# PI-REC
**Repository Path**: KafurTan/PI-REC
## Basic Information
- **Project Name**: PI-REC
- **Description**: :fire: PI-REC: Progressive Image Reconstruction Network With Edge and Color Domain. :fire: 图像翻译，条件GAN，AI绘画
- **Primary Language**: Python
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-03-08
- **Last Updated**: 2024-10-12
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
PI-REC
------------------------------------------------------------------------------------------------------
**Progressive Image Reconstruction Network With Edge and Color Domain**
### [Paper on arXiv](https://arxiv.org/abs/1903.10146) | [Paper Read Online](https://www.arxiv-vanity.com/papers/1903.10146/) | [BibTex](#citation)
-----
When I was a schoolchild,
I dreamed about becoming a painter.
With PI-REC, we realize it nowadays.
For you, for everyone.
-----
English | 中文版
🏳️🌈 Demo show time 🏳️🌈
------
#### Draft2Painting
#### Tool operation
Introduction
-----
We propose a universal image reconstruction method to represent detailed images purely from binary sparse edge and flat color domain.
Here is the open source code and the drawing tool.
*\*The codes of training for release are no completed yet, also waiting for release license of lab.*
**Find more details in our paper: [Paper on arXiv](https://arxiv.org/abs/1903.10146)**
Quick Overview of Paper
-----
### What can we do?
- Figure (a): Image reconstruction from extreme sparse inputs.
- Figure (b): Hand drawn draft translation.
- Figure (c): User-defined edge-to-image **(E2I)** translation.
### Model Architecture
We strongly recommend you to understand our model architecture before running our drawing tool. Refer to the paper for more details.
## Prerequisites
- Python 3+
- PyTorch `1.0` (`0.4` is not supported)
- NVIDIA GPU + CUDA cuDNN
## Installation
- Clone this repo
- Install PyTorch and dependencies from http://pytorch.org
- Install python requirements:
```bash
pip install -r requirements.txt
```
## Usage
#### We provide two ways in the project:
- **Basic command line mode** for batch test
- **Drawing tool GUI mode** for creation
Firstly, follow steps below to prepare pre-trained models with patience:
1. Download the pre-trained models you want here: Google Drive | Baidu (Extraction Code: 9qn1)
2. Unzip the `.7z` and put it under your dir `./models/`.
So make sure your path now is: `./models/celeba/`
3. Complete the above [Prerequisites](#pre) and [Installation](#ins)
#### Files are ready now! Read the [User Manual](USAGE.md) for firing operations.
中文版介绍 :mahjong:
-----
Demo演示
-----
自己看上面的咯~
简介
-----
我们提出了一种基于GAN的渐进式训练方法 PI-REC，能从超稀疏二值边缘以及色块中还原重建真实图像。
这属于*图像重建，图像翻译，条件图像生成，AI自动绘画*的前沿交叉领域，而非简单的以图搜图。更多相关可以阅读论文里的
Related Work。
这里包含了测试代码以及交互式绘画工具。
*\*由于训练过程过于复杂，用于训练的发布版代码还未完成*
**在我们的论文中你可以获得更多信息(强烈推荐阅读): [Paper on arXiv](https://arxiv.org/abs/1903.10146)**
论文概览
-----
### PI-REC能做啥？
- Figure (a): 超稀疏输入信息重建原图。
- Figure (b): 手绘草图转换。
- Figure (c): 用户自定义的 edge-to-image **(E2I)** 转换.
### 模型结构
我们强烈建议你先仔细阅读论文熟悉我们的模型结构，对运行使用大有裨益。
## 基础环境
- Python 3
- PyTorch `1.0` (`0.4` 会报错)
- NVIDIA GPU + CUDA cuDNN （当前版本已可选cpu，请修改`config.yml`中的`DEVICE`）
## 第三方库安装
- Clone this repo
- 安装PyTorch和torchvision --> http://pytorch.org
- 安装 python requirements:
```bash
pip install -r requirements.txt
```
## 运行使用
#### 我们提供以下两种方式运行：
- **基础命令行模式** 用来批处理测试整个文件夹的图片
- **绘画GUI工具模式** 用来创作
首先，请耐心地按照以下步骤做准备：
1. 在这里下载你想要的预训练模型文件：Google Drive | Baidu (提取码: 9qn1)
2. 解压，放到目录`./models`下
现在你的目录应该像这样： `./models/celeba/`
3. 完成上面的基础环境和第三方库安装
#### 啦啦啦啦，准备工作已完成，阅读[用户手册](USAGE.md#jump_zh)来开始运行程序咯~
Acknowledgment
-----
Code structure is modified from [Anime-InPainting](https://github.com/youyuge34/Anime-InPainting), which is based on [Edge-Connect](https://github.com/knazeri/edge-connect).
BibTex
-----
```
@article{you2019pirec,
title={PI-REC: Progressive Image Reconstruction Network With Edge and Color Domain},
author={You, Sheng and You, Ning and Pan, Minxue},
journal={arXiv preprint arXiv:1903.10146},
year={2019}
}
```