vtk-pure-rs 0.2.0

Pure Rust visualization toolkit — data structures, filters, I/O, rendering
Documentation 
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use crate::data::{AnyDataArray, DataArray, ImageData};

/// Binarize an ImageData using adaptive (local mean) thresholding.
///
/// For each voxel, the threshold is the local mean in a neighborhood
/// of `radius`, offset by `bias`. Produces a "Binary" array with 0/1 values.
pub fn image_adaptive_threshold(input: &ImageData, scalars: &str, radius: usize, bias: f64) -> ImageData {
    let arr = match input.point_data().get_array(scalars) {
        Some(a) => a, None => return input.clone(),
    };

    let dims = input.dimensions();
    let nx = dims[0] as usize; let ny = dims[1] as usize; let nz = dims[2] as usize;
    let n = nx*ny*nz;
    let r = radius.max(1) as i64;

    let mut buf = [0.0f64];
    let mut values: Vec<f64> = (0..n).map(|i| { arr.tuple_as_f64(i, &mut buf); buf[0] }).collect();

    let mut binary = vec![0.0f64; n];

    for k in 0..nz { for j in 0..ny { for i in 0..nx {
        let mut sum = 0.0; let mut count = 0;
        for dk in -r..=r { for dj in -r..=r { for di in -r..=r {
            let ii = (i as i64+di).clamp(0,nx as i64-1) as usize;
            let jj = (j as i64+dj).clamp(0,ny as i64-1) as usize;
            let kk = (k as i64+dk).clamp(0,nz as i64-1) as usize;
            sum += values[kk*ny*nx+jj*nx+ii]; count += 1;
        }}}
        let local_mean = sum / count as f64;
        let idx = k*ny*nx+j*nx+i;
        binary[idx] = if values[idx] > local_mean + bias { 1.0 } else { 0.0 };
    }}}

    let mut img = input.clone();
    img.point_data_mut().add_array(AnyDataArray::F64(DataArray::from_vec("Binary", binary, 1)));
    img
}

/// Simple global binarization: values >= threshold become 1.0, else 0.0.
pub fn image_binarize(input: &ImageData, scalars: &str, threshold: f64) -> ImageData {
    let arr = match input.point_data().get_array(scalars) {
        Some(a) => a, None => return input.clone(),
    };
    let n = arr.num_tuples();
    let mut buf = [0.0f64];
    let values: Vec<f64> = (0..n).map(|i| {
        arr.tuple_as_f64(i, &mut buf);
        if buf[0] >= threshold { 1.0 } else { 0.0 }
    }).collect();

    let mut img = input.clone();
    img.point_data_mut().add_array(AnyDataArray::F64(DataArray::from_vec("Binary", values, 1)));
    img
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn adaptive_threshold() {
        let mut img = ImageData::with_dimensions(5, 1, 1);
        img.point_data_mut().add_array(AnyDataArray::F64(
            DataArray::from_vec("v", vec![10.0, 10.0, 50.0, 10.0, 10.0], 1),
        ));

        let result = image_adaptive_threshold(&img, "v", 1, 0.0);
        let arr = result.point_data().get_array("Binary").unwrap();
        let mut buf = [0.0f64];
        arr.tuple_as_f64(2, &mut buf);
        assert_eq!(buf[0], 1.0); // spike above local mean
    }

    #[test]
    fn global_binarize() {
        let mut img = ImageData::with_dimensions(4, 1, 1);
        img.point_data_mut().add_array(AnyDataArray::F64(
            DataArray::from_vec("v", vec![0.1, 0.5, 0.9, 0.3], 1),
        ));

        let result = image_binarize(&img, "v", 0.5);
        let arr = result.point_data().get_array("Binary").unwrap();
        let mut buf = [0.0f64];
        arr.tuple_as_f64(0, &mut buf); assert_eq!(buf[0], 0.0);
        arr.tuple_as_f64(1, &mut buf); assert_eq!(buf[0], 1.0);
        arr.tuple_as_f64(2, &mut buf); assert_eq!(buf[0], 1.0);
    }

    #[test]
    fn missing_array() {
        let img = ImageData::with_dimensions(3, 1, 1);
        let r = image_adaptive_threshold(&img, "nope", 1, 0.0);
        assert!(r.point_data().get_array("Binary").is_none());
    }
}