Merge branch 'main' into Feature_Extractor

This commit is contained in:
Sven Vogel 2023-06-18 10:32:06 +02:00
commit 805397a6e1
23 changed files with 1406 additions and 35 deletions

3
.gitignore vendored
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@ -2,4 +2,5 @@
/Cargo.lock
.DS_Store
.idea
/.vscode
/.vscode
*.json

339
LICENSE Normal file
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@ -0,0 +1,339 @@
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This General Public License does not permit incorporating your program into
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Public License instead of this License.

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@ -1,12 +1,82 @@
# Programmentwurf
# Imsearch
Die Beschreibung der Aufgabenstellung ist unter [Programmentwurf.md](https://github.com/programmieren-mit-rust/programmentwurf/blob/main/Programmentwurf.md) zu finden. Diese `Readme.md` ist durch etwas Sinnvolles zu ersetzen.
Extensible library for creating an image based search engine. The library exposes the functionality to create databases which index various images stored as png files.
# WICHTIG!
Kleiner reminder, wenn ihr Sachen pushed in das repo, die eurer Anischt nach fertig sind (z.B für einen Pull-Request!), bitte mit den folgenden Commands auf Fehler/Warnings überprüfen:
- `cargo fmt` für formattierung
- `cargo clippy` für warnings
- `cargo test doc` für documentation tests
optional:
- `cargo test` für module tests
- `cargo bench` für benchmarks
Files can be compared for similarity by either premade features or custom ones. The basic idea of handling the library is as follows:
- Create a new database
- Add some features to the database
- Add some images to the database
- Search for some images in the database by a certain feature
- Save the database to disk
or:
- Load a database form disk
- Supply generator functions
- Search for some images in the database by a certain feature
- Add some images to the database
- Save the database to disk
# Examples:
## Define a new feature
```rust
/// Compute the average value of the tree color channels of a given image
fn average_rgb_value(image: Arc<Image<f32>>) -> (String, FeatureResult) {
let bright = image
.pixels()
.iter()
.map(|(r, g, b, _)| (r + g + b) / 3.0 / 255.0)
.sum::<f32>();
(
String::from("average_brightness"),
FeatureResult::Percent(bright / image.pixels().len() as f32),
)
}
```
## Create a new database
```rust
let files: Vec<PathBuf> = std::fs::read_dir("image/folder/")
.unwrap()
.map(|f| f.unwrap().path())
.collect();
let feats: Vec<FeatureGenerator> = vec![average_rgb_value];
let db = Database::new(&files, feats).unwrap();
db.write_to_file(json);
```
## Read a new database and search for similar images
```rust
let db = Database::from_file(Path::new("db.json"));
for results in db
.search(
std::path::Path::new("path/to/image.png"),
average_brightness,
)
.unwrap()
{
println!(
"path: {} similarity: {}",
results.0.as_os_str().to_str().unwrap(),
results.1
);
}
```
# Details
Processing of features for images are multithreaded. Features that are calculated for images only get their results stored. The generator function used to calculate won't get serialized. This implies that
in order to compute the features for images the generator functions have to be passed to the database
after it has been read from a file.
## Limiting thread usage
You can limit the number of threads to be used by calling `set_limit()` on the database.
Note that the thread pool will automatically try to detect the optimal number of threads to use.
As long as no edge case such as running in an over committed virtual machine applies this will be
good enough for most cases.
## Image formats
The library can only handle png files through the `png` crate. Note that not all colortypes are supported. Due to the poor capabilites of the crate pngs with indexed palettes are not functional
will cause functions to return error values.
## Memory usage
The database won't hold all images in ram at the same time. They are loaded on demand when calculating features for them. This may cause increased disk usage but will prevent ram overcommitment.

