Roboflow Python Library

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Roboflow makes managing, preprocessing, augmenting, and versioning datasets for computer vision seamless. This is the official Roboflow python package that interfaces with the Roboflow API. Key features of Roboflow:

• Import and Export image datasets into any supported formats
• Preprocess and augment data using Roboflow's dataset management tools
• Train computer vision models using Roboflow Train and deploy to production
• Use community curated projects to start building your own vision-powered products

Installation:

To install this package, please use Python 3.6 or higher. We provide three different ways to install the Roboflow package to use within your own projects.

Install from PyPi (Recommended):

pip install roboflow

Install from Source:

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

Quickstart

import roboflow

# Instantiate Roboflow object with your API key
rf = roboflow.Roboflow(api_key=YOUR_API_KEY_HERE)

# List all projects for your workspace
workspace = rf.workspace()

# Load a certain project, workspace url is optional
project = rf.project("PROJECT_ID")

# List all versions of a specific project
project.versions()

# Upload image to dataset
project.upload("UPLOAD_IMAGE.jpg")

# Retrieve the model of a specific project
project.version("1").model

# predict on a local image
prediction = model.predict("YOUR_IMAGE.jpg")

# Predict on a hosted image
prediction = model.predict("YOUR_IMAGE.jpg", hosted=True)

# Plot the prediction
prediction.plot()

# Convert predictions to JSON
prediction.json()

# Save the prediction as an image
prediction.save(output_path='predictions.jpg')

Using this package for a specifc project

If you have a specific project from your workspace you'd like to run in a notebook follow along on this tutorial Downloading Datasets from Roboflow for Training (Python)

Selecting the format you'd like your project to be exported as while choosing the show download code option will display code snippets you can use in either Jupyter or your terminal. These code snippets will include your api_key, project, and workspace names.

Deving locally

1. clone repo
2. create a virtual env: virtualenv local_dev
3. activate virtual env: source local_dev/bin/activate
4. install packages from repo: pip3 install -e ".[dev]"
5. create script on top level of ROBOFLOW-PYTHON and reference local roboflow directory: from roboflow import Roboflow
6. when done, uptick the pip package minor version number in setup.py
7. manually add any new dependencies to the requirements.txt and list of dependencies in setup.py (careful not to overwrite any packages that might screw up backwards dependencies for object detection, etc.)

Code Quality

We provide a Makefile to format and ensure code quality. Be sure to run them before creating a PR.

# format your code with `black` and `isort` run
make style
# check code with flake8
make check_code_quality

Tests

You need to have the following env variables,

ROBOFLOW_API_KEY="<YOUR_ROBOFLOW_API>"
PROJECT_NAME="<YOUR_PROJECT_NAME>"
PROJECT_VERSION="" # by default "1"

Then, assuming you have installed the dev packages (pip install -e ".[dev]), you can run the tests

python -m unittest