I am a bit confused with the parameters of getPerspectiveTransform as I cannot see a proper image. Here is my code. The original_image variable is the image that contains a square object (and some others) that I want to crop and create a new image (something like this Android OpenCV Find Largest Square or Rectangle). The variables p1, p2, p3, and p4 are the coordinates of the corners of the largest square/rectangle in the image. p1 is the upper left, p2 is the upper right, p3 is the lower right, and p4 is the lower left (clockwise assigning).
Mat src = new Mat(4,1,CvType.CV_32FC2);
src.put((int)p1.y,(int)p1.x, (int)p2.y,(int)p2.x, (int)p4.y,(int)p4.x, (int)p3.y,(int)p3.x);
Mat dst = new Mat(4,1,CvType.CV_32FC2);
dst.put(0,0, 0,original_image.width(), original_image.height(),original_image.width(), original_image.height(),0);
Mat perspectiveTransform = Imgproc.getPerspectiveTransform(src, dst);
Mat cropped_image = original_image.clone();
Imgproc.warpPerspective(untouched, cropped_image, perspectiveTransform, new Size(512,512));
When I try to display cropped_image, I get a "I don't know what it is" image. I think my parameters in getPerspectiveTransform() are incorrect (or is it). Please help. Thanks!
Update: When I debugged my code, I found out that the edges of my square/rectangle are incorrect, well some are quite right except for p4. This is my code to detect the edges of the square or rectangle in the image. My image is all black except for the contour of the largest square/rectangle which has a white outline.
//we will find the edges of the new_image (corners of the square/rectangle)
Point p1 = new Point(10000, 10000); //upper left; minX && minY
Point p2 = new Point(0, 10000); //upper right; maxX && minY
Point p3 = new Point(0, 0); //lower right; maxX && maxY
Point p4 = new Point(10000, 0); //lower left; minX && maxY
double[] temp_pixel_color;
for (int x=0; x<new_image.rows(); x++) {
for (int y=0; y<new_image.cols(); y++) {
temp_pixel_color = new_image.get(x, y); //we have a black and white image so we only have one color channel
if (temp_pixel_color[0] > 200) { //we found a white pixel
if (x<=p1.x && y<=p1.y) { //for p1, minX && minY
p1.x = x;
p1.y = y;
}
else if (x>=p2.x && y<=p2.y) { //for p2, maxX && minY
p2.x = x;
p2.y = y;
}
else if (x>=p3.x && y>=p3.y) { //for p3, maxX && maxY
p3.x = x;
p3.y = y;
}
else if (x<=(int)p4.x && y>=(int)p4.y) { //for p4, minX && maxY
p4.x = x;
p4.y = y;
}
}
}
}
Here is my sample image. ignore the colored circles as they are drawn after the edges are detected:
Update: July 16, 2013
I can detect the corners now using only the approxCurve of the maximum 4-pointed contour. Here is my code:
private Mat findLargestRectangle(Mat original_image) {
Mat imgSource = original_image;
//Mat untouched = original_image.clone();
//convert the image to black and white
Imgproc.cvtColor(imgSource, imgSource, Imgproc.COLOR_BGR2GRAY);
//convert the image to black and white does (8 bit)
Imgproc.Canny(imgSource, imgSource, 50, 50);
//apply gaussian blur to smoothen lines of dots
Imgproc.GaussianBlur(imgSource, imgSource, new Size(5, 5), 5);
//find the contours
List<MatOfPoint> contours = new ArrayList<MatOfPoint>();
Imgproc.findContours(imgSource, contours, new Mat(), Imgproc.RETR_LIST, Imgproc.CHAIN_APPROX_SIMPLE);