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@ -45,12 +45,15 @@ fn dot_parallel(a: Arc<Vec<f64>>, b: Arc<Vec<f64>>, threads: usize) {
for i in 0..threads {
// offset of the first element for the thread local vec
let chunk = i * steps;
// create a new strong reference to the vector
let aa = a.clone();
let bb = b.clone();
// launch a new thread
pool.enqueue(Task::new(
(chunk, steps, a.clone(), b.clone()),
|(block, inc, a, b)| {
let a = &a[block..(block + inc)];
let b = &b[block..(block + inc)];
(aa, bb, chunk, steps),
|(aa, bb, chunk, steps)| {
let a = &aa[chunk..(chunk + steps)];
let b = &bb[chunk..(chunk + steps)];
dot(a, b)
},
));
@ -115,12 +118,15 @@ fn pool_overusage(a: Arc<Vec<f64>>, b: Arc<Vec<f64>>, threads: usize) {
for i in 0..threads {
// offset of the first element for the thread local vec
let chunk = i * steps;
// create a new strong reference to the vector
let aa = a.clone();
let bb = b.clone();
// launch a new thread
pool.enqueue(Task::new(
(chunk, steps, a.clone(), b.clone()),
|(block, inc, a, b)| {
let a = &a[block..(block + inc)];
let b = &b[block..(block + inc)];
(aa, bb, chunk, steps),
|(aa, bb, chunk, steps)| {
let a = &aa[chunk..(chunk + steps)];
let b = &bb[chunk..(chunk + steps)];
dot(a, b)
},
));

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@ -40,7 +40,7 @@ pub trait Sample: Into<f32> + PartialEq + Default + Copy + From<u8> + PartialOrd
impl<T: Into<f32> + PartialEq + Default + Copy + From<u8> + PartialOrd> Sample for T {}
#[allow(unused)]
#[derive(Default)]
#[derive(Default, Debug)]
pub struct Image<T>
where
T: Sample,
@ -87,6 +87,10 @@ where
pub fn pixel(&self, index: usize) -> (T, T, T, T) {
*self.index(index)
}
/// Returns all pixel of the image
pub fn pixels(&self) -> &Vec<(T, T, T, T)> {
&self.pixels
}
/// Returns the path of the image
pub fn path(&self) -> &PathBuf {
&self.path

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@ -0,0 +1,333 @@
use crate::image::Image;
use png::BitDepth;
use std::fs::File;
use std::path::Path;
///# Image Loader
/// The image_loader function is a function which can use a path of an image to return some Metadata from the image.<br>
///It can also retrieve the rgba values of every pixel from the image and the path.<br>
///<br>
///# IMPORTANT<br>
/// Doesn't support pictures with the color type indexed!<br>
///<br>
///# Parameter
///The function has the parameter path with the type &Path.<br>
///<br>
///# Return variables
///The return value is a struct which includes four variables.<br>
///1. width [u32]: width contains the number of pixels in a row of the given png.<br>
///2. height [u32]: height contains the height of the picture in measured pixels.<br>
///3. pixel_vec [Vec<(f32, f32, f32, f32)>]: pixel_vec contains the rgba values of every pixel in the picture, saved in a vec themselves.<br>
///4. path [path.to_path_buf()]: is the path from the parameter.<br>
pub fn image_loader(path: &Path) -> Result<Image<f32>, &'static str> {
let decoder = match File::open(path) {
Ok(file) => png::Decoder::new(file),
Err(_) => return Err("failed the decoder"),
};
let mut reader = match decoder.read_info() {
Ok(reader) => reader,
Err(_) => return Err("Failed to read PNG info"),
};
let mut buf = vec![0; reader.output_buffer_size()];
let info = match reader.next_frame(&mut buf) {
Ok(info) => info,
Err(_) => return Err("Failed to read PNG frame"),
};
let bit_depth = reader.info().bit_depth;
let color_type = reader.info().color_type;
let width = reader.info().width;
let height = reader.info().height;
let idat = &buf[..info.buffer_size()];
let pixel_vec = match color_type {
png::ColorType::Grayscale => grayscale_to_rgba(idat, bit_depth),
png::ColorType::GrayscaleAlpha => grayscale_alpha_to_rgba(idat, bit_depth),
png::ColorType::Rgb => rgb_to_rgba(idat, bit_depth),
png::ColorType::Rgba => decode_rgba(idat, bit_depth),
_ => panic!("Unsupported color type or bit depth"),
}?;
let image: Image<f32> = Image::new(width, height, pixel_vec, path.to_path_buf());
Ok(image)
}
///# Grayscale to RGBA
///The grayscale_to_rgba function converts a IDAT chunk from an picture with the color type grayscale and the bit depth into an RGBA value.<br>
/// <br>
///# Parameter
///The function has the following two parameters:<br>
///1. IDAT [&[u8]]: This array cointans the IDAT chunk of the given image.<br>
///2. bit_depth [BitDepth]: This variable contains the bit depth of the given image.<br>
///<br>
///# Return variables
///This function returns a vector filled with vectors filled with four [f32] variables, which are the RGBA values.<br>
///Also this function sets the alpha channel of the RGBA value to 255.
