X_test = np.zeros((0, patchSize[0], patchSize[1]))
y_test = np.zeros(0)
for iImg in range(0, len(cfg['lPredictImg'])):
# patches and labels of reference/artifact
tmpPatches, tmpLabels = datapre.fPreprocessData(
cfg['lPredictImg'][iImg],
patchSize,
cfg['patchOverlap'],
1,
cfg['sLabeling'],
sTrainingMethod=sTrainingMethod)
X_test = np.concatenate((X_test, tmpPatches), axis=0)
y_test = np.concatenate(
(y_test, cfg['lLabelPredictImg'][iImg] * tmpLabels), axis=0)
allImg[iImg] = datapre.fReadData(cfg['lPredictImg'][iImg])
if sTrainingMethod == "MultiScaleSeparated":
X_test_p1 = scaling.fcutMiddelPartOfPatch(X_test, X_test, patchSize,
cfg['patchSize'])
X_train_p2, X_test_p2, scedpatchSize = scaling.fscaling(
[X_test], [X_test], patchSize, cfg['lScaleFactor'][0])
frunCNN_MS(
{
'X_test': X_test_p1,
'y_test': y_test,
'patchSize': patchSize,
'X_test_p2': X_test_p2[0],
'model_name': sPredictModel,
'patchOverlap': cfg['patchOverlap'],
'actualSize': cfg['correction']['actualSize']
def fPredictArtDetection():
# prediction
sNetworktype = cfg['network'].split("_")
if len(sPredictModel) == 0:
sPredictModel = cfg['selectedDatabase']['bestmodel'][sNetworktype[2]]
if sTrainingMethod == "MultiScaleSeparated":
patchSize = fcalculateInputOfPath2(cfg['patchSize'],
cfg['lScaleFactor'][0],
cfg['network'])
if len(patchSize) == 3:
X_test = np.zeros((0, patchSize[0], patchSize[1], patchSize[2]))
y_test = np.zeros((0))
allImg = np.zeros(
(len(cfg['lPredictImg']), cfg['correction']['actualSize'][0],
cfg['correction']['actualSize'][1],
cfg['correction']['actualSize'][2]))
else:
X_test = np.zeros((0, patchSize[0], patchSize[1]))
y_test = np.zeros(0)
for iImg in range(0, len(cfg['lPredictImg'])):
# patches and labels of reference/artifact
tmpPatches, tmpLabels = datapre.fPreprocessData(
cfg['lPredictImg'][iImg],
patchSize,
cfg['patchOverlap'],
1,
cfg['sLabeling'],
sTrainingMethod=sTrainingMethod)
X_test = np.concatenate((X_test, tmpPatches), axis=0)
y_test = np.concatenate(
(y_test, cfg['lLabelPredictImg'][iImg] * tmpLabels), axis=0)
allImg[iImg] = datapre.fReadData(cfg['lPredictImg'][iImg])
if sTrainingMethod == "MultiScaleSeparated":
X_test_p1 = scaling.fcutMiddelPartOfPatch(X_test, X_test, patchSize,
cfg['patchSize'])
X_train_p2, X_test_p2, scedpatchSize = scaling.fscaling(
[X_test], [X_test], patchSize, cfg['lScaleFactor'][0])
frunCNN_MS(
{
'X_test': X_test_p1,
'y_test': y_test,
'patchSize': patchSize,
'X_test_p2': X_test_p2[0],
'model_name': sPredictModel,
'patchOverlap': cfg['patchOverlap'],
'actualSize': cfg['correction']['actualSize']
},
cfg['network'],
lTrain,
sOutPath,
cfg['batchSize'],
cfg['lr'],
cfg['epochs'],
predictImg=allImg)
elif 'MS' in cfg['network']:
frunCNN_MS(
{
'X_test': X_test,
'y_test': y_test,
'patchSize': cfg['patchSize'],
'model_name': sPredictModel,
'patchOverlap': cfg['patchOverlap'],
'actualSize': cfg['correction']['actualSize']
},
cfg['network'],
lTrain,
sOutPath,
cfg['batchSize'],
cfg['lr'],
cfg['epochs'],
predictImg=allImg)
else:
fRunCNN(
{
'X_train': [],
'y_train': [],
'X_test': X_test,
'y_test': y_test,
'patchSize': patchSize,
'model_name': sPredictModel,
'patchOverlap': cfg['patchOverlap'],
'actualSize': cfg['correction']['actualSize']
}, cfg['network'], lTrain, cfg['sOpti'], sOutPath,
cfg['batchSize'], cfg['lr'], cfg['epochs'])