Python Images.append方法代码示例

# 需要导入模块: from lib.images import Images [as 别名]
# 或者: from lib.images.Images import append [as 别名]
    def qaSupplier(self):
        """Create and supply images for the report generated by qa task

        """
        qaImages = Images()
        softwareName = 'mrtrix'

        #Get images
        mask = self.getRegistrationImage('mask', 'resample')

        #Build qa images
        tags = (
            ('nufo', 5, 'nufo'),
            ('afd', 5,'afd')
            )

        for postfix, vmax, description in tags:
            image = self.getImage('dwi', postfix)
            if image:
                imageQa = self.plot3dVolume(
                        image, fov=mask, vmax=vmax,
                        colorbar=True, postfix=softwareName)
                qaImages.append((imageQa, description))

        return qaImages

# 需要导入模块: from lib.images import Images [as 别名]
# 或者: from lib.images.Images import append [as 别名]
    def qaSupplier(self):
        """Create and supply images for the report generated by qa task

        """
        qaImages = Images()
        softwareName = 'fsl'

        #Get images
        mask = self.getRegistrationImage('mask', 'resample')

        #Build qa images
        tags = (
            ('fa', 0.7, 'Fractional anisotropy'),
            ('ad', 0.005, 'Axial Diffusivity'),
            ('md', 0.005, 'Mean Diffusivity'),
            ('rd', 0.005, 'Radial Diffusivity'),
            )

        for postfix, vmax, description in tags:
            image = self.getImage('dwi', postfix)
            if image:
                imageQa = self.plot3dVolume(
                        image, fov=mask, vmax=vmax,
                        colorbar=True, postfix=softwareName)
                qaImages.append((imageQa, description))

        #Build SSE image
        sse = self.getImage('dwi', 'sse')
        sseQa = self.plot3dVolume(
                sse, fov=mask, postfix=softwareName, colorbar=True)
        qaImages.append((sseQa, 'Sum of squared errors'))

        return qaImages

# 需要导入模块: from lib.images import Images [as 别名]
# 或者: from lib.images.Images import append [as 别名]
    def qaSupplier(self):
        """Create and supply images for the report generated by qa task

        """
        qaImages = Images()

        #Get images
        dwiNative = self.getPreparationImage('dwi')
        dwiCorrected = self.getCorrectionImage('dwi', 'corrected')
        dwiDenoised = self.getDenoisingImage('dwi', 'denoise')
        noiseMask = self.getImage('mask', ['corrected', 'noisemask'])
        ccMask = self.getImage('aparc_aseg', ['253', 'mask', 'downsample'])
        b0 = self.getCorrectionImage('b0', 'corrected')

        #Build qa images
        tags = (
            (dwiNative, 'Native'),
            (dwiDenoised, 'denoised'),
            (dwiCorrected, 'Corrected'),
            )
        for dwi, description in tags:
            if dwi:
                qaImages = self.__noiseAnalysis(dwi, noiseMask, ccMask, qaImages, description)

        #Build qa masks images
        tags = (
            (noiseMask, 'Noise mask'),
            (ccMask, 'Corpus callosum mask'),
            )
        for mask, description in tags:
            maskPng = self.buildName(mask, None, 'png')
            self.slicerPng(b0, maskPng, maskOverlay=mask, boundaries=mask)
            qaImages.append((maskPng, description))

        return qaImages

# 需要导入模块: from lib.images import Images [as 别名]
# 或者: from lib.images.Images import append [as 别名]
    def qaSupplier(self):
        """Create and supply images for the report generated by qa task

        """
        qaImages = Images()
        softwareName = 'mrtrix'

        #Set information
        information = "Estimation using WLS with {} iteration(s)".format(self.get('iter'))
        qaImages.setInformation(information)

        #Get images
        mask = self.getRegistrationImage('mask', 'resample')

