How to use OpenCV SimpleBlobDetector

Python: Reads image blob.jpg and performs blob detection with different parameters.

#!/usr/bin/python

# Standard imports
import cv2
import numpy as np;

# Read image
im = cv2.imread("blob.jpg")

# Setup SimpleBlobDetector parameters.
params = cv2.SimpleBlobDetector_Params()

# Change thresholds
params.minThreshold = 10
params.maxThreshold = 200


# Filter by Area.
params.filterByArea = True
params.minArea = 1500

# Filter by Circularity
params.filterByCircularity = True
params.minCircularity = 0.1

# Filter by Convexity
params.filterByConvexity = True
params.minConvexity = 0.87

# Filter by Inertia
params.filterByInertia = True
params.minInertiaRatio = 0.01

# Create a detector with the parameters
# OLD: detector = cv2.SimpleBlobDetector(params)
detector = cv2.SimpleBlobDetector_create(params)


# Detect blobs.
keypoints = detector.detect(im)

# Draw detected blobs as red circles.
# cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS ensures
# the size of the circle corresponds to the size of blob

im_with_keypoints = cv2.drawKeypoints(im, keypoints, np.array([]), (0,0,255), cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)

# Show blobs
cv2.imshow("Keypoints", im_with_keypoints)
cv2.waitKey(0)

C++: Reads image blob.jpg and performs blob detection with different parameters.

#include "opencv2/opencv.hpp"

using namespace cv;
using namespace std;

int main(int argc, char** argv)
{
    // Read image
#if CV_MAJOR_VERSION < 3   // If you are using OpenCV 2
    Mat im = imread("blob.jpg", CV_LOAD_IMAGE_GRAYSCALE);
#else
    Mat im = imread("blob.jpg", IMREAD_GRAYSCALE);
#endif

    // Setup SimpleBlobDetector parameters.
    SimpleBlobDetector::Params params;

    // Change thresholds
    params.minThreshold = 10;
    params.maxThreshold = 200;

    // Filter by Area.
    params.filterByArea = true;
    params.minArea = 1500;

    // Filter by Circularity
    params.filterByCircularity = true;
    params.minCircularity = 0.1;

    // Filter by Convexity
    params.filterByConvexity = true;
    params.minConvexity = 0.87;

    // Filter by Inertia
    params.filterByInertia = true;
    params.minInertiaRatio = 0.01;

    // Storage for blobs
    std::vector<KeyPoint> keypoints;

#if CV_MAJOR_VERSION < 3   // If you are using OpenCV 2

    // Set up detector with params
    SimpleBlobDetector detector(params);

    // Detect blobs
    detector.detect(im, keypoints);
#else 

    // Set up detector with params
    Ptr<SimpleBlobDetector> detector = SimpleBlobDetector::create(params);

    // Detect blobs
    detector->detect(im, keypoints);
#endif 

    // Draw detected blobs as red circles.
    // DrawMatchesFlags::DRAW_RICH_KEYPOINTS flag ensures
    // the size of the circle corresponds to the size of blob

    Mat im_with_keypoints;
    drawKeypoints(im, keypoints, im_with_keypoints, Scalar(0, 0, 255), DrawMatchesFlags::DRAW_RICH_KEYPOINTS);

    // Show blobs
    imshow("keypoints", im_with_keypoints);
    waitKey(0);
}

The answer has been copied from this tutorial I wrote at LearnOpenCV.com explaining various parameters of SimpleBlobDetector. You can find additional details about the parameters in the tutorial.

You may store the parameters for the blob detector in a file, but this is not necessary. Example:

// set up the parameters (check the defaults in opencv's code in blobdetector.cpp)
cv::SimpleBlobDetector::Params params;
params.minDistBetweenBlobs = 50.0f;
params.filterByInertia = false;
params.filterByConvexity = false;
params.filterByColor = false;
params.filterByCircularity = false;
params.filterByArea = true;
params.minArea = 20.0f;
params.maxArea = 500.0f;
// ... any other params you don't want default value

// set up and create the detector using the parameters
cv::SimpleBlobDetector blob_detector(params);
// or cv::Ptr<cv::SimpleBlobDetector> detector = cv::SimpleBlobDetector::create(params)

// detect!
vector<cv::KeyPoint> keypoints;
blob_detector.detect(image, keypoints);

// extract the x y coordinates of the keypoints: 

for (int i=0; i<keypoints.size(); i++){
    float X = keypoints[i].pt.x; 
    float Y = keypoints[i].pt.y;
}

Note: all the examples here are using the OpenCV 2.X API.

In OpenCV 3.X, you need to use:

Ptr<SimpleBlobDetector> d = SimpleBlobDetector::create(params);

See also: the transition guide: http://docs.opencv.org/master/db/dfa/tutorial_transition_guide.html#tutorial_transition_hints_headers

// creation 
            cv::SimpleBlobDetector * blob_detector; 
            blob_detector = new SimpleBlobDetector(); 
            blob_detector->create("SimpleBlobDetector"); 
// change params - first move it to public!! 
            blob_detector->params.filterByArea = true; 
            blob_detector->params.minArea = 1; 
            blob_detector->params.maxArea = 32000; 
// or read / write them with file 
            FileStorage fs("test_fs.yml", FileStorage::WRITE); 
            FileNode fn = fs["features"]; 

            //blob_detector->read(fn); 
// detect 
            vector<KeyPoint> keypoints; 
            blob_detector->detect(img_text, keypoints); 
            fs.release(); 

I do know why, but params are protected. So I moved it in file features2d.hpp to be public:

  virtual void read( const FileNode& fn ); 
  virtual void write( FileStorage& fs ) const; 

public: 
Params params; 




protected: 
struct CV_EXPORTS Center 
  { 
      Point2d loc 

If you will not do this, the only way to change params is to create file (FileStorage fs("test_fs.yml", FileStorage::WRITE);), than open it in notepad, and edit. Or maybe there is another way, but I`m not aware of it.