I am working with OpenCV (C++) to concatenate some images but the result is not as expected.
Source images:
Result:
It looks good until zoomed to see the pixels. The line connecting two images is not clean. I do the same work with PhotoShop and here is the comparison:
How can I have a clean concat line with OpenCV?
My simple code in c++:
using namespace cv;
int main(int argc, char** argv)
{
Mat image = imread("1_segment.jpg", 4);
Mat image2 = imread("0_segment.jpg", 4);
Mat output;
vconcat(image, image2, output);
imwrite("output.jpg", output);
return 0;
}
Related
#include "opencv2/opencv.hpp"
using namespace std;
int main(int argc, char** argv) {
cv::Mat img = cv::imread("an_img_from_internet.png", cv::IMREAD_COLOR);
cv::Mat img_float;
img.convertTo(img_float, CV_32FC3, 1.0/255);
cv::Mat region = img_float(cv::Range(0, 5), cv::Range(0, 3));
return 0;
}
The code above tries to get a three-channel region from an image. But I just wanna get a single-channel region. I know a solution
std::vector<cv::Mat> img_float_vec;
cv::split(img_float, img_float_vec);
cv::Mat region = img_float_vec[0](cv::Range(0, 5), cv::Range(0, 3));
which is a little inconvenient. So I wonder if there is a better way to solve it in just one line, kind of like cv::Mat region = img_float(cv::Range(0, 5), cv::Range(0, 3))[0]?
I need to extract L(illuminative component)from RGB frame and obtain the inverted illuminative image (L).
adding the inverted image to the original L image.
My question is how can i add two images (L channel of lab and inverted image)?
Which function can do that?
i think http://answers.opencv.org/question/81947 is your question. so i tried to revise it as far as i understand your question. hope it will help.
source :
result :
#include "opencv2/opencv.hpp"
using namespace cv;
using namespace std;
void split_lab( Mat planes )
{
Mat lab, blurredL;
cvtColor( planes, lab, CV_BGR2Lab );
vector <Mat> splits;
split(lab, splits);
medianBlur( splits[0], blurredL, 31);
blurredL = 255 - blurredL;
cvtColor( blurredL, blurredL, CV_GRAY2BGR );
planes = planes + ( blurredL * 0.5 );
}
int main(int argc, char** argv)
{
Mat src= imread( argv[1] );
split_lab(src);
imshow( "result", src );
waitKey();
return 0;
}
http://inside.mines.edu/~whoff/courses/EENG512/lectures/HoughInOpenCV.pdf
Hi, i am going through the pdf tutorial in the link above.
I encounter problem on page 6 of the slides.
As we seee that the output of the code after inserting the canny edge detector, it should trace out all the edges on a photo.
I cannot get what is shown at page 6.
#include <opencv2/opencv.hpp>
#include <iostream>
using namespace std;
using namespace cv;
int main(int argc, char* argv[])
{
printf("Hello world\n");
// read an image
Mat imgInput = imread("a.png");
// create image window named "My Image"
namedWindow("My Image");
// Convert to gray if necessary
if (imgInput.channels() == 3)
cv::cvtColor(imgInput, imgInput, CV_BGR2GRAY);
// Apply Canny edge detector
Mat imgContours;
double thresh = 105; // try different values to see effect
Canny(imgInput, imgContours, 0.4*thresh, thresh); // low, high threshold
// show the image on window
imshow("My Image", imgInput);
// wait for xx ms (0 means wait until keypress)
waitKey(5000);
return 0;
}
And also, there is a line double thresh = xxx;//try different values
What values should i put? and what are the values mean?
Thank you
Just replace your imshow function with ,
imshow("My Image", imgContours);
and you can use thresh value approximately around 200.
Change threshold value and see effect of it and according to that you can select your threshold value.
The imgContours is your output map with all the edges. You should use imshow with imgContours.
