OpenCV - find bounding box of largest blob in binary image

Question

What is the most efficient way to find the bounding box of the largest blob in a binary image using OpenCV? Unfortunately, OpenCV does not have specific functionality for blob detection. Should I just use findContours() and search for the largest in the list?

Britain

It depends what you want to do with the blob afterwards, but your approach is valid :) – Hummock

If you say what language you're using, you may get more specific answers. – Men

I just read a bit about this. If you already have a binary image, it sounds like using Suzuki's method (findContours) is a really good fit. You could also incrementally find the first white pixel, then use floodFill to find the rest of that region, and so on. But I'm not sure that would be any faster. – Men

I just want to find the bounding box of the blob. I'm using OpenCV for Android, but all versions of OpenCV have pretty much the same functionality. – Snips

Answer 1

If you want to use OpenCV libs, check out OpenCVs SimpleBlobDetector. Here's another stack overflow showing a small tutorial of it: How to use OpenCV SimpleBlobDetector

This only gives you key points though. You could use this as an initial search to find the blob you want, and then possibly use the findContours algorithm around the most likely blobs.

Also the more information you know about your blob, you can provide parameters to filter out the blobs you don't want. You might want to test out the area parameters of the SimpleBlobDetector. Possibly could could compute the area based on the size of the area of the image and then iteratively allow for a smaller blob if the algorithm does not detect any blobs.

Here is the link to the main OpenCV documentation: http://docs.opencv.org/modules/features2d/doc/common_interfaces_of_feature_detectors.html#simpleblobdetector

Suppressive

Thanks for the link, this looks like a good alternative to findContours. Unfortunately it's not in OpenCV4Android, so I'll stick to my original plan. – Snips

Answer 2

Here. It. Is. (FYI: try not to be lazy and figure out what happens in my function below.

cv::Mat findBiggestBlob(cv::Mat & matImage){
    int largest_area=0;
    int largest_contour_index=0;

    vector< vector<Point> > contours; // Vector for storing contour
    vector<Vec4i> hierarchy;

    findContours( matImage, contours, hierarchy,CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE ); // Find the contours in the image

    for( int i = 0; i< contours.size(); i++ ) {// iterate through each contour. 
        double a=contourArea( contours[i],false);  //  Find the area of contour
        if(a>largest_area){
            largest_area=a;
            largest_contour_index=i;                //Store the index of largest contour
            //bounding_rect=boundingRect(contours[i]); // Find the bounding rectangle for biggest contour
        }
    }

    drawContours( matImage, contours, largest_contour_index, Scalar(255), CV_FILLED, 8, hierarchy ); // Draw the largest contour using previously stored index.
    return matImage;
}

Dawson

Isn't this a duplicate of my own answer? – Snips

Nope, my answer is in C++, the core language for OpenCV. As i said, it's for the lazy ones. – Dawson

@Dawson Could you help me with the java equivalent of drawContours() – Alphard

Answer 3

To find the bounding box of the largest blob, I used findContours, followed by the following code:

double maxArea = 0;
for (MatOfPoint contour : contours) {
    double area = Imgproc.contourArea(contour);
    if (area > maxArea) {
        maxArea = area;
        largestContour = contour;
    }
}
Rect boundingRect = Imgproc.boundingRect(largestContour);

Britain

What if there is a hole inside that contour and we want the blob with the largest area? – Stipule

Answer 4

Since no one has posted a complete OpenCV solution, here's a simple approach using thresholding + contour area filtering

---

Input image

Largest blob/contour highlighted in green

import cv2

# Load image, grayscale, Gaussian blur, and Otsu's threshold
image = cv2.imread('1.png')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]

# Find contours and sort using contour area
cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
cnts = sorted(cnts, key=cv2.contourArea, reverse=True)
for c in cnts:
    # Highlight largest contour
    cv2.drawContours(image, [c], -1, (36,255,12), 3)
    break

cv2.imshow('thresh', thresh)
cv2.imshow('image', image)
cv2.waitKey()

Farewell

Answer 5

TimZaman, your code has a bug but I can't comment so I start a new and correct answer. Here is my solution based on 1"'s and TimZaman's ideas:

Mat measure::findBiggestBlob(cv::Mat &src){
int largest_area=0;
int largest_contour_index=0;
Mat temp(src.rows,src.cols,CV_8UC1);
Mat dst(src.rows,src.cols,CV_8UC1,Scalar::all(0));
src.copyTo(temp);

vector<vector<Point>> contours; // storing contour
vector<Vec4i> hierarchy;

findContours( temp, contours, hierarchy,CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE );

for( int i = 0; i< contours.size(); i++ ) // iterate
{
    double a=contourArea( contours[i],false);  //Find the largest area of contour
    if(a>largest_area)
    {
        largest_area=a;
        largest_contour_index=i;
    }

}

drawContours( dst, contours,largest_contour_index, Scalar(255), CV_FILLED, 8, hierarchy ); 
// Draw the largest contour
return dst;
}

Aberdeen