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public class Grabcut extends Activity {
ImageView iv;
Bitmap bitmap;
Canvas canvas;
Scalar color = new Scalar(255, 0, 0, 255);
Point tl, br;
int counter;
Bitmap bitmapResult, bitmapBackground;
Mat dst = new Mat();
final String pathToImage = Environment.getExternalStorageDirectory()+"/gcut.png";
public static final String TAG = "Grabcut demo";
static {
if (!OpenCVLoader.initDebug()) {
// Handle initialization error
}
}
#Override
public void onCreate(Bundle savedInstanceState) {
super.onCreate(savedInstanceState);
setContentView(R.layout.grabcut_main);
iv = (ImageView) this.findViewById(R.id.imageView);
Bitmap bitmap = BitmapFactory.decodeResource(getResources(), R.drawable.grabcut);
Log.d(TAG, "bitmap: " + bitmap.getWidth() + "x" + bitmap.getHeight());
bitmap = bitmap.copy(Bitmap.Config.ARGB_8888, true);
Log.d(TAG, "bitmap 8888: " + bitmap.getWidth() + "x" + bitmap.getHeight());
//GrabCut part
Mat img = new Mat();
Utils.bitmapToMat(bitmap, img);
Log.d(TAG, "img: " + img);
int r = img.rows();
int c = img.cols();
Point p1 = new Point(c/5, r/5);
Point p2 = new Point(c-c/5, r-r/8);
Rect rect = new Rect(p1,p2);
//Rect rect = new Rect(50,30, 100,200);
Log.d(TAG, "rect: " + rect);
Mat mask = new Mat();
debugger(""+mask.type());
mask.setTo(new Scalar(125));
Mat fgdModel = new Mat();
fgdModel.setTo(new Scalar(255, 255, 255));
Mat bgdModel = new Mat();
bgdModel.setTo(new Scalar(255, 255, 255));
Mat imgC3 = new Mat();
Imgproc.cvtColor(img, imgC3, Imgproc.COLOR_RGBA2RGB);
Log.d(TAG, "imgC3: " + imgC3);
Log.d(TAG, "Grabcut begins");
Imgproc.grabCut(imgC3, mask, rect, bgdModel, fgdModel, 5, Imgproc.GC_INIT_WITH_RECT);
Mat source = new Mat(1, 1, CvType.CV_8U, new Scalar(3.0));
Core.compare(mask, source, mask, Core.CMP_EQ);
Mat foreground = new Mat(img.size(), CvType.CV_8UC3, new Scalar(255, 255, 255));
img.copyTo(foreground, mask);
Core.rectangle(img, p1, p2, color);
Mat background = new Mat();
try {
background = Utils.loadResource(getApplicationContext(),
R.drawable.wall2 );
} catch (IOException e) {
e.printStackTrace();
}
Mat tmp = new Mat();
Imgproc.resize(background, tmp, img.size());
background = tmp;
Mat tempMask = new Mat(foreground.size(), CvType.CV_8UC1, new Scalar(255, 255, 255));
Imgproc.cvtColor(foreground, tempMask, 6/* COLOR_BGR2GRAY */);
//Imgproc.threshold(tempMask, tempMask, 254, 255, 1 /* THRESH_BINARY_INV */);
Mat vals = new Mat(1, 1, CvType.CV_8UC3, new Scalar(0.0));
dst = new Mat();
background.setTo(vals, tempMask);
Imgproc.resize(foreground, tmp, mask.size());
foreground = tmp;
Core.add(background, foreground, dst, tempMask);
//convert to Bitmap
Log.d(TAG, "Convert to Bitmap");
Utils.matToBitmap(dst, bitmap);
iv.setBackgroundResource(R.drawable.wall2);
iv.setImageBitmap(bitmap);
//release MAT part
img.release();
imgC3.release();
mask.release();
fgdModel.release();
bgdModel.release();
}
public void debugger(String s){
Log.v("","########### "+s);
}
}
I had followed above tutorial.But problem is that output image I get has brighter colors than my input image. Why is that and how to solve it?
