From abb288f75094458356185a75cd680799bab4b9a3 Mon Sep 17 00:00:00 2001 From: Hans Johnson Date: Mon, 20 Jul 2026 19:46:27 -0500 Subject: [PATCH] COMP: Use @ref for module Doxygen cross-refs to silence CMP0219 CMake 4.4 policy CMP0219 warns when a macro (itk_module) receives arguments containing backslashes. The module DESCRIPTION strings carried Doxygen \ref group cross-references, spelled \\\\ref so a literal \ref survived the macro re-parse. Doxygen treats @ref identically to \ref, and @ is inert to both the macro and configure_file @ONLY, so switching to @ref removes the warning on every CMake version while keeping the generated Module.dox cross-links byte-for-byte unchanged. Ref: #6660 --- Modules/Filtering/AnisotropicSmoothing/itk-module.cmake | 2 +- Modules/Filtering/CurvatureFlow/itk-module.cmake | 2 +- Modules/Filtering/GPUAnisotropicSmoothing/itk-module.cmake | 2 +- Modules/Filtering/GPUSmoothing/itk-module.cmake | 2 +- Modules/Filtering/Smoothing/itk-module.cmake | 2 +- Modules/Registration/Common/itk-module.cmake | 4 ++-- Modules/Segmentation/Classifiers/itk-module.cmake | 2 +- Modules/Segmentation/ConnectedComponents/itk-module.cmake | 4 ++-- Modules/Segmentation/KLMRegionGrowing/itk-module.cmake | 2 +- Modules/Segmentation/LabelVoting/itk-module.cmake | 4 ++-- .../MarkovRandomFieldsClassifiers/itk-module.cmake | 2 +- Modules/Segmentation/RegionGrowing/itk-module.cmake | 2 +- 12 files changed, 15 insertions(+), 15 deletions(-) diff --git a/Modules/Filtering/AnisotropicSmoothing/itk-module.cmake b/Modules/Filtering/AnisotropicSmoothing/itk-module.cmake index 1e95ec830d1..12f8c67e8cd 100644 --- a/Modules/Filtering/AnisotropicSmoothing/itk-module.cmake +++ b/Modules/Filtering/AnisotropicSmoothing/itk-module.cmake @@ -4,7 +4,7 @@ anisotropic smoothing. This is an image denoising technique that strives for preserving edges on the images while smoothing regions of uniform intensity. This type of filtering is convenient as a preprocessing stage of segmentation algorithms. You may find useful as well the filters in -\\\\ref ITKCurvatureFlow and \\\\ref ITKSmoothing.") +@ref ITKCurvatureFlow and @ref ITKSmoothing.") itk_module( ITKAnisotropicSmoothing diff --git a/Modules/Filtering/CurvatureFlow/itk-module.cmake b/Modules/Filtering/CurvatureFlow/itk-module.cmake index 25ee26e8553..32fab3c8101 100644 --- a/Modules/Filtering/CurvatureFlow/itk-module.cmake +++ b/Modules/Filtering/CurvatureFlow/itk-module.cmake @@ -3,7 +3,7 @@ set(DOCUMENTATION Curvature Flow. This is a technique that uses an iterative solution of partial differential equations to implement image denoising image filtering. These classes are typically used as edge-preserving smoothing filters. You may also -find the \\\\ref ITKSmoothing and the \\\\ref ITKAnisotropicSmoothing useful +find the @ref ITKSmoothing and the @ref ITKAnisotropicSmoothing useful as well.") itk_module( diff --git a/Modules/Filtering/GPUAnisotropicSmoothing/itk-module.cmake b/Modules/Filtering/GPUAnisotropicSmoothing/itk-module.cmake index 04db0c4ddf8..e21469caa64 100644 --- a/Modules/Filtering/GPUAnisotropicSmoothing/itk-module.cmake +++ b/Modules/Filtering/GPUAnisotropicSmoothing/itk-module.cmake @@ -4,7 +4,7 @@ implement variations of anisotropic smoothing. This is an image denoising technique that strives for preserving edges on the images while smoothing regions of uniform intensity. This type of filtering is convenient as a preprocessing stage of segmentation algorithms. You may find useful as well the filters in the -\\\\ref ITKGPUSmoothing.") +@ref ITKGPUSmoothing.") itk_module( ITKGPUAnisotropicSmoothing diff --git a/Modules/Filtering/GPUSmoothing/itk-module.cmake b/Modules/Filtering/GPUSmoothing/itk-module.cmake index a44c7a3ac04..0cd6feaffdb 100644 --- a/Modules/Filtering/GPUSmoothing/itk-module.cmake +++ b/Modules/Filtering/GPUSmoothing/itk-module.cmake @@ -2,7 +2,7 @@ set(DOCUMENTATION "This module contains the GPU implementation of the most common image smoothing filters. For example, Gaussian and Median filters. You may also find it interesting to look at the -\\\\ref ITKAnisotropicSmoothing group of filters.") +@ref ITKAnisotropicSmoothing group of filters.") itk_module( ITKGPUSmoothing diff --git a/Modules/Filtering/Smoothing/itk-module.cmake b/Modules/Filtering/Smoothing/itk-module.cmake index 94b94c1ae65..58bc1c490a4 100644 --- a/Modules/Filtering/Smoothing/itk-module.cmake +++ b/Modules/Filtering/Smoothing/itk-module.cmake @@ -1,7 +1,7 @@ set(DOCUMENTATION "This module includes the most common image smoothing filters. For example, Gaussian and Median filters. You may also find it -interesting to look at the \\\\ref ITKAnisotropicSmoothing group of filters.") +interesting to look at the @ref ITKAnisotropicSmoothing group of filters.") itk_module( ITKSmoothing diff --git a/Modules/Registration/Common/itk-module.cmake b/Modules/Registration/Common/itk-module.cmake index 