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[submodule "third_party/SuperGluePretrainedNetwork"]
path = third_party/SuperGluePretrainedNetwork
url = https://github.com/magicleap/SuperGluePretrainedNetwork.git
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# LoFTR
## 论文
[LoFTR](https://arxiv.org/pdf/2104.00680.pdf)
## 模型简介
本文提出了Local Feature Transformers （LoFTR），这是一种新的Detector-free的局部特征匹配方法。受SuperGlue的启发，本文使用了具有自注意层和互注意层的Transformer模块来处理从卷积网络中提取的密集局部特征：本文首先在低特征分辨率（图像维度的1/8）上提取密集匹配，然后从这些匹配中选择具有高可信度的匹配，使用基于相关的方法将其细化到高分辨率的亚像素级别。这样，模型的大接受域使转换后的特征符能够体现出上下文和位置信息，通过多次自注意力和互注意层，LoFTR学习在GT中的匹配先验。另外，本文还采用Linear Attention方法将计算复杂度降低到可接受的水平.
![alt text](image-1.png)
## 环境依赖
- 列举基础环境需求，根据实际情况填写
| 软件 | 版本 |
| :------: | :------: |
| DTK | 25.04.1 |
| python | 3.11 |
| torch | 2.4.1+das.opt1.dtk25041 |
推荐使用镜像:
- 挂载地址 `-v` 根据实际模型情况修改
```bash
docker run -it --shm-size 50g --network=host --name loftr --privileged --device=/dev/kfd --device=/dev/dri --device=/dev/mkfd --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -u root -v /opt/hyhal/:/opt/hyhal/:ro -v /path/your_code_path/:/path/your_code_path/ image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.4.1-ubuntu22.04-dtk25.04.1-py3.11 bash
```
更多镜像可前往[光源](https://sourcefind.cn/#/service-list)下载使用。
关于本项目DCU显卡所需的特殊深度学习库可从[光合](https://developer.sourcefind.cn/tool/)开发者社区下载安装，其它包参照requirements.txt安装：
```
pip install -r requirements.txt
```
## 数据集
[original MegaDepth dataset](https://www.cs.cornell.edu/projects/megadepth/dataset/Megadepth_v1/MegaDepth_v1.tar.gz) \
[D2-Net preprocessed images](https://drive.google.com/drive/folders/1hxpOsqOZefdrba_BqnW490XpNX_LgXPB) \
[ScanNet](https://github.com/ScanNet/ScanNet#scannet-data)
或者也可以下载处理过的数据 https://drive.google.com/drive/folders/1DOcOPZb3-5cWxLqn256AhwUVjBPifhuf
## 训练
<!-- ### 单机训练
```bash
``` -->
### 多机训练
```bash
# ScanNet
scripts/reproduce_train/indoor_ds.sh
# MegaDepth
scripts/reproduce_train/outdoor_ds.sh
```
## 推理
### 单机推理
```bash
# with shell script
bash ./scripts/reproduce_test/indoor_ds.sh
# or
python test.py configs/data/scannet_test_1500.py configs/loftr/loftr_ds.py --ckpt_path weights/indoor_ds.ckpt --profiler_name inference --gpus=1 --accelerator="ddp"
```
<!-- ### 多机推理
```bash
``` -->
### 精度
DCU与GPU精度一致
## 预训练权重
| 模型名称 | 权重大小 | DCU型号 | 最低卡数需求 |下载地址|
|:-----:|:----------:|:----------:|:---------------------:|:----------:|
| indoor_ds | - | K100AI | 1 | [下载地址](https://drive.google.com/drive/folders/1xu2Pq6mZT5hmFgiYMBT9Zt8h1yO-3SIp) |
| indoor_ot | - | K100AI | 1 | [下载地址](https://drive.google.com/drive/folders/1xu2Pq6mZT5hmFgiYMBT9Zt8h1yO-3SIp) |
## 源码仓库及问题反馈
- https://developer.sourcefind.cn/codes/modelzoo/loftr-pytorch
## 参考资料
- https://github.com/zju3dv/LoFTR
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# LoFTR: Detector-Free Local Feature Matching with Transformers
### [Project Page](https://zju3dv.github.io/loftr) | [Paper](https://arxiv.org/pdf/2104.00680.pdf)
<br/>
> LoFTR: Detector-Free Local Feature Matching with Transformers
> [Jiaming Sun](https://jiamingsun.ml)<sup>\*</sup>, [Zehong Shen](https://zehongs.github.io/)<sup>\*</sup>, [Yu'ang Wang](https://github.com/angshine)<sup>\*</sup>, [Hujun Bao](http://www.cad.zju.edu.cn/home/bao/), [Xiaowei Zhou](http://www.cad.zju.edu.cn/home/xzhou/)
> CVPR 2021
![demo_vid](assets/loftr-github-demo.gif)
## TODO List and ETA
- [x] Inference code and pretrained models (DS and OT) (2021-4-7)
- [x] Code for reproducing the test-set results (2021-4-7)
- [x] Webcam demo to reproduce the result shown in the GIF above (2021-4-13)
- [x] Training code and training data preparation (expected 2021-6-10)
Discussions about the paper are welcomed in the [discussion panel](https://github.com/zju3dv/LoFTR/discussions).
