README.md
WARNING
A2RL_online should be used instead of A2RL, A2RL_online supports urls as input and is much faster than A2RL.
Overview
[Project] [Paper] [Online Demo] [API] [Related Work: GP-GAN (for Image Blending)]
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A2-RL (aka. Aesthetics Aware Reinforcement Learning) is the author's implementation of the RL-based automatic image cropping algorithm described in:
A2-RL: Aesthetics Aware Reinforcement Learning for Automatic Image Cropping
Debang Li, Huikai Wu, Junge Zhang, Kaiqi Huang
Given a source image, our algorithm could take actions step by step to find almost the best cropping window on source image.
Usage
Input
For client users, the input is a list of bytearray reading from image files.
input = bytearray(open('xxx.jpg', 'rb').read())
result = algo.pipe([input]).result
For web (javascript) users, the input is a dict with 'xxximagexxx' as the keys and base64 string of the images as the values.
...
<input id="file-input" type="file" name="name" style="display: none;"/>
...
document.getElementById('file-input').onchange = function (evt) {
var tgt = evt.target || window.event.srcElement;
files = tgt.files;
// FileReader support
if (FileReader && files && files.length) {
var reader = new FileReader();
reader.onload = function () {
Algorithmia.client("xxxxxxxxxxxxxxxx")
.algo("algo://wuhuikai/A2RL")
.pipe({'super_image_a': reader.result})
.then(function(output) {
...
});
}
reader.readAsDataURL(files[0]);
} else {
// Not supported
...
}
Output
output.result = [[xmin, ymin, xmax, ymax], [xmin, ymin, xmax, ymax], ..., [xmin, ymin, xmax, ymax]]
Examples
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Contents