pyOpenRiverCam
==============
[](https://pypi.org/project/pyopenrivercam)
[](https://anaconda.org/conda-forge/pyopenrivercam)
[](https://codecov.io/gh/localdevices/pyorc)
[](https://localdevices.github.io/pyorc/latest)
[](https://mybinder.org/v2/gh/localdevices/pyorc.git/main?labpath=examples)
[](https://github.com/localdevices/pyorc/blob/main/LICENSE)
**pyorc**, short for "pyOpenRiverCam" is a fully Open Source library for performing image-based river flow analysis. It is the underlying library for
computations on the fully open software stack OpenRiverCam. **pyorc** can only be successful if the underlying methods
are made available openly for all. Currently **pyorc** implements Large-scale Particle Image Velocimetry (LSPIV) based
flow analysis using the OpenPIV library and reprojections and image pre-processing with OpenCV. We wish to extend this
to Large-scale Particle Tracking Velocimetry (LSPTV) and Space-Time Image Velocimetry (STIV) for conditions that are less favourable for LSPIV using open
libraries or extensions to this code.
Image: Example of pyorc velocimetry over Ngwerere river at the Zambezi Road crossing - Lusaka, Zambia.
Current capabilities are:
* Reading of frames and reprojection to surface
* Velocimetry estimation at user-defined resolution
* Discharge estimation over provided cross-section
* Plotting of velocimetry results and cross-section flows in camera, geographical and orthoprojected perspectives.
We use the well-known **xarray** data models and computation pipelines (with dask) throughout the entire library to
guarantee an easy interoperability with other tools and methods, and allow for lazy computing.
We are seeking funding for the following frequently requested functionalities:
* A command-line interface for processing single or batch videos
* Implementation of better filtering in pre-processing
* Improved efficiency of processing
* Establishing on-site edge computation through a raspberry-pi camera setup
* Implementation of additional processing algorithms (STIV and LSPTV)
If you wish to fund this or other work on features, please contact us at info@rainbowsensing.com.
> **_note:_** For instructions how to get Anaconda (with lots of pre-installed libraries) or Miniconda (light weight) installed, please go to https://docs.conda.io/projects/conda/en/latest/
> **_manual:_** Please go to https://localdevices.github.io/pyorc for the latest documentation
> **_compatibility:_** At this moment **pyorc** works with any video compatible with OpenCV as long as it has proper metadata.
Installation
------------
To get started with **pyorc**, we recommend to setup a python virtual environment.
We recommend using a Miniconda or Anaconda environment as this will ease installation, and will allow you to use all
functionalities without any trouble. Especially geographical plotting with `cartopy` can be difficult to get installed.
With a `conda` environment and our `conda-forge` package this is solved. In the subsections below, you can find specific
instructions for different use cases.
### Installation for direct use
If you simply want to add **pyorc** to an existing python installation or virtual environment, then follow these
instructions.
First activate the environment you want **pyorc** to be installed in (if you don't care about virtual environments, then
simply skip this step). You can simply install pyorc with all its dependencies as follows:
```
conda activate <name-of-your-environment>
conda install -c conda-forge pyopenrivercam
```
If you use mamba as a package mananager, then the steps are the same, except for the installation step, which is:
```
mamba install pyopenrivercam
```
### Installation from latest code base
To install **pyorc** from scratch in a new virtual environment from the code base, go through these steps. Logical cases
when you wish to install from the code base are when you wish to have the very latest non-released version.
First, clone the code with `git` and move into the cloned folder.
```
git clone https://github.com/localdevices/pyorc.git
cd pyorc
```
Setup a virtual environment with all dependencies as follows:
```
conda env create -f envs/pyorc-dev.yml
conda activate pyorc-dev
```
then install **pyorc** from the code base as follows:
```
pip install .
```
> **_note:_** **pyorc** is now installed in a virtual environment called `pyorc-dev`. This means that if you wish to run
python with **pyorc**. You need to always first activate this environment before running python (or jupyter). This is
done with the following command:
```
conda activate pyorc-dev
```
### Installation from latest code base as developer
Clone the repository with ssh and move into the cloned folder.
