How to segment objects after OpenCV connectedComponents function - c++

Question

I have obtained a labeling with the connectedComponents function of C++ OpenCV, which looks like in the picture :
This is the output of the ccLabels variable, which is a cv::Mat of the same size with the original image.
So what I need to do is :
Count the occurences of each number, and select the ones that
occur more than N times, which are the "big" ones.
Segment the
areas of the "big" components, and then count the number of 4's and
0's inside that area.
My ultimate aim is to count the number of holes in the image, so I aim to infer number of holes from (number of 0's / number of 4's). This is probably not the prettiest way but the images are very uniform in terms of size and illumination, so it will meet my needs.
But I'm new to OpenCV and I don't have much idea how to accomplish this task.
Here is what I've done so far:
cv::Mat1b outImg;
cv::threshold(grayImg, outImg, 150, 255, 0); // Thresholded -binary- image
cv::Mat ccLabels;
cv::connectedComponents(outImg, ccLabels); // Each non-zero pixel is labeled with their connectedComponent ID's
// write the labels to file:
std::ofstream myfile;
myfile.open("ccLabels.txt");
cv::Size s = ccLabels.size();
myfile << "Size: " << s.height << " , " << s.width <<"\n";
for (int r1 = 0; r1 < s.height; r1++) {
for (int c1 = 0; c1 < s.height; c1++) {
myfile << ccLabels.at<int>(r1,c1);
}
myfile << "\n";
}
myfile.close();
Since I know how to iterate inside the matrix, counting the numbers should be OK, but first I have to separate(eliminate / ignore) the "background" pixels, which are the 0's outside the connected components. Then counting should be easy.
How can I segment these "big" components? Maybe obtaining a mask, and only consider pixels where mask(x,y) = 1?
Thanks for any help !
Edit
This is the thresholded image:
And this is what I get after Canny edge detection :
This is the actual image (thresholded) :

Answer 1

Here a simple procedure to find the number on the dices, starting from your thresholded image
find external contours
for each contour
eventually discard small blobs
draw the filled mask
use AND and XOR to isolate internal holes
find contours, again
count contours
Result:
Number: 5
Number: 2
Image:
Code:
#include <opencv2\opencv.hpp>
#include <iostream>
#include <vector>
using namespace std;
using namespace cv;
int main(void)
{
// Grayscale image
Mat1b img = imread("path_to_image", IMREAD_GRAYSCALE);
// Minimum area of the contour
double minContourArea = 10;
// Prepare outpot
Mat3b result;
cvtColor(img, result, COLOR_GRAY2BGR);
// Find contours
vector<vector<Point>> contours;
findContours(img.clone(), contours, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE);
for (int i = 0; i < contours.size(); ++i)
{
// Check area
if (contourArea(contours[i]) < minContourArea) continue;
// Black mask
Mat1b mask(img.rows, img.cols, uchar(0));
// Draw filled contour
drawContours(mask, contours, i, Scalar(255), CV_FILLED);
mask = (mask & img) ^ mask;
vector<vector<Point>> cntrs;
findContours(mask, cntrs, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE);
cout << "Number: " << cntrs.size() << endl;
// Just for showing results
drawContours(result, cntrs, -1, Scalar(0,0,255), CV_FILLED);
}
imshow("Result", result);
waitKey();
return 0;
}

Answer 2

The easier way is findContours method. You find the inner contours and calculate their area( since the inner contours will be holes) and process this information accordingly.

Answer 3

To solve your 1st problem consider you have a set of values in values.Count the occurences of each number that as appeared.
int m=0;
for(int n=0;n<256;n++)
{
int c=0;
for(int q=0;q<values.size();q++)
{
if(n==values[q])
{
//int c;
c++;
m++;
}
}
cout<<n<<"= "<< c<<endl;
}
cout<<"Total number of elements "<< m<<endl;
To solve your second problem find the largest contour in the image using findcontours, draw bounding rectangle around it and then crop it. Again use the above code to count the pixel value "4" and "0". You can find the link of it here https://stackoverflow.com/a/32998275/3853072