CODEWITHSUNDEEP
opencvimage-processingfeature-detection
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Reputation: 27125

OpenCV - find bounding box of largest blob in binary image

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?

Upvotes: 18

Views: 39834

Answers (5)

nathancy
nathancy

Reputation: 46680

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

Input image

enter image description here

Largest blob/contour highlighted in green

enter image description here

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()

Upvotes: 2

Christoph Schart
Christoph Schart

Reputation: 26

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;
}

Upvotes: 0

TimZaman
TimZaman

Reputation: 2707

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;
}

Upvotes: 8

1&#39;&#39;
1&#39;&#39;

Reputation: 27125

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);

Upvotes: 2

jluzwick
jluzwick

Reputation: 2025

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

Upvotes: 4

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