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Ikomia API is an open-source API to train, test, and build reusable Computer Vision workflows. You can select and chain the best state-of-the-art algorithms from your preferred frameworks such as OpenCV, Detectron2, OpenMMLab or YOLO.

Reusable Computer Vision workflows in a few lines of code.

Ikomia API

Ikomia CLI is a command line interface to manage and deploy your Computer Vision project. It allows you to create, build and run workflows right from the command line.

Manage and deploy your Computer Vision project from the command line.

Ikomia CLI

Tools · Ikomia Docs

Ikomia STUDIO is a simple drag-and-drop User Interface to prototype Computer Vision solutions. With its double window, you can watch the effects of your treatments in seconds.

Using the command line

Or download Linux Installer

Desktop prototyping app for Computer Vision.

Ikomia STUDIO

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Install Ikomia tools and start building Computer Vision solutions in a more scalable manner.

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Thank you for being a part of Ikomia SCALE! We are excited to accompany you on your journey and help you achieve your goals.
Best regards,

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<div align="center">
  <img src="https://raw.githubusercontent.com/Ikomia-hub/infer_emotion_fer_plus/main/icon/icon.png" alt="Algorithm icon">
  <h1 align="center">infer_emotion_fer_plus</h1>
</div>
<br />
<p align="center">
    <a href="https://github.com/Ikomia-hub/infer_emotion_fer_plus">
        <img alt="Stars" src="https://img.shields.io/github/stars/Ikomia-hub/infer_emotion_fer_plus">
    </a>
    <a href="https://app.ikomia.ai/hub/">
        <img alt="Website" src="https://img.shields.io/website/http/app.ikomia.ai/en.svg?down_color=red&down_message=offline&up_message=online">
    </a>
    <a href="https://github.com/Ikomia-hub/infer_emotion_fer_plus/blob/main/LICENSE.md">
        <img alt="GitHub" src="https://img.shields.io/github/license/Ikomia-hub/infer_emotion_fer_plus.svg?color=blue">
    </a>    
    <br>
    <a href="https://discord.com/invite/82Tnw9UGGc">
        <img alt="Discord community" src="https://img.shields.io/badge/Discord-white?style=social&logo=discord">
    </a> 
</p>

Facial expression recognition algorithm.

![example](https://raw.githubusercontent.com/Ikomia-hub/infer_emotion_fer_plus/main/icon/result.jpg)

## :rocket: Use with Ikomia API

#### 1. Install Ikomia API

We strongly recommend using a virtual environment. If you're not sure where to start, we offer a tutorial [here](https://www.ikomia.ai/blog/a-step-by-step-guide-to-creating-virtual-environments-in-python).

```sh
pip install ikomia
```

#### 2. Create your workflow

```python
from ikomia.dataprocess.workflow import Workflow
from ikomia.utils.displayIO import display

# Init your workflow
wf = Workflow()

# Add face detection algorithm
face_detector = wf.add_task(name="infer_face_detection_kornia", auto_connect=True)

# Add emotion recognition algorithm
algo = wf.add_task(name="infer_emotion_fer_plus", auto_connect=True)

# Run on your image
wf.run_on(url="https://raw.githubusercontent.com/Ikomia-hub/infer_emotion_fer_plus/main/icon/example_face.jpg")

# Get graphics
graphics = algo.get_output(1)

# Display results
display(algo.get_output(0).get_image_with_graphics(graphics))
```

## :sunny: Use with Ikomia Studio

Ikomia Studio offers a friendly UI with the same features as the API.

- If you haven't started using Ikomia Studio yet, download and install it from [this page](https://www.ikomia.ai/studio).

- For additional guidance on getting started with Ikomia Studio, check out [this blog post](https://www.ikomia.ai/blog/how-to-get-started-with-ikomia-studio).


## :mag: Explore algorithm outputs

Every algorithm produces specific outputs, yet they can be explored them the same way using the Ikomia API. For a more in-depth understanding of managing algorithm outputs, please refer to the [documentation](https://ikomia-dev.github.io/python-api-documentation/advanced_guide/IO_management.html).

```python
from ikomia.dataprocess.workflow import Workflow
from ikomia.utils.displayIO import display

# Init your workflow
wf = Workflow()

# Add face detection algorithm
face_detector = wf.add_task(name="infer_face_detection_kornia", auto_connect=True)

# Add emotion recognition algorithm
algo = wf.add_task(name="infer_emotion_fer_plus", auto_connect=True)

# Run on your image
wf.run_on(url="https://raw.githubusercontent.com/Ikomia-hub/infer_emotion_fer_plus/main/icon/example_face.jpg")

# Iterate over outputs
for output in algo.get_outputs():
    # Print information
    print(output)
    # Export it to JSON
    output.to_json()
```

infer_emotion_fer_plus

Training Deep Networks for Facial Expression Recognitionwith Crowd-Sourced Label Distribution

Facial emotion recognition using DNN trained from crowd-sourced label distribution.

Permissions	Conditions	Limitations
Commercial use	License and copyright notice	Liability
Modification		Warranty
Distribution
Private use

infer_emotion_fer_plus

About

🚀 Use with Ikomia API

1. Install Ikomia API

2. Create your workflow

☀️ Use with Ikomia Studio

🔍 Explore algorithm outputs

Developer

Ikomia

License

MIT License