54059821af4..20b4ff5ad67 100644 --- a/Modules/Registration/Common/itk-module.cmake +++ b/Modules/Registration/Common/itk-module.cmake @@ -7,9 +7,9 @@ registration procedure. There are classes to perform multi-resolution image registration and also classes to registrations other that image-to-image registrations, e.g. point set-to-image or point set-to-point set registrations. Transforms used in the registration can be found in -\\\\ref ITKTransform, and optimizers can be found in \\\\ref ITKOptimizers. To +@ref ITKTransform, and optimizers can be found in @ref ITKOptimizers. To compare the moving image to the fixed image with the image metric, an -interpolator is required-- these can be found in \\\\ref ITKImageFunction.") +interpolator is required-- these can be found in @ref ITKImageFunction.") if(BUILD_EXAMPLES) set(EXAMPLE_TEST_CASE_DEPENDANCIES diff --git a/Modules/Segmentation/Classifiers/itk-module.cmake b/Modules/Segmentation/Classifiers/itk-module.cmake index 36c430aebd2..e0c2bc45695 100644 --- a/Modules/Segmentation/Classifiers/itk-module.cmake +++ b/Modules/Segmentation/Classifiers/itk-module.cmake @@ -4,7 +4,7 @@ image. It can be used, for example, to identify pixel membership within a set of tissue types. Different algorithms are available including Bayesian classification, Gaussian models, and K-means clustering. After tissue labels have been assigned, they can be modified and applied with the -\\\\ref ITKLabelMap.") +@ref ITKLabelMap.") itk_module( ITKClassifiers diff --git a/Modules/Segmentation/ConnectedComponents/itk-module.cmake b/Modules/Segmentation/ConnectedComponents/itk-module.cmake index d318004a846..583f9fdeebc 100644 --- a/Modules/Segmentation/ConnectedComponents/itk-module.cmake +++ b/Modules/Segmentation/ConnectedComponents/itk-module.cmake @@ -1,8 +1,8 @@ set(DOCUMENTATION "This module contains modules to identify and modify connected components. Theses algorithms are commonly applied to binary or label map -images. See also \\\\ref ITKClassifiers, \\\\ref ITKLabelMap, and -\\\\ref ITKBinaryMathematicalMorphology.") +images. See also @ref ITKClassifiers, @ref ITKLabelMap, and +@ref ITKBinaryMathematicalMorphology.") itk_module( ITKConnectedComponents diff --git a/Modules/Segmentation/KLMRegionGrowing/itk-module.cmake b/Modules/Segmentation/KLMRegionGrowing/itk-module.cmake index 7a7ac9db74c..626562e90c8 100644 --- a/Modules/Segmentation/KLMRegionGrowing/itk-module.cmake +++ b/Modules/Segmentation/KLMRegionGrowing/itk-module.cmake @@ -2,7 +2,7 @@ set(DOCUMENTATION "This module contains classes to perform energy-based region growing for multiband images. Since this is based on G. Koepfler, C. Lopez and J. M. Morel's work, the acronym KLM is added to quality the region growing -method. See also \\\\ref ITKRegionGrowing.") +method. See also @ref ITKRegionGrowing.") itk_module( ITKKLMRegionGrowing diff --git a/Modules/Segmentation/LabelVoting/itk-module.cmake b/Modules/Segmentation/LabelVoting/itk-module.cmake index 8dfd7412f10..18e025b2093 100644 --- a/Modules/Segmentation/LabelVoting/itk-module.cmake +++ b/Modules/Segmentation/LabelVoting/itk-module.cmake @@ -3,8 +3,8 @@ set(DOCUMENTATION they count the number of pixels with a given label within a neighborhood and determine the output pixel based on the count. The operations on label images are similar to filtering on scalar images. See also -\\\\ref ITKBinaryMathematicalMorphology, \\\\ref ITKConnectedComponents, and -\\\\ref ITKLabelMap.") +@ref ITKBinaryMathematicalMorphology, @ref ITKConnectedComponents, and +@ref ITKLabelMap.") itk_module( ITKLabelVoting diff --git a/Modules/Segmentation/MarkovRandomFieldsClassifiers/itk-module.cmake b/Modules/Segmentation/MarkovRandomFieldsClassifiers/itk-module.cmake index 380703e3ac7..fbc8ee7ac63 100644 --- a/Modules/Segmentation/MarkovRandomFieldsClassifiers/itk-module.cmake +++ b/Modules/Segmentation/MarkovRandomFieldsClassifiers/itk-module.cmake @@ -1,7 +1,7 @@ set(DOCUMENTATION "This module contains classes to perform Markov Random Field classification of image pixels. An initial label image, perhaps generated by -\\\\ref ITKClassifiers, is improved by iteratively accounting for the spatial +@ref ITKClassifiers, is improved by iteratively accounting for the spatial coherence of the labels.") itk_module( diff --git a/Modules/Segmentation/RegionGrowing/itk-module.cmake b/Modules/Segmentation/RegionGrowing/itk-module.cmake index 0e127af493b..d378cfca5f4 100644 --- a/Modules/Segmentation/RegionGrowing/itk-module.cmake +++ b/Modules/Segmentation/RegionGrowing/itk-module.cmake @@ -2,7 +2,7 @@ set(DOCUMENTATION "This module contains classes to perform the region growing approach to image segmentation. A seed pixel is iteratively propagated to a region identifying a tissue type by testing if connected pixels pass a criteria. -See also \\\\ref ITKKLMRegionGrowing.") +See also @ref ITKKLMRegionGrowing.") itk_module( ITKRegionGrowing