OpenCV- Working with OpenCV

Connected-component labeling (alternatively connected-component analysis, blob extraction, region labeling, blob discovery, or region extraction) is an algorithmic application of graph theory, where subsets of connected components are uniquely labeled based on a given heuristic. Connected-component labeling is not to be confused with segmentation. i got the initial code from this URL: http://nghiaho.com/?p=1102 However the code did not compile with my setup of OpenCV 2.2, im guessing it was an older version. so a refactored and corrected the errors to come up with this Class class atsBlobFinder     {     public:         atsBlobFinder()         {         }         ///Original Code by http://nghiaho.com/?p=1102         ///Changed and added commments. Removed Errors         ///works with VS2010 and OpenCV 2.2+         void FindBlobs(const cv::Mat &binary, vector < vector<cv::Point>  > &blobs)         {             blobs.clear();             // Fill the la

Using my previous posts Class file and this reference: http://people.revoledu.com/kardi/tutorial/DecisionTree/how-to-measure-impurity.htm float computeGiniIndex(Mat r, Mat g, Mat b)     {         float giniIndex = 0.0;         float frequency = getFrequencyOfBin(r);         for( int i = 1; i < histSize; i++ )         {             float Hc = abs(getHistogramBinValue(r,i));             giniIndex += Hc*Hc;         }         frequency = getFrequencyOfBin(g);         for( int i = 1; i < histSize; i++ )         {             float Hc = abs(getHistogramBinValue(g,i));             giniIndex += Hc*Hc;         }         frequency = getFrequencyOfBin(b);         for( int i = 1; i < histSize; i++ )         {             float Hc = abs(getHistogramBinValue(b,i));             giniIndex += Hc*Hc;         }         giniIndex = 1 - (giniIndex);         //cout << giniIndex <<endl;         return giniIndex;     }

entropy is a measure of the uncertainty associated with a random variable. basically i want to get a single value representing the entropy of an image. 1. Assign 255 bins for the range of values between 0-255 2. separate the image into its 3 channels 3. compute histogram for each channel 4. normalize all 3 channels unifirmely 5. for each channel get the bin value (Hc) and use its absolute value (negative log is infinity) 6. compute Hc*log10(Hc) 7. add to entropy and continue with 5 until a single value converges 5. get the frequency of each channel - add all the values of the bin 6. for each bin get a probability - if bin 1 = 20 bin 2 = 30 then frequency is 50 and probability is 20/50 and 30/50 then compute using shannon formula  REFERENCE: http://people.revoledu.com/kardi/tutorial/DecisionTree/how-to-measure-impurity.htm class atsHistogram { public:     cv::Mat DrawHistogram(Mat src)     {         /// Separate the image in 3 places ( R, G and B )