public Segmenter(ImageUInt8 gray) {
// Downsample image to smaller size
int sampleWidth = 300;
this.scale = (1.0 * gray.getWidth()) / sampleWidth;
int sampleHeight = (int) (gray.getHeight() / scale);
ImageUInt8 sample = new ImageUInt8(sampleWidth, sampleHeight);
boofcv.alg.distort.DistortImageOps.scale(
gray, sample, boofcv.alg.interpolate.TypeInterpolate.NEAREST_NEIGHBOR);
this.sample = sample;
// Calculate mean
this.mean = GPixelMath.sum(gray) / (gray.width * gray.height);
this.threshold = this.calculateThreshold();
Log.i(
"Setalyzer",
"brightness histogram: " + Arrays.toString(this.brightnessHistogram.histogram));
// Blur image and store blurred image
this.blurred = boofcv.alg.filter.blur.BlurImageOps.mean(sample, null, BLUR_RADIUS, null);
// Create binary image and label blobs for threshold-based segmentation
// Log.i("Setalyzer", "THreshold value is " + this.mean);
// Threshold to generate binary image
ImageUInt8 binary =
boofcv.alg.filter.binary.ThresholdImageOps.threshold(
this.blurred, null, (int) this.mean, false);
ImageSInt32 blobImage =
FactoryImage.create(ImageSInt32.class, sample.getWidth(), sample.getHeight());
this.numBlobs = boofcv.alg.filter.binary.BinaryImageOps.labelBlobs4(binary, blobImage);
this.blobQuads = quadsFromBlobs(blobImage, numBlobs);
// Canny detect edges and store them
// Dynamic canny edge, which sets the threshold as a function of the image's edge intensity
DetectEdgeContour<ImageUInt8> cannyD =
FactoryDetectEdgeContour.canny(0.05, 0.15, true, ImageUInt8.class, ImageSInt16.class);
cannyD.process(blurred);
List<List<Point2D_I32>> edges = cannyD.getContours();
// Prune edges which are obviously not cards
List<List<Point2D_F64>> prunedEdgeList = new ArrayList<List<Point2D_F64>>();
for (List<Point2D_I32> edge : edges) {
// Disregard tiny contours
if (edge.size() < EDGE_SIZE_THRESHOLD) {
continue;
}
// Disregard shapes with blatantly non-card shapes
// if (!isCardShaped(edge)) {
// continue;
// }
List<Point2D_F64> f64Edge = new ArrayList<Point2D_F64>();
for (Point2D_I32 p : edge) {
f64Edge.add(new Point2D_F64(p.x, p.y));
}
prunedEdgeList.add(f64Edge);
}
this.prunedCannyEdgeList = prunedEdgeList;
}
/** Fits polygons to found contours around binary blobs. */
public static void fitBinaryImage(ImageFloat32 input) {
ImageUInt8 binary = new ImageUInt8(input.width, input.height);
BufferedImage polygon =
new BufferedImage(input.width, input.height, BufferedImage.TYPE_INT_RGB);
// the mean pixel value is often a reasonable threshold when creating a binary image
double mean = ImageStatistics.mean(input);
// create a binary image by thresholding
ThresholdImageOps.threshold(input, binary, (float) mean, true);
// reduce noise with some filtering
ImageUInt8 filtered = BinaryImageOps.erode8(binary, null);
filtered = BinaryImageOps.dilate8(filtered, null);
// Find the contour around the shapes
List<Contour> contours = BinaryImageOps.contour(filtered, 8, null);
// Fit a polygon to each shape and draw the results
Graphics2D g2 = polygon.createGraphics();
g2.setStroke(new BasicStroke(2));
for (Contour c : contours) {
// Fit the polygon to the found external contour. Note loop = true
List<PointIndex_I32> vertexes =
ShapeFittingOps.fitPolygon(c.external, true, toleranceDist, toleranceAngle, 100);
g2.setColor(Color.RED);
VisualizeShapes.drawPolygon(vertexes, true, g2);
// handle internal contours now
g2.setColor(Color.BLUE);
for (List<Point2D_I32> internal : c.internal) {
vertexes = ShapeFittingOps.fitPolygon(internal, true, toleranceDist, toleranceAngle, 100);
VisualizeShapes.drawPolygon(vertexes, true, g2);
}
}
ShowImages.showWindow(polygon, "Binary Blob Contours");
}