So I have an image which I want to apply a shear transformation on using OpenCV.
Here's a function I wrote,
Mat shearMat(Mat img){
Mat temp;
img.copyTo(temp);
Mat M(2,3,CV_32F);
M.at<uint16_t>(0,0) = 1;
M.at<uint16_t>(0,1) = 0;
M.at<uint16_t>(0,2) = 0;
M.at<uint16_t>(1,0) = 0;
M.at<uint16_t>(1,1) = 1;
M.at<uint16_t>(1,2) = 0;
warpAffine(temp, temp, M , Size(temp.cols,temp.rows));
imshow("Sheared_Image", temp);
M is the inputArray for warpAffine(<#InputArray src#>, <#OutputArray dst#>, <#InputArray M#>, <#Size dsize#>);
I need to know how M should look like if I want to apply a shear transformation. It says it should be a 2x3 Matrix. When I try to show the image now, nothing appears. I know that M.at<uint16_t>(1,0) should be the shear factor, but its not working with warpAffine.
Thanks!
You are using wrong data type.
M.at<float>(0,0) = 1;
M.at<float>(0,1) = 0;
M.at<float>(0,2) = 0;
M.at<float>(1,0) = 0.5;
M.at<float>(1,1) = 1;
M.at<float>(1,2) = 0;
Related
I want to create a 1D plot from an image. Then I want to determine the maxima and their distances to each other in c++.
I am looking for some tips on how I could approach this.
I load the image as cv::Mat. In opencv I have searched, but only found the histogram function, which is wrong. I want to get a cross section of the image - from left to right.
does anyone have an idea ?
Well I have the following picture:
From this I want to create a 1D plot like in the following picture (I created the plot in ImageJ).
Here you can see the maxima (I could refine it with "smooth").
I want to determine the positions of these maxima and then the distances between them.
I have to get to the 1D plot somehow. I suppose I can get to the maxima with a derivation?
++++++++++ UPDATE ++++++++++
Now i wrote this to get an 1D Plot:
cv::Mat img= cv::imread(imgFile.toStdString(), cv::IMREAD_ANYDEPTH | cv::IMREAD_COLOR);
cv::cvtColor(img, img, cv::COLOR_BGR2GRAY);
uint8_t* data = img.data;
int width = img.cols;
int height = img.rows;
int stride = img.step;
std::vector<double> vPlot(width, 0);
for (int i = 0; i < height; i++) {
for (int j = 0; j < width; j++) {
uint8_t val = data[ i * stride + j];
vPlot[j]=vPlot[j] + val;
}
}
std::ofstream file;
file.open("path\\plot.csv");
for(int i = 0; i < vPlot.size(); i++){
file << vPlot[i];
file << ";";
}
file.close();
When i plot this in excel i got this:
Thats looks not so smooth as in ImageJ. Did i something wrong?
I need it like in the Plot of ImageJ - more smooth.
ok I got it:
for (int i = 0; i < vPlot.size(); i++) {
vPlot[i] = vPlot[i] / height;
}
Ok but i don't know how to get the maxima an distances.
When i have the local maxima (i don't know how), i can calculate the distance between them with the index of the vetcor elements.
Has anybody an idea to get the local Maxima out of the vector, that I plot above ?
Now o wrote this to find the maxima:
// find maxima
std::vector<int> idxMax;
int flag = 0;
for(int i = 1; i < avg.size(); i++){
double diff = avg[i] - avg[i-1];
if(diff < 0){
if(flag>0){
idxMax.push_back(i);
flag = -1;
}
}
if(diff >= 0){
if(flag<=0){
flag = 1;
}
}
}
But more maxima are found than wanted. The length of the vector varies and also the number of peaks. These can be close together or far away. They are also not always the same height, as can be seen in the picture
Input and Output Images of my code are here.
I want output as complete object detection with every pixel. Here I get with some shadows as well as other background pixels and missing some object points.
So can anybody have idea how can I get complete object detection (foreground detection) with this input images (object image and background image)?
