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| # PythonSIFT | ||
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| 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. | ||
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| ### *Update 2/11/2020* | ||
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| 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. | ||
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| ## Dependencies | ||
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| `Python 3` | ||
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| `NumPy` | ||
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| `OpenCV-Python` | ||
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| Last tested successfully using `Python 3.8.5`, `Numpy 1.19.4` and `OpenCV-Python 4.3.0`. | ||
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| ## Usage | ||
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| ```python | ||
| import cv2 | ||
| from SIFT import SIFT | ||
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| image = cv2.imread('your_image.png', 0) | ||
| keypoints, descriptors = pysift.computeKeypointsAndDescriptors(image) | ||
| ``` | ||
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| It's as simple as that. Just like OpenCV. | ||
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| 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. | ||
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| ## Tutorial | ||
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| You can find a step-by-step, detailed explanation of the code in this repo in my two-part tutorial: | ||
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| [Implementing SIFT in Python: A Complete Guide (Part 1)](https://medium.com/@russmislam/implementing-sift-in-python-a-complete-guide-part-1-306a99b50aa5) | ||
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| [Implementing SIFT in Python: A Complete Guide (Part 2)](https://medium.com/@russmislam/implementing-sift-in-python-a-complete-guide-part-2-c4350274be2b) | ||
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| 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. | ||
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| ## Template Matching Demo | ||
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| 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 | ||
| ``` | ||
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| ## Questions, Concerns, Bugs | ||
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| Anyone is welcome to report and/or fix any bugs. I will resolve any opened issues as soon as possible. | ||
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| Any questions about the implementation, no matter how simple, are welcome. I will patiently explain my code to you. | ||
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| ### *Original Paper* | ||
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| ["Distinctive Image Features from Scale-Invariant Keypoints", David G. Lowe](https://www.cs.ubc.ca/~lowe/papers/ijcv04.pdf) | ||
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| Definitely worth a read! | ||
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| ### *Legal Notice* | ||
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| 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. | ||
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| 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.). | ||
| Main root of project |