public AutoRotateAction()
{
this.Name = "AutoRotate";
this.Action = (input) =>
{
this.Status = "Processing...";
var maxRotation = this.MaxRotationDegrees.DegToRad();
var clusterThreshold = this.ClusterThresholdDegrees.DegToRad();
var zeroRotation = this.searchFor == SearchForEnum.HorizontalLines
? 90.0
: 0.0;
var canny = input.Canny(this.Threshold1, this.Threshold2, 3);
// Apply Hough Lines.
var lines = canny.HoughLines(this.HoughRho, this.HoughTheta, (int)this.HoughThreshold);
var thetaValues = lines
.Select(l => (double)l.Theta)
.Where(t => t.DistanceToRad(zeroRotation, ignoreRotationDirection: true) < maxRotation);
// Detect biggest cluster.
var cluster = ClusterDetection1D.Detect(thetaValues, clusterThreshold);
var(important, nonImportant) = lines.Split(line => cluster.Contains(line.Theta));
// Draw lines.
Mat image;
if (this.Debug)
{
if (input.Channels() == 1)
{
image = input.CvtColor(ColorConversionCodes.GRAY2BGR);
input.Dispose();
}
else
{
image = input;
}
foreach (var line in nonImportant)
{
line.Draw(image, Scalar.Red);
}
foreach (var line in important)
{
line.Draw(image, Scalar.Green);
}
}
else
{
image = input;
}
// Rotate.
var rotation = !cluster.Any() ? 0.0
: cluster.Average();
if (rotation != 0.0)
{
var origin = this.SearchFor == SearchForEnum.HorizontalLines
? 270.0 : 180.0;
var transformMatrix = Cv2.GetRotationMatrix2D(new Point2f(input.Width / 2, input.Height / 2), origin + rotation.RadToDeg(), 1);
var size = input.Size();
image = image.WarpAffine(transformMatrix, size);
}
this.Status = $"{lines.Length} lines detected ({important.Count} used to determine rotation of {rotation.RadToDeg():F1}°)";
return(image);
};
}
public void DetectTest(double[] input, double[] expectedOutput)
{
var cluster = ClusterDetection1D.Detect(input, 0.1);
cluster.Should().BeEquivalentTo(expectedOutput);
}