def pbr(I_path, Im_path):
I = plt.imread(I_path)
Im = plt.imread(Im_path)
X, Xm = util.my_cpselect(I_path, Im_path)
if len(X[1]) == 1:
print('x must be larger than 1')
return
X = util.c2h(X)
Xm = util.c2h(Xm)
T = reg.ls_affine(X, Xm)
It, Xt = reg.image_transform(Im, T)
fig = plt.figure(figsize=(30, 30))
ax1 = fig.add_subplot(121)
ax1.set_title('Overlay of original images', fontsize=35)
ax1.axis('off')
im11 = ax1.imshow(I)
im12 = ax1.imshow(Im, alpha=0.7)
ax2 = fig.add_subplot(122)
ax2.set_title('Overlay transformed image over the fixed image',
fontsize=35)
ax2.axis('off')
im21 = ax2.imshow(I)
im22 = ax2.imshow(It, alpha=0.7)
return I, Im, It
def pbr(I_path, Im_path):
# Load in images
I = plt.imread(I_path)
Im = plt.imread(Im_path)
# Set control points
X, Xm = util.my_cpselect(I_path, Im_path)
Xh = util.c2h(X)
Xmh = util.c2h(Xm)
# Find optimal affine transformation
T = ls_affine(Xh, Xmh)
# Transform Im
Im_t, X_t = image_transform(Im, T)
# Display images
fig = plt.figure(figsize=(12, 5))
ax1 = fig.add_subplot(131)
im11 = ax1.imshow(I)
ax2 = fig.add_subplot(132)
im21 = ax2.imshow(Im)
ax3 = fig.add_subplot(133)
im31 = ax3.imshow(I)
im32 = ax3.imshow(Im_t, alpha=0.7)
ax1.set_title('Original image (I)')
ax2.set_title('Warped image (Im)')
ax3.set_title('Overlay with I and transformed Im')
def point_based_affine_registration(I, Im):
# let the user selecte points
X, Xm = util.my_cpselect(I, Im)
# find affine transformation matrix
T, reg_error = reg.ls_affine(util.c2h(X), util.c2h(Xm))
# transform the moving image according to T
Im = plt.imread(Im)
It, _ = reg.image_transform(Im, T)
return It, reg_error
def point_based_registration_demo():
# ---------------------------------
# Append the following code in jupiter to run:
# %matplotlib inline
# import sys
# sys.path.append("C:/Users/s163666/Documents/2019-2020/Medical Image Analysis/Mini_project_mia")
# from project_fout import point_based_registration_demo
# point_based_registration_demo()
# ---------------------------------
# read the fixed and moving images, 2x T1 or T1&T2
# change these in order to read different images
I_path = './data/image_data/3_1_t1.tif'
Im_path = './data/image_data/3_1_t2.tif'
#Select set of corresponding points using my_cpselect
X0, Xm0 = util.my_cpselect(I_path, Im_path)
#Compute the affine transformation between the pair of images
Xm = np.ones((3, Xm0.shape[1]))
Xm[0, :] = Xm0[0, :]
Xm[1, :] = Xm0[1, :]
X = np.ones((3, X0.shape[1]))
X[0, :] = X0[0, :]
X[1, :] = X0[1, :]
T, Etrain = reg.ls_affine(X, Xm)
Etest = reg.point_based_error(I_path, Im_path, T)
#read image
Im = plt.imread(Im_path)
#Apply the affine transformation to the moving image
It, Xt = reg.image_transform(Im, T)
#plot figure
fig = plt.figure(figsize=(12, 5))
#fig, ax = plt.subplots()
ax = fig.add_subplot(121)
im = ax.imshow(It)
ax.set_title("Transformed image")
ax.set_xlabel('x')
ax.set_ylabel('y')
print(Etrain)
print(Etest)
display(fig)
fig.savefig('./data/image_results/3_1_t1 + 3_1_t2.png')
def my_point_based_registration():
I = '../data/image_data/3_2_t1.tif'
I_d = '../data/image_data/3_2_t1_d.tif'
I_plt = plt.imread('../data/image_data/3_2_t1.tif')
I_d_plt = plt.imread('../data/image_data/3_2_t1_d.tif')
X, Xm = util.my_cpselect(I, I_d)
affine_transformation = reg.ls_affine(X, Xm)
transformed_moving_image, transformed_vector = reg.image_transform(
I_d_plt, affine_transformation)
fig = plt.figure(figsize=(12, 5))
ax1 = fig.add_subplot(131)
