def produceAlignedImagesStep(self, volumeIsCTFCorrected, fn, images):
from numpy import array, dot
fnOut = 'classes_aligned@' + fn
MDin = MetaData(images)
MDout = MetaData()
n = 1
hasCTF = MDin.containsLabel(xmipp.MDL_CTF_MODEL)
for i in MDin:
fnImg = MDin.getValue(xmipp.MDL_IMAGE, i)
fnImgRef = MDin.getValue(xmipp.MDL_IMAGE_REF, i)
maxCC = MDin.getValue(xmipp.MDL_MAXCC, i)
rot = MDin.getValue(xmipp.MDL_ANGLE_ROT, i)
tilt = MDin.getValue(xmipp.MDL_ANGLE_TILT, i)
psi = -1. * MDin.getValue(xmipp.MDL_ANGLE_PSI, i)
flip = MDin.getValue(xmipp.MDL_FLIP, i)
if flip:
psi = -psi
eulerMatrix = Euler_angles2matrix(0., 0., psi)
x = MDin.getValue(xmipp.MDL_SHIFT_X, i)
y = MDin.getValue(xmipp.MDL_SHIFT_Y, i)
shift = array([x, y, 0])
shiftOut = dot(eulerMatrix, shift)
[x, y, z] = shiftOut
if flip:
x = -x
id = MDout.addObject()
MDout.setValue(xmipp.MDL_IMAGE, fnImg, id)
MDout.setValue(xmipp.MDL_IMAGE_REF, fnImgRef, id)
MDout.setValue(xmipp.MDL_IMAGE1,
"%05d@%s" % (n, self._getExtraPath("diff.stk")), id)
if hasCTF:
fnCTF = MDin.getValue(xmipp.MDL_CTF_MODEL, i)
MDout.setValue(xmipp.MDL_CTF_MODEL, fnCTF, id)
MDout.setValue(xmipp.MDL_MAXCC, maxCC, id)
MDout.setValue(xmipp.MDL_ANGLE_ROT, rot, id)
MDout.setValue(xmipp.MDL_ANGLE_TILT, tilt, id)
MDout.setValue(xmipp.MDL_ANGLE_PSI, psi, id)
MDout.setValue(xmipp.MDL_SHIFT_X, x, id)
MDout.setValue(xmipp.MDL_SHIFT_Y, y, id)
MDout.setValue(xmipp.MDL_FLIP, flip, id)
MDout.setValue(xmipp.MDL_ENABLED, 1, id)
n += 1
MDout.write(fnOut, xmipp.MD_APPEND)
# Actually create the differences
img = Image()
imgRef = Image()
if hasCTF and volumeIsCTFCorrected:
Ts = MDin.getValue(xmipp.MDL_SAMPLINGRATE, MDin.firstObject())
for i in MDout:
img.readApplyGeo(MDout, i)
imgRef.read(MDout.getValue(xmipp.MDL_IMAGE_REF, i))
if hasCTF and volumeIsCTFCorrected:
fnCTF = MDout.getValue(xmipp.MDL_CTF_MODEL, i)
imgRef.applyCTF(fnCTF, Ts)
img.convert2DataType(DT_DOUBLE)
imgDiff = img - imgRef
imgDiff.write(MDout.getValue(xmipp.MDL_IMAGE1, i))
def produceAlignedImagesStep(self, volumeIsCTFCorrected, fn, images):
from numpy import array, dot
fnOut = "classes_aligned@" + fn
MDin = MetaData(images)
MDout = MetaData()
n = 1
hasCTF = MDin.containsLabel(xmipp.MDL_CTF_MODEL)
for i in MDin:
fnImg = MDin.getValue(xmipp.MDL_IMAGE, i)
fnImgRef = MDin.getValue(xmipp.MDL_IMAGE_REF, i)
maxCC = MDin.getValue(xmipp.MDL_MAXCC, i)
rot = MDin.getValue(xmipp.MDL_ANGLE_ROT, i)
tilt = MDin.getValue(xmipp.MDL_ANGLE_TILT, i)
psi = -1.0 * MDin.getValue(xmipp.MDL_ANGLE_PSI, i)
flip = MDin.getValue(xmipp.MDL_FLIP, i)
if flip:
psi = -psi
eulerMatrix = Euler_angles2matrix(0.0, 0.0, psi)
x = MDin.getValue(xmipp.MDL_SHIFT_X, i)
y = MDin.getValue(xmipp.MDL_SHIFT_Y, i)
shift = array([x, y, 0])
shiftOut = dot(eulerMatrix, shift)
[x, y, z] = shiftOut
if flip:
x = -x
id = MDout.addObject()
MDout.setValue(xmipp.MDL_IMAGE, fnImg, id)
MDout.setValue(xmipp.MDL_IMAGE_REF, fnImgRef, id)
MDout.setValue(xmipp.MDL_IMAGE1, "%05d@%s" % (n, self._getExtraPath("diff.stk")), id)
if hasCTF:
fnCTF = MDin.getValue(xmipp.MDL_CTF_MODEL, i)
MDout.setValue(xmipp.MDL_CTF_MODEL, fnCTF, id)
MDout.setValue(xmipp.MDL_MAXCC, maxCC, id)
MDout.setValue(xmipp.MDL_ANGLE_ROT, rot, id)
MDout.setValue(xmipp.MDL_ANGLE_TILT, tilt, id)
MDout.setValue(xmipp.MDL_ANGLE_PSI, psi, id)
MDout.setValue(xmipp.MDL_SHIFT_X, x, id)
MDout.setValue(xmipp.MDL_SHIFT_Y, y, id)
MDout.setValue(xmipp.MDL_FLIP, flip, id)
MDout.setValue(xmipp.MDL_ENABLED, 1, id)
n += 1
MDout.write(fnOut, xmipp.MD_APPEND)
# Actually create the differences
img = Image()
imgRef = Image()
if hasCTF and volumeIsCTFCorrected:
Ts = MDin.getValue(xmipp.MDL_SAMPLINGRATE, MDin.firstObject())
for i in MDout:
img.readApplyGeo(MDout, i)
imgRef.read(MDout.getValue(xmipp.MDL_IMAGE_REF, i))
if hasCTF and volumeIsCTFCorrected:
fnCTF = MDout.getValue(xmipp.MDL_CTF_MODEL, i)
imgRef.applyCTF(fnCTF, Ts)
img.convert2DataType(DT_DOUBLE)
imgDiff = img - imgRef
imgDiff.write(MDout.getValue(xmipp.MDL_IMAGE1, i))
