Feature Detection And Matching Save
Feature Detection and Matching with SIFT, SURF, KAZE, BRIEF, ORB, BRISK, AKAZE and FREAK through the Brute Force and FLANN algorithms using Python and OpenCV
Feature Detection and Matching
Feature Detection and Matching between two images using Local Feature Descriptors and Local Binary Descriptors through the Brute Force and FLANN algorithms.
From this application it is possible to solve several problems in the area of Computer Vision, such as: image recovery, motion tracking, motion structure detection, object detection, recognition and tracking, 3D object reconstruction, and others.
Overview
This project performs Feature Detection and Matching with SIFT, SURF, KAZE, BRIEF, ORB, BRISK, AKAZE and FREAK through the Brute Force and FLANN algorithms using Python (version 3.6.10) and OpenCV (version 3.3.1).
Dependencies
To install the dependencies run:
pip install -r requirements.txt
Usage
python main.py --detector <detector> --descriptor <descriptor> --matcher <matcher>
| Arguments | Info |
|---|---|
-h, --help |
Show help message and exit |
--detector |
Specify SIFT or SURF or KAZE or ORB or BRISK or AKAZE |
--descriptor |
Specify SIFT or SURF or KAZE or BRIEF or ORB or BRISK or AKAZE or FREAK |
--matcher |
Specify BF or FLANN |
Examples
Help
python main.py --help
Brute Force with ORB
python main.py --detector ORB --descriptor ORB --matcher BF
Recommended Readings
-
KRIG, Scott. Computer vision metrics: Survey, taxonomy, and analysis. Apress, 2014.
License
Code released under the MIT license.
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