79 lines
2.9 KiB
C++
79 lines
2.9 KiB
C++
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// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html.
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#ifndef OPENCV_IMG_HASH_H
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#define OPENCV_IMG_HASH_H
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#include "opencv2/img_hash/average_hash.hpp"
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#include "opencv2/img_hash/block_mean_hash.hpp"
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#include "opencv2/img_hash/color_moment_hash.hpp"
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#include "opencv2/img_hash/marr_hildreth_hash.hpp"
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#include "opencv2/img_hash/phash.hpp"
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#include "opencv2/img_hash/radial_variance_hash.hpp"
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/**
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@defgroup img_hash The module brings implementations of different image hashing algorithms.
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Provide algorithms to extract the hash of images and fast way to figure out most similar images in
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huge data set.
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Namespace for all functions is cv::img_hash.
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### Supported Algorithms
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- Average hash (also called Different hash)
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- PHash (also called Perceptual hash)
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- Marr Hildreth Hash
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- Radial Variance Hash
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- Block Mean Hash (modes 0 and 1)
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- Color Moment Hash (this is the one and only hash algorithm resist to rotation attack(-90~90 degree))
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You can study more about image hashing from following paper and websites:
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- "Implementation and benchmarking of perceptual image hash functions" @cite zauner2010implementation
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- "Looks Like It" @cite lookslikeit
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### Code Example
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@include samples/hash_samples.cpp
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### Performance under different attacks
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### Speed comparison with PHash library (100 images from ukbench)
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As you can see, hash computation speed of img_hash module outperform [PHash library](http://www.phash.org/) a lot.
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PS : I do not list out the comparison of Average hash, PHash and Color Moment hash, because I cannot
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find them in PHash.
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### Motivation
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Collects useful image hash algorithms into opencv, so we do not need to rewrite them by ourselves
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again and again or rely on another 3rd party library(ex : PHash library). BOVW or correlation
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matching are good and robust, but they are very slow compare with image hash, if you need to deal
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with large scale CBIR(content based image retrieval) problem, image hash is a more reasonable
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solution.
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### More info
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You can learn more about img_hash modules from following links, these links show you how to find
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similar image from ukbench dataset, provide thorough benchmark of different attacks(contrast, blur,
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noise(gaussion,pepper and salt), jpeg compression, watermark, resize).
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* [Introduction to image hash module of opencv](http://qtandopencv.blogspot.my/2016/06/introduction-to-image-hash-module-of.html)
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* [Speed up image hashing of opencv(img_hash) and introduce color moment hash](http://qtandopencv.blogspot.my/2016/06/speed-up-image-hashing-of-opencvimghash.html)
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### Contributors
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Tham Ngap Wei, thamngapwei@gmail.com
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*/
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#endif // OPENCV_IMG_HASH_H
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