README
¶
imghash
imghash computes the Perceptual Hash for a given input image.
The hash is returned as a 64 bit integer. It comes with two commandline
tools: img-index and img-find. Refer to their respective READMEs for
information on what they do.
Note that this toolset is mainly for educational purposes on my part. It is a partial implementation of an article on hackerfactor.com.
The package supports these hashing modes:
-
Average: Average computes a Perceptual Hash using a naive, but very fast method. It holds up to minor colour changes, changing brightness and contrast and is indifferent to aspect ratio and image size differences.
Average Hash is a great algorithm if you are looking for something specific. For example, if we have a small thumbnail of an image and we wish to know if the big one exists somewhere in our collection. Average Hash will find it very quickly. However, if there are modifications -- like text was added or a head was spliced into place, then Average Hash probably won't do the job.
The Average Hash is quick and easy, but it can generate false-misses if gamma correction or color histogram is applied to the image. This is because the colors move along a non-linear scale -- changing where the "average" is located and therefore changing which bits are above/below the average.
More may come at some point.
Usage
go get github.com/jteeuwen/imghash
License
Unless otherwise stated, all of the work in this project is subject to a 1-clause BSD license. Its contents can be found in the enclosed LICENSE file.
Documentation
¶
Overview ¶
imghash computes the Perceptual Hash for a given input image. The Perceptual Hash is returned as a 64 bit integer.
Comparing two images can be done by constructing the hash from each image and counting the number of bit positions that are different. This is a Hamming distance. A distance of zero indicates that it is likely a very similar picture (or a variation of the same picture). A distance of 5 means a few things may be different, but they are probably still close enough to be similar. But a distance of 10 or more? That's probably a very different picture.
Index ¶
- func Average(img image.Image) uint64
- func Distance(a, b uint64) uint64
- type Database
- func (d *Database) Find(hash, distance uint64) ResultSet
- func (d *Database) IndexFile(file string) int
- func (d *Database) IndexHash(hash uint64) []int
- func (d *Database) IsNew(file string, modtime int64) bool
- func (d *Database) Load(file string) (err error)
- func (d *Database) Save(file string) (err error)
- func (d *Database) Set(file string, modtime int64, hash uint64)
- type Entry
- type HashFunc
- type ResultSet
- type SearchResult
Constants ¶
This section is empty.
Variables ¶
This section is empty.
Functions ¶
func Average ¶
Average computes a Perceptual Hash using a naive, but very fast method. It holds up to minor colour changes, changing brightness and contrast and is indifferent to aspect ratio and image size differences.
Average Hash is a great algorithm if you are looking for something specific. For example, if we have a small thumbnail of an image and we wish to know if the big one exists somewhere in our collection. Average Hash will find it very quickly. However, if there are modifications -- like text was added or a head was spliced into place, then Average Hash probably won't do the job.
The Average Hash is quick and easy, but it can generate false-misses if gamma correction or color histogram is applied to the image. This is because the colors move along a non-linear scale -- changing where the "average" is located and therefore changing which bits are above/below the average.
Types ¶
type Database ¶
A Database holds a listing of Perceptual hashes, mapped to image file paths.
Note: This is a very naive implementation that can benefit a great deal from optimization.
func (*Database) Find ¶
Find finds all entries which have a Hamming Diance <= to the specified distance with the given hash. The list is sorted by relevance.
func (*Database) IndexHash ¶
IndexHash returns the indices for files with the given hash. There can be more than one of them.
func (*Database) IsNew ¶
IsNew returns true if the given file has been updated since it was last stored in the database.
func (*Database) Load ¶
Load loads a database from the given file. Leave the filename empty to use the default file.
type Entry ¶
type Entry struct {
Path string // Image path, relative to Database.Root
Hash uint64 // Perceptual Image hash.
ModTime int64 // Last-Modified timestamp for this file.
}
Entry represents a single database entry.
type ResultSet ¶
type ResultSet []*SearchResult
ResultSet holds search results, sortable by Hamming Distance.
type SearchResult ¶
type SearchResult struct {
Path string // Image path, relative to Database.Root
Hash uint64 // Perceptual Image hash.
Distance uint64 // Hamming Distance to search term.
}
SearchResult is returned by Database.Find.
Source Files
Directories