From 1f0e954b870df0ed2d0a66316874da8658199977 Mon Sep 17 00:00:00 2001 From: Collyn O'Kane <47607823+okaneco@users.noreply.github.com> Date: Tue, 19 Apr 2022 06:32:32 -0400 Subject: [PATCH] Initial commit --- .github/workflows/rust-cd.yml | 89 +++++++ .github/workflows/rust-ci.yml | 62 +++++ .gitignore | 5 + CHANGELOG.md | 4 + Cargo.lock | 377 ++++++++++++++++++++++++++++ Cargo.toml | 56 +++++ LICENSE-APACHE | 190 ++++++++++++++ LICENSE-MIT | 7 + README.md | 123 +++++++++ gfx/113016-append.jpg | Bin 0 -> 271646 bytes gfx/113016.jpg | Bin 0 -> 93851 bytes gfx/176039.jpg | Bin 0 -> 64476 bytes gfx/295087-176039.jpg | Bin 0 -> 245414 bytes gfx/295087.jpg | Bin 0 -> 75361 bytes gfx/55067-compactness.jpg | Bin 0 -> 35431 bytes gfx/55067.jpg | Bin 0 -> 36837 bytes src/bin/simple_clustering/args.rs | 53 ++++ src/bin/simple_clustering/main.rs | 122 +++++++++ src/bin/simple_clustering/utils.rs | 104 ++++++++ src/error.rs | 109 ++++++++ src/image.rs | 120 +++++++++ src/lib.rs | 229 +++++++++++++++++ src/seed.rs | 154 ++++++++++++ src/slic.rs | 385 +++++++++++++++++++++++++++++ src/snic.rs | 348 ++++++++++++++++++++++++++ 25 files changed, 2537 insertions(+) create mode 100644 .github/workflows/rust-cd.yml create mode 100644 .github/workflows/rust-ci.yml create mode 100644 .gitignore create mode 100644 CHANGELOG.md create mode 100644 Cargo.lock create mode 100644 Cargo.toml create mode 100644 LICENSE-APACHE create mode 100644 LICENSE-MIT create mode 100644 README.md create mode 100644 gfx/113016-append.jpg create mode 100644 gfx/113016.jpg create mode 100644 gfx/176039.jpg create mode 100644 gfx/295087-176039.jpg create mode 100644 gfx/295087.jpg create mode 100644 gfx/55067-compactness.jpg create mode 100644 gfx/55067.jpg create mode 100644 src/bin/simple_clustering/args.rs create mode 100644 src/bin/simple_clustering/main.rs create mode 100644 src/bin/simple_clustering/utils.rs create mode 100644 src/error.rs create mode 100644 src/image.rs create mode 100644 src/lib.rs create mode 100644 src/seed.rs create mode 100644 src/slic.rs create mode 100644 src/snic.rs diff --git a/.github/workflows/rust-cd.yml b/.github/workflows/rust-cd.yml new file mode 100644 index 0000000..e4b1edc --- /dev/null +++ b/.github/workflows/rust-cd.yml @@ -0,0 +1,89 @@ +name: Rust CD + +on: + push: + tags: + - "*.*.*" + +jobs: + publish-binary: + runs-on: ${{ matrix.platform.os }} + strategy: + matrix: + rust: [ stable ] + platform: + - os: macos-latest + os-name: macos + target: x86_64-apple-darwin + architecture: x86_64 + binary-postfix: "" + use-cross: false + - os: ubuntu-latest + os-name: linux + target: x86_64-unknown-linux-gnu + architecture: x86_64 + binary-postfix: "" + use-cross: false + - os: windows-latest + os-name: windows + target: x86_64-pc-windows-msvc + architecture: x86_64 + binary-postfix: ".exe" + use-cross: false + - os: ubuntu-latest + os-name: linux + target: aarch64-unknown-linux-gnu + architecture: arm64 + binary-postfix: "" + use-cross: true + - os: ubuntu-latest + os-name: linux + target: i686-unknown-linux-gnu + architecture: i686 + binary-postfix: "" + use-cross: true + + steps: + - name: Install Rust toolchain + uses: actions-rs/toolchain@v1 + with: + toolchain: ${{ matrix.rust }} + profile: minimal + override: true + + - name: Checkout repository + uses: actions/checkout@v2 + + - name: Cargo build + uses: actions-rs/cargo@v1 + with: + command: build + use-cross: ${{ matrix.platform.use-cross }} + toolchain: ${{ matrix.rust }} + args: --release --target ${{ matrix.platform.target }} + + - name: Package final binary + shell: bash + run: | + cd target/${{ matrix.platform.target }}/release + BINARY_NAME=simple_clustering${{ matrix.platform.binary-postfix }} + + ########## create tar.gz ########## + RELEASE_NAME=simple_clustering-${GITHUB_REF/refs\/tags\//}-${{ matrix.platform.os-name }}-${{ matrix.platform.architecture }} + tar czvf $RELEASE_NAME.tar.gz $BINARY_NAME + + ########## create sha256 ########## + if [[ ${{ runner.os }} == 'Windows' ]]; then + certutil -hashfile $RELEASE_NAME.tar.gz sha256 | grep -E [A-Fa-f0-9]{64} > $RELEASE_NAME.sha256 + else + shasum -a 256 $RELEASE_NAME.tar.gz > $RELEASE_NAME.sha256 + fi + + - name: Releasing assets + uses: softprops/action-gh-release@v1 + with: + files: | + target/${{ matrix.platform.target }}/release/simple_clustering-*.tar.gz + target/${{ matrix.platform.target }}/release/simple_clustering-*.sha256 + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} diff --git a/.github/workflows/rust-ci.yml b/.github/workflows/rust-ci.yml new file mode 100644 index 0000000..79fe647 --- /dev/null +++ b/.github/workflows/rust-ci.yml @@ -0,0 +1,62 @@ +name: Rust CI + +on: + push: + branches: master + pull_request: + branches: master + schedule: + - cron: "0 0 1 * *" # monthly + workflow_dispatch: # allow manual triggering of the action + +env: + RUSTFLAGS: "-Dwarnings" + +jobs: + build-crate: + name: Build and test crate/docs + runs-on: ${{ matrix.os }} + strategy: + matrix: + os: [ubuntu-latest, windows-latest] + toolchain: [nightly, beta, stable] + include: + - os: macos-latest + toolchain: stable + steps: + - uses: actions/checkout@v2 + - uses: actions-rs/toolchain@v1 + with: + profile: minimal + toolchain: ${{ matrix.toolchain }} + components: rust-docs + override: true + - name: Build library + run: cargo build -v --lib --no-default-features + - name: Build binary + run: cargo build -v --bins + - name: Test library + run: cargo test --no-default-features --lib + - name: Doc tests + run: cargo test --doc --no-default-features + - name: Build docs + run: cargo doc --no-deps --no-default-features + + clippy-rustfmt: + name: Clippy and rustfmt + runs-on: ubuntu-latest + continue-on-error: true + steps: + - uses: actions/checkout@v2 + - uses: actions-rs/toolchain@v1 + with: + profile: minimal + toolchain: stable + components: clippy, rustfmt + override: true + - name: clippy + run: cargo clippy + continue-on-error: true + - name: rustfmt + run: 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".github"] +description = "Implementations of image clustering and segmentation algorithms such as SLIC and SNIC." +homepage = "https://github.com/okaneco/simple_clustering" +repository = "https://github.com/okaneco/simple_clustering" +readme = "README.md" + +keywords = ["clustering", "superpixel", "image", "segmentation", "segments"] +categories = ["computer-vision", "multimedia::images", "mathematics"] +license = "MIT OR Apache-2.0" + +[features] +# Use default-features=false if using as a library +default = ["app"] + +# Features required for building the binary +app = [ + "clap", + "image", +] + +[dependencies.clap] +version = "3.1.18" +default-features = false +features = ["std", "suggestions", "derive"] +optional = true + +[dependencies.fxhash] +version = "0.2.1" +default-features = false + +[dependencies.image] +version = "0.24.2" +default-features = false +features = ["jpeg", "png"] +optional = true + +[dependencies.num-traits] +version = "0.2.15" +default-features = false + +[dependencies.palette] +version = "0.6" +default-features = false +features = ["std"] + +[profile.release] +strip = true + +[package.metadata.docs.rs] +no-default-features = true +features = [""] +targets = [] diff --git a/LICENSE-APACHE b/LICENSE-APACHE new file mode 100644 index 0000000..d0a4778 --- /dev/null +++ b/LICENSE-APACHE @@ -0,0 +1,190 @@ + Apache License + Version 2.0, January 2004 + https://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. diff --git a/README.md b/README.md new file mode 100644 index 0000000..ee7d3f1 --- /dev/null +++ b/README.md @@ -0,0 +1,123 @@ +# simple_clustering +[![Build Status](https://img.shields.io/github/workflow/status/okaneco/simple_clustering/Rust%20CI)](https://github.com/okaneco/simple_clustering) +[![Crates.io](https://img.shields.io/crates/v/simple_clustering.svg)](https://crates.io/crates/simple_clustering) +[![Docs.rs](https://docs.rs/simple_clustering/badge.svg)](https://docs.rs/simple_clustering) + +Image segmentation library and command line tool using clustering methods. + +Currently supported algorithms are the **SLIC** (*simple linear iterative +clustering*) and **SNIC** (*simple non-iterative clustering*) superpixel +algorithms. The crate also supports drawing basic contours around the image +segments. + +![Comparison of original and segmented images](/gfx/295087-176039.jpg) + +To use as a library, add the following to your `Cargo.toml`. Executable builds +can be found at https://github.com/okaneco/simple_clustering/releases. + +```toml +[dependencies.simple_clustering] +version = "0.1" +default-features = false +``` + +## Features +- image segmentation using SNIC and SLIC +- find mean colors of segmented regions +- draw contours around segment boundaries +- command line tool for generating segmented images from JPG/PNG input files + +## Examples + +*Images are from the [Berkeley Segmentation Dataset and Benchmark][BSD] +unless noted otherwise.* + +### SNIC and SLIC + +The algorithm can be selected with the `-a` option. By default, the algorithm +is `snic`. + +```bash +simple_clustering -i 295087.jpg +simple_clustering -i 295087.jpg -a slic +``` + +--- + +The suggested number of superpixels to find is specified with `-k` or `-n`. +Resulting superpixel counts may be slightly greater or less than this value due +to characteristics of seeding or enforcing connectivity of superpixel labels. +The default setting is `1000`. + +```bash +simple_clustering -i 295087.jpg -k 200 +``` + +--- + +The `-m` option is used to control the "compactness" of each superpixel. The +number ranges from `1` to `20` with a tradeoff between uniform superpixel size +and better boundary adherence. `1` will result in larger and more non-uniform +superpixels while `20` will result in more compact and uniform superpixels. By +default, `-m` is set to `10`. + +```bash +simple_clustering -i 55067.jpg -m 1 +simple_clustering -i 55067.jpg -m 20 +``` + +![Comparison of compactness parameter, mountains](./gfx/55067-compactness.jpg) +*Left image: `-m 1`. Right image: `-m 20`.* + +--- + +### Contours + +The segment color defaults to black, `#000`, but can be specified with the +`--segment-color` option followed by a 3 or 6 digit hexadecimal RGB color. The +following example sets the color to gray, `#777`. To save the segment contours +over the original image data, add the `--no-mean` flag to skip generating the +mean color image. + +```bash +simple_clustering -i 113016.jpg -k 200 --segments --segment-color 777 +simple_clustering -i 113016.jpg -k 200 --segments --no-mean +``` + +![Comparison of segment paramaters, horses](./gfx/113016-append.jpg) +*Left image: Segmented image with gray contours. Right image: Superpixel regions +drawn over original image data.* + +--- + +### Reference + +*Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., & Süsstrunk, S. SLIC +Superpixels. EPFL Technical Report no. 149300, June 2010.* + +*Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., & Süsstrunk, S. SLIC +Superpixels Compared to State-of-the-art Superpixel Methods. IEEE Transactions +on Pattern Analysis and Machine Intelligence, vol. 34, num. 11, p. 2274 – 2282, +May 2012.* + +*Achanta, R., & Süsstrunk, S. Superpixels and polygons using simple +non-iterative clustering. Proceedings of the IEEE Conference on Computer Vision +and Pattern Recognition, 2017.* + +*Martin, D., Fowlkes, C., Tal, D., & Malik, J. A Database of Human Segmented +Natural Images and its Application to Evaluating Segmentation Algorithms and +Measuring Ecological Statistics. Proc. 8th Int'l Conf. Computer Vision, vol. 2, +p. 416 – 423, July 2001.* + +## License +This crate is licensed under either +- the [MIT License](LICENSE-MIT), or +- the [Apache License (Version 2.0)](LICENSE-APACHE) + +at your option. + +Unless you explicitly state otherwise, any contribution intentionally submitted +for inclusion in the work by you, as defined in the Apache-2.0 license, shall be +dual licensed as above, without any additional terms or conditions. + +[BSD]: https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/ diff --git a/gfx/113016-append.jpg b/gfx/113016-append.jpg new file mode 100644 index 0000000000000000000000000000000000000000..b990bf95a78e1c8130e63bad56940f0440f7bea4 GIT binary patch literal 271646 zcmb6AgcIKkbe#oavtiWV(Uf=h9CcM2^|fKar!w#BU!cY;IE;u7S? z>+}9Tf50=>wddNiXJvMF&hFgz%s$OOEdk^Js3`x*e=X{Nf{ylIi-C@ghK`AWiTQsE z77jKh77i9BCN>^64(@;Qy!HYQ_r-sm|4#D1Ls2o%&@gbZFtPq`m;b+-r=I{KY!n%^ z-)JaA08}CrG$NFzUH}aMfP(RV>H+?rU;)rEFtO2aP*HH7#~ld)DCj6?=zwQs(J@}& 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{ + /// Input file. + #[clap(short, long, parse(from_os_str))] + pub input: std::path::PathBuf, + + /// Output file, defaults to PNG image output. + #[clap(short, long, parse(from_os_str))] + pub output: Option, + + /// Number of superpixel segments to find. + #[clap(short, short_alias = 'n', default_value_t = 1_000)] + pub k: u32, + + /// Compactness of superpixels, `m` parameter. Range of 1 to 20. + #[clap(short, default_value_t = 10)] + pub m: u8, + + /// Number of iterations to run (SLIC). + #[clap(long, default_value_t = 10)] + pub iter: u8, + + /// Disable saving an output image with mean superpixel segment colors. + #[clap(long)] + pub no_mean: bool, + + /// Segmentation algorithm used, such as SNIC or SLIC. + #[clap(short, long, default_value = "snic")] + pub algorithm: crate::utils::Algorithm, + + /// Print the number of segments found and time taken. + #[clap(short, long)] + pub verbose: bool, + + /// Save as a JPG or PNG file. + #[clap(long, default_value = "png")] + pub format: String, + + /// Development flag for testing speeds of calculation. + #[clap(long, hide = true)] + pub benchmark: bool, + + /// Save an image with segmented superpixel contours. + #[clap(long)] + pub segments: bool, + + /// Specify the hexadecimal RGB color for segment contours. + #[clap(long, default_value = "000")] + pub segment_color: String, +} diff --git a/src/bin/simple_clustering/main.rs b/src/bin/simple_clustering/main.rs new file mode 100644 index 0000000..6be26f2 --- /dev/null +++ b/src/bin/simple_clustering/main.rs @@ -0,0 +1,122 @@ +mod args; +mod utils; + +use crate::args::Opt; +use crate::utils::{generate_filename, save_image, Algorithm}; + +use clap::Parser; + +use palette::{FromColor, Lab, Pixel, Srgb}; +use simple_clustering::image::{count_colors, mean_colors, segment_contours}; +use std::fmt::Write; +use std::str::FromStr; + +fn main() { + if let Err(e) = try_main() { + eprintln!