%global _empty_manifest_terminate_build 0
Name: python-roboflow
Version: 1.0.3
Release: 1
Summary: python client for the Roboflow application
License: MIT License
URL: https://github.com/roboflow-ai/roboflow-python
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/b0/80/535e1d43b28e24db0716b4eda4988371534db6c171197d675c1959b76c4a/roboflow-1.0.3.tar.gz
BuildArch: noarch
Requires: python3-certifi
Requires: python3-chardet
Requires: python3-cycler
Requires: python3-idna
Requires: python3-kiwisolver
Requires: python3-matplotlib
Requires: python3-numpy
Requires: python3-opencv-python
Requires: python3-Pillow
Requires: python3-pyparsing
Requires: python3-dateutil
Requires: python3-dotenv
Requires: python3-requests
Requires: python3-six
Requires: python3-urllib3
Requires: python3-wget
Requires: python3-tqdm
Requires: python3-PyYAML
Requires: python3-requests-toolbelt
Requires: python3-flake8
Requires: python3-black
Requires: python3-isort
Requires: python3-responses
Requires: python3-twine
Requires: python3-wheel
%description
**Roboflow** streamlines your computer vision pipeline - upload data, label it, download datasets, train models, deploy models, and repeat.
The **Roboflow Python Package** is a python wrapper around the core Roboflow web application and REST API.
We also maintain an open source set of CV utililities and notebook tutorials in Python:
* :fire: https://github.com/roboflow/supervision :fire:
* :fire: https://github.com/roboflow/notebooks :fire:
## Installation
To install this package, please use `Python 3.6` or higher.
Install from PyPi (Recommended):
```bash
pip install roboflow
```
Install from Source:
```bash
git clone https://github.com/roboflow-ai/roboflow-python.git
cd roboflow-python
python3 -m venv env
source env/bin/activate
pip3 install -r requirements.txt
```
## Authentication
```python
import roboflow
roboflow.login()
```
## Quickstart
### Datasets
Download any of over 200,000 public computer vision datasets from [Roboflow Universe](universe.roboflow.com). Label and download your own datasets on app.roboflow.com.
```python
import roboflow
dataset = roboflow.download_dataset(dataset_url="universe.roboflow.com/...", model_format="yolov8")
#ex. dataset = roboflow.download_dataset(dataset_url="https://universe.roboflow.com/joseph-nelson/bccd/dataset/1", model_format="yolov8")
print(dataset.location)
```
### Models
Predict with any of over 50,000 public computer vision models. Train your own computer vision models on app.roboflow.com or train upload your model from open source models - see https://github.com/roboflow/notebooks
```python
img_url = "https://media.roboflow.com/quickstart/aerial_drone.jpeg?updatedAt=1678743716455"
universe_model_url = "https://universe.roboflow.com/brad-dwyer/aerial-solar-panels/model/6"
model = roboflow.load_model(model_url=universe_model_url)
pred = model.predict(img_url, hosted=True)
pred.plot()
```
## Library Structure
The Roboflow python library is structured by the core Roboflow application objects.
Workspace (workspace.py) --> Project (project.py) --> Version (version.py)
```python
from roboflow import Roboflow
rf = Roboflow()
workspace = rf.workspace("WORKSPACE_URL")
project = workspace.project("PROJECT_URL")
version = project.version("VERSION_NUMBER")
```
The workspace, project, and version parameters are the same that you will find in the URL addresses at app.roboflow.com and universe.roboflow.com.
Within the workspace object you can perform actions like making a new project, listing your projects, or performing active learning where you are using predictions from one project's model to upload images to a new project.
Within the project object, you can retrieve metadata about the project, list versions, generate a new dataset version with preprocessing and augmentation settings, train a model in your project, and upload images and annotations to your project.
Within the version object, you can download the dataset version in any model format, train the version on Roboflow, and deploy your own external model to Roboflow.
## Contributing
If you want to extend our Python library or if you find a bug, please open a PR!