fn grayscale_to_rgba(
idat: &[u8],
bit_depth: BitDepth,
) -> Result<Vec<(f32, f32, f32, f32)>, &'static str> {
let mut rgba_values = Vec::new();
let max_value: u32 = (1 << bit_depth as u32) - 1;
for byte in idat {
let grayscale = match bit_depth {
BitDepth::One => ((*byte & 0x01) as u32 * max_value) as f32,
BitDepth::Two => ((*byte & 0x03) as u32 * max_value / 3) as f32,
BitDepth::Four => ((*byte & 0x0F) as u32 * max_value / 15) as f32,
BitDepth::Eight => *byte as f32,
BitDepth::Sixteen => ((*byte as u32 * 255) / max_value) as f32,
};
let rgba = (grayscale, grayscale, grayscale, 255_f32);
rgba_values.push(rgba);
}
Ok(rgba_values)
}
///# Grayscale Alpha to RGBA
///The grayscale_alpha_to_rgba function converts a IDAT chunk from a picture with the color type grayscale alpha and the bit depth into an RGBA value.<br>
/// <br>
///# Parameter
///The function has the following two parameters:<br>
///1. IDAT [&[u8]]: This array contains the IDAT chunk of the given image.<br>
///2. bit_depth [BitDepth]: This variable contains the bit depth of the given image.<br>
///<br>
///# Return variables
///This function returns a vector filled with vectors filled with four [f32] variables, which are the RGBA values.
fn grayscale_alpha_to_rgba(
idat: &[u8],
bit_depth: BitDepth,
) -> Result<Vec<(f32, f32, f32, f32)>, &'static str> {
let mut rgba_values = Vec::new();
let chunk_size;
if bit_depth == BitDepth::Eight {
chunk_size = 2;
} else if bit_depth == BitDepth::Sixteen {
chunk_size = 4;
} else {
return Err("Invalid Bit Depth");
}
for pair in idat.chunks(chunk_size) {
if pair.len() < 2 {
return Err("Insufficient data");
}
let grayscale = match bit_depth {
BitDepth::Eight => pair[0] as f32,
BitDepth::Sixteen => ((pair[0] as u16) << 8 | pair[1] as u16) as f32,
_ => return Err("Unsupported bit depth"),
};
let alpha = match bit_depth {
BitDepth::Eight => pair[1] as f32,
BitDepth::Sixteen => ((pair[2] as u16) << 8 | pair[3] as u16) as f32,
_ => return Err("Unsupported bit depth"),
};
let rgba = (grayscale, grayscale, grayscale, alpha);
rgba_values.push(rgba);
}
Ok(rgba_values)
}
///# RGB to RGBA
///The tgb_to_rgba function converts a IDAT chunk from a picture with the color type rgb and the bit depth into an RGBA value.<br>
/// <br>
///# Parameter
///The function has the following two parameters:<br>
///1. IDAT [&[u8]]: This array contains the IDAT chunk of the given image.<br>
///2. bit_depth [BitDepth]: This variable contains the bit depth of the given image.<br>
///<br>
///# Return variables
///This function returns a vector filled with vectors filled with four [f32] variables, which are the RGBA values.
///Also this function sets the alpha channel of the RGBA value to 255.
fn rgb_to_rgba(
idat: &[u8],
bit_depth: BitDepth,
) -> Result<Vec<(f32, f32, f32, f32)>, &'static str> {
let mut rgba_values = Vec::new();
let chunk_size;
if bit_depth == BitDepth::Eight {
chunk_size = 3;
} else if bit_depth == BitDepth::Sixteen {
chunk_size = 6;
} else {
return Err("Invalid Bit Depth");
}
for group in idat.chunks(chunk_size) {
if group.len() < 3 {
return Err("Insufficient data");
}
let red = match bit_depth {
BitDepth::Eight => group[0] as f32,
BitDepth::Sixteen => ((group[0] as u16) << 8 | group[1] as u16) as f32,
_ => return Err("Unsupported bit depth"),
};
let green = match bit_depth {
BitDepth::Eight => group[1] as f32,
BitDepth::Sixteen => ((group[2] as u16) << 8 | group[3] as u16) as f32,
_ => return Err("Unsupported bit depth"),
};
let blue = match bit_depth {
BitDepth::Eight => group[2] as f32,
BitDepth::Sixteen => ((group[4] as u16) << 8 | group[5] as u16) as f32,
_ => return Err("Unsupported bit depth"),
};
let rgba = (red, green, blue, 255_f32);
rgba_values.push(rgba);
}
Ok(rgba_values)
}
///# Decode RGBA
///The decode_rgba function converts a IDAT chunk from a picture with the color type rgba and the bit depth into an RGBA value.<br>
/// <br>
///# Parameter
///The function has the following two parameters:<br>
///1. IDAT [&[u8]]: This array contains the IDAT chunk of the given image.<br>
///2. bit_depth [BitDepth]: This variable contains the bit depth of the given image.<br>
///<br>
///# Return variables
///This function returns a vector filled with vectors filled with four [f32] variables, which are the RGBA values.
