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One_Class_SVM.py
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One_Class_SVM.py
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"""
Created on Mon Apr 29 05:21:13 2019
"""
import numpy as np
from dipy.viz import window, actor
from dipy.tracking.streamline import transform_streamlines
import vtk.util.colors as colors
from dipy.tracking import utils
from dipy.tracking.streamline import set_number_of_points
from sklearn import svm
import nibabel as nib
from dipy.tracking.vox2track import streamline_mapping
import time
def show_tract(segmented_tract, color_positive ,segmented_tract_negative, color_negative, out_path):
"""Visualization of the segmented tract.
"""
affine=utils.affine_for_trackvis(voxel_size=np.array([1.25,1.25,1.25]))
bundle_native = transform_streamlines(segmented_tract, np.linalg.inv(affine))
bundle_nativeNeg = transform_streamlines(segmented_tract_negative, np.linalg.inv(affine))
renderer = window.Renderer()
stream_actor2 = actor.line(bundle_native,
colors=color_positive, linewidth=0.1)
stream_actorNeg = actor.line(bundle_nativeNeg, colors=color_negative,
opacity=0.01, linewidth=0.1)
renderer.set_camera(position=(408.85, -26.23, 92.12),
focal_point=(0.42, -14.03, 0.82),
view_up=(-0.09, 0.85, 0.51))
bar = actor.scalar_bar()
renderer.add(stream_actor2)
renderer.add(stream_actorNeg)
renderer.add(bar)
window.show(renderer, size=(1920, 1039), reset_camera=False)
renderer.camera_info()
"""Take a snapshot of the window and save it
"""
window.record(renderer, out_path = out_path, size=(1920, 1039))
def compute_dsc(estimated_tract, true_tract):
"""Compute the overlap between the segmented tract and ground truth tract
"""
aff=np.array([[-1.25, 0, 0, 90],[0, 1.25, 0, -126],[0, 0, 1.25, -72],[0, 0, 0, 1]])
#aff=utils.affine_for_trackvis(voxel_size=np.array([1.25,1.25,1.25]))
voxel_list_estimated_tract = streamline_mapping(estimated_tract, affine=aff).keys()
voxel_list_true_tract = streamline_mapping(true_tract, affine=aff).keys()
TP = len(set(voxel_list_estimated_tract).intersection(set(voxel_list_true_tract)))
vol_A = len(set(voxel_list_estimated_tract))
vol_B = len(set(voxel_list_true_tract))
DSC = 2.0 * float(TP) / float(vol_A + vol_B)
return DSC
def load(filename):
"""Load tractogram from TRK file
"""
wholeTract= nib.streamlines.load(filename)
wholeTract = wholeTract.streamlines
return wholeTract
def embedding(streamlines, no_of_points):
"""Resample streamlines using 12 points and also flatten the streamlines
"""
return np.array([set_number_of_points(s, no_of_points).ravel() for s in streamlines])
def create_train_data_set(train_subjectList,tract):
train_data=[]
for sub in train_subjectList:
print (sub)
T_filename=sub+tract
wholeTract = load (T_filename)
train_data=np.concatenate((train_data, wholeTract),axis=0)
print ("train data Shape")
resample_tract=embedding(train_data,no_of_points=no_of_points)
return resample_tract, train_data
def create_test_data_set(testTarget_brain):
print ("Preparing Test Data")
t_filename=testTarget_brain #"124422_af.left.trk"
test_data=load(t_filename)
resample_tractogram=embedding(test_data,no_of_points=no_of_points)
return resample_tractogram, test_data
if __name__ == '__main__':
train_subjectList =[ "124422", "111312", "100408", "100307", "856766"]
tract = "_cg.right.trk"
no_of_points=12
leafsize=10
################################ Train Data ######################################
print ("Preparing Train Data")
resample_tract_train, train_data= create_train_data_set(train_subjectList, tract)
###################### Test Data################################
testTarget = "161731"
testTarget_brain = "full1M_"+testTarget+".trk"
t0=time.time()
resample_tract_test, test_data= create_test_data_set(testTarget_brain)
trueTract=load(testTarget + tract)
t1=t0-time.time()
"""########################### one class SVM Linear ######################"""
gamma_value = 0.001
clf = svm.OneClassSVM(nu=0.1, kernel="linear", gamma=gamma_value)
clf.fit(resample_tract_train)
""" linear poly rbf """
"""#########################################"""
x_pred_train = clf.predict(resample_tract_train.tolist())
n_error_test = x_pred_train[x_pred_train==-1].size
print('number of error for training =', n_error_test)
x_pred_test=clf.predict(resample_tract_test.tolist())
n_error_test = x_pred_test[x_pred_test==-1].size
print('number of error for testing=',n_error_test)
########################### visualize tract ######################
test_data=np.array(test_data)
segmented_tract_positive= test_data[np.where(x_pred_test==1)]
segmented_tract_negative= test_data[np.where(x_pred_test==-1)]
dsc=compute_dsc(segmented_tract_positive,trueTract)
print("Accuracy for linear: ",dsc)
"""########################### one class SVM Poly ######################"""
gamma_value = 0.001
clf = svm.OneClassSVM(nu=0.1, kernel="poly", gamma=gamma_value)
clf.fit(resample_tract_train)
""" linear poly rbf """
"""#########################################"""
x_pred_train = clf.predict(resample_tract_train.tolist())
n_error_test = x_pred_train[x_pred_train==-1].size
print('number of error for training =', n_error_test)
x_pred_test=clf.predict(resample_tract_test.tolist())
n_error_test = x_pred_test[x_pred_test==-1].size
print('number of error for testing=',n_error_test)
########################### visualize tract ######################
test_data=np.array(test_data)
segmented_tract_positive= test_data[np.where(x_pred_test==1)]
segmented_tract_negative= test_data[np.where(x_pred_test==-1)]
dsc=compute_dsc(segmented_tract_positive,trueTract)
print("Accuracy for poly: ",dsc)
"""########################### one class SVM RBF######################"""
t2=time.time()
gamma_value = 0.001
clf = svm.OneClassSVM(nu=0.1, kernel="rbf", gamma=gamma_value)
clf.fit(resample_tract_train)
x_pred_train = clf.predict(resample_tract_train.tolist())
n_error_test = x_pred_train[x_pred_train==-1].size
print('number of error for training =', n_error_test)
x_pred_test=clf.predict(resample_tract_test.tolist())
n_error_test = x_pred_test[x_pred_test==-1].size
print('number of error for testing=',n_error_test)
########################### visualize tract ######################
test_data=np.array(test_data)
segmented_tract_positive= test_data[np.where(x_pred_test==1)]
segmented_tract_negative= test_data[np.where(x_pred_test==-1)]
###########################Calculating Dice Similarity Co-efficient###########################
dsc=compute_dsc(segmented_tract_positive,trueTract)
print("Accuracy for rbf: ",dsc)
print("Total amount of time to compute svm is %f seconds" % ((time.time()-t2)+t1))
print("Show the tract")
out_path="images\\svm\\"+str(len(train_subjectList) )+"_sub_"+testTarget+"_"+tract+"_SVMResult.png" # Save image in this path
color_positive= colors.green
color_negative=colors.red
show_tract(segmented_tract_positive, color_positive, segmented_tract_negative, color_negative, out_path)#,color_negative)#,segmented_tract_negative)