("simple_clustering: {}", e); + std::process::exit(1); + } +} + +fn try_main() -> Result<(), Box> { + let opt = Opt::parse(); + + let output_image = if let Some(output) = opt.output { + output + } else { + generate_filename(&opt)?.into() + }; + + let input_image = image::open(opt.input)?.into_rgb8(); + let (width, height) = input_image.dimensions(); + let input_buffer = Srgb::from_raw_slice(input_image.as_raw()); + let mut input_lab: Vec> = Vec::new(); + input_lab.try_reserve_exact(input_buffer.len())?; + input_lab.extend( + input_buffer + .iter() + .map(|&c| Lab::from_color(c.into_format())), + ); + + let mut display_string = String::new(); + let mut output_buffer = Vec::new(); + output_buffer.try_reserve_exact(input_image.as_raw().len())?; + output_buffer.extend((0..input_image.as_raw().len()).map(|_| 0)); + + if opt.benchmark { + let t0 = std::time::Instant::now(); + let _ = simple_clustering::slic(opt.k, opt.m, width, height, Some(opt.iter), &input_lab)?; + writeln!(&mut display_string, "SLIC: {:?}", t0.elapsed())?; + + let t0 = std::time::Instant::now(); + let _ = simple_clustering::snic(opt.k, opt.m, width, height, &input_lab)?; + writeln!(&mut display_string, "SNIC: {:?}", t0.elapsed())?; + + print!("{display_string}"); + return Ok(()); + } + + let labels = match opt.algorithm { + Algorithm::Snic => { + let t0 = std::time::Instant::now(); + let labels = simple_clustering::snic(opt.k, opt.m, width, height, &input_lab)?; + let t1 = t0.elapsed(); + if opt.verbose { + write!(&mut display_string, "SNIC: {:?}", t1)?; + } + labels + } + Algorithm::Slic => { + let t0 = std::time::Instant::now(); + let labels = + simple_clustering::slic(opt.k, opt.m, width, height, Some(opt.iter), &input_lab)?; + let t1 = t0.elapsed(); + if opt.verbose { + write!(&mut display_string, "SLIC: {:?}", t1)?; + } + labels + } + }; + + let segment_color = *Srgb::from_str(opt.segment_color.as_str()) + .or(Err("Segment color is invalid hex"))? + .as_raw(); + + if !opt.no_mean { + let num_segments = mean_colors( + &mut output_buffer, + usize::try_from(opt.k)?, + &labels, + &input_lab, + )?; + + // Draw segment contours over mean image + if opt.segments { + segment_contours(&mut output_buffer, width, height, &labels, segment_color)?; + } + + save_image(output_image.as_ref(), &output_buffer, width, height)?; + + if opt.verbose { + write!(&mut display_string, ", {num_segments} segments")?; + } + } else { + // Save segmented original image + if opt.segments { + output_buffer.copy_from_slice(&input_image); + segment_contours(&mut output_buffer, width, height, &labels, segment_color)?; + save_image(output_image.as_ref(), &output_buffer, width, height)?; + } + + // Otherwise, count individual labels for verbose output + if opt.verbose { + write!(&mut display_string, ", {} segments", count_colors(&labels))?; + } + } + + if opt.verbose { + println!("{display_string}"); + } + + Ok(()) +} diff --git a/src/bin/simple_clustering/utils.rs b/src/bin/simple_clustering/utils.rs new file mode 100644 index 0000000..45346c2 --- /dev/null +++ b/src/bin/simple_clustering/utils.rs @@ -0,0 +1,104 @@ +use image::codecs::png::{CompressionType, FilterType, PngEncoder}; +use image::{codecs::jpeg::JpegEncoder, ColorType, ImageEncoder}; + +#[derive(Debug)] +pub enum Algorithm { + Snic, + Slic, +} + +impl std::str::FromStr for Algorithm { + type Err = simple_clustering::error::ScError; + + fn from_str(s: &str) -> Result { + match s { + s if s.eq_ignore_ascii_case("snic") => Ok(Self::Snic), + s if s.eq_ignore_ascii_case("slic") => Ok(Self::Slic), + _ => Err(Self::Err::General("Invalid algorithm")), + } + } +} + +impl std::fmt::Display for Algorithm { + fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result { + match self { + Self::Snic => write!(f, "snic"), + Self::Slic => write!(f, "slic"), + } + } +} + +// Create a file name displaying the algorithm, `k`, and `m` values used. +pub fn generate_filename(opt: &crate::args::Opt) -> Result> { + let mut filename = opt + .input + .file_stem() + .ok_or("No file stem")? + .to_str() + .ok_or("Could not convert file stem to string")? + .to_string(); + + let format = + if opt.format.eq_ignore_ascii_case("jpg") || opt.format.eq_ignore_ascii_case("jpeg") { + "jpg" + } else { + opt.format.as_str() + }; + + use std::fmt::Write; + write!( + &mut filename, + "-{algo}-{k}-{m:02}", + algo = opt.algorithm, + k = opt.k, + m = opt.m + )?; + + if opt.no_mean { + write!(&mut filename, "-orig")?; + } else { + write!(&mut filename, "-mean")?; + } + + if opt.segments { + write!(&mut filename, "-segments")?; + } + write!(&mut filename, ".{format}")?; + + Ok(filename) +} + +// Saves image buffer to file. +pub fn save_image( + output: &std::path::Path, + imgbuf: &[u8], + width: u32, + height: u32, +) -> Result<(), Box> { + let w = std::io::BufWriter::new(std::fs::File::create(output)?); + + // Save as jpg if it matches the extension + if let Some(ext) = output.extension() { + if ext.eq_ignore_ascii_case("jpg") || ext.eq_ignore_ascii_case("jpeg") { + let mut encoder = JpegEncoder::new_with_quality(w, 90); + + if let Err(err) = encoder.encode(imgbuf, width, height, ColorType::Rgb8) { + eprintln!("simple_clustering: {}", err); + std::fs::remove_file(output)?; + } + + return Ok(()); + } + } + + // Sub filter seemed to result in better filesize compared to Adaptive + let encoder = PngEncoder::new_with_quality(w, CompressionType::Best, FilterType::Sub); + + // Clean up if file is created but there's a problem writing to it + if let Err(err) = encoder.write_image(imgbuf, width, height, ColorType::Rgb8) { + eprintln!("simple_clustering: {}", err); + std::fs::remove_file(output)?; + } + + Ok(()) +} diff --git a/src/error.rs b/src/error.rs new file mode 100644 index 0000000..a7baad6 --- /dev/null +++ b/src/error.rs @@ -0,0 +1,109 @@ +//! Superpixel error enums. +use std::collections::TryReserveError; + +/// Error for SLIC calculations. +#[derive(Clone, Debug)] +pub enum ScError { + /// The image `width` and/or `height` is equal to `0`. + InvalidImageDimension, + /// The number of superpixels to find is equal to `0`. + ZeroSuperpixelCount, + /// The number of superpixels to find is greater than or equal to the number + /// of pixels in the image. + InvalidSuperpixelCount, + /// The calculated grid interval is equal to `0`. + ZeroGridInterval, + /// The calculated grid interval is too large. + InvalidGridInterval, + /// The SLIC image buffer length does not match the dimensions. + MismatchedSlicBuffer, + /// The SNIC image buffer length does not match the dimensions. + MismatchedSnicBuffer, + /// A distance calculated during SNIC resulted in a NaN. + NanDistance, + /// An error occured while initializing or perturbing superpixel seeds. + SeedError(SeedErrorKind), + /// Space could not be reserved for a collection required in superpixel + /// calculation. + Reserve(TryReserveError), + /// A general error occurred. + General(&'static str), +} + +impl std::fmt::Display for ScError { + fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result { + match self { + Self::InvalidImageDimension => write!(f, "Image dimension cannot be 0"), + Self::ZeroSuperpixelCount => write!(f, "Number of superpixels cannot be 0"), + Self::InvalidSuperpixelCount => write!( + f, + "Number of superpixels greater than or equal to pixels in image" + ), + Self::ZeroGridInterval => write!(f, "Grid interval cannot be 0"), + Self::InvalidGridInterval => write!(f, "Grid interval larger than u32"), + Self::MismatchedSlicBuffer => { + write!