double maxArea = -1;
int maxAreaIdx = -1;
MatOfPoint temp_contour = contours.get(0); //the largest is at the index 0 for starting point
MatOfPoint2f approxCurve = new MatOfPoint2f();
MatOfPoint2f maxCurve = new MatOfPoint2f();
List<MatOfPoint> largest_contours = new ArrayList<MatOfPoint>();
for (int idx = 0; idx < contours.size(); idx++) {
temp_contour = contours.get(idx);
double contourarea = Imgproc.contourArea(temp_contour);
//compare this contour to the previous largest contour found
if (contourarea > maxArea) {
//check if this contour is a square
MatOfPoint2f new_mat = new MatOfPoint2f( temp_contour.toArray() );
int contourSize = (int)temp_contour.total();
Imgproc.approxPolyDP(new_mat, approxCurve, contourSize*0.05, true);
if (approxCurve.total() == 4) {
maxCurve = approxCurve;
maxArea = contourarea;
maxAreaIdx = idx;
largest_contours.add(temp_contour);
}
}
}
//create the new image here using the largest detected square
Mat new_image = new Mat(imgSource.size(), CvType.CV_8U); //we will create a new black blank image with the largest contour
Imgproc.cvtColor(new_image, new_image, Imgproc.COLOR_BayerBG2RGB);
Imgproc.drawContours(new_image, contours, maxAreaIdx, new Scalar(255, 255, 255), 1); //will draw the largest square/rectangle
double temp_double[] = maxCurve.get(0, 0);
Point p1 = new Point(temp_double[0], temp_double[1]);
Core.circle(new_image, new Point(p1.x, p1.y), 20, new Scalar(255, 0, 0), 5); //p1 is colored red
String temp_string = "Point 1: (" + p1.x + ", " + p1.y + ")";
temp_double = maxCurve.get(1, 0);
Point p2 = new Point(temp_double[0], temp_double[1]);
Core.circle(new_image, new Point(p2.x, p2.y), 20, new Scalar(0, 255, 0), 5); //p2 is colored green
temp_string += "\nPoint 2: (" + p2.x + ", " + p2.y + ")";
temp_double = maxCurve.get(2, 0);
Point p3 = new Point(temp_double[0], temp_double[1]);
Core.circle(new_image, new Point(p3.x, p3.y), 20, new Scalar(0, 0, 255), 5); //p3 is colored blue
temp_string += "\nPoint 3: (" + p3.x + ", " + p3.y + ")";
temp_double = maxCurve.get(3, 0);
Point p4 = new Point(temp_double[0], temp_double[1]);
Core.circle(new_image, new Point(p4.x, p4.y), 20, new Scalar(0, 255, 255), 5); //p1 is colored violet
temp_string += "\nPoint 4: (" + p4.x + ", " + p4.y + ")";
TextView temp_text = (TextView)findViewById(R.id.temp_text);
temp_text.setText(temp_string);
return new_image;
}
Here is the sample result image:
I have drawn circles for the corners of the square/rectangle and I also added a textview to display all the four points.
This worked for me. in the src_mat.put you should have 0,0 at first and then the float values for the coordinates.
Mat mat=Highgui.imread("inputImage.jpg");
Mat src_mat=new Mat(4,1,CvType.CV_32FC2);
Mat dst_mat=new Mat(4,1,CvType.CV_32FC2);
src_mat.put(0,0,407.0,74.0,1606.0,74.0,420.0,2589.0,1698.0,2589.0);
dst_mat.put(0,0,0.0,0.0,1600.0,0.0, 0.0,2500.0,1600.0,2500.0);
Mat perspectiveTransform=Imgproc.getPerspectiveTransform(src_mat, dst_mat);
Mat dst=mat.clone();
Imgproc.warpPerspective(mat, dst, perspectiveTransform, new Size(1600,2500));
Highgui.imwrite("resultImage.jpg", dst);
Related
I am trying to draw the contour of every element in a picture with separated musical notations.
