range(4)]), num_res_units = 0)
state_dict = T.hub.load_state_dict_from_url("https://github.com/VemundFredriksen/LungTumorMask/releases/download/0.0/dc_student.pth", progress=True, map_location=gpu_device)
model.load_state_dict(state_dict)
model.eval()
return model
def mask(image_path, save_path, lung_filter, threshold, radius, batch_size):
print("Loading model...")
model = load_model()
print("Preprocessing image...")
preprocess_dump = preprocess(image_path, batch_size)
print("Looking for tumors...")
left = model(preprocess_dump['left_lung']).squeeze(0).squeeze(0).detach().numpy()
right = model(preprocess_dump['right_lung']).squeeze(0).squeeze(0).detach().numpy()
print("Post-processing image...")
inferred = post_process(left, right, preprocess_dump, lung_filter, threshold, radius)
print(f"Storing segmentation at {save_path}")
nimage = nibabel.Nifti1Image(inferred, preprocess_dump['org_affine'])
nibabel.save(nimage, save_path)
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