Metadata-Version: 2.0
Name: sdhash
Version: 0.0.4
Summary: Library for image hashing and deduplication.
Home-page: http://github.com/horia141/sdhash
Author: Horia Coman
Author-email: horia141@gmail.com
License: MIT
Keywords: photo image gif hash perceptual dedup deduplication fft
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: POSIX
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.6
Classifier: Programming Language :: Python :: 2.7
Classifier: Topic :: Multimedia :: Graphics
Classifier: Topic :: Multimedia :: Video
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Dist: pillow
Requires-Dist: numpy
Requires-Dist: scipy

# SDHash [![Build Status](https://travis-ci.org/horia141/sdhash.svg?branch=master)](https://travis-ci.org/horia141/sdhash)

A Python library for computing hashes of images which ignore perceptual differences.

## Usage

```python
import sdhash
from PIL import Image

i1 = Image.open('test1.png')
i2 = Image.open('test1_noise.png')
i3 = Image.open('test2.png')

sdhash.test_duplicate(i1, i2) # True
sdhash.test_duplicate(i1, i3) # False
sdhash.hash_image(i1) # [ an md5 output ]
```

## Background

Suppose you want to test that two images are identical. The naive approach of simply comparing the byte-array representation of the two is not good.

## Algorithm

## Installation ##

The Python image library and NumPy/SciPy etc.

Installation is simple, via `pip`:

```bash
pip install sdhash
```

## TODO

Resistance to rotation, mirroring etc.
Tunable knobs (for similarity detection etc.)