(f, "SLIC buffer length does not equal image dimensions") + } + Self::MismatchedSnicBuffer => { + write!(f, "SNIC buffer length does not equal image dimensions") + } + Self::NanDistance => write!(f, "NaN encountered during SNIC"), + Self::SeedError(e) => write!(f, "{e}"), + Self::Reserve(e) => write!(f, "{e}"), + Self::General(e) => write!(f, "{e}"), + } + } +} + +/// Errors that can occur while initializing the superpixel seeds. +#[derive(Clone, Debug)] +pub enum SeedErrorKind { + /// Index out of bounds for seed initialization. + InvalidImageIndex, + /// The total number of seeds is too large to be stored in a vector. + InvalidTotalSeeds, + /// An integer conversion error occurred while perturbing superpixel seeds. + PerturbConversion, +} + +impl std::fmt::Display for SeedErrorKind { + fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result { + match self { + Self::InvalidImageIndex => { + write!(f, "Invalid image index for seed initialization") + } + Self::InvalidTotalSeeds => write!(f, "Total number of seeds too large"), + Self::PerturbConversion => write!(f, "Could not convert integer in seed perturbation"), + } + } +} + +impl std::error::Error for ScError { + fn source(&self) -> Option<&(dyn std::error::Error + 'static)> { + match self { + Self::Reserve(e) => e.source(), + Self::InvalidImageDimension + | Self::ZeroSuperpixelCount + | Self::InvalidSuperpixelCount + | Self::ZeroGridInterval + | Self::InvalidGridInterval + | Self::MismatchedSlicBuffer + | Self::MismatchedSnicBuffer + | Self::NanDistance + | Self::SeedError(_) + | Self::General(_) => None, + } + } +} + +impl std::convert::From for ScError { + fn from(error: TryReserveError) -> Self { + Self::Reserve(error) + } +} + +impl std::convert::From<&'static str> for ScError { + fn from(error: &'static str) -> Self { + Self::General(error) + } +} diff --git a/src/image.rs b/src/image.rs new file mode 100644 index 0000000..0b25836 --- /dev/null +++ b/src/image.rs @@ -0,0 +1,120 @@ +//! Functions for interacting with image labels and manipulating images. +use crate::{error::ScError, get_in_bounds}; +use fxhash::{FxHashMap, FxHashSet}; +use palette::{FromColor, Lab, Pixel, Srgb}; + +/// Count the number of unique labels in a slice of superpixel labels. +pub fn count_colors(labels: &[usize]) -> usize { + labels + .iter() + .copied() + .collect::>() + .len() +} + +/// Modify `output` to contain an RGB image of superpixel segments filled with +/// the mean color of that region. The return value is the count of superpixels +/// in the image. +pub fn mean_colors( + output: &mut [u8], + k: usize, + labels: &[usize], + image: &[Lab], +) -> Result { + if Some(output.len()) != image.len().checked_mul(3) { + return Err(ScError::General( + "Mean color buffer does not match image length", + )); + } + + let mut map = FxHashMap::, f64)>::default(); + map.try_reserve(k)?; + + for (&idx, &color) in labels.iter().zip(image.iter()) { + let _ = map + .entry(idx) + .and_modify(|e| { + e.0 += color; + e.1 += 1.0; + }) + .or_insert((color, 1.0)); + } + + let mut rgb_map = FxHashMap::>::default(); + rgb_map.try_reserve(map.len())?; + + rgb_map.extend(map.iter().map(|(&key, &(color, count))| { + let rgb: Srgb = Srgb::from_color(color / count).into_format(); + (key, rgb) + })); + + output + .chunks_exact_mut(3) + .zip(labels.iter().filter_map(|a| rgb_map.get(a))) + .for_each(|(chunk, color)| chunk.copy_from_slice(color.as_raw())); + + Ok(map.len()) +} + +/// Modify `output` to contain an RGB image with colored contours based on +/// superpixel labels. +pub fn segment_contours( + output: &mut [u8], + width: u32, + height: u32, + labels: &[usize], + segment_color: [u8; 3], +) -> Result<(), ScError> { + let mut segment = Vec::new(); + segment.try_reserve_exact(labels.len())?; + segment.extend((0..labels.len()).map(|_| false)); + + let width_i = i64::from(width); + let height_i = i64::from(height); + + let mut chunks_iter = output.chunks_exact_mut(3).enumerate(); + let mut label_iter = labels.iter(); + for y in 0..height_i { + for x in 0..width_i { + let label = label_iter.next().ok_or("Labels exhausted")?; + let (chunk_idx, chunk) = chunks_iter.next().ok_or("Chunks exhausted")?; + let neighbors = [ + get_in_bounds(width_i, height_i, x - 1, y, labels), + get_in_bounds(width_i, height_i, x - 1, y - 1, labels), + get_in_bounds(width_i, height_i, x, y - 1, labels), + get_in_bounds(width_i, height_i, x + 1, y - 1, labels), + get_in_bounds(width_i, height_i, x + 1, y, labels), + get_in_bounds(width_i, height_i, x + 1, y + 1, labels), + get_in_bounds(width_i, height_i, x, y + 1, labels), + get_in_bounds(width_i, height_i, x - 1, y + 1, labels), + ]; + let neighbor_segments = [ + get_in_bounds(width_i, height_i, x - 1, y, &segment), + get_in_bounds(width_i, height_i, x - 1, y - 1, &segment), + get_in_bounds(width_i, height_i, x, y - 1, &segment), + get_in_bounds(width_i, height_i, x + 1, y - 1, &segment), + get_in_bounds(width_i, height_i, x + 1, y, &segment), + get_in_bounds(width_i, height_i, x + 1, y + 1, &segment), + get_in_bounds(width_i, height_i, x, y + 1, &segment), + get_in_bounds(width_i, height_i, x - 1, y + 1, &segment), + ]; + + // Count neighboring labels that are different from current label + // and aren't already a border segment + if neighbors + .iter() + .zip(neighbor_segments.iter()) + .filter(|(&n, &ns)| ns == Some(&false) && n != Some(label)) + .count() + >= 2 + { + chunk.copy_from_slice(&segment_color); + if let Some(s) = segment.get_mut(chunk_idx) { + *s = true; + } + } + } + } + + Ok(()) +} diff --git a/src/lib.rs b/src/lib.rs new file mode 100644 index 0000000..7106aa0 --- /dev/null +++ b/src/lib.rs @@ -0,0 +1,229 @@ +//! Image segmentation based on clustering methods. +//! +//! Currently supported algorithms are the **SLIC** (*simple linear iterative +//! clustering*) and **SNIC** (*simple non-iterative clustering*) superpixel +//! algorithms. The crate also supports drawing basic contours around the image +//! segments. +//! +//! The library uses the `palette` crate for some of its color types. The +//! current version used is `palette 0.6`. +//! +//! ## Usage +//! +//! Note that the convenience methods [`slic_from_bytes`] and +//! [`snic_from_bytes`] also exist to allow for calculation of superpixel labels +//! without having to convert to `Lab`. +//! +//! ### SNIC +//! +//! ``` +//! use palette::{FromColor, Lab, Pixel, Srgb}; +//! use simple_clustering::snic; +//! +//! # fn main() -> Result<(), Box> { +//! # let (width, height) = (1, 3); +//! # let image = [0u8, 0, 0, 127, 127, 127, 255, 255, 255]; +//! # let (k, m) = (1, 10); +//! let lab_buffer: Vec> = Srgb::from_raw_slice(&image) +//! .iter() +//! .map(|&c| Lab::from_color(c.into_format())) +//! .collect(); +//! let labels = snic(k, m, width, height, &lab_buffer)?; +//! +//! # Ok(()) +//! # } +//! ``` +//! +//! ### SLIC +//! +//! ``` +//! use palette::{FromColor, Lab, Pixel, Srgb}; +//! use simple_clustering::slic; +//! +//! # fn main() -> Result<(), Box> { +//! # let (width, height) = (1, 3); +//! # let image = [0u8, 0, 0, 127, 127, 127, 255, 255, 255]; +//! # let (k, m) = (1, 10); +//! let lab_buffer: Vec> = Srgb::from_raw_slice(&image) +//! .iter() +//! .map(|&c| Lab::from_color(c.into_format())) +//! .collect(); +//! let labels = slic(k, m, width, height, None, &lab_buffer)?; +//! # Ok(()) +//! # } +//! ``` +//! +//! ### Mean color segments and drawing segment contours +//! +//! Using the labels from SNIC or SLIC, the mean colors can be found of each +//! segment and output as an RGB image buffer. Contours can also be drawn +//! around those segments. +//! +//! ``` +//! # use palette::{FromColor, Lab, Pixel, Srgb}; +//! # use simple_clustering::snic; +//! use simple_clustering::image::{mean_colors, segment_contours}; +//! +//! # fn main() -> Result<(), Box> { +//! # let (width, height) = (1, 3); +//! # let image = [0u8, 0, 0, 127, 127, 127, 255, 255, 255]; +//! # let (k, m) = (1, 10); +//! # let lab_buffer: Vec> = Srgb::from_raw_slice(&image) +//! # .iter() +//! # .map(|&c| Lab::from_color(c.into_format())) +//! # .collect(); +//! # let labels = snic(k, m, width, height, &lab_buffer)?; +//! # let mut output_buffer = [0; 9]; +//! # let k = 1; +//! let _ = mean_colors(&mut output_buffer, k, &labels, &lab_buffer)?; +//! segment_contours(&mut output_buffer, width, height, &labels, [0; 3])?; +//! +//! # Ok(()) +//! # } +//! ``` +#![forbid( + absolute_paths_not_starting_with_crate, + missing_docs, + non_ascii_idents, + noop_method_call, + unsafe_code, + unused_results +)] +#![warn( + clippy::cast_lossless, + clippy::cast_possible_truncation, + clippy::cast_possible_wrap, + clippy::cast_precision_loss, + clippy::cast_sign_loss +)] + +use num_traits::{Float, One, Unsigned, Zero}; +use palette::{white_point::WhitePoint, FloatComponent, Lab}; +use std::ops::{Add, Div, Rem}; + +pub mod error; +pub mod image; +pub mod seed; +mod slic; +mod snic; + +pub use slic::{slic, slic_from_bytes}; +pub use snic::{snic, snic_from_bytes}; + +/// Calculate the superpixel side length, `S`. +/// +/// `S * S` is the approximate size of each superpixel in pixels. The formula is +/// `S = (N / K).sqrt()`, where `N` is the number of pixels and `K` is the +/// number of desired superpixels. +#[inline] +fn calculate_grid_interval(width: u32, height: u32, superpixels: u32) -> f64 { + ((f64::from(width) * f64::from(height)) / f64::from(superpixels)).sqrt() +} + +/// Calculate the distance between two `Lab` colors. +#[inline] +fn distance_lab(lhs: Lab, rhs: Lab) -> T +where + Wp: WhitePoint, + T: FloatComponent, +{ + (rhs.l - lhs.l).powi(2) + (rhs.a - lhs.a).powi(2) + (rhs.b - lhs.b).powi(2) +} + +/// Calculate the distance between two two-dimensional points. +#[inline] +fn distance_xy(lhs: (T, T), rhs: (T, T)) -> T { + (rhs.0 - lhs.0).powi(2) + (rhs.1 - lhs.1).powi(2) +} + +/// Calculate the `s` distance. +#[inline] +fn distance_s(m_div_s: T, d_lab: T, d_xy: T) -> T { + d_lab + m_div_s * d_xy +} + +/// Calculate the superpixel scaling factor. +/// +/// `m_div_s` is `(m / s).powi(2)`. +#[inline] +fn m_div_s(m: f64, s: f64) -> f64 { + (m / s).powi(2) +} + +/// Calculates the quotient of `lhs` and `rhs`, rounding the result towards +/// positive infinity. +// FIXME: Remove when stable +#[inline] +fn div_ceil(lhs: T, rhs: T) -> T +where + T: PartialOrd + Copy + Div + Rem + Add + Unsigned + Zero + One, +{ + let d = lhs / rhs; + let r = lhs % rhs; + if r > T::zero() && rhs > T::zero() { + d + T::one() + } else { + d + } +} + +/// Checks if the index is in bounds and returns a reference to the data at that +/// point if it exists. +#[inline] +fn get_in_bounds(width: i64, height: i64, x: i64, y: i64, image: &[T]) -> Option<&T> { + if (0..width).contains(&x) && (0..height).contains(&y) { + let i = u64::try_from(y) + .ok()? + .checked_mul(u64::try_from(width).ok()?)? + .checked_add(u64::try_from(x).ok()?) + .and_then(|i| usize::try_from(i).ok())?; + image.get(i) + } else { + None + } +} + +/// Checks if the index is in bounds and returns a mutable referance to the data +/// at that point if it exists. +#[inline] +fn get_mut_in_bounds( + width: i64, + height: i64, + x: i64, + y: i64, + image: &mut [T], +) -> Option<&mut T> { + if (0..width).contains(&x) && (0..height).contains(&y) { + let i = u64::try_from(y) + .ok()? + .checked_mul(u64::try_from(width).ok()?)? + .checked_add(u64::try_from(x).ok()?) + .and_then(|i| usize::try_from(i).ok())?; + image.get_mut(i) + } else { + None + } +} + +/// Struct containing a superpixel's color, X-coordinate, and Y-coordinate in +/// an image. +#[derive(Debug, Clone, Copy)] +pub struct Superpixel { + /// Superpixel color. + pub data: T, + /// X-position coordinate. + pub x: u32, + /// Y-position coordinate. + pub y: u32, +} + +impl Default for Superpixel { + #[inline] + fn default() -> Self { + Self { + data: Default::default(), + x: Default::default(), + y: Default::default(), + } + } +} diff --git a/src/seed.rs b/src/seed.rs new file mode 100644 index 0000000..69c86b8 --- /dev/null +++ b/src/seed.rs @@ -0,0 +1,154 @@ +//! Functions for initializing superpixel seeds. +use crate::error::{ScError, SeedErrorKind}; +use crate::{distance_lab, div_ceil, get_in_bounds, Superpixel}; + +use num_traits::ToPrimitive; +use palette::{white_point::WhitePoint, FloatComponent, Lab}; + +/// Initialize the superpixel seed centers. +/// +/// `width`, `height`, `s`, and `k` must not be `0`. +pub fn init_seeds( + width: u32, + height: u32, + s: u32, + k: u32, + image: &[T], + seeds: &mut Vec>, +) -> Result<(), ScError> { + seeds.clear(); + let width = width; + let height = height; + let s = s; + let half_s = div_ceil(s, 2); + let mut x_seeds = div_ceil(width, s); + let mut y_seeds = div_ceil(height, s); + + // The seeds per row and column might be too high due to the div_ceil + if u64::from(s) * u64::from(x_seeds) > u64::from(width) { + x_seeds -= 1; + } + if u64::from(s) * u64::from(y_seeds) > u64::from(height) { + y_seeds -= 1; + } + + // If the seed count is larger than k, reduce until we're below (we may add + // seeds in the "enforce connectivity" step later for SLIC) + while u64::from(x_seeds) * u64::from(y_seeds) > u64::from(k) { + x_seeds -= 1; + y_seeds -= 1; + } + + // Edge case for very small image sizes where no clusters would be produced + if x_seeds == 0 { + x_seeds += 1; + } + if y_seeds == 0 { + y_seeds += 1; + } + + // Error correction for spreading the seeds out more evenly along rows/cols + let x_correction = (f64::from(width) - f64::from(x_seeds) * f64::from(s)) / f64::from(x_seeds); + let y_correction = (f64::from(height) - f64::from(y_seeds) * f64::from(s)) / f64::from(y_seeds); + + let total_seeds = usize::try_from(u64::from(x_seeds) * u64::from(y_seeds)) + .or(Err(ScError::SeedError(SeedErrorKind::InvalidTotalSeeds)))?; + + if total_seeds > seeds.capacity() { + seeds.try_reserve_exact(total_seeds - seeds.capacity())?; + } + + for ydx in 0..y_seeds { + let y_correct = (f64::from(ydx) * y_correction) + .to_u32() + .ok_or("Could not convert Y correction")?; + for xdx in 0..x_seeds { + let x_correct = (f64::from(xdx) * x_correction) + .to_u32() + .ok_or("Could not convert X correction")?; + let x = xdx + .saturating_mul(s) + .saturating_add(half_s) + .saturating_add(x_correct); + let y = ydx + .saturating_mul(s) + .saturating_add(half_s) + .saturating_add(y_correct); + let i = usize::try_from( + u64::from(y) + .saturating_mul(u64::from(width)) + .saturating_add(u64::from(x)), + ) + .or(Err(ScError::SeedError(SeedErrorKind::InvalidImageIndex)))?; + if x < width && y < height && i < image.len() { + seeds.push(Superpixel { + data: *image + .get(i) + .ok_or(ScError::SeedError(SeedErrorKind::InvalidImageIndex))?, + x, + y, + }); + } + } + } + + Ok(()) +} + +/// Find the lowest gradient in a 3x3 neighborhood for a seed. +/// +/// This step minimizes the chance that a noisy pixel is chosen as a seed. +pub fn perturb( + seed: &mut Superpixel>, + width: i64, + height: i64, + image: &[Lab], +) -> Result<(), ScError> +where + Wp: WhitePoint, + T: FloatComponent, +{ + let mut min = T::infinity(); + let default = Lab::::default(); + let sp_x = i64::from(seed.x); + let sp_y = i64::from(seed.y); + + // Gradient equation is + // fn gradient() -> f64 { + // (I[x + 1, y] - I[x - 1, y]).powi(2) + + // (I[x, y + 1] - I[x, y - 1]).powi(2) + // } + for ydx in -1..