Append new data to the leaf @param data Value which will be associated with the hash this leaf represents

Append a new hashing algorithm which will be executed after all hash algorithms passed the test. @param algo The algorithms to be added @param

Compute the category of the supplied image. A category is a collection of similar images mapped to a common hash which minimizes the distance of all h

{@inheritDoc} <p> The distance returned by this method calls indicates how many percent of the trees in the random forest agree with the decision ran

Hashing algorithms may only be added and removed as long as no image has been added to this set. <p> {@inheritDoc}

Compute the distance between an image and a category cluster midpoint. This method is used to compute the minimum distance and therefore might cut the

Reads a hash from a serialization file and returns it. Only hashes can be read from file that got saved by the same class instance using {@link #toFil

Return the number of hashes that currently make up this hash. After calling reset the added count will be set to 1. @return the count

Map an integer to a bucket of the bloom filter @param hashCode the integer used as seed @param i secondary seed value @return index of a bucke

Get a list of available categories this matcher matched images to. Each category represents a set of images with high similarity. @return A list of i

Get a h2 database connection with the supplied settings. @param subname the database file name. By default the file looks at the base

Get a database image matcher which previously got serialized by calling {@link #serializeToDatabase(int)} on the object. @param subname the database

Return a list of images that are considered matching by the definition of this matcher. <p> This method is propagated by the super class allowing to u

Convert the image to a numerical hashcode @param hasher the hashing algorithm to use @param image the image to hash @return an integer hashcode

@param depth

Construct a persistent image matcher from a serialization file and rebuild it's internal state. @param saveLocation the serialization file as specif

Remove the first occurance of a {@link dev.brachtendorf.jimagehash.hashAlgorithms.filter.Filter Filter} from this hashing algorithm. <p> Be aware tha

Serialize this image matcher to a file. Serialized matchers keep their internal state and can be reconstructed at a later stage without needing to reh

Index the image. This enables the image matcher to find the image in future searches. The database image matcher does not store the image data itself

Add this image to the image matcher. @param uniqueId the unique id to refer to during lookup @param image the image to add

@return the number of buckets set

A box filter is a filter which applies the same factor to squared region of pixels and can be counted to the blurring filters. <pre> Example factor:

This method will return usable results after the first image has been added @return the percentage of buckets set. [0-1]

Make sure that the supplied connection is a h2 database connection. @param dbConnection The connection to check @return the supplied connection if it

Method invoked after {@link #recomputeClusters(int)} is being called.

Method invoked before {@link #recomputeClusters(int)} is being called.

Compute the dimension for the resize operation. We want to get to close to a quadratic images as possible to counteract scaling bias. @param bitResol

Compute the dimension for the resize operation. We want to get to close to a quadratic images as possible to counteract scaling bias. @param bitResol

Compute the dimension for the resize operation. We want to get to close to a quadratic images as possible to counteract scaling bias. @param bitResol

Create a tree node at the given point in the tree. <p> If a new node was found which improves distinction an inner node is returned. The algorithm cop

Compute the ring partition this specific pixel will fall into. @param originalX the x pixel index in the picture @param originalY the y pixel index i

Compute the ring partition this specific pixel will fall into. @param originalX the x pixel index in the picture @param originalY the y pixel index i

Create and set a child of the current node @param left if true create the left child if false create the right child @return the created node

Create and set a child of the current node @param left if true create the left child if false create the right child @return the created node

@param trees number of trees to create @param numVars number of variables to try at each

As variables for our tree we take the distance to random points in our hash space. @param preComputedHashes @return

The internal structure of hashing algorithms may change once in a while, but the hamming distance and normalized hamming distance should be unaffected

Reads a hash from a serialization file and returns it. Only hashes can be read from file that got saved by the same class instance using {@link #toFil

Return the approximate number of distinct elements added to this set. <p> This method will return usable results after the first image has been added

Return the cluster index whose centeroid is most similar to the supplied hash @param testHash the hash to check against the clusters @return the cate

Get the fuzzy hash representing the specified cluster @param cluster the cluster index @return the centeroid of the cluster

Get the number of images that are were added in this category. @param category to retrieve the number of images from. @return he number of images tha

Search for all similar images passing the algorithm filters supplied to this matcher. If the image itself was added to the matcher it will be returned

Return the maximum fuzzy distance for this bit. The maximum distance is the distance to either the 0 bit or the 1 bit which ever is greater. @param b

Assuming the optimal k value used as calculated by {@link #getOptimalNumberHashFunctions(int, int)}. Get the optimal number of bits to achieve the tar

Get the optimal number of hash functions needed to reduce false positive errors. @param m bit size of the bloom filter @param n number of maximum ele

Checks which cluster has a high chance to contain the closest neighbor to this hash @param testHash the hash to check which cluster it belongs to @pa

Check if an image has already been added to the categorizer @param uniqueId the unique id of the image @return true if it has been added, false other