Expand description
§The module brings implementations of different image hashing algorithms.
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” zauner2010implementation
- “Looks Like It” lookslikeit
§Code Example
@include samples/hash_samples.cpp
§Performance under different attacks
§Speed comparison with PHash library (100 images from ukbench)
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, thamngapwei@gmail.com
Modules§
Structs§
- Computes average hash value of the input image
- Image hash based on block mean.
- Image hash based on color moments.
- The base class for image hash algorithms
- Marr-Hildreth Operator Based Hash, slowest but more discriminative.
- pHash
- Image hash based on Radon transform.
Enums§
Constants§
- use fewer block and generate 16*16/8 uchar hash value
- use block blocks(step sizes/2), generate 31*31/8 + 1 uchar hash value
Traits§
- Mutable methods for crate::img_hash::AverageHash
- Constant methods for crate::img_hash::AverageHash
- Mutable methods for crate::img_hash::BlockMeanHash
- Constant methods for crate::img_hash::BlockMeanHash
- Mutable methods for crate::img_hash::ColorMomentHash
- Constant methods for crate::img_hash::ColorMomentHash
- Mutable methods for crate::img_hash::ImgHashBase
- Constant methods for crate::img_hash::ImgHashBase
- Mutable methods for crate::img_hash::MarrHildrethHash
- Constant methods for crate::img_hash::MarrHildrethHash
- Mutable methods for crate::img_hash::PHash
- Constant methods for crate::img_hash::PHash
- Mutable methods for crate::img_hash::RadialVarianceHash
- Constant methods for crate::img_hash::RadialVarianceHash
Functions§
- Calculates img_hash::AverageHash in one call
- Computes block mean hash of the input image
- Computes block mean hash of the input image
- Computes color moment hash of the input, the algorithm is come from the paper “Perceptual Hashing for Color Images Using Invariant Moments”
- Computes average hash value of the input image
- Computes average hash value of the input image
- Computes pHash value of the input image
- Computes radial variance hash of the input image
- Computes radial variance hash of the input image