=1 { + for xdx in -1..=1 { + let superpixel = + if let Some(color) = get_in_bounds(width, height, sp_x + xdx, sp_y + ydx, image) { + (*color, sp_x + xdx, sp_y + ydx) + } else { + continue; + }; + let a_x = sp_x + xdx + 1; + let b_x = sp_x + xdx - 1; + let ab_y = sp_y + ydx; + let cd_x = sp_x + xdx; + let c_y = sp_y + ydx + 1; + let d_y = sp_y + ydx - 1; + + let a = *get_in_bounds(width, height, a_x, ab_y, image).unwrap_or(&default); + let b = *get_in_bounds(width, height, b_x, ab_y, image).unwrap_or(&default); + let c = *get_in_bounds(width, height, cd_x, c_y, image).unwrap_or(&default); + let d = *get_in_bounds(width, height, cd_x, d_y, image).unwrap_or(&default); + + let gradient = distance_lab(a, b) + distance_lab(c, d); + if gradient < min { + min = gradient; + seed.data = superpixel.0; + seed.x = u32::try_from(superpixel.1) + .or(Err(ScError::SeedError(SeedErrorKind::PerturbConversion)))?; + seed.y = u32::try_from(superpixel.2) + .or(Err(ScError::SeedError(SeedErrorKind::PerturbConversion)))?; + } + } + } + + Ok(()) +} diff --git a/src/slic.rs b/src/slic.rs new file mode 100644 index 0000000..3e338b4 --- /dev/null +++ b/src/slic.rs @@ -0,0 +1,385 @@ +use crate::error::ScError; +use crate::seed::{init_seeds, perturb}; +use crate::{ + calculate_grid_interval, distance_lab, distance_s, distance_xy, get_in_bounds, + get_mut_in_bounds, m_div_s, +}; + +use num_traits::ToPrimitive; +use palette::{white_point::WhitePoint, FromColor, Lab, Pixel, Srgb}; + +/// Information for tracking image pixels' nearest superpixel cluster and +/// distance to that cluster during SLIC. +#[derive(Debug, Clone)] +struct SlicInfo { + /// Vector of nearest superpixel distances. + pub distances: Vec, + /// Vector of nearest superpixel labels. + pub labels: Vec, +} + +impl SlicInfo { + /// Create a [`SlicInfo`]. + #[must_use] + #[inline] + pub fn new() -> Self { + Self::default() + } +} + +impl Default for SlicInfo { + #[inline] + fn default() -> Self { + Self { + distances: Vec::new(), + labels: Vec::new(), + } + } +} + +/// Struct used for accumulating and calculating superpixel clusters in SLIC. +#[derive(Debug, Clone, Copy)] +struct SlicUpdate { + /// Color data. + pub data: T, + /// X-coordinate. + pub x: f64, + /// Y-coordinate. + pub y: f64, + /// Total elements in the cluster. + pub count: f64, +} + +impl SlicUpdate { + /// Create a [`SlicUpdate`]. + #[inline] + #[must_use] + pub fn new() -> Self { + Self::default() + } +} + +impl Default for SlicUpdate { + #[inline] + fn default() -> Self { + Self { + data: Default::default(), + x: Default::default(), + y: Default::default(), + count: Default::default(), + } + } +} + +/// Calculate SLIC by providing a buffer of RGB component bytes as `&[u8]`. +/// +/// `iter` will default to `10` if `None` is supplied. +/// +/// `k` must not be `0`. +/// `m` is clamped to be between `1` and `20`. +/// `width` and `height` must not be `0`. +pub fn slic_from_bytes( + k: u32, + m: u8, + width: u32, + height: u32, + iter: Option, + image: &[u8], +) -> Result, ScError> { + if usize::try_from(u64::from(width) * u64::from(height)) + .or(Err("Invalid image dimensions in SLIC from bytes"))? + != image.len() / 3 + { + return Err(ScError::MismatchedSlicBuffer); + } + let input_buffer = Srgb::from_raw_slice(image); + let mut input_lab: Vec> = Vec::new(); + input_lab.try_reserve_exact(input_buffer.len())?; + input_lab.extend( + input_buffer + .iter() + .map(|&c| Lab::from_color(c.into_format())), + ); + + slic(k, m, width, height, iter, &input_lab) +} + +/// Calculate SLIC. +/// +/// `iter` will default to `10` if `None` is supplied. +/// +/// `k` must not be `0`. +/// `m` is clamped to be between `1` and `20`. +/// `width` and `height` must not be `0`. +/// +/// ## Reference +/// +/// *Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., & Süsstrunk, S. SLIC +/// Superpixels. EPFL Technical Report no. 149300, June 2010.* +/// +/// *Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., & Süsstrunk, S. SLIC +/// Superpixels Compared to State-of-the-art Superpixel Methods. IEEE Transactions +/// on Pattern Analysis and Machine Intelligence, vol. 34, num. 11, p. 2274 – 2282, +/// May 2012.* +pub fn slic( + k: u32, + m: u8, + width: u32, + height: u32, + iter: Option, + image: &[Lab], +) -> Result, ScError> +where + Wp: WhitePoint, +{ + // Validate input parameters + let m = m.clamp(1, 20); + let iter = iter.unwrap_or(10); + if k == 0 { + return Err(ScError::ZeroSuperpixelCount); + } + + if width == 0 || height == 0 { + return Err(ScError::InvalidImageDimension); + } + + match u64::from(k).cmp(&(u64::from(width) * u64::from(height))) { + std::cmp::Ordering::Less => {} + std::cmp::Ordering::Equal | std::cmp::Ordering::Greater => { + return Err(ScError::InvalidSuperpixelCount); + } + } + + // Calculate S + let s = calculate_grid_interval(width, height, k) + .to_u32() + .ok_or(ScError::InvalidGridInterval)?; + + if s == 0 { + return Err(ScError::ZeroGridInterval); + } + + let m_s_term = m_div_s(f64::from(m), f64::from(s)); + + // Init seeds and shuffle them to a hopefully non-noisy pixel + let mut clusters = Vec::new(); + init_seeds(width, height, s, k, image, &mut clusters)?; + + for seed in &mut clusters { + perturb(seed, i64::from(width), i64::from(height), image)?; + } + + // Bookkeeping for tracking pixel clusters and updating cluster centers + let mut info = SlicInfo::::new(); + info.distances.try_reserve_exact(image.len())?; + info.labels.try_reserve_exact(image.len())?; + info.distances + .extend((0..image.len()).map(|_| f64::INFINITY)); + info.labels.extend((0..image.len()).map(|_| 0)); + + let mut updates: Vec>> = Vec::new(); + updates.try_reserve_exact(clusters.len())?; + updates.extend((0..clusters.len()).map(|_| SlicUpdate::new())); + + for _ in 0..iter { + // Search a pixel area of 2S x 2S size and match cluster centers to + // pixels with the lowest distance measure + for (center_index, center) in clusters.iter().enumerate() { + for y in center.y.saturating_sub(s)..center.y.saturating_add(s).min(height) { + for x in center.x.saturating_sub(s)..center.x.saturating_add(s).min(width) { + let idx = usize::try_from( + u64::from(y) + .saturating_mul(u64::from(width)) + .saturating_add(u64::from(x)), + ) + .or(Err("Index out of bounds for finding new neighbors"))?; + let color = *image.get(idx).ok_or("Image index out of bounds")?; + + let distance = distance_s( + m_s_term, + distance_lab(color, center.data), + distance_xy( + (f64::from(x), f64::from(y)), + (f64::from(center.x), f64::from(center.y)), + ), + ); + + if idx < info.distances.len() && idx < info.labels.len() { + let info_distance = info + .distances + .get_mut(idx) + .ok_or("Distance index out of bounds")?; + if distance < *info_distance { + *info_distance = distance; + *info.labels.get_mut(idx).ok_or("Info index out of bounds")? = + center_index; + } + } + } + } + } + + // Compute new centers and update + for y in 0..height { + for x in 0..width { + let idx = usize::try_from( + u64::from(y) + .saturating_mul(u64::from(width)) + .saturating_add(u64::from(x)), + ) + .or(Err("Invalid update index"))?; + + if idx < image.len() && idx < info.labels.len() { + let color = *image.get(idx).ok_or("Image index out of bounds")?; + let index = *info + .labels + .get(idx) + .ok_or("Info update index out of bounds")?; + if let Some(update) = updates.get_mut(index) { + update.data += color; + update.x += f64::from(x); + update.y += f64::from(y); + update.count += 1.0; + } + } else { + return Err(ScError::General("Update index out of bounds")); + } + } + } + + for (update, center) in updates.iter_mut().zip(&mut clusters) { + if update.count == 0.0 { + continue; + } + center.data = update.data / update.count; + center.x = (update.x / update.count) + .to_u32() + .ok_or("Update X out of bounds")?; + center.y = (update.y / update.count) + .to_u32() + .ok_or("Update Y out of bounds")?; + *update = SlicUpdate::new(); + } + } + + enforce_connectivity(width, height, s, &mut info.labels)?; + + Ok(info.labels) +} + +// Relabel disjoint labels to the largest, nearest neighbor cluster. +fn enforce_connectivity( + width: u32, + height: u32, + s: u32, + labels: &mut [usize], +) -> Result<(), ScError> { + let width_i = i64::from(width); + let height_i = i64::from(height); + let cluster_threshold = + usize::try_from(u64::from(s).pow(2) / 4).or(Err("Could not convert cluster threshold"))?; + let mut new_labels = Vec::new(); + new_labels.try_reserve_exact(labels.len())?; + new_labels.extend((0..labels.len()).map(|_| usize::MAX)); + + // This will be reused for searching each superpixel cluster. + // For now, the size of the queue is 8 superpixels to start. + let mut label_queue = Vec::new(); + label_queue.try_reserve( + usize::try_from(u64::from(s).pow(2).saturating_mul(8)) + .or(Err("Could not calculate label set size"))?, + )?; + + // Adjacent pixels, clockwise order West-North-East-South + let neighbors = [(-1, 0), (0, -1), (1, 0), (0, 1)]; + + // Assign new labels to pixels by finding connected pixel clusters + let mut neighbor_label = 0; + let mut new_label = 0_usize; + + for y in 0..height { + for x in 0..width { + let idx_usize = usize::try_from( + u64::from(y) + .saturating_mul(u64::from(width)) + .saturating_add(u64::from(x)), + ) + .or(Err("Invalid connectivity index"))?; + let old_label = *labels.get(idx_usize).ok_or("Could not get old label")?; + + // If no assigned label, assign current_label + if new_labels.get(idx_usize) == Some(&usize::MAX) { + *new_labels + .get_mut(idx_usize) + .ok_or("Label index out of bounds")? = new_label; + + // Find neighbor label that borders current pixel if it exists. + // Ending on South seems to have best results. This label will + // be used to label the cluster if the current label is too + // small. + for &neighbor in &neighbors { + let neighbor_x = i64::from(x) + neighbor.0; + let neighbor_y = i64::from(y) + neighbor.1; + if let Some(l) = + get_in_bounds(width_i, height_i, neighbor_x, neighbor_y, &new_labels) + { + if *l != usize::MAX { + neighbor_label = *l; + } + } + } + + // "One component at a time" search for pixels that share the + // same label. The members go into a queue so they can be + // reassigned a neighboring label if it's a disjoint cluster. + label_queue.clear(); + label_queue.push((i64::from(x), i64::from(y))); + let mut label_queue_idx = 0; + let mut label_count = 1_usize; + + while label_queue_idx < label_count { + for &neighbor in &neighbors { + let entry = label_queue + .get(label_queue_idx) + .ok_or("Could not get label")?; + let new_vx = entry.0 + neighbor.0; + let new_vy = entry.1 + neighbor.1; + + if let (Some(old_visit_label), Some(new_visit_label)) = ( + get_in_bounds(width_i, height_i, new_vx, new_vy, labels), + get_mut_in_bounds(width_i, height_i, new_vx, new_vy, &mut new_labels), + ) { + // If new label is unassigned and matches old_label, assign it the current cluster + if *old_visit_label == old_label && *new_visit_label == usize::MAX { + if label_queue.capacity() == label_queue.len() { + label_queue.try_reserve(1)?; + } + label_queue.push((new_vx, new_vy)); + *new_visit_label = new_label; + label_count = label_count.saturating_add(1); + } + } + } + label_queue_idx = label_queue_idx.saturating_add(1); + } + + // If a label set is smaller than some threshold, relabel that + // set as the nearest neighboring label. Don't increment label + // if too small of a set. Currently set to a quarter of a + // superpixel size. + if label_count <= cluster_threshold { + for &(l_x, l_y) in &label_queue { + *get_mut_in_bounds(width_i, height_i, l_x, l_y, &mut new_labels) + .ok_or("New label index out of bounds")? = neighbor_label; + } + continue; + } + new_label = new_label.saturating_add(1); + } + } + } + + labels.copy_from_slice(&new_labels); + + Ok(()) +} diff --git a/src/snic.rs b/src/snic.rs new file mode 100644 index 0000000..7750b7a --- /dev/null +++ b/src/snic.rs @@ -0,0 +1,348 @@ +use std::cmp::Reverse; + +use crate::error::ScError; +use crate::seed::{init_seeds, perturb}; +use crate::{ + calculate_grid_interval, distance_lab, distance_s, distance_xy, get_in_bounds, + get_mut_in_bounds, m_div_s, +}; + +use num_traits::ToPrimitive; +use palette::{white_point::D65, FromColor, Lab, Pixel, Srgb}; + +/// Struct used for accumulating and calculating superpixel clusters in SNIC. +#[derive(Debug, Clone, Copy)] +struct SnicUpdate { + /// Accumulated color data, divide by count for the mean color. + pub accum: T, + /// X-coordinate. + pub x: f64, + /// Y-coordinate. + pub y: f64, + /// Total elements in the cluster. + pub count: f64, +} + +impl SnicUpdate { + /// Create a [`SnicUpdate`]. + #[inline] + #[must_use] + pub fn new() -> Self { + Self::default() + } +} + +impl Default for SnicUpdate { + #[inline] + fn default() -> Self { + Self { + accum: Default::default(), + x: Default::default(), + y: Default::default(), + count: Default::default(), + } + } +} + +/// Calculate SNIC by providing a buffer of RGB component bytes as `&[u8]`. +/// +/// `k` must not be `0`. +/// `m` is clamped to be between `1` and `20`. +/// `width` and `height` must not be `0`. +pub fn snic_from_bytes( + k: u32, + m: u8, + width: u32, + height: u32, + image: &[u8], +) -> Result, ScError> { + if usize::try_from(u64::from(width) * u64::from(height)) + .or(Err("Invalid image dimensions in SNIC from bytes"))? + != image.len() / 3 + { + return Err(ScError::MismatchedSnicBuffer); + } + let input_buffer = Srgb::from_raw_slice(image); + let mut input_lab: Vec> = Vec::new(); + input_lab.try_reserve_exact(input_buffer.len())?; + input_lab.extend( + input_buffer + .iter() + .map(|&c| Lab::from_color(c.into_format())), + ); + + snic(k, m, width, height, &input_lab) +} + +/// Calculate SNIC. +/// +/// `k` must not be `0`. +/// `m` is clamped to be between `1` and `20`. +/// `width` and `height` must not be `0`. +/// +/// ## Reference +/// +/// *Achanta, R., & Süsstrunk, S. Superpixels and polygons using simple +/// non-iterative clustering. Proceedings of the IEEE Conference on Computer Vision +/// and Pattern Recognition, 2017.