This is the code that I am running in android/java:
public static Bitmap findNotationContours(Bitmap inputImage) {
Mat inputImageMat = new Mat();
Utils.bitmapToMat(inputImage, inputImageMat);
Imgproc.cvtColor(inputImageMat, inputImageMat, Imgproc.COLOR_BGR2GRAY);
Imgproc.GaussianBlur(inputImageMat, inputImageMat, new Size(5, 5), 0);
Imgproc.adaptiveThreshold(inputImageMat, inputImageMat, 255, 1, 1, 11, 2);
List<MatOfPoint> contours = new ArrayList<>();
Mat hierarchy = new Mat();
Imgproc.findContours(inputImageMat, contours, hierarchy, Imgproc.RETR_LIST, Imgproc.CHAIN_APPROX_SIMPLE);
int contourColor = android.R.color.holo_red_dark;
Scalar contourScalar = new Scalar(Color.red(contourColor), Color.green(contourColor), Color.blue(contourColor));
for (int i = 0; i < contours.size(); i++) {
Rect rect = Imgproc.boundingRect(contours.get(i));
Imgproc.rectangle(inputImageMat,
new Point(rect.x, rect.y),
new Point(rect.x + rect.width, rect.y + rect.height),
contourScalar, 3);
}
Utils.matToBitmap(inputImageMat, inputImage);
return inputImage;
}
The result I am getting is:
If you zoom in enough you can see that the contour for the notations are there, but I need to keep the original picture with just a rectangle contour around each of them, so I can save those as a pattern.
Can you please tell me what I am doing wrong?
With the crucial information from #Dan MaĊĦek the problem was fixed, I will add the revised code that needs no explanation, just that we keep a Mat with the initial 3-channel image which we use to draw the contour rectangles on.
public static Bitmap findNotationContours(Bitmap inputImage) {
Mat inputImageMat = new Mat();
Mat resultImageMat = new Mat();
Utils.bitmapToMat(inputImage, inputImageMat);
Utils.bitmapToMat(inputImage, resultImageMat);
Imgproc.cvtColor(inputImageMat, inputImageMat, Imgproc.COLOR_BGR2GRAY);
Imgproc.GaussianBlur(inputImageMat, inputImageMat, new Size(5, 5), 0);
Imgproc.adaptiveThreshold(inputImageMat, inputImageMat, 255, 1, 1, 11, 2);
List<MatOfPoint> contours = new ArrayList<>();
Mat hierarchy = new Mat();
Imgproc.findContours(inputImageMat, contours, hierarchy, Imgproc.RETR_LIST, Imgproc.CHAIN_APPROX_SIMPLE);
Scalar contourScalar = new Scalar(255,0,0);
for (int i = 0; i < contours.size(); i++) {
Rect rect = Imgproc.boundingRect(contours.get(i));
Imgproc.rectangle(resultImageMat,
new Point(rect.x, rect.y),
new Point(rect.x + rect.width, rect.y + rect.height),
contourScalar, 3);
Log.i("contour", "contour" + i + " x:" + rect.x + " y:" + rect.y);
}
Utils.matToBitmap(resultImageMat, inputImage);
return inputImage;
}
final result
My mRgba object has dimensions 0X0 so it doesn't return any lines on the picture
at all.I guess it is empty. What is the problem in the code? Is there a way to show lines just on
the black background?
