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https://github.com/ikomia-dev/ikomiaapi
Deploy Computer Vision solutions with a few lines of code.
https://github.com/ikomia-dev/ikomiaapi
computer-vision computer-vision-ai computer-vision-algorithms computer-vision-opencv computer-vision-tools computervision deep-learning detectron2 human-pose-estimation image-processing machine-learning object-detection opencv openmmlab pose-estimation python pytorch tensorflow yolo
Last synced: 3 days ago
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Deploy Computer Vision solutions with a few lines of code.
- Host: GitHub
- URL: https://github.com/ikomia-dev/ikomiaapi
- Owner: Ikomia-dev
- License: apache-2.0
- Created: 2021-02-15T15:50:57.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2024-12-13T11:12:34.000Z (about 1 month ago)
- Last Synced: 2025-01-18T18:08:37.227Z (3 days ago)
- Topics: computer-vision, computer-vision-ai, computer-vision-algorithms, computer-vision-opencv, computer-vision-tools, computervision, deep-learning, detectron2, human-pose-estimation, image-processing, machine-learning, object-detection, opencv, openmmlab, pose-estimation, python, pytorch, tensorflow, yolo
- Language: Python
- Homepage: https://ikomia-dev.github.io/python-api-documentation/
- Size: 17 MB
- Stars: 231
- Watchers: 7
- Forks: 14
- Open Issues: 12
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
Awesome Lists containing this project
README
Ikomia: Simplifying Computer Vision Deployment
Welcome to Ikomia, where we transform intricate research algorithms into user-friendly, deployable solutions for computer vision enthusiasts and professionals alike.
## **🌟 Why Choose Ikomia?**
- **Research Meets Reality**: We bridge the gap between cutting-edge research and real-world applications. With Ikomia, you get access to algorithms from renowned sources like OpenCV, Detectron2, OpenMMLab, and Hugging Face.
- **Unified Framework**: Say goodbye to integration complexities. Craft workflows and blend algorithms seamlessly, all under one roof.
- **Empowerment**: We're not just about providing tools; we're about building a community. By democratizing AI and computer vision technologies, we aim to foster collaboration and innovation.
## **🚀 Getting Started**
### **Installation**
```bash
pip install ikomia
```
### **Quick Examples**
#### Object Detection
```python
from ikomia.dataprocess.workflow import Workflow
from ikomia.utils.displayIO import display
wf = Workflow()
yolov7 = wf.add_task(name="infer_yolo_v7", auto_connect=True)
wf.run_on(url="https://raw.githubusercontent.com/Ikomia-dev/notebooks/main/examples/img/img_fireman.jpg")
display(yolov7.get_image_with_graphics())
```
#### Pose Estimation
```python
# ... [similar imports]
wf = Workflow()
pose_estimation = wf.add_task(name="infer_mmlab_pose_estimation", auto_connect=True)
wf.run_on(url="https://raw.githubusercontent.com/Ikomia-dev/notebooks/main/examples/img/img_fireman.jpg")
display(pose_estimation.get_image_with_graphics())
```
### **Discover with 'ik'**
Our auto-completion system, 'ik', is designed to assist developers in discovering available algorithms in Ikomia HUB. Dive into our detailed documentation to explore its capabilities.
```python
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())
```
## **📦 Exporting Your Workflow**
With Ikomia, sharing your crafted workflows is a breeze. Whether you want to collaborate with peers or integrate with Ikomia STUDIO, our export feature has got you covered.
```python
from ikomia.dataprocess.workflow import Workflow
from ikomia.utils import ik
wf = Workflow("Instance Segmentation with YOLOv7")
yolov7 = wf.add_task(ik.infer_yolo_v7_instance_segmentation(), auto_connect=True)
filter_task = wf.add_task(ik.ik_instance_segmentation_filter(categories="dog", confidence="0.90"), auto_connect=True)
wf.save("path/to/your_workflow.json")
```
Once you've exported your workflow, you can easily share it with others, ensuring reproducibility and collaboration.
## **🧪 Notebooks**
You can find some notebooks [here](https://github.com/Ikomia-dev/notebooks).
We provide some Google Colab tutorials:
Notebooks | Google Colab
-- | ---
[How to make a simple workflow](https://github.com/Ikomia-dev/notebooks/blob/main/examples/HOWTO_make_a_simple_workflow_with_Ikomia_API.ipynb) | [](https://colab.research.google.com/github/Ikomia-dev/notebooks/blob/main/examples/HOWTO_make_a_simple_workflow_with_Ikomia_API.ipynb)
[How to run Neural Style Transfer](https://github.com/Ikomia-dev/notebooks/blob/main/examples/HOWTO_run_Neural_Style_Transfer_with_Ikomia_API.ipynb) | [](https://colab.research.google.com/github/Ikomia-dev/notebooks/blob/main/examples/HOWTO_run_Neural_Style_Transfer_with_Ikomia_API.ipynb)
[How to train and run YOLO v7 on your datasets](https://github.com/Ikomia-dev/notebooks/blob/main/examples/HOWTO_train_YOLO_v7_with_Ikomia_API.ipynb) | [](https://colab.research.google.com/github/Ikomia-dev/notebooks/blob/main/examples/HOWTO_train_YOLO_v7_with_Ikomia_API.ipynb)
[How to use Detectron2 Object Detection](https://github.com/Ikomia-dev/notebooks/blob/main/examples/HOWTO_use_Detectron2_Object_Detection_with_Ikomia_API.ipynb) | [](https://colab.research.google.com/github/Ikomia-dev/notebooks/blob/main/examples/HOWTO_use_Detectron2_Object_Detection_with_Ikomia_API.ipynb)
## **📚 Comprehensive Documentation**
For those who love details, our [comprehensive documentation](https://ikomia-dev.github.io/python-api-documentation/) is a treasure trove of information. From basic setups to advanced configurations, we've got you covered.
## **🤝 Contributing**
We believe in the power of community. If you have suggestions, improvements, or want to contribute in any way, we're all ears! Stay tuned for our detailed contribution guidelines.
## **📜 License**
We believe in open-source. Ikomia is licensed under the Apache-2.0 License, promoting collaboration with transparency.
## **💖 Support & Feedback**
Your feedback drives our progress. If you find Ikomia useful, give us a :star:! For queries, issues, or just to say hi, drop us an email at [email protected] or join our [discord channel](https://discord.com/invite/82Tnw9UGGc).
## They like us, we love them :heart_eyes:
[](https://github.com/Ikomia-dev/IkomiaApi/stargazers)
## Star History
[](https://star-history.com/#Ikomia-dev/IkomiaAPI&Date)
## Citing Ikomia
If you use Ikomia in your research, please use the following BibTeX entry.
```BibTeX
@misc{DeBa2019Ikomia,
author = {Guillaume Demarcq and Ludovic Barusseau},
title = {Ikomia},
howpublished = {\url{https://github.com/Ikomia-dev/IkomiaAPI}},
year = {2019}
}
```