TF-A2RL: Automatic Image Cropping

[Project] [Paper] [Online Demo] [API] [Related Work: GP-GAN (for Image Blending)]

The official implementation for A2-RL: Aesthetics Aware Rinforcement Learning for Automatic Image Cropping

Overview

source	step 1	step 2	step 3	step 4	step 5	output

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.

Contact: Hui-Kai Wu (huikaiwu@icloud.com)

View Finding Network

Evaluation We provide the evaluation script to reproduce our evaluation results on Flickr cropping dataset. For example,

$ python vfn_eval.py --spp false --snapshot snapshots/model-wo-spp You will need to get sliding_window.json and the test images from the Flickr cropping dataset and specify the path of your model when running vfn_eval.py. You can also try our pre-trained model, which can be downloaded from [here].

Copy pre-trained model to View_Finding Direcotry

If you want to get an aesthetic score of a patch, please take a look at the example featured by [ModelDepot]

Getting started

• Install the python libraries. (See Requirements).
• Download the code from GitHub:

git clone https://github.com/pjhool/TF-A2RL-Test.git
cd TF-A2RL-Test

• Download the pretrained models vfn_rl.pk from Google Drive, then put them in current directory (TF-A2RL/).

• Run the python script:

python A2RL.py --image_path ./test_images --save_path ./test_images_cropped

or

sh example.sh

• To A2RL Test for AVA Landscape Images , download AVA Landscape Images

Results compared with baseline methods (more results)

Source	VFN+Sliding window	A2-RL	Ground Truth

Requirements

The code requires the following 3rd party libraries:

• pickle
• numpy
• skimage

pip install scikit-image

Details see the official README for installing skimage.

• TensorFlow 1.4

Details see the official README for installing TensorFlow.

Command line arguments:

Type python A2RL.py --help for a complete list of the arguments.

• --image_path: path of the input image
• --save_path: path of output image

Citation

@article{li2017a2,
  title={A2-RL: Aesthetics Aware Reinforcement Learning for Automatic Image Cropping},
  author={Li, Debang and Wu, Huikai and Zhang, Junge and Huang, Kaiqi},
  journal={arXiv preprint arXiv:1709.04595},
  year={2017}
}