| Name | Paidiverpy JSON |
| Version |
0.1.1
JSON |
| download |
| home_page | None |
| Summary | A library to preprocess image data. |
| upload_time | 2025-02-12 18:05:02 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
| license | Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
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APPENDIX: How to apply the Apache License to your work.
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Copyright 2024 Tobias Ferreira
Licensed under the Apache License, Version 2.0 (the "License");
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|
| keywords |
data
paidiver
noc
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
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No Travis.
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|
[![DOI][zenodo-badge]][zenodo-link]
[![Documentation][rtd-badge]][rtd-link]
[![Pypi][pip-badge]][pip-link]
[zenodo-badge]: https://zenodo.org/badge/DOI/10.5281/zenodo.14644007.svg
[zenodo-link]: https://doi.org/10.5281/zenodo.14644007
[rtd-badge]: https://img.shields.io/readthedocs/paidiverpy?logo=readthedocs
[rtd-link]: https://paidiverpy.readthedocs.io/en/latest/?badge=latest
[pip-badge]: https://img.shields.io/pypi/v/paidiverpy
[pip-link]: https://pypi.org/project/paidiverpy/
**Paidiverpy** is a Python package designed to create pipelines for preprocessing image data for biodiversity analysis.
> **Note:** This package is still in active development, and frequent updates and changes are expected. The API and features may evolve as we continue improving it.
## Documentation
The official documentation is hosted on ReadTheDocs.org: https://paidiverpy.readthedocs.io/
> **Note:** Comprehensive documentation is under construction.
## Installation
To install paidiverpy, run:
```bash
pip install paidiverpy
```
### Build from Source
You can install `paidiverpy` locally or on a notebook server such as JASMIN or the NOC Data Science Platform (DSP). The following steps are applicable to both environments, but steps 2 and 3 are required if you are using a notebook server.
1. Clone the repository:
```bash
# ssh
git clone git@github.com:paidiver/paidiverpy.git
# https
# git clone https://github.com/paidiver/paidiverpy.git
cd paidiverpy
```
2. (Optional) Create a Python virtual environment to manage dependencies separately from other projects. For example, using `conda`:
```bash
conda init
# Command to restart the terminal. This command may not be necessary if mamba init has already been successfully run before
exec bash
conda env create -f environment.yml
conda activate Paidiverpy
```
3. (Optional) For JASMIN or DSP users, you also need to install the environment in the Jupyter IPython kernel. Execute the following command:
```bash
python -m ipykernel install --user --name Paidiverpy
```
4. Install the paidiverpy package:
Finally, you can install the paidiverpy package:
```bash
pip install -e .
```
## Package Organisation
### Configuration File
First, create a configuration file. Example configuration files for processing the sample datasets are available in the `example/config` directory. You can use these files to test the example notebooks described in the [Usage section](#usage). Note that running the examples will automatically download the sample data.
The configuration file should follow the JSON schema described in the [configuration file schema](src/paidiverpy/configuration-schema.json). An online tool to validate configuration files is available [here](https://paidiver.github.io/paidiverpy/config_check.html).
### Metadata
To use this package, you may need a metadata file, which can be an IFDO.json file (following the IFDO standard) or a CSV file. For CSV files, ensure the `filename` column uses one of the following headers: `['image-filename', 'filename', 'file_name', 'FileName', 'File Name']`.
Other columns like datetime, latitude, and longitude should follow these conventions:
- Datetime: `['image-datetime', 'datetime', 'date_time', 'DateTime', 'Datetime']`
- Latitude: `['image-latitude', 'lat', 'latitude_deg', 'latitude', 'Latitude', 'Latitude_deg', 'Lat']`
- Longitude: `['image-longitude', 'lon', 'longitude_deg', 'longitude', 'Longitude', 'Longitude_deg', 'Lon']`
Examples of CSV and IFDO metadata files are in the `example/metadata` directory.