Here is the code
mat = new Mat();
edges = new Mat();
Size kernel = new Size(5, 5);
Mat gauss = new Mat();
Mat mRgba = new Mat(612,816, CvType.CV_8UC1);
Mat lines = new Mat(612,816, CvType.CV_8UC1);
binary_image = new Mat();
Utils.bitmapToMat(bitmap, mat);
Imgproc.GaussianBlur(mat, gauss, kernel, 10000, 10000);
Imgproc.Canny(gauss, edges, 50, 90);
Imgproc.threshold(edges, binary_image, 0, 255, Imgproc.THRESH_BINARY_INV);
int threshold = 50;
int minLineSize = 20;
int lineGap = 20;
Imgproc.HoughLinesP(binary_image, lines, 1, Math.PI / 180,threshold,minLineSize,lineGap);
for (int x = 0; x < lines.cols(); x++) {
double[] vec = lines.get(0, x);
double x1 = vec[0],
y1 = vec[1],
x2 = vec[2],
y2 = vec[3];
Point start = new Point(x1, y1);
Point end = new Point(x2, y2);
Core.line(mRgba, start, end, new Scalar(255, 0, 0), 3);
}
Bitmap bmp = Bitmap.createBitmap(mRgba.cols(), mRgba.rows(), Bitmap.Config.ARGB_8888);
Utils.matToBitmap(mRgba, bmp);
bitmap=bmp;
EDIT: The problem has been solved by removing GaussianBlur and threshold methods
mat = new Mat();
edges = new Mat();
Mat mRgba = new Mat(612,816, CvType.CV_8UC1);
Mat lines = new Mat();
Utils.bitmapToMat(bitmap, mat);
Imgproc.Canny(mat, edges, 50, 90);
int threshold = 50;
int minLineSize = 20;
int lineGap = 20;
Imgproc.HoughLinesP(edges, lines, 1, Math.PI / 180,threshold,minLineSize,lineGap);
for (int x = 0; x < lines.cols(); x++) {
double[] vec = lines.get(0, x);
double x1 = vec[0],
y1 = vec[1],
x2 = vec[2],
y2 = vec[3];
Point start = new Point(x1, y1);
Point end = new Point(x2, y2);
Core.line(mRgba, start, end, new Scalar(255, 0, 0), 3);
}
Bitmap bmp = Bitmap.createBitmap(mRgba.cols(), mRgba.rows(), Bitmap.Config.ARGB_8888);
Utils.matToBitmap(mRgba, bmp);
bitmap=bmp;
You are not specifying a size for mRgba when you are creating it, it should has the same size with the image that you are trying to find lines.
You should check lines object, to see if you are able to find lines or
not. You can understand it by checking mRgba.
This doesn't seem to be the whole code, there is no image loading here. It might be better for you to share whole code and an example image as well.
Currently I'm developing an app that will detect colored circles. I'm trying to do this by following this tutorial, where guy detects red circles on image with Python. I've written the same code, just for Java.
Mat mat = new Mat(bitmap.getWidth(), bitmap.getHeight(),
CvType.CV_8UC3);
Mat hsv_image = new Mat();
Utils.bitmapToMat(bitmap, mat);
Imgproc.cvtColor(mat, hsv_image, Imgproc.COLOR_BGR2HSV);
Mat lower_red_hue_range = new Mat();
Mat upper_red_hue_range = new Mat();
Core.inRange(hsv_image, new Scalar(0, 100, 100), new Scalar(10, 255, 255), lower_red_hue_range);
Core.inRange(hsv_image, new Scalar(160, 100, 100), new Scalar(179, 255, 255), upper_red_hue_range);
Utils.matToBitmap(hsv_image, bitmap);
mutableBitmap = bitmap.copy(Bitmap.Config.ARGB_8888, true);
image.setImageBitmap(mutableBitmap);
Image I use is identical to one from tutorial:
This is image with applied BGR2HSV:
When I execute the code using lower red hue range, it detects the blue circle. When I use upper red hue range it gives me black bmp(doesn't detect anything). How can it be? What am I doing wrong? This is literally copy moved from python to Java. Why's the result different then?
Thanks in advance.
Your mat is of CvType.CV_8UC1 image, i.e. you are working on a grayscale image. Try with CvType.CV_8UC3
Mat mat = new Mat(bitmap.getWidth(), bitmap.getHeight(), CvType.CV_8UC3);
hsv_image should look like this:
How to select a custom range:
You may want to detect a green circle.
Well, in HSV, tipically the range is:
H in [0,360]
S,V in [0,100]
However, for CV_8UC3 images, each component H,S,V can be represented by only 256 values at most, since it's stored in 1 byte. So, in OpenCV, the ranges H,S,V for CV_8UC3 are:
H in [0,180] <- halved to fit in the range
S,V in [0,255] <- stretched to fit the range
So to switch from typical range to OpenCV range you need to:
opencv_H = typical_H / 2;
opencv_S = typical_S * 2.55;
opencv_V = typical_V * 2.55;
So, green colors are around the value of hue of 120. The hue can have a value in the interval [0,360].