```
git clone git@github.com:localdevices/pyorc.git
cd pyorc
```
Setup a virtual developers environment and install the package as follows:
```
conda env create -f envs/pyorc-dev.yml
conda activate pyorc-dev
pip install -e .
```
Using pyorc
-----------
To use **pyorc**, you can use the API for processing. A command-line interface is forthcoming pending funding.
A manual is also still in the making.
Acknowledgement
---------------
The first development of pyorc has been supported by the World Meteorological Organisation - HydroHub.
License
-------
**pyorc** is licensed under AGPL Version 3 (see [LICENSE](./LICENSE) file).
**pyorc** uses the following libraries and software with said licenses.
| Package | Version | License |
|------------|---------|------------------------------------|
| numpy | 1.23.2 | BSD License |
| opencv2 | 4.6.0 | MIT License |
| openpiv | 0.23.8 | GPLv3 |
| matplotlib | 3.5.3 | Python Software Foundation License |
| geopandas | 0.10.2 | BSD License |
| pandas | 1.4.3 | BSD License |
Project organisation
--------------------
.
├── README.md
├── LICENSE
├── setup.py <- setup script compatible with pip
├── environment.yml <- YML-file for setting up a conda environment with dependencies
├── docs <- Sphinx documentation source code
├── ... <- Sphinx source code files
├── examples <- Jupyter notebooks with examples how to use the API
├── ... <- individual notebooks and folder with example data files
├── pyorc <- pyorc library
├── ... <- pyorc functions and API files
├── tests <- pytest suite
├── ... <- pytest functions on API level
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"description": "pyOpenRiverCam\n==============\n\n[](https://pypi.org/project/pyopenrivercam)\n[](https://anaconda.org/conda-forge/pyopenrivercam)\n[](https://codecov.io/gh/localdevices/pyorc)\n[](https://localdevices.github.io/pyorc/latest)\n[](https://mybinder.org/v2/gh/localdevices/pyorc.git/main?labpath=examples)\n[](https://github.com/localdevices/pyorc/blob/main/LICENSE)\n\n\n**pyorc**, short for \"pyOpenRiverCam\" is a fully Open Source library for performing image-based river flow analysis. It is the underlying library for \ncomputations on the fully open software stack OpenRiverCam. **pyorc** can only be successful if the underlying methods\nare made available openly for all. Currently **pyorc** implements Large-scale Particle Image Velocimetry (LSPIV) based\nflow analysis using the OpenPIV library and reprojections and image pre-processing with OpenCV. We wish to extend this \nto Large-scale Particle Tracking Velocimetry (LSPTV) and Space-Time Image Velocimetry (STIV) for conditions that are less favourable for LSPIV using open\nlibraries or extensions to this code. \n\n\nImage: Example of pyorc velocimetry over Ngwerere river at the Zambezi Road crossing - Lusaka, Zambia.\n\nCurrent capabilities are:\n* Reading of frames and reprojection to surface\n* Velocimetry estimation at user-defined resolution\n* Discharge estimation over provided cross-section\n* Plotting of velocimetry results and cross-section flows in camera, geographical and orthoprojected perspectives.\n\nWe use the well-known **xarray** data models and computation pipelines (with dask) throughout the entire library to \nguarantee an easy interoperability with other tools and methods, and allow for lazy computing. \n\nWe are seeking funding for the following frequently requested functionalities:\n* A command-line interface for processing single or batch videos\n* Implementation of better filtering in pre-processing\n* Improved efficiency of processing\n* Establishing on-site edge computation through a raspberry-pi camera setup\n* Implementation of additional processing algorithms (STIV and LSPTV)\n\nIf you wish to fund this or other work on features, please contact us at info@rainbowsensing.com.\n\n> **_note:_** For instructions how to get Anaconda (with lots of pre-installed libraries) or Miniconda (light weight) installed, please go to https://docs.conda.io/projects/conda/en/latest/\n\n> **_manual:_** Please go to https://localdevices.github.io/pyorc for the latest documentation\n\n> **_compatibility:_** At this moment **pyorc** works with any video compatible with OpenCV as long as it has proper metadata.