### Layers
The package is organised into multiple layers:
The `Paidiverpy` class serves as the main container for image processing functions. It manages several subclasses for specific processing tasks: `OpenLayer`, `ConvertLayer`, `PositionLayer`, `ResampleLayer`, and `ColourLayer`.
Supporting classes include:
- `Configuration`: Parses and manages configuration files.
- `Metadata`: Handles metadata.
- `ImagesLayer`: Stores outputs from each image processing step.
The `Pipeline` class integrates all processing steps defined in the configuration file.
## Usage
While comprehensive documentation is forthcoming, you can explore various use cases through sample notebooks in the `examples/example_notebooks` directory:
- [Open and display a configuration file and a metadata file](examples/example_notebooks/config_metadata_example.ipynb)
- [Run processing steps without creating a pipeline](examples/example_notebooks/simple_processing.ipynb)
- [Run a pipeline and interact with outputs](examples/example_notebooks/pipeline.ipynb)
- [Run pipeline steps in test mode](examples/example_notebooks/pipeline_testing_steps.ipynb)
- [Create pipelines programmatically](examples/example_notebooks/pipeline_generation.ipynb)
- [Rerun pipeline steps with modified configurations](examples/example_notebooks/pipeline_interaction.ipynb)
- [Use parallelization with Dask](examples/example_notebooks/pipeline_dask.ipynb)
- [Create a LocalCluster and run a pipeline](examples/example_notebooks/pipeline_cluster.ipynb)
- [Run a pipeline using a public dataset with IFDO metadata](examples/example_notebooks/pipeline_ifdo.ipynb)
- [Run a pipeline using a data on a object store](examples/example_notebooks/pipeline_remote_data.ipynb)
- [Add a custom algorithm to a pipeline](examples/example_notebooks/pipeline_custom_algorithm.ipynb)
### Example Data
If you'd like to manually download example data for testing, you can use the following command:
```python
from paidiverpy import data
data.load(DATASET_NAME)
```
Available datasets:
- pelagic_csv
- benthic_csv
- benthic_ifdo
Example data will be automatically downloaded when running the example notebooks.
### Command-Line Arguments
Pipelines can be executed via command-line arguments. For example:
```bash
paidiverpy -c examples/config_files/config_simple.yaml
```
This runs the pipeline according to the configuration file, saving output images to the directory defined in the `output_path`.
## Docker
You can run **Paidiverpy** using Docker by either building the container locally or pulling a pre-built image from **GitHub Container Registry (GHCR)** or **Docker Hub**.
### Build or Pull the Docker Image
You have three options to obtain the Paidiverpy Docker image:
#### **Option 1: Build the container locally**
Clone the repository and build the image:
```bash
git clone git@github.com:paidiver/paidiverpy.git
cd paidiverpy
docker build -t paidiverpy .
```
#### **Option 2: Pull from Docker Hub**
Fetch the latest image from Docker Hub:
```bash
docker pull soutobias/paidiverpy:latest
docker tag soutobias/paidiverpy:latest paidiverpy:latest
```
#### **Option 3: Pull from GitHub Container Registry (GHCR)**
Fetch the latest image from GitHub:
```bash
docker pull ghcr.io/paidiver/paidiverpy:latest
docker tag ghcr.io/paidiver/paidiverpy:latest paidiverpy:latest
```
### Running the Container
To run the container with local input, output, and metadata directories, use the following command:
```bash
docker run --rm \
-v <INPUT_PATH>:/app/input/ \
-v <OUTPUT_PATH>:/app/output/ \
-v <METADATA_PATH>:/app/metadata/ \
-v <CONFIG_DIR>:/app/config_files/ \
paidiverpy -c /app/examples/config_files/<CONFIG_FILE>
```
#### **Arguments Explained**
- `<INPUT_PATH>`: Local directory containing input images (as defined in the configuration file).
- `<OUTPUT_PATH>`: Local directory where processed images will be saved.
