Quad detection: new fallback algorithms

app/src/main/java/org/fairscan/app/DocumentDetection.kt

@@ -16,8 +16,13 @@ package org.fairscan.app
 
 import android.graphics.Bitmap
 import androidx.core.graphics.createBitmap
+import org.fairscan.app.ImageSegmentationService.Segmentation
+import org.fairscan.app.quad.detectDocumentQuadFromProbmap
+import org.fairscan.app.quad.findQuadFromRightAngles
+import org.fairscan.app.quad.minAreaRect
 import org.opencv.android.Utils
 import org.opencv.core.Core
+import org.opencv.core.CvType
 import org.opencv.core.Mat
 import org.opencv.core.MatOfPoint
 import org.opencv.core.MatOfPoint2f
@@ -26,14 +31,39 @@ import org.opencv.imgproc.Imgproc
 import kotlin.math.abs
 import kotlin.math.max
 
-fun detectDocumentQuad(mask: Bitmap, minQuadAreaRatio: Double = 0.02): Quad? {
-    val mat = Mat()
-    Utils.bitmapToMat(mask, mat)
+fun detectDocumentQuad(mask: Segmentation, isLiveAnalysis: Boolean, minQuadAreaRatio: Double = 0.02): Quad? {
+    val mat = mask.toMat()
+    val (biggest: MatOfPoint2f?, area) = biggestContour(mat)
+    var vertices: List<Point>?
+    if (biggest != null && biggest.total() == 4L && area > mask.width * mask.height * minQuadAreaRatio) {
+        vertices = biggest.toList()?.map { Point(it.x, it.y) }
+    } else {
 
-    val gray = Mat()
-    Imgproc.cvtColor(mat, gray, Imgproc.COLOR_BGR2GRAY)
+        // Fallback 1: adjust threshold
+        val thresholds =
+            if (isLiveAnalysis) listOf(25.0, 50.0, 75.0) else (0..12).map { 0.2 + it * 0.05 }
+        vertices = detectDocumentQuadFromProbmap(mat, thresholds)
+            ?.map { Point(it.x, it.y) }
+        if (vertices == null && biggest != null && biggest.total() > 4) {
 
-    val refinedMask = refineMask(gray)
+            // Fallback 2: look for right angles
+            val polygon = biggest.toList().map { Point(it.x, it.y) }
+            vertices = findQuadFromRightAngles(polygon, mask.width, mask.height)
+            if (vertices == null && !isLiveAnalysis) {
+
+                // Fallback 3: bounding rectangle
+                vertices = minAreaRect(polygon, mask.width, mask.height)
+            }
+        }
+    }
+    return if (vertices?.size == 4) createQuad(vertices) else null
+}
+
+private fun biggestContour(mat: Mat): Pair<MatOfPoint2f?, Double> {
+    val mat8u = Mat()
+    mat.convertTo(mat8u, CvType.CV_8UC1, 255.0)
+
+    val refinedMask = refineMask(mat8u)
 
     val blurred = Mat()
     Imgproc.GaussianBlur(refinedMask, blurred, Size(5.0, 5.0), 0.0)
@@ -54,21 +84,13 @@ fun detectDocumentQuad(mask: Bitmap, minQuadAreaRatio: Double = 0.02): Quad? {
         val approx = MatOfPoint2f()
         Imgproc.approxPolyDP(contour2f, approx, 0.02 * peri, true)
 
-        if (approx.total() == 4L) {
-            val area = abs(Imgproc.contourArea(approx))
-            if (area > maxArea) {
-                maxArea = area
-                biggest = approx
-            }
+        val area = abs(Imgproc.contourArea(approx))
+        if (area > maxArea) {
+            maxArea = area
+            biggest = approx
         }
     }
-
-    if (maxArea < mask.width * mask.height * minQuadAreaRatio) {
-        return null
-    }
-
-    val vertices = biggest?.toList()?.map { Point(it.x, it.y) }
-    return if (vertices?.size == 4) createQuad(vertices) else null
+    return Pair(biggest, maxArea)
 }
 
