Develop Reference

K-Means clustering for color based segmentation using Opencv in Android

I want to implement K-means clustering for segmenting an image based on color intensity and actually i do not know how to get the segmented image and ROI after applying Core.kmeans function. I followed the steps in the question in here here but there is no answer to how to proceed from this point. Any help would be appreciated.
Thanks in advance.
This the code I am using based on different sources and actually it is not working
//convert to lab with three channels
Mat imgLab = new Mat();
Mat imgMat = new Mat();
Imgproc.cvtColor(imgMat, imgLab, Imgproc.COLOR_RGB2Lab, 3);
// separate channels
List<Mat> lab_planes = new ArrayList<Mat>(3);
Core.split(imgMat, lab_planes);
Mat channel = lab_planes.get(2);
channel = Mat.zeros(imgLab.rows(), imgLab.cols(), CvType.CV_8UC1);
// use only AB channels in Lab color space
lab_planes.set(2, channel);
Core.merge(lab_planes,imgLab);
Mat samples = imgLab.reshape(1, imgLab.cols() * imgLab.rows());
Mat samples32f = new Mat();
samples.convertTo(samples32f, CvType.CV_32F, 1.0 / 255.0);
Mat labels = new Mat();
TermCriteria criteria = new TermCriteria(TermCriteria.EPS + TermCriteria.MAX_ITER, 100, 1);
Mat centers = new Mat();
//Mat clusteredLab = new Mat();
int nColors = 3; //number of clusters (k)
int attempt = 3; //number of attempts
// repeat the clustering 3 times to avoid local minima
Core.kmeans(samples32f, nColors, labels, criteria, attempt, Core.KMEANS_PP_CENTERS, centers);
centers.convertTo(centers, CvType.CV_8UC1);
centers.reshape(3);
//the rest of code for RGB image not Lab
List<Mat> clusters = new ArrayList<Mat>();
for (int i = 0; i < centers.rows(); i++) {
clusters.add(Mat.zeros(samples.size(), samples.type()));
}
Map<Integer, Integer> counts = new HashMap<Integer, Integer>();
for (int i = 0; i < centers.rows(); i++) counts.put(i, 0);
int rows = 0;
for (int y = 0; y < samples.rows(); y++) {
for (int x = 0; x < samples.cols(); x++) {
int label = (int) labels.get(rows, 0)[0];
int r = (int) centers.get(label, 2)[0];
int g = (int) centers.get(label, 1)[0];
int b = (int) centers.get(label, 0)[0];
counts.put(label, counts.get(label) + 1);
clusters.get(label).put(y, x, b, g, r);
rows++;
}
}
return clusters;
}

Android OpenCv people count

I am developing an Android application to count number of people in real time video. I used OpenCv to detect people but not finding a way to count them. If any one knows how to do it then please help me.
Here is the code to detect people in coming video frame
#Override
public Mat onCameraFrame(CameraBridgeViewBase.CvCameraViewFrame inputFrame) {
List<MatOfPoint> list = new ArrayList<>();
Mat frame = new Mat();
Mat gray = new Mat();
Mat hierarchy = new Mat();
Mat originalFrame = inputFrame.rgba();
Core.transpose(originalFrame,mRgbaT);
Imgproc.resize(mRgbaT,mRgbaF,mRgbaF.size(),0,0,0);
Core.flip(mRgbaF,originalFrame,1);
Imgproc.medianBlur(originalFrame,originalFrame,3);
Imgproc.cvtColor(originalFrame, gray, Imgproc.CV_BGR2GRAY, 0);
HOGDescriptor hog = new HOGDescriptor();
//Получаем стандартный определитель людей и устанавливаем его нашему дескриптору
MatOfFloat descriptors = HOGDescriptor.getDefaultPeopleDetector();
// MatOfFloat dfdfdf=HOGDescriptor.getDefaultPeopleDetector();
hog.setSVMDetector(descriptors);
MatOfRect locations = new MatOfRect();
MatOfDouble weights = new MatOfDouble();
hog.detectMultiScale(gray, locations, weights);
Point rectPoint1 = new Point();
Point rectPoint2 = new Point();
Point fontPoint = new Point();
if (locations.rows() > 0) {
List<Rect> rectangles = locations.toList();
for (Rect rect : rectangles) {
rectPoint1.x = rect.x;
rectPoint1.y = rect.y;
fontPoint.x = rect.x;
fontPoint.y = rect.y - 4;
rectPoint2.x = rect.x + rect.width;
rectPoint2.y = rect.y + rect.height;
final Scalar rectColor = new Scalar( 0 , 0 , 0 );
// Добавляем на изображения найденную информацию
Imgproc.rectangle(originalFrame, rectPoint1, rectPoint2, rectColor, 2);
}
}
frame.release();
gray.release();
hierarchy.release();
int i=list.size();
list.clear();
return originalFrame;
}

