<h1 align="center"> MCCNN </h1>
<h4 align="center">MCCNN neural network for stereo matching cost.</h4>
<p align="center">
<a href="https://github.com/CNES/Pandora_MCCNN/actions"> <img src="https://github.com/CNES/Pandora_MCCNN/actions/workflows/mccnn_ci.yml/badge.svg?branch=master"></a>
<a href="https://opensource.org/licenses/Apache-2.0/"><img src="https://img.shields.io/badge/License-Apache%202.0-blue.svg"></a>
</p>
<p align="center">
<a href="#overview">Overview</a> •
<a href="#install">Install</a> •
<a href="#usage">Usage</a> •
<a href="#usage">Pretrained Weights for MCCNN networks</a> •
<a href="#related">Related</a> •
<a href="#references">References</a>
</p>
## Overview
Pytorch implementation of [[MCCNN]](#1.) neural network which computes a similarity measure on pair of small image patches.
## Install from Pypi
**MCCNN** is available on Pypi and can be installed by:
```bash
pip install MCCNN
```
## Developer install
After cloning source code from repository, do a local pip install in a virtualenv through MCCNN Makefile:
```bash
make install
```
## Usage
Documentation explains how to train and use MCCNN convolutional neural network.
To generate it, please execute the following commands:
```bash
make docs
```
Let's see [pandora_plugin_mccnn](https://github.com/CNES/Pandora_plugin_mccnn) for real life example.
## Pretrained Weights for MCCNN networks
### Download weights files
Pretrained weights for mc-cnn fast and mc-cnn accurate neural networks are available in the weights directory :
- mc_cnn_fast_mb_weights.pt and mc_cnn_accurate_mb_weights.pt are the weights of the pretrained networks on the Middlebury dataset [[Middlebury]](#Middlebury)
- mc_cnn_fast_data_fusion_contest.pt and mc_cnn_accurate_data_fusion_contest.pt are the weights of the pretrained networks on the Data Fusion Contest dataset [[DFC]](#DFC)
To download the pretrained weights:
```bash
wget https://raw.githubusercontent.com/CNES/Pandora_MCCNN/master/mc_cnn/weights/mc_cnn_fast_mb_weights.pt
wget https://raw.githubusercontent.com/CNES/Pandora_MCCNN/master/mc_cnn/weights/mc_cnn_fast_data_fusion_contest.pt
wget https://raw.githubusercontent.com/CNES/Pandora_MCCNN/master/mc_cnn/weights/mc_cnn_accurate_mb_weights.pt
wget https://raw.githubusercontent.com/CNES/Pandora_MCCNN/master/mc_cnn/weights/mc_cnn_accurate_data_fusion_contest.pt
```
### Access weights from pip package
Pretrained weights are stored into the pip package and downloaded for any installation of mc_cnn pip package.
To access it, use the `weights` submodule :
```python
from mc_cnn.weights import get_weights
mc_cnn_fast_mb_weights_path = get_weights(arch="fast", training_dataset="middlebury")
mc_cnn_fast_data_fusion_contest_path = get_weights(arch="fast", training_dataset="dfc")
mc_cnn_accurate_mb_weights_path = get_weights(arch="accurate", training_dataset="middlebury")
mc_cnn_accurate_data_fusion_contest = get_weights(arch="accurate", training_dataset="dfc")
```
## References
Please cite the following paper when using MCCNN:
*Defonte, V., Dumas, L., Cournet, M., & Sarrazin, E. (2021, July). Evaluation of MC-CNN Based Stereo Matching Pipeline for the CO3D Earth Observation Program. In 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS (pp. 7670-7673). IEEE.*
*Cournet, M., Sarrazin, E., Dumas, L., Michel, J., Guinet, J., Youssefi, D., Defonte, V., Fardet, Q., 2020. Ground-truth generation and disparity estimation for optical satellite imagery. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences.*
<a id="1.">[MCCNN]</a>
*Zbontar, J., & LeCun, Y. (2016). Stereo matching by training a convolutional neural network to compare image patches. J. Mach. Learn. Res., 17(1), 2287-2318.*
<a id="Middlebury">[Middlebury]</a>
*Scharstein, D., Hirschmüller, H., Kitajima, Y., Krathwohl, G., Nešić, N., Wang, X., & Westling, P. (2014, September). High-resolution stereo datasets with subpixel-accurate ground truth. In German conference on pattern recognition (pp. 31-42). Springer, Cham.*
<a id="DFC">[DFC]</a>
*Bosch, M., Foster, K., Christie, G., Wang, S., Hager, G. D., & Brown, M. (2019, January). Semantic stereo for incidental satellite images. In 2019 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 1524-1532). IEEE.*
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