@Override
public void apply(Mat src, Mat dst) {
Core.split(src, mChannels);
final Mat r = mChannels.get(0);
final Mat g = mChannels.get(1);
final Mat b = mChannels.get(2);
Core.min(b, r, b);
Core.min(b, g, b);
Core.merge(mChannels, dst);
}
public static Mat Histogram(Mat im) {
Mat img = im;
Mat equ = new Mat();
img.copyTo(equ);
// Imgproc.blur(equ, equ, new Size(3, 3));
Imgproc.cvtColor(equ, equ, Imgproc.COLOR_BGR2YCrCb);
List<Mat> channels = new ArrayList<Mat>();
Core.split(equ, channels);
Imgproc.equalizeHist(channels.get(0), channels.get(0));
Core.merge(channels, equ);
Imgproc.cvtColor(equ, equ, Imgproc.COLOR_YCrCb2BGR);
Mat gray = new Mat();
Imgproc.cvtColor(equ, gray, Imgproc.COLOR_BGR2GRAY);
Mat grayOrig = new Mat();
Imgproc.cvtColor(img, grayOrig, Imgproc.COLOR_BGR2GRAY);
System.out.println("Histogram work ///");
return grayOrig;
}
/**
* @param inputImg
* @return Mat
*/
public static Mat kmeans(Mat inputImg) {
Mat rgba = inputImg;
Mat tempMat = inputImg;
rgba = new Mat(inputImg.cols(), inputImg.rows(), CvType.CV_8UC3);
inputImg.copyTo(rgba);
List<Mat> hsv_planes_temp = new ArrayList<Mat>(3);
Core.split(tempMat, hsv_planes_temp);
double threshValue1 = PreProcessingOperation.getHistAverage(inputImg, hsv_planes_temp.get(0));
sample.util.Estimate.setFirstHistAverageValue(threshValue1);
System.out.println("Defore eqau " + threshValue1);
System.out.println(
Estimate.getBlueAverage() + " ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;");
if (threshValue1 > 140) {
if (Estimate.getBlueAverage() > 110) {
rgba.convertTo(rgba, -1, 10d * 31 / 100, 0);
System.out.println("11");
} else {
rgba.convertTo(rgba, -1, 10d * 40 / 100, 0);
System.out.println("12");
}
} else if (threshValue1 > 135) {
rgba.convertTo(rgba, -1, 10d * 32 / 100, 0);
System.out.println("21");
} else if (threshValue1 > 125) {
if (Estimate.getBlueAverage() > 110) {
rgba.convertTo(rgba, -1, 10d * 30 / 100, 0);
rgba = PreProcessing.Dilate(rgba, 5);
System.out.println("31");
} else {
rgba.convertTo(rgba, -1, 10d * 37 / 100, 0);
System.out.println("32");
}
} else if (threshValue1 > 120) {
rgba.convertTo(rgba, -1, 10d * 35 / 100, 0);
System.out.println("41");
} else if (threshValue1 > 110) {
if (Estimate.getBlueAverage() > 110) {
rgba.convertTo(rgba, -1, 10d * 35 / 100, 0);
rgba = PreProcessing.Dilate(rgba, 5);
System.out.println("51");
}
} else if (threshValue1 > 100) {
if (Estimate.getBlueAverage() > 107) {
rgba.convertTo(rgba, -1, 10d * 24 / 100, 0);
rgba = PreProcessing.Dilate(rgba, 5);
System.out.println("61");
} else if (Estimate.getBlueAverage() > 90) {
rgba.convertTo(rgba, -1, 10d * 30 / 100, 0);
rgba = PreProcessing.Dilate(rgba, 5);
System.out.println("62");
}
} else if (threshValue1 > 50) {
if (Estimate.getBlueAverage() > 160) {
rgba.convertTo(rgba, -1, 10d * 30 / 100, 0);
rgba = PreProcessing.Dilate(rgba, 3);
System.out.println("81");
} else if (Estimate.getBlueAverage() > 160) {
rgba.convertTo(rgba, -1, 10d * 27 / 100, 0);
rgba = PreProcessing.Dilate(rgba, 9);
System.out.println("82");
} else if (Estimate.getBlueAverage() > 130) {
rgba.convertTo(rgba, -1, 10d * 30 / 100, 0);
rgba = PreProcessing.Dilate(rgba, 9);
System.out.println("83");
} else if (Estimate.getBlueAverage() > 70) {
rgba.convertTo(rgba, -1, 10d * 29 / 100, 0);
rgba = PreProcessing.Dilate(rgba, 9);
System.out.println("84");
}
} else if (threshValue1 > 30) {
if (Estimate.getBlueAverage() > 190) {
rgba.convertTo(rgba, -1, 10d * 25 / 100, 0);
System.out.println("91");
} else if (Estimate.getBlueAverage() > 160) {
rgba.convertTo(rgba, -1, 10d * 35 / 100, 0);
System.out.println("92");
}
} else {
if (Estimate.getBlueAverage() > 240) {
rgba.convertTo(rgba, -1, 10d * 24 / 100, 0);
System.out.println("7");
} else {
rgba.convertTo(rgba, -1, 10d * 17 / 100, 0);
System.out.println("7");
}
}
tempMat.release();
Mat mHSV = new Mat();
Imgproc.cvtColor(rgba, mHSV, Imgproc.COLOR_RGBA2RGB, 3);
Imgproc.cvtColor(rgba, mHSV, Imgproc.COLOR_RGB2HSV, 3);
List<Mat> hsv_planes = new ArrayList<Mat>(3);
Core.split(mHSV, hsv_planes);
Mat channel = hsv_planes.get(0);
channel = Mat.zeros(mHSV.rows(), mHSV.cols(), CvType.CV_8UC1);
hsv_planes.set(2, channel);
Core.merge(hsv_planes, mHSV);
mHSV.convertTo(mHSV, CvType.CV_8UC1);
mHSV = Histogram(mHSV);
/*
Mat clusteredHSV = new Mat();
mHSV.convertTo(mHSV, CvType.CV_32FC3);
TermCriteria criteria = new TermCriteria(TermCriteria.EPS + TermCriteria.MAX_ITER,100,0.1);
Core.kmeans(mHSV, 1, clusteredHSV, criteria, 20, Core.KMEANS_PP_CENTERS);
Mat hsvImg = new Mat();
List<Mat> hsvPlanes = new ArrayList<>();
Mat thresholdImg = new Mat();
int thresh_type = Imgproc.THRESH_BINARY_INV;
hsvImg.create(mHSV.size(), CvType.CV_8U);
Imgproc.cvtColor(mHSV, hsvImg, Imgproc.COLOR_BGR2HSV);
Core.split(hsvImg, hsvPlanes);
Imgproc.threshold(hsvPlanes.get(1), thresholdImg, 0 , 200 , thresh_type);
double threshValue = PreProcessingOperation.getHistAverage(hsvImg, hsvPlanes.get(0));
Estimate.setSecondHistAverageValue(threshValue);
System.out.println("After equa " + Estimate.getSecondHistAverageValue());*/
Imgproc.threshold(mHSV, mHSV, 0, 150, Imgproc.THRESH_BINARY_INV);
// mHSV.convertTo(mHSV, CvType.CV_8UC1);
return mHSV;
}