My input image is Here and output image is here.Output image is actually screenshot of my application where large one is input image and small one with black background is output image.
I finally found solution to my problem. Here is code to grab cut background of an image using Grabcut algorithm in opencv for android.
public void grabcutAlgo(Bitmap bit){
Bitmap b = bit.copy(Bitmap.Config.ARGB_8888, true);
Point tl=new Point();
Point br=new Point();
//GrabCut part
Mat img = new Mat();
Utils.bitmapToMat(b, img);
Imgproc.cvtColor(img, img, Imgproc.COLOR_RGBA2RGB);
int r = img.rows();
int c = img.cols();
Point p1 = new Point(c / 100, r / 100);
Point p2 = new Point(c - c / 100, r - r / 100);
Rect rect = new Rect(p1, p2);
//Rect rect = new Rect(tl, br);
Mat background = new Mat(img.size(), CvType.CV_8UC3,
new Scalar(255, 255, 255));
Mat firstMask = new Mat();
Mat bgModel = new Mat();
Mat fgModel = new Mat();
Mat mask;
Mat source = new Mat(1, 1, CvType.CV_8U, new Scalar(Imgproc.GC_PR_FGD));
Mat dst = new Mat();
Imgproc.grabCut(img, firstMask, rect, bgModel, fgModel, 5, Imgproc.GC_INIT_WITH_RECT);
Core.compare(firstMask, source, firstMask, Core.CMP_EQ);
Mat foreground = new Mat(img.size(), CvType.CV_8UC3, new Scalar(255, 255, 255));
img.copyTo(foreground, firstMask);
Scalar color = new Scalar(255, 0, 0, 255);
Imgproc.rectangle(img, tl, br, color);
Mat tmp = new Mat();
Imgproc.resize(background, tmp, img.size());
background = tmp;
mask = new Mat(foreground.size(), CvType.CV_8UC1,
new Scalar(255, 255, 255));
Imgproc.cvtColor(foreground, mask, Imgproc.COLOR_BGR2GRAY);
Imgproc.threshold(mask, mask, 254, 255, Imgproc.THRESH_BINARY_INV);
System.out.println();
Mat vals = new Mat(1, 1, CvType.CV_8UC3, new Scalar(0.0));
background.copyTo(dst);
background.setTo(vals, mask);
Core.add(background, foreground, dst, mask);
Bitmap grabCutImage = Bitmap.createBitmap(dst.cols(), dst.rows(), Bitmap.Config.ARGB_8888);
Bitmap processedImage = Bitmap.createBitmap(dst.cols(), dst.rows(), Bitmap.Config.RGB_565);
Utils.matToBitmap(dst, grabCutImage);
dst.copyTo(sampleImage);
imageView.setImageBitmap(grabCutImage);
firstMask.release();
source.release();
bgModel.release();
fgModel.release();
}
I am very new to android and openCV, I'M working on "Android application: plant disease Analyzer".
Below is my workflow:
1.I have static plant diseases in my gallery
2.End-user can capture an plant disease and submit to my application.