OpenCV Histogram Matching of Color Image

I am trying to write an Android App that performs histogram matching of color images using OpenCV3.1. I found this code example in C++, and converted it to java.
I tried to match each RGB chanel separately but it did not gave me the desired results. So now I'm converting the images to YUV color space and then match the Y chanels. What I hope to achieve is a brightnes matching, so if the source is brighter then the target I will get darker image.
So far it seems that the Y histogram of the final(output) image is somewhat close to the Y histogram of target image, but the actual output dosen't look like the target.
Here is the relevan code:
private Mat calculateLUT(Mat in_cdf_mat, Mat dst_cdf_mat) {
int last = 0;
double epsilon = Double.parseDouble(epsilonTextView.getText().toString());// epsilon set to 0.01
Mat M = new Mat(256, 1,CvType.CV_8UC1);
for(int j=0; j<in_cdf_mat.rows(); j++) {
double F1j = in_cdf_mat.get(j,0)[0];
for(int k = last; k < dst_cdf_mat.rows(); k++) {
double F2k = dst_cdf_mat.get(k,0)[0];
if(Math.abs(F2k - F1j) < epsilon || F2k > F1j) {
double[] data = {k} ;
M.put(j, 0, data);
last = k;
break;
}
}
}
return M;
}
private void calculateCDF (Mat channel, Mat cdf)
{
// channel holds the histogram. The indices represents the pixel color
// and the value is the amount of pixels of that color in the image
for (int i = 1; i < 256; i++) {
double[] data = new double[1];
data[0] = cdf.get(i-1,0)[0] + channel.get(i,0)[0];
cdf.put(i, 0, data);
}
}
private void calcHistogram(String imgPath, Mat y_hist, Mat y_cdf) {
Mat image;
Mat ycrcb = new Mat();
image = Imgcodecs.imread(imgPath);
Imgproc.cvtColor(image, ycrcb, Imgproc.COLOR_RGB2YCrCb);
image.release();
List<Mat> ycrcbChannels= new ArrayList<>();
Core.split(ycrcb,ycrcbChannels);
List<Mat> yList = new ArrayList<>();
yList.add(ycrcbChannels.get(0));
MatOfInt histSize = new MatOfInt(256);
MatOfFloat histRange = new MatOfFloat(0f, 256f);
Imgproc.calcHist(yList, new MatOfInt(0), new Mat(), y_hist, histSize, histRange, false);
Core.normalize(y_hist, y_hist, 3, 255, Core.NORM_MINMAX);
calculateCDF(y_hist, y_cdf);
Core.normalize(y_cdf, y_cdf, 3, 255, Core.NORM_MINMAX);
}
private void transformLight(Mat inputImage, Mat outputImage, Mat ylut) {
Mat imageYCrCb = new Mat();
Imgproc.cvtColor(inputImage, imageYCrCb, Imgproc.COLOR_RGB2YCrCb);
Mat y_chanel = new Mat();
Core.extractChannel(imageYCrCb, y_chanel, 0);
Mat cr_chanel = new Mat();
Core.extractChannel(imageYCrCb, cr_chanel, 1);
Mat cb_chanel = new Mat();
Core.extractChannel(imageYCrCb, cb_chanel, 2);
Core.LUT(y_chanel, ylut,y_chanel);
ArrayList<Mat> ycrcbDest = new ArrayList<>();
ycrcbDest.add(y_chanel);
ycrcbDest.add(cr_chanel);
ycrcbDest.add(cb_chanel);
Core.merge(ycrcbDest,outputImage);
Imgproc.cvtColor(outputImage, outputImage, Imgproc.COLOR_YCrCb2RGB);
}
private static void drawLine (Mat mat, int i, long bin_w, int hist_h, Mat histImage, Scalar color) {
// bin_w set to 1
Point p0 = new Point(bin_w * (i - 1), hist_h - Math.round(mat.get(i-1,0)[0]) );
Point p1 = new Point(bin_w * (i), hist_h - Math.round(mat.get(i,0)[0]) );
Imgproc.line(histImage, p0, p1, color, 5, 8, 0);
}
private void drawHistogram(Mat histImage, Mat graph, Scalar color) {
for (int i = 1; i < 256; i++) {
drawLine(graph, i, bin_w, histImage.rows(), histImage, color);
}
}
private void histNCDFtoFile(String filename, Mat hist, Mat cdf, Scalar histColor, Scalar cdfColor) {
Mat histImage = new Mat(256, 256, CvType.CV_8UC3);
drawHistogram(histImage, hist, histColor);
drawHistogram(histImage, cdf, cdfColor);
saveImage(filename, histImage);
}
private Mat matchHistograms(String input, String traget) {
Mat input_y_hist = new Mat();
Mat target_y_hist = new Mat();
calcHistogram(input, input_y_hist, input_y_cdf_mat);
histNCDFtoFile("inputHistNCDF.jpg", input_y_hist, input_y_cdf_mat, inputHistColor, inputCDFColor);
calcHistogram(traget, target_y_hist, target_y_cdf_mat);
histNCDFtoFile("targetHistNCDF.jpg", target_y_hist, target_y_cdf_mat, targetHistColor, targetCDFColor);
Mat ylut = calculateLUT(input_y_cdf_mat, target_y_cdf_mat);
Mat image;
Mat dst = new Mat(); // this Matrix will hold the transformed image
image = Imgcodecs.imread(input);
transformLight(image, dst, ylut);
return dst;
}
Here is an exmaple image from pixabay that I want to transform:
And this is the image that I use as a target:
And this is the result:
The CDF of the result and the target is here:
The light green is the target CDF and the light blue is the result CDF

