diff --git a/demo/demo_mace3D.py b/demo/demo_mace3D.py
index f0cdc371bae99ccd373f03d1b34a4d24e6a64042..67a90337a6817ac3868958708ae01f50798eec53 100644
--- a/demo/demo_mace3D.py
+++ b/demo/demo_mace3D.py
@@ -44,9 +44,8 @@ save_path = './output/mace3D/'
os.makedirs(save_path, exist_ok=True)
# Geometry parameters
-dist_source_detector = 839.0472 # Distance between the X-ray source and the detector in units of ALU
+dist_source_detector = 4*839.0472 # Distance between the X-ray source and the detector in units of ALU
magnification = 5.572128439964856 # magnification = (source to detector distance)/(source to center-of-rotation distance)
-delta_pixel_detector = 0.25 # Scalar value of detector pixel spacing in units of ALU
num_det_rows = 28 # number of detector rows
num_det_channels = 240 # number of detector channels
@@ -91,8 +90,7 @@ print("Generating sinogram ...")
angles = np.linspace(0, 2 * np.pi, num_views, endpoint=False)
sino = mbircone.cone3D.project(phantom, angles,
num_det_rows, num_det_channels,
- dist_source_detector, magnification,
- delta_pixel_detector=delta_pixel_detector)
+ dist_source_detector, magnification)
sino_weights = mbircone.cone3D.calc_weights(sino, weight_type='transmission')
# Add transmission noise
@@ -104,7 +102,6 @@ sino_noisy = sino + noise
# ###########################################################################
print("Performing qGGMRF reconstruction ...")
recon_qGGMRF = mbircone.cone3D.recon(sino_noisy, angles, dist_source_detector, magnification,
- delta_pixel_detector=delta_pixel_detector,
sharpness=sharpness, weight_type='transmission',
verbose=1)
@@ -136,7 +133,6 @@ recon_mace = mbircone.mace.mace3D(sino_noisy, angles, dist_source_detector, magn
max_admm_itr=max_admm_itr,
init_image=recon_qGGMRF,
sharpness=sharpness,
- delta_pixel_detector=delta_pixel_detector,
weight_type='transmission',
verbose=1)
recon_shape = recon_mace.shape
diff --git a/demo/demo_mace3D_fast.py b/demo/demo_mace3D_fast.py
index 7b7a8f86ae48a2530a3a88de0ac98ffddc474b95..81a6593d6cff1e9515a22dfd70dc0c1ceb9a43da 100644
--- a/demo/demo_mace3D_fast.py
+++ b/demo/demo_mace3D_fast.py
@@ -44,9 +44,8 @@ save_path = './output/mace3D_fast/'
os.makedirs(save_path, exist_ok=True)
# Geometry parameters
-dist_source_detector = 839.0472 # Distance between the X-ray source and the detector in units of ALU
+dist_source_detector = 4*839.0472 # Distance between the X-ray source and the detector in units of ALU
magnification = 5.572128439964856 # magnification = (source to detector distance)/(source to center-of-rotation distance)
-delta_pixel_detector = 0.25 # Scalar value of detector pixel spacing in units of ALU
num_det_rows = 29 # number of detector rows
num_det_channels = 120 # number of detector channels
@@ -104,8 +103,7 @@ print("Generating sinogram ...")
angles = np.linspace(0, 2 * np.pi, num_views, endpoint=False)
sino = mbircone.cone3D.project(phantom, angles,
num_det_rows, num_det_channels,
- dist_source_detector, magnification,
- delta_pixel_detector=delta_pixel_detector)
+ dist_source_detector, magnification)
sino_weights = mbircone.cone3D.calc_weights(sino, weight_type='transmission')
# Add transmission noise
@@ -117,7 +115,6 @@ sino_noisy = sino + noise
# ###########################################################################
print("Performing qGGMRF reconstruction ...")
recon_qGGMRF = mbircone.cone3D.recon(sino_noisy, angles, dist_source_detector, magnification,
- delta_pixel_detector=delta_pixel_detector,
sharpness=sharpness, weight_type='transmission',
verbose=1)
@@ -147,7 +144,6 @@ recon_mace = mbircone.mace.mace3D(sino_noisy, angles, dist_source_detector, magn
max_admm_itr=max_admm_itr,
init_image=recon_qGGMRF,
sharpness=sharpness,
- delta_pixel_detector=delta_pixel_detector,
weight_type='transmission',
verbose=1)
recon_shape = recon_mace.shape
diff --git a/demo/demo_mace3D_old.py b/demo/demo_mace3D_old.py
new file mode 100644
index 0000000000000000000000000000000000000000..f0cdc371bae99ccd373f03d1b34a4d24e6a64042
--- /dev/null
+++ b/demo/demo_mace3D_old.py
@@ -0,0 +1,164 @@
+import os, sys
+import numpy as np
+import math
+import urllib.request
+import tarfile
+import mbircone
+import demo_utils, denoiser_utils
+
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
+
+"""
+This script is a demonstration of the mace3D reconstruction algorithm. Demo functionality includes
+ * downloading phantom and denoiser data from specified urls
+ * generating synthetic sinogram data by forward projecting the phantom and then adding transmission noise
+ * performing a 3D MACE and qGGMRF reconstructions and displaying them.
+"""
+print('This script is a demonstration of the mace3D reconstruction algorithm. Demo functionality includes \
+\n\t * downloading phantom and denoiser data from specified urls \
+\n\t * generating synthetic sinogram data by forward projecting the phantom and then adding transmission noise\
+\n\t * performing a 3D MACE and qGGMRF reconstructions and displaying them.')
+
+# ###########################################################################
+# Set the parameters to get the data and do the recon
+# ###########################################################################
+
+# Change the parameters below for your own use case.
+
+# The url to the data repo.
+data_repo_url = 'https://github.com/cabouman/mbir_data/raw/master/'
+
+# Download url to the index file.
+# This file will be used to retrieve urls to files that we are going to download
+yaml_url = os.path.join(data_repo_url, 'index.yaml')
+
+# Choice of phantom file.
+# These should be valid choices specified in the index file.
+# The url to phantom data will be parsed from data_repo_url and the choices of phantom specified below.
+phantom_name = 'bottle_cap_3D'
+
+# destination path to download and extract the phantom and NN weight files.
+target_dir = './demo_data/'
+# path to store output recon images
+save_path = './output/mace3D/'
+os.makedirs(save_path, exist_ok=True)
+
+# Geometry parameters
+dist_source_detector = 839.0472 # Distance between the X-ray source and the detector in units of ALU
+magnification = 5.572128439964856 # magnification = (source to detector distance)/(source to center-of-rotation distance)
+delta_pixel_detector = 0.25 # Scalar value of detector pixel spacing in units of ALU
+num_det_rows = 28 # number of detector rows
+num_det_channels = 240 # number of detector channels
+
+# Simulated sinogram parameters
+num_views = 75 # number of projection views
+sino_noise_sigma = 0.01 # transmission noise level
+
+# MACE recon parameters
+sharpness = 1.0 # Parameter to control regularization level of reconstruction.
+max_admm_itr = 10 # max ADMM iterations for MACE reconstruction
+# ######### End of parameters #########
+
+
+# ###########################################################################
+# Download and extract data
+# ###########################################################################
+
+# Download the url index file and return path to local file.
+index_path = demo_utils.download_and_extract(yaml_url, target_dir)
+# Load the url index file as a directionary
+url_index = demo_utils.load_yaml(index_path)
+# get urls to phantom and denoiser parameter file
+phantom_url = os.path.join(data_repo_url, url_index['phantom'][phantom_name]) # url to download the 3D image volume phantom file
+denoiser_url = os.path.join(data_repo_url, url_index['denoiser']['dncnn_ct']) # url to download the denoiser parameter file
+
+# download phantom file
+phantom_path = demo_utils.download_and_extract(phantom_url, target_dir)
+# download and extract NN weights and structure files used for MACE denoiser
+denoiser_path = demo_utils.download_and_extract(denoiser_url, target_dir)
+
+# load original phantom
+phantom = np.load(phantom_path)
+print("shape of phantom = ", phantom.shape)
+
+
+# ###########################################################################
+# Generate sinogram
+# ###########################################################################
+print("Generating sinogram ...")
