def run(input_dir, phantom, save_volume,
suffix="_0filter_fdk.raw",
es_params=None,
compute_noise_level=False,
output_dir="../data/simresults",
**bf_kwargs
):
"""
Run postfilter routine on all files in input_dir that have the specified
suffix.
phantom | str, prefix for analysis files
save_volume | bool, whether to save the postfiltered volume or not
es_params | None or tuple(roi_name, sigma)
output_dir | Run this in scripts/ if not specified
bf_kwargs | sigmaISq, sigmaP, spacing, kernelScaling
"""
raw_nofilter_files = [f for f in listdir(input_dir) if f.endswith(suffix)]
COMPUTE_EDGESHARPNESS = es_params is not None
COMPUTE_ROI_VARIANCE = compute_noise_level
if COMPUTE_EDGESHARPNESS:
EDGESHARPNESS_FILEPATH = os.path.join(output_dir,
EDGESHARPNESS_FILE.format(phantom, *es_params))
ROI_VARIANCES_FILEPATH = os.path.join(output_dir, NOISELEVEL_FILE.format(phantom))
for i,file in tqdm(enumerate(raw_nofilter_files)):
# convert to float32 because pyopencl cannot handle float16.
img_in = np.fromfile(
os.path.join(input_dir, file),
dtype=np.float16).reshape((250,512,311)).astype(np.float32)
img_out = _apply_bilateral_filter(img_in, queue, **bf_kwargs)
# Analysis
if COMPUTE_ROI_VARIANCE:
variances = compute_var_from_volume(img_out)
write_header = not os.path.exists(ROI_VARIANCES_FILEPATH)
with open(ROI_VARIANCES_FILEPATH, 'a+') as rvf:
if write_header:
rvf.write("#fmode,fmrs,flux,var1,var2,var3\n")
rvf.write("{},{},{},{},{},{}\n".format(
2,
file[:3],
file[8:13],
*variances
)
)
if COMPUTE_EDGESHARPNESS:
es, esd = compute_es_from_volume(img_out, *es_params[::-1])
write_header = not os.path.exists(EDGESHARPNESS_FILEPATH)
with open(EDGESHARPNESS_FILEPATH, 'a+') as esf:
if write_header:
esf.write("#fmode,frms,flux,shrpns,uncrty\n")
esf.write("{},{},{},{},{}\n".format(
2, # post filtermode
file[:3],
file[8:13],
es,
esd
)
)
if save_volume:
# volume output directory is the input directory, i.e. on the server.
img_out.astype(np.float16).tofile(
os.path.join(
input_dir,
file.replace(suffix, "_2filter_fdk.raw")))
)
with open(EDGESHARPNESS_FILEPATH, 'a+') as esf:
esf.write(
"{},{},{},{},{},{},{},{}\n".format(
filMode,
frms,
flux,
es,
esd,
RECO_MODES[recoMode],
numRuns,
tvGrad
)
)
if COMPUTE_NOISE_LEVEL:
var1, var2, var3 = compute_var_from_volume(img_cropped)
with open(NOISELEVEL_FILEPATH, 'a+') as nlf:
nlf.write(
"{},{},{},{},{},{},{},{},{}\n".format(
filMode,
frms,
flux,
var1, var2, var3,
RECO_MODES[recoMode],
numRuns,
tvGrad
)
)
if COMPUTE_RMSE:
rmse = compute_rmse_from_arrays(RMSE_GROUND_TRUTH,
img_cropped.flatten(), RMSE_MASK)
print("Cropping volume and converting to 16bit-float, scaling ...")
rawFilePath = "{}.raw".format(tmp_fpath)
img = np.fromfile(rawFilePath, dtype=np.float32).reshape((512, 512, 512))
scaling = IR_SCALING
if recoMode == "FDK":
# Scale w.r.t. region that is supposed to be 1
scaling = 1.0 / np.mean(img[220:225, 260:360, 150:350])
with open(FDK_SCALING_FILEPATH, "a+") as fsf:
fsf.write("#{}\n".format(fname))
fsf.write("{}\n".format(scaling))
img[:, :, :] = scaling * img[:, :, :]
#
# !!!!!!!!!!!!!!!!!!!11
#
img_cropped = img[150:400, :, 100:411]
if COMPUTE_EDGE_SHARPNESS:
es, esd = compute_es_from_volume(
img_cropped, EDGESHARPNESS_PREFILTER_SIGMA, EDGESHARPNESS_ROI_NAME
)
with open(EDGESHARPNESS_FILEPATH, "a+") as esf:
esf.write("{},{},{},{},{}\n".format(filMode, frms, flux, es, esd))
if COMPUTE_NOISE_LEVEL:
variances = compute_var_from_volume(img_cropped)
with open(NOISELEVEL_FILEPATH, "a+") as nlf:
nlf.write("{},{},{},{},{},{}\n".format(filMode, frms, flux, *variances))
if SAVE_VOLUME:
img_cropped.astype(np.float16).tofile("{}.raw".format(fpath))
os.remove(rawFilePath)