General RANSAC solver using only NumPy as dependency. The implemented RANSAC solver can be used basically with any model fitting method to make it robust against outliers.
https://omyllymaki.medium.com/algorithms-from-scratch-ransac-f5a03bed2fde
Clone repo and update submodules (for samples):
git clone https://github.com/omyllymaki/ransac-solver.git --recurse-submodules
- Python >= 3.6
- Requirements
Install requirements by running (also sample dependencies are installed):
pip3 install -r samples/requirements.txt
To use RANSAC solver, user needs to implement abstract Data and Model classes:
class Data(BaseData):
...
class Model(BaseModel):
...
After that, solver can be initialized with the model and used with the data:
solver = RansacSolver(model=Model(...), ...)
inlier_indices = solver.fit(Data(...))
See samples folder for more detailed usage. Run all the samples by typing
./run_samples.sh
Curve fit with and without RANSAC:
ICP fit with and without RANSAC:
Polynomial fit with RANSAC:
