Learning opencv Lecture 4 (transforming images with morphological operations)

In this chapter, we will cover:

• Eroding and dilating images using morphological filters
• Opening and Closing images using morphological filters
• Detecting edges and corners using morphological filters
• Segmenting Images Using watersheds Watershed Algorithm
• Extracting foreground objects with the grabcut Algorithm

Eroding, dilating, opening, closing

#include <opencv2/core/core.hpp>#include <opencv2/imgproc/imgproc.hpp>#include <opencv2/highgui/highgui.hpp>int main(){      // Read input image     cv::Mat image= cv::imread("../binary.bmp" );      if (!image.data)            return 0;    // Display the image     cv::namedWindow( "Image");     cv::imshow( "Image",image);      // Erode the image     cv::Mat eroded;     cv::erode(image,eroded,cv::Mat());    // Display the eroded image     cv::namedWindow( "Eroded Image");     cv::imshow( "Eroded Image",eroded);      // Dilate the image     cv::Mat dilated;     cv::dilate(image,dilated,cv::Mat());    // Display the dialted image     cv::namedWindow( "Dilated Image");     cv::imshow( "Dilated Image",dilated);      // Erode the image with a larger s.e.     cv::Mat element(7,7,CV_8U,cv::Scalar(1));     cv::erode(image,eroded,element);    // Display the eroded image     cv::namedWindow( "Eroded Image (7x7)");     cv::imshow( "Eroded Image (7x7)",eroded);      // Erode the image 3 times.     cv::erode(image,eroded,cv::Mat(),cv::Point(-1,-1),3);    // Display the eroded image     cv::namedWindow( "Eroded Image (3 times)");     cv::imshow( "Eroded Image (3 times)",eroded);      // Close the image     cv::Mat element5(5,5,CV_8U,cv::Scalar(1));     cv::Mat closed;     cv::morphologyEx(image,closed,cv::MORPH_CLOSE,element5);    // Display the opened image     cv::namedWindow( "Closed Image");     cv::imshow( "Closed Image",closed);      // Open the image     cv::Mat opened;     cv::morphologyEx(image,opened,cv::MORPH_OPEN,element5);    // Display the opened image     cv::namedWindow( "Opened Image");     cv::imshow( "Opened Image",opened);      // Close and Open the image     cv::morphologyEx(image,image,cv::MORPH_CLOSE,element5);     cv::morphologyEx(image,image,cv::MORPH_OPEN,element5);    // Display the close/opened image     cv::namedWindow( "Closed and Opened Image");     cv::imshow( "Closed and Opened Image",image);     cv::imwrite( "binaryGroup.bmp",image);      // Read input image     image= cv::imread("../binary.bmp");      // Open and Close the image     cv::morphologyEx(image,image,cv::MORPH_OPEN,element5);     cv::morphologyEx(image,image,cv::MORPH_CLOSE,element5);    // Display the close/opened image     cv::namedWindow( "Opened and Closed Image");     cv::imshow( "Opened and Closed Image",image);     cv::waitKey();      return 0;}

Results:

 

