I am new to Opencv. I am using it in java. Could you please tell me where I went wrong in the following coding? I always get the unsupported format error - "Unsupported format or combination of formats ([Start]FindContours supports only CV_8UC1 images when mode != CV_RETR_FLOODFILL otherwise supports CV_32SC1 images only) in cvStartFindContours".
Thanks in advance.
Mat dst = new Mat();
destination.convertTo(dst,CvType.CV_32SC1);
List<MatOfPoint> points = new ArrayList<MatOfPoint>();
Mat hierarchy = new Mat();
Imgproc.findContours(dst, points, hierarchy, Imgproc.RETR_FLOODFILL, Imgproc.CHAIN_APPROX_SIMPLE);
Mat cImg = new Mat(dst.size(), dst.type());
for (int i = 0; i < points.size(); i++) {
Imgproc.drawContours(cImg, points, i, new Scalar(0, 255,0), -1);
}
Imgcodecs.imwrite("contour.jpg", cImg);
Related
I am detecting a rectangle and comparing the color to a urine test strip.
How can i detect all of squares? I want to detect the remaining squares in the picture below. I have tried changing the brightness and contrast
Here is my code:
MainActivity.java
...
#Override
protected void onActivityResult(int requestCode, int resultCode, Intent data) {
...
Bitmap img = BitmapFactory.decodeStream(in);
in.close();
Bitmap changeImg = changeBitmapContrastBrightness(img, (float)1, 10);
Mat cMap = new Mat();
Utils.bitmapToMat(changeImg, cMap);
List<MatOfPoint> squres = processImage(cMap);
for (int i = 0; i < squres.size(); i++) {
setLabel(cMap, String.valueOf(i), squres.get(i));
}
Bitmap resultBitmap = Bitmap.createBitmap(cMap.cols(), cMap.rows(), Bitmap.Config.ARGB_8888);
Utils.matToBitmap(cMap, resultBitmap);
imgView.setImageBitmap(resultBitmap);
...
}
...
private static List<MatOfPoint> processImage(Mat img){
ArrayList<MatOfPoint> squares = new ArrayList<>();
Mat matGray = new Mat();
Mat matCny = new Mat();
Mat matBlur = new Mat();
Mat matThresh = new Mat();
Mat close = new Mat();
// 노이즈 제거위해 다운스케일 후 업스케일
// Imgproc.pyrDown(matInit, matBase, matBase.size());
// Imgproc.pyrUp(matBase, matInit, matInit.size());
// GrayScale
Imgproc.cvtColor(img, matGray, Imgproc.COLOR_BGR2GRAY);
// Blur
Imgproc.medianBlur(matGray, matBlur, 5);
// // Canny Edge 검출
// Imgproc.Canny(matBlur, matCny, 0, 255);
// // Binary
Imgproc.threshold(matBlur, matThresh, 160, 255, Imgproc.THRESH_BINARY_INV);
Imgproc.morphologyEx(matThresh, close, Imgproc.MORPH_CLOSE, Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(3,3)));
// // 노이즈 제거
// Imgproc.erode(matCny, matCny, Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new org.opencv.core.Size(6, 6)));
// Imgproc.dilate(matCny, matCny, Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new org.opencv.core.Size(12, 12)));
List<MatOfPoint> contours = new ArrayList<>();
Mat hierarchy = new Mat();
Imgproc.findContours(close, contours, hierarchy, Imgproc.RETR_EXTERNAL, Imgproc.CHAIN_APPROX_SIMPLE);
double min_area = 0;
double max_area = 10000;
for(MatOfPoint cnt : contours){
double contourArea = Imgproc.contourArea(cnt);
if(contourArea > min_area && contourArea < max_area){
squares.add(cnt);
}
}
return squares;
}
App result Image
Original Image
Please help me..
Your code is correctly identifying the smaller boxes and ignoring the very large box which is the strip, so the basics are all in place.
It is not recognising the smaller boxes on the strip - given that your contour finding is clearly working this suggests that the threshold value in your threshold function (160 in your code above) may need to be adjusted so it includes the color boxes on the strip which do not have a black contour. The black contour will be definitely detached.
