https://github.com/pwlmc/imghash
Perceptual image hashing for Node.js
https://github.com/pwlmc/imghash
computer-vision image-processing imghash webscraping
Last synced: about 2 months ago
JSON representation
Perceptual image hashing for Node.js
- Host: GitHub
- URL: https://github.com/pwlmc/imghash
- Owner: pwlmc
- Created: 2016-02-12T14:03:21.000Z (over 10 years ago)
- Default Branch: master
- Last Pushed: 2026-04-25T07:32:07.000Z (about 2 months ago)
- Last Synced: 2026-04-25T08:36:04.985Z (about 2 months ago)
- Topics: computer-vision, image-processing, imghash, webscraping
- Language: TypeScript
- Homepage:
- Size: 956 KB
- Stars: 195
- Watchers: 4
- Forks: 18
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# imghash  
Promise-based image perceptual hash calculation for Node.js
## Installation
```
npm install imghash
```
:information_source: You can find the command-line interface [here](https://github.com/pwlmaciejewski/imghash-cli).
## Basic usage
```js
const imghash = require("imghash");
const hash1 = await imghash.hash("./path/to/file");
console.log(hash1); // "f884c4d8d1193c07"
// Custom hex length and result in binary
const hash2 = await imghash.hash("./path/to/file", 4, "binary");
console.log(hash2); // "1000100010000010"
```
## Finding similar images
To measure similarity between images you can use [Hamming distance](https://en.wikipedia.org/wiki/Hamming_distance) or [Levenshtein Distance](https://en.wikipedia.org/wiki/Levenshtein_distance).
The following example uses the latter one:
```js
const imghash = require("imghash");
const leven = require("leven");
const hash1 = await imghash.hash("./img1");
const hash2 = await imghash.hash("./img2");
const distance = leven(hash1, hash2);
console.log(`Distance between images is: ${distance}`);
if (distance <= 12) {
console.log("Images are similar");
} else {
console.log("Images are NOT similar");
}
```
## API
##### `.hash(filepath[, bits][, format])`
Returns: ES6 `Promise`, resolved returns hash string in specified format and length (eg. `f884c4d8d1193c07`)
Parameters:
- `filepath` - path to the image (supported formats are `png` and `jpeg`) or `Buffer`
- `bits` (optional) - hash length [default: `8`]
- `format` (optional) - output format [default: `hex`]
---
##### `.hashRaw(data, bits)`
Returns: hex hash
Parameters:
- `data` - image data descriptor in form `{ width: [width], height: [height], data: [decoded image pixels] }`
- `bits` - hash length
---
##### `.hexToBinary(s)`
Returns: hex string, eg. `f884c4d8d1193c07`.
Parameters:
- `s` - binary hash string eg. `1000100010000010`
---
##### `.binaryToHex(s)`
Returns: hex string, eg. `1000100010000010`.
Parameters:
- `s` - hex hash string eg. `f884c4d8d1193c07`
## Further reading
`imghash` takes advantage of block mean value based hashing method:
- [http://stackoverflow.com/questions/14377854/block-mean-value-hashing-method](http://stackoverflow.com/questions/14377854/block-mean-value-hashing-method)
- [http://commonsmachinery.se/2014/09/digital-image-matching-part-1-hashing/](http://commonsmachinery.se/2014/09/digital-image-matching-part-1-hashing/)