However, for Mat3b HSV images, the range for H is in [0,180], i.e. is halved so it can fit in a 8 bit representation with at most 256 possible values.
So, you want the H value to be around 120 / 2 = 60, say from 50 to 70.
You also set a minimum value for S,V to 100 in order to prevent very dark (almost black) colors.
Mat green_hue_range
inRange(hsv_image, cv::Scalar(50, 100, 100), cv::Scalar(70, 255, 255), green_hue_range);
use the following code and pass color to Blob detector and then pass an image to the detector
private Scalar converScalarRgba2HSV(Scalar rgba) {
Mat pointMatHsv= new Mat();
Mat pointMatRgba = new Mat(1, 1, CvType.CV_8UC3, rgba);
Imgproc.cvtColor(pointMatRgba,pointMatHsv, Imgproc.COLOR_RGB2HSV_FULL, 4);
return new Scalar(pointMatHsv.get(0, 0));}
// Blob Detector
public class ColorBlobDetector {
// Lower and Upper bounds for range checking in HSV color space
private Scalar mLowerBound = new Scalar(0);
private Scalar mUpperBound = new Scalar(0);
// Minimum contour area in percent for contours filtering
private static double mMinContourArea = 0.1;
// Color radius for range checking in HSV color space
private Scalar mColorRadius = new Scalar(25,50,50,0);
private Mat mSpectrum = new Mat();
private List<MatOfPoint> mContours = new ArrayList<MatOfPoint>();
Mat mPyrDownMat = new Mat();
Mat mHsvMat = new Mat();
Mat mMask = new Mat();
Mat mDilatedMask = new Mat();
Mat mHierarchy = new Mat();
public void setColorRadius(Scalar radius) {
mColorRadius = radius;
}
public void setHsvColor(Scalar hsvColor) {
double minH = (hsvColor.val[0] >= mColorRadius.val[0]) ? hsvColor.val[0]-mColorRadius.val[0] : 0;
double maxH = (hsvColor.val[0]+mColorRadius.val[0] <= 255) ? hsvColor.val[0]+mColorRadius.val[0] : 255;
mLowerBound.val[0] = minH;
mUpperBound.val[0] = maxH;
mLowerBound.val[1] = hsvColor.val[1] - mColorRadius.val[1];
mUpperBound.val[1] = hsvColor.val[1] + mColorRadius.val[1];
mLowerBound.val[2] = hsvColor.val[2] - mColorRadius.val[2];
mUpperBound.val[2] = hsvColor.val[2] + mColorRadius.val[2];
mLowerBound.val[3] = 0;
mUpperBound.val[3] = 255;
Mat spectrumHsv = new Mat(1, (int)(maxH-minH), CvType.CV_8UC3);
for (int j = 0; j < maxH-minH; j++) {
byte[] tmp = {(byte)(minH+j), (byte)255, (byte)255};
spectrumHsv.put(0, j, tmp);
}
Imgproc.cvtColor(spectrumHsv, mSpectrum, Imgproc.COLOR_HSV2RGB_FULL, 4);
}
public Mat getSpectrum() {
return mSpectrum;
}
public void setMinContourArea(double area) {
mMinContourArea = area;
}
public void process(Mat rgbaImage) {
Imgproc.pyrDown(rgbaImage, mPyrDownMat);
Imgproc.pyrDown(mPyrDownMat, mPyrDownMat);
Imgproc.cvtColor(mPyrDownMat, mHsvMat, Imgproc.COLOR_RGB2HSV_FULL);
Core.inRange(mHsvMat, mLowerBound, mUpperBound, mMask);
Imgproc.dilate(mMask, mDilatedMask, new Mat());
List<MatOfPoint> contours = new ArrayList<MatOfPoint>();
Imgproc.findContours(mDilatedMask, contours, mHierarchy, Imgproc.RETR_EXTERNAL, Imgproc.CHAIN_APPROX_SIMPLE);
// Find max contour area
double maxArea = 0;
Iterator<MatOfPoint> each = contours.iterator();