fn decode_rgba(
idat: &[u8],
bit_depth: BitDepth,
) -> Result<Vec<(f32, f32, f32, f32)>, &'static str> {
let mut rgba_values = Vec::new();
let chunk_size;
if bit_depth == BitDepth::Eight {
chunk_size = 4;
} else if bit_depth == BitDepth::Sixteen {
chunk_size = 8;
} else {
return Err("Invalid Bit Depth");
}
for group in idat.chunks(chunk_size) {
if group.len() < 4 {
return Err("Insufficient data");
}
let red = match bit_depth {
BitDepth::Eight => group[0] as f32,
BitDepth::Sixteen => ((group[0] as u16) << 8 | group[1] as u16) as f32,
_ => return Err("Unsupported bit depth"),
};
let green = match bit_depth {
BitDepth::Eight => group[1] as f32,
BitDepth::Sixteen => ((group[2] as u16) << 8 | group[3] as u16) as f32,
_ => return Err("Unsupported bit depth"),
};
let blue = match bit_depth {
BitDepth::Eight => group[2] as f32,
BitDepth::Sixteen => ((group[4] as u16) << 8 | group[5] as u16) as f32,
_ => return Err("Unsupported bit depth"),
};
let alpha = match bit_depth {
BitDepth::Eight => group[3] as f32,
BitDepth::Sixteen => ((group[6] as u16) << 8 | group[7] as u16) as f32,
_ => return Err("Unsupported bit depth"),
};
let rgba = (red, green, blue, alpha);
rgba_values.push(rgba);
}
Ok(rgba_values)
}
#[cfg(test)]
mod test {
use std::path::PathBuf;
use super::*;
#[test]
fn test_image_loader() {
let path = Path::new("res/test_img/red_image.png");
let test = image_loader(path);
let image = Image::new(
4,
4,
vec![
(255., 0., 0., 255.),
(0., 255., 0., 255.),
(0., 0., 255., 255.),
(255., 255., 255., 255.),
(127., 127., 127., 255.),
(0., 255., 255., 255.),
(255., 255., 0., 255.),
(255., 0., 0., 255.),
(127., 127., 127., 255.),
(255., 0., 255., 255.),
(255., 0., 0., 255.),
(255., 0., 0., 255.),
(255., 0., 0., 255.),
(255., 0., 0., 255.),
(255., 0., 0., 255.),
(0., 0., 0., 255.),
],
PathBuf::default(),
);
assert_eq!(test.unwrap().pixels(), image.pixels())
}
#[test]
#[should_panic]
fn test_wrong_img() {
let path = Path::new("res/test_img/wrong pixel count.png");
let test = image_loader(path);
let image = Image::new(
4,
4,
vec![
(255., 0., 0., 255.),
(255., 0., 0., 255.),
(255., 0., 0., 255.),
(255., 0., 0., 255.),
(255., 0., 0., 255.),
(255., 0., 0., 255.),
(255., 0., 0., 255.),
(255., 0., 0., 255.),
(255., 0., 0., 255.),
(255., 0., 0., 255.),
(255., 0., 0., 255.),
(255., 0., 0., 255.),
(255., 0., 0., 255.),
(255., 0., 0., 255.),
(255., 0., 0., 255.),
(255., 0., 0., 255.),
],
PathBuf::default(),
);
//should panic because we are looking at a corrupt picture
assert_eq!(test.unwrap().pixels(), image.pixels())
}
}

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@ -1,19 +1,6 @@
extern crate core;
pub mod image;
pub mod image_loader;
pub mod multithreading;
pub fn add(left: usize, right: usize) -> usize {
left + right
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn it_works() {
let result = add(2, 2);
assert_eq!(result, 4);
}
}
pub mod search_index;

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@ -174,6 +174,13 @@ where
}
}
/// Set a new limit to the amount of used threads.
/// This will only take effect when new threads are created. Currently running threads
/// will continue to do so.
pub fn set_limt(&mut self, limit: NonZeroUsize) {
self.limit = limit;
}
/// Put a new job into the queue to be executed by a thread in the future.
/// The priority of the job will determine if the job will be put at the start or end of the queue.
/// See [`crate::multithreading::Priority`].

542
src/search_index/mod.rs Normal file
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@ -0,0 +1,542 @@
//!
//! This module provides the Database for Images and compare methods to search in it.
//!
//! The database Struct provides the Images and has a threadpool to efficiently process all given features for all Images
//!
//!
//! to generate a database you need a vector of paths of picture that you want to save and search in it.
//! You also need a Vector of Feature generator functions that generates the feature of every image
//!
//!
//!```rust ignore
//! # use std::path::{Path, PathBuf};
//! # use imsearch::image::Image;
//! # use imsearch::search_index;
//! use imsearch::search_index::FeatureGenerator;
//!
