I'm new to OpenCV a have some trouble regarding writing to a subrange of a Mat-Object.
The code below iterates a given Image. For each pixel, it takes pixel within a range of 5x5, finds the brightest pixel, and put all other pixel to 0.
I call the function multiple times. After a random number of calls the function gives me a segmentation fault or "malloc memory corruption". Sometimes I can call the function 10 times with no problems sometimes only twice, then the program stops.
I tracked down the problem to the line, where I write to the original image using the subimage.
subimage.at<uchar>(rowSubimage,colSubimage) = 0;
There is the function that drives me crazy:
void findMaxAndBlackout(Mat& image, int size){
Point centralPoint;
Size rangeSize = Size(size,size);
Mat subimage;
Rect range;
// iterate the image
for(int row = 0; row <= image.rows-size; row++){
for(int col = 0; col <= image.cols-size; col++){
centralPoint = Point(col,row);
range = Rect(centralPoint, rangeSize);
// slice submatrix and find max
subimage = image(range);
double max;
minMaxLoc( subimage, NULL, &max, NULL, NULL );
// iterate the surrounding
for(int rowSubimage = 0; rowSubimage <= subimage.rows; rowSubimage++){
for(int colSubimage = 0; colSubimage <= subimage.cols; colSubimage++){
if(subimage.at<uchar>(rowSubimage,colSubimage) < max){
//this line cause the trouble
subimage.at<uchar>(rowSubimage,colSubimage) = 0;
}
}
}
}
}}
The Mat-Object is generated using:
Mat houghImage = imread("small_schachbrett1_cam.png", CV_LOAD_IMAGE_GRAYSCALE);
Please help me understand the problem.
If you know a better or more efficient way to achieve the same result please let me know. I am open for any improvements
Regards
benniz
You are out of range:
row <= image.rows-size
col <= image.cols-size
rowSubimage <= subimage.rows
colSubimage <= subimage.cols
should be
row < image.rows-size
col < image.cols-size
rowSubimage < subimage.rows
colSubimage < subimage.cols
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
I try to modify a BGRA mat using a pointer like this:
//Bound the value between 0 to 255
uchar boundPixelValue(double c) {
c = int(c);
if (c > 255)
c = 255;
if (c < 0)
c = 0;
return (uchar) c;
}
for (int i = 0; i < rows; i++)
for (int j = 0; j < cols; j++)
for (int k = 0; k < 3; k++){
//This loop is accessing the first three channels
mat.ptr<Vec4b>(i)[j][k] = boundPixelValue(
1.0 * mat.ptr<Vec4b>(i)[j][k] * max / avg[k]);
}
But this gives different outputs every time, sometimes work and sometimes give a white blank image. I am suspecting if this is due to the noncontinuous data, can anyone help?
One extra question, usually we access the columns of a 2D array first before accessing the rows because it is usually faster. However, I have to access the pixel using mat.ptr<Vec4b>(row)[col]. So, should I loop through the rows first then column?
Easier, less intensive way of doing is:
std::vector<cv::Mat> matArray;
cv::split(toBoundMat, matArray);
matArray[0].setTo(0, matArray[0] < 0);
matArray[0].setTo(255, matArray[0] > 255);
matArray[1].setTo(0, matArray[1] < 0);
matArray[1].setTo(255, matArray[1] > 255);
matArray[2].setTo(0, matArray[2] < 0);
matArray[2].setTo(255, matArray[2] > 255);
cv::Mat boundedMat;
cv::merge(matArray, boundedMat);
But I really don't understand what you are trying to do. Your double data may have values between 1.7E +/- 308. You either are expecting a very specific kind of data, or you are going to mess it up. If you want to make a Mat visualizable, just normalize it like this:
cv::normalize(inMat, destMat, 0, 255, CV_MINMAX);
cv::cvtColor(destMat, destMat, CV_8UC1) //--(8 bit visualizable mat)
This will check the min and max of your current Mat and will set the minimum to 0, the maximum to 255, and all the in between values proportionally :)
I want to segment car plate to get separate characters.
I found some article, where such segmentation performed using brightness histograms (as i understand - sum of all non-zero pixels).
How can i calculate such histogram? I would really appreciate for any help!
std::vector<int> computeColumnHistogram(const cv::Mat& in) {
std::vector<int> histogram(in.cols,0); //Create a zeroed histogram of the necessary size
for (int y = 0; y < in.rows; y++) {
p_row = in.ptr(y); ///Get a pointer to the y-th row of the image
for (int x = 0; x < in.cols; x++)
histogram[x] += p_row[x]; ///Update histogram value for this image column
}
//Normalize if you want (you'll get the average value per column):
// for (int x = 0; x < in.cols; x++)
// histogram[x] /= in.rows;
return histogram;
}
Or use reduce as suggested by Berak, either calling
cv::reduce(in, out, 0, CV_REDUCE_AVG);
or
cv::reduce(in, out, 0, CV_REDUCE_SUM, CV_32S);
out is a cv::Mat, and it will have a single row.
What is the fastest way of assigning a vector to a matrix row in a loop? I want to fill a data matrix along its rows with vectors. These vectors are computed in a loop. This loop last until all the entries of data matrix is filled those vectors.
Currently I am using cv::Mat::at<>() method for accessing the elements of the matrix and fill them with the vector, however, it seems this process is quite slow. I have tried another way by using cv::Mat::X.row(index) = data_vector, it works fast but fill my matrix X with some garbage values which I can not understand, why.
I read that there exists another way of using pointers (fastest way), however, I can not able to understand. Can somebody explain how to use them or other different methods?
