I am trying to pre multiply a Homography matrix before I send it it the warpperspective function, but I cannot figure out how to do this. I am trying to use gemm for multiplying the matrices. Also How do you specify an element (like HomOffset(0,0)) in a matrix obj then multiply it by a scalar? I have been reading the opencv documentation but did not come across this. Code is below. Thanks in advance.
cv:: Mat Hom = cv::findHomography(scene,obj, CV_RANSAC);
cv:: Mat HomOffset[3][3] = {
{ 1, 0, 25 },
{ 0, 1, 25 },
{ 0, 0, 1 }
};
error for declartion of HomOffSet code is int to cv:: Mat is ambigious
gemm(Hom,HomOffset,1,0,0,H);
Multiple errors for the gemm function.
you need to assign your Matrix's values (HomOffset) correctly. Use at operator: see it here
Related
I have a bgr image and convert to lab channels.
I tried to check if the idft image of the result of dft of L channel image is the same.
// MARK: Split LAB Channel each
cv::Mat lab_resized_host_image;
cv::cvtColor(resized_host_image, lab_resized_host_image, cv::COLOR_BGR2Lab);
imshow("lab_resized_host_image", lab_resized_host_image);
cv::Mat channel_L_host_image, channel_A_host_image, channel_B_host_image;
std::vector<cv::Mat> channel_LAB_host_image(3);
cv::split(lab_resized_host_image, channel_LAB_host_image);
// MARK: DFT the channel_L host image.
channel_L_host_image = channel_LAB_host_image[0];
imshow("channel_L_host_image", channel_L_host_image);
cv::Mat padded_L;
int rows_L = getOptimalDFTSize(channel_L_host_image.rows);
int cols_L = getOptimalDFTSize(channel_L_host_image.cols);
copyMakeBorder(channel_L_host_image, padded_L, 0, rows_L - channel_L_host_image.rows, 0, cols_L - channel_L_host_image.cols, BORDER_CONSTANT, Scalar::all(0));
Mat planes_L[] = {Mat_<float>(padded_L), Mat::zeros(padded_L.size(), CV_32F)};
Mat complexI_L;
merge(planes_L, 2, complexI_L);
dft(complexI_L, complexI_L);
// MARK: iDFT Channel_L.
Mat complexI_channel_L = complexI_L;
Mat complexI_channel_L_idft;
cv::dft(complexI_L, complexI_channel_L_idft, cv::DFT_INVERSE|cv::DFT_REAL_OUTPUT);
normalize(complexI_channel_L_idft, complexI_channel_L_idft, 0, 1, NORM_MINMAX);
imshow("complexI_channel_L_idft", complexI_channel_L_idft);
Each imshow give me different image... I think normalization would be error...
what is wrong? help!
original image
idft
OpenCV’s FFT is not normalized by default. One of the forward/backward transform pair must be normalized for the pair to reproduce the input values. Simply add cv::DFT_SCALE to the options:
cv::dft(complexI_mid_frequency_into_channel_A, iDFT_mid_frequency_into_channel_A, cv::DFT_INVERSE|cv::DFT_REAL_OUTPUT|cv::DFT_SCALE);
I have a non square matrix in OpenCV.
I want to calculate it's rank.
I understood you need to do SVD decomposition and count the rows or on one of the parts of it? Not sure...
I could really use code example in OpenCV(C/C++), because there is too much room for me to make errors...
I found this thread... opencv calculate matrix rank
But it has no code example...
So if there is no code example maybe you could explain the steps to find the rank of a non square matrix in OpenCV?
As mentioned here, you need to find the number of non-zero singular value. So, first find the singular values with SVD decomposition, and then count the number of non zero values. You may need to apply a small threshold to account for numeric errors:
#include <opencv2\opencv.hpp>
using namespace cv;
int main()
{
// Your matrix
Mat1d M = (Mat1d(4,5) << 1, 0, 0, 0, 2,
0, 0, 3, 0, 0,
0, 0, 0, 0, 0,
0, 2, 0, 0, 0);
// Compute SVD
Mat1d w, u, vt;
SVD::compute(M, w, u, vt);
// w is the matrix of singular values
// Find non zero singular values.
