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Grayscale: fix perf issue in percentile computation #236

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Merged
pynicolas merged 1 commit into from
Sep 14, 2026
+21 −20
Merged

Grayscale: fix perf issue in percentile computation #236

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37 changes: 19 additions & 18 deletions imageprocessing/src/main/java/org/fairscan/imageprocessing/PostProcessing.kt
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Original file line number Diff line number Diff line change
Expand Up @@ -175,6 +175,7 @@ fun multiScaleRetinexOnL(bgr: Mat): Mat {
return result
}

// Expects values in [0..255]
fun percentileL(l: Mat, p: Double): Double {
val hist = Mat()
Imgproc.calcHist(
Expand All @@ -199,6 +200,22 @@ fun percentileL(l: Mat, p: Double): Double {
return 255.0
}

// Estimate 2 percentiles in `src` on a subsample rather than the full image.
private fun percentiles(src: Mat, low: Double, high: Double): Pair<Double, Double> {
val sample = resizeForMaxPixels(src, 500_000.0, Imgproc.INTER_NEAREST)
val flat = Mat()
sample.reshape(1, 1).copyTo(flat)
val sorted = Mat()
Core.sort(flat, sorted, Core.SORT_ASCENDING)
val n = sorted.cols()
val pLow = sorted.get(0, (n * low).toInt().coerceIn(0, n - 1))[0]
val pHigh = sorted.get(0, (n * high).toInt().coerceIn(0, n - 1))[0]
flat.release()
sorted.release()
sample.release()
return pLow to pHigh
}

fun enhanceGrayscaleImage(img: Mat): Mat {

// -- 1. Convert to grayscale --------
Expand Down Expand Up @@ -247,16 +264,7 @@ fun enhanceGrayscaleImage(img: Mat): Mat {
// gray areas more visible.
val retinexExp = Mat()
Core.exp(retinex, retinexExp)

val flat = Mat()
retinexExp.reshape(1, 1).copyTo(flat)
val sorted = Mat()
Core.sort(flat, sorted, Core.SORT_ASCENDING)
val n = sorted.cols()
val pLow = sorted.get(0, (n * 0.004).toInt())[0]
val pHigh = sorted.get(0, (n * 0.99).toInt())[0]
flat.release(); sorted.release()

val (pLow, pHigh) = percentiles(retinexExp, 0.004, 0.99)
val normalized = Mat()
Core.subtract(retinexExp, Scalar(pLow), normalized)
val scale = if (pHigh > pLow) 255.0 / (pHigh - pLow) else 1.0
Expand Down Expand Up @@ -291,14 +299,7 @@ fun enhanceGrayscaleImage(img: Mat): Mat {
if (modeVal >= 254) {
val grayF = Mat()
gray.convertTo(grayF, CvType.CV_32F)
val grayFlat = Mat()
grayF.reshape(1, 1).copyTo(grayFlat)
val graySorted = Mat()
Core.sort(grayFlat, graySorted, Core.SORT_ASCENDING)
val gN = graySorted.cols()
val gLow = graySorted.get(0, (gN * 0.01).toInt())[0]
val gHigh = graySorted.get(0, (gN * 0.99).toInt())[0]
grayFlat.release(); graySorted.release()
val (gLow, gHigh) = percentiles(grayF, 0.01, 0.99)
Core.subtract(grayF, Scalar(gLow), grayF)
Core.multiply(grayF, Scalar(255.0 / (gHigh - gLow + 1e-6)), grayF)
Core.min(grayF, Scalar(255.0), grayF)
Expand Down
4 changes: 2 additions & 2 deletions imageprocessing/src/main/java/org/fairscan/imageprocessing/Utils.kt
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Expand Up @@ -23,15 +23,15 @@ import org.opencv.imgproc.Imgproc
import java.io.IOException
import kotlin.math.sqrt

fun resizeForMaxPixels(img: Mat, maxPixels: Double): Mat {
fun resizeForMaxPixels(img: Mat, maxPixels: Double, interpolation: Int = Imgproc.INTER_AREA): Mat {
val origPixels = img.width() * img.height()
if (origPixels <= maxPixels) {
return img.clone()
}
val scale = sqrt(maxPixels / origPixels)
val size = Size(img.width() * scale, img.height() * scale)
val resizedImg = Mat()
Imgproc.resize(img, resizedImg, size, 0.0, 0.0, Imgproc.INTER_AREA)
Imgproc.resize(img, resizedImg, size, 0.0, 0.0, interpolation)
return resizedImg
}

Expand Down
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