Develop Reference

Find rectangle with inner rectangle using OpenCV

I am trying to extract a table row containing a filled rectangle in an image file using openCV. I have used findcontours and boundingrect. Disclaimer: I am completely new to opencv and image processing, so this might not be an optimal solution.
This is what I have done so far, it is getting me all the tables in the image including the row i want. How can i filter to just that row?
Imgcodecs imageCodecs = new Imgcodecs();
Mat sourceMat = imageCodecs.imread("image.png");
Mat grayMat = imageCodecs.imread("image.png");
Mat threshold = imageCodecs.imread("image.png");
Mat threshold1 = imageCodecs.imread("image.png");
Imgproc.cvtColor(sourceMat, grayMat, Imgproc.COLOR_BGR2GRAY);
Imgproc.threshold(grayMat, threshold, 70, 255, Imgproc.THRESH_BINARY_INV);
Imgproc.threshold(grayMat, threshold1, 270, 255, Imgproc.THRESH_BINARY);
Core.bitwise_not(grayMat, threshold);
Imgcodecs imgcodecs1 = new Imgcodecs();
imgcodecs1.imwrite("imagethreshold.png", threshold);
List<MatOfPoint> whiteContours = new ArrayList<>();
MatOfPoint heirarchy = new MatOfPoint();
Imgproc.findContours(threshold.clone(), whiteContours, heirarchy, Imgproc.RETR_CCOMP, Imgproc.CHAIN_APPROX_SIMPLE);
int count = 0;
// find appropriate bounding rectangles
for (int i = 0; i < whiteContours.size(); i++) {
RotatedRect boundingRect = Imgproc.minAreaRect(new MatOfPoint2f(whiteContours.get(i).toArray()));
Point rotated_rect_points[] = new Point[4];
boundingRect.points(rotated_rect_points);
Rect rect = Imgproc.boundingRect(new MatOfPoint(rotated_rect_points));
Mat roiMat = sourceMat.submat(rect);
if (rect.area()>15000 && heirarchy.get(0,i) != null) {
// checking if heirarchy has parent, next or previous contour is -1
if(heirarchy.get(0,i)[3]!=-1 && heirarchy.get(0,i)[0] !=-1 && heirarchy.get(0,i)[1] ==-1){
// write to image file
Imgcodecs imgcodecs = new Imgcodecs();
imgcodecs.imwrite("image" + count + ".png", roiMat);
count++;
}
}
}```

Detect Biggest Rectangle in the Image using Java Opencv [SOLVED]

How can I detect the four corner points of the biggest square (at center of the image) using opencv in java
I have solved this using findContours.
Original Image
Output Image
Please find the code below. I don't now how to detect the end points of center square. I tried to detect lines using HoughLinesP but it is returning only 1 verticle line instead of giving all the 4 lines.
System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
String path = "/Users/saurabhsaluja/Desktop/cimg.jpg";
Mat img = Imgcodecs.imread(path);
Mat destination = new Mat(img.rows(),img.cols(),img.type());
Core.addWeighted(img, 1.3, destination, -0.7, 0, destination);
Mat cannyOutput = new Mat();
int threshold = 15;
Mat srcGray = new Mat();
Imgproc.cvtColor(destination, srcGray, Imgproc.COLOR_BGR2GRAY);
Imgproc.Canny(srcGray, cannyOutput, threshold, threshold* 4);
Mat element = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(10,10));
Mat element2 = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(10,10));
Imgproc.dilate(cannyOutput, cannyOutput, element);
Imgproc.dilate(cannyOutput, cannyOutput, element2);
element = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(9,9));
element2 = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(9,9));
Imgproc.erode(cannyOutput, cannyOutput, element);
Imgproc.erode(cannyOutput, cannyOutput, element2);
Imgcodecs.imwrite("/Users/saurabhsaluja/Desktop/cannyOutput.jpg", cannyOutput); //THE IMAGE YOU ARE LOOKING AT
Mat lines = new Mat();
Imgproc.HoughLinesP(cannyOutput, lines, 1, Math.PI / 180, 50, 20, 20);
for(int i = 0; i < lines.cols(); i++) {
double[] val = lines.get(0, i);
Imgproc.line(img, new Point(val[0], val[1]), new Point(val[2], val[3]), new Scalar(0, 0, 255), 2);
}
Imgcodecs.imwrite("/Users/saurabhsaluja/Desktop/finalimg.jpg", img);
Solution:
List<MatOfPoint> contours = new ArrayList<MatOfPoint>();
Imgproc.findContours(cannyOutput, contours, new Mat(), Imgproc.RETR_LIST, Imgproc.CHAIN_APPROX_SIMPLE);
double inf = 0;
Rect max_rect = null;
for(int i=0; i< contours.size();i++){
Rect rect = Imgproc.boundingRect(contours.get(i));
double area = rect.area();
if(inf < area) {
max_rect = rect;
inf = area;
//Imgcodecs.imwrite("/Users/saurabhsaluja/Desktop/input"+i+".jpg", img);
}
if(area > 50000) {
System.out.println(area);
Imgproc.rectangle(img, new Point(rect.x,rect.y), new Point(rect.x+rect.width,rect.y+rect.height),new Scalar(0,0,0),5);
}
}
Now just get the biggest by looking area of each counter.
Thanks.
Solution Image:

List<MatOfPoint> contours = new ArrayList<MatOfPoint>();
Imgproc.findContours(cannyOutput, contours, new Mat(), Imgproc.RETR_LIST, Imgproc.CHAIN_APPROX_SIMPLE);
double inf = 0;
Rect max_rect = null;
for(int i=0; i< contours.size();i++){
Rect rect = Imgproc.boundingRect(contours.get(i));
double area = rect.area();
if(inf < area) {
max_rect = rect;
inf = area;
//Imgcodecs.imwrite("/Users/saurabhsaluja/Desktop/input"+i+".jpg", img);
}
if(area > 50000) {
System.out.println(area);
Imgproc.rectangle(img, new Point(rect.x,rect.y), new Point(rect.x+rect.width,rect.y+rect.height),new Scalar(0,0,0),5);
}
}
Output:

OpenCV detect rectangle with the largest width

I'm trying to get this rectangle from that image:
Found this solution using OpenCV:
private Bitmap findRectangle(Bitmap src) throws Exception {
Mat imageMat = new Mat();
Utils.bitmapToMat(src, imageMat);
Mat imgSource=imageMat.clone();
Imgproc.cvtColor(imgSource, imageMat, Imgproc.COLOR_BGR2GRAY);
//find the contours
List<MatOfPoint> contours = new ArrayList<MatOfPoint>();
Imgproc.findContours(imageMat, contours, new Mat(), Imgproc.RETR_LIST, Imgproc.CHAIN_APPROX_NONE);
Imgproc.Canny(imageMat,imageMat,0,255);
Bitmap canny=Bitmap.createBitmap(imageMat.cols(),imageMat.rows(),Bitmap.Config.ARGB_8888);
Utils.matToBitmap(imageMat,canny);
Imgproc.GaussianBlur(imageMat, imageMat, new org.opencv.core.Size(1, 1), 2, 2);
Bitmap blur=Bitmap.createBitmap(imageMat.cols(),imageMat.rows(),Bitmap.Config.ARGB_8888);
Utils.matToBitmap(imageMat,blur);
MatOfPoint temp_contour = contours.get(0); //the largest is at the index 0 for starting point
for (int idx = 0; idx < contours.size(); idx++) {
temp_contour = contours.get(idx);
//check if this contour is a square
MatOfPoint2f new_mat = new MatOfPoint2f( temp_contour.toArray() );
int contourSize = (int)temp_contour.total();
MatOfPoint2f approxCurve_temp = new MatOfPoint2f();
Imgproc.approxPolyDP(new_mat, approxCurve_temp, contourSize*0.05, true);
if (approxCurve_temp.total() == 4) {
MatOfPoint points = new MatOfPoint( approxCurve_temp.toArray() );
Rect rect = Imgproc.boundingRect(points);
Imgproc.rectangle(imgSource, new Point(rect.x,rect.y), new Point(rect.x+rect.width,rect.y+rect.height), new Scalar(255, 0, 0, 255), 3);
}
}
Bitmap analyzed=Bitmap.createBitmap(imgSource.cols(),imgSource.rows(),Bitmap.Config.ARGB_8888);
Utils.matToBitmap(imgSource,analyzed);
return analyzed;
}
The best i got was this:
The problem is that the rectangle isn't perfect, maybe find the white numbers inside of that can be a best option, but i don't know too much of OpenCV.
Original image:

