infer_uniface_detection

About

1.0.0

MIT

Lightweight production-ready face analysis library built on ONNX Runtime.

Task: Object detection

uniface

detection

face detection

Run inference for multi-face detection using UniFace library. UniFace is a lightweight production-ready face analysis library built on ONNX Runtime, supporting multiple face detection models including RetinaFace, YOLOv5Face, SCRFD, and YOLOv8Face.

This algorithm also includes optional face anonymization capabilities with 5 different blur methods (gaussian, pixelate, blackout, elliptical, median) for privacy protection.

Example image

🚀 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.

pip install ikomia

2. Create your workflow

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

# Init your workflow
wf = Workflow()

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

# Run the workflow on image
wf.run_on(url="https://raw.githubusercontent.com/yakhyo/uniface/refs/heads/main/assets/scientists.png")

# Display result
display(detector.get_image_with_graphics())

☀️ 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.
For additional guidance on getting started with Ikomia Studio, check out this blog post.

📝 Set algorithm parameters

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

# Init your workflow
wf = Workflow()

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

detector.set_parameters({
    "model_name": "retinaface",
    "conf_thres": "0.6",
    "nms_thres": "0.4",
    "output_anonymized": "Ture",
    "blur_method": "pixelate",
    "blur_strength": "3.0",
    "pixel_blocks": "15",
    "margin": "20"
})

# Run the workflow on image
wf.run_on(url="https://raw.githubusercontent.com/yakhyo/uniface/refs/heads/main/assets/scientists.png")

# Display result
display(detector.get_image_with_graphics())

Detection Parameters:

model_name (str, default="retinaface"): Face detection model to use. Options: "retinaface", "yolov5face", "scrfd", "yolov8face".
conf_thres (float, default="0.6"): Object detection confidence threshold.
nms_thres (float, default="0.4"): Non-maximum suppression threshold.

Anonymization Parameters:

output_anonymized (bool, default="False"): Enable output of anonymized image with blurred faces.
blur_method (str, default="pixelate"): Blur method to use. Options: "gaussian", "pixelate", "blackout", "elliptical", "median".
blur_strength (float, default="3.0"): Blur intensity for gaussian, elliptical, and median methods (range: 1.0-10.0).
pixel_blocks (int, default="15"): Size of pixelation blocks for pixelate method (range: 1-50).
margin (int, default="20"): Extra margin around face regions in pixels (range: 0-100).

Note: parameter key and value should be in string format when added to the dictionary.

🔍 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.

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

# Init your workflow
wf = Workflow()

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

# Run the workflow on image
wf.run_on(url="https://raw.githubusercontent.com/yakhyo/uniface/refs/heads/main/assets/scientists.png")

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

The algorithm outputs:

Output 0: Original image with detection overlays (graphics)
Output 1: Detection graphics (bounding boxes)
Output 2: Anonymized image (only when output_anonymized=True)

🔧 Face Anonymization Example

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

# Init your workflow
wf = Workflow()

# Add face detection with anonymization
detector = wf.add_task(name="infer_uniface_detection", auto_connect=True)

# Enable anonymization with pixelate method
detector.set_parameters({
    "model_name": "retinaface",
    "conf_thres": "0.6",
    "output_anonymized": "True",
    "blur_method": "pixelate",
    "pixel_blocks": "15"
})

# Run the workflow on image
wf.run_on(url="https://raw.githubusercontent.com/yakhyo/uniface/refs/heads/main/assets/scientists.png")

# Display anonymized image
anonymized_img = detector.get_output(2)
display(anonymized_img.get_image())

Developer

Ikomia

License

MIT License

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