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implemented gftt_response #306

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0d3d062
gftt implementation
aayushhzz Mar 22, 2025
a75cb33
fixed clippy errors
aayushhzz Mar 22, 2025
85b9e09
updated arrow version
aayushhzz Mar 22, 2025
39e8595
Merge branch 'main' of https://github.com/kornia/kornia-rs into gftt-…
aayushhzz Apr 5, 2025
6e679e1
moved gftt response function to responses.rs
aayushhzz Apr 5, 2025
a6a98d4
added benchmarks
aayushhzz Apr 5, 2025
f53932b
improved spatial gradient and gaussian blur
aayushhzz Apr 6, 2025
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1 change: 1 addition & 0 deletions crates/kornia-imgproc/Cargo.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -14,6 +14,7 @@ version.workspace = true
[dependencies]
fast_image_resize = "5.1.0"
kornia-tensor = { workspace = true }
kornia-tensor-ops = { workspace = true }
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why

kornia-image = { workspace = true }
num-traits = { workspace = true }
rayon = "1.10"
Expand Down
36 changes: 34 additions & 2 deletions crates/kornia-imgproc/benches/bench_features.rs
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Expand Up @@ -9,7 +9,7 @@ use rand::Rng;
fn bench_fast_corner_detect(c: &mut Criterion) {
let mut group = c.benchmark_group("FastCornerDetect");

let img_rgb8 = io::read_image_any_rgb8("/home/edgar/Downloads/kodim08_grayscale.png").unwrap();
let img_rgb8 = io::read_image_any_rgb8("../../tests/data/dog-rgb8.png").unwrap();
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use existing image; otherwise if it's light we can add


let new_size = [1920, 1080].into();
let mut img_resized = Image::from_size_val(new_size, 0).unwrap();
Expand All @@ -32,6 +32,38 @@ fn bench_fast_corner_detect(c: &mut Criterion) {
);
}

fn bench_gftt_response(c: &mut Criterion) {
let mut group = c.benchmark_group("Features");
let mut rng = rand::thread_rng();

for (width, height) in [(224, 224), (512, 512), (1920, 1080)].iter() {
group.throughput(criterion::Throughput::Elements((*width * *height) as u64));

let parameter_string = format!("{}x{}", width, height);

// input image
let image_data: Vec<f32> = (0..(*width * *height))
.map(|_| rng.gen_range(0.0..1.0))
.collect();
let image_size = [*width, *height].into();

let image_f32: Image<f32, 1> = Image::new(image_size, image_data).unwrap();

// output image
let response_f32: Image<f32, 1> = Image::from_size_val(image_size, 0.0).unwrap();

group.bench_with_input(
BenchmarkId::new("gftt", &parameter_string),
&(&image_f32, &response_f32),
|b, i| {
let (src, mut dst) = (i.0, i.1.clone());
b.iter(|| black_box(gftt_response(src, &mut dst)))
},
);
}
group.finish();
}

fn bench_harris_response(c: &mut Criterion) {
let mut group = c.benchmark_group("Features");
let mut rng = rand::thread_rng();
Expand Down Expand Up @@ -100,7 +132,7 @@ fn bench_dog_response(c: &mut Criterion) {
criterion_group!(
name = benches;
config = Criterion::default().warm_up_time(std::time::Duration::new(10, 0));
targets = bench_harris_response, bench_dog_response, bench_fast_corner_detect
targets = bench_harris_response, bench_dog_response, bench_fast_corner_detect, bench_gftt_response
);
criterion_main!(benches);

Expand Down
98 changes: 97 additions & 1 deletion crates/kornia-imgproc/src/features/responses.rs
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Original file line number Diff line number Diff line change
@@ -1,5 +1,6 @@
use crate::filter::gaussian_blur;
use crate::filter::{gaussian_blur, spatial_gradient_float_parallel_row};
use kornia_image::{Image, ImageError, ImageSize};
use kornia_tensor_ops::TensorOps;
use rayon::prelude::*;

/// Method to calculate gradient for feature response
Expand All @@ -24,6 +25,64 @@ fn _get_kernel_size(sigma: f32) -> usize {

ksize
}
///Compute the Shi-Tomasi cornerness function.
///
/// The Shi-Tomasi cornerness function is computed as the minimum eigenvalue of the gradient matrix.
///
/// Args:
/// src: The source image.
/// dst: The destination image.
pub fn gftt_response(src: &Image<f32, 1>, dst: &mut Image<f32, 1>) -> Result<(), ImageError> {
if src.size() != dst.size() {
return Err(ImageError::InvalidImageSize(
src.cols(),
src.rows(),
dst.cols(),
dst.rows(),
));
}
let size = src.size();
let mut dx = Image::from_size_val(size, 0.0)?;
let mut dy = Image::from_size_val(size, 0.0)?;
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because of all this buffers, we might want this implemented as a struct to hold the buffers there

struct GfttResponse {
  dx_buf: Image<f32, 1>,
  dy_buf: Image<f32, 1>,
  ...
};

or something like that


let mut dx2_g: Image<f32, 1> = Image::from_size_val(size, 0.0)?;
let mut dy2_g: Image<f32, 1> = Image::from_size_val(size, 0.0)?;
let mut dxy_g: Image<f32, 1> = Image::from_size_val(size, 0.0)?;
spatial_gradient_float_parallel_row(src, &mut dx, &mut dy)?;

