def load(self, target_graph):
if self.config[self.KEYS.LORS_PATH].endswith(".npy"):
lors = np.load(self.config[self.KEYS.LORS_PATH])
elif self.config[self.KEYS.LORS_PATH].endswith(".h5"):
data = load_h5(self.config[self.KEYS.LORS_PATH])
lors_point = np.hstack((data['fst'], data['snd']))
lors = np.hstack(
(lors_point, data['tof'].reshape(data['tof'].size, 1)))
# tof_sigma2 = (limit**2)/9
lors = recon_process(lors, self.config[self.KEYS.TOF_RES])
lors = {k: lors[str2axis(k)] for k in ('x', 'y', 'z')}
projection_data = ListModeDataSplit(
**{
k: ListModeData(lors[k], np.ones([lors[k].shape[0]],
np.float32))
for k in lors
})
emap = Image(
np.load(self.config[self.KEYS.EMAP_PATH]).astype(np.float32),
self.config[self.KEYS.CENTER], self.config[self.KEYS.SIZE])
result = {'projection_data': projection_data, 'efficiency_map': emap}
if self.psf_flag:
psf_xy, psf_z = self._load_psf()
result.update({'psf_xy': psf_xy, 'psf_z': psf_z})
return result, ()
def lm2bin(config, target):
path_file = config['input']['listmode']['path_file']
data = load_h5(path_file)
coo_data = np.hstack((data['fst'], data['snd']))
bin_data = np.hstack(
(coo_data, data['weight'].reshape(data['weight'].size, 1)))
out = bin_data.astype(np.float32)
out.tofile((Path(target) + 'input.txt').abs)
f = open((Path(target) + 'input.txt.hdr').abs, 'w')
f.write(str(out.shape[0]))
def run(cls, kernel_xy_para_dir, kernel_z_para_dir, grid: list,
voxsize: list, refined_factor, output_mat_dir: str,
output_kernel_dir: str, map_file: str, psf_map_file: str,
epsilon: float):
from srf.io.listmode import load_h5
grid = np.array(grid)
voxsize = np.array(voxsize)
kernel_xy_dict = load_h5(kernel_xy_para_dir)
kernel_z_dict = load_h5(kernel_z_para_dir)
print("step 1/3: making xy kernel...")
cls().xy_main(kernel_xy_dict, grid[0:2], voxsize[0:2], refined_factor,
output_mat_dir)
print("step 2/3: making z kernel...")
cls().z_main(kernel_z_dict, grid[2], voxsize[2], output_kernel_dir)
print("step 3/3: generating psf efficiency map...")
cls().map_process(output_mat_dir, output_kernel_dir, map_file,
psf_map_file, epsilon)
print("Task complete!")
def load(self, target_graph):
if self.config[self.KEYS.LORS_PATH].endswith(".npy"):
lors = np.load(self.config[self.KEYS.LORS_PATH])
elif self.config[self.KEYS.LORS_PATH].endswith(".h5"):
data = load_h5(self.config[self.KEYS.LORS_PATH])
lors_point = np.hstack((data['fst'], data['snd']))
lors = np.hstack(
(lors_point, data['weight'].reshape(data['weight'].size, 1)))
projection_data = ListModeData(lors,
np.ones([lors.shape[0]], np.float32))
return {'projection_data': projection_data}, ()
def generate_data_and_header(config, target):
with open(config, 'r') as fin:
c = json.load(fin)
scanner = get_scanner(c['scanner']['petscanner'])
path_data = Path(target)
gen_sino_script(c['scanner']['petscanner'], (path_data.abs + 'sinogram'))
path_file = c['input']['listmode']['path_file']
data = load_h5(path_file)
lor = List(
LoR(PositionEvent(data['fst'][i, ]),
PositionEvent(data['snd'][i, ]),
weight=data['weight'][i]) for i in range(0, data['weight'].size))
lm2sino(scanner, lor, (path_data.abs + '/sinogram.s'))
def load(self, target_graph):
if self.config[self.KEYS.LORS_PATH].endswith(".npy"):
lors = np.load(self.config[self.KEYS.LORS_PATH])
elif self.config[self.KEYS.LORS_PATH].endswith(".h5"):
data = load_h5(self.config[self.KEYS.LORS_PATH])
lors_point = np.hstack((data['fst'], data['snd']))
lors = np.hstack(
(lors_point, data['weight'].reshape(data['weight'].size, 1)))
listmode = ListModeData(lors, np.ones([lors.shape[0]], np.float32))
# sino = np.load('sinogram_data.npy')
sino = _listMode2Sino(listmode, self.config[self.KEYS.PJ_CONFIG])
# np.save('sinogram_data.npy', sino)
emap = Image(
np.load(self.config[self.KEYS.EMAP_PATH]).astype(np.float32),
self.config[self.KEYS.CENTER], self.config[self.KEYS.SIZE])
return {'projection_data': sino, 'efficiency_map': emap}, ()
def load(self, target_graph):
if self.config[self.KEYS.LORS_PATH].endswith(".npy"):
lors = np.load(self.config[self.KEYS.LORS_PATH])
elif self.config[self.KEYS.LORS_PATH].endswith(".h5"):
data = load_h5(self.config[self.KEYS.LORS_PATH])
lors_point = np.hstack((data['fst'], data['snd']))
if 'tof' in data.keys():
lors = np.hstack(
(lors_point, data['tof'].reshape(data['tof'].size, 1)))
projection_data = ListModeData(lors, lors[:, 6])
else:
lors = np.hstack((lors_point, np.ones(
(lors_point.shape[0], 1))))
projection_data = ListModeData(lors, lors[:, 6])
emap = Image(
np.load(self.config[self.KEYS.EMAP_PATH]).astype(np.float32),
self.config[self.KEYS.CENTER], self.config[self.KEYS.SIZE])
result = {'projection_data': projection_data, 'efficiency_map': emap}
if self.psf_flag:
psf_xy, psf_z = self._load_psf()
result.update({'psf_xy': psf_xy, 'psf_z': psf_z})
return result, ()