Also be sure to test your code the `unittest` command at the `/root` level directory.
Run tests:
```bash
python -m unittest
```
When creating new functions, please follow the [Google style Python docstrings](https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html). See example below:
```python
def example_function(param1: int, param2: str) -> bool:
"""Example function that does something.
Args:
param1: The first parameter.
param2: The second parameter.
Returns:
The return value. True for success, False otherwise.
"""
```
We provide a `Makefile` to format and ensure code quality. **Be sure to run them before creating a PR**.
```bash
# format code with `black` and `isort`
make style
# check code with flake8
make check_code_quality
```
**Note** These tests will be run automatically when you commit thanks to git hooks.
%package -n python3-roboflow
Summary: python client for the Roboflow application
Provides: python-roboflow
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-roboflow
**Roboflow** streamlines your computer vision pipeline - upload data, label it, download datasets, train models, deploy models, and repeat.
The **Roboflow Python Package** is a python wrapper around the core Roboflow web application and REST API.
We also maintain an open source set of CV utililities and notebook tutorials in Python:
* :fire: https://github.com/roboflow/supervision :fire:
* :fire: https://github.com/roboflow/notebooks :fire:
## Installation
To install this package, please use `Python 3.6` or higher.
Install from PyPi (Recommended):
```bash
pip install roboflow
```
Install from Source:
```bash
git clone https://github.com/roboflow-ai/roboflow-python.git
cd roboflow-python
python3 -m venv env
source env/bin/activate
pip3 install -r requirements.txt
```
## Authentication
```python
import roboflow
roboflow.login()
```
## Quickstart
### Datasets
Download any of over 200,000 public computer vision datasets from [Roboflow Universe](universe.roboflow.com). Label and download your own datasets on app.roboflow.com.
```python
import roboflow
dataset = roboflow.download_dataset(dataset_url="universe.roboflow.com/...", model_format="yolov8")
#ex. dataset = roboflow.download_dataset(dataset_url="https://universe.roboflow.com/joseph-nelson/bccd/dataset/1", model_format="yolov8")
print(dataset.location)
```
### Models
Predict with any of over 50,000 public computer vision models. Train your own computer vision models on app.roboflow.com or train upload your model from open source models - see https://github.com/roboflow/notebooks
```python
img_url = "https://media.roboflow.com/quickstart/aerial_drone.jpeg?updatedAt=1678743716455"
universe_model_url = "https://universe.roboflow.com/brad-dwyer/aerial-solar-panels/model/6"
model = roboflow.load_model(model_url=universe_model_url)
pred = model.predict(img_url, hosted=True)
pred.plot()
```
## Library Structure
The Roboflow python library is structured by the core Roboflow application objects.
Workspace (workspace.py) --> Project (project.py) --> Version (version.py)
```python
from roboflow import Roboflow
rf = Roboflow()
workspace = rf.workspace("WORKSPACE_URL")
project = workspace.project("PROJECT_URL")
version = project.version("VERSION_NUMBER")
```
The workspace, project, and version parameters are the same that you will find in the URL addresses at app.roboflow.com and universe.roboflow.com.
Within the workspace object you can perform actions like making a new project, listing your projects, or performing active learning where you are using predictions from one project's model to upload images to a new project.
Within the project object, you can retrieve metadata about the project, list versions, generate a new dataset version with preprocessing and augmentation settings, train a model in your project, and upload images and annotations to your project.
Within the version object, you can download the dataset version in any model format, train the version on Roboflow, and deploy your own external model to Roboflow.
## Contributing
If you want to extend our Python library or if you find a bug, please open a PR!
Also be sure to test your code the `unittest` command at the `/root` level directory.
Run tests:
```bash
python -m unittest
```
When creating new functions, please follow the [Google style Python docstrings](https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html). See example below:
```python
def example_function(param1: int, param2: str) -> bool:
"""Example function that does something.
Args:
param1: The first parameter.
param2: The second parameter.
Returns:
The return value. True for success, False otherwise.