        #Build qa images
        tags = (
            ('fa', 0.7, 'Fractional anisotropy'),
            ('ad', 0.005, 'Axial Diffusivity'),
            ('md', 0.005, 'Mean Diffusivity'),
            ('rd', 0.005, 'Radial Diffusivity'),
            )

        for postfix, vmax, description in tags:
            image = self.getImage('dwi', postfix)
            if image:
                imageQa = self.plot3dVolume(
                        image, fov=mask, vmax=vmax,
                        colorbar=True, postfix=softwareName)
                qaImages.append((imageQa, description))

        return qaImages

# 需要导入模块: from lib.images import Images [as 别名]
# 或者: from lib.images.Images import append [as 别名]
    def qaSupplier(self):
        """Create and supply images for the report generated by qa task

        """
        qaImages = Images()

        # Get images
        dwiNative = self.getPreparationImage("dwi")
        dwiCorrected = self.getCorrectionImage("dwi", "corrected")
        dwiDenoised = self.getDenoisingImage("dwi", "denoise")
        noiseMask = self.getImage("mask", ["corrected", "noisemask"])
        ccMask = self.getImage("aparc_aseg", ["253", "mask", "downsample"])
        b0 = self.getCorrectionImage("b0", "corrected")

        # Build qa images
        tags = ((dwiNative, "Native"), (dwiDenoised, "denoised"), (dwiCorrected, "Corrected"))
        for dwi, description in tags:
            if dwi:
                qaImages = self.__noiseAnalysis(dwi, noiseMask, ccMask, qaImages, description)

        # Build qa masks images
        tags = ((noiseMask, "Noise mask"), (ccMask, "Corpus callosum mask"))
        for mask, description in tags:
            maskPng = self.buildName(mask, None, "png")
            self.slicerPng(b0, maskPng, maskOverlay=mask, boundaries=mask)
            qaImages.append((maskPng, description))

        return qaImages

# 需要导入模块: from lib.images import Images [as 别名]
# 或者: from lib.images.Images import append [as 别名]
    def qaSupplier(self):
        """Create and supply images for the report generated by qa task

        """
        qaImages = Images()

        information = "Warning: due to storage restriction, streamlines were " \
                      "downsampled. Even if there is no difference in structural " \
                      "connectivity, you should be careful before computing any " \
                      "metrics along these streamlines.\n To run toad without this " \
                      "downsampling, please refer to the documentation."

        if self.defaultQuery:
            # get images
            norm = self.getRegistrationImage("norm", "resample")
            self.__buildNameTractQuerierOutputs()
            # images production
            tags = (
                (self.queries[0],
                 'Corpus Callosum',
                 95, 60, 40, -80, 0, 160),
                (self.queries[1],
                 'Inferior Fronto Occipital tract left',
                 95, 80, 40, -90, 0, 90),
                (self.queries[2],
                 'Inferior Fronto Occipital tract right',
                 95, 80, 40, -90, 0, -90),
                (self.queries[3],
                 'inferior Longitudinal Fasciculus left',
                 95, 80, 40, -90, 0, 90),
                (self.queries[4],
                 'Inferior Longitudinal Fasciculus right',
                 95, 80, 40, -90, 0, -90),
                (self.queries[5],
                 'Uncinate Fasciculus left',
                 95, 80, 40, -90, 0, 90),
                (self.queries[6],
                 'Uncinate Fasciculus right',
                 95, 80, 40, -90, 0, -90),
                (self.queries[7],
                 'Corticospinal tract Left',
                 95, 80, 40, -90, 0, 160),
                (self.queries[8],
                 'Corticospinal tract right',
                 95, 80, 40, -90, 0, 200),
                )

            for data, description, xSlice, ySlice, zSlice, xRot, yRot, zRot in tags:
                if data:
                    imageQa = self.plotTrk(data, norm, None, xSlice, ySlice, zSlice, xRot, yRot, zRot)
                    qaImages.append((imageQa, description))
        else:
            information = """
            Because you didn't choose default queries and dictionnary,
            we are not able to create proper screenshots of the output bundles.
            """
        qaImages.setInformation(information)

        return qaImages

# 需要导入模块: from lib.images import Images [as 别名]
# 或者: from lib.images.Images import append [as 别名]
    def meetRequirement(self):
        images = Images((self.getPreparationImage('dwi'), 'diffusion weighted'),
                        (self.getParcellationImage('norm'), 'freesurfer normalize'),
                        (self.getParcellationImage('mask'), 'freesurfer mask'),
                        (self.getPreparationImage('grad', None, 'bvals'), 'gradient .bvals encoding file'),
                        (self.getPreparationImage('grad', None, 'bvecs'), 'gradient .bvecs encoding file'),
                        (self.getPreparationImage('grad', None, 'b'), 'gradient .b encoding file'))

