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package com.sothr.imagetools.hash
import edu.emory.mathcs.jtransforms.dct.FloatDCT_2D import grizzled.slf4j.Logging
/**
* Created by dev on 1/22/14. */ object PHash extends PerceptualHasher with Logging { def getHash(imageData: Array[Array[Int]]): Long = { //convert the imageData into a FloatArray
val width = imageData.length val height = imageData(0).length //debug(s"Starting with image of ${height}x${width} for PHash")
val imageDataFloat:Array[Array[Float]] = Array.ofDim[Float](height, width) for (row <- 0 until height) { for (col <- 0 until width) { imageDataFloat(row)(col) = imageData(row)(col).toFloat } } //debug("Copied image data to float array for transform")
//debug(s"\n${imageDataFloat.deep.mkString("\n")}")
//perform transform on the data
val dct:FloatDCT_2D = new FloatDCT_2D(height,width) dct.forward(imageDataFloat, true) //debug("Converted image data into DCT")
//debug(s"\n${imageDataFloat.deep.mkString("\n")}")
//extract the DCT data
val dctDataWidth:Int = width / 4 val dctDataHeight:Int = height / 4
//calculate the mean
var total = 0.0f for (row <- 0 until dctDataHeight) { for (col <- 0 until dctDataWidth) { total += imageDataFloat(row)(col) } } val mean = total / (dctDataHeight * dctDataWidth) //debug(s"Calculated mean as $mean from ${total}/${dctDataHeight * dctDataWidth}")
//calculate the hash
var hash = 0L for (row <- 0 until dctDataHeight by 2) { //process each column
for (col <- 0 until dctDataWidth by 1) { hash <<= 1 val pixel = imageDataFloat(row)(col) //If the current pixel is at or above the mean, store it as a one, else store it as a zero
if (pixel >= mean) hash |= 1 else hash |= 0 }
if ((row +1) < dctDataWidth) { val nextRow = row + 1 //process each column
for (col <- (dctDataWidth - 1) to 0 by -1) { hash <<= 1 val pixel = imageDataFloat(nextRow)(col) if (pixel >= mean) hash |= 1 else hash |= 0 } } } //debug(s"Computed PHash: $hash from ${dctDataWidth * dctDataHeight} pixels")
hash } }
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