//! let path: Vec<PathBuf> = Vec::new();
//! let features: Vec<FeatureGenerator> = Vec::new();
//!
//! let mut database = search_index::Database::new(&path, features).unwrap();
//! database.add_image(Path::new("testpath")).unwrap();
//! ```
//!
//!
//!This Library provides some Feature generator functions but you can also create your own.
//!The Feature generator has to fit in the "FeatureGenerator" type to work with the database.
//!
//!
use crate::image::Image;
use crate::multithreading::{Task, ThreadPool};
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::default::Default;
use std::fs;
use std::num::NonZeroUsize;
use std::path::{Path, PathBuf};
use std::sync::Arc;
///this trait provides a function to compare objects and returns a f32 between 0 and 1.
/// 1 is identical and 0 is different. with this trait you get the similarity between the objects
trait WeightedCmp {
fn weighted(&self, other: &Self) -> f32;
}
/// Every feature returns a known and sized type from this enum
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum FeatureResult {
/// A boolean. Just a boolean
Bool(bool),
/// Signed 32-bit integer
I32(i32),
/// 32-bit single precision floating point
/// can be used for aspect ratio or luminance
F32(f32),
/// Vector for nested multidimensional
Vec(Vec<FeatureResult>),
/// Standard RGBA color
Rgba(f32, f32, f32, f32),
/// Indices intended for the usage in histograms
Indices(Vec<u64>),
///A Character :)
Char(char),
///A String ;)
String(String),
///a f32 between 0 and 1 where 1 is 100% and 0 is 0%
Percent(f32),
}
impl Default for FeatureResult {
fn default() -> Self {
FeatureResult::Bool(false)
}
}
/// For some feature return type we want to implement a custom compare function
impl PartialEq for FeatureResult {
fn eq(&self, other: &Self) -> bool {
match (self, other) {
(Self::Bool(l0), Self::Bool(r0)) => l0 == r0,
(Self::I32(l0), Self::I32(r0)) => l0 == r0,
(Self::F32(l0), Self::F32(r0)) => l0 == r0,
(Self::Vec(l0), Self::Vec(r0)) => l0 == r0,
(Self::Rgba(l0, l1, l2, l3), Self::Rgba(r0, r1, r2, r3)) => {
l0 == r0 && l1 == r1 && l2 == r2 && l3 == r3
}
(Self::Indices(l), Self::Indices(r)) => l == r,
(Self::Char(l0), Self::Char(r0)) => l0 == r0,
(Self::String(l), Self::String(r)) => l == r,
(Self::Percent(l0), Self::Percent(r0)) => l0 == r0,
_ => false,
}
}
}
///in this trait we compare the types to get the similarity between them where 1 is identical and 0 is completly different
///
/// the Vec type compares each member recursive.
/// the Rgba type returns the Delta E similarity of the Colors
/// the Indices type is compared with the cosines similarity
/// the Percent type returns the 1 - difference
///
///
impl WeightedCmp for FeatureResult {
fn weighted(&self, other: &Self) -> f32 {
match (self, other) {
(Self::Bool(l0), Self::Bool(r0)) => {
if l0 == r0 {
1.
} else {
0.
}
}
(Self::I32(l0), Self::I32(r0)) => {
if l0 == r0 {
1.
} else {
0.
}
}
(Self::F32(l0), Self::F32(r0)) => {
if (l0 - r0).abs() < 1e-4 {
1.
} else {
0.
}
}
(Self::Vec(l), Self::Vec(r)) => {
if l.len() == r.len() {
let mut b: f32 = 0.;
for a in l.iter().enumerate() {
b += a.1.weighted(&r[a.0]);
}
b / l.len() as f32
} else {
0.
}
}
(Self::Rgba(l0, l1, l2, _), Self::Rgba(r0, r1, r2, _)) => {
let lableft = rgb_to_lab(vec![*l0, *l1, *l2]);
let labright = rgb_to_lab(vec![*r0, *r1, *r2]);
let mut result = ((lableft[0] - labright[0]) * (lableft[0] - labright[0])
+ (lableft[1] - labright[1]) * (lableft[1] - labright[1])
+ (lableft[2] - labright[2]) * (lableft[2] - labright[2]))
.sqrt(); //euclidian distance between two colors: Delta E
if result > 100. {
result = 0.;
} else {
result = 1. - result / 100.;
}
result
}
(Self::Indices(l), Self::Indices(r)) => {
let mut up = 0_u64;
let mut left = 0_u64;
let mut right = 0_u64;
for (a, b) in l.iter().zip(r.iter()).map(|(a, b)| (a, b)) {
left += a * a;
right += b * b;
up += a * b;
}
let mut result = up as f32 / ((left * right) as f32).sqrt(); //cosines similarity
if result.is_nan() {
if left == right {
result = 1.;
} else {
result = 0.