Whatever the root causes you'll probably find the easiest way to debug it is to output and look at the intermediate images generated - this will allow you check visually yourself very quickly the result of your blurring and thresholding.
You could also take a look at using adaptive thresholding if you are working with multiple images and find the threshold is not something you can reliably determine in advance. The documentation is here: https://docs.opencv.org/2.4/modules/imgproc/doc/miscellaneous_transformations.html?highlight=adaptivethreshold and there is a very nice example in this answer here: https://stackoverflow.com/a/31290735/334402
adaptiveThreshold parameters allow you fine tune its behaviour and it is worth experimenting with them see what works best for a given type if image:
I'm using java with opencv,javacv for an image processing project. The image is not taking from the camera. I'm giving the image as below.
Mat image = Imgcodecs.imread("E:/resources/PPHOTOO/test.jpg");
and using haarcascades I'm cropping the face image too.
CascadeClassifier faceDetector = new CascadeClassifier("C:\\opencv\\haarcascades\\haarcascade_frontalface_alt.xml");
String faces;
MatOfRect faceDetections = new MatOfRect();
Mat face;
Mat crop = null;
for (int i = 0; i< faceDetections.toArray().length; i++){
faces = "Face"+i+".png";
face = image.submat(faceDetections.toArray()[i]);
crop = face.submat(4, (2*face.width())/3, 0, face.height());
Imgcodecs.imwrite(faces, face);
}
I want to find whether person is person is straight looking or not. Which means not side faces. I need to know how to find this part.
straight looking image
side face image
I'm trying to implement Exposure Fusion with OpenCV 3.0.0 on Android using MergeMertens class.
The problem is that image is returned in blue. Here is the screenshot of how it looks like: http://take.ms/agYSD
I suppose the problem is with RGB/BGR representation of files. I tried to convert from RGB to BGR and vice versa before and after applying merger, anyway got the problem with color.
If I'm using grayscaled image then everything is allright.
Here is the code that I'm using:
public void process(String[] InFiles, float[] InTimes, String OutImage) {
List<Mat> images = new ArrayList<Mat>();
String path = Environment.getExternalStorageDirectory().toString() + "/" + _App.getPackageName() + "/";
Mat hdrImage = new Mat();
Mat ldrImage = new Mat();
Mat times = new MatOfFloat(InTimes);
for(int i = 0; i < InFiles.length; i++) {
Mat m = Imgcodecs.imread(path + InFiles[i]);
images.add(m);
}
Photo.createMergeMertens().process(images, hdrImage);
Core.multiply(hdrImage, new Scalar(255.0), ldrImage);
Imgcodecs.imwrite(path + OutImage, ldrImage);
}
I'm currently working on an application that will split a scanned image (that contains multiple receipts) into individual receipt images.
Below is the sample image:
sample image
I was able to detect the edges of each receipts in the scanned image using canny function of OpenCV.
Below is the sample image with detected edges:
sample image with detected edges
... and the sample code is
Mat src = Highgui.imread(filename);
Mat gray = new Mat();
int threshold = 12;
Imgproc.cvtColor(src, gray, Imgproc.COLOR_BGR2GRAY);
Imgproc.blur(gray, gray, new Size(3, 3));
Imgproc.Canny(gray, gray, threshold, threshold * 3, 3, true);
List<MatOfPoint> contours = new ArrayList<>();
Mat hierarchy = new Mat();
Imgproc.findContours(gray, contours, hierarchy,
Imgproc.RETR_CCOMP,
Imgproc.CHAIN_APPROX_SIMPLE);
if (hierarchy.size().height > 0 && hierarchy.size().width > 0) {
for (int idx = 0; idx >= 0; idx = (int) hierarchy.get(0, idx)[0]) {
Rect rect = Imgproc.boundingRect(contours.get(idx));
Core.rectangle(src, new Point(rect.x, rect.y),
new Point(rect.x + rect.width, rect.y + rect.height),
new Scalar(255, 0, 0));
}
}
Now my problem is, I don't know how am I going to identify the 3rd receipt since unlike with the first 2 it is not enclosed in one rectangular shape which I will use as the basis for splitting the image.
I've heard that for me to extract the 3rd image, I must use a clustering algorithm like DBSCAN, unfortunately I can't find one.
