pip install ikomia
About
1.1.2
Apache-2.0
Face detection using the Kornia API
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.
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.
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If you haven't started using Ikomia Studio yet, download and install it from this page.
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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:
- Forwaded original image (CImageIO)
- Objects detection output (CObjectDetectionIO)
Developer
Ikomia
License
Apache License 2.0
A permissive license whose main conditions require preservation of copyright and license notices. Contributors provide an express grant of patent rights. Licensed works, modifications, and larger works may be distributed under different terms and without source code.
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|---|---|---|
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This is not legal advice: this description is for informational purposes only and does not constitute the license itself. Provided by choosealicense.com.