int imageProcess(cv::Mat frame)
{
int countLabel = 0;
//ROS_INFO("Part 4");
Mat grayFrame;
//ROS_INFO("Part 5");
cvtColor(frame,grayFrame,CV_RGB2GRAY);
//ROS_INFO("Part 6");
//Watershed Segmentation
// Get the binary map
cv::Mat binary;
//binary = cv::imread("binary.bmp", 0); // prevent loading of pre-converted image
cvtColor(frame, grayFrame, CV_BGR2GRAY); // instead convert original
grayFrame = grayFrame < 60; // apply threshold
// Display the binary image
cv::namedWindow("Binary Image");
// cv::imshow("Binary Image", grayFrame);
// Eliminate noise and smaller objects
cv::Mat fg;
cv::erode(grayFrame, fg, cv::Mat(), cv::Point(-1, -1), 6);
// Display the foreground image
// cv::namedWindow("Foreground Image");
// cv::imshow("Foreground Image", fg);
// Identify image pixels without objects
cv::Mat bg;
cv::dilate(grayFrame, bg, cv::Mat(), cv::Point(-1, -1), 6);
cv::threshold(bg, bg, 1, 128, cv::THRESH_BINARY_INV);
// Display the background image
// cv::namedWindow("Background Image");
// cv::imshow("Background Image", bg);
// Show markers image
cv::Mat markers(grayFrame.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(frame);
//ROS_INFO("Part 7");
cv::Mat mutableImage;
mutableImage = segmenter.getSegmentation();
string name = "simpleWall";
std::stringstream sstm;
sstm << name << hallwayCenter;
++countLabel;
imwrite(sstm.str()+".jpg", segmenter.getSegmentation());
cv::namedWindow("testVision");
cv::imshow("testVision",mutableImage);
// cv::imwrite("imgForPoster", mutableImage);
//ROS_INFO("Part 8");
//Set starting x,y values
int y_ex= 260;
int x_ex= 320;
//ROS_INFO("Part 9");
Scalar intensity;
for (int i=x_ex; i>0; i--) {
//ROS_INFO("Part 9.1");
Scalar intensity = (int)mutableImage.at<uchar>(y_ex, i);
//ROS_INFO("Part 9.2");
Scalar intensity1 = (int)mutableImage.at<uchar>(y_ex, i-10);
//ROS_INFO("Part 9.3");
int floorVal= (int)*intensity.val;
//ROS_INFO("Part 9.4");
int floorVal1= (int)*intensity1.val;
if (floorVal==128 && floorVal1!= 128 ){
//ROS_INFO("Part 9.41");
a1=i;
}
}
//ROS_INFO("Part 10");
for (int i=x_ex; i<590; i++) {
Scalar intensity = (int)mutableImage.at<uchar>(y_ex, i);
Scalar intensity2 = (int)mutableImage.at<uchar>(y_ex, i+30);
int floorVal= (int)*intensity.val;
int floorVal2= (int)*intensity2.val;
if(floorVal==128 && floorVal2 != 128) {
a2=i;
}
}
//ROS_INFO("Part 11");
hallwayCenter=(a1+a2)/2;
int imageCenter= 300;
//ROS_INFO("Part 12");
int error= hallwayCenter-imageCenter;
//ROS_INFO("Part 13");
double tempYawValue= error*kValue;
double yawArray[10000];
yawArray[countLabel] = tempYawValue;
//MAIN Y Value for ROS implemenation. Should be teling me where the center is in relation to one of the walls.
