vitaly-z/vishash

Visual Hash for matching copies of visually similar images.

Visual hash for comparing visual similarity of images; which works on a wide variety of image types such as photos, logos, figures, diagrams and drawings.

VisHash

This repository is the official implementation of VisHash: Visual Similarity Preserving Image Hashing for Diagram Retrieval.

Requirements

• PIL or pillow
• scikit-image
• matplotlib
• cairosvg (optional: necessary for reading SVG images)

Calculate image hashes

calc_vishash.py --dataname example --image_path /path/to/images

Input is a directory containing images. A hash will be computed for each image. Two files are written: filenames_example.csv and signatures_example.npy, so that the order of the hashes (signatures) matches the order given in filenames_example.csv. Any unreadable or unsupported files will generate a log message, but processing on other images will continue.

Find duplicate images

calc_matches.py --postfix example --threshold 0.2

This script will read filenames_example.csv and signatures_example.npy (such as produced by calc_vishash.py). The distance between pairs of image hashes is calculated, then filtered based on the given threshold to output a list of matches in matches_example.csv.

Evaluation

Evaluation of the algorithm is performed by finding the closest matches in a given dataset, and then evaluating (by hand) whether these matches are correct. The results of this analysis are used to generate a plot of precision versus number of retrieved pairs.

calc_mindist_allpairs.py --postfix example --n_matches 1000

The above example will generate a list of 1000 image-pairs with the lowest distance. The script will read filenames_example.csv and signatures_example.npy (such as produced by calc_vishash.py); and write mindist_example.csv with the list of 1000 matches.

calc_query_matches.py --postfix example -k 5

The above match-per-query example will generate a list of the top-5 matches (hashes with lowest distance) for each of the hashes in the given array of hashes. The script will read filenames_example.csv and signatures_example.npy (such as produced by calc_vishash.py); and write topk_example.csv with the list of top-5 matches per hash.

Contributing

Send email to contact author (dianeoyen) with any suggestions, concerns or interest in contributing. See license.