pip install ikomia
About
1.0.0
MIT
Inference of Privacy-Preserving Portrait Matting (P3M)
This algorithm proposes inference with Privacy-Preserving Portrait Matting (P3M) model.
🚀 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 the real_esrgan algorithm
algo = wf.add_task(name = 'infer_p3m_portrait_matting', auto_connect=True)
# Run on your image
wf.run_on(url="https://raw.githubusercontent.com/Ikomia-dev/notebooks/main/examples/img/img_portrait.jpg")
# Inspect your results
display(algo.get_input(0).get_image())
display(algo.get_output(0).get_image())
☀️ 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
- model_name (str) - default 'resnet34': Name of the model, resnet34 or vitae-s
- input_size (int) - default: '1024': Size of the input image (stride of 32)
- method (str) - default: 'HYBRID': Choice of the inference method 'HYBRID' or 'RESIZE'
- cuda (bool): If True, CUDA-based inference (GPU). If False, run on CPU.
from ikomia.dataprocess.workflow import Workflow
from ikomia.utils.displayIO import display
# Init your workflow
wf = Workflow()
# Add the p3m process to the workflow
algo = wf.add_task(name="infer_p3m_portrait_matting", auto_connect=True)
# Set process parameters
algo.set_parameters({
"model_name" : "resnet34",
"input_size" : "1024",
"method": 'HYBRID',
"cuda" : "True"})
# Run workflow on the image
wf.run_on(url="https://raw.githubusercontent.com/Ikomia-dev/notebooks/main/examples/img/img_portrait.jpg")
# Inspect your results
display(det.get_input(0))
display(det.get_output(1))
🔍 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.
import ikomia
from ikomia.dataprocess.workflow import Workflow
# Init your workflow
wf = Workflow()
# Add algorithm
algo = wf.add_task(name="infer_p3m_portrait_matting", auto_connect=True)
# Run on your image
wf.run_on(url="https://raw.githubusercontent.com/Ikomia-dev/notebooks/main/examples/img/img_portrait.jpg")
# Iterate over outputs
for output in algo.get_outputs():
# Print information
print(output)
# Export it to JSON
output.to_json()
Developer
Ikomia
License
MIT License
A short and simple permissive license with conditions only requiring preservation of copyright and license notices. Licensed works, modifications, and larger works may be distributed under different terms and without source code.
| Permissions | Conditions | Limitations |
|---|---|---|
Commercial use | License and copyright notice | Liability |
Modification | Warranty | |
Distribution | ||
Private use |
This is not legal advice: this description is for informational purposes only and does not constitute the license itself. Provided by choosealicense.com.