while (each.hasNext()) {
MatOfPoint wrapper = each.next();
double area = Imgproc.contourArea(wrapper);
if (area > maxArea)
maxArea = area;
}
// Filter contours by area and resize to fit the original image size
mContours.clear();
each = contours.iterator();
while (each.hasNext()) {
MatOfPoint contour = each.next();
if (Imgproc.contourArea(contour) > mMinContourArea*maxArea) {
Core.multiply(contour, new Scalar(4,4), contour);
mContours.add(contour);
}
}
}
public List<MatOfPoint> getContours() {
return mContours;
}}
now set detector
public void initDetector() {
mDetector = new ColorBlobDetector();
mSpectrum = new Mat();
mBlobColorRgba = new Scalar(255);
mBlobColorHsv = new Scalar(255);
SPECTRUM_SIZE = new org.opencv.core.Size(500, 64);
CONTOUR_COLOR = new Scalar(0, 255, 0, 255);
mDetector.setHsvColor(converScalarRgba2HSV(new Scalar(0,255,255,255)));
Imgproc.resize(mDetector.getSpectrum(), mSpectrum, SPECTRUM_SIZE, 0, 0, Imgproc.INTER_LINEAR_EXACT);
mIsColorSelected = true;
}
now pass an image to a detector object
Mat mRgba = new Mat(inputFrame.height(), inputFrame.width(), CvType.CV_8UC4);
mRgba = inputFrame;
mDetector.process(mRgba);
List<MatOfPoint> contours = mDetector.getContours();
Log.e(TAG, "Contours count: " + contours.size());
drawContours(mRgba, contours, -1, CONTOUR_COLOR);
return mRgba;
Happy Codeing !!!
I am working on opencv in android and i want to change eye pupil color through Hue channel and i achieve this already but the problem is that the region i detected is in rectangle but i want this region circular as eye pupil is circular region. kindly help how i achieve this.
private Mat get_template(CascadeClassifier clasificator, Rect area,int size){
Mat template = new Mat();
Mat mROI = mGray.submat(area);
MatOfRect eyes = new MatOfRect();
Point iris = new Point();
Rect eye_template = new Rect();
clasificator.detectMultiScale(mROI, eyes, 1.15, 2,Objdetect.CASCADE_FIND_BIGGEST_OBJECT|Objdetect.CASCADE_SCALE_IMAGE, new Size(30,30),new Size());
Rect[] eyesArray = eyes.toArray();
for (int i = 0; i < eyesArray.length; i++){
Rect e = eyesArray[i];
e.x = area.x + e.x;
e.y = area.y + e.y;
Rect eye_only_rectangle = new Rect((int)e.tl().x,(int)( e.tl().y + e.height*0.4),(int)e.width,(int)(e.height*0.6));
mROI = mGray.submat(eye_only_rectangle);
Mat vyrez = mRgba.submat(eye_only_rectangle);
Core.MinMaxLocResult mmG = Core.minMaxLoc(mROI);
Core.circle(vyrez, mmG.minLoc,2, new Scalar(255, 255, 255, 255),2);
iris.x = mmG.minLoc.x + eye_only_rectangle.x;
iris.y = mmG.minLoc.y + eye_only_rectangle.y;
eye_template = new Rect((int)iris.x-size/2,(int)iris.y-size/2 ,size,size);
Core.rectangle(mRgba,eye_template.tl(),eye_template.br(),new Scalar(255, 0, 0, 255), 2);
template = (mGray.submat(eye_template)).clone();
return template;
}
return template;
}
Some potential solutions:
the simplest, although it might not be very robust is to calculate the inscribed circle (the circle bound by the rectangle) and change it's color - if your pupil detection is very accurate this solution may work fine.