3.I want to compare the processed image with my gallery(Diseases) to get the most similar disease
Can any one tell me What will be the best algorithm to use?,
I have been searching on google but no luck,I tried with
below code snippet, which I tried using openCV :
BitmapFactory.Options bmOptions = new BitmapFactory.Options();
Bitmap camerabitmap = BitmapFactory.decodeFile(cameraimage,
bmOptions);
Bitmap galareybitmap =
BitmapFactory.decodeFile(galImage.getAbsolutePath(), bmOptions);
private double imageProcessing(Bitmap cameraimage,Bitmap
galimagebitmap,String galimagename) throws IOException {
Mat img1 = new Mat();
Utils.bitmapToMat(cameraimage, img1);
Mat img2 = new Mat();
Utils.bitmapToMat(gallimagebitmap, img2);
Imgproc.cvtColor(img1, img1, Imgproc.COLOR_RGBA2GRAY);
Imgproc.cvtColor(img2, img2, Imgproc.COLOR_RGBA2GRAY);
img1.convertTo(img1, CvType.CV_32F);
img2.convertTo(img2, CvType.CV_32F);
//Log.d("ImageComparator", "img1:"+img1.rows()+"x"+img1.cols()+" img2:"+img2.rows()+"x"+img2.cols());
Mat hist1 = new Mat();
Mat hist2 = new Mat();
MatOfInt histSize = new MatOfInt(180);
MatOfInt channels = new MatOfInt(0);
ArrayList<Mat> bgr_planes1= new ArrayList<Mat>();
ArrayList<Mat> bgr_planes2= new ArrayList<Mat>();
Core.split(img1, bgr_planes1);
Core.split(img2, bgr_planes2);
MatOfFloat histRanges = new MatOfFloat(0f, 256f);
boolean accumulate = false;
Imgproc.calcHist(bgr_planes1, channels, new Mat(), hist1, histSize, histRanges);
Core.normalize(hist1, hist1, 0, hist1.rows(), Core.NORM_MINMAX, -1, new Mat());
Imgproc.calcHist(bgr_planes2, channels, new Mat(), hist2, histSize, histRanges);
Core.normalize(hist2, hist2, 0, hist2.rows(), Core.NORM_MINMAX, -1, new Mat());
/ img1.convertTo(img1, CvType.CV_32F);
// img2.convertTo(img2, CvType.CV_32F);
hist1.convertTo(hist1, CvType.CV_32F);
hist2.convertTo(hist2, CvType.CV_32F);
return Imgproc.compareHist(hist1, hist2,3);
}
I tried with template matching also in opencv
int match_method=Imgproc.TM_CCOEFF_NORMED;
Mat temp = Imgcodecs.imread(tempim,Imgcodecs.CV_LOAD_IMAGE_GRAYSCALE );
Mat img = Imgcodecs.imread(sourceim,Imgcodecs.CV_LOAD_IMAGE_GRAYSCALE
);
Size sz = new Size(200, 200);
Mat resizeimage = new Mat();
Imgproc.resize(img, resizeimage, sz);
Mat sourceimage = resizeimage;
Mat resizeimage2 = new Mat();
Imgproc.resize(temp, resizeimage2, sz);
Mat templateimage = resizeimage2;
int result_cols = sourceimage.cols() - templateimage.cols() + 1;
int result_rows = sourceimage.rows() - templateimage.rows() + 1;
Mat result = new Mat(result_rows, result_cols, CvType.CV_32FC1);
Imgproc.matchTemplate(sourceimage,templateimage, result, match_method);
//Core.normalize(result, result, 0, 1, Core.NORM_MINMAX, -1, new Mat());
Imgproc.threshold(result, result,0.1,1,Imgproc.THRESH_TOZERO);
Point matchLoc,maxLoc,minLoc;
Core.MinMaxLocResult mmr;
boolean iterate = true;
double minlocvalue,maxlocvalue,minminvalue,maxmaxvalue;
while(true){
mmr = Core.minMaxLoc(result);
if (match_method == Imgproc.TM_SQDIFF || match_method == Imgproc.TM_SQDIFF_NORMED) {
matchLoc = mmr.minLoc;
minminvalue = mmr.minVal; // test
} else {
matchLoc = mmr.maxLoc;
maxmaxvalue = mmr.minVal; // test
}
Log.d(TAG, "mmr.maxVal : "+mmr.maxVal);
if(mmr.maxVal >=0.2)
{
Log.d(TAG, "imagemathed..");
Imgproc.rectangle(sourceimage, matchLoc, new Point(matchLoc.x + templateimage.cols(),
matchLoc.y + templateimage.rows()), new Scalar(0, 255, 0));
Imgcodecs.imwrite(outFile, img);
Mat image = Imgcodecs.imread(outFile);
try {
Bitmap bm = Bitmap.createBitmap(image.cols(), image.rows(), Bitmap.Config.RGB_565);
Utils.matToBitmap(image, bm);
System.out.println("MinVal "+bm);
}catch(Exception e){
e.printStackTrace();
}
return true;
}else {
Log.d(TAG, "image not mathced..");
return false;
}
}
But every time I'M not getting the proper output, fault images are coming in the output.Please help me out, AM I following right approach, If not can some one suggest me which approach i have to follow.
