c++ - OpenCV: Computing superpixel centroids -


background:

i have computed slic superpixels of image using gslicr, gives "per-pixel map" of image superpixels indices (0 number of superpixels-1).

this map pointer integer const array (const int*) containing indices.

i want compute centroids of each superpixel using opencv.

coming matlab background, using regionprops:

segments = vl_slic(myimage, regionsize, regularizer); stats = regionprops(segments, 'centroid'); centroids = cat(1, stats.centroid);

i don't know how done using opencv.

questions:

(i) how convert const int* array cv::mat?

(ii) how compute superpixel centroids matrix in (i)?

as first questions seems answered, focus on second question. used following code compute mean coordinates (i.e. spatial centroids) of each superpixel:

/** \brief compute mean coordinates of each superpixel (i.e. spatial centroids).  * \param[in] labels matrix of type cv_32sc1 holding labels each pixel  * \param[out] means spatial centroids (or means in y , x axes) of superpixels  */ void getmeans(const cv::mat &labels, std::vector<cv::vec2f> &means) {      // count superpixels or highest superpixel index:     int superpixels = 0;     (int = 0; < labels.rows; ++i) {         (int j = 0; j < labels.cols; ++j) {             if (labels.at<int>(i, j) > superpixels) {                 superpixels = labels.at<int>(i, j);             }         }     }      superpixels++;      // setup means 0 vectors.     means.clear();     means.resize(superpixels);     (int k = 0; k < superpixels; k++)     {         means[k] = cv::vec2f(0, 0);     }      std::vector<int> counts(superpixels, 0);      // sum y , x coordinates each superpixel:     (int = 0; < labels.rows; ++i) {         (int j = 0; j < labels.cols; ++j) {             means[labels.at<int>(i, j)][0] += i; // computing mean (i.e. row or y axis)             means[labels.at<int>(i, j)][1] += j; // computing mean j (i.e. column or x axis)              counts[labels.at<int>(i, j)]++;         }     }      // obtain averages dividing size (=number of pixels) of superpixels.     (int k = 0; k < superpixels; ++k) {         means[k] /= counts[k];     } }  // means ...

if need mean color, method require image argument, remaining code can adapted computing mean colors.


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