a more robust solution would be to detect the area of the pupil based on color or gradient (edge detection)
I am working on opencv eye detection project and i have sucessfully detect rectangular region of both eyes through the help of haar cascade for boths eyes. now i want to detect the eye balls from both eyes, the problem is that i have no haar cascade for eye ball tracking. kindly help me if anyone of you have this xml and suggest other solution.
here is my code of eye detection
private Mat get_template(CascadeClassifier clasificator, Rect area,int size)
{
Mat eye = new Mat();
Mat template = new Mat();
Mat mROI = mGray.submat(area);
MatOfRect eyes = new MatOfRect();
Point iris = new Point();
Rect eye_template = new Rect();
clasificator.detectMultiScale(mROI, eyes, 1.15, 2, Objdetect.CASCADE_FIND_BIGGEST_OBJECT|Objdetect.CASCADE_SCALE_IMAGE, new Size(30,30), new Size());
Rect[] eyesArray = eyes.toArray();
for (int i = 0; i < eyesArray.length; i++)
{
Rect e = eyesArray[i];
e.x = area.x + e.x;
e.y = area.y + e.y;
Core.rectangle(mROI, e.tl(), e.br(), new Scalar(25, 50, 0, 255));
Rect eye_only_rectangle = new Rect((int)e.tl().x, (int)( e.tl().y + e.height*0.4), (int)e.width, (int)(e.height*0.6));
//reduce ROI
mROI = mGray.submat(eye_only_rectangle);
Mat vyrez = mRgba.submat(eye_only_rectangle);
Core.MinMaxLocResult mmG = Core.minMaxLoc(mROI);
//Draw pink circle on eyeball
int radius = vyrez.height()/2;
// Core.circle(vyrez, mmG.minLoc, 2, new Scalar(0, 255, 0, 1), radius);
//Core.circle(vyrez, mmG.minLoc,2, new Scalar(255, 0, 255),1);
iris.x = mmG.minLoc.x + eye_only_rectangle.x;
iris.y = mmG.minLoc.y + eye_only_rectangle.y;
eye_template = new Rect((int)iris.x-size/2,(int)iris.y-size/2 ,size,size);
//draw red rectangle around eyeball
//Core.rectangle(mRgba,eye_template.tl(),eye_template.br(),new Scalar(255, 0, 0, 255), 2);
eye = (mRgba.submat(eye_only_rectangle));
template = (mGray.submat(eye_template)).clone();
//return template;
Mat eyeball_HSV = new Mat();
Mat dest = new Mat();
//Mat eye = new Mat();
//eye = mRgba.submat(eye_only_rectangle);
List<Mat> hsv_channel = new ArrayList<Mat>();
//convert image to HSV
Imgproc.cvtColor(eye, eyeball_HSV, Imgproc.COLOR_RGB2HSV, 0);
// get HSV channel
//hsv_channel[0] is hue
//hsv_channel[1] is saturation
//hsv_channel[2] is visibility
Core.split(eyeball_HSV, hsv_channel);
try
{
hsv_channel.get(0).setTo(new Scalar(145));
Log.v(TAG, "Got the Channel!");
}
catch(Exception ex)
{
ex.printStackTrace();
Log.v(TAG, "Didn't get any channel");
}
Core.merge(hsv_channel, eyeball_HSV);
Imgproc.cvtColor(eyeball_HSV, dest, Imgproc.COLOR_HSV2RGB);
Imgproc.cvtColor(dest, eye, Imgproc.COLOR_RGB2RGBA);
}
return eye;
}`enter code here`
If you are willing to consider other solutions then haar cascades, you can use facial landmark detection code. Facial landmark packages can give the location of the eyes in the image (usually, center of the eye and left and right borders).
Examples of landmark detection packages:
STASM:
http://www.milbo.users.sonic.net/stasm/
Flandmark detector:
http://cmp.felk.cvut.cz/~uricamic/flandmark/