pip install ikomia
skimage_threshold
About
Compilation of well-known thresholding methods from scikit-image library.
Compilation of well-known thresholding methods from scikit-image library: Otsu, Multi-Otsu, Yen, IsoData, Li, Mean, Minimum, Local, Niblack, Sauvola Triangle, Hysteresis.
🚀 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
[Change the sample image URL to fit algorithm purpose]
from ikomia.dataprocess.workflow import Workflow
from ikomia.utils.displayIO import display
# Init your workflow
wf = Workflow()
# Add algorithm
algo = wf.add_task(name="skimage_threshold", auto_connect=True)
# Run on your image
wf.run_on(url="https://cdn.pixabay.com/photo/2023/09/10/00/49/lovebird-8244066_960_720.jpg")
# Display result
display(algo.get_output(0).get_image())
☀️ 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.
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For additional guidance on getting started with Ikomia Studio, check out this blog post.
📝 Set algorithm parameters
- local_method (str, default="Otsu"): Method used for thresholding. Must be one of:
- "Otsu"
- "Yen"
- "Iso data"
- "Li"
- "Mean"
- "Minimum"
- "Local"
- "Niblack"
- "Sauvola"
- "Triangle"
- "Multi otsu"
- "Hysteresis"
You can find more information about what these methods do and what are the complementary parameters here skimage doc
Note: parameter key and value should be in string format when added to the dictionary.
from ikomia.dataprocess.workflow import Workflow
# Init your workflow
wf = Workflow()
# Add algorithm
algo = wf.add_task(name="skimage_threshold", auto_connect=True)
algo.set_parameters({
"local_model": "Iso data",
"isodata_nbins": "128",
})
# Run on your image
wf.run_on(url="https://cdn.pixabay.com/photo/2023/09/10/00/49/lovebird-8244066_960_720.jpg")
🔍 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
# Init your workflow
wf = Workflow()
# Add algorithm
algo = wf.add_task(name="skimage_threshold", auto_connect=True)
# Run on your image
wf.run_on(url="https://cdn.pixabay.com/photo/2023/09/10/00/49/lovebird-8244066_960_720.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.
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Commercial use | License and copyright notice | Liability |
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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.