Detecting edges and corners using morphological filtersMorphofeatures. h

#if !defined MORPHOF#define MORPHOF#include <opencv2/core/core.hpp>#include <opencv2/highgui/highgui.hpp>#include <opencv2/imgproc/imgproc.hpp>class MorphoFeatures {private:      // threshold to produce binary image      int threshold;      // structuring elements used in corner detection     cv::Mat cross;     cv::Mat diamond;     cv::Mat square;     cv::Mat x;public:     MorphoFeatures() : threshold(-1),                 cross(5, 5, CV_8U, cv::Scalar(0)),                 diamond(5, 5, CV_8U, cv::Scalar(0)),                 square(5, 5, CV_8U, cv::Scalar(0)),                 x(5, 5, CV_8U, cv::Scalar(0)) {            // Creating the cross-shaped structuring element            for (int i = 0; i < 5; i++) {                 cross.at<uchar>(2, i) = 1;                 cross.at<uchar>(i, 2) = 1;            }            // Creating the diamond-shaped structuring element            diamond.at<uchar>(0, 0) = 0;            diamond.at<uchar>(0, 1) = 0;            diamond.at<uchar>(1, 0) = 0;            diamond.at<uchar>(4, 4) = 0;            diamond.at<uchar>(3, 4) = 0;            diamond.at<uchar>(4, 3) = 0;            diamond.at<uchar>(4, 0) = 0;            diamond.at<uchar>(4, 1) = 0;            diamond.at<uchar>(3, 0) = 0;            diamond.at<uchar>(0, 4) = 0;            diamond.at<uchar>(0, 3) = 0;            diamond.at<uchar>(1, 4) = 0;            // Creating the x-shaped structuring element            for (int i = 0; i < 5; i++) {                 x.at<uchar>(i, i) = 1;                 x.at<uchar>(4 - i, i) = 1;            }     }      void setThreshold(int t) {            if (t > 0)                 threshold = t;     }      int getThreshold() const {            return threshold;     }     cv::Mat getEdges(const cv::Mat &image) {            // Get the gradient image            cv::Mat result;            cv::morphologyEx(image, result, cv::MORPH_GRADIENT, cv::Mat());            // Apply threshold to obtain a binary image            applyThreshold(result);            return result;     }      void applyThreshold(cv::Mat &result) {            // Apply threshold on result            if (threshold > 0) {                 cv::threshold(result, result, threshold, 255, cv::THRESH_BINARY_INV);            }     }     cv::Mat getCorners(const cv::Mat &image) {            cv::Mat result;            // Dilate with a cross            cv::dilate(image, result, cross);            // Erode with a diamond            cv::erode(result, result, diamond);            cv::Mat result2;            // Dilate with a x            cv::dilate(image, result2, x);            // Erode with a square            cv::erode(result2, result2, square);            // Corners are obtained by differencing            // the two closed images            cv::absdiff(result2, result, result);            // Apply threshold to obtain a binary image            applyThreshold(result);            return result;     }      void drawOnImage(const cv::Mat &binary, cv::Mat &image) {            cv::Mat_<uchar>::const_iterator it = binary.begin<uchar>();            cv::Mat_<uchar>::const_iterator itend = binary.end<uchar>();            // for each pixel            for (int i = 0; it != itend; ++it, ++i) {                 if (!*it) {                      cv::circle(image,                            cv::Point(i%image.step, i/image.step),                            5, cv::Scalar(255, 0, 0));                 }            }     }};#endif

Morph. cpp

#include <iostream>#include "morphoFeatures.h"int main() {     cv::Mat image = cv::imread( "../building.jpg");     cv::cvtColor(image, image, CV_BGR2GRAY);      // Create the morphological features instance     MorphoFeatures morpho;     morpho.setThreshold(40);      // Get the edges     cv::Mat edges;     edges = morpho.getEdges(image);     cv::namedWindow( "Edges Image", CV_WINDOW_AUTOSIZE);     cv::imshow( "Edges Image", edges);      // Get the corners     cv::Mat corners;     corners = morpho.getCorners(image);      // Display the corner on the image     morpho.drawOnImage(corners, image);     cv::namedWindow( "Corners on Image", CV_WINDOW_AUTOSIZE);     cv::imshow( "Corners on Image", image);     cv::waitKey(0);      return 0;}

Results:

 

Segmenting Images Using watershedsWatershedsegment. h

#include <opencv2/core/core.hpp>#include <opencv2/highgui/highgui.hpp>#include <opencv2/imgproc/imgproc.hpp>class WatershedSegmenter {private:     cv::Mat markers;public:      void setMarkers(const cv::Mat &markerImage) {                       // Convert to image of ints            markerImage.convertTo(markers, CV_32S);     }     cv::Mat process(const cv::Mat &image) {            // Apply watershed            cv::watershed(image, markers);            return markers;     }      // Return result in the form of an image     cv::Mat getSegmentation() {            cv::Mat tmp;            // all segment with label higher than 255            // will be assigned value 255            markers.convertTo(tmp, CV_8U);            return tmp;     }      // Return watershed in the form of an image     cv::Mat getWatersheds() {                       cv::Mat tmp;            // Each pixel p is transform into            // 255p + 255 befor conversion            markers.convertTo(tmp, CV_8U, 255, 255);            return tmp;     }};

　　

// Read input image     cv::Mat image = cv::imread( "../group.jpg");      if (!image.data) {            return 0;     }      // Display the image     cv::namedWindow( "Original Image");     cv::imshow( "Original Image", image);      // Get the binary image     cv::Mat binary;     binary = cv::imread( "../binary.bmp", 0);      // Display the binary image     cv::namedWindow( "Binary Image");     cv::imshow( "Binary Image", binary);      // Eliminate noise and smaller objects     cv::Mat fg;     cv::erode(binary, fg, cv::Mat(), cv::Point(-1, -1), 6);      // Display the foreground image     cv::namedWindow( "Foreground Image");     cv::imshow( "Foreground Image", fg);

Results:

// Identify image pixels without objects     cv::Mat bg;     cv::dilate(binary, bg, cv::Mat(), cv::Point(-1, -1), 6);     cv::threshold(bg, bg, 1, 128, cv::THRESH_BINARY_INV);      // Display the backgroud image     cv::namedWindow( "Background Image");     cv::imshow( "Background Image", bg);

Results:

// Show markers image     cv::Mat markers(binary.size(), CV_8U, cv::Scalar(0));     markers = fg + bg;     cv::namedWindow( "Markers");     cv::imshow( "Markers", markers);

　　

// Create watershed segmentation object     WatershedSegmenter segmenter;      // Set markers and process     segmenter.setMarkers(markers);     segmenter.process(image);      // Display segmentation result     cv::namedWindow( "Segmentation");     cv::imshow( "Segmentation", segmenter.getSegmentation());      // Display watersheds     cv::namedWindow( "Watershed");     cv::imshow( "Watershed", segmenter.getWatersheds());

　　

// Open another image------------------------------------     image = cv::imread( "../tower.jpg");      // Identify background pixels     cv::Mat imageMask(image.size(), CV_8U, cv::Scalar(0));     cv::rectangle(imageMask, cv::Point(5, 5), cv::Point(image.cols - 5, image.rows - 5), cv::Scalar(255), 3);      // Identify forground pixels (in the middle of the image)     cv::rectangle(imageMask, cv::Point(image.cols / 2 - 10, image.rows / 2 - 10),                                      cv::Point(image.cols / 2 + 10, image.rows / 2 + 10), cv::Scalar(1), 10);      // Set markers and process     segmenter.setMarkers(imageMask);     segmenter.process(image);      // Display the image with markers     cv::rectangle(image, cv::Point(5, 5), cv::Point(image.cols - 5, image.rows - 5), cv::Scalar(255, 255, 255), 3);     cv::rectangle(image, cv::Point(image.cols / 2 - 10, image.rows / 2 - 10),                                 cv::Point(image.cols / 2 + 10, image.rows / 2 + 10), cv::Scalar(1, 1, 1), 10);     cv::namedWindow( "Image with marker");     cv::imshow( "Image with marker", image);      // Display watersheds     cv::namedWindow( "Watersheds of foreground object");     cv::imshow( "Watersheds of foreground object", segmenter.getWatersheds());

Results:

 

Extracting foreground objects with the grabcut Algorithm

// Open another image     image = cv::imread( "../tower.jpg");      // define bounding rectange     cv::Rect rectangle(50, 70, image.cols - 150, image.rows - 180);     cv::Mat result;  // segmentation result (4 possible values)     cv::Mat bgModel, fgModel; // the models (internally used)      // GrabCut segmentation     cv::grabCut(image,          // input image            result,                     // segmentation result            rectangle,            // rectangle containing foreground            bgModel, fgModel,     // models            1,                          //number of iterations            cv::GC_INIT_WITH_RECT// use rectangle            );      // Get the pixles marked as likely foreground     cv::compare(result, cv::GC_PR_FGD, result, cv::CMP_EQ);      // Generate output image     cv::Mat foreground(image.size(), CV_8UC3, cv::Scalar(255, 255, 255));     image.copyTo(foreground, result); // bg pixels not copied      // draw rectangle on original image     cv::rectangle(image, rectangle, cv::Scalar(255,255,255),1);     cv::namedWindow( "Image");     cv::imshow( "Image",image);      // display result     cv::namedWindow( "Segmented Image");     cv::imshow( "Segmented Image",foreground);

　　

// Open another image     image= cv::imread("../group.jpg");      // define bounding rectangle     cv::Rect rectangle2(10,100,380,180);     cv::Mat bkgModel,fgrModel; // the models (internally used)      // GrabCut segmentation     cv::grabCut(image,   // input image            result, // segmentation result           rectangle2,bkgModel,fgrModel,5,cv::GC_INIT_WITH_RECT);      // Get the pixels marked as likely foreground      //   cv::compare(result,cv::GC_PR_FGD,result,cv::CMP_EQ);     result= result&1;     foreground.create(image.size(),CV_8UC3);     foreground.setTo(cv::Scalar(255,255,255));     image.copyTo(foreground,result); // bg pixels not copied      // draw rectangle on original image     cv::rectangle(image, rectangle2, cv::Scalar(255,255,255),1);     cv::namedWindow( "Image 2");     cv::imshow( "Image 2",image);      // display result     cv::namedWindow( "Foreground objects");     cv::imshow( "Foreground objects",foreground);