Below is the code I have tried.
cv::Mat ImgObject, ImgBck;
ImgObject = imread("Object.jpg");
ImgBck = imread("Background.jpg");
imwrite("ImgObject.jpg", ImgObject);
imwrite("ImgBck.jpg", ImgBck);
cv::Mat diffImage;
ImgBck = ImgBck + Scalar(-20, -20 - 20);/* decrease brightness of background
because of brightness changes after putting object */
cv::absdiff(ImgObject, ImgBck, diffImage);
float threshold = (float)50;
float dist = 0.0f;
for (int j = 0; j < diffImage.rows; ++j)
{
for (int i = 0; i<diffImage.cols; ++i)
{
cv::Vec3b pix = diffImage.at<cv::Vec3b>(j, i);
dist = (pix[0] * pix[0] + pix[1] * pix[1] + pix[2] * pix[2]);
dist = sqrt(dist);
cv::Point3_<uchar>* pFinal = ImgObject.ptr<Point3_<uchar> >(j, i);
if (dist <= threshold)
{
pFinal->x = 255; // fill blue as background
pFinal->y = 0;
pFinal->z = 0;
}
}
}
imwrite("Obj.jpg", ImgObject);
ImgObject.release();
ImgBck.release();
Do not use direct light on the object(To reduce Shadow and Reflection).
Firstly, I need to say that this is not an object detection task, but a saliency detection or segmentation task.
Second, as #Kartik Maheshwari said, you are facing a lightning issue which is not a solved problem in Computer Vision.
As an alternative answer, take a look at this.
So, I've been trying to get a 3D cloud point from a sequence of images of an object. I have successfully obtained a decent point cloud with two images. I got that from matching features on both images, finding the fundamental matrix and from that, extracting P' (the camera matrix for the second view). For the first view, I set P = K(I | 0), where K is the matrix for the camera intrinsics. But I haven't been able to extend this approach to several images. My idea was to do this sliding the two image window through the sequence of images(e.g. match image1 with image2, find 3d points, match image2 with image3 and then find the more 3d points, and so on). For the following image pairs, P would be made of a cumulative rotation matrix and a cumulative translation vector (this would allow me to keep bringing the points to the first camera coordinate system). But this is not working at all. I'm using OpenCV. What I wanna know is if this approach makes sense at all.
In the code, P_prev is P and Pl is P'. This is just the part that I think it's relevant.
Mat combinedPointCloud;
Mat P_prev;
P_prev = (Mat_<double>(3,4) << cameraMatrix.at<double>(0,0), cameraMatrix.at<double>(0,1), cameraMatrix.at<double>(0,2), 0,
cameraMatrix.at<double>(1,0), cameraMatrix.at<double>(1,1), cameraMatrix.at<double>(1,2), 0,
cameraMatrix.at<double>(2,0), cameraMatrix.at<double>(2,1), cameraMatrix.at<double>(2,2), 0);
for(int i = 1; i < images.size(); i++) {
Mat points3D;
image1 = images[i-1];
image2 = images[i];
matchTwoImages(image1, image2, imgpts1, imgpts2);
P = findSecondProjectionMatrix(cameraMatrix, imgpts1, imgpts2);
P.col(0).copyTo(R.col(0));
P.col(1).copyTo(R.col(1));
P.col(2).copyTo(R.col(2));
P.col(3).copyTo(t.col(0));
if(i == 1) {
Pl = P;
triangulatePoints(P_prev, Pl, imgpts1, imgpts2, points3D); //points3D is 4xN
//Transforming to euclidean by hand, because couldn't make
// opencv's convertFromHomogeneous work
aux.create(3, points3D.cols, CV_64F);// aux is 3xN
for(int i = 0; i < points3D.cols; i++) {
aux.at<float>(0, i) = points3D.at<float>(0, i)/points3D.at<float>(3, i);
aux.at<float>(1, i) = points3D.at<float>(1, i)/points3D.at<float>(3, i);
aux.at<float>(2, i) = points3D.at<float>(2, i)/points3D.at<float>(3, i);
