image, new_spacing = mt.resample(full_scan, old_spacing) #resample
print('Resample Done. time:{}s'.format(time.time() - start_time))
#make a real nodule visualization
real_nodules = []
for annotation in annotations:
real_nodule = np.int_([
(annotation[2] - origin[0]) / new_spacing[0],
(annotation[1] - origin[1]) / new_spacing[1],
(annotation[0] - origin[2]) / new_spacing[2]
])
real_nodules.append(real_nodule)
if 'vision_path' in dir() and 'vision_path' is not None:
annotation_vision = cvm.view_coordinations(image,
real_nodules,
window_size=10,
reverse=False,
slicewise=False,
show=False)
np.save(vision_path + "/" + uid + "_annotations.npy",
annotation_vision)
candidate_results = nd.slic_candidate(image)
if candidate_results is None:
continue
candidate_coords, candidate_labels, cluster_labels = candidate_results
if 'vision_path' in dir() and vision_path is not None:
np.save(vision_path + "/" + uid + "_segmask.npy", cluster_labels)
#segresult = lc.segment_vision(image, cluster_labels)
#np.save(vision_path + "/" + uid + "_segresult.npy", segresult)
print('Candidate Done. time:{}s'.format(time.time() - start_time))
image, new_spacing = mt.resample(full_scan, old_spacing) #resample
print('Resample Done. time:{}s'.format(time.time() - start_time))
#make a real nodule visualization
real_nodules = []
for annotation in annotations:
real_nodule = np.int_([
(annotation[2] - origin[0]) / new_spacing[0],
(annotation[1] - origin[1]) / new_spacing[1],
(annotation[0] - origin[2]) / new_spacing[2]
])
real_nodules.append(real_nodule)
if 'vision_path' in dir() and 'vision_path' is not None:
annotation_vision = cvm.view_coordinations(image,
real_nodules,
window_size=10,
reverse=False,
slicewise=False,
show=False)
np.save(vision_path + "/" + uid + "_annotations.npy",
annotation_vision)
candidate_coords = nd.luna_candidate(image, uid, origin, new_spacing,
candidate_file)
if 'vision_path' in dir() and vision_path is not None:
volume_candidate = cvm.view_coordinations(image,
candidate_coords,
window_size=10,
reverse=False,
slicewise=False,
show=False)
np.save(vision_path + "/" + uid + "_candidate.npy",