#include <opencv2/opencv.hpp>
#include <iostream>
using namespace std;
using namespace cv;
int main(int argc, char* argv[])
{
printf("Hello world\n");
// read an image
Mat imgInput = imread("a.png");
// create image window named "My Image"
namedWindow("My Image");
// Convert to gray if necessary
if (imgInput.channels() == 3)
cv::cvtColor(imgInput, imgInput, CV_BGR2GRAY);
// Apply Canny edge detector
Mat imgContours;
double thresh = 105; // try different values to see effect
Canny(imgInput, imgContours, 0.4*thresh, thresh); // low, high threshold
// show the image on window
imshow("My Image", imgContours);
// wait for xx ms (0 means wait until keypress)
waitKey(5000);
return 0;
}
Reference:
http://docs.opencv.org/modules/imgproc/doc/feature_detection.html?highlight=canny#canny
I am trying to smooth output image edges using opencv framework, I am trying following steps. Steps took from here https://stackoverflow.com/a/17175381/790842
int lowThreshold = 10.0;
int ratio = 3;
int kernel_size = 3;
Mat src_gray,detected_edges,dst,blurred;
/// Convert the image to grayscale
cvtColor( result, src_gray, CV_BGR2GRAY );
/// Reduce noise with a kernel 3x3
cv::blur( src_gray, detected_edges, cv::Size(5,5) );
/// Canny detector
cv::Canny( detected_edges, detected_edges, lowThreshold, lowThreshold*ratio, kernel_size );
//Works fine upto here I am getting perfect edge mask
cv::dilate(detected_edges, blurred, result);
//I get Assertion failed (src.channels() == 1 && func != 0) in countNonZero ERROR while doing dilate
result.copyTo(blurred, blurred);
cv::blur(blurred, blurred, cv::Size(3.0,3.0));
blurred.copyTo(result, detected_edges);
UIImage *image = [UIImageCVMatConverter UIImageFromCVMat:result];
I want help whether if I am going in right way, or what am I missing?
Thanks for any suggestion and help.
Updated:
I have got an image like below got from grabcut algorithm, now I want to apply edge smoothening to the image, as you can see the image is not smooth.
Do you want to get something like this?
If yes, then here is the code:
#include <iostream>
#include <vector>
#include <string>
#include <fstream>
#include <opencv2/opencv.hpp>
using namespace cv;
using namespace std;
int main(int argc, char **argv)
{
cv::namedWindow("result");
Mat img=imread("TestImg.png");
Mat whole_image=imread("D:\\ImagesForTest\\lena.jpg");
whole_image.convertTo(whole_image,CV_32FC3,1.0/255.0);
cv::resize(whole_image,whole_image,img.size());
img.convertTo(img,CV_32FC3,1.0/255.0);
Mat bg=Mat(img.size(),CV_32FC3);
bg=Scalar(1.0,1.0,1.0);
// Prepare mask
Mat mask;
Mat img_gray;
cv::cvtColor(img,img_gray,cv::COLOR_BGR2GRAY);
img_gray.convertTo(mask,CV_32FC1);
threshold(1.0-mask,mask,0.9,1.0,cv::THRESH_BINARY_INV);
cv::GaussianBlur(mask,mask,Size(21,21),11.0);
imshow("result",mask);
cv::waitKey(0);
// Reget the image fragment with smoothed mask
Mat res;
vector<Mat> ch_img(3);
vector<Mat> ch_bg(3);
cv::split(whole_image,ch_img);
cv::split(bg,ch_bg);
ch_img[0]=ch_img[0].mul(mask)+ch_bg[0].mul(1.0-mask);
ch_img[1]=ch_img[1].mul(mask)+ch_bg[1].mul(1.0-mask);
ch_img[2]=ch_img[2].mul(mask)+ch_bg[2].mul(1.0-mask);
cv::merge(ch_img,res);
cv::merge(ch_bg,bg);
imshow("result",res);
cv::waitKey(0);
cv::destroyAllWindows();
}
And I think this link will be interestiong for you too: Poisson Blending
I have followed the following steps to smooth the edges of the Foreground I got from GrabCut.
Create a binary image from the mask I got from GrabCut.
Find the contour of the binary image.
Create an Edge Mask by drawing the contour points. It gives the boundary edges of the Foreground image I got from GrabCut.
Then follow the steps define in https://stackoverflow.com/a/17175381/790842
I am now learning a code from the opencv codebook (OpenCV 2 Computer Vision Application Programming Cookbook): Chapter 5, Segmenting images using watersheds, page 131.
