Information
# Photo Optimizing Adversarial Net with Policy Gradient Method
Photo retouching is a time-consuming and challenging task that requires advanced skills beyond the abilities of casual photographers. This project aims to construct an agent which learns to edit photo in human-like fashion. Recently, theres has been lots of tasks enhance their robustness and performance via adversarial learning. We borrow the concept of **adversarial learning** into photo aesthetic improving task, and use the **policy gradient** to optimize the policy network whose action space is **human-like**.
## Requirement
- python 2.7
- scipy
- Tensorflow
- PIL
## Usage
We need two pools of images.
Our model will learn to edit photo from source pool to make it looks like the one in target pool.
\`\`\`
pyhton main.py --source_path --target_path
\`\`\`
## Method
### Human Level Editing: Curve
The curves tool is perhaps the most powerful and flexible image transformation. The curves tool can take input tones and selectively stretch or compress them. Unlike levels however, which only has black, white and midpoint control, a tonal curve is controlled using any number of anchor points. The result of a given curve can be visualized by following a test input tone up to the curve, then over to its resulting output tone. A diagonal line through the center will therefore leave tones unchanged. In our experiments, we modify the coordinates (x and y) of N points on the curve, which correspond to policy’s actions \`\`\`a\`\`\`.
### Policy Gradient
For each photo, we will pick several continuous actions \`\`\`a = \{a1 , a2 ...an \}\`\`\`, which correspond to the points on curve mentioned in previous section. The model is a stochastic policy \`\`\`pi(a|x)\`\`\` where \`\`\`x\`\`\` is the input photo. Here we model curve adjusting as a continuous control task. Each action represent the \`\`\`μ\`\`\` value of a Gaussian distribution. In order to reduce action space, we fix sigma to a constant. While training, we will sample actions from the distribution. After acquiring actions, we will process the photo based on \`\`\`a\`\`\`, which form a generative model \`\`\`G(y|x)\`\`\` where \`\`\`y\`\`\` is the output photo been processed with curve mentioned in the previous section. We directly maximize the reward \`\`\`R(a)\`\`\`.
### Style Learning
## Citing
If you find this repository helpful please cite as
\`\`\`
@misc\{Chuang2017,
author = \{Ching-Yao Chuang\},
title = \{Photo Optimizing Adversarial Net with Policy Gradient Method\},
year = \{2017\},
publisher = \{GitHub\},
journal = \{GitHub repository\},
howpublished = \{\url\{https://github.com/JamesChuanggg/photo-editing-tensorflow\}\},
commit = \{4bceb19d88dcb6d97e2dbb1fe6e59f84df5865ff\}
\}
\`\`\`
## Requirement
- python 2.7
- scipy
- Tensorflow
- PIL
## Usage
We need two pools of images.
Our model will learn to edit photo from source pool to make it looks like the one in target pool.
\`\`\`
pyhton main.py --source_path 
### Style Learning
## Citing
If you find this repository helpful please cite as
\`\`\`
@misc\{Chuang2017,
author = \{Ching-Yao Chuang\},
title = \{Photo Optimizing Adversarial Net with Policy Gradient Method\},
year = \{2017\},
publisher = \{GitHub\},
journal = \{GitHub repository\},
howpublished = \{\url\{https://github.com/JamesChuanggg/photo-editing-tensorflow\}\},
commit = \{4bceb19d88dcb6d97e2dbb1fe6e59f84df5865ff\}
\}
\`\`\`