This is a very simple C++ implementation which tries to search for the text box. The accuracy of the detection depends on three parameters:
The thresh value provided to cv::threshold function to convert gray image to binary.
The height/width ratio, since the height of the text box is relatively smaller than the width, and the area of the text box.
Mat img = imread("image.jpg",-1), gray, binary;
/*pre-processing steps*/
uchar thresh = 80;
cvtColor(img, gray, cv::COLOR_BGR2GRAY);
GaussianBlur(gray, gray, Size(7,7), 0);
// change the thresh value to fine tune this program for your images
threshold(gray, binary, thresh, 255, cv::THRESH_BINARY_INV);
/*contour searching*/
std::vector<std::vector<Point>> contours;
std::vector<Vec4i> hierarchy;
findContours(binary, contours, hierarchy, cv::RETR_LIST, cv::CHAIN_APPROX_SIMPLE);
/*Filtering contours based on height/width ratio and bounding box area*/
std::vector<Rect> boxes;
double box_ratio = 0.3;
int box_area = 20000;
for(auto& cnt : contours)
{
auto box = minAreaRect(cnt).boundingRect();
// we are searching for a rectangle which a has relatively large area,
// and the height is smaller than the width, so the
// height/width ratio should be small. Change the these two values for fine tuning
if((min(box.width,box.height)/double(max(box.width,box.height)) < box_ratio) && box.area() > box_area )
{
boxes.push_back(box);
}
}
Mat txt_box = img(boxes.at(0));

Here is the almost same solution on java:
private Bitmap findRoi(Bitmap sourceBitmap) {
Mat sourceMat = new Mat(sourceBitmap.getWidth(), sourceBitmap.getHeight(), CV_8UC3);
Utils.bitmapToMat(sourceBitmap, sourceMat);
Mat grayMat = new Mat(sourceBitmap.getWidth(), sourceBitmap.getHeight(), CV_8UC3);
Imgproc.cvtColor(sourceMat, grayMat, Imgproc.COLOR_BGR2GRAY);
Imgproc.threshold(grayMat, grayMat, 125, 200, Imgproc.THRESH_BINARY);
// find contours
List<MatOfPoint> whiteContours = new ArrayList<>();
Rect largestRect = null;
Imgproc.findContours(grayMat, whiteContours, new Mat(), Imgproc.RETR_LIST, Imgproc.CHAIN_APPROX_SIMPLE);
// find appropriate bounding rectangles
for (MatOfPoint contour : whiteContours) {
RotatedRect boundingRect = Imgproc.minAreaRect(new MatOfPoint2f(contour.toArray()));
double rectangleArea = boundingRect.size.area();
// test min ROI area in pixels
if (rectangleArea > 10000) {
Point rotated_rect_points[] = new Point[4];
boundingRect.points(rotated_rect_points);
Rect rect = Imgproc.boundingRect(new MatOfPoint(rotated_rect_points));
// test horizontal ROI orientation and aspect ratio
if (rect.width > 3 * rect.height) {
if (largestRect == null) {
largestRect = rect;
} else {
if (rect.width > largestRect.width) {
largestRect = rect;
}
}
}
}
}
Mat roiMat = new Mat(sourceMat, largestRect);
Bitmap bitmap = Bitmap.createBitmap(roiMat.cols(), roiMat.rows(), Bitmap.Config.ARGB_8888);
Utils.matToBitmap(roiMat, bitmap);
return bitmap;
}
Also, you can use additional information: red number places on the right.

OpenCV change RGB parameters in rectangle

I have 4 points. For example...
A(1;1)
B(2;5)
C(4;4)
D(3;2)
How I can change RGB parameters in this rectangle (for all pixels)?
Like this:
double[] data = mat.get(x, y);
data[0] = data[0]+30;
data[1] = data[1]+20;
data[2] = data[2]+10;
mat.put(x, y, data);

Try something like that for implementing approach described in Dan Mašek comment:
...
Bitmap sourceBitmap = BitmapFactory.decodeResource(getResources(), R.drawable.<your_image>);
Mat sourceMat = new Mat(sourceBitmap.getWidth(), sourceBitmap.getHeight(), CvType.CV_8UC3);
Utils.bitmapToMat(sourceBitmap, sourceMat);
Mat maskMat = new Mat(sourceBitmap.getWidth(), sourceBitmap.getHeight(), CvType.CV_8UC4);
Mat resultMat = new Mat(sourceBitmap.getWidth(), sourceBitmap.getHeight(), CvType.CV_8UC4);
// create color, which added to sourceMat region (+100 - for red channel)
Scalar color = new Scalar(100, 0, 0, 255);
// or you can try Scalar color = new Scalar(10, 20, 30); as in your question
Point[] region = new Point[4];
// your coords multiplied by 50 for visualization convenience
region[0] = new Point(50, 50);
region[1] = new Point(100, 250);
region[2] = new Point(200, 200);
region[3] = new Point(150, 100);
List<MatOfPoint> contours = new ArrayList();
MatOfPoint regionMat = new MatOfPoint(region);
contours.add(regionMat);
// create mask
Imgproc.drawContours(maskMat, contours, 0, color, -1);
// apply mask to source
Core.add(maskMat, sourceMat, resultMat);
// just for visualisation
Bitmap bitmap = Bitmap.createBitmap(sourceMat.cols(), sourceMat.rows(), Bitmap.Config.ARGB_8888);
Utils.matToBitmap(resultMat, bitmap);
<your_ImageView>.setImageBitmap(bitmap);
...
NB! This is just example of masking, not optimized.

