This is a C++ implementation from this paper https://arxiv.org/abs/2006.10172 that published on 2020, the repo is for sky mask post-processing. but I didn't implemente the "Density Estimation" mentioned in the paper.

About Sky segmentation, I trained the sky-segmentation model by U-2-Net, the result looks good. please refer to https://github.com/xuebinqin/U-2-Net about training detail

Dependency：OpenCV, ncnn

seg_demo.cpp is for sky-seg and input is image

mask_refine.cpp is for mask post-process to refine the mask. inputs are image and the mask inferenced by model.

The Sky-mask Post-Processing show a good performence in the scene of tree as below. it retain much more details.In addition, the post-process is only for sky-mask.perhaps it won't get the same good performance when you apply it on other class segmentation.

2023/11/22 Update: EGE-unet and u2netp speed test

2021/12/29 Update: upload code interenced by onnxruntime, you need to install the package by pip install onnxruntime

onnx model(167M) baiduyun：https://pan.baidu.com/s/1bE38w422STSwuJwjPpRIMw code：4tmm

2021/10/13 Update

Upload a small sky-seg model of 2Mb（traind by u2netp） for demo（We couldn't public the high-precision model because it used in our product）

Upload a sky-seg demo cpp inferenced by ncnn

but it also has some defect：in the scene of building, some detail of building will be considered as sky by mistake

For some special textured clouds, The algorithm has some flaws as below

Next TODO: the U-2-Net couldn't run in real-time in mobile device(about 300ms in Snapdragon 888). even though u2netp size is much smaller than u2net, but the interence speed doesn't improve obviously. I plan to train a real-time model by normanl unet so that it could run in real-time in mobile device.