"""
```
We provide a `Makefile` to format and ensure code quality. **Be sure to run them before creating a PR**.
```bash
# format code with `black` and `isort`
make style
# check code with flake8
make check_code_quality
```
**Note** These tests will be run automatically when you commit thanks to git hooks.
%package help
Summary: Development documents and examples for roboflow
Provides: python3-roboflow-doc
%description help
**Roboflow** streamlines your computer vision pipeline - upload data, label it, download datasets, train models, deploy models, and repeat.
The **Roboflow Python Package** is a python wrapper around the core Roboflow web application and REST API.
We also maintain an open source set of CV utililities and notebook tutorials in Python:
* :fire: https://github.com/roboflow/supervision :fire:
* :fire: https://github.com/roboflow/notebooks :fire:
## Installation
To install this package, please use `Python 3.6` or higher.
Install from PyPi (Recommended):
```bash
pip install roboflow
```
Install from Source:
```bash
git clone https://github.com/roboflow-ai/roboflow-python.git
cd roboflow-python
python3 -m venv env
source env/bin/activate
pip3 install -r requirements.txt
```
## Authentication
```python
import roboflow
roboflow.login()
```
## Quickstart
### Datasets
Download any of over 200,000 public computer vision datasets from [Roboflow Universe](universe.roboflow.com). Label and download your own datasets on app.roboflow.com.
```python
import roboflow
dataset = roboflow.download_dataset(dataset_url="universe.roboflow.com/...", model_format="yolov8")
#ex. dataset = roboflow.download_dataset(dataset_url="https://universe.roboflow.com/joseph-nelson/bccd/dataset/1", model_format="yolov8")
print(dataset.location)
```
### Models
Predict with any of over 50,000 public computer vision models. Train your own computer vision models on app.roboflow.com or train upload your model from open source models - see https://github.com/roboflow/notebooks
```python
img_url = "https://media.roboflow.com/quickstart/aerial_drone.jpeg?updatedAt=1678743716455"
universe_model_url = "https://universe.roboflow.com/brad-dwyer/aerial-solar-panels/model/6"
model = roboflow.load_model(model_url=universe_model_url)
pred = model.predict(img_url, hosted=True)
pred.plot()
```
## Library Structure
The Roboflow python library is structured by the core Roboflow application objects.
Workspace (workspace.py) --> Project (project.py) --> Version (version.py)
```python
from roboflow import Roboflow
rf = Roboflow()
workspace = rf.workspace("WORKSPACE_URL")
project = workspace.project("PROJECT_URL")
version = project.version("VERSION_NUMBER")
```
The workspace, project, and version parameters are the same that you will find in the URL addresses at app.roboflow.com and universe.roboflow.com.
Within the workspace object you can perform actions like making a new project, listing your projects, or performing active learning where you are using predictions from one project's model to upload images to a new project.
Within the project object, you can retrieve metadata about the project, list versions, generate a new dataset version with preprocessing and augmentation settings, train a model in your project, and upload images and annotations to your project.
Within the version object, you can download the dataset version in any model format, train the version on Roboflow, and deploy your own external model to Roboflow.
## Contributing
If you want to extend our Python library or if you find a bug, please open a PR!
Also be sure to test your code the `unittest` command at the `/root` level directory.
Run tests:
```bash
python -m unittest
```
When creating new functions, please follow the [Google style Python docstrings](https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html). See example below:
```python
def example_function(param1: int, param2: str) -> bool:
"""Example function that does something.
Args:
param1: The first parameter.
param2: The second parameter.
Returns:
The return value. True for success, False otherwise.
"""
```
We provide a `Makefile` to format and ensure code quality. **Be sure to run them before creating a PR**.
```bash
# format code with `black` and `isort`
make style
# check code with flake8
make check_code_quality
```
**Note** These tests will be run automatically when you commit thanks to git hooks.
%prep
%autosetup -n roboflow-1.0.3
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-roboflow -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Tue Apr 11 2023 Python_Bot - 1.0.3-1
- Package Spec generated