Edited answer due to comment below:
This is OpenCV C++ code but you can easily port it to JAVA. Also my code assumes the points always represent a rectangle.
// read image
Mat image=imread("image.jpg",-1);
// region of interest, shape=rectangle
Point p1(50,50), p2(100,80);
Rect roi(p1.x,p1.y,p2.x,p2.y);
// vector hold channels
std::vector<Mat> channels(3);
// split original image to bgr channels
cv::split(image, channels);
// Mat to hold ROI
Mat extractedRoi;
//For channel B
extractedRoi = channels.at(0)(roi);
extractedRoi += 30;
//For channel G
extractedRoi = channels.at(1)(roi);
extractedRoi += 20;
//For channel R
extractedRoi = channels.at(2)(roi);
extractedRoi += 10;
// merge channels back together
cv::merge(channels, image);
Edit2: A faster approach.
Mat image=imread("/home/haseebullah/Pictures/S1.jpg",-1);
Point p1(50,50), p2(100,80);
Rect roi(p1.x,p1.y,p2.x,p2.y);
Mat extractedRoi;
extractedRoi = image(roi);
Scalar constants(30,20,10);
extractedRoi += constants

how to find the corner of rectangle in openCv with java

I wrote this code that detects rectangle but I can not write a code that detects corners.
public class RectDetection {
public static void main(String[] args) {
System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
Mat rectengle=Imgcodecs.imread("D:\\sepano\\rect.png");
Mat img =new Mat();
img=rectengle.clone();
Imgproc.cvtColor(rectengle, img, Imgproc.COLOR_BGR2GRAY);
Imgproc.GaussianBlur(img, img, new org.opencv.core.Size(1, 1), 2, 2);
Imgproc.Canny(img,img,3, 3,5,false);
List<MatOfPoint> contours = new ArrayList<MatOfPoint>();
Imgproc.findContours(img, contours, new Mat(), Imgproc.RETR_LIST, Imgproc.CHAIN_APPROX_SIMPLE);
MatOfPoint temp_contour = contours.get(0); //the largest is at the index 0 for starting point
for (int idx = 0; idx < contours.size(); idx++) {
temp_contour = contours.get(idx);
MatOfPoint2f new_mat = new MatOfPoint2f( temp_contour.toArray() );
int contourSize = (int)temp_contour.total();
MatOfPoint2f approxCurve_temp = new MatOfPoint2f();
Imgproc.approxPolyDP(new_mat, approxCurve_temp, contourSize*0.05, true);
if (approxCurve_temp.total()==8) {
MatOfPoint points = new MatOfPoint( approxCurve_temp.toArray() );
Rect rect = Imgproc.boundingRect(points);
Imgproc.rectangle(img, new Point(rect.x,rect.y), new Point(rect.x+rect.width,rect.y+rect.height), new Scalar(170,0,150,0), 5);}}
Here is a python code for corner detection but I can not convert it to java:
import numpy as np
import cv2
from matplotlib import pyplot as plt
img = cv2.imread('simple.jpg')
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
corners = cv2.goodFeaturesToTrack(gray,25,0.01,10)
corners = np.int0(corners)
for i in corners:
x,y = i.ravel()
cv2.circle(img,(x,y),3,255,-1)
plt.imshow(img),plt.show()
can any help me????

Look at your java code closely...
In this line:
Imgproc.rectangle(img, new Point(rect.x,rect.y), new Point(rect.x+rect.width,rect.y+rect.height), new Scalar(170,0,150,0), 5);
Point(rect.x,rect.y) corresponds to the top left corner of your rectangle, and Point(rect.x+rect.width,rect.y+rect.height) corresponds to the bottom right corner of your rectangle.
Rectangle detection code should suffice, and the 4 corners are as follows:
Point(rect.x,rect.y) //Top Left
Point(rect.x+rect.width,rect.y) //Top Right
Point(rect.x,rect.y+rect.height) //Bottom Left
Point(rect.x+rect.width,rect.y+rect.height) //Bottom Right