infer_face_detection_kornia

Run inference for multi-face detection using Kornia based one the YuNet model.The model implementation is based on Pytorch framework.

🚀 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_face_detection_kornia", auto_connect=True)

# Run the workflow on imageontent.com/Ikomia-hub/infer_face_detection_kornia/main/images/people.jpg")

# Display result
display(detector.get_image_with_graphics(), title="Kornia face detector")

☀️ 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_face_detection_kornia", auto_connect=True)

detector.set_parameters({
    "conf_thres": "0.6",
    "cuda": "True",
})

# Run the workflow on image
wf.run_on(url="https://raw.githubusercontent.com/Ikomia-hub/infer_face_detection_kornia/main/images/people.jpg")

# Display result
display(detector.get_image_with_graphics(), title="Kornia face detector")

• conf_thresh (float, default="0.6"): object detection confidence.
• cuda (bool, default=True): CUDA acceleration if True, run on CPU otherwise.

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_face_detection_kornia", auto_connect=True)

# Run the workflow on image
wf.run_on(url="https://raw.githubusercontent.com/Ikomia-hub/infer_face_detection_kornia/main/images/people.jpg")

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

Kornia face detection algorithm generates 2 outputs:

1. Forwaded original image (CImageIO)
2. Objects detection output (CObjectDetectionIO)