y_train = hf['y_train'][:]
y_test = hf['y_test'][:]
patchSize = hf['patchSize'][:]
else: # perform patching
dAllPatches = np.zeros((patchSize[0], patchSize[1], 0))
dAllLabels = np.zeros(0)
dAllPats = np.zeros((0, 1))
#selectedDatabase值为*id001，所以直接选motion_head的dataref:t1...0002和dataart:t1...0003
lDatasets = cfg['selectedDatabase']['dataref'] + cfg['selectedDatabase']['dataart']
iLabels = cfg['selectedDatabase']['labelref'] + cfg['selectedDatabase']['labelart'] #[0,1]
for ipat, pat in enumerate(dbinfo.lPats):
for iseq, seq in enumerate(lDatasets):
# patches and labels of reference/artifact
#import utils.DataPreprocessing as datapre，函数没有问题，只有选到了数据不全的病人才会在这出错
tmpPatches, tmpLabels = datapre.fPreprocessData(os.path.join(dbinfo.sPathIn, pat, dbinfo.sSubDirs[1], seq), cfg['patchSize'], cfg['patchOverlap'], 1 )
dAllPatches = np.concatenate((dAllPatches, tmpPatches), axis=2)
dAllLabels = np.concatenate((dAllLabels, iLabels[iseq]*tmpLabels), axis=0)
dAllPats = np.concatenate((dAllPats,ipat*np.ones((tmpLabels.shape[0],1), dtype=np.int)), axis=0)
# perform splitting
X_train, y_train, X_test, y_test = ttsplit.fSplitDataset(dAllPatches, dAllLabels, dAllPats, cfg['sSplitting'], patchSize, cfg['patchOverlap'], cfg['dSplitval'], '')
# save to file (deprecated)
# sio.savemat(sOutPath + os.sep + sFSname + str(patchSize[0]) + str(patchSize[1]) + '_input.mat', {'X_train': X_train, 'y_train': y_train, 'X_test': X_test, 'y_test': y_test, 'patchSize': cfg['patchSize']})
with h5py.File(sDatafile, 'w') as hf:
hf.create_dataset('X_train', data=X_train)
hf.create_dataset('X_test', data=X_test)
hf.create_dataset('y_train', data=y_train)
hf.create_dataset('y_test', data=y_test)
hf.create_dataset('patchSize', data=patchSize)
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'])
else:
dAllPatches = np.zeros((0, scpatchSize[0], scpatchSize[1]))
dAllLabels = np.zeros(0)
dAllPats = np.zeros((0, 1))
lDatasets = cfg['selectedDatabase']['dataref'] + cfg[
'selectedDatabase']['dataart']
iLabels = cfg['selectedDatabase']['labelref'] + cfg[
'selectedDatabase']['labelart']
for ipat, pat in enumerate(dbinfo.lPats):
if os.path.exists(dbinfo.sPathIn + os.sep + pat + os.sep +
dbinfo.sSubDirs[1]):
for iseq, seq in enumerate(lDatasets):
# patches and labels of reference/artifact
tmpPatches, tmpLabels = datapre.fPreprocessData(
os.path.join(dbinfo.sPathIn, pat,
dbinfo.sSubDirs[1], seq), scpatchSize,
cfg['patchOverlap'], 1, cfg['sLabeling'],
sTrainingMethod, cfg['range'])
dAllPatches = np.concatenate((dAllPatches, tmpPatches),
axis=0)
dAllLabels = np.concatenate(
(dAllLabels, iLabels[iseq] * tmpLabels), axis=0)
dAllPats = np.concatenate((dAllPats, ipat * np.ones(
(tmpLabels.shape[0], 1), dtype=np.int)),
axis=0)
else:
pass
print('Start splitting')
# perform splitting: sp for split
if cfg['sSplitting'] == 'crossvalidation_data':
spX_train, spy_train, spX_test, spy_test = ttsplit.fSplitDataset(
def fTrainArtDetection():
# training for artifact detection
# check if file is already existing -> skip patching
if glob.glob(sOutPath + os.sep + sFSname +
''.join(map(str, patchSize)).replace(" ", "") +