}
points3D.create(3, points3D.cols, CV_64F);
aux.copyTo(points3D);
}
else {
R_aux = R_prev * R;
t_aux = t_prev + t;
R_aux.col(0).copyTo(Pl.col(0));
R_aux.col(1).copyTo(Pl.col(1));
R_aux.col(2).copyTo(Pl.col(2));
t_aux.col(0).copyTo(Pl.col(3));
triangulatePoints(P_prev, Pl, imgpts1, imgpts2, points3D);
//Transforming to euclidean by hand, because couldn't make
// opencv's convertFromHomogeneous work
aux.create(3, points3D.cols, CV_64F);// aux is 3xN
for(int i = 0; i < points3D.cols; i++) {
aux.at<float>(0, i) = points3D.at<float>(0, i)/points3D.at<float>(3, i);
aux.at<float>(1, i) = points3D.at<float>(1, i)/points3D.at<float>(3, i);
aux.at<float>(2, i) = points3D.at<float>(2, i)/points3D.at<float>(3, i);
}
points3D.create(3, points3D.cols, CV_64F);
aux.copyTo(points3D);
}
Pl.col(0).copyTo(R_prev.col(0));
Pl.col(1).copyTo(R_prev.col(1));
Pl.col(2).copyTo(R_prev.col(2));
Pl.col(3).copyTo(t_prev.col(0));
P_prev = Pl;
if(i==1) {
points3D.copyTo(combinedPointCloud);
} else {
hconcat(combinedPointCloud, points3D, combinedPointCloud);
}
}
show3DCloud(comninedPointCloud);
I'm working with the kinect, using OpenNI 2.x, c++, OpenCV.
I am able to get the kinect depth streaming and obtain a grey-scale cv::Mat. just to show how it is defined:
cv::Mat m_depthImage;
m_depthImage= cvCreateImage(cvSize(640, 480), 8, 1);
I suppose that the closest value is represented by "0" and the farthest by "255".
After that, I make a conversion between depth coordinates to World coordinates. I do it element by element of the cv::Mat grey-scale matrix, and i collect data in PointsWorld[640*480].
In order to display these data, I adjust the scale in order to collect value in a 2000x2000x2000 matrix.
cv::Point3f depthPoint;
cv::Point3f PointsWorld[640*480];
for (int j=0;j<m_depthImage.rows;j++)
{
for(int i=0;i<m_depthImage.cols; i++)
{
depthPoint.x = (float) i;
depthPoint.y = (float) j;
depthPoint.z = (float) m_depthImage.at<unsigned char>(j, i);
if (depthPoint.z!=255)
{
openni::CoordinateConverter::convertDepthToWorld(*m_depth,depthPoint.x,depthPoint.y,depthPoint.z, &wx,&wy,&wz);
wx = wx*7,2464; //138->1000
if (wx<-999) wx = -999;
if (wx>999) wx = 999;
wy = wy*7,2464; //111->1000 with 9,009
if (wy<-999) wy = -999;
if (wy>999) wy = 999;
wz=wz*7,8431; //255->2000
if (wz>1999) wy = 1999;
Xsp = P-floor(wx);
Ysp = P+floor(wy);
Zsp = 2*P-floor(wz);
PointsWorld[k].x = Xsp;
PointsWorld[k].y = Ysp;
PointsWorld[k].z = Zsp;
k++;
}
}
}
But i'm sure that doing that do not allow me to have a comprehension of the real distance between points. An x,y,z coordinate what will mean?
There is a way to know the real distance between points, to know how much far is, for example, the grey value of the matrix "255"? and the wx,wy,wz what they are for?
If you have OpenCV built with OpenNI support you should be able to do something like:
int ptcnt;
cv::Mat real;
cv::Point3f PointsWorld[640*480];
if( capture.retrieve(real, CV_CAP_OPENNI_POINT_CLOUD_MAP)){
for (int j=0;j<m_depthImage.rows;j++)
{
for(int i=0;i<m_depthImage.cols; i++){
PointsWorld[ptcnt] = real.at<cv::Vec3f>(i,j);
ptcnt++;
}
}
}
I have searched internet and stackoverflow thoroughly, but I haven't found answer to my question:
How can I get/set (both) RGB value of certain (given by x,y coordinates) pixel in OpenCV? What's important-I'm writing in C++, the image is stored in cv::Mat variable. I know there is an IplImage() operator, but IplImage is not very comfortable in use-as far as I know it comes from C API.