//ROS_INFO("Part 14");
putText(mutableImage, "X", cvPoint(hallwayCenter,y_ex), 0, 2.5, cvScalar(0,0,0,255), 3, CV_AA);
cout<<"It is working "<<hallwayCenter;
//ROS_INFO("Part 15");
//ROS_INFO("Part 16");
ROS_INFO("Frame Error: [%d]", error);
ros::NodeHandle n;
ros::Publisher tempYawValue_pub = n.advertise<geometry_msgs::Twist>("cmd_vel", 1000);
geometry_msgs::Twist yaw_access;
yaw_access.angular.z = tempYawValue;
tempYawValue_pub.publish(yaw_access);
for(int i = 0; i < 10000; i++)
{
ROS_INFO("y[%f] ",yawArray[i]);
}
return tempYawValue;
}
int main()
{
//ocl::setUseOpenCL(true);
VideoCapture video("/Users/sonhojun/Downloads/data/all.mp4");
if(!video.isOpened())
{
cout<<"video open error"<<endl;
return 0 ;
}
Mat pri;
Mat aft;
Mat binary;
Mat frame1,frame2;
while (1)
{
video.read(frame1);
video.read(frame2);
// Mat cropFrame1, cropFrame2;
// Mat oriConv = frame1(Rect(10,340,700,200));
//
//// cropFrame1 = frame1(Rect(10,340,700,200));
//// cropFrame2 = frame2(Rect(10,340,700,200));
//
// Mat grayImage1,grayImage2;
// Mat differenceImage;
// Mat thresholdImage;
// Vec<double,4> totalDiff = 0.0;
//
// cropFrame1 = frame1(Rect(10,340,700,200));
// cvtColor(cropFrame1, grayImage1, COLOR_BGR2GRAY);
//
// cropFrame2 =frame2(Rect(10,340,700,200));
//
// cvtColor(cropFrame2, grayImage2, COLOR_BGR2GRAY);
// absdiff(grayImage1,grayImage2,differenceImage);
//
// totalDiff = sum(differenceImage) / (690 * 140);
// cout << "sum diff: " <<totalDiff<<endl;
//
// if(totalDiff[0] > 14.0)
// continue;
//
// threshold(differenceImage, thresholdImage, SENSITIVITY_VALUE, 255, THRESH_BINARY);
//
// blur(thresholdImage, thresholdImage , Size(BLUR_SIZE,BLUR_SIZE));
//
// threshold(thresholdImage,thresholdImage,SENSITIVITY_VALUE,255,THRESH_BINARY);
//
//
// //if tracking enabled, search for contours in our thresholded image
//
// //searchForMovement(thresholdImage, oriConv);
//
//
// //show our captured frame
// imshow("moving",thresholdImage);
// imshow("ori",temp);
Mat cropRGB = frame2(Rect(10,340,700,200));
Mat cropG;
cvtColor(cropRGB, cropG, CV_RGB2GRAY);
GaussianBlur(cropG, cropG, Size(5,5), 0,0);
threshold(cropG,binary,0,255,THRESH_OTSU|THRESH_BINARY);
// adaptiveThreshold(cropG, binary, 255, <#int adaptiveMethod#>, THRESH_OTSU|THRESH_BINARY, 3, 0.);
// bitwise_xor(thresholdImage,binary,binary);
imshow("bin",binary);
Mat fg;
erode(binary, fg, Mat(), Point(-1, -1), 6);
namedWindow("Foreground Image");
imshow("Foreground Image", fg);
Mat bg;
dilate(binary,bg,Mat(),Point(-1,-1),6);
threshold(bg,bg,1,128,THRESH_BINARY_INV);
namedWindow("Background Image");
imshow("Background Image",bg);
// Mat dist;
// distanceTransform(binary, dist, CV_DIST_L2, 5);
// normalize(dist, dist, 0, 1., NORM_MINMAX);
// imshow("dist",dist);
//
// threshold(dist, dist, .4, 1., CV_THRESH_BINARY);
// // Dilate a bit the dist image
// Mat kernel1 = Mat::ones(3, 3, CV_8UC1);
// dilate(dist, dist, kernel1);
// imshow("Peaks", dist);
Mat markers(binary.size(),CV_8U,Scalar(0));
markers= fg+bg;
namedWindow("Markers");
imshow("Markers",markers);
WatershedSegmenter segmenter;
segmenter.setMarkers(markers);
segmenter.process(cropRGB);
namedWindow("Segmentation");
imshow("Segmentation",segmenter.getSegmentation());
// segmenter.getSegmentation()
// namedWindow("Watersheds"); // 워터쉐드 띄워 보기
// imshow("Watersheds",segmenter.getWatersheds());
//111 Mat kernel = Mat::ones(3,3,CV_8U);
//
// Mat morpho;
// morphologyEx(binary,morpho,MORPH_OPEN,kernel);
//
// imshow("open",morpho);
//
// Mat sure_bg;
// dilate(morpho,sure_bg,kernel);
