This repository contains solution for automatic detection of solar modules. Main aim was to prepare a tool that can be configured and based on that configuration produce detected modules in to be defined formats, commonly used in machine learning but not only.
docker build -t pvpd:0.0.1 -t pvpd .
Try running:
docker run pvpd:0.1.0 -h
docker run -v ${PWD}/data:/usr/data pvpd:0.1.0 -c PlasmaConfig -o /usr/data --f /usr/data/raw/3.JPG -t raw -cm plasma -l LabelMeLabeler
-v ${PWD}/data:/usr/datashares directorydatafrom repository to/usr/datapvpd:0.1.0version of docker image-c PlasmaConfigwhich config will be used for image annotation-o /usr/data/data will be saved in the shared directorydata-f /usr/data/plasma/1.JPGfile1.JPGis going to be analyzing-t rawfile1.JPGis in a raw format. There is a need to extract thermal information-cm plasmawhich color map will be used for thermal image values
- Required Python > 3.7. Check python version
python --version
- For extraction exif data (thermal data) from raw images: exiftool
Install the needed python packages commands below:
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
./detector_cli.py
Configs are parts of this project that every user adjust for private images. All config can be found those in config directory. Using them requires some knowledge about classic image processing methods.
Whole detection is split into distinct steps that could be configured using each step
Param. It is highly suggested taking a look at those.
For now available Configs can be found using ./detector_cli.py -h
Labeler is an entity that can be selected to generate a file containing data and metadata from detection process. New labelers can be easily created to meet individual needs. All labelers can be found in labelers directory
For now available Labelers can be found using ./detector_cli.py -h