Here is my main code:
#include "opencv2/opencv.hpp"
#include <string>
using namespace cv;
using namespace std;
class WatershedSegmenter {
private:
cv::Mat markers;
public:
void setMarkers(const cv::Mat& markerImage){
markerImage.convertTo(markers, CV_32S);
}
cv::Mat process(const cv::Mat &image){
cv::watershed(image,markers);
return markers;
}
};
int main ()
{
cv::Mat image = cv::imread("/Users/yaozhongsong/Pictures/IMG_1648.JPG");
// Eliminate noise and smaller objects
cv::Mat fg;
cv::erode(binary,fg,cv::Mat(),cv::Point(-1,-1),6);
// 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);
// Create markers image
cv::Mat markers(binary.size(),CV_8U,cv::Scalar(0));
markers= fg+bg;
// Create watershed segmentation object
WatershedSegmenter segmenter;
// Set markers and process
segmenter.setMarkers(markers);
segmenter.process(image);
imshow("a",image);
std::cout<<".";
cv::waitKey(0);
}
However, it doesn't work. How could I initialize a binary image? And how could I make this segmentation code work?
I am not very clear about this part of the book.
Thanks in advance!
There's a couple of things that should be mentioned about your code:
Watershed expects the input and the output image to have the same size;
You probably want to get rid of the const parameters in the methods;
Notice that the result of watershed is actually markers and not image as your code suggests; About that, you need to grab the return of process()!
This is your code, with the fixes above:
// Usage: ./app input.jpg
#include "opencv2/opencv.hpp"
#include <string>
using namespace cv;
using namespace std;
class WatershedSegmenter{
private:
cv::Mat markers;
public:
void setMarkers(cv::Mat& markerImage)
{
markerImage.convertTo(markers, CV_32S);
}
cv::Mat process(cv::Mat &image)
{
cv::watershed(image, markers);
markers.convertTo(markers,CV_8U);
return markers;
}
};
int main(int argc, char* argv[])
{
cv::Mat image = cv::imread(argv[1]);
cv::Mat binary;// = cv::imread(argv[2], 0);
cv::cvtColor(image, binary, CV_BGR2GRAY);
cv::threshold(binary, binary, 100, 255, THRESH_BINARY);
imshow("originalimage", image);
imshow("originalbinary", binary);
// Eliminate noise and smaller objects
cv::Mat fg;
cv::erode(binary,fg,cv::Mat(),cv::Point(-1,-1),2);
imshow("fg", fg);
// Identify image pixels without objects
cv::Mat bg;
cv::dilate(binary,bg,cv::Mat(),cv::Point(-1,-1),3);
cv::threshold(bg,bg,1, 128,cv::THRESH_BINARY_INV);
imshow("bg", bg);
// Create markers image
cv::Mat markers(binary.size(),CV_8U,cv::Scalar(0));
markers= fg+bg;
imshow("markers", markers);
// Create watershed segmentation object
WatershedSegmenter segmenter;
segmenter.setMarkers(markers);
cv::Mat result = segmenter.process(image);
result.convertTo(result,CV_8U);
imshow("final_result", result);
cv::waitKey(0);
return 0;
}
I took the liberty of using Abid's input image for testing and this is what I got:
Below is the simplified version of your code, and it works fine for me. Check it out :
#include "opencv2/objdetect/objdetect.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
using namespace cv;
using namespace std;
int main ()
{
Mat image = imread("sofwatershed.jpg");
Mat binary = imread("sofwsthresh.png",0);
// Eliminate noise and smaller objects
Mat fg;
erode(binary,fg,Mat(),Point(-1,-1),2);
// Identify image pixels without objects
Mat bg;
dilate(binary,bg,Mat(),Point(-1,-1),3);
threshold(bg,bg,1,128,THRESH_BINARY_INV);
// Create markers image
Mat markers(binary.size(),CV_8U,Scalar(0));
markers= fg+bg;
markers.convertTo(markers, CV_32S);
watershed(image,markers);
markers.convertTo(markers,CV_8U);
imshow("a",markers);
waitKey(0);
}
Below is my input image :
Below is my output image :
See the code explanation here : Simple watershed Sample in OpenCV
I had the same problem as you, following the exact same code sample of the cookbook (great book btw).
Just to place the matter I was coding under Visual Studio 2013 and OpenCV 2.4.8. After a lot of searching and no solutions I decided to change the IDE.
It's still Visual Studio BUT it's 2010!!!! And boom it works!
Becareful of how you configure Visual Studio with OpenCV. Here's a great tutorial for installation here
Good day to all