Enum img_hash::HashAlg [−][src]
pub enum HashAlg { Mean, Gradient, VertGradient, DoubleGradient, Blockhash, // some variants omitted }
Hash algorithms implemented by this crate.
Implemented primarily based on the high-level descriptions on the blog Hacker Factor written by Dr. Neal Krawetz: http://www.hackerfactor.com/
Note that hash_width
and hash_height
in these docs refer to the parameters of
HasherConfig::hash_size()
.
Choosing an Algorithm
Each algorithm has different performance characteristics
Variants
The Mean hashing algorithm.
The image is converted to grayscale, scaled down to hash_width x hash_height
,
the mean pixel value is taken, and then the hash bits are generated by comparing
the pixels of the descaled image to the mean.
This is the most basic hash algorithm supported, resistant only to changes in resolution, aspect ratio, and overall brightness.
Further Reading: http://www.hackerfactor.com/blog/?/archives/432-Looks-Like-It.html
The Gradient hashing algorithm.
The image is converted to grayscale, scaled down to (hash_width + 1) x hash_height
,
and then in row-major order the pixels are compared with each other, setting bits
in the hash for each comparison. The extra pixel is needed to have hash_width
comparisons
per row.
This hash algorithm is as fast or faster than Mean (because it only traverses the hash data once) and is more resistant to changes than Mean.
Further Reading: http://www.hackerfactor.com/blog/index.php?/archives/529-Kind-of-Like-That.html
The Vertical-Gradient hashing algorithm.
Equivalent to Gradient
but operating on the columns of the image
instead of the rows.
The Double-Gradient hashing algorithm.
An advanced version of Gradient
;
resizes the grayscaled image to (width / 2 + 1) x (height / 2 + 1)
and compares columns
in addition to rows.
This algorithm is slightly slower than Gradient
(resizing the image dwarfs
the hash time in most cases) but the extra comparison direction may improve results (though
you might want to consider increasing
hash_size
to accommodate the extra comparisons).
The Blockhash.io algorithm.
Compared to the other algorithms, this does not require any preprocessing steps and so may be significantly faster at the cost of some resilience.
The algorithm is described in a high level here: https://github.com/commonsmachinery/blockhash-rfc/blob/master/main.md
Trait Implementations
impl<'de> Deserialize<'de> for HashAlg
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impl<'de> Deserialize<'de> for HashAlg
[src]fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error> where
__D: Deserializer<'de>,
[src]
__D: Deserializer<'de>,
impl StructuralEq for HashAlg
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impl StructuralEq for HashAlg
[src]impl StructuralPartialEq for HashAlg
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impl StructuralPartialEq for HashAlg
[src]Auto Trait Implementations
impl RefUnwindSafe for HashAlg
impl RefUnwindSafe for HashAlg
impl UnwindSafe for HashAlg
impl UnwindSafe for HashAlg
Blanket Implementations
impl<T> DeserializeOwned for T where
T: for<'de> Deserialize<'de>,
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impl<T> DeserializeOwned for T where
T: for<'de> Deserialize<'de>,
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