- `<METADATA_PATH>`: Local directory containing the metadata file.
- `<CONFIG_DIR>`: Local directory containing the configuration file.
- `<CONFIG_FILE>`: Name of the configuration file.
The processed images will be saved in the `output_path` specified in the configuration file.
### Running with Remote Data (Object Store)
If your input data is stored remotely (e.g., in an object store), you **do not** need to mount local volumes for input data. However, to upload processed images to an object store, you must provide authentication credentials via an environment file.
Use the following command:
```bash
docker run --rm \
-v <CONFIG_DIR>:/app/config_files/ \
--env-file .env \
paidiverpy -c /app/examples/config_files/<CONFIG_FILE>
```
#### **Environment File (`.env`)**
Create a `.env` file with your object store credentials:
```bash
OS_SECRET=your_secret
OS_TOKEN=your_token
OS_ENDPOINT=your_endpoint
```
This will allow Paidiverpy to authenticate and interact with the remote storage system.
Raw data
{
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"home_page": null,
"name": "Paidiverpy",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.10",
"maintainer_email": "Tobias Ferreira <tobias.ferreira@noc.ac.uk>",
"keywords": "data, paidiver, noc",
"author": null,
"author_email": "Tobias Ferreira <tobias.ferreira@noc.ac.uk>, Mojtaba Masoudi <mojtaba.masoudi@noc.ac.uk>, Van Lo\u00efc Audenhaege <loic.audenhaege@noc.ac.uk>, Erik Orenstein <erik.orenstein@noc.ac.uk>, Colin Sauze <colin.sauze@noc.ac.uk>, Jennifer Durden <jennifer.durden@noc.ac.uk>",
"download_url": "https://files.pythonhosted.org/packages/3a/e2/12d07f98579eee5f1adeb81f250dc1fbd535704790bc8a4c44678301d32c/paidiverpy-0.1.1.tar.gz",
"platform": null,
"description": "[![DOI][zenodo-badge]][zenodo-link]\n[![Documentation][rtd-badge]][rtd-link]\n[![Pypi][pip-badge]][pip-link]\n\n[zenodo-badge]: https://zenodo.org/badge/DOI/10.5281/zenodo.14644007.svg\n[zenodo-link]: https://doi.org/10.5281/zenodo.14644007\n[rtd-badge]: https://img.shields.io/readthedocs/paidiverpy?logo=readthedocs\n[rtd-link]: https://paidiverpy.readthedocs.io/en/latest/?badge=latest\n[pip-badge]: https://img.shields.io/pypi/v/paidiverpy\n[pip-link]: https://pypi.org/project/paidiverpy/\n\n\n\n\n**Paidiverpy** is a Python package designed to create pipelines for preprocessing image data for biodiversity analysis.\n\n> **Note:** This package is still in active development, and frequent updates and changes are expected. The API and features may evolve as we continue improving it.\n\n\n## Documentation\n\nThe official documentation is hosted on ReadTheDocs.org: https://paidiverpy.readthedocs.io/\n\n> **Note:** Comprehensive documentation is under construction.\n\n## Installation\n\nTo install paidiverpy, run:\n\n ```bash\npip install paidiverpy\n ```\n\n### Build from Source\n\nYou can install `paidiverpy` locally or on a notebook server such as JASMIN or the NOC Data Science Platform (DSP). The following steps are applicable to both environments, but steps 2 and 3 are required if you are using a notebook server.\n\n1. Clone the repository:\n\n ```bash\n # ssh\n git clone git@github.com:paidiver/paidiverpy.git\n\n # https\n # git clone https://github.com/paidiver/paidiverpy.git\n\n cd paidiverpy\n ```\n\n2. (Optional) Create a Python virtual environment to manage dependencies separately from other projects. For example, using `conda`:\n\n ```bash\n conda init\n\n # Command to restart the terminal. This command may not be necessary if mamba init has already been successfully run before\n exec bash\n\n conda env create -f environment.yml\n conda activate Paidiverpy\n ```\n\n3. (Optional) For JASMIN or DSP users, you also need to install the environment in the Jupyter IPython kernel. Execute the following command:\n\n ```bash\n python -m ipykernel install --user --name Paidiverpy\n ```\n\n4. Install the paidiverpy package:\n\n Finally, you can install the paidiverpy package:\n\n ```bash\n pip install -e .