        #if fieldmap available
        if Images(self.getPreparationImage("mag") , self.getPreparationImage("phase")).isAllImagesExists():
            images.append((self.getParcellationImage('anat', 'freesurfer'),"freesurfer anatomical"))

        return images

# 需要导入模块: from lib.images import Images [as 别名]
# 或者: from lib.images.Images import append [as 别名]
    def isDirty(self):

        images = Images()

        dwi = self.getUpsamplingImage('dwi', 'upsample')

        if mriutil.getNbDirectionsFromDWI(dwi) <= 45  and not self.get('forceHardi'):
            if 'deterministic' in self.get('algorithm'):
                images.append((
                    self.getImage('dwi', 'tensor_det', 'trk'),
                    "deterministic tensor connectome matrix from a streamlines"
                    ))

            if 'probabilistic' in self.get('algorithm'):
                images.append((
                    self.getImage('dwi', 'tensor_prob', 'trk'),
                    "probabilistic tensor connectome matrix from a streamlines"
                    ))

        else:
            images.append((
                    self.getImage('dwi', 'hardi_prob', 'trk'),
                    "tckgen hardi probabilistic streamlines tractography"
                    ))

            if self.get('sift'):
                images.append((
                    self.getImage('dwi', 'tcksift', 'trk'), 'tcksift'))

        return images

# 需要导入模块: from lib.images import Images [as 别名]
# 或者: from lib.images.Images import append [as 别名]
    def qaSupplier(self):
        """Create and supply images for the report generated by qa task

        """
        qaImages = Images()

        #Information on denoising algorithm
        information = 'Denoising was done using the {} algorithm'.format(self.algorithm)

        if self.algorithm == "nlmeans" and \
            self.config.get("denoising", "number_array_coil") == "32":
            information = "NLMEANS algorithm is not yet implemented for 32 " \
                "coils channels images, "
            if self.config.getboolean("general", "matlab_available"):
                information += "set algorithm to `lpca` or `aonlm` or "
            information += "set `ignore: True` into the [denoising] section " \
                    "of your config.cfg file."

        if self.matlabWarning:
            information = "Algorithm `aonlm` or `lpca` was set for the " \
                    "denoising, but Matlab is not available for this server. "\
                    "Please install and configure Matlab or set `ignore: True`"\
                    " into the [denoising] section of your config.cfg file."
            qaImages.extend(Images((False, 'Denoised diffusion image')))

        qaImages.setInformation(information)

        #Get images
        dwi = self.getPreparationImage("dwi")
        dwiDenoised = self.getImage('dwi', 'denoise')
        brainMask = self.getImage('mask', 'resample')
        b0 = self.getImage('b0')
        noiseMask = self.getImage('dwi', 'noise_mask')

        #Build qa images
        if dwiDenoised:

            dwiDenoisedQa = self.plot4dVolume(dwiDenoised, fov=brainMask)
            qaImages.append((dwiDenoisedQa, 'Denoised diffusion image'))

            dwiCompareQa = self.compare4dVolumes(
                    dwi, dwiDenoised, fov=brainMask)
            qaImages.append((dwiCompareQa, 'Before and after denoising'))

            if self.algorithm == "nlmeans":
                if self.sigmaVector != None:
                    sigmaQa = self.plotSigma(self.sigmaVector, dwiDenoised)
                    qaImages.append(
                            (sigmaQa, 'Sigmas from the nlmeans algorithm'))