Opencv - select rectangle and apply transformation from image in android?

I want to detect paper sheet from image.I applied median Blur, Canny ,dilate,threshold,Etc. algorithms to find.i am able to find sheet but don't know how to crop rectangle and apply transformation
This is my code :-
Mat blurred = new Mat();
Imgproc.medianBlur(src, blurred, 9);
// Set up images to use.
Mat gray0 = new Mat(blurred.size(), CvType.CV_8U);
Mat gray = new Mat();
// For Core.mixChannels.
List<MatOfPoint> contours = new ArrayList<MatOfPoint>();
List<MatOfPoint2f> rectangles = new ArrayList<MatOfPoint2f>();
List<Mat> sources = new ArrayList<Mat>();
sources.add(blurred);
List<Mat> destinations = new ArrayList<Mat>();
destinations.add(gray0);
// To filter rectangles by their areas.
int srcArea = src.rows() * src.cols();
// Find squares in every color plane of the image.
for (int c = 0; c < 3; c++) {
int[] ch = {c, 0};
MatOfInt fromTo = new MatOfInt(ch);
Core.mixChannels(sources, destinations, fromTo);
// Try several threshold levels.
for (int l = 0; l < N; l++) {
if (l == 0) {
// HACK: Use Canny instead of zero threshold level.
// Canny helps to catch squares with gradient shading.
// NOTE: No kernel size parameters on Java API.
Imgproc.Canny(gray0, gray, 0, CANNY_THRESHOLD);
// Dilate Canny output to remove potential holes between edge segments.
Imgproc.dilate(gray, gray, Mat.ones(new Size(3, 3), 0));
} else {
int threshold = (l + 1) * 255 / N;
Imgproc.threshold(gray0, gray, threshold, 255, Imgproc.THRESH_BINARY);
}
// Find contours and store them all as a list.
Imgproc.findContours(gray, contours, new Mat(), Imgproc.RETR_LIST, Imgproc.CHAIN_APPROX_SIMPLE);
int i=0;
for (MatOfPoint contour : contours) {
MatOfPoint2f contourFloat = GeomUtils.toMatOfPointFloat(contour);
double arcLen = Imgproc.arcLength(contourFloat, true) * 0.02;
// Approximate polygonal curves.
MatOfPoint2f approx = new MatOfPoint2f();
Imgproc.approxPolyDP(contourFloat, approx, arcLen, true);
if (isRectangle(approx, srcArea)) {
Imgproc.drawContours(src, contours, i, new Scalar(255, 0, 0), 3);
//rectangles.add(approx);
/*Rect rect = Imgproc.boundingRect(contour);
Log.e("Rectangle Finder:-" + i, "Height:-" + rect.height + ", Width:-" + rect.width + " and Area:-" + rect.area() + "\nX:-" + rect.x + ",Y:-" + rect.y);*/
}
i++;
}
}
I want to select only white papersheet.please help me
Thanks in advance

Why is pointPolygonTest() method of OpenCV4Android returning -1 for every pixel?