}
}
result
}
(Self::Char(l0), Self::Char(r0)) => {
if l0 == r0 {
1.
} else {
0.
}
}
(Self::String(l0), Self::String(r0)) => {
if l0 == r0 {
1.
} else {
0.
}
}
(Self::Percent(l0), Self::Percent(r0)) => 1. - (l0 - r0).abs(),
_ => 0.,
}
}
}
///this function transforms rgb values to lab values
fn rgb_to_lab(rgb: Vec<f32>) -> [f32; 3] {
let r = rgb[0] / 255.0;
let g = rgb[1] / 255.0;
let b = rgb[2] / 255.0;
let r = if r > 0.04045 {
((r + 0.055) / 1.055).powf(2.4)
} else {
r / 12.92
};
let g = if g > 0.04045 {
((g + 0.055) / 1.055).powf(2.4)
} else {
g / 12.92
};
let b = if b > 0.04045 {
((b + 0.055) / 1.055).powf(2.4)
} else {
b / 12.92
};
let x = r * 0.4124 + g * 0.3576 + b * 0.1805;
let y = r * 0.2126 + g * 0.7152 + b * 0.0722;
let z = r * 0.0193 + g * 0.1192 + b * 0.9505;
let x = x / 0.95047;
let y = y / 1.0;
let z = z / 1.08883;
let x = if x > 0.008856 {
x.powf(1.0 / 3.0)
} else {
(7.787 * x) + (16.0 / 116.0)
};
let y = if y > 0.008856 {
y.powf(1.0 / 3.0)
} else {
(7.787 * y) + (16.0 / 116.0)
};
let z = if z > 0.008856 {
z.powf(1.0 / 3.0)
} else {
(7.787 * z) + (16.0 / 116.0)
};
let l = (116.0 * y) - 16.0;
let a = 500.0 * (x - y);
let b = 200.0 * (y - z);
[l, a, b]
}
pub type FeatureGenerator = fn(Arc<Image<f32>>) -> (String, FeatureResult);
///The Database stores the images with the feature generators.
///It also stores the threadpool
///the images of the Database can get serialized using Serde_Json. the complete Database cant get serialized
#[derive(Default)]
pub struct Database {
images: IndexedImages,
/// keep feature generator for the case when we add a new image
/// this field is not serialized and needs to be wrapped in an option
generators: Vec<FeatureGenerator>,
threadpool: ThreadPool<Arc<Image<f32>>, (String, FeatureResult)>,
}
impl Database {
///This function search the Database after the Similarity to a given Image in a specific feature.
/// It returns a Vector of all images and a f32 value which represents the Similarity in percent.
///
pub fn search(
&self,
imagepath: &Path,
feature: FeatureGenerator,
) -> Result<Vec<(PathBuf, f32)>, &'static str> {
self.images.search(imagepath, feature)
}
///the new function generates a new Database out of a vector of the Paths of the Images and a Vector of features
pub fn new(
imagepaths: &Vec<PathBuf>,
features: Vec<FeatureGenerator>,
) -> Result<Self, &'static str> {
let mut threadpool = ThreadPool::new();
let images = match IndexedImages::new(imagepaths, &features, &mut threadpool) {
Ok(images) => images,
Err(e) => return Err(e),
};
Ok(Self {
images,
generators: features,
threadpool,
})
}
/// Limit the amount of threads to use when processing features
pub fn limit_thread_usage(&mut self, limit: NonZeroUsize) {
self.threadpool.set_limt(limit);
}
/// Register the supplied generators.
/// All previously registered generators will be replaced with the new generators
pub fn register_generators(&mut self, generators: Vec<FeatureGenerator>) {
self.generators = generators;
}
/// with add_image you can add images in a existing database.
/// databases from a file are read only.
pub fn add_image(&mut self, path: &Path) -> Result<(), &'static str> {
if !self.generators.is_empty() {
self.images
.add_image(path, &self.generators, &mut self.threadpool)
} else {
panic!("database without generator functions is immutable")
}
}
/// with from_file you can generate a Database out of a given path to a serialized database
pub fn from_file(path: &Path) -> Self {
let filestring = fs::read_to_string(path).expect("can't read that file");
let images = serde_json::from_str::<IndexedImages>(&filestring)
.expect("unable to deserialize the file");
Self {
images,
generators: Vec::new(),
threadpool: ThreadPool::new(),
}
}
pub fn write_to_file(&self, path: &Path) {
let filestring = serde_json::to_string(&self.images).expect("unable to serialize the file");
fs::write(path, filestring).expect("unable to write the file");
}
}
///IndexedImages stores the images of the Database and is serializable
#[derive(Serialize, Deserialize, Default, PartialEq, Debug)]
struct IndexedImages {
images: HashMap<PathBuf, HashMap<String, FeatureResult>>,
}
impl IndexedImages {
///the new function generates all images and generates every feature so it can store these.
