# LET-NET

**Repository Path**: sun_6s/LET-NET

## Basic Information

- **Project Name**: LET-NET
- **Description**: No description available
- **Primary Language**: C++
- **License**: Not specified
- **Default Branch**: dev
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-08-30
- **Last Updated**: 2024-08-30

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# LET-NET: A lightweight CNN network for sparse corners extraction and tracking

LET-NET implements an extremely lightweight network for feature point extraction and image consistency computation. The network can process a 240 x 320 image on a CPU in about 5ms. Combined with LK optical flow, it breaks the assumption of brightness consistency and performs well on dynamic lighting as well as blurred images.


## 1. Prerequisites 

- OpenCV (https://docs.opencv.org/3.4/d7/d9f/tutorial_linux_install.html)
- ncnn (https://github.com/Tencent/ncnn/wiki/how-to-build#build-for-linux)

> Notes: After installing ncnn, you need to change the path in CMakeLists.txt

```
set(ncnn_DIR "<your_path>/install/lib/cmake/ncnn" CACHE PATH "Directory that contains ncnnConfig.cmake")
```
## 2. Build 

```
mkdir build && cd build
cmake .. && make -j4
```

## 3. Run demo 

You can enter the path to a video or two images.
```
./build/demo <path_param> <path_bin> <path_video>
```
or 
```
./build/demo <path_param> <path_bin> <path_img_1> <path_img_2>
```
For example using the data we provide:

```
./build/demo ../model/model.param ../model/model.bin ../assets/nyu_snippet.mp4
```

You should see the following output from the NYU sequence snippet:

<img src="assets/demo.gif" width="260">

## 4. Examples 

### Dynamic lighting

The left is ours and the right is the original optical flow algorithm.

<table><tr>
<td><img src=assets/3.png border=0 width="260"></td>
<td><img src=assets/4.png border=0 width="260"></td>
</tr></table>

### Underwater

The left is ours and the right is the original optical flow algorithm.

<table><tr>
<td><img src=assets/1.png border=0 width="260"></td>
<td><img src=assets/2.png border=0 width="260"></td>
</tr></table>

### Active light source

The left is ours and the right is the original optical flow algorithm.

<table><tr>
<td><img src=assets/7.png border=0 width="260"></td>
<td><img src=assets/8.png border=0 width="260"></td>
</tr></table>