 /**
@@ -77,7 +99,7 @@ fun detectDocumentQuad(mask: Bitmap, minQuadAreaRatio: Double = 0.02): Quad? {
 fun refineMask(original: Mat): Mat {
     // Step 0: Ensure the mask is binary (just in case)
     val binaryMask = Mat()
-    Imgproc.threshold(original, binaryMask, 0.0, 255.0, Imgproc.THRESH_BINARY)
+    Imgproc.threshold(original, binaryMask, 128.0, 255.0, Imgproc.THRESH_BINARY)
 
     // Step 1: Closing (fills small holes)
     val kernelClose = Imgproc.getStructuringElement(Imgproc.MORPH_ELLIPSE, Size(5.0, 5.0))

app/src/main/java/org/fairscan/app/ImageSegmentation.kt

@@ -28,6 +28,8 @@ import kotlinx.coroutines.isActive
 import kotlinx.coroutines.sync.Mutex
 import kotlinx.coroutines.sync.withLock
 import kotlinx.coroutines.withContext
+import org.opencv.core.CvType
+import org.opencv.core.Mat
 import org.tensorflow.lite.DataType
 import org.tensorflow.lite.Interpreter
 import org.tensorflow.lite.support.common.FileUtil
@@ -116,29 +118,36 @@ class ImageSegmentationService(private val context: Context) {
         outputBuffer.rewind()
         interpreter.run(tensorImage.tensorBuffer.buffer, outputBuffer)
         outputBuffer.rewind()
-        val mask = generateMaskFromOutputBuffer(outputBuffer, w, h)
-        return Segmentation(mask)
+        return Segmentation(outputToArray(outputBuffer, w, h), w, h)
     }
 
-    private fun generateMaskFromOutputBuffer(outputBuffer: ByteBuffer, width: Int, height: Int): Bitmap {
+    private fun outputToArray(outputBuffer: ByteBuffer, width: Int, height: Int): FloatArray {
         outputBuffer.rewind()
-        val floatArray = FloatArray(width * height)
-        outputBuffer.asFloatBuffer()[floatArray]
-
-        val pixels = IntArray(width * height)
-        for (i in floatArray.indices) {
-            val value = floatArray[i].coerceIn(0f, 1f)
-            val gray = (value * 255).toInt()
-            pixels[i] = Color.rgb(gray, gray, gray)
+        val maskFloats = FloatArray(width * height)
+        outputBuffer.asFloatBuffer()[maskFloats]
+        for (i in maskFloats.indices) {
+            maskFloats[i] = maskFloats[i].coerceIn(0f, 1f)
         }
-
-        val bitmap = createBitmap(width, height, Bitmap.Config.ARGB_8888)
-        bitmap.setPixels(pixels, 0, width, 0, 0, width, height)
-        return bitmap
+        return maskFloats
     }
 
-    data class Segmentation(val mask: Bitmap) {
-        fun toBinaryMask(): Bitmap = mask
+    data class Segmentation(private val probmap: FloatArray, val width: Int, val height: Int) {
+        fun get(x: Int, y: Int): Float = probmap[y * width + x]
+        fun toBinaryMask(): Bitmap {
+            val bmp = createBitmap(width, height, Bitmap.Config.ARGB_8888)
+            val pixels = IntArray(width * height)
+            for (i in probmap.indices) {
+                val v = (probmap[i].coerceIn(0f, 1f) * 255f).toInt()
+                pixels[i] = Color.rgb(v, v, v)
+            }
+            bmp.setPixels(pixels, 0, width, 0, 0, width, height)
+            return bmp
+        }
+        fun toMat(): Mat {
+            val mat = Mat(height, width, CvType.CV_32FC1)
+            mat.put(0, 0, probmap)
+            return mat
+        }
     }
 