In the following code, I have carried out the following steps:
Loaded an image from sdcard.
Converted it to HSV format.
Used inRange function to mask out the red color.
Used findContours to find the contours.
Find the largest contour from those contours.
Created an ROI around the largest contour using boundingRect and submat functions.
Converted this ROI Mat to HSV format.
Iterated through the ROI Mat, and check for each pixel if it lies within the largest contour. I used the method pointPolygonTest to find this out, but it returns -1 for every pixel, as can be seen from the Log.i output I have pasted here. The question is why? How can I correct this.
private Scalar detectColoredBlob() {
rgbaFrame = Highgui.imread("/mnt/sdcard/DCIM/rgbaMat4Mask.bmp");
Mat hsvImage = new Mat();
Imgproc.cvtColor(rgbaFrame, hsvImage, Imgproc.COLOR_BGR2HSV);
Highgui.imwrite("/mnt/sdcard/DCIM/hsvImage.bmp", hsvImage);// check
Mat maskedImage = new Mat();
Core.inRange(hsvImage, new Scalar(0, 100, 100), new Scalar(10, 255, 255), maskedImage);
Highgui.imwrite("/mnt/sdcard/DCIM/maskedImage.bmp", maskedImage);// check
List<MatOfPoint> contours = new ArrayList<MatOfPoint>();
Imgproc.findContours(maskedImage, contours, new Mat(), Imgproc.RETR_LIST, Imgproc.CHAIN_APPROX_SIMPLE);
// \/ We will use only the largest contour. Other contours (any other possible blobs of this color range) will be ignored.
MatOfPoint largestContour = contours.get(0);
double largestContourArea = Imgproc.contourArea(largestContour);
for (int i = 1; i < contours.size(); ++i) {// NB Notice the prefix increment.
MatOfPoint currentContour = contours.get(i);
double currentContourArea = Imgproc.contourArea(currentContour);
if (currentContourArea > largestContourArea) {
largestContourArea = currentContourArea;
largestContour = currentContour;
}
}
MatOfPoint2f largestContour2f = new MatOfPoint2f(largestContour.toArray());// Required on Line 289. See http://stackoverflow.com/questions/11273588/how-to-convert-matofpoint-to-matofpoint2f-in-opencv-java-api
Rect detectedBlobRoi = Imgproc.boundingRect(largestContour);
Mat detectedBlobRgba = rgbaFrame.submat(detectedBlobRoi);
Highgui.imwrite("/mnt/sdcard/DCIM/detectedBlobRgba.bmp", detectedBlobRgba);// check
Mat detectedBlobHsv = new Mat();
Imgproc.cvtColor(detectedBlobRgba, detectedBlobHsv, Imgproc.COLOR_BGR2HSV);
Highgui.imwrite("/mnt/sdcard/DCIM/roiHsv.bmp", detectedBlobHsv);// check
for (int firstCoordinate = 0; firstCoordinate < detectedBlobHsv.rows(); firstCoordinate++) {
for (int secondCoordinate = 0; secondCoordinate < detectedBlobHsv.cols(); secondCoordinate++) {
Log.i(TAG, "HAPPY " + Arrays.toString(detectedBlobHsv.get(firstCoordinate, secondCoordinate)));
if (Imgproc.pointPolygonTest(largestContour2f, new Point(firstCoordinate, secondCoordinate), false) == -1) {
Log.i(TAG, "HAPPY ....................... OUTSIDE");
}
}
}
Highgui.imwrite("/mnt/sdcard/DCIM/processedcontoured.bmp", detectedBlobHsv);// check
EDIT:
I am doing this because I need to compute the average HSV color of pixels lying inside the contour (i.e. the average HSV color of the biggest red colored blob). If I computed the average color of the ROI detectedBlobHsv by the normal formula, I would do something like
Scalar averageHsvColor= new Scalar(256);
Scalar sumHsvOfPixels = new Scalar(256);