'*_input.mat'): # deprecated
sDatafile = sOutPath + os.sep + sFSname + ''.join(map(
str, patchSize)).replace(" ", "") + '_input.mat'
try:
conten = sio.loadmat(sDatafile)
except:
f = h5py.File(sDatafile, 'r')
conten = {}
conten['X_train'] = np.transpose(np.array(f['X_train']),
(3, 2, 0, 1))
conten['X_test'] = np.transpose(np.array(f['X_test']),
(3, 2, 0, 1))
conten['y_train'] = np.transpose(np.array(f['y_train']))
conten['y_test'] = np.transpose(np.array(f['y_test']))
conten['patchSize'] = np.transpose(np.array(f['patchSize']))
X_train = conten['X_train']
X_test = conten['X_test']
y_train = conten['y_train']
y_test = conten['y_test']
elif glob.glob(sDatafile):
with h5py.File(sDatafile, 'r') as hf:
X_train = hf['X_train'][:]
X_test = hf['X_test'][:]
y_train = hf['y_train'][:]
y_test = hf['y_test'][:]
patchSize = hf['patchSize'][:]
if sTrainingMethod == "MultiScaleSeparated":
X_train_p2 = hf['X_train_p2'][:]
X_test_p2 = hf['X_test_p2'][:]
y_train_p2 = hf['y_train_p2'][:]
y_test_p2 = hf['y_test_p2'][:]
patchSize_down = hf['patchSize_down'][:]
else: # perform patching
X_train = []
scpatchSize = [0 for i in range(len(patchSize))]
if sTrainingMethod == "None" or sTrainingMethod == "ScaleJittering":
lScaleFactor = [1]
if sTrainingMethod == "MultiScaleSeparated":
lScaleFactor = lScaleFactor[:-1]
# images will be split into pathces with size scpatchSize and then scaled to patchSize
for iscalefactor in lScaleFactor:
# Calculate the patchsize according to scale factor and training method
scpatchSize = patchSize
if iscalefactor != 1:
if sTrainingMethod == "MultiScaleSeparated":
scpatchSize = fcalculateInputOfPath2(
patchSize, iscalefactor, cfg['network'])
elif sTrainingMethod == "MultiScaleTogether":
scpatchSize = [
int(psi / iscalefactor) for psi in patchSize
]
if len(scpatchSize) == 3:
dAllPatches = np.zeros(
(0, scpatchSize[0], scpatchSize[1], scpatchSize[2]))
else:
dAllPatches = np.zeros((0, scpatchSize[0], scpatchSize[1]))
dAllLabels = np.zeros(0)
dAllPats = np.zeros((0, 1))
lDatasets = cfg['selectedDatabase']['dataref'] + cfg[
'selectedDatabase']['dataart']
iLabels = cfg['selectedDatabase']['labelref'] + cfg[
'selectedDatabase']['labelart']
for ipat, pat in enumerate(dbinfo.lPats):
if os.path.exists(dbinfo.sPathIn + os.sep + pat + os.sep +
dbinfo.sSubDirs[1]):
for iseq, seq in enumerate(lDatasets):
# patches and labels of reference/artifact
tmpPatches, tmpLabels = datapre.fPreprocessData(
os.path.join(dbinfo.sPathIn, pat,
dbinfo.sSubDirs[1], seq),
scpatchSize,
cfg['patchOverlap'],
1,
cfg['sLabeling'],
sTrainingMethod=sTrainingMethod,
range_norm=cfg['range'])
dAllPatches = np.concatenate((dAllPatches, tmpPatches),
axis=0)
dAllLabels = np.concatenate(
(dAllLabels, iLabels[iseq] * tmpLabels), axis=0)
dAllPats = np.concatenate((dAllPats, ipat * np.ones(
(tmpLabels.shape[0], 1), dtype=np.int)),
axis=0)
else:
pass
print('Start splitting')
# perform splitting: sp for split
if cfg['sSplitting'] == 'crossvalidation_data':
spX_train, spy_train, spX_test, spy_test = ttsplit.fSplitDataset(
dAllPatches,
dAllLabels,
