Benchmarks gauss NEON

Cargo.toml

@@ -19,7 +19,7 @@ rayon = "1.10.0"
 
 [dev-dependencies]
 criterion = "0.5.1"
-opencv = { version = "0.93.1", default-features = false, features = ["imgcodecs", "imgproc"] }
+opencv = { version = "0.93.1", default-features = false, features = ["imgcodecs", "imgproc", "clang-runtime"] }
 
 [[bench]]
 name = "gauss"

README.md

@@ -29,14 +29,15 @@ cargo add libblur
 
 ```rust
 let blurred = gaussian_blur_image(
-img,
-61,
-0.,
-EdgeMode::Clamp,
-GaussianPreciseLevel::INTEGRAL,
-ThreadingPolicy::Adaptive,
+    img,
+    61,
+    0.,
+    EdgeMode::Clamp,
+    GaussianPreciseLevel::INTEGRAL,
+    ThreadingPolicy::Adaptive,
 )
 .unwrap();
+
 blurred
 .save_with_format("blurred.jpg", ImageFormat::Jpeg)
 .unwrap();
@@ -245,14 +246,14 @@ By the nature of this filter the more spatial sigma are the faster method is.
 
 ```rust
 fast_bilateral_filter(
-src_bytes,
-& mut dst_bytes,
-dimensions.0,
-dimensions.1,
-kernel_size,
-spatial_sigma,
-range_sigma,
-FastBlurChannels::Channels3,
+    src_bytes,
+    & mut dst_bytes,
+    dimensions.0,
+    dimensions.1,
+    kernel_size,
+    spatial_sigma,
+    range_sigma,
+    FastBlurChannels::Channels3,
 );
 ```
 

src/main.rs

@@ -338,14 +338,15 @@ fn main() {
 
     let motion_kernel = generate_motion_kernel(225, 15.);
 
-    filter_2d_rgb_fft::<u8, f32, f32>(
+    filter_2d_rgb(
         &bytes,
         &mut dst_bytes,
         ImageSize::new(dimensions.0 as usize, dimensions.1 as usize),
         &motion_kernel,
         KernelShape::new(225, 225),
         EdgeMode::Clamp,
         Scalar::new(255.0, 0., 0., 255.0),
+        ThreadingPolicy::Adaptive,
     )
     .unwrap();