//
// imshow("dilate",sure_bg);
//
// Mat dist;
// distanceTransform(binary, dist, CV_DIST_L2, 5);
// // Normalize the distance image for range = {0.0, 1.0}
// // so we can visualize and threshold it
// normalize(dist, dist, 0, 1., NORM_MINMAX);
// imshow("Distance Transform Image", dist);
// // Threshold to obtain the peaks
// // This will be the markers for the foreground objects
// threshold(dist, dist, .4, 1., CV_THRESH_BINARY);
// // Dilate a bit the dist image
// Mat kernel1 = Mat::ones(3, 3, CV_8UC1);
// dilate(dist, dist, kernel1);
// imshow("Peaks", dist);
// // Create the CV_8U version of the distance image
// // It is needed for findContours()
// Mat dist_8u;
// dist.convertTo(dist_8u, CV_8U);
// // Find total markers
// vector<vector<Point> > contours;
// findContours(dist_8u, contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);
// // Create the marker image for the watershed algorithm
// Mat markers = Mat::zeros(dist.size(), CV_32SC1);
// // Draw the foreground markers
// for (size_t i = 0; i < contours.size(); i++)
// drawContours(markers, contours, static_cast<int>(i), Scalar::all(static_cast<int>(i)+1), -1);
// // Draw the background marker
// circle(markers, Point(5,5), 3, CV_RGB(255,255,255), -1);
// imshow("Markers", markers*10000);
// // Perform the watershed algorithm
// watershed(cropRGB, markers);
//// Mat mark = Mat::zeros(markers.size(), CV_8UC1);
//// markers.convertTo(mark, CV_8UC1);
//// bitwise_not(mark, mark);
//// imshow("Markers_v2", mark); // uncomment this if you want to see how the mark
// // image looks like at that point
// // Generate random colors
// vector<Vec3b> colors;
// for (size_t i = 0; i < contours.size(); i++)
// {
// int b = theRNG().uniform(0, 255);
// int g = theRNG().uniform(0, 255);
// int r = theRNG().uniform(0, 255);
// colors.push_back(Vec3b((uchar)b, (uchar)g, (uchar)r));
// }
// // Create the result image
// Mat dst = Mat::zeros(markers.size(), CV_8UC3);
// // Fill labeled objects with random colors
// for (int i = 0; i < markers.rows; i++)
// {
// for (int j = 0; j < markers.cols; j++)
// {
// int index = markers.at<int>(i,j);
// if (index > 0 && index <= static_cast<int>(contours.size()))
// dst.at<Vec3b>(i,j) = colors[index-1];
// else
// dst.at<Vec3b>(i,j) = Vec3b(0,0,0);
// }
// }
// // Visualize the final image
// imshow("Final Result", dst);
//
//
// 111 1
cvWaitKey(10);
}
return 0;
}
int main()
{
// Read input image
cv::Mat image= cv::imread("../images/group.jpg");
if (!image.data)
return 0;
// Display the image
cv::namedWindow("Original Image");
cv::imshow("Original Image",image);
// Get the binary map
cv::Mat binary;
binary= cv::imread("../images/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);
// 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 background image
cv::namedWindow("Background Image");
cv::imshow("Background Image",bg);
// 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("Watersheds");
cv::imshow("Watersheds",segmenter.getWatersheds());
// Open another image
image= cv::imread("../images/tower.jpg");
if (!image.data)
return 0;
// 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 foreground 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());
// Open another image
image= cv::imread("../images/tower.jpg");
// define bounding rectangle
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 pixels 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("../images/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);
cv::waitKey();
return 0;
}