\n\nInstallation\n------------\n\nTo get started with **pyorc**, we recommend to setup a python virtual environment. \nWe recommend using a Miniconda or Anaconda environment as this will ease installation, and will allow you to use all\nfunctionalities without any trouble. Especially geographical plotting with `cartopy` can be difficult to get installed. \nWith a `conda` environment and our `conda-forge` package this is solved. In the subsections below, you can find specific\ninstructions for different use cases. \n\n### Installation for direct use\n\nIf you simply want to add **pyorc** to an existing python installation or virtual environment, then follow these \ninstructions.\n\nFirst activate the environment you want **pyorc** to be installed in (if you don't care about virtual environments, then \nsimply skip this step). You can simply install pyorc with all its dependencies as follows:\n\n```\nconda activate <name-of-your-environment>\nconda install -c conda-forge pyopenrivercam\n```\n\nIf you use mamba as a package mananager, then the steps are the same, except for the installation step, which is:\n```\nmamba install pyopenrivercam\n```\n\n### Installation from latest code base\n\nTo install **pyorc** from scratch in a new virtual environment from the code base, go through these steps. Logical cases\nwhen you wish to install from the code base are when you wish to have the very latest non-released version.\n\nFirst, clone the code with `git` and move into the cloned folder.\n\n```\ngit clone https://github.com/localdevices/pyorc.git\ncd pyorc\n```\n\nSetup a virtual environment with all dependencies as follows:\n```\nconda env create -f envs/pyorc-dev.yml\nconda activate pyorc-dev\n```\nthen install **pyorc** from the code base as follows:\n```\npip install .\n```\n> **_note:_** **pyorc** is now installed in a virtual environment called `pyorc-dev`. This means that if you wish to run\npython with **pyorc**. You need to always first activate this environment before running python (or jupyter). This is\ndone with the following command:\n```\nconda activate pyorc-dev\n```\n### Installation from latest code base as developer\n\nClone the repository with ssh and move into the cloned folder.\n\n```\ngit clone git@github.com:localdevices/pyorc.git\ncd pyorc\n```\n\nSetup a virtual developers environment and install the package as follows:\n```\nconda env create -f envs/pyorc-dev.yml\nconda activate pyorc-dev\npip install -e .\n```\n\nUsing pyorc\n-----------\nTo use **pyorc**, you can use the API for processing. A command-line interface is forthcoming pending funding. \nA manual is also still in the making.\n\nAcknowledgement\n---------------\nThe first development of pyorc has been supported by the World Meteorological Organisation - HydroHub. \n\nLicense\n-------\n**pyorc** is licensed under AGPL Version 3 (see [LICENSE](./LICENSE) file).\n\n**pyorc** uses the following libraries and software with said licenses.\n\n| Package | Version | License |\n|------------|---------|------------------------------------|\n| numpy | 1.23.2 | BSD License |\n| opencv2 | 4.6.0 | MIT License | \n| openpiv | 0.23.8 | GPLv3 | \n| matplotlib | 3.5.3 | Python Software Foundation License | \n| geopandas | 0.10.2 | BSD License | \n | pandas | 1.4.3 | BSD License | \n\nProject organisation\n--------------------\n\n .\n \u251c\u2500\u2500 README.md\n \u251c\u2500\u2500 LICENSE\n \u251c\u2500\u2500 setup.py <- setup script compatible with pip\n \u251c\u2500\u2500 environment.yml <- YML-file for setting up a conda environment with dependencies\n \u251c\u2500\u2500 docs <- Sphinx documentation source code\n \u251c\u2500\u2500 ... <- Sphinx source code files\n \u251c\u2500\u2500 examples <- Jupyter notebooks with examples how to use the API\n \u251c\u2500\u2500 ... <- individual notebooks and folder with example data files\n \u251c\u2500\u2500 pyorc <- pyorc library\n \u251c\u2500\u2500 ... <- pyorc functions and API files\n \u251c\u2500\u2500 tests <- pytest suite\n \u251c\u2500\u2500 ... <- pytest functions on API level\n\n\n\n",
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