I'm OpenCV learner. I was trying Image Comparison. I have used OpenCV 2.4.13.3
I have these two images 1.jpg and cam1.jpg.
When I use the following command in openCV
File sdCard = Environment.getExternalStorageDirectory();
String path1, path2;
path1 = sdCard.getAbsolutePath() + "/1.jpg";
path2 = sdCard.getAbsolutePath() + "/cam1.jpg";
FeatureDetector detector = FeatureDetector.create(FeatureDetector.ORB);
DescriptorExtractor extractor = DescriptorExtractor.create(DescriptorExtractor.BRIEF);
DescriptorMatcher matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_HAMMING);
Mat img1 = Highgui.imread(path1);
Mat img2 = Highgui.imread(path2);
Mat descriptors1 = new Mat();
MatOfKeyPoint keypoints1 = new MatOfKeyPoint();
detector.detect(img1, keypoints1);
extractor.compute(img1, keypoints1, descriptors1);
//second image
// Mat img2 = Imgcodecs.imread(path2);
Mat descriptors2 = new Mat();
MatOfKeyPoint keypoints2 = new MatOfKeyPoint();
detector.detect(img2, keypoints2);
extractor.compute(img2, keypoints2, descriptors2);
//matcher image descriptors
MatOfDMatch matches = new MatOfDMatch();
matcher.match(descriptors1,descriptors2,matches);
// Filter matches by distance
MatOfDMatch filtered = filterMatchesByDistance(matches);
int total = (int) matches.size().height;
int Match= (int) filtered.size().height;
Log.d("LOG", "total:" + total + " Match:"+Match);
Method filterMatchesByDistance
static MatOfDMatch filterMatchesByDistance(MatOfDMatch matches){
List<DMatch> matches_original = matches.toList();
List<DMatch> matches_filtered = new ArrayList<DMatch>();
int DIST_LIMIT = 30;
// Check all the matches distance and if it passes add to list of filtered matches
Log.d("DISTFILTER", "ORG SIZE:" + matches_original.size() + "");
for (int i = 0; i < matches_original.size(); i++) {
DMatch d = matches_original.get(i);
if (Math.abs(d.distance) <= DIST_LIMIT) {
matches_filtered.add(d);
}
}
Log.d("DISTFILTER", "FIL SIZE:" + matches_filtered.size() + "");
MatOfDMatch mat = new MatOfDMatch();
mat.fromList(matches_filtered);
return mat;
}
Log
total:122 Match:30
As we can see from the log match is 30.
But as we can see both images have same visual element (in).
How can I get match=90 using openCV?
It would be great if somebody can help with code snippet.
If using opencv it is not possible then what are the other
alternatives we can look for?
But as we can see both images have same visual element (in).
So, we should compare not whole images, but "same visual element" on it. You can improve Match value more if you do not compare the "template" and "camera" images themselves, but processed same way (converted to binary black/white, for example) "template" and "camera" images. For example, try to find blue (background of template logo) square on both ("template" and "camera") images and compare that squares (Region Of Interest). The code may be something like that:
Bitmap bmImageTemplate = <get your template image Bitmap>;
Bitmap bmTemplate = findLogo(bmImageTemplate); // process template image
Bitmap bmImage = <get your camera image Bitmap>;
Bitmap bmLogo = findLogo(bmImage); // process camera image same way
compareBitmaps(bmTemplate, bmLogo);
where
private Bitmap findLogo(Bitmap sourceBitmap) {
Bitmap roiBitmap = null;
Mat sourceMat = new Mat(sourceBitmap.getWidth(), sourceBitmap.getHeight(), CvType.CV_8UC3);
Utils.bitmapToMat(sourceBitmap, sourceMat);
Mat roiTmp = sourceMat.clone();
final Mat hsvMat = new Mat();
sourceMat.copyTo(hsvMat);
// convert mat to HSV format for Core.inRange()
Imgproc.cvtColor(hsvMat, hsvMat, Imgproc.COLOR_RGB2HSV);
Scalar lowerb = new Scalar(85, 50, 40); // lower color border for BLUE
Scalar upperb = new Scalar(135, 255, 255); // upper color border for BLUE
Core.inRange(hsvMat, lowerb, upperb, roiTmp); // select only blue pixels
// find contours
List<MatOfPoint> contours = new ArrayList<>();
List<Rect> squares = new ArrayList<>();
Imgproc.findContours(roiTmp, contours, new Mat(), Imgproc.RETR_LIST, Imgproc.CHAIN_APPROX_SIMPLE);
// find appropriate bounding rectangles
for (MatOfPoint contour : contours) {
MatOfPoint2f areaPoints = new MatOfPoint2f(contour.toArray());
RotatedRect boundingRect = Imgproc.minAreaRect(areaPoints);
double rectangleArea = boundingRect.size.area();
// test min ROI area in pixels
if (rectangleArea > 400) {
Point rotated_rect_points[] = new Point[4];
boundingRect.points(rotated_rect_points);
Rect rect = Imgproc.boundingRect(new MatOfPoint(rotated_rect_points));
double aspectRatio = rect.width > rect.height ?
