Python Images.extend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 extend [as 别名]
    def qaSupplier(self):
        """Create and supply images for the report generated by qa task

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

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

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

        for postfix, description in tags:
            image = self.getImage('dwi', postfix)
            if image:
                qaImage = self.buildName(image, softwareName, 'png')
                self.slicerPng(image, qaImage, boundaries=mask)
                qaImages.extend(Images((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 extend [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'),
            (dwiCorrected, 'Corrected'),
            (dwiDenoised, 'denoised'),
            )
        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.extend(Images((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 extend [as 别名]
    def qaSupplier(self):
        """Create and supply images for the report generated by qa task

        """
        qaImages = Images()

        #Get images
        b0 = self.getUpsamplingImage('b0', 'upsample')
        whiteMatter = self.getImage("tt5", ["resample", "wm", "mask"])
        interfaceGmWm = self.getImage("tt5", ["register", "5tt2gmwmi"])
        area253 = self.getImage('aparc_aseg',['253','mask'])
        area1024 = self.getImage('aparc_aseg',['1024','mask'])

        #Build qa images
        tags = (
            (whiteMatter, 'resample white segmented mask'),
            #(interfaceGmWm, 'grey matter, white matter interface'),
            #(area253, 'area 253 from aparc_aseg atlas'),
            #(area1024, 'area 1024 from aparc_aseg atlas'),
            )
        for image, description in tags:
            qaImage = self.buildName(image, None, 'png')
            self.slicerPng(b0, qaImage, maskOverlay=image, boundaries=image)
            qaImages.extend(Images((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 extend [as 别名]
    def qaSupplier(self):
        """Create and supply images for the report generated by qa task

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

        #Produce tensor ellipsoids png image
        rgb = self.getImage('dwi', ['tensor', 'rgb'])
        cc = self.getMaskingImage('aparc_aseg', ['253','mask'])
        ellipsoidsPng = self.buildName(rgb, 'ellipsoids', 'png')
        self.tensorPng(self.__fit, cc, ellipsoidsPng)
        qaImages.extend(Images((ellipsoidsPng, 'Tensor ellipsoids in a part of the CC')))

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

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

        for postfix, description in tags:
            image = self.getImage('dwi', postfix)
            if image:
                qaImage = self.buildName(image, softwareName, 'png')
                self.slicerPng(image, qaImage, boundaries=mask)
                qaImages.extend(Images((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 extend [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 extend [as 别名]
    def qaSupplier(self):
        """Create and supply images for the report generated by qa task

        """
        qaImages = Images()

        #Information on distorsion correction
        b0_ap = self.getPreparationImage('b0_ap')
        b0_pa = self.getPreparationImage('b0_pa')
        mag = self.getPreparationImage('mag')
        phase = self.getPreparationImage('phase')
        information = ''
        if b0_ap and b0_pa:
            information = 'Distortion correction done with AP and PA images'
        elif mag and phase:
            information = 'Distorsion correction done with fieldmap images'
        else:
            information = 'No distortion correction done'
        qaImages.setInformation(information)

        #Get images
        dwi = self.getPreparationImage('dwi')
        dwiCorrected = self.getImage('dwi', 'corrected')
        brainMask = self.getImage('mask', 'corrected')
        eddyParameterFiles = self.getImage('dwi', None, 'eddy_parameters')
        bVecs=  self.getPreparationImage('grad',  None, 'bvecs')
        bVecsCorrected = self.getImage('grad',  None, 'bvecs')

        #Build qa names
        dwiCorrectedGif = self.buildName(dwiCorrected, None, 'gif')
        dwiCompareGif = self.buildName(dwiCorrected, 'compare', 'gif')
        translationsPng = self.buildName(dwiCorrected, 'translations', 'png')
        rotationPng = self.buildName(dwiCorrected, 'rotations', 'png')
        bVecsGif = self.buildName(dwiCorrected, 'vectors', 'gif')