                if noiseMask:
                    noiseMaskQa = self.plot3dVolume(
                            b0, edges=noiseMask, fov=noiseMask)
                    qaImages.append(
                            (noiseMaskQa, 'Noise mask from the nlmeans algorithm'))

        return qaImages

# 需要导入模块: from lib.images import Images [as 别名]
# 或者: from lib.images.Images import append [as 别名]
    def isDirty(self):

        images = Images()

        if self.__nbDirections <= 45:
            if 'deterministic' in self.get('algorithm'):
                images.extend(Images((self.getImage('dwi', 'tensor_det', 'tck'), "deterministic tensor connectome matrix from a streamlines")))

            if 'probabilistic' in self.get('algorithm'):
                images.extend(Images((self.getImage('dwi', 'tensor_prob', 'tck'), "probabilistic tensor connectome matrix from a streamlines")))

        else:
            if 'hardi' in self.get('algorithm'):
                images.append((self.getImage('dwi', 'hardi_prob', 'tck'), "tckgen hardi probabilistic streamlines tractography"))

            if 'sift' in self.get('algorithm'):
                images.extend(Images((self.getImage('dwi', 'tcksift', 'tck'), 'tcksift')))

        return images

# 需要导入模块: from lib.images import Images [as 别名]
# 或者: from lib.images.Images import append [as 别名]
    def qaSupplier(self):
        """Create and supply images for the report generated by qa task

        """
        qaImages = Images()
        softwareName = 'dipy'

        #Set information
        information = "Fit method: {}".format(self.get('fitMethod'))
        qaImages.setInformation(information)

        #  mask image
        mask = self.getRegistrationImage('mask', 'resample')

        #  Produce tensor ellipsoids image
        dwi = self.getUpsamplingImage('dwi', 'upsample')
        cc = self.getMaskingImage('aparc_aseg', ['253','mask'])
        ellipsoidsQa = self.plotReconstruction(
                self.__fit, mask, cc, 'tensor', dwi)
        qaImages.append((
            ellipsoidsQa,
            'Coronal slice of tensor ellipsoids in the Corpus Callosum'))

        #  Build qa images
        tags = (
            #(['tensor', 'rgb'], 'RGB map'),
            ('fa', 0.7, 'Fractional anisotropy'),
            ('ad', 0.005, 'Axial Diffusivity'),
            ('md', 0.005, 'Mean Diffusivity'),
            ('rd', 0.005, 'Radial Diffusivity'),
            )

        for postfix, vmax, description in tags:
            image = self.getImage('dwi', postfix)
            if image:
                imageQa = self.plot3dVolume(
                        image, fov=mask, vmax=vmax,
                        colorbar=True, postfix=softwareName)
                qaImages.append((imageQa, description))

        return qaImages

# 需要导入模块: from lib.images import Images [as 别名]
# 或者: from lib.images.Images import append [as 别名]
    def qaSupplier(self):
        """Create and supply images for the report generated by qa task

        """
        qaImages = Images()

        tags = (
            ('anat', self.plot3dVolume, 'High resolution anatomical image'),
            ('dwi', self.plot4dVolume, 'Diffusion weighted image'),
            ('b0_ap', self.plot3dVolume, 'B0 AP image'),
            ('b0_pa', self.plot3dVolume, 'B0 PA image'),
            ('mag', self.plot3dVolume, 'Magnitude image'),
            ('phase', self.plot3dVolume, 'Phase image'),
            )
        for prefix, plotMethod, description in tags:
            source = self.getImage(prefix)
            if source:
                qaImage = plotMethod(source)
                qaImages.append((qaImage, description))

        return qaImages

# 需要导入模块: from lib.images import Images [as 别名]
# 或者: from lib.images.Images import append [as 别名]
    def qaSupplier(self):
        """Create and supply images for the report generated by qa task

        """
        qaImages = Images()
        softwareName = "dipy"

        # Get images
        dwi = self.getUpsamplingImage("dwi", "upsample")
        cc = self.getMaskingImage("aparc_aseg", ["253", "mask"])
        mask = self.getRegistrationImage("mask", "resample")