     data class SegmentationResult(

app/src/main/java/org/fairscan/app/MainViewModel.kt

@@ -105,11 +105,12 @@ class MainViewModel(
             imageSegmentationService.segmentation
                 .filterNotNull()
                 .map {
+                    // TODO Should we really call toBinaryMask if it's used only in debug mode?
                     val binaryMask = it.segmentation.toBinaryMask()
                     LiveAnalysisState(
                         inferenceTime = it.inferenceTime,
                         binaryMask = binaryMask,
-                        documentQuad = detectDocumentQuad(binaryMask),
+                        documentQuad = detectDocumentQuad(it.segmentation, isLiveAnalysis = true),
                         timestamp = System.currentTimeMillis(),
                     )
                 }
@@ -190,8 +191,8 @@ class MainViewModel(
         val bitmap = imageProxy.toBitmap()
         val segmentation = imageSegmentationService.runSegmentationAndReturn(bitmap, 0)
         if (segmentation != null) {
-            val mask = segmentation.segmentation.toBinaryMask()
-            var quad = detectDocumentQuad(mask)
+            val mask = segmentation.segmentation
+            var quad = detectDocumentQuad(mask, isLiveAnalysis = false)
             if (quad == null) {
                 val now = System.currentTimeMillis()
                 lastSuccessfulLiveAnalysisState?.timestamp?.let {

app/src/main/java/org/fairscan/app/quad/AdaptiveThreshold.kt

@@ -0,0 +1,129 @@
+/*
+ * Copyright 2025 Pierre-Yves Nicolas
+ *
+ * This program is free software: you can redistribute it and/or modify it
+ * under the terms of the GNU General Public License as published by the Free
+ * Software Foundation, either version 3 of the License, or (at your option)
+ * any later version.
+ * This program is distributed in the hope that it will be useful, but WITHOUT
+ * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
+ * FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for
+ * more details.
+ * You should have received a copy of the GNU General Public License along with
+ * this program. If not, see <https://www.gnu.org/licenses/>.
+ */
+package org.fairscan.app.quad
+
+import org.opencv.core.Mat
+import org.opencv.core.CvType
+import org.opencv.core.Size
+import org.opencv.core.Point
+import org.opencv.core.Scalar
+import org.opencv.core.MatOfPoint
+import org.opencv.core.MatOfPoint2f
+import org.opencv.core.Core
+import org.opencv.imgproc.Imgproc
+import kotlin.math.abs
+
+// Look for a threshold for which we find a quad in the mask
+fun detectDocumentQuadFromProbmap(
+    probmap: Mat,
+    thresholds: List<Double>,
+    useOtsu: Boolean = true,
+    minQuadAreaRatio: Double = 0.02
+): List<Point>? {
+    val probmapU8 = Mat()
+    probmap.convertTo(probmapU8, CvType.CV_8U, 255.0)
+    val probmapSmooth = Mat()
+    Imgproc.GaussianBlur(probmapU8, probmapSmooth, Size(3.0, 3.0), 0.0)
+
+    var bestScore = 0.0
+    var bestQuad: List<Point>? = null
+
+    // 1) Otsu
+    if (useOtsu) {
+        val otsu = Mat()
+        Imgproc.threshold(probmapSmooth, otsu, 0.0, 255.0, Imgproc.THRESH_BINARY + Imgproc.THRESH_OTSU)
