pub fn canny(image: &GrayImage, low_threshold: f32, high_threshold: f32) -> GrayImage {
    assert!(high_threshold >= low_threshold);
    // Heavily based on the implementation proposed by wikipedia.
    // 1. Gaussian blur.
    const SIGMA: f32 = 1.4;
    let blurred = gaussian_blur_f32(image, SIGMA);

    // 2. Intensity of gradients.
    let gx = horizontal_sobel(&blurred);
    let gy = vertical_sobel(&blurred);
    let g: Vec<f32> = gx
        .iter()
        .zip(gy.iter())
        .map(|(h, v)| (*h as f32).hypot(*v as f32))
        .collect::<Vec<f32>>();

    let g = ImageBuffer::from_raw(image.width(), image.height(), g).unwrap();

    // 3. Non-maximum-suppression (Make edges thinner)
    let thinned = non_maximum_suppression(&g, &gx, &gy);

    // 4. Hysteresis to filter out edges based on thresholds.
    hysteresis(&thinned, low_threshold, high_threshold)
}
/// Runs the canny edge detection algorithm.
///
/// Returns a binary image where edge pixels have a value of 255
///  and non-edge pixels a value of 0.
///
/// # Params
///
/// - `low_threshold`: Low threshold for the hysteresis procedure.
/// Edges with a strength higher than the low threshold will appear
/// in the output image, if there are strong edges nearby.
/// - `high_threshold`: High threshold for the hysteresis procedure.
/// Edges with a strength higher than the high threshold will always
/// appear as edges in the output image.
///
/// The greatest possible edge strength (and so largest sensible threshold)
/// is`sqrt(5) * 2 * 255`, or approximately 1140.39.
///
/// This odd looking value is the result of using a standard
/// definition of edge strength: the strength of an edge at a point `p` is
/// defined to be `sqrt(dx^2 + dy^2)`, where `dx` and `dy` are the values
/// of the horizontal and vertical Sobel gradients at `p`.
Runs the canny edge detection algorithm.
Returns a binary image where edge pixels have a value of 255
and non-edge pixels a value of 0.
Runs the canny edge detection algorithm . Returns a binary image where edge pixels have a value of 255 and non - edge pixels a value of 0 .
pub fn canny(image: &GrayImage, low_threshold: f32, high_threshold: f32) -> GrayImage {
    assert!(high_threshold >= low_threshold);
    // Heavily based on the implementation proposed by wikipedia.
    // 1. Gaussian blur.
    const SIGMA: f32 = 1.4;
    let blurred = gaussian_blur_f32(image, SIGMA);
    // 2. Intensity of gradients.
    let gx = horizontal_sobel(&blurred);
    let gy = vertical_sobel(&blurred);
    let g: Vec<f32> = gx
        .iter()
        .zip(gy.iter())
        .map(|(h, v)| (*h as f32).hypot(*v as f32))
        .collect::<Vec<f32>>();
    let g = ImageBuffer::from_raw(image.width(), image.height(), g).unwrap();
    // 3. Non-maximum-suppression (Make edges thinner)
    let thinned = non_maximum_suppression(&g, &gx, &gy);
    // 4. Hysteresis to filter out edges based on thresholds.
    hysteresis(&thinned, low_threshold, high_threshold)
}
pub fn canny ( image : & GrayImage , low_threshold : f32 , high_threshold : f32 ) -> GrayImage { assert ! ( high_threshold low_threshold ) ; // Heavily based on the implementation proposed by wikipedia. // 1. Gaussian blur. const SIGMA : f32 = 1.4 ; let blurred = gaussian_blur_f32 ( image , SIGMA ) ; // 2. Intensity of gradients. let gx = horizontal_sobel ( & blurred ) ; let gy = vertical_sobel ( & blurred ) ; let g : Vec < f32 > = gx . iter ( ) . zip ( gy . iter ( ) ) . map ( | ( h , v ) | ( * h as f32 ) . hypot ( * v as f32 ) ) . collect :: < Vec < f32 > > ( ) ; let g = ImageBuffer :: from_raw ( image . width ( ) , image . height ( ) , g ) . unwrap ( ) ; // 3. Non-maximum-suppression (Make edges thinner) let thinned = non_maximum_suppression ( & g , & gx , & gy ) ; // 4. Hysteresis to filter out edges based on thresholds. hysteresis ( & thinned , low_threshold , high_threshold ) }
Runs the canny edge detection algorithm.
Runs the canny edge detection algorithm .
image
&GrayImage
low_threshold
f32
high_threshold
f32
image
&GrayImage
low_threshold
f32
high_threshold
f32
params
`low_threshold`: Low threshold for the hysteresis procedure.
Edges with a strength higher than the low threshold will appear
in the output image, if there are strong edges nearby.
`high_threshold`: High threshold for the hysteresis procedure.
Edges with a strength higher than the high threshold will always
appear as edges in the output image.

The greatest possible edge strength (and so largest sensible threshold)
is`sqrt(5) * 2 * 255`, or approximately 1140.39.

This odd looking value is the result of using a standard
definition of edge strength: the strength of an edge at a point `p` is
defined to be `sqrt(dx^2 + dy^2)`, where `dx` and `dy` are the values
of the horizontal and vertical Sobel gradients at `p`.
` low_threshold ` : Low threshold for the hysteresis procedure . Edges with a strength higher than the low threshold will appear in the output image if there are strong edges nearby . ` high_threshold ` : High threshold for the hysteresis procedure . Edges with a strength higher than the high threshold will always appear as edges in the output image . The greatest possible edge strength ( and so largest sensible threshold ) is ` sqrt ( 5 ) * 2 * 255 ` or approximately 1140 . 39 . This odd looking value is the result of using a standard definition of edge strength : the strength of an edge at a point ` p ` is defined to be ` sqrt ( dx^2 + dy^2 ) ` where ` dx ` and ` dy ` are the values of the horizontal and vertical Sobel gradients at ` p ` .