An example project using PipelineX, Kedro, OpenCV, Scikit-image, and TensorFlow/Keras for image processing.
Pipeline visualized by Kedro-viz
conf
- YAML config files for PipelineX project
data
- empty folders (output files will be saved here)
logs
- empty folders (log files will be saved here)
src
empty_area.py
- The algorithm to estimate empty area ratio
roi.py
- Supplementary algorithm to compute ROI (Region of Interest) from segmentation image
semantic_segmentation.py
- Semantic segmentation using PSPNet model pretrained with ADE20K dataset
$ pip install pipelinex opencv-python scikit-image ocrd-fork-pylsd Pillow pandas numpy requests kedro mlflow kedro-viz
Note: mlflow
and kedro-viz
are optional.
$ pip install "tensorflow<2" keras-segmentation Keras
If you want to use TensorFlow 2.x, install fork of keras-segmentation modified to work with TensorFlow 2.x
$ pip install "tensorflow>=2.0.0" Keras
$ pip install git+https://github.com/Minyus/image-segmentation-keras.git
$ git clone https://github.com/Minyus/pipelinex_image_processing.git
$ cd pipelinex_image_processing
$ python main.py
As configured in catalog.yml, the following 2 images will be downloaded by http requests and then processed using opencv-python
, scikit-image
, and ocrd-fork-pylsd
packages.
$ mlflow server --host 0.0.0.0 --backend-store-uri sqlite:///mlruns/sqlite.db --default-artifact-root ./mlruns/experiment_001
Experiment logs in MLflow's UI
- Python 3.6.8
This project was created from the GitHub template repository at https://github.com/Minyus/pipelinex_template
To use for a new project, fork the template repository and hit Use this template
button next to Clone or download
.