dAllPats,
cfg['sSplitting'],
scpatchSize,
cfg['patchOverlap'],
cfg['dSplitval'],
'',
nfolds=nFolds)
else:
spX_train, spy_train, spX_test, spy_test = ttsplit.fSplitDataset(
dAllPatches, dAllLabels, dAllPats, cfg['sSplitting'],
scpatchSize, cfg['patchOverlap'], cfg['dSplitval'], '')
print('Start scaling')
# perform scaling: sc for scale
scX_train, scX_test, scedpatchSize = scaling.fscaling(
spX_train, spX_test, scpatchSize, iscalefactor)
if sTrainingMethod == "MultiScaleSeparated":
X_train_p2 = scX_train
X_test_p2 = scX_test
y_train_p2 = spy_train
y_test_p2 = spy_test
patchSize_down = scedpatchSize
X_train_cut, X_test_cut = scaling.fcutMiddelPartOfPatch(
spX_train, spX_test, scpatchSize, patchSize)
X_train = X_train_cut
X_test = X_test_cut
y_train = spy_train
y_test = spy_test
else:
if len(X_train) == 0:
X_train = scX_train
X_test = scX_test
y_train = spy_train
y_test = spy_test
else:
X_train = np.concatenate((X_train, scX_train), axis=1)
X_test = np.concatenate((X_test, scX_test), axis=1)
y_train = np.concatenate((y_train, spy_train), axis=1)
y_test = np.concatenate((y_test, spy_test), axis=1)
print('Start saving')
# save to file (deprecated)
if lSave:
# sio.savemat(sOutPath + os.sep + sFSname + str(patchSize[0]) + str(patchSize[1]) + '_input.mat', {'X_train': X_train, 'y_train': y_train, 'X_test': X_test, 'y_test': y_test, 'patchSize': cfg['patchSize']})
with h5py.File(sDatafile, 'w') as hf:
hf.create_dataset('X_train', data=X_train)
hf.create_dataset('X_test', data=X_test)
hf.create_dataset('y_train', data=y_train)
hf.create_dataset('y_test', data=y_test)
hf.create_dataset('patchSize', data=patchSize)
hf.create_dataset('patchOverlap', data=cfg['patchOverlap'])
if sTrainingMethod == "MultiScaleSeparated":
hf.create_dataset('X_train_p2', data=X_train_p2)
hf.create_dataset('X_test_p2', data=X_test_p2)
hf.create_dataset('y_train_p2', data=y_train_p2)
hf.create_dataset('y_test_p2', data=y_test_p2)
hf.create_dataset('patchSize_down', data=patchSize_down)
# perform training
for iFold in range(0, len(X_train)):
if len(X_train) != 1:
CV_Patient = iFold + 1
else:
CV_Patient = 0
if 'MultiPath' in cfg['network']:
frunCNN_MS(
{
'X_train': X_train[iFold],
'y_train': y_train[iFold],
'X_test': X_test[iFold],
'y_test': y_test[iFold],
'patchSize': patchSize,
'X_train_p2': X_train_p2[iFold],
'y_train_p2': y_train_p2[iFold],
'X_test_p2': X_test_p2[iFold],
'y_test_p2': y_test_p2[iFold],
'patchSize_down': patchSize_down,
'ScaleFactor': lScaleFactor[0]
}, cfg['network'], lTrain, sOutPath, cfg['batchSize'],
cfg['lr'], cfg['epochs'], CV_Patient)
elif 'MS' in cfg['network']:
frunCNN_MS(
{
'X_train': X_train[iFold],
'y_train': y_train[iFold],
'X_test': X_test[iFold],
'y_test': y_test[iFold],
'patchSize': patchSize
}, cfg['network'], lTrain, sOutPath, cfg['batchSize'],
cfg['lr'], cfg['epochs'], CV_Patient)
else:
fRunCNN(
{
'X_train': X_train[iFold],
'y_train': y_train[iFold],
'X_test': X_test[iFold],
'y_test': y_test[iFold],
'patchSize': patchSize
}, cfg['network'], lTrain, cfg['sOpti'], sOutPath,
cfg['batchSize'], cfg['lr'], cfg['epochs'], CV_Patient)
