akaze 0.7.0

AKAZE feature extraction algorithm for computer vision
Documentation 
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use crate::image::{gaussian_blur, GrayFloatImage};
use log::*;

/// This function computes a good empirical value for the k contrast factor
/// given an input image, the percentile (0-1), the gradient scale and the
/// number of bins in the histogram.
///
/// # Arguments
/// * `image` Input imagm
/// * `percentile` - Percentile of the image gradient histogram (0-1)
/// * `gradient_histogram_scale` - Scale for computing the image gradient histogram
/// * `nbins` - Number of histogram bins
/// # Return value
/// k contrast factor
#[allow(non_snake_case)]
pub fn compute_contrast_factor(
    image: &GrayFloatImage,
    percentile: f64,
    gradient_histogram_scale: f64,
    num_bins: usize,
) -> f64 {
    let mut num_points: f64 = 0.0;
    let mut histogram = vec![0; num_bins];
    let gaussian = gaussian_blur(image, gradient_histogram_scale as f32);
    let Lx = crate::derivatives::scharr_horizontal(&gaussian, 1);
    let Ly = crate::derivatives::scharr_vertical(&gaussian, 1);
    let hmax = (1..gaussian.height() - 1)
        .flat_map(|y| (1..gaussian.width() - 1).map(move |x| (x, y)))
        .map(|(x, y)| Lx.get(x, y).powi(2) as f64 + Ly.get(x, y).powi(2) as f64)
        .map(float_ord::FloatOrd)
        .max()
        .unwrap()
        .0
        .sqrt();
    for y in 1..(gaussian.height() - 1) {
        for x in 1..(gaussian.width() - 1) {
            let modg = (Lx.get(x, y).powi(2) as f64 + Ly.get(x, y).powi(2) as f64).sqrt();
            if modg != 0.0 {
                let mut bin_number = f64::floor((num_bins as f64) * (modg / hmax)) as usize;
                if bin_number == num_bins {
                    bin_number -= 1;
                }
                histogram[bin_number] += 1;
                num_points += 1f64;
            }
        }
    }
    let threshold: usize = (num_points * percentile) as usize;
    let mut k: usize = 0;
    let mut num_elements: usize = 0;
    while num_elements < threshold && k < num_bins {
        num_elements += histogram[k];
        k += 1;
    }
    debug!(
        "hmax: {}, threshold: {}, num_elements: {}",
        hmax, threshold, num_elements
    );
    if num_elements >= threshold {
        hmax * (k as f64) / (num_bins as f64)
    } else {
        0.03
    }
}