def processTomoFolder(projectionsF, referencesF, darkFieldF, outputFolder):
print('Process Tomo')
Ir = spytIO.openImage(referencesF[0])
darkField = spytIO.openImage(darkFieldF)
Ir = corr.normalization2D(Ir, darkField)
dxFolder = outputFolder + '/dx/'
dyFolder = outputFolder + '/dy/'
phiFolder = outputFolder + '/phi/'
phi2Folder = outputFolder + '/phi2/'
phi3Folder = outputFolder + '/phi3/'
if not (os.path.exists(outputFolder)):
os.mkdir(outputFolder)
if not (os.path.exists(dxFolder)):
os.mkdir(dxFolder)
if not (os.path.exists(dyFolder)):
os.mkdir(dyFolder)
if not (os.path.exists(phiFolder)):
os.mkdir(phiFolder)
if not (os.path.exists(phi2Folder)):
os.mkdir(phi2Folder)
if not (os.path.exists(phi3Folder)):
os.mkdir(phi3Folder)
for proj in projectionsF:
print(proj)
Is = spytIO.openImage(proj)
Is = corr.normalization2D(Is, darkField)
#Ir=corr.registerRefAndSample(Ir,Is,1000)
result = processOneProjection(Is, Ir)
dx = result['dx']
dx = np.asarray(dx.real, np.float32)
dxFilename = dxFolder + '/dx_' + os.path.basename(proj)
spytIO.saveEdf(dx, dxFilename)
dy = result['dy']
dy = np.asarray(dy.real, np.float32)
dyFilename = dyFolder + '/dy_' + os.path.basename(proj)
spytIO.saveEdf(dy, dyFilename)
phi = result['phi']
phi = np.asarray(phi.real, np.float32)
phiFilename = phiFolder + '/phi_' + os.path.basename(proj)
spytIO.saveEdf(phi, phiFilename)
phi2 = result['phi2']
phi2 = np.asarray(phi2.real, np.float32)
phi2Filename = phi2Folder + '/phi2_' + os.path.basename(proj)
spytIO.saveEdf(phi2, phi2Filename)
phi3 = result['phi3']
phi3 = np.asarray(phi3.real, np.float32)
phi3Filename = phi3Folder + '/phi3' + os.path.basename(proj)
spytIO.saveEdf(phi3, phi3Filename)
print phi3Filename
def processAverage(self):
print('Average ')
dat = openImage(self.fileNames[0])
shape = dat.shape
self.output = np.zeros(shape)
for filenam in self.fileNames:
dat = openImage(filenam)
self.output += dat
self.output = np.divide(self.output, len(self.fileNames))
def opticalFlowSolverOneThread(inputDictionary, outputFolder):
projectionFiles = inputDictionary['projections']
referenceFilename = inputDictionary['references'][0]
Ir = spytIO.openImage(referenceFilename)
i = 0
for projectionFileName in projectionFiles:
print(projectionFileName)
Is = spytIO.openImage(projectionFileName)
result = OpticalFlow.processOneProjection(Is, Ir)
saveResult(result, i, outputFolder)
i += 1
def processMedian(self):
print('Median ')
dat = openImage(self.fileNames[0])
shape = dat.shape
self.bigMat = np.zeros(
(len(self.fileNames), dat.shape[0], dat.shape[1]))
cpt = 0
for filenam in self.fileNames:
dat = openImage(filenam)
self.bigMat[cpt] += dat
cpt += 1
self.output = np.median(self.bigMat, axis=0)
def convert32BitFolder(inputFolder,outputFolder):
listofFiles=glob.glob(inputFolder+'/*.edf')
for filename in listofFiles:
base=os.path.basename(filename)
data=spytIO.openImage(filename)
dataToStore=np.asarray(data,np.float32)
outputfilename=outputFolder+'/'+base
print ('Copying \n'+str(filename)+' \n in \n'+str(outputfilename))
spytIO.saveEdf(dataToStore,outputfilename)
