ximgproc: optimize guidedfilter function for ARM64 using NEON intrinsics #3979
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asmorkalov
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| inline bool CPU_SUPPORT_NEON() | ||
| { | ||
| static const bool is_supported = cv::checkHardwareSupport(CV_CPU_NEON); | ||
| return is_supported; | ||
| } |
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The checkHardwareSupport function is very simple:
bool checkHardwareSupport(int feature)
{
CV_DbgAssert( 0 <= feature && feature <= CV_HARDWARE_MAX_FEATURE );
return currentFeatures->have[feature];
}
No need to add static field for caching.
| } | ||
| } | ||
| #elif CV_NEON | ||
| if (CPU_SUPPORT_NEON()) |
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There is CV_CPU_HAS_SUPPORT_NEON macro for this:
include/opencv2/core/cv_cpu_helper.h:411:# define CV_CPU_HAS_SUPPORT_NEON (cv::checkHardwareSupport(CV_CPU_NEON))
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Hi @asmorkalov ,
I’ve removed the custom CPU_SUPPORT_NEON() helper and updated the code to use the existing CV_CPU_HAS_SUPPORT_NEON macro instead.
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I agree to contribute to the project under Apache 2 License.
To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
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This PR introduces an ARM64-specific optimization for the add_mul function in edgeaware_filters_common.cpp using NEON intrinsics.
The optimization is applied only when CV_NEON is defined and the runtime NEON check (cv::checkHardwareSupport(CV_CPU_NEON)) passes.
The SIMD implementation leverages NEON instructions (vld1q_f32, vmulq_f32, vaddq_f32, vst1q_f32) to accelerate the fused multiply-add operation on 4-element float vectors.
This brings parity with existing x64 SIMD optimizations using SSE1.
The addition of the ARM64 NEON optimization for the add_mul function in edgeaware_filters_common.cpp has led to performance improvements in some tests of GuidedFilter function.