:thinking: **FAQ**
1. Undistorted images from D2Net are not available anymore.
For a temporal alternative, please use the undistorted images provided by the MegaDepth_v1 (should be downloaded along with the required depth files). We numerically compared these images and only found very subtle difference.
:triangular_flag_on_post: **Updates**
- Check out [QuadTreeAttention](https://github.com/Tangshitao/QuadTreeAttention), a new attention machanism that improves the efficiency and performance of LoFTR with less demanding GPU requirements for training.
- :white_check_mark: Integrated to [Huggingface Spaces](https://huggingface.co/spaces) with [Gradio](https://github.com/gradio-app/gradio). See [Gradio Web Demo](https://huggingface.co/spaces/akhaliq/Kornia-LoFTR)
## Colab demo
Want to run LoFTR with custom image pairs without configuring your own GPU environment? Try the Colab demo:
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1BgNIOjFHauFoNB95LGesHBIjioX74USW?usp=sharing)
## Using from kornia
LoFTR is integrated into [kornia](https://github.com/kornia/kornia) library since version 0.5.11.
```
pip install kornia
```
Then you can import it as
```python3
from kornia.feature import LoFTR
```
See tutorial on using LoFTR from kornia [here](https://kornia-tutorials.readthedocs.io/en/latest/image_matching.html).
## Installation
```shell
# For full pytorch-lightning trainer features (recommended)
conda env create -f environment.yaml
conda activate loftr
# For the LoFTR matcher only
pip install torch einops yacs kornia
```
We provide the [download link](https://drive.google.com/drive/folders/1DOcOPZb3-5cWxLqn256AhwUVjBPifhuf?usp=sharing) to
- the scannet-1500-testset (~1GB).
- the megadepth-1500-testset (~600MB).
- 4 pretrained models of indoor-ds, indoor-ot, outdoor-ds and outdoor-ot (each ~45MB).
By now, the environment is all set and the LoFTR-DS model is ready to go!
If you want to run LoFTR-OT, some extra steps are needed:
<details>
<summary>[Requirements for LoFTR-OT]</summary>
We use the code from [SuperGluePretrainedNetwork](https://github.com/magicleap/SuperGluePretrainedNetwork) for optimal transport. However, we can't provide the code directly due its strict LICENSE requirements. We recommend downloading it with the following command instead.
```shell
cd src/loftr/utils
wget https://raw.githubusercontent.com/magicleap/SuperGluePretrainedNetwork/master/models/superglue.py
```
</details>
## Run LoFTR demos
### Match image pairs with LoFTR
<details>
<summary>[code snippets]</summary>
```python
from src.loftr import LoFTR, default_cfg
# Initialize LoFTR
matcher = LoFTR(config=default_cfg)
matcher.load_state_dict(torch.load("weights/indoor_ds.ckpt")['state_dict'])
matcher = matcher.eval().cuda()
# Inference
with torch.no_grad():
matcher(batch) # batch = {'image0': img0, 'image1': img1}
mkpts0 = batch['mkpts0_f'].cpu().numpy()
mkpts1 = batch['mkpts1_f'].cpu().numpy()
```
</details>
An example is given in `notebooks/demo_single_pair.ipynb`.
### Online demo
Run the online demo with a webcam or video to reproduce the result shown in the GIF above.