phi3Filename = phi3Folder + '/phi3' + os.path.basename(proj)
spytIO.saveEdf(phi3, phi3Filename)
print phi3Filename
if __name__ == "__main__":
inputFolder = '/Volumes/ID17/speckle/md1097/id17/SpeckleSacroIliaque/HA1100_sacroIlliaque_speckle_xss_6um_52kev__001__008_'
outputFolder = '/Volumes/ID17/speckle/md1097/id17/SpeckleSacroIliaque/OuputSacro_Floor0_'
projectionsFileNames, referenceFileNames, darkFieldFilename = parseESRFTomoFolder(
inputFolder)
processTomoFolder(projectionsFileNames, referenceFileNames,
darkFieldFilename, outputFolder)
print(' Optical Flow Tomo ')
print('Test One File')
Ir = spytIO.openImage('ref1-1.edf')
Is = spytIO.openImage('samp1-1.edf')
result = processOneProjection(Is, Ir)
dx = result['dx']
dy = result['dy']
phi = result['phi']
phi2 = result['phi2']
phi3 = result['phi3']
spytIO.saveEdf(dx, 'output/dx.edf')
spytIO.saveEdf(dy.real, 'output/dy.edf')
spytIO.saveEdf(phi.real, 'output/phi.edf')
spytIO.saveEdf(phi2.real, 'output/phi2.edf')
spytIO.saveEdf(phi3.real, 'output/phi3.edf')
#offsett=corr.registerRefAndSample(Ir,Is,1000)
referenceFiles = qt.QFileDialog.getOpenFileNames(
None, 'Open a set of Images', '/', 'Image files (*.edf *.tif *.tiff)')
referenceFiles = referenceFiles[0]
directory = os.path.dirname(str(referenceFiles[0]))
sampleFiles = (qt.QFileDialog.getOpenFileNames(
None, 'Open a set of Images', directory,
'Image files (*.edf *.tif *.tiff)'))
sampleFiles = sampleFiles[0]
saveFolder = (qt.QFileDialog.getExistingDirectory(
None, 'Open directory to save the images', directory))
saveFolder = str(saveFolder)
Ir = spytIO.openImage(str(referenceFiles[0]))
Is = spytIO.openImage(str(sampleFiles[0]))
result = processOneProjection(Is, Ir)
dx = result['dx']
dy = result['dy']
phi = result['phi']
phi2 = result['phi2']
phi3 = result['phi3']
spytIO.saveEdf(dx, saveFolder + '/dx.edf')
spytIO.saveEdf(dy.real, saveFolder + '/dy.edf')
spytIO.saveEdf(phi.real, saveFolder + '/phi.edf')
spytIO.saveEdf(phi2.real, saveFolder + '/phi2.edf')
spytIO.saveEdf(phi3.real, saveFolder + '/phi3.edf')
Ir3D = spytIO.openSeq(referenceFiles)
spytIO.saveEdf(phi3.real,
outputFolder + '/phiLarkin/phiLarkin_' + textProj + '.edf')
spytIO.saveEdf(
gradientNorm.real,
outputFolder + '/gradientNorm/gradientNorm_' + textProj + '.edf')
if __name__ == "__main__":
originalFolder = '/Users/embrun/Downloads/Mantela_aurantiaca_1/cine1/'
fileSequence = glob.glob(originalFolder + '/*.tif')
fileSequence.sort()
outputFolder = '/Users/embrun/Downloads/Mantela_aurantiaca_1/Mantela_cine_OpticalFlow/'
mkdir(outputFolder)
createFolders(outputFolder)
Ir = spytIO.openImage(fileSequence[0])
Ir = np.asarray(Ir, np.float32)
firstIm = spytIO.openImage(fileSequence[0])
cpt = 0
for filename in fileSequence:
print(filename)
if cpt > 0:
Is = spytIO.openImage(fileSequence[cpt])
Is = np.asarray(Is, np.float32)
result = OpticalFlow.processOneProjection(Is, Ir)
saveResults(result, outputFolder, cpt)
textProj = '%4.4d' % cpt
spytIO.saveEdf(Is - Ir,
outputFolder + '/diff/diff' + textProj + '.edf')
# multiplication to be in micron
img_thickness = img_thickness * 1e6
