Various open source salient maps.
Example using the Deep Gaze model.
const models = require('salient-maps');
const cv = require('opencv4nodejs');
const Deep = models.deep.load();
const deep = new Deep({ width: 200, height: 200 });
const salientMap = deep.computeSaliency(cv.imread('myimage.jpg'));
| Option | Type | Default | Info |
|---|---|---|---|
| width | number |
200 |
Width of saliency map. It's not recommended to go above 300 or below 100. |
| height | number |
200 |
Height of saliency map. It's not recommended to go above 300 or below 100. |
While it's entirely up to you how use these maps, the original intent of this project was to be paired with the salient-autofocus project for providing fast image auto-focus capabilities.
| ID | Description | License | Usage |
|---|---|---|---|
| deep | MIT | Deep Gaze port of FASA (Fast, Accurate, and Size-Aware Salient Object Detection) algorithm | Recommended for most static usage where high accuracy is important, and near-realtime is sufficient performance (tunable by reducing map size). May not be ideal for video unless you drop map size to 150^2 or lower. |
| deep-rgb | MIT | A varient of Deep Gaze port but leveraging the RGB colour space instead of LAB. | Not recommended. Useful for comparison. Can perform better. |
| spectral | BSD | A port of the Spectral Residual model from OpenCV Contributions. | Amazing performance, great for video, but at the cost of quality/accuracy. |
| fine | BSD | A port of the Fine Grained model from OpenCV Contributions. | Interesting for testing but useless for realtime applications. |
Typical local setup.
git clone git@github.com:asilvas/salient-maps.git
cd salient-maps
npm i
By default testing looks at trainer/image-source, so you can put any images you like there.
Or follow the below instructions to import a known dataset.
- Download and extract CAT2000
- Run
node trainer/scripts/import-CAT2000.js {path-to-CAT2000}
The benefit of using the above script is it'll seperate the truth maps into trainer/image-truth,
which are optional.
You can run visual previews of the available saliency maps against the dataset via:
npm run preview
Compare performance data between models:
npm run benchmark
Also available is the ability to export the salient map data to trainer/image-saliency folder, broken
down by the saliency model. This permits review of maps from disk, in addition to being in a convenient
format for submission to the mit saliency benchmark for
quality analysis against other models.
npm run export
While this project falls under an MIT license, each of the models are subject to their own license. See Models for details.