```bash
cd demo
./run_demo.sh
```
<details>
<summary>[run_demo.sh]</summary>
```bash
#!/bin/bash
set -e
# set -x
if [ ! -f utils.py ]; then
echo "Downloading utils.py from the SuperGlue repo."
echo "We cannot provide this file directly due to its strict licence."
wget https://raw.githubusercontent.com/magicleap/SuperGluePretrainedNetwork/master/models/utils.py
fi
# Use webcam 0 as input source.
input=0
# or use a pre-recorded video given the path.
# input=/home/sunjiaming/Downloads/scannet_test/$scene_name.mp4
# Toggle indoor/outdoor model here.
model_ckpt=../weights/indoor_ds.ckpt
# model_ckpt=../weights/outdoor_ds.ckpt
# Optionally assign the GPU ID.
# export CUDA_VISIBLE_DEVICES=0
echo "Running LoFTR demo.."
eval "$(conda shell.bash hook)"
conda activate loftr
python demo_loftr.py --weight $model_ckpt --input $input
# To save the input video and output match visualizations.
# python demo_loftr.py --weight $model_ckpt --input $input --save_video --save_input
# Running on remote GPU servers with no GUI.
# Save images first.
# python demo_loftr.py --weight $model_ckpt --input $input --no_display --output_dir="./demo_images/"
# Then convert them to a video.
# ffmpeg -framerate 15 -pattern_type glob -i '*.png' -c:v libx264 -r 30 -pix_fmt yuv420p out.mp4
```
</details>
### Reproduce the testing results with pytorch-lightning
You need to setup the testing subsets of ScanNet and MegaDepth first. We create symlinks from the previously downloaded datasets to `data/{{dataset}}/test`.
```shell
# set up symlinks
ln -s /path/to/scannet-1500-testset/* /path/to/LoFTR/data/scannet/test
ln -s /path/to/megadepth-1500-testset/* /path/to/LoFTR/data/megadepth/test
```
```shell
conda activate loftr
# with shell script
bash ./scripts/reproduce_test/indoor_ds.sh
# or
python test.py configs/data/scannet_test_1500.py configs/loftr/loftr_ds.py --ckpt_path weights/indoor_ds.ckpt --profiler_name inference --gpus=1 --accelerator="ddp"
```
For visualizing the results, please refer to `notebooks/visualize_dump_results.ipynb`.
<br/>
<!-- ### Image pair info for training on ScanNet
You can download the data at [here](https://drive.google.com/file/d/1fC2BezUSsSQy7_H65A0ZfrYK0RB3TXXj/view?usp=sharing).
<details>
<summary>[data format]</summary>
```python
In [14]: npz_path = './cfg_1513_-1_0.2_0.8_0.15/scene_data/train/scene0000_01.npz'
In [15]: data = np.load(npz_path)
In [16]: data['name']
Out[16]:
array([[ 0, 1, 276, 567],
[ 0, 1, 1147, 1170],
[ 0, 1, 541, 5757],
...,
[ 0, 1, 5366, 5393],
[ 0, 1, 2607, 5278],
[ 0, 1, 736, 5844]], dtype=uint16)
In [17]: data['score']
Out[17]: array([0.2903, 0.7715, 0.5986, ..., 0.7227, 0.5527, 0.4148], dtype=float16)
In [18]: len(data['name'])
Out[18]: 1684276
In [19]: len(data['score'])
Out[19]: 1684276
```
`data['name']` is the image pair info, organized as [`scene_id`, `seq_id`, `image0_id`, `image1_id`].
`data['score']` is the overlapping score defined in [SuperGlue](https://arxiv.org/pdf/1911.11763) (Page 12).
</details> -->
## Training
See [Training LoFTR](./docs/TRAINING.md) for more details.
## Citation
If you find this code useful for your research, please use the following BibTeX entry.
```bibtex
@article{sun2021loftr,
title={{LoFTR}: Detector-Free Local Feature Matching with Transformers},
author={Sun, Jiaming and Shen, Zehong and Wang, Yuang and Bao, Hujun and Zhou, Xiaowei},
journal={{CVPR}},
year={2021}
}
```
## Copyright
This work is affiliated with ZJU-SenseTime Joint Lab of 3D Vision, and its intellectual property belongs to SenseTime Group Ltd.
```
Copyright SenseTime. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
```
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0022_0.3_0.5
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