imgddcore 0.1.3

Performance-first perceptual hashing library; perfect for handling large datasets. Designed to quickly process nested folder structures, commonly found in image datasets
Documentation 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23

use anyhow::Result;
use image::{imageops::FilterType, DynamicImage};

/// Normalizes an image by resizing it to a given resolution and converting it to grayscale.
///
/// # Arguments
/// * `image` - A reference to a `DynamicImage` to be normalized.
/// * `filter` - The down sampling method to use during resizing.
///     - **Options:** [`Nearest`, `Triangle`, `CatmullRom`, `Gaussian`, `Lanczos3`]
/// * `width` - The desired width of the resized image.
/// * `height` - The desired height of the resized image.
///
/// # Returns
/// * A `DynamicImage` that has been resized to the given dimensions and converted to grayscale.
#[inline]
pub fn proc(
    image: &DynamicImage,
    filter: FilterType,
    width: u32,
    height: u32,
) -> Result<DynamicImage> {
    Ok(image.resize_exact(width, height, filter).grayscale())
}