Create the default tables used if they do not yet exist. @param conn The database connection @throws SQLException if an sql error occurs

Define how the algorithm shall handle images with alpha value. Hashing algorithms usually depend on the luminosity value, which by default will be tre

Subtract a hash from this object and lazily update the values as required. A subtraction operation looks at every individual bit and increments or dec

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

Query if the database contains a table with the given name @param tableName The table name to check for @return true if a table with the name exists,

Creates a gaussian blur. <p> A gaussian filter blurs the kernel with decreasing amount depending on the distance to the pixel and produces a more nat

Check if the bit at the given position is set. @param position of the bit. An index of 0 points to the lowest (rightmost bit) @return

Get the category of this result object. The category uniquely identifies the cluster this image was matched to. @return the category

@return how many hashes were added to the tree

Retrieve the hash that is the most similar to the queried hash. The closest hash is the hash with the smallest distance. @param hash to search the ne

Return an identity kernel. This kernel is a 1x1 kernel and copies the original value to the new array @return a identity kernel

Removing all previous knowledge of hashes added while keeping the current cluster hash in place treating it as the only hash added. <p> This operatio

Check if the bit at the given position of the hash is set. This method does not check the bounds of the supplied argument. @param position of the bit

Initialize the hash fields with the first added hash. as soon as we know the correct length @param algorithmId the algorithm id to set this fuzzy has

Add a {@link dev.brachtendorf.jimagehash.hashAlgorithms.filter.Filter Filter} to this hashing algorithm which will be used to alter the image before t

Add a hashing algorithm to the matcher with a weight multiplier of 1. In order for images to match they have to be beneath the threshold of the summed

Ensure that the weight probability array is up to date. Call this method when ever the array is needed.

@return a strong reference to the arraylist backing this leaf

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

A box filter is a filter which applies the same factor to squared region of pixels and can be counted to the blurring filters. <p> This filter is nor

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

Check if modification of the filter is allowed.

Remove all hashing algorithms used by this image matcher instance. At least one algorithm has to be supplied before imaages can be checked for similar

Create a table to hold image hashes for a particular image hashing algorithm @param hasher the hashing algorithm @throws SQLException if an SQL error

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

Return all url descriptors which describe images within the provided hammington distance of the supplied hash @param targetHash The hash to check th

Return the cluster index for the data point of the given index @param index of the datapoint when the cluster method was called @return the cluster i

Reconstruct a hash value from the database @param hasher The hashing algorithm used to create the hash @param bytes the byte array stored in the dat

Subtract a hash from this object and lazily update the values as required. A subtraction operation looks at every individual bit and increments or dec

Creates a visual representation of the hash mapping the hash values to the section of the rescaled image used to generate the hash assuming default bi