@Test public void performLearning() { float interp_factor = 0.075f; ImageFloat32 a = new ImageFloat32(20, 25); ImageFloat32 b = new ImageFloat32(20, 25); ImageMiscOps.fill(a, 100); ImageMiscOps.fill(b, 200); CirculantTracker<ImageFloat32> alg = new CirculantTracker<ImageFloat32>(1f / 16, 0.2, 1e-2, 0.075, 1.0, 64, 255, interp); alg.initialize(a, 0, 0, 20, 25); // copy its internal value ImageFloat64 templateC = new ImageFloat64(alg.template.width, alg.template.height); templateC.setTo(alg.template); // give it two images alg.performLearning(b); // make sure the images aren't full of zero assertTrue(Math.abs(ImageStatistics.sum(templateC)) > 0.1); assertTrue(Math.abs(ImageStatistics.sum(alg.template)) > 0.1); int numNotSame = 0; // the result should be an average of the two for (int i = 0; i < a.data.length; i++) { if (Math.abs(a.data[i] - alg.templateNew.data[i]) > 1e-4) numNotSame++; // should be more like the original one than the new one double expected = templateC.data[i] * (1 - interp_factor) + interp_factor * alg.templateNew.data[i]; double found = alg.template.data[i]; assertEquals(expected, found, 1e-4); } // make sure it is actually different assertTrue(numNotSame > 100); }
protected void performThresholding(float threshLow, float threshHigh, ImageUInt8 output) { if (hysteresisPts != null) { hysteresisPts.process(suppressed, direction, threshLow, threshHigh); // if there is an output image write the contour to it if (output != null) { ImageMiscOps.fill(output, 0); for (EdgeContour e : hysteresisPts.getContours()) { for (EdgeSegment s : e.segments) for (Point2D_I32 p : s.points) output.unsafe_set(p.x, p.y, 1); } } } else { hysteresisMark.process(suppressed, direction, threshLow, threshHigh, output); } }
@Override public void renderTarget(ImageFloat32 original, List<CalibrationObservation> solutions) { ImageMiscOps.fill(original, 255); int numRows = config.numRows * 2 - 1; int numCols = config.numCols * 2 - 1; int square = original.getWidth() / (Math.max(numRows, numCols) + 4); int targetWidth = square * numCols; int targetHeight = square * numRows; int x0 = (original.width - targetWidth) / 2; int y0 = (original.height - targetHeight) / 2; for (int i = 0; i < numRows; i += 2) { int y = y0 + i * square; for (int j = 0; j < numCols; j += 2) { int x = x0 + j * square; ImageMiscOps.fillRectangle(original, 0, x, y, square, square); } } int pointsRow = numRows + 1; int pointsCol = numCols + 1; CalibrationObservation set = new CalibrationObservation(); int gridIndex = 0; for (int i = 0; i < pointsRow; i++) { for (int j = 0; j < pointsCol; j++, gridIndex++) { double y = y0 + i * square; double x = x0 + j * square; set.add(new Point2D_F64(x, y), gridIndex); } } solutions.add(set); }
@Test public void checkRender() { // Easier to make up a plane in this direction Se3_F64 cameraToPlane = new Se3_F64(); ConvertRotation3D_F64.eulerToMatrix( EulerType.XYZ, UtilAngle.degreeToRadian(0), 0, 0, cameraToPlane.getR()); cameraToPlane.getT().set(0, -5, 0); Se3_F64 planeToCamera = cameraToPlane.invert(null); CreateSyntheticOverheadViewMS<ImageFloat32> alg = new CreateSyntheticOverheadViewMS<ImageFloat32>( TypeInterpolate.BILINEAR, 3, ImageFloat32.class); alg.configure(param, planeToCamera, centerX, centerY, cellSize, overheadW, overheadH); MultiSpectral<ImageFloat32> input = new MultiSpectral<ImageFloat32>(ImageFloat32.class, width, height, 3); for (int i = 0; i < 3; i++) ImageMiscOps.fill(input.getBand(i), 10 + i); MultiSpectral<ImageFloat32> output = new MultiSpectral<ImageFloat32>(ImageFloat32.class, overheadW, overheadH, 3); alg.process(input, output); for (int i = 0; i < 3; i++) { ImageFloat32 o = output.getBand(i); // check parts that shouldn't be in view assertEquals(0, o.get(0, 300), 1e-8); assertEquals(0, o.get(5, 0), 1e-8); assertEquals(0, o.get(5, 599), 1e-8); // check areas that should be in view assertEquals(10 + i, o.get(499, 300), 1e-8); } }