def lung_slic_tool(filename):
prename, extname = os.path.splitext(filename)
pathname = os.path.split(filename)[0]
if (extname == '.npy'):
segresult = np.load(filename)
labels = np.load(prename + '.label.npy')
elif (extname == '.mhd'):
npyname = filename + '.npy'
filelist = [pathname + '/' + i for i in os.listdir(pathname)]
if npyname in filelist:
segresult = np.load(npyname)
labels = np.load(filename + '.label.npy')
else:
full_image_info = sitk.ReadImage(filename)
full_scan = sitk.GetArrayFromImage(full_image_info)
old_spacing = np.array(full_image_info.GetSpacing())[::-1]
image, new_spacing = mt.resample(full_scan, old_spacing)
#####
#image = image[195:200]
#####
print("slic segmenting")
labels = lc.slic_segment(image, view_result=True)
print("segmentation complete")
#mbb.z_top = 195
#mbb.z_bottom = 205
#mbb.y_top = 110
#mbb.y_bottom = 120
#mbb.x_top = 105
#mbb.x_bottom = 115
image_bounded = image[mbb.z_top:mbb.z_bottom+1, mbb.y_top:mbb.y_bottom+1, mbb.x_top:mbb.x_bottom+1]
print('Lung Segmentation Done. time:{}s' .format(time.time()-start_time))
#nodule_matrix, cindex = cd.candidate_detection(segimage)
#cluster_labels = lc.seed_volume_cluster(nodule_matrix, cluster_size=30, result_vision=False)
num_segments = int(image_bounded.shape[0] * image_bounded.shape[1] * image_bounded.shape[2] / 27) #the volume of a 3mm nodule is 27 voxels
#num_segments = 20
print('cluster number:%d' %(num_segments))
cluster_labels = 0 - np.ones(shape=image.shape, dtype=int)
cluster_labels_bounded = lc.slic_segment(image_bounded, compactness=0.01, num_segments=num_segments)
print('Clustering Done. time:{}s' .format(time.time()-start_time))
cluster_labels[mbb.z_top:mbb.z_bottom+1, mbb.y_top:mbb.y_bottom+1, mbb.x_top:mbb.x_bottom+1] = cluster_labels_bounded
cluster_labels[np.logical_or(segmask==0, image<-600)] = -1
#cluster_labels_filtered_fast = lc.cluster_filter_fast(image, cluster_labels)
#print('Cluster Filtering Fast Done. time:{}s'.format(time.time() - start_time))
#cluster_labels_filtered = lc.cluster_filter(image, cluster_labels) #the clusters with no tissue are filetered out
#candidate_coords_slow, candidate_labels_slow = lc.cluster_centers_fast(cluster_labels_filtered)
#volume_candidated = cvm.view_coordinations(image, candidate_coords_slow, window_size=10, reverse=False, slicewise=True, show=False)
#np.save(vision_path+"/"+uid+"_candidate_slow.npy", volume_candidated)
#print('candidate slow number:%d' %(len(candidate_coords_slow)))
#print('Cluster Filtering Done. time:{}s'.format(time.time() - start_time))
#segresultfast = lc.segment_vision(image, cluster_labels_filtered_fast)
#np.save(vision_path+"/"+uid+"_segresultfast.npy", segresultfast)
if RESULT_VISION: