imagesearch 0.1.8

Measure visual similiarity of a reference image to other images.

imagesearch

imagesearch measures visual similiarity between a reference image and a set of other images. This can be used to search for a similar image in a large/deep directory structure.

Installation

pip install imagesearch

Examples

• Compare a reference image to all images in a search path:

    > imagesearch needle.jpg haystack\
    28      haystack\0.jpg
    38      haystack\1.jpg
    12      haystack\2.jpg
    18      haystack\3.jpg
    32      haystack\4.jpg
    29      haystack\5.jpg
    0       haystack\6.jpg
    29      haystack\7.jpg
    5       haystack\8.jpg
    28      haystack\9.jpg
  

  In this example, haystack\6.jpg is most similar.

• Compare against a single image:

    > imagesearch needle.jpg haystack\1.jpg
    38       haystack\1.jpg
  

• Only return images with similarity less than or equal to 10:

    > imagesearch needle.jpg haystack\ --threshold 10
    0       haystack\6.jpg
    5       haystack\8.jpg
  

• Return the first image found under the threshold (0, in this case) and stop searching immediately:

    > imagesearch needle.jpg haystack\ -t 0 -1
    0       haystack\6.jpg
  

• Specify a different algorithm:

    > imagesearch needle.jpg haystack\ --algorithm colorhash
    ...
  

• Get more help:

    > imagesearch --help
    ...
  

Visual Similiarity

imagesearch returns a nonnegative integer that quantifies the visual similarity between the reference image and another image. It does this by creating an image fingerprint and looking at the difference between them.

A critical feature of these fingerprints is that they can be numerically compared (by Hamming Distance). Images that are different will have large differences in their fingerprints, and vice versa

A 0 value indicates the highest level of similarity, or possibly a true match.

Values should be treated as opaque and relative. It is dependent on the algorithm used to create the fingerprints and your subjective criteria for what "similar" is.

This project uses the imagehash library to produce these fingerprints, and more information about the techniques can be found there.

Algorithms

All the fingerprinting algorithms in imagesearch come from imagehash. In imagesearch, you may specify which algorithm to use by passing the appropriate option value to the -a or --algorithm flag:

• ahash: Average hashing (aHash)
• phash: 2-axis perceptual hashing (pHash)
• phash-simple: 1-axis perceptual hashing (pHash)
• dhash: Horizontal difference hashing (dHash)
• dhash-vert: Vertical difference hashing (dHash)
• whash-haar: Haar wavelet hashing (wHash)
• whash-db4: Daubechies wavelet hashing (wHash)
• colorhash: HSV color hashing (colorhash)

See this section of the imagehash documentation for examples of different methods producing the same fingerprint for different images. These are the analog to cryptographic hash collosions, so it's important to understand what kinds of scenarios may cause this!

Contributing

Bug Fixes/Features

Submit a PR from an appropriately named feature branch off of master.

Releasing

1. Bump the version with bumpversion [patch|minor|major]. This will update the version number around the project, commit and tag it.
2. Push the repo. A Github release will be made and published to PyPI.