def test_2D():
X = np.loadtxt('data/fish_target.txt')
Y = np.loadtxt('data/fish_source.txt')
reg = deformable_registration(**{'X': X, 'Y': Y})
TY, _ = reg.register()
assert_array_almost_equal(X, TY, decimal=1)
def main():
X = np.loadtxt('data/fish_target.txt')
Y = np.loadtxt('data/fish_source.txt')
fig = plt.figure()
fig.add_axes([0, 0, 1, 1])
callback = partial(visualize, ax=fig.axes[0])
reg = deformable_registration(**{'X': X, 'Y': Y})
reg.register(callback)
plt.show()
def main():
fish = loadmat('./data/fish.mat')
X = fish['X']
Y = fish['Y']
fig = plt.figure()
fig.add_axes([0, 0, 1, 1])
callback = partial(visualize, ax=fig.axes[0])
reg = deformable_registration(X, Y)
reg.register(callback)
plt.show()
def main():
fish = loadmat('../data/fish.mat')
X = np.zeros((fish['X'].shape[0], fish['X'].shape[1] + 1))
X[:,:-1] = fish['X']
Y = np.zeros((fish['Y'].shape[0], fish['Y'].shape[1] + 1))
Y[:,:-1] = fish['Y']
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
callback = partial(visualize, ax=ax)
reg = deformable_registration(X, Y)
reg.register(callback)
plt.show()
def CPD(X, Y, draw=True):
if draw:
fig = plt.figure()
fig.add_axes([0, 0, 1, 1])
callback = partial(visualize, ax=fig.axes[0], draw=draw)
else:
callback = []
reg = deformable_registration(**{'X': X, 'Y': Y})
reg.register(callback)
if draw:
plt.show()
return reg.TY
def test_3D():
fish_target = np.loadtxt('data/fish_target.txt')
X1 = np.zeros((fish_target.shape[0], fish_target.shape[1] + 1))
X1[:, :-1] = fish_target
X2 = np.ones((fish_target.shape[0], fish_target.shape[1] + 1))
X2[:, :-1] = fish_target
X = np.vstack((X1, X2))
fish_source = np.loadtxt('data/fish_source.txt')
Y1 = np.zeros((fish_source.shape[0], fish_source.shape[1] + 1))
Y1[:, :-1] = fish_source
Y2 = np.ones((fish_source.shape[0], fish_source.shape[1] + 1))
Y2[:, :-1] = fish_source
Y = np.vstack((Y1, Y2))
reg = deformable_registration(**{'X': X, 'Y': Y})
TY, _ = reg.register()
assert_array_almost_equal(TY, X, decimal=0)
def get_cpd_transformation(target_points,
points_to_register,
alpha,
beta,
tolerance=1e-6,
max_iter=100,
callback=lambda *args: None):
reg = deformable_registration(X=target_points,
Y=points_to_register,
alpha=alpha,
beta=beta,
tolerance=tolerance,
max_iterations=max_iter)
def cb(*args, **kwargs):
callback(reg)
reg.register(callback=cb)
return reg.TY
def main():
fish_target = np.loadtxt('data/fish_target.txt')
X1 = np.zeros((fish_target.shape[0], fish_target.shape[1] + 1))
X1[:, :-1] = fish_target
X2 = np.ones((fish_target.shape[0], fish_target.shape[1] + 1))
X2[:, :-1] = fish_target
X = np.vstack((X1, X2))
fish_source = np.loadtxt('data/fish_source.txt')
Y1 = np.zeros((fish_source.shape[0], fish_source.shape[1] + 1))
Y1[:, :-1] = fish_source
Y2 = np.ones((fish_source.shape[0], fish_source.shape[1] + 1))
Y2[:, :-1] = fish_source
Y = np.vstack((Y1, Y2))
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
callback = partial(visualize, ax=ax)
reg = deformable_registration(**{'X': X, 'Y': Y})
reg.register(callback)
plt.show()
def main(ID_target, ID_source):
target_load = o3d.io.read_point_cloud(
'C:/Users/monre/OneDrive - Imperial College London/ME4/FYP/DATA/OAI-ZIB/processed_data/05.corresponded_deformable_no_scaling_PCDs/femur_bone_corresponded_deformable_' + ID_target + '.ply')
femur_target = np.asarray(target_load.points)/8
source_load = o3d.io.read_point_cloud(
'C:/Users/monre/OneDrive - Imperial College London/ME4/FYP/DATA/OAI-ZIB/processed_data/05.trimmed_no_scaling_PCDs/femur_bone_trimmed_' + ID_source + '.ply')
femur_source = np.asarray(source_load.points)/8
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
callback = partial(visualize, ax=ax)
reg = deformable_registration(**{'X': femur_target, 'Y': femur_source})
reg.register(callback)
#plt.show()
dictionary = {"femur": reg.register(callback)[0],
"probability": reg.register(callback)[2]}
return dictionary
def main():
flat = np.array(flat_trans[0:-70])
flat = flat.reshape((np.size(flat, 0), np.size(flat, 2)))
print(np.shape(flat))
curv = np.array(curv_trans[95:-120])
curv = curv.reshape((np.size(curv, 0), np.size(curv, 2)))
sshape = np.array(sshape_trans)
X = np.zeros((flat.shape[0], flat.shape[1] + 1))
X[:, :-1] = flat
Y = np.zeros((curv.shape[0], curv.shape[1] + 1))
Y[:, :-1] = curv
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
callback = partial(visualize, ax=ax)
reg = deformable_registration(X, Y)
reg.register(callback)
plt.show()
#im2 = tf.get_t()
im1 = gaussian3d(im1)
im2 = gaussian3d(im2)
peaks1 = findpeaks3d(im1 * np.array(im1 > np.quantile(im1,.99)))
peaks2 = findpeaks3d(im2 * np.array(im2 > np.quantile(im2,.99)))
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
callback = partial(visualize, ax=ax)
reg = deformable_registration(**{ 'X': peaks1, 'Y': peaks2 })
reg.register(callback)
plt.show()
#reg.register()
'''
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(peaks1[:,0], peaks1[:,1], peaks1[:,2], color='red', label='Target')
ax.scatter(peaks2[:,0], peaks2[:,1], peaks2[:,2], color='blue', label='Source')
plt.show()