(double) rect.height / (double) rect.width : (double) rect.width / (double) rect.height;
if (aspectRatio >= 0.9) {
squares.add(rect);
}
}
}
Mat logoMat = extractSquareMat(roiTmp, getBiggestSquare(squares));
roiBitmap = Bitmap.createBitmap(logoMat.cols(), logoMat.rows(), Bitmap.Config.ARGB_8888);
Utils.matToBitmap(logoMat, roiBitmap);
return roiBitmap;
}
method extractSquareMat() just extract Region of Interest (logo) from whole image
public static Mat extractSquareMat(Mat sourceMat, Rect rect) {
Mat squareMat = null;
int padding = 50;
if (rect != null) {
Rect truncatedRect = new Rect((int) rect.tl().x + padding, (int) rect.tl().y + padding,
rect.width - 2 * padding, rect.height - 2 * padding);
squareMat = new Mat(sourceMat, truncatedRect);
}
return squareMat ;
}
and compareBitmaps() just wrapper for your code:
private void compareBitmaps(Bitmap bitmap1, Bitmap bitmap2) {
Mat mat1 = new Mat(bitmap1.getWidth(), bitmap1.getHeight(), CvType.CV_8UC3);
Utils.bitmapToMat(bitmap1, mat1);
Mat mat2 = new Mat(bitmap2.getWidth(), bitmap2.getHeight(), CvType.CV_8UC3);
Utils.bitmapToMat(bitmap2, mat2);
compareMats(mat1, mat2);
}
your code as method:
private void compareMats(Mat img1, Mat img2) {
FeatureDetector detector = FeatureDetector.create(FeatureDetector.ORB);
DescriptorExtractor extractor = DescriptorExtractor.create(DescriptorExtractor.BRIEF);
DescriptorMatcher matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_HAMMING);
Mat descriptors1 = new Mat();
MatOfKeyPoint keypoints1 = new MatOfKeyPoint();
detector.detect(img1, keypoints1);
extractor.compute(img1, keypoints1, descriptors1);
//second image
// Mat img2 = Imgcodecs.imread(path2);
Mat descriptors2 = new Mat();
MatOfKeyPoint keypoints2 = new MatOfKeyPoint();
detector.detect(img2, keypoints2);
extractor.compute(img2, keypoints2, descriptors2);
//matcher image descriptors
MatOfDMatch matches = new MatOfDMatch();
matcher.match(descriptors1,descriptors2,matches);
// Filter matches by distance
MatOfDMatch filtered = filterMatchesByDistance(matches);
int total = (int) matches.size().height;
int Match= (int) filtered.size().height;
Log.d("LOG", "total:" + total + " Match:" + Match);
}
static MatOfDMatch filterMatchesByDistance(MatOfDMatch matches){
List<DMatch> matches_original = matches.toList();
List<DMatch> matches_filtered = new ArrayList<DMatch>();
int DIST_LIMIT = 30;
// Check all the matches distance and if it passes add to list of filtered matches
Log.d("DISTFILTER", "ORG SIZE:" + matches_original.size() + "");
for (int i = 0; i < matches_original.size(); i++) {
DMatch d = matches_original.get(i);
if (Math.abs(d.distance) <= DIST_LIMIT) {
matches_filtered.add(d);
}
}
Log.d("DISTFILTER", "FIL SIZE:" + matches_filtered.size() + "");
MatOfDMatch mat = new MatOfDMatch();
mat.fromList(matches_filtered);
return mat;
}
As result for resized (scaled for 50%) images saved from your question result is:
D/DISTFILTER: ORG SIZE:237
D/DISTFILTER: FIL SIZE:230
D/LOG: total:237 Match:230
NB! This is a quick and dirty example just to demonstrate approach only for given template.