+        val quad = findQuadFromBinaryMask(otsu, minQuadAreaRatio)
+        if (quad != null) {
+            val probFloat = Mat()
+            probmap.convertTo(probFloat, CvType.CV_32F)
+            val sc = scoreQuadAgainstProbmap(quad, probFloat)
+            if (sc > bestScore) {
+                bestScore = sc
+                bestQuad = quad
+            }
+        }
+    }
+
+    // 2) Threshold sweep
+    for (thr in thresholds) {
+        val bin = Mat()
+        Imgproc.threshold(probmapSmooth, bin, thr * 255.0, 255.0, Imgproc.THRESH_BINARY)
+        val kernel = Imgproc.getStructuringElement(Imgproc.MORPH_ELLIPSE, Size(5.0, 5.0))
+        Imgproc.morphologyEx(bin, bin, Imgproc.MORPH_CLOSE, kernel)
+        val quad = findQuadFromBinaryMask(bin, minQuadAreaRatio)
+        if (quad != null) {
+            val probFloat = Mat()
+            probmap.convertTo(probFloat, CvType.CV_32F)
+            val sc = scoreQuadAgainstProbmap(quad, probFloat)
+            if (sc > bestScore) {
+                bestScore = sc
+                bestQuad = quad
+            }
+        }
+    }
+
+    return bestQuad
+}
+
+// Fill polygon and return binary mask (0/1)
+fun makePolygonMask(size: Size, polygon: List<Point>): Mat {
+    val mask = Mat.zeros(size, CvType.CV_8U)
+    val pts = MatOfPoint(*polygon.toTypedArray())
+    Imgproc.fillPoly(mask, listOf(pts), Scalar(1.0))
+    return mask
+}
+
+// Compute score between quad and probmap
+fun scoreQuadAgainstProbmap(quad: List<Point>, probmap: Mat): Double {
+    val mask = makePolygonMask(probmap.size(), quad)
+    val maskFloat = Mat()
+    mask.convertTo(maskFloat, CvType.CV_32F)
+    val masked = Mat()
+    Core.multiply(probmap, maskFloat, masked)
+    val meanProb = Core.sumElems(masked).`val`[0] / Core.sumElems(maskFloat).`val`[0]
+    val areaRatio = Core.sumElems(maskFloat).`val`[0] / (probmap.rows() * probmap.cols())
+    return meanProb * (0.7 + 0.3 * areaRatio)
+}
+
+// Find largest quadrilateral in a binary mask
+fun findQuadFromBinaryMask(binMask: Mat, minQuadAreaRatio: Double = 0.02): List<Point>? {
+    val blurred = Mat()
+    Imgproc.GaussianBlur(binMask, blurred, Size(5.0, 5.0), 0.0)
+    val edges = Mat()
+    Imgproc.Canny(blurred, edges, 75.0, 200.0)
+
+    val contours = mutableListOf<MatOfPoint>()
+    Imgproc.findContours(edges, contours, Mat(), Imgproc.RETR_LIST, Imgproc.CHAIN_APPROX_SIMPLE)
+
+    var biggest: MatOfPoint2f? = null
+    var maxArea = 0.0
+    for (cnt in contours) {
+        val cnt2f = MatOfPoint2f(*cnt.toArray())
+        val peri = Imgproc.arcLength(cnt2f, true)
+        val approx = MatOfPoint2f()
+        Imgproc.approxPolyDP(cnt2f, approx, 0.02 * peri, true)
+        if (approx.rows() == 4) {
+            val area = abs(Imgproc.contourArea(approx))
+            if (area > maxArea) {
+                maxArea = area
+                biggest = approx
+            }
+        }
+    }
+    val totalArea = binMask.rows() * binMask.cols().toDouble()
+    return if (maxArea > totalArea * minQuadAreaRatio && biggest != null) {
+        biggest.toList()
+    } else null
+}

