Very simply, this project demonstrates how to match an image to a bank of pre-existing images. It contains a simple front-end and image bank. The python implementation of the image bank can be easily adapted for other applications.

The image comparisons use SURF: Speeded Up Robust Features which is scale, orientation, and to some degree affine invariant.

A common problem in managing large numbers of images is detecting slight duplicates. Using a library like OpenCV which is widely available across platforms and languages is a great way to detect these duplicates.

To add an image to the bank:

• Compute SURF descriptors for the image
• Concatenate the descriptor to a "mega matrix" of pre-existing ones, making note of it's position.

To look up an image:

• Compute SURF descriptors for the image
• Perform a knn search in the "mega matrix" for the SURF descriptors found above
• For all matches, if the two are within a certain distance threshold, we increment a similary value with respect to that candidate by 1. This creates an arbitrary similarity index.
• Return the top results

The server is implemented using flask and the front end uses react

Need to install opencv and imagemagick (todo: add links)

pip install sqlite3
pip install numpy
pip install flask
pip install wand
pip install flask
npm install

compile front end webpack

watch for changes on front end webpack --watch

run server: python server.py

watch for changes on server: uncomment this line in server.py app.debug = True note: this is by default on

• The implementation is poorly optimized, there is a rudimentary attempt to distribute the "mega matrix" to take advantage of multiple cores. At any sort of scale, you probably want to look into doing some sort of distributed nearest neighbor search.

• By default the server persists the bank data in bank.db which is a simple sqlite database with pickled python objects. This is merely for convenience between server restarts. While it is running, the server keeps everything in local memory.

• isk-daemon

• Tested with around 200k images without issues.

• This is only tested on OS X Mavericks, it shouldn't have any problems on linux. It is completely untested on windows.

• A Sample dataset. untar it and just POST them all to the server find <MY_DATASET_DIR> -name "*.<IMAGE_EXTENSION>" -exec curl -i -F file=@{} \;

mineye source code is released under the MIT License

The SURF and SIFT algorithms implemented by OpenCV are patented You will have to switch out the feature detector for something else.