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https://github.com/rostyq/pico-detect
Rust library for Pixel Intensity Comparison-based Object (PICO) Detection.
https://github.com/rostyq/pico-detect
computer-vision detection
Last synced: about 21 hours ago
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Rust library for Pixel Intensity Comparison-based Object (PICO) Detection.
- Host: GitHub
- URL: https://github.com/rostyq/pico-detect
- Owner: rostyq
- License: mit
- Created: 2020-08-25T07:53:43.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2024-05-14T13:22:00.000Z (6 months ago)
- Last Synced: 2024-10-31T11:35:33.319Z (20 days ago)
- Topics: computer-vision, detection
- Language: Rust
- Homepage:
- Size: 1.84 MB
- Stars: 16
- Watchers: 1
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
[![crates-badge]][crates]
[![docs-badge]][docs]
![license-badge]
# pico-detect
This library is a reimplementation of _Pixel Intensity Comparison-based Object_ (PICO) detection algorithms in Rust:
- `Detector`: Cascade of binary classifiers from [pico];
- `Localizer`: Localization with an ensemble of randomized trees from [picojs](https://github.com/nenadmarkus/picojs) (see `lploc.js`);
- `Shaper`: Alignment with an ensemble of regression trees from [dlib](https://github.com/davisking/dlib) (see `shape_predictor`).
## Example
To run CLI example, which takes an image, finds all faces, detects some landmarks and pupils:
> **NOTE**: [Git LFS](https://git-lfs.github.com/) is needed to resolve binary files with `git clone`.
>
> If you don't want to use Git LFS you can download models (and test image) direct from this repo
> (see **model** column in the table below)
> and put them under [`models/`](./models) directory.
```sh
cargo run --release --example detect-faces -- --models-dir models -i "assets/test.png" --score 35.0 -o result.png
```
Output image `result.png` should be like this:
## Models
Each algorithm requires to be loaded with correspondent binary model.
| model | algorithm | source | Description |
|---------------------------|-------------|------------------------------------|---------------------------|
| [face.detector.bin] | `Detector` | [pico] | Human face classifier |
| [pupil.localizer.bin] | `Localizer` | [puploc] | Human eye pupil localizer |
| [face-5.shaper.bin] | `Shaper` | [shape_predictor_5_face_landmarks] | Human 5 face landmarks |
## References
1. [N. Markus, M. Frljak, I. S. Pandzic, J. Ahlberg and R. Forchheimer, "Object Detection with Pixel Intensity Comparisons Organized in Decision Trees"](http://arxiv.org/abs/1305.4537)
2. [Eye pupil localization with an ensemble of randomized trees](https://across.fer.hr/_download/repository/PR4885.pdf)
3. [One Millisecond Face Alignment with an Ensemble of Regression Trees](https://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Kazemi_One_Millisecond_Face_2014_CVPR_paper.pdf)
[crates]: https://crates.io/crates/pico-detect
[docs]: https://docs.rs/pico-detect
[docs-badge]: https://docs.rs/pico-detect/badge.svg
[crates-badge]: https://img.shields.io/crates/v/pico-detect
[license-badge]: https://img.shields.io/crates/l/pico-detect
[pico]: https://github.com/nenadmarkus/pico
[face.detector.bin]: https://github.com/rostyq/pico-detect/raw/master/models/face.detector.bin
[pupil.localizer.bin]: https://github.com/rostyq/pico-detect/raw/master/models/pupil.localizer.bin
[face-5.shaper.bin]: https://github.com/rostyq/pico-detect/raw/master/models/face-5.shaper.bin
[puploc]: https://drone.nenadmarkus.com/data/blog-stuff/puploc.bin
[shape_predictor_5_face_landmarks]: https://github.com/davisking/dlib-models#shape_predictor_5_face_landmarksdatbz2