app/src/main/java/org/fairscan/app/quad/MinAreaRect.kt

@@ -0,0 +1,111 @@
+/*
+ * Copyright 2025 Pierre-Yves Nicolas
+ *
+ * This program is free software: you can redistribute it and/or modify it
+ * under the terms of the GNU General Public License as published by the Free
+ * Software Foundation, either version 3 of the License, or (at your option)
+ * any later version.
+ * This program is distributed in the hope that it will be useful, but WITHOUT
+ * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
+ * FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for
+ * more details.
+ * You should have received a copy of the GNU General Public License along with
+ * this program. If not, see <https://www.gnu.org/licenses/>.
+ */
+package org.fairscan.app.quad
+
+import org.fairscan.app.Point
+import kotlin.math.cos
+import kotlin.math.sin
+
+// Look for a minimal rectangle that covers a given polygon
+fun minAreaRect(polygon: List<Point>, imgWidth: Int? = null, imgHeight: Int? = null): List<Point>? {
+    if (polygon.size < 3) return null
+
+    val hull = convexHull(polygon)
+    if (hull.size < 3) return hull
+
+    var bestArea = Double.POSITIVE_INFINITY
+    var bestRect: List<Point>? = null
+
+    // Test 90 rotation angles between 0 and π/2
+    for (deg in 0 until 90) {
+        val angle = Math.toRadians(deg.toDouble())
+        val cosA = cos(angle)
+        val sinA = sin(angle)
+
+        // Rotation matrix
+        val rotX = { p: Point -> p.x * cosA - p.y * sinA }
+        val rotY = { p: Point -> p.x * sinA + p.y * cosA }
+
+        val rotated = hull.map { Point(rotX(it), rotY(it)) }
+
+        val minX = rotated.minOf { it.x }
+        val maxX = rotated.maxOf { it.x }
+        val minY = rotated.minOf { it.y }
+        val maxY = rotated.maxOf { it.y }
+
+        val area = (maxX - minX) * (maxY - minY)
+        if (area < bestArea) {
+            bestArea = area
+
+            val rectRot = listOf(
+                Point(minX, minY),
+                Point(maxX, minY),
+                Point(maxX, maxY),
+                Point(minX, maxY)
+            )
+
+            // Apply inverse rotation
+            val invX = { p: Point -> p.x * cosA + p.y * sinA }
+            val invY = { p: Point -> -p.x * sinA + p.y * cosA }
+            val rect = rectRot.map { Point(invX(it), invY(it)) }
+
+            bestRect = rect
+        }
+    }
+
+    if (bestRect == null) return null
+
+    // Optionally clip within image bounds
+    if (imgWidth != null && imgHeight != null) {
+        val w = imgWidth - 1.0
+        val h = imgHeight - 1.0
+        return bestRect.map {
+            Point(it.x.coerceIn(0.0, w), it.y.coerceIn(0.0, h))
+        }
+    }
+
+    return bestRect
+}
+
+fun convexHull(points: List<Point>): List<Point> {
+    val unique = points.distinctBy { Pair(it.x, it.y) }
+    if (unique.size <= 3) return unique
+
+    val sorted = unique.sortedWith(compareBy({ it.x }, { it.y }))
+
+    fun cross(o: Point, a: Point, b: Point): Double {
+        return (a.x - o.x) * (b.y - o.y) - (a.y - o.y) * (b.x - o.x)
+    }
+
+    val lower = mutableListOf<Point>()
+    for (p in sorted) {
+        while (lower.size >= 2 && cross(lower[lower.size - 2], lower.last(), p) <= 0f) {
+            lower.removeAt(lower.lastIndex)
+        }
+        lower.add(p)
+    }
+
+    val upper = mutableListOf<Point>()
+    for (p in sorted.asReversed()) {
+        while (upper.size >= 2 && cross(upper[upper.size - 2], upper.last(), p) <= 0f) {
+            upper.removeAt(upper.lastIndex)
+        }
+        upper.add(p)
+    }
+
+    // Remove last element of each list to avoid duplication
+    val hull = lower.dropLast(1) + upper.dropLast(1)
+    return hull
+}