sumHsvOfPixels = Core.sumElems(detectedBlobHsv);
int numOfPixels = detectedBlobHsv.width() * detectedBlobHsv.height();
for (int channel=0; channel<sumHsvOfPixels.val.length; channel++) {
averageHsvColor = sumHsvOfPixels.val[channel]/numOfPixels;
}
So somebody here on SO (probably you?) had suggested me a way to exclude pixels outside my contour a while back. I'd implement that like:
//Giving pixels outside contour of interest an HSV value of `double[]{0,0,0}`, so that they don't affect the computation of `sumHsvOfPixels` while computing average,
//and while keeping track of the number of pixels removed from computation this way, so we can subtract that number from the `$numOfPixels` during computation of average.
int pixelsRemoved = 0;
for (int row=0; row<detectedBlobHsv.rows(); row++) {
for (int col=0; col<detectedBlobHsv.cols(); col++) {
if (Imgproc.pointPolygonTest(largestContour2f, new Point(row, col), false) == -1) {
detectedBlobHsv.put(row, col, new double[]{0,0,0});
pixelsRemoved++;
}
}
}
Then compute the average like
Scalar averageHsvColor= new Scalar(256);
Scalar sumHsvOfPixels = new Scalar(256);
sumHsvOfPixels = Core.sumElems(detectedBlobHsv); //This will now exclude pixels outside the contour
int numOfPixels = ( detectedBlobHsv.width()*detectedBlobHsv.height() )-pixelsRemoved;
for (int channel=0; channel<sumHsvOfPixels.val.length; channel++) {
averageHsvColor = sumHsvOfPixels.val[channel]/numOfPixels;
}
EDIT 1:
Towards the end of the following method, I have created the mask with a list of MatOfPoints which contains the largest contour only. When I wrote it to SDCard, I got
I don't know where I messed up!
private Scalar detectColoredBlob() {
//Highgui.imwrite("/mnt/sdcard/DCIM/rgbaFrame.jpg", rgbaFrame);// check
rgbaFrame = Highgui.imread("/mnt/sdcard/DCIM/rgbaMat4Mask.bmp");
//GIVING A UNIFORM VALUE OF 255 TO THE V CHANNEL OF EACH PIXEL (255 IS THE MAXIMUM VALUE OF V ALLOWED - Simulating a maximum light condition)
for (int firstCoordinate = 0; firstCoordinate < rgbaFrame.rows(); firstCoordinate++) {
for (int secondCoordinate = 0; secondCoordinate < rgbaFrame.cols(); secondCoordinate++) {
double[] pixelChannels = rgbaFrame.get(firstCoordinate, secondCoordinate);
pixelChannels[2] = 255;
rgbaFrame.put(firstCoordinate, secondCoordinate, pixelChannels);
}
}
Mat hsvImage = new Mat();
Imgproc.cvtColor(rgbaFrame, hsvImage, Imgproc.COLOR_BGR2HSV);
Highgui.imwrite("/mnt/sdcard/DCIM/hsvImage.bmp", hsvImage);// check
Mat maskedImage = new Mat();
Core.inRange(hsvImage, new Scalar(0, 100, 100), new Scalar(10, 255, 255), maskedImage);
Highgui.imwrite("/mnt/sdcard/DCIM/maskedImage.bmp", maskedImage);// check
// Mat dilatedMat = new Mat();
// Imgproc.dilate(maskedImage, dilatedMat, new Mat());
// Highgui.imwrite("/mnt/sdcard/DCIM/dilatedMat.jpg", dilatedMat);// check
List<MatOfPoint> contours = new ArrayList<MatOfPoint>();
Imgproc.findContours(maskedImage, contours, new Mat(), Imgproc.RETR_LIST, Imgproc.CHAIN_APPROX_SIMPLE);
//FINDING THE BIGGEST CONTOUR
// \/ We will use only the largest contour. Other contours (any other possible blobs of this color range) will be ignored.
MatOfPoint largestContour = contours.get(0);
double largestContourArea = Imgproc.contourArea(largestContour);
for (int i = 1; i < contours.size(); ++i) {// NB Notice the prefix increment.
