How was the visualization results visualized? about vit-v-net_for_3d_image_registration_pytorch HOT 1 CLOSED

def plot_grid(gridx,gridy, **kwargs):
    for i in range(gridx.shape[1]):
        plt.plot(gridx[i,:], gridy[i,:], **kwargs)
    for i in range(gridx.shape[0]):
        plt.plot(gridx[:,i], gridy[:,i], **kwargs)

# Assume an image has size 384 x 384
x = np.arange(0, 384, 1)
y = np.arange(0, 384, 1)
X, Y = np.meshgrid(x, y)
u = displacement_field[0, 0, :, :] #batch, channel, size_x, size_y
v = displacement_field[0, 1, :, :] #batch, channel, size_x, size_y
for i in range(0, 384):
    for j in range(0, 384):
        # Apply displacements
        X[i, j] = X[i, j] - u[i, j]
        Y[i, j] = Y[i, j] - v[i, j]
plt.figure()
plot_grid(X, Y, color="C0")
plt.title('Deformed grid')
plt.show()

# Assume an image has size 160 x 192 x 224
def mk_grid_img(grid_step, line_thickness=1, grid_sz=(160, 192, 224)):
    grid_img = np.zeros(grid_sz)
    for j in range(0, grid_img.shape[1], grid_step):
        grid_img[:, j+line_thickness-1, :] = 1
    for i in range(0, grid_img.shape[2], grid_step):
        grid_img[:, :, i+line_thickness-1] = 1
    grid_img = grid_img[None, None, ...]
    grid_img = torch.from_numpy(grid_img).cuda()
    return grid_img

grid_img = mk_grid_img(8, 1, (160, 192, 224))
def_grid = spatial_transformation_function(grid_img.float(), displacement_field.cuda())
def_grid = def_grid.detach().cpu().numpy()[0, 0, :, :, :]

plt.figure()
plt.imshow(img[80, :, :], cmap='gray') # visualize an arbitrary slice
plt.title('Deformed grid')
plt.show()