public static Mat getCCH(Mat image) {
ArrayList<MatOfPoint> contours = new ArrayList<MatOfPoint>();
Mat hierarchy = new Mat();
Imgproc.findContours(
image, contours, hierarchy, Imgproc.RETR_EXTERNAL, Imgproc.CHAIN_APPROX_NONE);
Mat chainHistogram = Mat.zeros(1, 8, CvType.CV_32F);
int n = 0;
MatOfPoint2f approxCurve = new MatOfPoint2f();
for (MatOfPoint contour : contours) {
// get the freeman chain code from the contours
int rows = contour.rows();
// System.out.println("\nrows"+rows+"\n"+contour.dump());
int direction = 7;
Mat prevPoint = contours.get(0).row(0);
n += rows - 1;
for (int i = 1; i < rows; i++) {
// get the current point
double x1 = contour.get(i - 1, 0)[1];
double y1 = contour.get(i - 1, 0)[0];
// get the second point
double x2 = contour.get(i, 0)[1];
double y2 = contour.get(i, 0)[0];
if (x2 == x1 && y2 == y1 + 1) direction = 0;
else if (x2 == x1 - 1 && y2 == y1 + 1) direction = 1;
else if (x2 == x1 - 1 && y2 == y1) direction = 2;
else if (x2 == x1 - 1 && y2 == y1 - 1) direction = 3;
else if (x2 == x1 && y2 == y1 - 1) direction = 4;
else if (x2 == x1 + 1 && y2 == y1 - 1) direction = 5;
else if (x2 == x1 + 1 && y2 == y1) direction = 6;
else if (x2 == x1 + 1 && y2 == y1 + 1) direction = 7;
else System.out.print("err");
double counter = chainHistogram.get(0, direction)[0];
chainHistogram.put(0, direction, ++counter);
System.out.print(direction);
}
}
System.out.println("\n" + chainHistogram.dump());
Scalar alpha = new Scalar(n); // the factor
Core.divide(chainHistogram, alpha, chainHistogram);
System.out.println("\nrows=" + n + " " + chainHistogram.dump());
return chainHistogram;
}
/**
* Identifies the color in the frame
*
* @param in the Mat image in the region of interest
* @return the color
*/
public char identifyColor(Mat in) {
// Mat blue = new Mat(in.rows(), in.cols(), CvType.CV_8UC1);
// Mat green = new Mat(in.rows(), in.cols(), CvType.CV_8UC1);
// Mat red = new Mat(in.rows(), in.cols(), CvType.CV_8UC1);
// split the channels of the image
Mat blue = new Mat(); // default is CV_8UC3
Mat green = new Mat();
Mat red = new Mat();
List<Mat> channels = new ArrayList<Mat>(3);
Core.split(in, channels);
blue = channels.get(0); // makes all 3 CV_8UC1
green = channels.get(1);
red = channels.get(2);
// System.out.println(blue.toString());
// add the intensities
Mat intensity = new Mat(in.rows(), in.cols(), CvType.CV_32F);
// Mat mask = new Mat();
Core.add(blue, green, intensity); // , mask, CvType.CV_32F);
Core.add(intensity, red, intensity); // , mask, CvType.CV_32F);
// not sure if correct from here to ...
Mat inten = new Mat();
Core.divide(intensity, Scalar.all(3.0), inten);
// System.out.println(intensity.toString());
// Core.divide(3.0, intensity, inten);
// if intensity = intensity / 3.0; means element-wise division
// use intensity.muls(Mat m)
// so make new Mat m of same size that has each element of 1/3
/*
* or
* About per-element division you can use Core.divide()
Core.divide(A,Scalar.all(d), B);
It's equivalent to B=A/d
*/
// find normalized values
Mat bnorm = new Mat();
Mat gnorm = new Mat();
Mat rnorm = new Mat();
// blue.convertTo(blue, CvType.CV_32F);
// green.convertTo(green, CvType.CV_32F);
// red.convertTo(red, CvType.CV_32F);
Core.divide(blue, inten, bnorm);
Core.divide(green, inten, gnorm);
Core.divide(red, inten, rnorm);
// find average norm values
Scalar val = new Scalar(0);
val = Core.mean(bnorm);
String value[] = val.toString().split(",");
String s = value[0].substring(1);
double bavg = Double.parseDouble(s);
val = Core.mean(gnorm);
String value1[] = val.toString().split(",");
String s1 = value1[0].substring(1);
double gavg = Double.parseDouble(s1);
val = Core.mean(rnorm);
String value2[] = val.toString().split(",");
String s2 = value2[0].substring(1);
double ravg = Double.parseDouble(s2);
// ... here
// original values
/*
// define the reference color values
//double RED[] = {0.4, 0.5, 1.8};
//double GREEN[] = {1.0, 1.2, 1.0};
double BLUE[] = {1.75, 1.0, 0.5};
//double YELLOW[] = {0.82, 1.7, 1.7};
double ORANGE[] = {0.2, 1.0, 2.0};
double WHITE[] = {2.0, 1.7, 1.7};
//double BLACK[] = {0.0, 0.3, 0.3};
*/
// define the reference color values
// double RED[] = {0.4, 0.5, 1.8};
// double GREEN[] = {1.0, 1.2, 1.0};
double BLUE[] = {1.75, 1.0, 0.5};
// double YELLOW[] = {0.82, 1.7, 1.7};
double ORANGE[] = {0.2, 1.0, 2.0};
double WHITE[] = {2.0, 1.7, 1.7};
// double BLACK[] = {0.0, 0.3, 0.3};
// compute the square error relative to the reference color values
// double minError = 3.0;
double minError = 2.0;
double errorSqr;
char bestFit = 'x';
// test++;
// System.out.print("\n\n" + test + "\n\n");
// check BLUE fitness
errorSqr = normSqr(BLUE[0], BLUE[1], BLUE[2], bavg, gavg, ravg);
System.out.println("Blue: " + errorSqr);
if (errorSqr < minError) {
minError = errorSqr;
bestFit = COLOR_BLUE;
}
// check ORANGE fitness
errorSqr = normSqr(ORANGE[0], ORANGE[1], ORANGE[2], bavg, gavg, ravg);
System.out.println("Orange: " + errorSqr);
if (errorSqr < minError) {
minError = errorSqr;
bestFit = COLOR_ORANGE;
}
// check WHITE fitness
errorSqr = normSqr(WHITE[0], WHITE[1], WHITE[2], bavg, gavg, ravg);
System.out.println("White: " + errorSqr);
if (errorSqr < minError) {
minError = errorSqr;
bestFit = COLOR_WHITE;
}
// check BLACK fitness
/*errorSqr = normSqr(BLACK[0], BLACK[1], BLACK[2], bavg, gavg, ravg);
System.out.println("Black: " + errorSqr);
if(errorSqr < minError)
{
minError = errorSqr;
bestFit = COLOR_BLACK;
}*/
// return the best fit color label
return bestFit;
}
