This repository contains a vectorized implementation of Lowe's Scale Invariant Feature Transform. It is meant as an accessible and well-documented implementation that can be used as a study aid. A central goal was to keep the code semantically consistent with the mental model a student may have of SIFT. For example, the main feature detection loop uses high-level naming and hides implementation details.
gauss_octaves = build_gaussian_octaves(img)
for octave_idx, gauss_octave in enumerate(gauss_octaves):
dog_octave = build_dog_octave(gauss_octave)
extrema = find_dog_extrema(dog_octave)
keypoint_coords = find_keypoints(extrema, dog_octave)
keypoints = assign_reference_orientations(keypoint_coords, gauss_octave, octave_idx)
keypoints = assign_descriptor(keypoints, gauss_octave, octave_idx)
Install the requirements using pip install -r requirements.txt.
Execute python sift.py to find and match features in images/{box|box_in_scene}.png.
The notebooks provide a look into the intermediate steps of SIFT feature creation. See what each layer of a difference of gaussian (DoG) octave looks like; See how the vector fields of image gradients build a reference orientation; Or visualize the descriptor grid and the final histogram that is the SIFT feature.
Note: The notebooks have been pushed with their state intact. One can view the visualization in Github by opening the notebook.
| Name | Purpose |
|---|---|
| sift.py | Main function. Detects and plots matching SIFT features in two images. |
| octaves.py | Builds increasingly blurred Gaussian and DoG octaves. |
| keypoints.py | Infers candidate keypoints from DoG octave extrema. |
| reference_orientation.py | Assigns a reference orientation to a keypoint based on local gradients. |
| descriptor.py | Assigns a descriptor to a keypoint based on gradients in a large local neighborhood. |
| match.py | Performs brute-force matching of SIFT features between two images. |
| const.py | The parameters of the SIFT algorithm. These are set according to the literature. |