Here is a part of my code:
#define OFFSET 2
cv::Mat im = cv::imread("001.png", CV_LOAD_IMAGE_GRAYSCALE);
cv::Mat X = cv::Mat((im.rows - 2*OFFSET)*(im.cols - 2*OFFSET), 25, CV_64FC1); // Holds the training data. Data contains image patches
cv::Mat patch = cv::Mat(5, 5, im.type()); // Holds a cropped image patch
typedef cv::Vec<float, 25> Vec25f;
int ind = 0;
for (int row = 0; row < (im.rows - 2*OFFSET); row++){
for (int col = 0; col < (im.cols - 2*OFFSET); col++){
cv::Mat temp_patch = im(cv::Rect(col, row, 5, 5)); // crop an image patch (5x5) at each pixel
patch = temp_patch.clone(); // Needs to do this because temp_patch is not continuous in memory
patch.convertTo(patch, CV_64FC1);
Vec25f data_vector = patch.reshape(0, 1); // make it row vector (1X25).
for (int i = 0; i < 25; i++)
{
X.at<float>(ind, i) = data_vector[i]; // Currently I am using this way (quite slow).
}
//X_train.row(ind) = patch.reshape(0, 1); // Tried this but it assigns some garbage values to the data matrix!
ind += 1;
}
}
To do it the regular opencv way you could do :-
ImageMat.row(RowIndex) = RowMat.clone();
or
RowMat.copyTo(ImageMat.row(RowIndex));
Haven't tested for correctness or speed.
Just a couple of edits in your code
double * xBuffer = X.ptr<double>(0);
for (int row = 0; row < (im.rows - 2*OFFSET); row++){
for (int col = 0; col < (im.cols - 2*OFFSET); col++){
cv::Mat temp_patch = im(cv::Rect(col, row, 5, 5)); // crop an image patch (5x5) at each pixel
patch = temp_patch.clone(); // Needs to do this because temp_patch is not continuous in memory
patch.convertTo(patch, CV_64FC1);
memcpy(xBuffer, patch.data, 25*sizeof(double));
xBuffer += 25;
}
}
Also, you dont seem to do any computation in patch just extract grey level values, so you can create X with the same type as im, and convert it to double at the end. In this way, you could memcpy each row of your patch, the address in memory beeing `unsigned char* buffer = im.ptr(row) + col
According to the docs:
if you need to process a whole row of matrix, the most efficient way is to get the pointer to the row first, and then just use plain C operator []:
// compute sum of positive matrix elements
// (assuming that M is double-precision matrix)
double sum=0;
for(int i = 0; i < M.rows; i++)
{
const double* Mi = M.ptr<double>(i);
for(int j = 0; j < M.cols; j++)
sum += std::max(Mi[j], 0.);
}
I am trying to create my personal Blob Detection algorithm
As far as I know I first must create different Gaussian Kernels with different sigmas (which I am doing using Mat kernel= getGaussianKernel(x,y);) Then get the Laplacian of that kernel and then filter the Image with that so I create my scalespace. Now I need to find the Local Maximas in each result Image of the scalespace. But I cannot seem to find a proper way to do so.... my Code so far is
vector <Point> GetLocalMaxima(const cv::Mat Src,int MatchingSize, int Threshold)
{
vector <Point> vMaxLoc(0);
if ((MatchingSize % 2 == 0) ) // MatchingSize has to be "odd" and > 0
{
return vMaxLoc;
}
vMaxLoc.reserve(100); // Reserve place for fast access
Mat ProcessImg = Src.clone();
int W = Src.cols;
int H = Src.rows;
int SearchWidth = W - MatchingSize;
int SearchHeight = H - MatchingSize;
int MatchingSquareCenter = MatchingSize/2;
uchar* pProcess = (uchar *) ProcessImg.data; // The pointer to image Data
int Shift = MatchingSquareCenter * ( W + 1);
int k = 0;
for(int y=0; y < SearchHeight; ++y)
{
int m = k + Shift;
for(int x=0;x < SearchWidth ; ++x)
{
if (pProcess[m++] >= Threshold)
{
Point LocMax;
Mat mROI(ProcessImg, Rect(x,y,MatchingSize,MatchingSize));
minMaxLoc(mROI,NULL,NULL,NULL,&LocMax);
if (LocMax.x == MatchingSquareCenter && LocMax.y == MatchingSquareCenter)
{
vMaxLoc.push_back(Point( x+LocMax.x,y + LocMax.y ));
// imshow("W1",mROI);cvWaitKey(0); //For gebug
}
}
}
k += W;
}
return vMaxLoc;
}
which I found in this thread here, which it supposedly returns a vector of points where the maximas are. it does return a vector of points but all the x and y coordinates of each point are always -17891602... What to do???
Please if you are to lead me in something else other than correcting my code be informative because I know nothing about opencv. I am just learning
The problem here is that your LocMax point is declared inside the inner loop and never initialized, so it's returning garbage data every time. If you look back at the StackOverflow question you linked, you'll see that their similar variable Point maxLoc(0,0) is declared at the top and constructed to point at the middle of the search window. It only needs to be initialized once. Subsequent loop iterations will replace the value with the minMaxLoc function result.
In summary, remove this line in your inner loop:
Point LocMax; // delete this
And add a slightly altered version near the top:
vector <Point> vMaxLoc(0); // This was your original first line
Point LocMax(0,0); // your new second line
That should get you started anyway.
I found it guys. The problem was my threshold was too high. I do not understand why it gave me negative points instead of zero points but lowering the threshold worked