added feature module
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//! # Prebuild features
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//! This module provides a set of prebuild features ready to be used with a database
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//! to index images.
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//! Features include:
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//! - distribution of colors (via histogram)
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//! - distribution of luminance (via histogram)
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//! - average luminance
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//! - aspect ratio of images computed a width/height
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//! All features are designed to used with sRGB color channels only.
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use std::sync::Arc;
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use crate::{image::Image, search_index::FeatureResult};
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#[allow(unused)]
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// from https://github.com/programmieren-mit-rust/pr-ferrisgroup/issues/8 by @SirTalksalot75
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/// Compute a basic distribution of values from all color channels and count their apprearances in buckets.
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/// This function will use 5 buckets per channel.
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fn color_distribution(image: Arc<Image<f32>>) -> (String, FeatureResult) {
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const N: usize = 5;
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let mut histogram = vec![0u64; N * 3 + 1];
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const INV_255: f32 = 1./255. * N as f32;
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for (r, g, b, _) in image.iter() {
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// map linear channel value to bin index
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histogram[ (r * INV_255) as usize] += 1;
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histogram[ (g * INV_255) as usize * 2 ] += 1;
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histogram[ (b * INV_255) as usize * 3 ] += 1;
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}
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(String::from("luminance-distribution"), FeatureResult::Indices(histogram))
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}
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#[allow(unused)]
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// from https://github.com/programmieren-mit-rust/pr-ferrisgroup/issues/8 by @SirTalksalot75
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/// Compute a basic distribution of luminance values and count their apprearances in buckets.
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/// Luminance is calculated via Digital ITU BT.601 and NOT the more common Photometric ITU BT.709
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fn luminance_distribution(image: Arc<Image<f32>>) -> (String, FeatureResult) {
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let mut histogram = vec![0u64; 256]; // Assuming 256 bins for the histogram
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for (r, g, b, _) in image.iter() {
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// map luminance to bin index
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// luminance is a value between 0 and 255.
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let luminance = (0.299 * r + 0.587 * g + 0.114 * b) as usize;
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histogram[luminance] += 1;
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}
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(String::from("luminance-distribution"), FeatureResult::Indices(histogram))
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}
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#[allow(unused)]
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// from https://github.com/programmieren-mit-rust/pr-ferrisgroup/issues/8 by @SirTalksalot75
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/// Compute the average luminance of all pixels in a given image.
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/// Luminance is calculated via Digital ITU BT.601 and NOT the more common Photometric ITU BT.709
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fn average_luminance(image: Arc<Image<f32>>) -> (String, FeatureResult) {
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let num_pixels = image.pixels().len() as u32;
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let total_brightness: f32 = image
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.iter()
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.map(|(r, g, b, _)| (0.299 * r + 0.587 * g + 0.114 * b) / 255.0) // Calculate Y for each pixel
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.sum();
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let average_brightness = total_brightness / num_pixels as f32;
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let feature_name = String::from("average-brightness");
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let feature_result = FeatureResult::Percent(average_brightness);
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(feature_name, feature_result)
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}
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#[allow(unused)]
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// from https://github.com/programmieren-mit-rust/pr-ferrisgroup/issues/8 by @SirTalksalot75
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fn aspect_ratio(image: Arc<Image<f32>>) -> (String, FeatureResult) {
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let a = image.width() as f32 / image.height() as f32;
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(String::from("aspect-ratio"), FeatureResult::Percent(a))
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}
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#[cfg(test)]
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mod test {
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use std::path::Path;
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use crate::search_index::{Database, FeatureGenerator};
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use super::*;
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#[test]
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fn test_histogram() {
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let files: Vec<std::path::PathBuf> = std::fs::read_dir("res/integration/")
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.unwrap()
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.map(|f| f.unwrap().path())
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.collect();
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let feats: Vec<FeatureGenerator> = vec![color_distribution];
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let db = Database::new(&files, feats).unwrap();
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for (path, sim) in db.search(Path::new("res/integration/gray_image.png"), color_distribution).unwrap() {
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let file_name = path.file_name().unwrap().to_str().unwrap();
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if file_name.eq("gray_image.png") {
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assert_eq!(sim, 1.);
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}
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println!("{} {}", file_name, sim);
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}
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}
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#[test]
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fn test_average_luminance() {
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let files: Vec<std::path::PathBuf> = std::fs::read_dir("res/integration/")
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.unwrap()
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.map(|f| f.unwrap().path())
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.collect();
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let feats: Vec<FeatureGenerator> = vec![average_luminance];
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let db = Database::new(&files, feats).unwrap();
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for (path, sim) in db.search(Path::new("res/integration/gray_image.png"), average_luminance).unwrap() {
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let file_name = path.file_name().unwrap().to_str().unwrap();
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if file_name.eq("gray_image.png") {
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assert_eq!(sim, 1.);
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}
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println!("{} {}", file_name, sim);
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}
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}
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#[test]
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fn test_aspect_ratio() {
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let files: Vec<std::path::PathBuf> = std::fs::read_dir("res/integration/")
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.unwrap()
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.map(|f| f.unwrap().path())
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.collect();
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let feats: Vec<FeatureGenerator> = vec![aspect_ratio];
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let db = Database::new(&files, feats).unwrap();
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for (path, sim) in db.search(Path::new("res/integration/gray_image.png"), aspect_ratio).unwrap() {
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let file_name = path.file_name().unwrap().to_str().unwrap();
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if file_name.eq("gray_image.png") {
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assert_eq!(sim, 1.);
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}
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println!("{} {}", file_name, sim);
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}
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}
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}
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19
src/lib.rs
19
src/lib.rs
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//! # Imsearch
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//! Extensible library for creating an image based search engine.
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//! The library exposes the functionality to create databases which index various images stored as png files.
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//! # Examples
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//! ```ignore
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//! let files: Vec<PathBuf> = std::fs::read_dir("image/folder/")
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//! .unwrap()
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//! .map(|f| f.unwrap().path())
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//! .collect();
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//!
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//! let feats: Vec<FeatureGenerator> = vec![average_rgb_value];
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//!
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//! let db = Database::new(&files, feats).unwrap();
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//!
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//! db.write_to_file(json);
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//! ```
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extern crate core;
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extern crate core;
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pub mod image;
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pub mod image;
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pub mod image_loader;
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pub mod image_loader;
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pub mod multithreading;
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pub mod multithreading;
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pub mod search_index;
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pub mod search_index;
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pub mod feature;
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