is-photo
| Crates.io | is-photo |
| lib.rs | is-photo |
| version | |
| source | src |
| created_at | 2024-12-04 05:43:16.279146 |
| updated_at | 2024-12-04 05:45:40.217654 |
| description | Utility to determine if an image is likely a photograph or a 2D graphic |
| homepage | |
| repository | https://github.com/Lantern-chat/is-photo |
| max_upload_size | |
| id | 1471237 |
| Cargo.toml error: | TOML parse error at line 23, column 1 | 23 | autolib = false | ^^^^^^^ unknown field `autolib`, expected one of `name`, `version`, `edition`, `authors`, `description`, `readme`, `license`, `repository`, `homepage`, `documentation`, `build`, `resolver`, `links`, `default-run`, `default_dash_run`, `rust-version`, `rust_dash_version`, `rust_version`, `license-file`, `license_dash_file`, `license_file`, `licenseFile`, `license_capital_file`, `forced-target`, `forced_dash_target`, `autobins`, `autotests`, `autoexamples`, `autobenches`, `publish`, `metadata`, `keywords`, `categories`, `exclude`, `include` |
| size | 0 |
Nova (novacrazy)
documentation
https://docs.rs/is-photo
README
is-photo
Utility to determine if an image is likely a photograph or a 2D graphic, such as a logo, illustration, or digital art.
It does this by taking various statistics from the image and running them through a pre-trained logistic regression model, along with a few sure-fire heuristics.
On my test set of around 1500 images, it has a 94% accuracy rate. Feel free to submit links to additional image sets to train on in an issue!
Example
# fn main() -> Result<(), Box<dyn std::error::Error>> {
let img = image::open("test.jpg")?;
let analysis = is_photo::analyze(&img).expect("Failed to analyze image");
let is_photo = analysis.is_photo(&is_photo::STANDARD_MODEL);
# Ok(()) }
Future work may include training on a larger dataset, and possibly using a full neural network instead of logistic regression.