MatOfPoint currentContour = contours.get(i);
double currentContourArea = Imgproc.contourArea(currentContour);
if (currentContourArea > largestContourArea) {
largestContourArea = currentContourArea;
largestContour = currentContour;
}
}
Rect detectedBlobRoi = Imgproc.boundingRect(largestContour);
Mat detectedBlobRgba = rgbaFrame.submat(detectedBlobRoi);
Highgui.imwrite("/mnt/sdcard/DCIM/detectedBlobRgba.bmp", detectedBlobRgba);// check
Mat detectedBlobHsv = new Mat();
Imgproc.cvtColor(detectedBlobRgba, detectedBlobHsv, Imgproc.COLOR_BGR2HSV);
Highgui.imwrite("/mnt/sdcard/DCIM/roiHsv.bmp", detectedBlobHsv);// check
List<MatOfPoint> largestContourList = new ArrayList<>();
largestContourList.add(largestContour);
Mat roiWithMask = new Mat(detectedBlobHsv.rows(), detectedBlobHsv.cols(), CvType.CV_8UC3);
roiWithMask.setTo(new Scalar(0,0,0));
Imgproc.drawContours(roiWithMask, largestContourList, 0, new Scalar(0, 255, 255), -1);//TODO Using -1 instead of CV_FILLED.
Highgui.imwrite("/mnt/sdcard/DCIM/roiWithMask.bmp", roiWithMask);// check
// CALCULATING THE AVERAGE COLOR OF THE DETECTED BLOB
// STEP 1:
double [] averageHsvColor = new double[]{0,0,0};
int numOfPixels = 0;
for (int firstCoordinate = 0; firstCoordinate < detectedBlobHsv.rows(); ++firstCoordinate) {
for (int secondCoordinate = 0; secondCoordinate < detectedBlobHsv.cols(); ++secondCoordinate) {
double hue = roiWithMask.get(firstCoordinate, secondCoordinate)[0];
double saturation = roiWithMask.get(firstCoordinate, secondCoordinate)[1];
double value = roiWithMask.get(firstCoordinate, secondCoordinate)[2];
averageHsvColor[0] += hue;
averageHsvColor[1] += saturation;
averageHsvColor[2] += value;
numOfPixels++;
}
}
averageHsvColor[0] /= numOfPixels;
averageHsvColor[1] /= numOfPixels;
averageHsvColor[1] /= numOfPixels;
return new Scalar(averageHsvColor);
}
EDIT 2:
I corrected my 3 channel mask and made a single channel mask
Mat roiMask = new Mat(rgbaFrame.rows(), rgbaFrame.cols(), CvType.CV_8UC1);
roiMask.setTo(new Scalar(0));
Imgproc.drawContours(roiMask, largestContourList, 0, new Scalar(255), -1);
and this resulted in the correct roiMask:
Then, before the comment // CALCULATING THE AVERAGE COLOR OF THE DETECTED BLOB, I added:
Mat newImageWithRoi = new Mat(rgbaFrame.rows(), rgbaFrame.cols(), CvType.CV_8UC3);
newImageWithRoi.setTo(new Scalar(0, 0, 0));
rgbaFrame.copyTo(newImageWithRoi, roiMask);
Highgui.imwrite("/mnt/sdcard/DCIM/newImageWithRoi.bmp", newImageWithRoi);//check
This resulted in:
Now again I don't know how to proceed. :s

You don't need to use pointPolygonTest, because you already have the mask.
You can simply sum up the values that lies on the mask. Something along the lines of (not able to test this):
// Initialize at 0!!!
Scalar averageHsvColor= new Scalar(0,0,0);
int numOfPixels = 0;
for(int r=0; r<detectedBlobHsv.height(); ++r)
{
for(int c=0; c<detectedBlobHsv.width(); ++c)
{
if( /* value of mask(r,c) > 0 */)
{
int H = // get H value of pixel at (r, c)
int S = // get S value of pixel at (r, c)
int V = // get V value of pixel at (r, c)
// Sum values
averageHsvColor[0] += H;
averageHsvColor[1] += S;
averageHsvColor[2] += V;
// Increment number of pixels inside mask
numOfPixels ++;
}
}
}
// Compute average
averageHsvColor[0] /= numOfPixels ;
averageHsvColor[1] /= numOfPixels ;
averageHsvColor[2] /= numOfPixels ;