* +pub fn snic( + k: u32, + m: u8, + width: u32, + height: u32, + image: &[Lab], +) -> Result, ScError> { + let width_i = i64::from(width); + let height_i = i64::from(height); + // Validate input parameters + let m = m.clamp(1, 20); + if k == 0 { + return Err(ScError::ZeroSuperpixelCount); + } + + if width == 0 || height == 0 { + return Err(ScError::InvalidImageDimension); + } + + match u64::from(k).cmp(&(u64::from(width) * u64::from(height))) { + std::cmp::Ordering::Less => {} + std::cmp::Ordering::Equal | std::cmp::Ordering::Greater => { + return Err(ScError::InvalidSuperpixelCount); + } + } + + // Calculate S + let s = calculate_grid_interval(width, height, k) + .to_u32() + .ok_or(ScError::InvalidGridInterval)?; + + if s == 0 { + return Err(ScError::ZeroGridInterval); + } + + let m_s_term = m_div_s(f64::from(m), f64::from(s)); + + // Init seeds and shuffle them to a hopefully non-noisy pixel + let mut clusters = Vec::new(); + init_seeds(width, height, s, k, image, &mut clusters)?; + + for seed in &mut clusters { + perturb(seed, i64::from(width), i64::from(height), image)?; + } + + // Output labels + let mut labels = Vec::new(); + labels.try_reserve_exact(image.len())?; + labels.extend((0..image.len()).map(|_| 0_usize)); + + // Leave the first entry vacant since label k starts at 1 + let mut updates: Vec>> = Vec::new(); + updates.try_reserve_exact(clusters.len().saturating_add(1))?; + updates.extend((0..=clusters.len()).map(|_| SnicUpdate::new())); + + // Reuse element and copy it into pq + let mut element = SnicElement::default(); + + // Min-heap priority queue that will pop the lowest distance to a k_th + // cluster. Push all current centers onto the queue with 0.0 distance. Label + // values start at 1. + let mut pq = std::collections::BinaryHeap::with_capacity(image.len() / 5); + for (k, &cluster) in clusters.iter().skip(1).enumerate() { + element.distance = Reverse(NonNanFloat(0.0)); + element.k = k.saturating_add(1); + element.x = cluster.x; + element.y = cluster.y; + pq.push(element); + } + + // 4 way connectivity for neighboring pixels going clockwise from west + let neighbors = [(-1, 0), (0, -1), (1, 0), (0, 1)]; + + // Cache next element if its distance is less than the pq root to avoid + // reheaping + let mut swap_elem = None; + + // Remember that we have to offset down by 1 for indexing k + while !pq.is_empty() { + let elem = if let Some(elem) = swap_elem.take().or_else(|| pq.pop()) { + elem + } else { + break; + }; + if let Some(label) = get_mut_in_bounds( + width_i, + height_i, + i64::from(elem.x), + i64::from(elem.y), + &mut labels, + ) { + if *label == 0 { + *label = elem.k; + + // Update C[k_i] + let update = updates + .get_mut(elem.k) + .ok_or("Update index out of bounds")?; + // Subtract 1 to index into cluster since k is 1-indexed + let cluster = clusters + .get_mut(elem.k - 1) + .ok_or("Cluster index out of bounds")?; + update.accum += *get_in_bounds( + width_i, + height_i, + i64::from(elem.x), + i64::from(elem.y), + image, + ) + .ok_or("Element color out of bounds")?; + update.x += f64::from(elem.x); + update.y += f64::from(elem.y); + update.count += 1.0; + + cluster.data = update.accum * update.count.recip(); + cluster.x = (update.x * update.count.recip()) + .to_u32() + .ok_or("Invalid x update coordinate")?; + cluster.y = (update.y * update.count.recip()) + .to_u32() + .ok_or("Invalid y update coordinate")?; + + // Pushpop array to possibly skip a heap balancing operation + let mut arr_neighbors = [None; 4]; + + for (&neighbor, arr) in neighbors.iter().zip(arr_neighbors.iter_mut()) { + let n_x = i64::from(elem.x) + neighbor.0; + let n_y = i64::from(elem.y) + neighbor.1; + + if let (Some(n_label), Some(color)) = ( + get_in_bounds(width_i, height_i, n_x, n_y, &labels), + get_in_bounds(width_i, height_i, n_x, n_y, image), + ) { + if *n_label == 0 { + let distance = distance_s( + m_s_term, + distance_lab(*color, cluster.data), + distance_xy( + ( + n_x.to_f64().ok_or("Could not convert x neighbor")?, + n_y.to_f64().ok_or("Could not convert y neighbor")?, + ), + (f64::from(cluster.x), f64::from(cluster.y)), + ), + ); + + if distance.is_nan() { + return Err(ScError::NanDistance); + } + + element.distance = Reverse(NonNanFloat(distance)); + element.k = elem.k; + element.x = u32::try_from(n_x).or(Err("Invalid neighbor x"))?; + element.y = u32::try_from(n_y).or(Err("Invalid neighbor y"))?; + *arr = Some(element); + } + } + } + + // Pushpop: Find the min value and if it's less than the root, + // assign it to swap_elem. Because we're using cmp::Reverse, the + // smallest value will be the last element of the array. + arr_neighbors.sort_unstable(); + if let Some(min) = arr_neighbors[3] { + if let Some(mut peek) = pq.peek_mut() { + // Swap element is less than root: don't push to heap. + // (.distance field is wrapped in cmp::Reverse) + if min.distance > peek.distance { + swap_elem = Some(min); + } else { + // Root is less than element: swap and heapify + swap_elem = Some(*peek); + *peek = min; + } + } else { + swap_elem = arr_neighbors[3]; + } + pq.extend(arr_neighbors[..3].iter().flatten()); + } + } + } + } + + enforce_connectivity(width_i, height_i, &mut labels); + + Ok(labels) +} + +// Enforce connectivity if algorithm fails to do so, iterate in WNES order. +// BSDS300-images\BSDS300\images\test\295087.jpg (desert rocks with tree) +// showed some stray white pixels at k=1000, m=10. +fn enforce_connectivity(width: i64, height: i64, labels: &mut [usize]) { + for y in 0..height { + for x in 0..width { + if let Some(first) = get_in_bounds(width, height, x, y, labels) { + let neighbors = [ + get_in_bounds(width, height, x - 1, y, labels).copied(), + get_in_bounds(width, height, x, y - 1, labels).copied(), + get_in_bounds(width, height, x + 1, y, labels).copied(), + get_in_bounds(width, height, x, y + 1, labels).copied(), + ]; + if !neighbors.iter().any(|&n| n == Some(*first)) { + for &n in neighbors.iter().flatten() { + // We know this pixel is inbounds from `if let` + *get_mut_in_bounds(width, height, x, y, labels).unwrap() = n; + } + } + } + } + } +} + +/// Queue element used in SNIC computation. +#[derive(Debug, Default, PartialEq, Clone, Copy)] +struct SnicElement { + /// Distance to nearest superpixel. + pub distance: Reverse, + /// Superpixel label. + pub k: usize, + /// X-coordinate. + pub x: u32, + /// Y-coordinate. + pub y: u32, +} + +impl Eq for SnicElement {} + +impl PartialOrd for SnicElement { + #[inline] + fn partial_cmp(&self, other: &Self) -> Option { + self.distance.partial_cmp(&other.distance) + } +} + +impl Ord for SnicElement { + #[inline] + fn cmp(&self, other: &Self) -> std::cmp::Ordering { + self.partial_cmp(other).unwrap() + } +} + +/// Floating point number used for distance in SNIC as the min-heap key. This +/// number must not be a `NaN`. +#[derive(Debug, Default, PartialEq, Clone, Copy)] +struct NonNanFloat(f64); + +impl Eq for NonNanFloat {} + +impl PartialOrd for NonNanFloat { + #[inline] + fn partial_cmp(&self, other: &Self) -> Option { + self.0.partial_cmp(&other.0) + } +} + +impl Ord for NonNanFloat { + #[inline] + fn cmp(&self, other: &Self) -> std::cmp::Ordering { + self.partial_cmp(other).unwrap() + } +}

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