Anyone knows how am I going to identify the 3rd image?
Thank you in advance!
I was trying to implement watershed function from OpenCV on Android. However my program always crashed at the place where watershed function is called. I can output the marker's result perfectly fine. But the watershed function always just crashes. Here is my code:
Mat threeChannel = new Mat();
Imgproc.cvtColor(mRgba, threeChannel, Imgproc.COLOR_BGR2GRAY);
Imgproc.threshold(threeChannel, threeChannel, 100, 255, Imgproc.THRESH_BINARY);
Mat fg = new Mat(mRgba.size(),CvType.CV_8U);
Imgproc.erode(threeChannel,fg,new Mat(),new Point(-1,-1),2);
Mat bg = new Mat(mRgba.size(),CvType.CV_8U);
Imgproc.dilate(threeChannel,bg,new Mat(),new Point(-1,-1),3);
Imgproc.threshold(bg,bg,1, 128,Imgproc.THRESH_BINARY_INV);
Mat markers = new Mat(mRgba.size(),CvType.CV_8U, new Scalar(0));
Core.add(fg, bg, markers);
WatershedSegmenter segmenter = new WatershedSegmenter();
segmenter.setMarkers(markers);
Mat result = segmenter.process(mRgba);
return result;
WatershedSegmenter calss is as follows:
public class WatershedSegmenter{
public Mat markers;
public void setMarkers(Mat markerImage)
{
markerImage.convertTo(markers, CvType.CV_32S);
}
public Mat process(Mat image)
{
Imgproc.watershed(image, markers);
markers.convertTo(markers,CvType.CV_8U);
return markers;
}
}
Has anybody managed to get this working on Android before? I managed to get it to work in C++ with Qt before following this tutorial: link. However I haven't got any luck on Android at the moment.
I found out the reason of crash now. watershed is taking a 8 bit 3 channel format of data, and RGBA is a 4 channel data. I just convert it from RGBA to RGB, and it solved all the issues.
Your Mat doesn't match the correct .depth() and/or .channel().
The first step is to double-check each Mat has the type you think it does by using the myMat.depth() and myMat.channels() functions. The function watershed uses two Mat arguments. The first should be an 8-bit, 3-channel image, and the second should be a 32-bit single-channel image.
If they are not the right kind of image, use cvtColor to convert from what you have to what you need.
Try out this solution
BitmapFactory.Options o = new BitmapFactory.Options();
o.inDither = false;
o.inSampleSize=4;
int width , height ;
width = src_Bitmap.getWidth();
height = src_Bitmap.getHeight();
Mat rgba = new Mat();
Mat gray_mat= new Mat();
Mat threeChannel = new Mat();
Utils.bitmapToMat(src_Bitmap,gray_mat);
Imgproc.cvtColor(gray_mat,rgba , Imgproc.COLOR_RGBA2RGB);
Imgproc.cvtColor(rgba, threeChannel, Imgproc.COLOR_RGB2GRAY);
Imgproc.threshold(threeChannel, threeChannel, 100, 255, Imgproc.THRESH_OTSU);
Mat fg = new Mat(rgba.size(),CvType.CV_8U);
Imgproc.erode(threeChannel,fg,new Mat(),new Point(-1,-1),2);
Mat bg = new Mat(rgba.size(),CvType.CV_8U);
Imgproc.dilate(threeChannel,bg,new Mat(),new Point(-1,-1),3);
Imgproc.threshold(bg,bg,1, 128,Imgproc.THRESH_BINARY_INV);
Mat markers = new Mat(rgba.size(),CvType.CV_8U, new Scalar(0));
Core.add(fg, bg, markers);
// Start the WaterShed Segmentation :
Mat marker_tempo = new Mat();
markers.convertTo(marker_tempo, CvType.CV_32S);
Imgproc.watershed(rgba, marker_tempo);
marker_tempo.convertTo(markers,CvType.CV_8U);
result_Bitmap=Bitmap.createBitmap(width,height,Bitmap.Config.RGB_565);
Imgproc.applyColorMap( markers, markers,4 );
Utils.matToBitmap( markers,result_Bitmap);
myImageView.setImageBitmap(result_Bitmap);