let dx2 = Image(dx.mul(&dx).unwrap());
let dy2 = Image(dy.mul(&dy).unwrap());
let dxy = Image(dx.mul(&dy).unwrap());

gaussian_blur(&dx2, &mut dx2_g, (7, 7), (1.0, 1.0))?;
gaussian_blur(&dy2, &mut dy2_g, (7, 7), (1.0, 1.0))?;
gaussian_blur(&dxy, &mut dxy_g, (7, 7), (1.0, 1.0))?;
unsafe {
let det = dx2_g
.mul(&dy2_g)
.unwrap_unchecked()
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very likely you can implement all the ops in the same loop with; each ops call here you will loop over all the image pixels which will be very expensive

.sub(&dxy_g.mul(&dxy_g).unwrap_unchecked())
.unwrap_unchecked();
let trace = dx2_g.add(&dy2_g).unwrap_unchecked();

let trace_sq = trace.mul(&trace).unwrap_unchecked();
let four_det = det.mul_scalar(4.0);
let discr = trace_sq.sub(&four_det).unwrap_unchecked().abs().powf(0.5);
let half = 0.5;
let e1 = trace.add(&discr).unwrap_unchecked().mul_scalar(half);
let e2 = trace.sub(&discr).unwrap_unchecked().mul_scalar(half);

let score = e1.min(&e2).unwrap_unchecked();

let sd = score.as_slice();
let dd = dst.as_slice_mut();
for i in 0..sd.len() {
dd[i] = sd[i];
}
}

Ok(())
}

/// Compute the Hessian response of an image.
///
Expand Down Expand Up @@ -325,7 +384,44 @@ pub fn dog_response(
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_gftt_response() -> Result<(), ImageError> {
#[rustfmt::skip]
let src = Image::from_size_slice(
[9, 9].into(),
&[
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0,
0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0,
0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0,
0.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0,
0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0,
0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0,
0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
],
)?;

let mut dst = Image::from_size_val([9, 9].into(), 0.0)?;
gftt_response(&src, &mut dst)?;

#[rustfmt::skip]
let expected_center_value = 0.1274;
assert!(
(dst.as_slice()[4 * 9 + 4] - expected_center_value).abs() < 1e-4,
"Center value should be close to expected value"
);
let max = dst
.as_slice()
.iter()
.max_by(|a, b| a.partial_cmp(b).unwrap())
.unwrap();
assert!(
(*max - expected_center_value).abs() < 1e-4,
"Max value should be close to centre value"
);
Ok(())
}
#[test]
fn test_hessian_response() -> Result<(), ImageError> {
#[rustfmt::skip]
Expand Down
45 changes: 23 additions & 22 deletions crates/kornia-imgproc/src/filter/ops.rs
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Expand Up @@ -220,37 +220,38 @@ pub fn spatial_gradient_float_parallel_row<const C: usize>(
}

let (sobel_x, sobel_y) = kernels::normalized_sobel_kernel3();
let rows = src.rows();
let cols = src.cols();

let src_data = src.as_slice();

dx.as_slice_mut()
let dx_data = dx.as_slice_mut();
let dy_data = dy.as_slice_mut();

dx_data
.par_chunks_mut(cols * C)
.zip(dy.as_slice_mut().par_chunks_mut(cols * C))
.zip(dy_data.par_chunks_mut(cols * C))
.enumerate()
.for_each(|(r, (dx_row, dy_row))| {
dx_row
.chunks_mut(C)
.zip(dy_row.chunks_mut(C))
.enumerate()
.for_each(|(c, (dx_c, dy_c))| {
let mut sum_x = [0.0; C];
let mut sum_y = [0.0; C];
for dy in 0..3 {
for dx in 0..3 {
let row = (r + dy).min(src.rows()).max(1) - 1;
let col = (c + dx).min(src.cols()).max(1) - 1;
for ch in 0..C {
let src_pix_offset = (row * src.cols() + col) * C + ch;
let val = unsafe { src_data.get_unchecked(src_pix_offset) };
sum_x[ch] += val * sobel_x[dy][dx];
sum_y[ch] += val * sobel_y[dy][dx];
}
for c in 0..cols {
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you are breaking the functionality here -- not parallel anymore

let mut sum_x = [0.0f32; C];
let mut sum_y = [0.0f32; C];
for ky in 0..3 {
let y = (r + ky).saturating_sub(1).min(rows - 1);
for kx in 0..3 {
let x = (c + kx).saturating_sub(1).min(cols - 1);
let src_idx = (y * cols + x) * C;
for ch in 0..C {
let val = unsafe { *src_data.get_unchecked(src_idx + ch) };
sum_x[ch] += val * sobel_x[ky][kx];
sum_y[ch] += val * sobel_y[ky][kx];
}
}
dx_c.copy_from_slice(&sum_x);
dy_c.copy_from_slice(&sum_y);
});
}
let out_idx = c * C;
dx_row[out_idx..out_idx + C].copy_from_slice(&sum_x);
dy_row[out_idx..out_idx + C].copy_from_slice(&sum_y);
}
});

Ok(())
Expand Down
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