fn new(
imagepaths: &Vec<PathBuf>,
features: &[FeatureGenerator],
threadpool: &mut ThreadPool<Arc<Image<f32>>, (String, FeatureResult)>,
) -> Result<Self, &'static str> {
let mut images_with_feats = HashMap::new();
for path in imagepaths {
let image = match crate::image_loader::image_loader(path) {
Ok(image) => Arc::new(image),
Err(desc) => return Err(desc),
};
let mut feats = HashMap::new();
for generator in features.iter() {
threadpool.enqueue(Task::new(image.clone(), *generator));
}
let vec = threadpool.get_results();
for (name, result) in vec {
feats.insert(name, result);
}
images_with_feats.insert(image.path().clone(), feats);
}
Ok(Self {
images: images_with_feats,
})
}
///This function search the Database after the Similarity to a given Image in a specific feature.
/// It returns a Vector of all images and a f32 value which represents the Similarity in percent.
///
fn search(
&self,
imagepath: &Path,
feature: FeatureGenerator,
) -> Result<Vec<(PathBuf, f32)>, &'static str> {
let image = match crate::image_loader::image_loader(imagepath) {
Ok(image) => Arc::new(image),
Err(desc) => return Err(desc),
};
let search_feat = feature(image);
let mut result: Vec<(PathBuf, f32)> = Vec::new();
for image in &self.images {
for feat in image.1 {
if search_feat.0 == *feat.0 {
result.push((image.0.clone(), search_feat.1.weighted(feat.1)));
}
}
}
Ok(result)
}
///this function lets you add images to the Indexed Image struct
fn add_image(
&mut self,
path: &Path,
generator: &Vec<FeatureGenerator>,
threadpool: &mut ThreadPool<Arc<Image<f32>>, (String, FeatureResult)>,
) -> Result<(), &'static str> {
let image = match crate::image_loader::image_loader(path) {
Ok(image) => Arc::new(image),
Err(desc) => return Err(desc),
};
let mut feats = HashMap::new();
for gen in generator {
threadpool.enqueue(Task::new(image.clone(), *gen));
}
let vec = threadpool.get_results();
for (name, result) in vec {
feats.insert(name, result);
}
self.images.insert(image.path().clone(), feats);
Ok(())
}
}
#[cfg(test)]
mod tests {
use super::*;
///this function tests the Serialization of the Database
#[test]
fn conversion() {
let mut images: HashMap<PathBuf, HashMap<String, FeatureResult>> = HashMap::new();
let mut feat: HashMap<String, FeatureResult> = HashMap::new();
feat.insert(String::from("average-brightness"), FeatureResult::F32(0.0));
images.insert(PathBuf::new(), feat);
let data = IndexedImages { images };
let _as_json = serde_json::to_string(&data).expect("couldnt convert");
println!("{:?}", _as_json);
let data_after_conversion =
serde_json::from_str::<IndexedImages>(&_as_json).expect("couldnt convert from string");
assert_eq!(data, data_after_conversion);
}
///this function tests Edgecases for the cosine_similarity in the weightet function
#[test]
fn cosine_similarity() {
let vec1 = FeatureResult::Indices(vec![1, 3, 4]);
let vec2 = FeatureResult::Indices(vec![1, 3, 4]);
assert_eq!(1., vec1.weighted(&vec2)); // both are identical
let vec2 = FeatureResult::Indices(vec![0, 0, 0]);
assert_eq!(0., vec1.weighted(&vec2)); // one is 0
let vec1 = FeatureResult::Indices(vec![0, 0, 0]);
assert_eq!(1., vec1.weighted(&vec2)); // both are 0
assert_eq!(1., vec2.weighted(&vec1)); // it shouldn't change if the Values are switched
let vec1 = FeatureResult::Indices(vec![7, 3, 4]);
let vec2 = FeatureResult::Indices(vec![1, 5, 2]);
assert_eq!(vec1.weighted(&vec2), vec2.weighted(&vec1));
println!("{:?}", vec1.weighted(&vec2));
let mut vec1 = vec![5; 9999];
vec1.push(1);
let vec1 = FeatureResult::Indices(vec1);
let vec2 = FeatureResult::Indices(vec![7; 10000]);
println!("{:?}", vec1.weighted(&vec2));
}
///this function tests all of the weighted function
#[test]
fn weighted() {
let vec1 = FeatureResult::Vec(vec![
FeatureResult::Bool(true),
FeatureResult::Char('c'),