\n ```\n\n## Package Organisation\n\n### Configuration File\n\nFirst, create a configuration file. Example configuration files for processing the sample datasets are available in the `example/config` directory. You can use these files to test the example notebooks described in the [Usage section](#usage). Note that running the examples will automatically download the sample data.\n\nThe configuration file should follow the JSON schema described in the [configuration file schema](src/paidiverpy/configuration-schema.json). An online tool to validate configuration files is available [here](https://paidiver.github.io/paidiverpy/config_check.html).\n\n### Metadata\n\nTo use this package, you may need a metadata file, which can be an IFDO.json file (following the IFDO standard) or a CSV file. For CSV files, ensure the `filename` column uses one of the following headers: `['image-filename', 'filename', 'file_name', 'FileName', 'File Name']`.\n\nOther columns like datetime, latitude, and longitude should follow these conventions:\n\n- Datetime: `['image-datetime', 'datetime', 'date_time', 'DateTime', 'Datetime']`\n- Latitude: `['image-latitude', 'lat', 'latitude_deg', 'latitude', 'Latitude', 'Latitude_deg', 'Lat']`\n- Longitude: `['image-longitude', 'lon', 'longitude_deg', 'longitude', 'Longitude', 'Longitude_deg', 'Lon']`\n\nExamples of CSV and IFDO metadata files are in the `example/metadata` directory.\n\n### Layers\n\nThe package is organised into multiple layers:\n\n\n\nThe `Paidiverpy` class serves as the main container for image processing functions. It manages several subclasses for specific processing tasks: `OpenLayer`, `ConvertLayer`, `PositionLayer`, `ResampleLayer`, and `ColourLayer`.\n\nSupporting classes include:\n\n- `Configuration`: Parses and manages configuration files.\n- `Metadata`: Handles metadata.\n- `ImagesLayer`: Stores outputs from each image processing step.\n\nThe `Pipeline` class integrates all processing steps defined in the configuration file.\n\n## Usage\n\nWhile comprehensive documentation is forthcoming, you can explore various use cases through sample notebooks in the `examples/example_notebooks` directory:\n\n- [Open and display a configuration file and a metadata file](examples/example_notebooks/config_metadata_example.ipynb)\n- [Run processing steps without creating a pipeline](examples/example_notebooks/simple_processing.ipynb)\n- [Run a pipeline and interact with outputs](examples/example_notebooks/pipeline.ipynb)\n- [Run pipeline steps in test mode](examples/example_notebooks/pipeline_testing_steps.ipynb)\n- [Create pipelines programmatically](examples/example_notebooks/pipeline_generation.ipynb)\n- [Rerun pipeline steps with modified configurations](examples/example_notebooks/pipeline_interaction.ipynb)\n- [Use parallelization with Dask](examples/example_notebooks/pipeline_dask.ipynb)\n- [Create a LocalCluster and run a pipeline](examples/example_notebooks/pipeline_cluster.ipynb)\n- [Run a pipeline using a public dataset with IFDO metadata](examples/example_notebooks/pipeline_ifdo.ipynb)\n- [Run a pipeline using a data on a object store](examples/example_notebooks/pipeline_remote_data.ipynb)\n- [Add a custom algorithm to a pipeline](examples/example_notebooks/pipeline_custom_algorithm.ipynb)\n\n### Example Data\n\nIf you'd like to manually download example data for testing, you can use the following command:\n\n```python\nfrom paidiverpy import data\ndata.load(DATASET_NAME)\n```\n\nAvailable datasets:\n\n- pelagic_csv\n- benthic_csv\n- benthic_ifdo\n\nExample data will be automatically downloaded when running the example notebooks.