# unpadding
img_thickness = img_thickness[padRow:padRow + width,
padCol:padCol + height]
img_thickness += bg_val
return img_thickness
if __name__ == "__main__":
# testOneImage()
irName = 'Ref0367.edf'
isName = 'im0007.edf'
imageSample = openImage(isName)
imageReference = openImage(irName)
imageSample = imageSample[411:411 + 400, 768:768 + 400]
imageReference = imageReference[411:411 + 400, 768:768 + 400]
# darkName='/VOLUMES/ID17/broncho/IHR_April2018/HA800_Patte21_3um_Gap90_75_Speckle02_/dark.edf'
# result=processOneImage(isName,irName)
beta = 2.7274690492888E-12
delta = 1.0430137117588E-07
z1 = 142 # (m) distance from source to object
z2 = 11.0 # (m) distance from the object to the detector
pix_size = 6.1e-6
result = tie_Pavlovetal2019(imageSample, imageReference, 52, z1, z2,
pix_size, delta, beta, 0, 12)
import os
import glob
import spytIO
import numpy as np
import utils
if __name__ == "__main__":
inputFolder='/Volumes/ID17/speckle/md1097/id17/Phantoms/ThreeDimensionalPhantom/OpticalFlow/dy/'
outputFolder='/Volumes/ID17/speckle/md1097/id17/Phantoms/ThreeDimensionalPhantom/OpticalFlow/dy_32/'
utils.spytMkDir(outputFolder)
files=glob.glob(inputFolder+'/*.edf')
files.sort()
for filename in files:
print(filename)
data=spytIO.openImage(filename)
data=np.asarray(data,np.float32)
outputFilename=outputFolder+'/'+os.path.basename(filename)
print(outputFilename)
spytIO.saveEdf(data,outputFilename)
dI = (Is - Ir * (np.mean(gaussian_filter(Is,sigma=2.)) / np.mean(gaussian_filter(Ir,sigma=2.))))
alpha=np.finfo(np.float32).eps
dx, dy = derivativesByOpticalflow(Is, dI, alpha=alpha, sig_scale=sigma)
phi = fc.frankotchellappa(dx, dy, False)
phi3 = kottler(dx, dy)
phi2 = LarkinAnissonSheppard(dx, dy)
return {'dx': dx, 'dy': dy, 'phi': phi, 'phi2': phi2,'phi3': phi3,'gradientNorm':gradientNorm}
if __name__ == "__main__":
Is = spytIO.openImage('im0007.edf')
Ir = spytIO.openImage('Ref0367.edf')
result = processOneProjection(Is, Ir)
dx = result['dx']
dy = result['dy']
phi = result['phi']
phi2 = result['phi2']
phi3 = result['phi3']
spytIO.saveEdf(dx, 'output/dx.edf')
spytIO.saveEdf(dy.real, 'output/dy.edf')
spytIO.saveEdf(phi.real, 'output/phi.edf')
spytIO.saveEdf(phi2.real, 'output/phiLarkinson.edf')
gradientNorm = np.sqrt(dxPlus**2 + dyPlus**2)
return {
'dx': dx,
'dy': dy,
'phi': phi,
'phi2': phi2,
'phi3': phi3,
'gradientNorm': gradientNorm
}
if __name__ == "__main__":
Ir = spytIO.openImage(
'/Users/embrun/Codes/specklematching/Experiments/MoucheSimapAout2017/ref/ref_40kV_5.2um_30s_12cm_53cm_speck14.tif'
)
Is = spytIO.openImage(
'/Users/embrun/Codes/specklematching/Experiments/MoucheSimapAout2017/sample/mouche_40kV_5.2um_30s_12cm_53cm_speck14.tif'
)
result = processOneProjection(Is, Ir)
#IrNames=glob.glob('/Users/embrun/Codes/specklematching/Experiments/MoucheSimapAout2017/ref/*.tif')
#IsNames= glob.glob('/Users/embrun/Codes/specklematching/Experiments/MoucheSimapAout2017/sample/*.tif')
#Ir=spytIO.openSeq(IrNames)
#Is= spytIO.openSeq(IsNames)
#result = processProjectionSet(Is, Ir)
dx = result['dx']
dy = result['dy']
phi = result['phi']
