We are thrilled to announce that the Graph-Cut RANSAC algorithm has been officially included in OpenCV. This inclusion marks a significant enhancement in the capabilities of the OpenCV library, empowering developers and researchers to achieve more robust and efficient model fitting in their computer vision applications.

What is Graph-Cut RANSAC?

Graph-Cut RANSAC is an innovative algorithm introduced by Daniel Barath and Jiri Matas in their paper presented at the CVPR 2018. It extends the traditional RANSAC framework by incorporating a graph-cut optimization technique that improves the robustness of model fitting by taking advantage of spatial coherence. To delve deeper into this algorithm, you can refer to the journal paper for additional insights.

Installation Procedure

To utilize Graph-Cut RANSAC, follow these steps for installation:

• Install from PyPI using pip:

You can quickly install the Python package with the command:

pip install pygcransac

Thanks to @akaszynski for contributions!

• Installation for C++:

To build and install the C++ version:

git clone https://github.com/daninig/graph-cut-ransac
cd build
cmake ..
make

Usage Examples

The following code snippets demonstrate how to use Graph-Cut RANSAC:

import pygcransac

h1, w1 = img1.shape
h2, w2 = img2.shape

H, mask = pygcransac.findHomography(src_pts, dst_pts, h1, w1, h2, w2, 3.0)
F, mask = pygcransac.findFundamentalMatrix(src_pts, dst_pts, h1, w1, h2, w2, 3.0)

Think of Graph-Cut RANSAC as a meticulous gardener armed with powerful pruning shears. In this analogy, the gardener (RANSAC) systematically surveys the plant beds (data points) to identify the healthiest plants (inliers). Once identified, the gardener employs the shears (graph-cut optimization) to remove any unproductive branches (outliers), ensuring the garden thrives maximally.

Jupyter Notebook Examples

Explore various examples and tutorials through the following links:

• Homography fitting example
• Fundamental matrix fitting example
• Essential matrix fitting example
• 6D pose fitting example

Troubleshooting Tips

If you encounter any issues during installation or usage:

• Ensure that you have all the required dependencies installed:
• Eigen 3.0 or higher
• CMake 2.8.12 or higher
• OpenCV 3.0 or higher
• A modern compiler with C++17 support
• Double-check your installation commands for any typos.
• For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.