+
+# Generate view angles and sinogram with weights
+angles = np.linspace(0, 2 * np.pi, num_views, endpoint=False)
+sino = mbircone.cone3D.project(phantom, angles,
+ num_det_rows, num_det_channels,
+ dist_source_detector, magnification,
+ delta_pixel_detector=delta_pixel_detector)
+sino_weights = mbircone.cone3D.calc_weights(sino, weight_type='transmission')
+
+# Add transmission noise
+noise = sino_noise_sigma * 1. / np.sqrt(sino_weights) * np.random.normal(size=(num_views, num_det_rows, num_det_channels))
+sino_noisy = sino + noise
+
+# ###########################################################################
+# Perform qGGMRF reconstruction
+# ###########################################################################
+print("Performing qGGMRF reconstruction ...")
+recon_qGGMRF = mbircone.cone3D.recon(sino_noisy, angles, dist_source_detector, magnification,
+ delta_pixel_detector=delta_pixel_detector,
+ sharpness=sharpness, weight_type='transmission',
+ verbose=1)
+
+# ###########################################################################
+# Set up the denoiser
+# ###########################################################################
+# The MACE reconstruction algorithm requires the use of a denoiser as a prior model.
+# This demo includes a custom DnCNN denoiser trained on CT images, which can be used for other applications.
+print("Loading denoiser function and model ...")
+# Load denoiser model structure and pre-trained model weights
+denoiser_model = denoiser_utils.DenoiserCT(checkpoint_dir=os.path.join(denoiser_path, 'model_dncnn_ct'))
+# Define the denoiser using this model. This version requires some interface code to match with MACE.
+def denoiser(img_noisy):
+ img_noisy = np.expand_dims(img_noisy, axis=1)
+ upper_range = denoiser_utils.calc_upper_range(img_noisy)
+ img_noisy = img_noisy/upper_range
+ testData_obj = denoiser_utils.DataLoader(img_noisy)
+ img_denoised = denoiser_model.denoise(testData_obj, batch_size=256)
+ img_denoised = img_denoised*upper_range
+ return np.squeeze(img_denoised)
+
+
+# ###########################################################################
+# Perform MACE reconstruction
+# ###########################################################################
+print("Performing MACE reconstruction ...")
+recon_mace = mbircone.mace.mace3D(sino_noisy, angles, dist_source_detector, magnification,
+ denoiser=denoiser, denoiser_args=(),
+ max_admm_itr=max_admm_itr,
+ init_image=recon_qGGMRF,
+ sharpness=sharpness,
+ delta_pixel_detector=delta_pixel_detector,
+ weight_type='transmission',
+ verbose=1)
+recon_shape = recon_mace.shape
+print("Reconstruction shape = ", recon_shape)
+
+
+# ###########################################################################
+# Generating phantom and reconstruction images
+# ###########################################################################
+print("Generating phantom and reconstruction images ...")
+# Plot axial slices of phantom and recon
+display_slices = [7, 12, 17, 22]
+for display_slice in display_slices:
+ demo_utils.plot_image(phantom[display_slice], title=f'phantom, axial slice {display_slice}',
+ filename=os.path.join(save_path, f'phantom_slice{display_slice}.png'), vmin=0, vmax=0.5)
+ demo_utils.plot_image(recon_mace[display_slice], title=f'MACE reconstruction, axial slice {display_slice}',
+ filename=os.path.join(save_path, f'recon_mace_slice{display_slice}.png'), vmin=0, vmax=0.5)
+ demo_utils.plot_image(recon_qGGMRF[display_slice], title=f'qGGMRF reconstruction, axial slice {display_slice}',
+ filename=os.path.join(save_path, f'recon_qGGMRF_slice{display_slice}.png'), vmin=0, vmax=0.5)
+# Plot 3D phantom and recon image volumes as gif images.
+demo_utils.plot_gif(phantom, save_path, 'phantom', vmin=0, vmax=0.5)
+demo_utils.plot_gif(recon_mace, save_path, 'recon_mace', vmin=0, vmax=0.5)
+demo_utils.plot_gif(recon_qGGMRF, save_path, 'recon_qGGMRF', vmin=0, vmax=0.5)
+
+input("press Enter")