c_init = [
b_init[0] + r_c * np.cos(theta_b_init + theta_c_init),
b_init[1] + r_c * np.sin(theta_b_init + theta_c_init)
]
a = [0., 0.]
b = [
a_init[0] + r_b * np.cos(theta_b_init),
a_init[1] + r_b * np.sin(theta_b_init)
]
c = [
b[0] + r_c * np.cos(theta_b_init + theta_c_init),
b[1] + r_c * np.sin(theta_b_init + theta_c_init)
]
template_init = fun_gen.generate_image_2articulations(space, a, b, c,
0.1 * width)
template = template_init.copy()
#template = 10*space.element(scipy.ndimage.filters.gaussian_filter(template_init.asarray(), 10))
template.show('template', clim=[0, 1])
plt.plot(a[0], a[1], 'xr')
plt.plot(b[0], b[1], 'xr')
plt.plot(c[0], c[1], 'xr')
proj_data = forward_op(template)
# Add white Gaussion noise onto the noiseless data
noise = 0.25 * odl.phantom.noise.white_noise(forward_op.range)
# Create the noisy projection data
noise_proj_data = proj_data + noise
theta_b_init = 0.5*np.pi theta_c_init = 0.1*np.pi a_init =[0., 0.] b_init = [a_init[0] + r_b*np.cos(theta_b_init), a_init[1] + r_b*np.sin(theta_b_init)] c_init = [b_init[0] + r_c*np.cos(theta_b_init + theta_c_init), b_init[1] + r_c*np.sin(theta_b_init + theta_c_init)] a = [0., 0.] b = [a_init[0] + r_b*np.cos(theta_b_init), a_init[1] + r_b*np.sin(theta_b_init)] c = [b[0] + r_c*np.cos(theta_b_init+theta_c_init), b[1] + r_c*np.sin(theta_b_init+theta_c_init)] template_init = fun_gen.generate_image_2articulations(space, a, b, c, width) template = space.element(scipy.ndimage.filters.gaussian_filter(template_init.asarray(),1)) template.show() plt.plot(a[0], a[1],'xr') plt.plot(b[0], b[1],'xr') plt.plot(c[0], c[1],'xr') proj_data = forward_op(template) # Add white Gaussion noise onto the noiseless data noise =0.25 * odl.phantom.noise.white_noise(forward_op.range) # Create the noisy projection data noise_proj_data = proj_data + noise
nb_ab = int((ab_norm + 0.2*width) / sigma) +1
nb_bc = int((bc_norm + 0.2*width) / sigma) +1
Cont = rd.uniform(-2, 2, [nbdata, 2])
sblur = 5
for i in range(nbdata):
a = param.T[i][0:2]
b = param.T[i][2:4]
c = param.T[i][4:6]
truth_temp = generate_truthblurred_from_param(param.T[i], Cont[i,0], Cont[i,1], sblur).copy()
#truth_temp = space.tangent_bundle.element(truth_temp)
image_list.append(fun_gen.generate_image_2articulations(space, a, b, c, width))
vector_fields_list.append(truth_temp.copy())
points, vectors = cmp.compute_pointsvectors_2articulations_bis_nb(a, b, c, width, sigma, n_orth, nb_ab, nb_bc)
points_list.append(points.copy())
vectors_list.append(vectors.copy())
#
if False:
a = param.T[i][0:2]
b = param.T[i][2:4]
c = param.T[i][4:6]
v_temp0 = fun_gen.generate_vectorfield_2articulations_0(space, a, b, c, width).copy()
v_temp1 = fun_gen.generate_vectorfield_2articulations_1(space, a, b, c, width).copy()
v_temp1.show()
plt.plot(param.T[i][::2], param.T[i][1::2])
vectors_list = []
cov_mat_list = []
param_list = []
A_inner_prod_list = []
image_list = []
nbdatamax = 5
for i in range(nbdatamax):
structured_list.append(np.loadtxt(name + 'structured' + str(i)))
unstructured_list.append(np.loadtxt(name + 'unstructured' + str(i)))
vectors_i = structured_list[i][dim:2 * dim]
points_list.append(np.loadtxt(name + 'points' + str(i)))
vectors_list.append(np.loadtxt(name + 'vectors' + str(i)))
param_tmp = np.loadtxt(name + 'param' + str(i))
param_list.append(param_tmp)
image_temp = fun_gen.generate_image_2articulations(
space, param_tmp[0:2], param_tmp[2:4],
param_tmp[2:4] + 0.85 * (param_tmp[4:6] - param_tmp[2:4]),
0.5 * width).copy()
image_list.append(
space.element(scipy.ndimage.filters.gaussian_filter(image_temp, 10)))
cov_mat_list.append(
struct.make_covariance_matrix(points_list[i], kernel_np))
A_inner_prod_list.append(
np.dot(cov_mat_list[i], np.dot(vectors_i.T, vectors_list[i])).T)
#
param_list = np.array(param_list).T
nb_points = len(points_list[0][0])
nb_vectors = len(vectors_list[0][0])
points = space.points()
nb_bc = 8
Cont = rd.uniform(-2, 2, [nbdata, 2])
sblur = 5
for i in range(nbdata):
a = param.T[i][0:2]
b = param.T[i][2:4]
c = param.T[i][4:6]
truth_temp = generate_truthblurred_from_param(param.T[i], Cont[i, 0],
Cont[i, 1], sblur).copy()
#truth_temp = space.tangent_bundle.element(truth_temp)
image_list.append(
fun_gen.generate_image_2articulations(space, a, b, c, width))
vector_fields_list.append(truth_temp.copy())
points, vectors = cmp.compute_pointsvectors_2articulations_thin(
a, b, c, nb_ab, nb_bc)
points_list.append(points.copy())
vectors_list.append(vectors.copy())
#
if False:
a = param.T[i][0:2]
b = param.T[i][2:4]
c = param.T[i][4:6]
v_temp0 = fun_gen.generate_vectorfield_2articulations_0(
space, a, b, c, width).copy()
v_temp1 = fun_gen.generate_vectorfield_2articulations_1(