\n\n### Command-Line Arguments\n\nPipelines can be executed via command-line arguments. For example:\n\n```bash\npaidiverpy -c examples/config_files/config_simple.yaml\n```\n\nThis runs the pipeline according to the configuration file, saving output images to the directory defined in the `output_path`.\n\n## Docker\n\nYou can run **Paidiverpy** using Docker by either building the container locally or pulling a pre-built image from **GitHub Container Registry (GHCR)** or **Docker Hub**.\n\n### Build or Pull the Docker Image\n\nYou have three options to obtain the Paidiverpy Docker image:\n\n#### **Option 1: Build the container locally**\nClone the repository and build the image:\n\n```bash\ngit clone git@github.com:paidiver/paidiverpy.git\ncd paidiverpy\ndocker build -t paidiverpy .\n```\n\n#### **Option 2: Pull from Docker Hub**\nFetch the latest image from Docker Hub:\n\n```bash\ndocker pull soutobias/paidiverpy:latest\ndocker tag soutobias/paidiverpy:latest paidiverpy:latest\n```\n\n#### **Option 3: Pull from GitHub Container Registry (GHCR)**\nFetch the latest image from GitHub:\n\n```bash\ndocker pull ghcr.io/paidiver/paidiverpy:latest\ndocker tag ghcr.io/paidiver/paidiverpy:latest paidiverpy:latest\n```\n\n### Running the Container\n\nTo run the container with local input, output, and metadata directories, use the following command:\n\n```bash\ndocker run --rm \\\n -v <INPUT_PATH>:/app/input/ \\\n -v <OUTPUT_PATH>:/app/output/ \\\n -v <METADATA_PATH>:/app/metadata/ \\\n -v <CONFIG_DIR>:/app/config_files/ \\\n paidiverpy -c /app/examples/config_files/<CONFIG_FILE>\n```\n\n#### **Arguments Explained**\n- `<INPUT_PATH>`: Local directory containing input images (as defined in the configuration file).\n- `<OUTPUT_PATH>`: Local directory where processed images will be saved.\n- `<METADATA_PATH>`: Local directory containing the metadata file.\n- `<CONFIG_DIR>`: Local directory containing the configuration file.\n- `<CONFIG_FILE>`: Name of the configuration file.\n\nThe processed images will be saved in the `output_path` specified in the configuration file.\n\n### Running with Remote Data (Object Store)\n\nIf your input data is stored remotely (e.g., in an object store), you **do not** need to mount local volumes for input data. However, to upload processed images to an object store, you must provide authentication credentials via an environment file.\n\nUse the following command:\n\n```bash\ndocker run --rm \\\n -v <CONFIG_DIR>:/app/config_files/ \\\n --env-file .env \\\n paidiverpy -c /app/examples/config_files/<CONFIG_FILE>\n```\n\n#### **Environment File (`.env`)**\nCreate a `.env` file with your object store credentials:\n\n```bash\nOS_SECRET=your_secret\nOS_TOKEN=your_token\nOS_ENDPOINT=your_endpoint\n```\n\nThis will allow Paidiverpy to authenticate and interact with the remote storage system.\n",
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"license": "Apache License\n Version 2.0, January 2004\n http://www.apache.org/licenses/\n \n TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n \n 1. Definitions.\n \n \"License\" shall mean the terms and conditions for use, reproduction,\n and distribution as defined by Sections 1 through 9 of this document.\n \n \"Licensor\" shall mean the copyright owner or entity authorized by\n the copyright owner that is granting the License.\n \n \"Legal Entity\" shall mean the union of the acting entity and all\n other entities that control, are controlled by, or are under common\n control with that entity. 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