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simple-camera-pipeline

(★ 350)

A simple and light-weight camera image processing pipeline

파일 탐색기

최종 버전 다운로드 (.zip)

- 0001_GT_RAW_010.MAT
- 0001_METADATA_RAW_010.MAT
- colorchart.dng
- apply_cmatrix.m
- cfa_pattern.m
- CropActiveArea.m
- demo.m
- FixOrientation.m
- GetActiveArea.m
- GetCropArea.m
- GetNLF.m
- Load_Data_and_Metadata_from_DNG.m
- run_pipeline.m
- tone_curve.mat
- wbmask.m
- __init__.py
- demo1.py
- demo2.py
- demo_multi_demosaic.py
- dng_opcode.py
- exif_data_formats.py
- exif_utils.py
- pipeline.py
- pipeline_utils.py
- requirements.txt
- tone_curve.mat
- dng_spec.pdf
- dng_spec_1.4.0.0.pdf
LICENSE
README.md

# 설치 가이드

1. 코드 내려받기

git clone https://github.com/AbdoKamel/simple-camera-pipeline

깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.

cd simple-camera-pipeline

방금 내려받은 프로젝트 폴더 안으로 이동합니다.

2. Python

쉬움 추천

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Python 3 설치 시 'Add Python to PATH' 옵션을 꼭 체크하세요 (Windows).

pip install -r python/requirements.txt

requirements.txt 등에 명시된 파이썬 라이브러리를 설치합니다.

python <실행할 파일명>.py # README에서 정확한 실행 파일명을 확인하세요

파이썬 스크립트(또는 모듈)를 실행합니다.

✅ 에러 메시지 없이 실행되고 터미널에 안내 문구가 출력되면 정상입니다.

// repository documentation

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# simple-camera-pipeline
A simple and light-weight camera image signal processor (ISP) pipeline implemented in MATLAB and Python.

Stages currently included in the pipeline:
* Normalization
* Lens shading correction (Python only)
* White balance
* Demosaicing
* Color space transformation (CIE XYZ)
* Color space transformation (sRGB)
* Gamma correction
* Global tone mapping

Start by running:

Matlab: [matlab/demo.m](matlab/demo.m)

Python:

[python/demo1.py](python/demo1.py)

[python/demo2.py](python/demo2.py)

Python requirements ([python/requirements.txt](python/requirements.txt)) -- _other versions may work_
```
numpy==1.17.2
scipy==1.3.1
opencv-python==4.1.1.26
rawpy==0.14.0
exifread==2.1.2
colour-demosaicing==0.1.5
```

This is the code used to render the sRGB images from the Raw-RGB images of the [Smartphone Image Denoising Dataset (SIDD)](https://www.eecs.yorku.ca/~kamel/sidd/).

This code is helpful for participants of the real image denoising challenges on CodaLab:

[NTIRE 2020 Real Image Denoising Challenge - Track 1: rawRGB](https://competitions.codalab.org/competitions/22230)

[NTIRE 2020 Real Image Denoising Challenge - Track 2: sRGB](https://competitions.codalab.org/competitions/22231)

[NTIRE 2019 Real Image Denoising Challenge - Track 1: Raw-RGB](https://competitions.codalab.org/competitions/21258)

[NTIRE 2019 Real Image Denoising Challenge - Track 2: sRGB](https://competitions.codalab.org/competitions/21266)

### Paper
Abdelhamed, A., Lin, S., & Brown, M. S. (2018). A High-Quality Denoising Dataset for Smartphone Cameras. In 2018 {IEEE}/{CVF} Conference on Computer Vision and Pattern Recognition. {IEEE}. Retrieved from https://doi.org/10.1109%2Fcvpr.2018.00182

Enjoy!