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Algorithms are the building blocks of your Computer Vision workflows. You can create and share your own algorithms or use the ones provided by the community on Ikomia HUB.

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Use Python for full control and complex chaining.

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Choose how you want to create your workflow, depending on your experience level.

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Unlock all deployment options—including Instance CPU and GPU, and get monthly recurring free credits.
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By migrating to the enhanced free plan, you'll gain access to all deployment options—including CPU and GPU instances, and receive monthly recurring free credits.
However, you will lose the previous plan's 2000 free serverless deployment hits. This action cannot be undone.

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Projects lets you organize multiple Computer Vision workflows.

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Publish workflow as App

Python API for building and running workflows locally.

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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

Download Windows Installer

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

Ikomia tools

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Don't forget to check out our quick start guide to quickly get acquainted with our application and start enjoying its benefits.

If you have any questions, concerns, or need assistance, please do not hesitate to contact us at support@ikomia.ai. Our team is here to assist you every step of the way.

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,

The Ikomia SCALE Team

Published App

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Workflow settings

infer_raft_optical_flow

Estimate the optical flow from a video using a RAFT model.

infer_rf_detr

blur_faces

This workflow blurs all faces in an image or video.

### Capabilities
- Face anonymization by blurring detected faces.
- Robust face detection in various conditions, including large occlusions and varying lighting conditions.
- Suitable for both images and videos.

### Known limitations
- May miss faces in crowded scenes when too many faces are present.

### Recommendations
- Best results are achieved with high-resolution images and videos.
- On critical applications, implement guardrails to verify anonymization quality.

### Model information
- Based on a custom fine-tuned version of `RF-DETR` for face detection.
- Implemented a custom task for optimal face blurring.

## Usage
### Python

1. First, **deploy the workflow**.

2. Install Python library:

    ```bash
    pip install ikomia-client
    ```

3. Integrate the workflow into your Python code:

    **Process an image**

    ```python
    from ikclient.core.client import Client
    from ikclient.core.io import ImageIO, StorageObjectIO

    with Client(
        url="https://your.scale.endpoint.url",
        token="your-api-token",  # default to os.environ.get("IKOMIA_TOKEN")
    ) as face_blur:
        results = face_blur.run("path/to/your/image.jpg")
        image_output = results.get_output(assert_type=ImageIO)
        image_output.to_pil().save("blurred_faces_image.jpg")
    ```

    **Process a video**

    ```python
    from ikclient.core.client import Client
    from ikclient.core.io import StorageObjectIO

    with Client(
        url="https://your.scale.endpoint.url",
        token="your-api-token",  # default to os.environ.get("IKOMIA_TOKEN")
    ) as face_blur:
        results = face_blur.run("path/to/your/video.mp4")
        video_output = results.get_output(assert_type=StorageObjectIO)

        with face_blur.storage.read(video_output["metadata"], streaming=True) as stream, \
             open("blurred_faces_video.mp4", "wb") as out_file:
            for chunk in stream.iter_bytes():
                out_file.write(chunk)
    ```

    For more details, refer to the [documentation](https://docs.ikomia.ai/scale/integration/python/getting-started).

### JavaScript

1. First, **deploy the workflow**.

2. Install JavaScript library:

    ```bash
    npm install @ikomia/ikclient
    ```

3. Integrate the workflow into your JavaScript code:

    **Process an image**

    ```javascript
    import { Client, ImageIO } from "@ikomia/ikclient";
    import fs from "fs/promises";

    const client = new Client({
      url: "https://your.scale.endpoint.url",
      token: "your-api-token", // default to process.env.IKOMIA_TOKEN
    });

    const results = await client.run({
      inputs: [
        "https://your.image.url/image.jpg",
      ],
    });
    const imageOutput = results.getOutput(0, ImageIO);
    await fs.writeFile(
      'blurred_faces_image.png',
      Buffer.from(imageOutput.toArrayBuffer())
    );
    ```

    **Process a video**

    ```javascript
    import { Client, StorageObjectIO } from "@ikomia/ikclient";
    import fs from "fs/promises";

    const client = new Client({
      url: "https://your.scale.endpoint.url",
      token: "your-api-token", // default to process.env.IKOMIA_TOKEN
    });

    const results = await client.run({
      inputs: [
        "https://your.video.url/video.mp4",
      ],
    });

    const videoOutput = results.getOutput(0, StorageObjectIO);
    const response = await client.storage.read(videoOutput.data.metadata);
    await fs.writeFile(
      'blurred_faces_video.mp4',
      Buffer.from(response.arrayBuffer())
    );
    ```

    For more details, refer to the [documentation](https://docs.ikomia.ai/scale/integration/javascript/getting-started).

### REST API

Refer to the [documentation](https://docs.ikomia.ai/scale/integration/rest) for integration using the REST API.

Face Anonymization

Automatic face-blurring for privacy protection

Ikomia HUB

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