build_training_sets.py

"""
Created on July 2020
Demo for traning a 2D FBSEM net 


@author: Abi Mehranian
abolfazl.mehranian@kcl.ac.uk
"""


import numpy as np
from matplotlib import pyplot as plt
from geometry.BuildGeometry_v4 import BuildGeometry_v4 
from models.deeplib import buildBrainPhantomDataset


# build PET recontruction object 
temPath = r'C:\pythonWorkSpace\tmp003'
PET = BuildGeometry_v4('mmr',0.5) #scanner mmr, with radial crop factor of 50%
PET.loadSystemMatrix(temPath,is3d=False)

# get some info of Pet object
print('is3d:',PET.is3d)
print('\nscanner info:', PET.scanner.as_dict())
print('\nimage info:',PET.image.as_dict())
print('\nsinogram info:',PET.sinogram.as_dict())


# this will take hours (5 phantoms, 5 random rotations each, lesion & sinogram simulation, 3 different recon,...)
# see 'buildBrainPhantomDataset' for default values, e.g. count level, psf, no. lesions, lesion size, no. rotations, rotation range,....
# LD/ld stands for low-definition low-dose, HD/hd stands for high-definition high-dose

phanPath = r'C:\phantoms\brainWeb'
save_training_dir = r'C:\MoDL\trainingDatasets\brainweb\2D'
phanType ='brainweb'
phanNumber = np.arange(0,5,1) # use first 5 brainweb phantoms out of 20

buildBrainPhantomDataset(PET, save_training_dir, phanPath, phanType =phanType,  phanNumber = phanNumber,is3d = False, num_rand_rotations=5)


# check out the strcuture of the produced datasets, e.g. data-0.npy
d = np.load(save_training_dir+ '\\' + 'data-0.npy',allow_pickle=True).item()
d.keys()


fig, ax = plt.subplots(1,4,figsize=(20,10))
ax[0].imshow(d['mrImg'],cmap='gist_gray'),ax[0].set_title('mrImg',fontsize=20)
ax[1].imshow(d['imgHD'],cmap='gist_gray_r'),ax[1].set_title('imgHD',fontsize=20)
ax[2].imshow(d['imgLD'],cmap='gist_gray_r'),ax[2].set_title('imgLD',fontsize=20)
ax[3].imshow(d['imgLD_psf'],cmap='gist_gray_r'),ax[3].set_title('imgLD_psf',fontsize=20)


fig, ax = plt.subplots(1,2,figsize=(20,10))
ax[0].imshow(d['sinoLD']),ax[0].set_title('sinoLD',fontsize=20)
ax[1].imshow(d['AN']),ax[1].set_title('Atten. factors * Norm. Factors (AN)',fontsize=20)