        # Produce hardi odfs image
        data = {"dwiData": self.__dwiData, "csdModel": self.__csdModel}
        odfsQa = self.plotReconstruction(data, mask, cc, "hardi_odf", dwi)
        qaImages.append((odfsQa, "Coronal slice of hardi CSD ODFs in the Corpus Callosum"))

        # Produce hardi peaks image
        peaksQa = self.plotReconstruction(self.__csdPeaks, mask, cc, "hardi_peak", dwi)
        qaImages.append((peaksQa, "Coronal slice of hardi CSD Peaks in the Corpus Callosum"))

        # Build qa images
        tags = (("gfa", 1.5, "Generalised Fractional Anisotropy"), ("nufo", 5, "nufo"))

        for postfix, vmax, description in tags:
            image = self.getImage("dwi", postfix)
            if image:
                imageQa = self.plot3dVolume(image, fov=mask, vmax=vmax, colorbar=True, postfix=softwareName)
                qaImages.append((imageQa, description))

        return qaImages

# 需要导入模块: from lib.images import Images [as 别名]
# 或者: from lib.images.Images import append [as 别名]
    def qaSupplier(self):
        """Create and supply images for the report generated by qa task

        """
        qaImages = Images()

        information = "Warning: due to storage restriction, streamlines were " \
                "downsampled. Even if there is no difference in structural " \
                "connectivity, you should be careful before computing any " \
                "metrics along these streamlines.\n To run toad without this " \
                "downsampling, please refer to the documentation."
        qaImages.setInformation(information)

        #get images
        norm = self.getRegistrationImage("norm", "resample")
        mask253 = self.getMaskingImage('aparc_aseg',['253','mask'])

        #images production
        if self.__nbDirections <= 45 and not self.get('forceHardi'):
            tags = (
                (self.__tckDetRoiTrk,
                'fiber crossing aparc_aseg area 253 from a deterministic tensor streamlines'),
                (self.__tckProbRoiTrk,
                'fiber crossing aparc_aseg area 253 from a probabilistic tensor streamlines'),
                )
        else:
            tags = (
                (self.__tckgenRoiTrk,
                'fiber crossing aparc_aseg area 253 from a probabilistic hardi streamlines'),
                (self.__tcksiftRoiTrk,
                'fiber crossing aparc_aseg area 253 from a probabilistic hardi streamlines with sift'),
                )

        for data, description in tags:
            if data is not None:
                imageQa = self.plotTrk(data, norm, mask253, None, None, 65, -70, 2.5, 185)
                qaImages.append((imageQa, description))

        return qaImages

# 需要导入模块: from lib.images import Images [as 别名]
# 或者: from lib.images.Images import append [as 别名]
    def qaSupplier(self):
        """Create and supply images for the report generated by qa task

        """
        qaImages = Images()
        softwareName = 'dipy'

        #Get images
        dwi = self.getUpsamplingImage('dwi', 'upsample')
        cc = self.getMaskingImage('aparc_aseg', ['253','mask'])
        mask = self.getRegistrationImage('mask', 'resample')

        #Produce hardi odfs image
        data = {'dwiData':self.__dwiData, 'csdModel':self.__csdModel}
        odfsQa = self.plotReconstruction(data, mask, cc, 'hardi_odf', dwi)
        qaImages.append((
            odfsQa, 'Coronal slice of hardi CSD ODFs in the Corpus Callosum'))

        #Produce hardi peaks image
        peaksQa = self.plotReconstruction(
                self.__csdPeaks, mask, cc, 'hardi_peak', dwi)
        qaImages.append((
            peaksQa, "Coronal slice of hardi CSD Peaks in the Corpus Callosum"))

        #Build qa images
        tags = (
            ('gfa', 1.5, 'Generalised Fractional Anisotropy'),
            ('nufo', 5, 'nufo'),
            )

        for postfix, vmax, description in tags:
            image = self.getImage('dwi', postfix)
            if image:
                imageQa = self.plot3dVolume(
                        image, fov=mask, vmax=vmax,
                        colorbar=True, postfix=softwareName)
                qaImages.append((imageQa, description))

        return qaImages