Yes, I'm aware that there was already this Pixel access in OpenCV 2.2 thread, but it was only about black and white bitmaps.
EDIT:
Thank you very much for all your answers. I see there are many ways to get/set RGB value of pixel. I got one more idea from my close friend-thanks Benny! It's very simple and effective. I think it's a matter of taste which one you choose.
Mat image;
(...)
Point3_<uchar>* p = image.ptr<Point3_<uchar> >(y,x);
And then you can read/write RGB values with:
p->x //B
p->y //G
p->z //R
Try the following:
cv::Mat image = ...do some stuff...;
image.at<cv::Vec3b>(y,x); gives you the RGB (it might be ordered as BGR) vector of type cv::Vec3b
image.at<cv::Vec3b>(y,x)[0] = newval[0];
image.at<cv::Vec3b>(y,x)[1] = newval[1];
image.at<cv::Vec3b>(y,x)[2] = newval[2];
The low-level way would be to access the matrix data directly. In an RGB image (which I believe OpenCV typically stores as BGR), and assuming your cv::Mat variable is called frame, you could get the blue value at location (x, y) (from the top left) this way:
frame.data[frame.channels()*(frame.cols*y + x)];
Likewise, to get B, G, and R:
uchar b = frame.data[frame.channels()*(frame.cols*y + x) + 0];
uchar g = frame.data[frame.channels()*(frame.cols*y + x) + 1];
uchar r = frame.data[frame.channels()*(frame.cols*y + x) + 2];
Note that this code assumes the stride is equal to the width of the image.
A piece of code is easier for people who have such problem. I share my code and you can use it directly. Please note that OpenCV store pixels as BGR.
cv::Mat vImage_;
if(src_)
{
cv::Vec3f vec_;
for(int i = 0; i < vHeight_; i++)
for(int j = 0; j < vWidth_; j++)
{
vec_ = cv::Vec3f((*src_)[0]/255.0, (*src_)[1]/255.0, (*src_)[2]/255.0);//Please note that OpenCV store pixels as BGR.
vImage_.at<cv::Vec3f>(vHeight_-1-i, j) = vec_;
++src_;
}
}
if(! vImage_.data ) // Check for invalid input
printf("failed to read image by OpenCV.");
else
{
cv::namedWindow( windowName_, CV_WINDOW_AUTOSIZE);
cv::imshow( windowName_, vImage_); // Show the image.
}
The current version allows the cv::Mat::at function to handle 3 dimensions. So for a Mat object m, m.at<uchar>(0,0,0) should work.
uchar * value = img2.data; //Pointer to the first pixel data ,it's return array in all values
int r = 2;
for (size_t i = 0; i < img2.cols* (img2.rows * img2.channels()); i++)
{
if (r > 2) r = 0;
if (r == 0) value[i] = 0;
if (r == 1)value[i] = 0;
if (r == 2)value[i] = 255;
r++;
}
const double pi = boost::math::constants::pi<double>();
cv::Mat distance2ellipse(cv::Mat image, cv::RotatedRect ellipse){
float distance = 2.0f;
float angle = ellipse.angle;
cv::Point ellipse_center = ellipse.center;
float major_axis = ellipse.size.width/2;
float minor_axis = ellipse.size.height/2;
cv::Point pixel;
float a,b,c,d;
for(int x = 0; x < image.cols; x++)
{
for(int y = 0; y < image.rows; y++)
{
auto u = cos(angle*pi/180)*(x-ellipse_center.x) + sin(angle*pi/180)*(y-ellipse_center.y);
auto v = -sin(angle*pi/180)*(x-ellipse_center.x) + cos(angle*pi/180)*(y-ellipse_center.y);
distance = (u/major_axis)*(u/major_axis) + (v/minor_axis)*(v/minor_axis);
if(distance<=1)
{
image.at<cv::Vec3b>(y,x)[1] = 255;
}
}
}
return image;
}