FeatureResult::Vec(vec![FeatureResult::Percent(0.5)]),
FeatureResult::F32(44.543),
]);
let vec2 = FeatureResult::Vec(vec![
FeatureResult::Bool(true),
FeatureResult::Char('c'),
FeatureResult::Vec(vec![FeatureResult::Percent(0.5)]),
FeatureResult::F32(44.543),
]);
assert_eq!(1., vec2.weighted(&vec1));
let vec2 = FeatureResult::Vec(vec![
FeatureResult::Bool(true),
FeatureResult::Char('c'),
FeatureResult::F32(44.543),
FeatureResult::Vec(vec![FeatureResult::Percent(0.5)]),
]);
assert_eq!(0.5, vec2.weighted(&vec1));
println!("{:?}", vec1.weighted(&vec2));
let value1 = FeatureResult::F32(44.543);
let value2 = FeatureResult::F32(44.543);
assert_eq!(1., value1.weighted(&value2));
let value1 = FeatureResult::Bool(true);
let value2 = FeatureResult::Bool(false);
assert_eq!(0., value1.weighted(&value2));
let value1 = FeatureResult::String(String::from("Testing"));
let value2 = FeatureResult::String(String::from("notTesting"));
assert_eq!(0., value1.weighted(&value2));
let value2 = FeatureResult::String(String::from("Testing"));
assert_eq!(1., value1.weighted(&value2));
}
///this test is for the rgba values in the weighted function
#[test]
fn weighted_rgba() {
let value1 = FeatureResult::Rgba(32.6754, 42.432, 43.87, 255.);
let value2 = FeatureResult::Rgba(32.6754, 42.432, 43.87, 255.);
assert_eq!(1., value1.weighted(&value2));
let value1 = FeatureResult::Rgba(255., 255., 0., 255.);
let value2 = FeatureResult::Rgba(0., 0., 0., 255.);
//assert_eq!(1., value1.weighted(&value2)) ;
println!("Yellow to Black: {:?}", value1.weighted(&value2));
let value1 = FeatureResult::Rgba(255., 255., 0., 255.);
let value2 = FeatureResult::Rgba(200., 255., 55., 255.);
//assert_eq!(1., value1.weighted(&value2)) ;
println!("yellow to light green: {:?}", value1.weighted(&value2));
let value1 = FeatureResult::Rgba(3., 8., 255., 255.);
let value2 = FeatureResult::Rgba(3., 106., 255., 255.);
//assert_eq!(1., value1.weighted(&value2)) ;
println!("blue to dark blue: {:?}", value1.weighted(&value2));
let value1 = FeatureResult::Rgba(255., 106., 122., 255.);
let value2 = FeatureResult::Rgba(255., 1., 28., 255.);
//assert_eq!(1., value1.weighted(&value2)) ;
println!("Red to light red: {:?}", value1.weighted(&value2));
}
}

82
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@ -0,0 +1,82 @@
use std::{path::Path, sync::Arc};
use imsearch::{
image::Image,
search_index::{Database, FeatureGenerator, FeatureResult},
};
fn average_brightness(image: Arc<Image<f32>>) -> (String, FeatureResult) {
let bright = image
.pixels()
.iter()
.map(|(r, g, b, _)| (r + g + b) / 3.0 / 255.0)
.sum::<f32>()
/ image.pixels().len() as f32;
(
String::from("average_brightness"),
FeatureResult::Percent(bright),
)
}
#[test]
fn test_database_basic() {
let files: Vec<std::path::PathBuf> = std::fs::read_dir("res/integration/")
.unwrap()
.map(|f| f.unwrap().path())
.collect();
let feats: Vec<FeatureGenerator> = vec![average_brightness];
let db = Database::new(&files, feats).unwrap();
for results in db
.search(
std::path::Path::new("res/integration/gray_image.png"),
average_brightness,
)
.unwrap()
{
println!(
"path: {} similarity: {}",
results.0.as_os_str().to_str().unwrap(),
results.1
);
}
}
#[test]
fn test_database_files() {
let json = Path::new("db.json");
{
let files: Vec<std::path::PathBuf> = std::fs::read_dir("res/integration/")
.unwrap()
.map(|f| f.unwrap().path())
.collect();
let feats: Vec<FeatureGenerator> = vec![average_brightness];
let db = Database::new(&files, feats).unwrap();
db.write_to_file(json);
}
{
let db = Database::from_file(json);
for results in db
.search(
std::path::Path::new("res/integration/gray_image.png"),
average_brightness,
)
.unwrap()
{
println!(
"path: {} similarity: {}",
results.0.as_os_str().to_str().unwrap(),
results.1
);
}
}
}