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https://github.com/rmislam/PythonSIFT
A clean and concise Python implementation of SIFT (Scale-Invariant Feature Transform)
https://github.com/rmislam/PythonSIFT
computer-vision feature-matching image-processing opencv python sift template-matching
Last synced: 3 days ago
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A clean and concise Python implementation of SIFT (Scale-Invariant Feature Transform)
- Host: GitHub
- URL: https://github.com/rmislam/PythonSIFT
- Owner: rmislam
- License: mit
- Created: 2015-07-02T00:27:34.000Z (over 9 years ago)
- Default Branch: master
- Last Pushed: 2021-01-01T02:31:29.000Z (almost 4 years ago)
- Last Synced: 2024-10-30T05:57:49.298Z (14 days ago)
- Topics: computer-vision, feature-matching, image-processing, opencv, python, sift, template-matching
- Language: Python
- Homepage:
- Size: 364 KB
- Stars: 927
- Watchers: 9
- Forks: 255
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 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 3.8.5`, `Numpy 1.19.4` and `OpenCV-Python 4.3.0`.
## Usage
```python
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)](https://medium.com/@russmislam/implementing-sift-in-python-a-complete-guide-part-1-306a99b50aa5)
[Implementing SIFT in Python: A Complete Guide (Part 2)](https://medium.com/@russmislam/implementing-sift-in-python-a-complete-guide-part-2-c4350274be2b)
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
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](https://www.cs.ubc.ca/~lowe/papers/ijcv04.pdf)
Definitely worth a read!
### *Legal Notice*
SIFT *was* patented, but it has expired.
This repo is primarily meant for educational purposes, but feel free to use my code any way you want, commercial or otherwise. All I ask is that you cite or share this repo.
You can find the original (now expired) patent [here](https://patents.google.com/patent/US6711293B1/en) (Inventor: David G. Lowe. Assignee: University of British Columbia.).