P.S. getBiggestSquare() can be like that (based on compare by area):
public static Rect getBiggestSquare(List<Rect> squares) {
Rect biggestSquare = null;
if (squares != null && squares.size() >= 1) {
Rect square;
double maxArea;
int ixMaxArea = 0;
square = squares.get(ixMaxArea);
maxArea = square.area();
for (int ix = 1; ix < squares.size(); ix++) {
square = squares.get(ix);
if (square.area() > maxArea) {
maxArea = square.area();
ixMaxArea = ix;
}
}
biggestSquare = squares.get(ixMaxArea);
}
return biggestSquare;
}
I am trying to implement Paper detection through OpenCV. I am able to understand the concept of how can I get it,
Input-> Canny-> Blur-> Find Conture-> Search (closed)Quadrilateral-> Draw Conture
but still, I am new to OpenCV programming. So having issues in implementing it. I was able to find help through this answer
Android OpenCV Paper Sheet detection
but it's drawing contour on every possible lining. Here is the code I am trying to implement.
public Mat onCameraFrame(CvCameraViewFrame inputFrame) {
mRgba = inputFrame.rgba();
Imgproc.drawContours(mRgba,findContours(mRgba), 0, new Scalar(0 , 255, 0), 5);
return mRgba;
}
public static class Quadrilateral {
public MatOfPoint contour;
public Point[] points;
public Quadrilateral(MatOfPoint contour, Point[] points) {
this.contour = contour;
this.points = points;
}
}
public static Quadrilateral findDocument( Mat inputRgba ) {
ArrayList<MatOfPoint> contours = findContours(inputRgba);
Quadrilateral quad = getQuadrilateral(contours);
return quad;
}
private static ArrayList<MatOfPoint> findContours(Mat src) {
double ratio = src.size().height / 500;
int height = Double.valueOf(src.size().height / ratio).intValue();
int width = Double.valueOf(src.size().width / ratio).intValue();
Size size = new Size(width,height);
Mat resizedImage = new Mat(size, CvType.CV_8UC4);
Mat grayImage = new Mat(size, CvType.CV_8UC4);
Mat cannedImage = new Mat(size, CvType.CV_8UC1);
Imgproc.resize(src,resizedImage,size);
Imgproc.cvtColor(resizedImage, grayImage, Imgproc.COLOR_RGBA2GRAY, 4);
Imgproc.GaussianBlur(grayImage, grayImage, new Size(5, 5), 0);
Imgproc.Canny(grayImage, cannedImage, 75, 200);
ArrayList<MatOfPoint> contours = new ArrayList<MatOfPoint>();
Mat hierarchy = new Mat();
Imgproc.findContours(cannedImage, contours, hierarchy, Imgproc.RETR_LIST, Imgproc.CHAIN_APPROX_SIMPLE);
hierarchy.release();
Collections.sort(contours, new Comparator<MatOfPoint>() {
#Override
public int compare(MatOfPoint lhs, MatOfPoint rhs) {
return Double.valueOf(Imgproc.contourArea(rhs)).compareTo(Imgproc.contourArea(lhs));
}
});
resizedImage.release();
grayImage.release();
cannedImage.release();
return contours;
}
private static Quadrilateral getQuadrilateral(ArrayList<MatOfPoint> contours) {
for ( MatOfPoint c: contours ) {
MatOfPoint2f c2f = new MatOfPoint2f(c.toArray());
double peri = Imgproc.arcLength(c2f, true);
MatOfPoint2f approx = new MatOfPoint2f();
Imgproc.approxPolyDP(c2f, approx, 0.02 * peri, true);
Point[] points = approx.toArray();
// select biggest 4 angles polygon
