/** * 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; }