Revise 3D shepp logan demo

demo/demo_3D_shepp_logan.py

@@ -4,46 +4,32 @@ import mbircone
 from demo_utils import plot_image, plot_gif, plt_cmp_3dobj
 
 # Set sinogram shape
-num_det_rows = 200
-num_det_channels = 256
+num_det_rows = 128
+num_det_channels = 128
 num_views = 144
 
 # Reconstruction parameters
-sharpness = 2.0
-snr_db = 31.0
-
-# magnification is unitless.
-magnification = 2
-
-# All distances are in unit of 1 ALU = 1 mm.
-dist_source_detector = 600
-delta_pixel_detector = 0.9
-delta_pixel_image = 1
-channel_offset = 0
-row_offset = 0
-max_iterations = 20
+sharpness = 1.0                                               # Controls sharpness of reconstruction
+magnification = 2.0                                           # Ratio of (source to detector)/(source to center of rotation)
+dist_source_detector = 2*num_det_channels                     # distance from source to detector in ALU
+angles = np.linspace(0, 2 * np.pi, num_views, endpoint=False) # set of projection angles
 
 # Display parameters
-vmin = 1.0
-vmax = 1.1
+vmin = 1.0  # minimum display value
+vmax = 1.1  # maximum display value
 filename = 'output/3D_shepp_logan/results.png'
 
 # Generate a 3D shepp logan phantom.
-ROR, boundary_size= mbircone.cone3D.compute_img_size(num_views, num_det_rows, num_det_channels,
-                                              dist_source_detector,
-                                              magnification,
-                                              channel_offset=channel_offset, row_offset=row_offset,
-                                              delta_pixel_detector=delta_pixel_detector,
-                                              delta_pixel_image=delta_pixel_image)
-Nz, Nx, Ny = ROR
+ROR, boundary_size = mbircone.cone3D.compute_img_size(num_views, num_det_rows, num_det_channels, dist_source_detector, magnification)
+Nz, Nx, Ny = ROR   # function ensures that Nx=Ny
 img_slices_boundary_size, img_rows_boundary_size, img_cols_boundary_size = boundary_size
 print('ROR of the recon is:', (Nz, Nx, Ny))
 
-# Set phantom parameters to generate a phantom inside ROI according to ROR and boundary_size.
-# All valid pixels should be inside ROI.
-num_rows_cols = Nx - 2 * img_rows_boundary_size  # Assumes a square image
-num_slices_phantom = Nz - 2 * img_slices_boundary_size
-print('ROI and shape of phantom is:', num_slices_phantom, num_rows_cols, num_rows_cols)
+# Determine required phantom size based on ROI.
+# ROI is determine by ROR - 2*boundary_size
+num_rows_cols = Nx - 2 * img_rows_boundary_size                                         # determines the width and height of the ROI 
+num_slices_phantom = Nz - 2 * img_slices_boundary_size                                  # determines the depth of the ROI
+print('ROI and shape of phantom is:', num_slices_phantom, num_rows_cols, num_rows_cols) # assumes that width=height
 
 # Set display indexes
 display_slice = img_slices_boundary_size + int(0.4*num_slices_phantom)
@@ -51,22 +37,17 @@ display_x = num_rows_cols // 2
 display_y = num_rows_cols // 2
 display_view = 0
 
-# Generate a phantom
-phantom = mbircone.phantom.gen_shepp_logan_3d(num_rows_cols, num_rows_cols, num_slices_phantom)
-print('Generated phantom shape = ', np.shape(phantom))
-phantom = mbircone.cone3D.pad_roi2ror(phantom, boundary_size)
-print('Padded phantom shape = ', np.shape(phantom))
+# Generate a 3D shepp logan phantom
+phantom = mbircone.phantom.gen_shepp_logan_3d(num_rows_cols, num_rows_cols, num_slices_phantom) # generate phatom within ROI
+print('Generated ROI phantom shape = ', np.shape(phantom))
+phantom = mbircone.cone3D.pad_roi2ror(phantom, boundary_size)                                   # generate phantom wihtin ROR
+print('Padded ROR phantom shape = ', np.shape(phantom))
 
-# Generate simulated data using forward projector on the 3D shepp logan phantom.
-angles = np.linspace(0, 2 * np.pi, num_views, endpoint=False)
+# Compute the forward projection of the phantom
+sino = mbircone.cone3D.project(phantom, angles, num_det_rows, num_det_channels, dist_source_detector, magnification)
 
-# After setting the geometric parameter, the shape of the input phantom should be equal to the calculated geometric parameter.
-# Input a phantom with wrong shape will generate a bunch of issue in C.
-sino = mbircone.cone3D.project(phantom, angles,
-                               num_det_rows=num_det_rows, num_det_channels=num_det_channels,
-                               dist_source_detector=dist_source_detector, magnification=magnification,
-                               delta_pixel_detector=delta_pixel_detector, delta_pixel_image=delta_pixel_image,
-                               channel_offset=channel_offset, row_offset=row_offset)
+# Reconstruction sinogram data
+recon = mbircone.cone3D.recon(sino, angles, dist_source_detector, magnification, sharpness=sharpness )
 
 # create output folder
 os.makedirs('output/3D_shepp_logan/', exist_ok=True)
@@ -75,12 +56,6 @@ print('sino shape = ', np.shape(sino), sino.dtype)
 plot_image(sino[display_view, :, :], title='sino', filename='output/3D_shepp_logan/sino-shepp-logan-3D-view(%.2f).png' % angles[0])
 # plot_gif(sino, 'output', 'sino-shepp-logan-3D')
 
-recon = mbircone.cone3D.recon(sino, angles,
-                              dist_source_detector=dist_source_detector, magnification=magnification,
-                              delta_pixel_detector=delta_pixel_detector, delta_pixel_image=delta_pixel_image,
-                              channel_offset=channel_offset, row_offset=row_offset,
-                              sharpness=sharpness, snr_db=snr_db, max_iterations=max_iterations)
-
 print('recon shape = ', np.shape(recon))
 np.save('output/3D_shepp_logan/recon.npy', recon)