        #Build qa images
        self.slicerGif(dwiCorrected, dwiCorrectedGif, boundaries=brainMask)
        self.slicerGifCompare(dwi, dwiCorrected, dwiCompareGif, boundaries=brainMask)
        self.plotMovement(eddyParameterFiles, translationsPng, rotationPng)
        self.plotvectors(bVecs, bVecsCorrected, bVecsGif)

        qaImages.extend(Images(
            (dwiCorrectedGif, 'DWI corrected'),
            (dwiCompareGif, 'Before and after corrections'),
            (translationsPng, 'Translation correction by eddy'),
            (rotationPng, 'Rotation correction by eddy'),
            (bVecsGif, 'Gradients vectors on the unitary sphere. Red : raw bvec | Blue : opposite bvec     | Black + : movement corrected bvec'),
            ))

        return qaImages

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

        """
        qaImages = Images()
        algorithm = self.get("algorithm")

        #Information on denoising algorithm
        information = 'Algorithm {} is set'.format(algorithm)
        if self.matlabWarning:
            information += ' but matlab is not available on this server'
        qaImages.setInformation(information)

        #Get images
        dwi = self.__getDwiImage()
        dwiDenoised = self.getImage('dwi', 'denoise')
        brainMask = self.getCorrectionImage('mask', 'corrected')
        b0 = self.getCorrectionImage('b0', 'corrected')
        noiseMask = self.getImage('dwi', 'noise_mask')

        #Build qa images
        if dwiDenoised:
            dwiDenoisedGif = self.buildName(dwiDenoised, None, 'gif')
            dwiCompareGif = self.buildName(dwiDenoised, 'compare', 'gif')
            self.slicerGif(dwiDenoised, dwiDenoisedGif, boundaries=brainMask)
            self.slicerGifCompare(dwi, dwiDenoised, dwiCompareGif, boundaries=brainMask)
            qaImages.extend(Images(
                (dwiDenoisedGif, 'Denoised diffusion image'),
                (dwiCompareGif, 'Before and after denoising'),
                ))

            if algorithm == "nlmeans":
                sigmaPng = self.buildName(dwiDenoised, 'sigma', 'png')
                noiseMaskPng = self.buildName(noiseMask, None, 'png')
                self.plotSigma(self.sigmaVector, sigmaPng)
                self.slicerPng(b0, noiseMaskPng, maskOverlay=noiseMask, boundaries=noiseMask)
                qaImages.extend(Images(
                    (sigmaPng, 'Sigmas from nlmeans algorithm'),
                    (noiseMaskPng, 'Noise mask from 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 extend [as 别名]
    def qaSupplier(self):
        """Create and supply images for the report generated by qa task

        """
        qaImages = Images()

        tags = (
            ('anat', 'png', self.slicerPng, 'High resolution anatomical image'),
            ('dwi', 'gif', self.slicerGif, 'Diffusion weighted image'),
            ('b0_ap', 'png', self.slicerPng, 'B0 AP image'),
            ('b0_pa', 'png', self.slicerPng, 'B0 PA image'),
            ('mag', 'png', self.slicerPng, 'Magnitude image'),
            ('phase', 'png', self.slicerPng, 'Phase image'),
            )
        for prefix, imageFormat, slicerMethod, description in tags:
            image = self.getImage(prefix)
            if image:
                qaImage = self.buildName(image, None, imageFormat)
                slicerMethod(image, qaImage)
                qaImages.extend(Images((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 extend [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', 'nufo'),
            )

        for postfix, description in tags:
            image = self.getImage('dwi', postfix)
            if image:
                qaImage = self.buildName(image, softwareName, 'png')
                self.slicerPng(image, qaImage, boundaries=mask)
                qaImages.extend(Images((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 extend [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 extend [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.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')
        noise = self.getImage('dwi','noise')
        residuals = self.getImage('dwi', 'residuals')

        #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 mp-pca algorithm'))

            if self.algorithm == "mp-pca":
                if noise:
                    noiseQa = self.plot3dVolume(
                            noise, fov=noise)
                    qaImages.append(
                            (noiseQa, 'Noise from the mp-pca algorithm'))

                if residuals:
                    resQa = self.plot4dVolume(
                            residuals, fov=residuals)
                    qaImages.append(
                            (resQa, 'Residuals from the mp-pca algorithm'))

        return qaImages