Ikomia API is an open source tool to easily build and deploy your Computer Vision solutions. You can mix your preferred frameworks such as OpenCV, Detectron2, OpenMMLab or YOLO with the best state-of-the-art algorithms from individual repos.
No effort, just choose what you want and Ikomia runs everything in a few lines of code.
pip install ikomia
With Ikomia, when you want to use an algorithm, it's always the same code pattern which is useful when you want to test multiple algorithms effortlessly.
from ikomia.dataprocess.workflow import Workflow
from ikomia.utils.displayIO import display
# Init your workflow
wf = Workflow()
# Add YOLO and connect it to your input data
yolov7 = wf.add_task(name="infer_yolo_v7", auto_connect=True)
# Run directly on your image
wf.run_on(url="https://raw.githubusercontent.com/Ikomia-dev/notebooks/main/examples/img/img_fireman.jpg")
# YOLO output image with bounding boxes
display(yolov7.get_image_with_graphics())
And finally, you can also export your results as JSON files.
# Get all object detection
json_results = yolov7.get_results().to_json()
print(json_results)from ikomia.dataprocess.workflow import Workflow
from ikomia.utils.displayIO import display
# Init your workflow
wf = Workflow()
# Add YOLO and connect it to your input data
yolov7 = wf.add_task(name="infer_mmlab_pose_estimation", auto_connect=True)
# Run directly on your image
wf.run_on(url="https://raw.githubusercontent.com/Ikomia-dev/notebooks/main/examples/img/img_fireman.jpg")
# YOLO output image with bounding boxes
display(yolov7.get_image_with_graphics())
ik is an auto-completion system designed to help developers to find available algorithms in Ikomia HUB. See the documentation for more explanations here.
from ikomia.dataprocess.workflow import Workflow
from ikomia.utils import ik
from ikomia.utils.displayIO import display
wf = Workflow()
yolov7 = wf.add_task(ik.infer_yolo_v7_instance_segmentation(), auto_connect=True)
# wf.run_on(path="path/to/your/image.png")
wf.run_on(url="https://raw.githubusercontent.com/Ikomia-dev/notebooks/main/examples/img/img_dog.png")
display(yolov7.get_image_with_graphics())
display(yolov7.get_image_with_mask())
display(yolov7.get_image_with_mask_and_graphics())
You can find some notebooks here.
We provide some Google Colab tutorials:
| Notebooks | Google Colab |
|---|---|
| How to make a simple workflow |  |
| How to run Neural Style Transfer | |
| How to train and run YOLO v7 on your datasets | |
| How to use Detectron2 Object Detection | |
Python API documentation can be found here. You will find Ikomia HUB algorithms code source in our Ikomia HUB GitHub.
This part is coming soon...:)
Distributed under the Apache-2.0 License. See LICENSE.md for more information.
If you use Ikomia in your research, please use the following BibTeX entry.
@misc{DeBa2019Ikomia,
author = {Guillaume Demarcq and Ludovic Barusseau},
title = {Ikomia},
howpublished = {\url{https://github.com/Ikomia-dev/IkomiaAPI}},
year = {2019}
}Contributions, issues, and feature requests are welcome! Give a ⭐ if you like this project!
Ikomia - @IkomiaOfficial - [email protected]
Project Link: https://github.com/Ikomia-dev/IkomiaAPI