Im1 = ax1.imshow(I_plt) # Fixed image or Transformed image
Im2 = ax1.imshow(transformed_moving_image,
alpha=0.7) # Transformed image or Fixed image
def pbr(I_path, Im_path):
# Load in images
I = plt.imread(I_path)
Im = plt.imread(Im_path)
# Set control points
X, Xm = util.my_cpselect(I_path, Im_path)
Xh = util.c2h(X)
Xmh = util.c2h(Xm)
# Find optimal affine transformation
T = ls_affine(Xh, Xmh)
# Transform Im
Im_t, X_t = image_transform(Im, T)
return Im_t, X_t, X, Xm, T
def point_based_error(I_path,Im_path,T):
#Select set of corresponding points using my_cpselect
X0, Xm0 = util.my_cpselect(I_path, Im_path)
Xm = np.ones((3, Xm0.shape[1]))
Xm[0,:] = Xm0[0,:]
Xm[1,:]= Xm0[1,:]
X = np.ones((3, X0.shape[1]))
X[0,:] = X0[0,:]
X[1,:]= X0[1,:]
b1 = np.transpose(X[0,:])
b2 = np.transpose(X[1,:])
#Compute the affine transformation between the pair of images
_, Etestx = reg.ls_solve(T,b1)
_, Etesty = reg.ls_solve(T,b2)
Etest = np.array([[Etestx],[Etesty]])
return Etest
def my_point_based_registration_2():
I2 = '../data/image_data/3_2_t1.tif' # Tweede opdracht T1 slide
I_d_2 = '../data/image_data/3_2_t2.tif' # T2 slice
I_plt_2 = plt.imread('../data/image_data/3_2_t1.tif')
I_d_plt_2 = plt.imread('../data/image_data/3_2_t1_d.tif')
X2, Xm2 = util.my_cpselect(I2, I_d_2)
affine_transformation_2 = reg.ls_affine(X2, Xm2)
transformed_moving_image_2, transformed_vector_2 = reg.image_transform(
I_d_plt_2, affine_transformation_2)
fig = plt.figure(figsize=(12, 5))
ax1 = fig.add_subplot(131)
Im1 = ax1.imshow(
I_plt_2) # Fixed image or Transformed image, Je mag wisselen
Im2 = ax1.imshow(
transformed_moving_image_2,
alpha=0.7) # Transformed image or Fixed image Mag wisselen
#
#
##calculate correlation
#display("Calculate correlation")
#corr_I1 = reg.correlation(I1, It1)
#
##calculate mutual information
#display("Calculate mutual information")
#p1 = reg.joint_histogram(I1, It1)
#mi_I1 = reg.mutual_information(p1)
"""
Part 2: T1 and T2
"""
##choose fiducial points and save them for T1 and T1 moving
X1, X2 = util.my_cpselect(I1_path, I2_path)
Th2, fig2, It2 = proj.point_based_registration(I1_path, I2_path, X1, X2)
np.save(
path + "/transformation_matrix_T1_and_T2_moving_" + str(numberofpoints),
Th2)
plt.savefig(path + "/transformed_T2_image_" + str(numberofpoints) + ".png")
#calculate correlation
display("Calculate correlation")
corr_I2 = reg.correlation(I1, It2)
#calculate mutual information
display("Calculate mutual information")
p2 = reg.joint_histogram(I1, It2)
mi_I2 = reg.mutual_information(p2)
"""
#DO NOT CHANGE FOLLOWING LINES
#Select images: T1 and T1 moving and T2 slice
#paths of the images
I1_path = '../data/image_data/1_1_t1.tif'
Im1_path = '../data/image_data/1_1_t1_d.tif'
I2_path = '../data/image_data/1_1_t2.tif'
#read the images
I1 = plt.imread(I1_path)
Im1 = plt.imread(Im1_path)
Im2 = plt.imread(I2_path)
if set_of_images == 1:
full_path = path + "/transformation_matrix_T1_and_T1_moving_" + str(
numberofpoints) + ".npy"
X1_target, Xm1_target = util.my_cpselect(I1_path, Im1_path)
else:
full_path = path + "/transformation_matrix_T1_and_T2_" + str(
numberofpoints) + ".npy"
X1_target, Xm1_target = util.my_cpselect(I1_path, I2_path)
transformation_matrix = np.load(full_path)
"""
Evaluation of point-based affine image registration
"""
Reg_error1 = proj.Evaluate_point_based_registration(transformation_matrix,
X1_target, Xm1_target)
if set_of_images == 1:
print('Registration error for pair of T1 and T1 moving: {:.2f}'.format(
