PythonSIFT

This is an implementation of SIFT (David G. Lowe's scale-invariant feature transform) done entirely in Python with the help of NumPy. This implementation is based on OpenCV's implementation and returns OpenCV KeyPoint objects and descriptors, and so can be used as a drop-in replacement for OpenCV SIFT. This repository is intended to help computer vision enthusiasts learn about the details behind SIFT.

Update 2/11/2020

PythonSIFT has been reimplemented (and greatly improved!) in Python 3. You can find the original Python 2 version in the legacy branch. However, I strongly recommend you use master (the new Python 3 implementation). It's much better.

Dependencies

Python 3

NumPy

OpenCV-Python

Last tested successfully using Python 3.7.6 and OpenCV-Python 4.2.0.

Usage

import cv2
import pysift

image = cv2.imread('your_image.png', 0)
keypoints, descriptors = pysift.computeKeypointsAndDescriptors(image)

It's as simple as that. Just like OpenCV.

The returned keypoints are a list of OpenCV KeyPoint objects, and the corresponding descriptors are a list of 128 element NumPy vectors. They can be used just like the objects returned by OpenCV-Python's SIFT detectAndCompute member function. Note that this code is not optimized for speed, but rather designed for clarity and ease of understanding, so it will take a few minutes to run on most images.

Tutorial

You can find a step-by-step, detailed explanation of the code in this repo in my two-part tutorial:

Implementing SIFT in Python: A Complete Guide (Part 1)

Implementing SIFT in Python: A Complete Guide (Part 2)

I'll walk you through each function, printing and plotting things along the way to develop a solid understanding of SIFT and its implementation details.

Template Matching Demo

I've adapted OpenCV's SIFT template matching demo to use PythonSIFT instead. The OpenCV images used in the demo are included in this repo for your convenience.

python template_matching_demo.py

Questions, Concerns, Bugs

Anyone is welcome to report and/or fix any bugs. I will resolve any opened issues as soon as possible.

Any questions about the implementation, no matter how simple, are welcome. I will patiently explain my code to you.

Original Paper

"Distinctive Image Features from Scale-Invariant Keypoints", David G. Lowe

Definitely worth a read!

Patent Notice

SIFT is patented, so the code in this repo may not be used for commerical purposes. Let me be clear that this repo is for educational purposes only.

You can find the patent here (Inventor: David G. Lowe. Assignee: University of British Columbia.).