if (points.length == 4) {
Point[] foundPoints = sortPoints(points);
return new Quadrilateral(c, foundPoints);
}
}
return null;
}
private static Point[] sortPoints(Point[] src) {
ArrayList<Point> srcPoints = new ArrayList<>(Arrays.asList(src));
Point[] result = { null , null , null , null };
Comparator<Point> sumComparator = new Comparator<Point>() {
#Override
public int compare(Point lhs, Point rhs) {
return Double.valueOf(lhs.y + lhs.x).compareTo(rhs.y + rhs.x);
}
};
Comparator<Point> diffComparator = new Comparator<Point>() {
#Override
public int compare(Point lhs, Point rhs) {
return Double.valueOf(lhs.y - lhs.x).compareTo(rhs.y - rhs.x);
}
};
// top-left corner = minimal sum
result[0] = Collections.min(srcPoints, sumComparator);
// bottom-right corner = maximal sum
result[2] = Collections.max(srcPoints, sumComparator);
// top-right corner = minimal diference
result[1] = Collections.min(srcPoints, diffComparator);
// bottom-left corner = maximal diference
result[3] = Collections.max(srcPoints, diffComparator);
return result;
}
The answer suggests that I should use Quadrilateral Object and call it with Imgproc.drawContours(), but this function takes in ArrayList as argument where as Quadrilateral object contains MatofPoint and Point[]. Can someone help me through this..I am using OpenCV(3.3) and Android (1.5.1)?
Here is the sample what it should look like
I am trying to detect red circles on camera frames,i was trying to do the same as here https://solarianprogrammer.com/2015/05/08/detect-red-circles-image-using-opencv/ and here Android OpenCV Color detection, but it only detects blue circle O_o(screenshots link: https://drive.google.com/open?id=0B-pbp_K-xNkEWmxRa2tSMUZlUEE), here goes my code:
public Mat onCameraFrame(CvCameraViewFrame inputFrame) {
Mat lowerRed = new Mat();
Mat upperRed = new Mat();
Mat redHueImage = new Mat();
Mat green;
Mat blue;
Mat orange;
Mat rgba = inputFrame.rgba();
Core.flip(rgba, rgba, 1);
Mat HSVMat = new Mat();
Imgproc.medianBlur(rgba,rgba,3);
Imgproc.cvtColor(rgba, HSVMat, Imgproc.COLOR_BGR2HSV, 0);
Core.inRange(HSVMat,new Scalar(0,100,100),new Scalar(10,255,255),lowerRed);
Core.inRange(HSVMat,new Scalar(160,100,100),new Scalar(179,255,255),upperRed);
Core.addWeighted(lowerRed,1.0,upperRed,1.0,0.0,redHueImage);
Imgproc.GaussianBlur(redHueImage, redHueImage, new Size(9, 9), 2, 2);
double dp = 1.2d;
double minDist = 100;
int minRadius = 0;
int maxRadius = 0;
double param1 = 100, param2 = 20;
Mat circles = new Mat();
Imgproc.HoughCircles(redHueImage, circles, Imgproc.HOUGH_GRADIENT, dp, redHueImage.rows()/8, param1, param2, minRadius, maxRadius);
int numCircles = (circles.rows() == 0) ? 0 : circles.cols();
for (int i = 0; i < numCircles; i++) {
double[] circleCoordinates = circles.get(0, i);
int x = (int) circleCoordinates[0], y = (int) circleCoordinates[1];
Point center = new Point(x, y);
int radius = (int) circleCoordinates[2];
Imgproc.circle(rgba, center, radius, new Scalar(0, 255, 0), 4);
}
lowerRed.release();
upperRed.release();
HSVMat.release();
return rgba;
}