From 1fd35a970eb31b319ba2f8670084cf3918db848e Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Ra=C3=BCl=20Ahmad?= <raulahmad@hotmail.com>
Date: Sat, 8 Aug 2020 13:46:42 +0200
Subject: [PATCH] [NEW] Readme updated

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-# gatys-style-transfer
-TensorFlow 2 impelemntation of Gatys et al. style transfer algorithm
+# Neural Style Transfer
+### Implementation of Gatys et al. algorithm using Tensorflow 2
+
+The following repository contains an implementation of the Neural Style Transfer algorithm described by Gatys et al. in their paper [A Neural Algorithm of Artistic Style](https://arxiv.org/pdf/1508.06576).
+The algorithm has been implemented using Tensorflow 2, and unlike many existing implementations, it has been written using its new *eager execution* feature. It also deploys Tensorflow's built-in VGG model, included in the Keras module.
+
+### Background of the algorithm
+The original algorithm, the one implemented in this repository, is of an iterative nature: a randomly generated initial image successively converges to the desired image, one mixing both the **content** of an image and the **style**  of another. 
+
+<p align="center">
+    <img src="data/input-example.jpg" alt="Content Image" height=350px width=350px/>
+    <img src="data/style-example.jpg" alt="Style Image" height=350px width=350px/>
+</p>
+
+Output:
+
+<p align="center">
+    <img src="data/out_example3000.jpg" alt="Content Image" height=350px/>
+</p>
+
+
+
+At each iteration, a loss function is evaluated, its gradient computed, and the image being iterated, updated. The loss function is made up of 2 parts:
+
+* Content loss <img src="https://render.githubusercontent.com/render/math?math=J_c">: loss aimed to evaluate how close the content of an image is to the original content of the reference image. Let <img src="https://render.githubusercontent.com/render/math?math=l"> be a given layer, <img src="https://render.githubusercontent.com/render/math?math=f_{l}(x)">  the function that computes the flattened feature maps of that layer <img src="https://render.githubusercontent.com/render/math?math=l"> for a given image <img src="https://render.githubusercontent.com/render/math?math=x">. Then, the content loss at each iteration is defined as <img src="https://render.githubusercontent.com/render/math?math=f(a)-f(b)">, where <img src="https://render.githubusercontent.com/render/math?math=a"> is the original reference image, and <img src="https://render.githubusercontent.com/render/math?math=b"> the image being iterated. 
+* Style loss <img src="https://render.githubusercontent.com/render/math?math=J_s">: similarly, loss aimed to evaluate how close the style of two images are from one another. Let <img src="https://render.githubusercontent.com/render/math?math=\{l_i\}"> be a sequence of layers. Let <img src="https://render.githubusercontent.com/render/math?math=G_i(x)"> be the Gram Matrix of the flattened feature maps of layer <img src="https://render.githubusercontent.com/render/math?math=l_i"> for an input image <img src="https://render.githubusercontent.com/render/math?math=x">. Then, the style loss is defined as the sum over all <img src="https://render.githubusercontent.com/render/math?math=l_i"> of <img src="https://render.githubusercontent.com/render/math?math=G_i(a)-G_i(b)">.
+
+The total loss <img src="https://render.githubusercontent.com/render/math?math=J"> is defined as <img src="https://render.githubusercontent.com/render/math?math=J = \alpha J_c %2B \beta J_s">, where <img src="https://render.githubusercontent.com/render/math?math=\alpha"> and <img src="https://render.githubusercontent.com/render/math?math=\beta"> are fixed weights. 
+
+
+### Details
+
+The source code includes:
+* A Python script named `main-transfer.py`, which transfers the style of an image to another image. The script is designed either to be called using command-line arguments, or be imported as a Python module to be used at other projects.
+* A Jupyter notebook, with the same code as the `main-transfer.py` file, but explaining thoroughly each step. This file is aimed both to do further research with the code and give some insight on how Tensorflow works.
+
+
+When executing the script through the command-line, several arguments are required, although all of them come with a default value. Please, execute `python src/main-transfer.py --help` to get help for the arguments. All default values are contained inside the `src/constants.py` file.
+
+When executing `python src/main-transfer.py`, the script will look for the image `data/input-example.jpg`, which will be the content image, and the image `data/style-example.jpg`, and will transfer the style of the latter to the former. The result, after 3000 iterations (epochs), can be found at `out_example3000.jpg`. These images can be changed using command-line arguments as described in the previous section. 
+
+The code has been developed (or tested) in the following platform; see the `requirements.txt` file for further information on Python modules' versions:
+* Ubuntu 18.04.03 / Windows 10 Pro version 1909 18363.959
+* Tensorflow 2.2.0
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