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void | cv::img_hash::averageHash (cv::InputArray inputArr, cv::OutputArray outputArr) |
| Calculates img_hash::AverageHash in one call. More...
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void | cv::img_hash::blockMeanHash (cv::InputArray inputArr, cv::OutputArray outputArr, int mode=BLOCK_MEAN_HASH_MODE_0) |
| Computes block mean hash of the input image. More...
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void | cv::img_hash::colorMomentHash (cv::InputArray inputArr, cv::OutputArray outputArr) |
| Computes color moment hash of the input, the algorithm is come from the paper "Perceptual Hashing for Color Images
Using Invariant Moments". More...
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void | cv::img_hash::marrHildrethHash (cv::InputArray inputArr, cv::OutputArray outputArr, float alpha=2.0f, float scale=1.0f) |
| Computes average hash value of the input image. More...
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void | cv::img_hash::pHash (cv::InputArray inputArr, cv::OutputArray outputArr) |
| Computes pHash value of the input image. More...
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void | cv::img_hash::radialVarianceHash (cv::InputArray inputArr, cv::OutputArray outputArr, double sigma=1, int numOfAngleLine=180) |
| Computes radial variance hash of the input image. More...
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Provide algorithms to extract the hash of images and fast way to figure out most similar images in huge data set.
Namespace for all functions is cv::img_hash.
Supported Algorithms
- Average hash (also called Different hash)
- PHash (also called Perceptual hash)
- Marr Hildreth Hash
- Radial Variance Hash
- Block Mean Hash (modes 0 and 1)
- Color Moment Hash (this is the one and only hash algorithm resist to rotation attack(-90~90 degree))
You can study more about image hashing from following paper and websites:
- "Implementation and benchmarking of perceptual image hash functions" [225]
- "Looks Like It" [103]
Code Example
#include <iostream>
template <typename T>
inline void test_one(
const std::string &title,
const Mat &a,
const Mat &b)
{
cout << "=== " << title << " ===" << endl;
func = T::create();
cout <<
"compute1: " << tick.
getTimeMilli() <<
" ms" << endl;
cout <<
"compute2: " << tick.
getTimeMilli() <<
" ms" << endl;
cout <<
"compare: " << func->
compare(hashA, hashB) << endl << endl;;
}
int main(int argc, char **argv)
{
if (argc != 3)
{
cerr << "must input the path of input image and target image. ex : hash_samples lena.jpg lena2.jpg" << endl;
return -1;
}
test_one<AverageHash>("AverageHash", input, target);
test_one<PHash>("PHash", input, target);
test_one<MarrHildrethHash>("MarrHildrethHash", input, target);
test_one<RadialVarianceHash>("RadialVarianceHash", input, target);
test_one<BlockMeanHash>("BlockMeanHash", input, target);
return 0;
}
Performance under different attacks
Performance chart
Speed comparison with PHash library (100 images from ukbench)
Hash Computation chart
Hash comparison chart
As you can see, hash computation speed of img_hash module outperform PHash library a lot.
PS : I do not list out the comparison of Average hash, PHash and Color Moment hash, because I cannot find them in PHash.
Motivation
Collects useful image hash algorithms into opencv, so we do not need to rewrite them by ourselves again and again or rely on another 3rd party library(ex : PHash library). BOVW or correlation matching are good and robust, but they are very slow compare with image hash, if you need to deal with large scale CBIR(content based image retrieval) problem, image hash is a more reasonable solution.
More info
You can learn more about img_hash modules from following links, these links show you how to find similar image from ukbench dataset, provide thorough benchmark of different attacks(contrast, blur, noise(gaussion,pepper and salt), jpeg compression, watermark, resize).
Introduction to image hash module of opencv Speed up image hashing of opencv(img_hash) and introduce color moment hash
Contributors
Tham Ngap Wei, thamn.nosp@m.gapw.nosp@m.ei@gm.nosp@m.ail..nosp@m.com
§ BlockMeanHashMode
Enumerator |
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BLOCK_MEAN_HASH_MODE_0 Python: cv.img_hash.BLOCK_MEAN_HASH_MODE_0 | use fewer block and generate 16*16/8 uchar hash value
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BLOCK_MEAN_HASH_MODE_1 Python: cv.img_hash.BLOCK_MEAN_HASH_MODE_1 | use block blocks(step sizes/2), generate 31*31/8 + 1 uchar hash value
|
§ averageHash()
Python: |
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| outputArr | = | cv.img_hash.averageHash( | inputArr[, outputArr] | ) |
Calculates img_hash::AverageHash in one call.
- Parameters
-
inputArr | input image want to compute hash value, type should be CV_8UC4, CV_8UC3 or CV_8UC1. |
outputArr | Hash value of input, it will contain 16 hex decimal number, return type is CV_8U |
§ blockMeanHash()
Python: |
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| outputArr | = | cv.img_hash.blockMeanHash( | inputArr[, outputArr[, mode]] | ) |
Computes block mean hash of the input image.
- Parameters
-
inputArr | input image want to compute hash value, type should be CV_8UC4, CV_8UC3 or CV_8UC1. |
outputArr | Hash value of input, it will contain 16 hex decimal number, return type is CV_8U |
mode | |
§ colorMomentHash()
Python: |
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| outputArr | = | cv.img_hash.colorMomentHash( | inputArr[, outputArr] | ) |
Computes color moment hash of the input, the algorithm is come from the paper "Perceptual Hashing for Color Images
Using Invariant Moments".
- Parameters
-
inputArr | input image want to compute hash value, type should be CV_8UC4, CV_8UC3 or CV_8UC1. |
outputArr | 42 hash values with type CV_64F(double) |
§ marrHildrethHash()
Python: |
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| outputArr | = | cv.img_hash.marrHildrethHash( | inputArr[, outputArr[, alpha[, scale]]] | ) |
Computes average hash value of the input image.
- Parameters
-
inputArr | input image want to compute hash value, type should be CV_8UC4, CV_8UC3, CV_8UC1. |
outputArr | Hash value of input, it will contain 16 hex decimal number, return type is CV_8U |
alpha | int scale factor for marr wavelet (default=2). |
scale | int level of scale factor (default = 1) |
§ pHash()
Python: |
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| outputArr | = | cv.img_hash.pHash( | inputArr[, outputArr] | ) |
Computes pHash value of the input image.
- Parameters
-
inputArr | input image want to compute hash value, type should be CV_8UC4, CV_8UC3, CV_8UC1. |
outputArr | Hash value of input, it will contain 8 uchar value |
§ radialVarianceHash()
Python: |
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| outputArr | = | cv.img_hash.radialVarianceHash( | inputArr[, outputArr[, sigma[, numOfAngleLine]]] | ) |
Computes radial variance hash of the input image.
- Parameters
-
inputArr | input image want to compute hash value, type should be CV_8UC4, CV_8UC3, CV_8UC1. |
outputArr | Hash value of input |
sigma | Gaussian kernel standard deviation |
numOfAngleLine | The number of angles to consider |