app/src/main/java/org/fairscan/app/quad/RightAngles.kt

@@ -0,0 +1,133 @@
+/*
+ * Copyright 2025 Pierre-Yves Nicolas
+ *
+ * This program is free software: you can redistribute it and/or modify it
+ * under the terms of the GNU General Public License as published by the Free
+ * Software Foundation, either version 3 of the License, or (at your option)
+ * any later version.
+ * This program is distributed in the hope that it will be useful, but WITHOUT
+ * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
+ * FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for
+ * more details.
+ * You should have received a copy of the GNU General Public License along with
+ * this program. If not, see <https://www.gnu.org/licenses/>.
+ */
+package org.fairscan.app.quad
+
+import org.fairscan.app.Point
+import kotlin.math.abs
+import kotlin.math.acos
+import kotlin.math.sqrt
+import kotlin.math.sign
+
+// Look for 3 consecutive angles that are (almost) right angles
+fun findQuadFromRightAngles(
+    points: List<Point>,
+    imgWidth: Int,
+    imgHeight: Int,
+    angleMin: Float = 60f,
+    angleMax: Float = 120f
+): List<Point>? {
+    if (points.size < 4) return null
+    val n = points.size
+
+    val angles = mutableListOf<Double>()
+    for (i in 0 until n) {
+        val a = points[(i + n - 1) % n]
+        val b = points[i]
+        val c = points[(i + 1) % n]
+        angles.add(orientedAngle(a, b, c))
+    }
+
+    var bestQuad: List<Point>? = null
+    var bestScore = Double.POSITIVE_INFINITY
+
+    for (i in 0 until n) {
+        val triplet = listOf(angles[i % n], angles[(i + 1) % n], angles[(i + 2) % n])
+        if (triplet.all { it in angleMin..angleMax }) {
+            val a = points[(i + n - 1) % n]
+            val b = points[i]
+            val c = points[(i + 1) % n]
+            val d = points[(i + 2) % n]
+            val e = points[(i + 3) % n]
+
+            val inter = lineIntersection2(a, b, d, e) ?: continue
+
+            val quad = listOf(b, c, d, inter)
+
+            // ensure inside image bounds
+            if (quad.any { it.x < 0 || it.x >= imgWidth || it.y < 0 || it.y >= imgHeight }) continue
+
+            // ensure convex
+            if (!isConvex(quad)) continue
+
+            val score = quadAngleError(quad)
+            if (score < bestScore) {
+                bestScore = score
+                bestQuad = quad
+            }
+        }
+    }
+    return bestQuad
+}
+
+fun angleBetween(v1: Point, v2: Point): Float {
+    val norm1 = sqrt(v1.x * v1.x + v1.y * v1.y) + 1e-9f
+    val norm2 = sqrt(v2.x * v2.x + v2.y * v2.y) + 1e-9f
+    val dot = (v1.x * v2.x + v1.y * v2.y) / (norm1 * norm2)
+    val cosAngle = dot.coerceIn(-1.0, 1.0)
+    return Math.toDegrees(acos(cosAngle).toDouble()).toFloat()
+}
+
+fun orientedAngle(a: Point, b: Point, c: Point): Double {
+    val v1 = Point(a.x - b.x, a.y - b.y)
+    val v2 = Point(c.x - b.x, c.y - b.y)
+    val norm1 = sqrt(v1.x * v1.x + v1.y * v1.y) + 1e-9f
+    val norm2 = sqrt(v2.x * v2.x + v2.y * v2.y) + 1e-9f
+    val dot = ((v1.x * v2.x + v1.y * v2.y) / (norm1 * norm2)).coerceIn(-1.0, 1.0)
+    val cross = v1.x * v2.y - v1.y * v2.x
+    var angle = Math.toDegrees(acos(dot))
+    if (cross < 0) angle = 360.0 - angle
+    return angle
+}
+
+fun lineIntersection2(p1: Point, p2: Point, p3: Point, p4: Point): Point? {
+    val denom = (p1.x - p2.x) * (p3.y - p4.y) - (p1.y - p2.y) * (p3.x - p4.x)
+    if (abs(denom) < 1e-6f) return null
+    val numX = (p1.x * p2.y - p1.y * p2.x)
+    val numY = (p3.x * p4.y - p3.y * p4.x)
+    val px = (numX * (p3.x - p4.x) - (p1.x - p2.x) * numY) / denom
+    val py = (numX * (p3.y - p4.y) - (p1.y - p2.y) * numY) / denom
+    return Point(px, py)
+}
+
+fun quadAngleError(quad: List<Point>): Double {
+    var err = 0.0
+    for (i in 0 until 4) {
+        val a = quad[(i + 3) % 4]
+        val b = quad[i]
+        val c = quad[(i + 1) % 4]
+        val ang = angleBetween(Point(a.x - b.x, a.y - b.y), Point(c.x - b.x, c.y - b.y))
+        err += abs(ang - 90.0)
+    }
+    return err
+}
+
+fun isConvex(quad: List<Point>): Boolean {
+    if (quad.size != 4) return false
+    var sign = 0
+    for (i in quad.indices) {
+        val a = quad[i]
+        val b = quad[(i + 1) % 4]
+        val c = quad[(i + 2) % 4]
+        val cross = (b.x - a.x) * (c.y - b.y) - (b.y - a.y) * (c.x - b.x)
+        val currentSign = cross.sign.toInt()
+        if (sign == 0 && currentSign != 0) {
+            sign = currentSign
+        } else if (currentSign != 0 && currentSign != sign) {
+            return false
+        }
+    }
+    return true
+}
+