// Use a small threshold to account for numeric errors
Mat1b nonZeroSingularValues = w > 0.0001;
// Count the number of non zero
int rank = countNonZero(nonZeroSingularValues);
return 0;
}
Is there a direct way to compute the column-wise standard deviation for a matrix in opencv? Similar to std in Matlab. I've found one for the mean:
cv::Mat col_mean;
reduce(A, col_mean, 1, CV_REDUCE_AVG);
but I cannot find such a function for the standard deviation.
Here's a quick answer to what you're looking for. I added both the standard deviation and mean for each column. The code can easily be modified for rows.
cv::Mat A = ...; // FILL IN THE DATA FOR YOUR INPUT MATRIX
cv::Mat meanValue, stdValue;
cv::Mat colSTD(1, A.cols, CV_64FC1);
cv::Mat colMEAN(1, A.cols, CV_64FC1);
for (int i = 0; i < A.cols; i++){
cv::meanStdDev(A.col(i), meanValue, stdValue);
colSTD.at<double>(i) = stdValue.at<double>(0);
colMEAN.at<double>(i) = meanValue.at<double>(0);
}
The following is not in a single line,but it is another version without loops:
reduce(A, meanOfEachCol, 0, CV_REDUCE_AVG); // produces single row of columnar means
Mat repColMean;
cv::repeat(meanOfEachCol, rows, 1, repColMean); // repeat mean vector 'rows' times
Mat diffMean = A - repColMean; // get difference
Mat diffMean2 = diffMean.mul(diffMean); // per element square
Mat varMeanF;
cv::reduce(diffMean2, varMeanF, 0, CV_REDUCE_AVG); // sum each column's elements to get single row
Mat stdMeanF;
cv::sqrt(varMeanF, stdMeanF); // get standard deviation
I have what should be a simple exercise in OpenCV, but can't seem to get it working. I'm trying to determine the density of edges in a section of an image. This is the process I follow:
1. pull subimage from image
2. use Canny to find edges in subImage
3. threshold to create binary image
4. create histogram for binary image
5. get number of pixels in binary image that are "on" (255)
6. calculate "edge density" as numPixelsOn/totalPixels
I've checked the results of 1,2,and 3 above, and results seem ok. Steps 4 and 5 seem to be giving me trouble.
Here's my code for calculating the histogram:
int histSize = 256; // bin size
float range[] = { 0, 256} ;
const float* histRange = { range };
bool uniform = true;
bool accumulate = false;
Mat hist;
/// Compute the histograms:
calcHist( &gray, 1, 0, Mat(), hist, 1, &histSize, &histRange, uniform, accumulate );
This doesn't seem to be working. When I check hist after calling calcHist, it has no data (i.e. data == 0)... or maybe I don't understand what I'm looking at.
Now for accessing the "bins" in the histogram, I've tried a number of things. First I tried this:
uchar* p;
p = hist.ptr<uchar>(0);
double edgePixels = p[255];
I also tried to use:
cvQueryHistValue_1D(hist,255); // #include <opencv2/legacy/compat.hpp>
This wouldn't compile. Gave 2 errors: 'cv::Mat' does not have an overloaded member 'operator ->', and 'bins': is not a member of 'cv::Mat'
I guess I need some help on this.
There is an error in your 3rd param - channels, that should be an array so you should call it like this
int histSize = 256; // bin size
float range[] = { 0, 256} ;
const float* histRange = { range };
bool uniform = true;
bool accumulate = false;
Mat hist;
int channels[] = {0};
/// Compute the histograms:
calcHist( &gray, 1, channels, Mat(), hist, 1, &histSize, &histRange, uniform, accumulate );
You should also call:
hist.at<float>(0);
to get your value, OpenCV stores them as floats, this is the reason you're getting 0 when using uchar as uchar is smaller than float and the numbers stores as small enough to not fill the first bites.
Im trying to create my own sobel edge detection based off of the gx and gy matrices on three channels i have in my code below.
[[0,1,2],
[-1,0,1],
[-2,-1,0]]
and
[-2,-1,0],
[-1,0,1],
[0,1,2]]
I edited the variables j and i in my code further down but it is not working, how can i create a sobel edge detection on those three channels
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
void salt(cv::Mat &image, int n) {
int i,j;
for (int k=0; k<n; k++) {
// rand() is the MFC random number generator
i= rand()%image.cols;
j= rand()%image.rows;
if (image.channels() == 1) { // gray-level image
image.at<uchar>(j,i)= 255;
} else if (image.channels() == 3) { // color image
image.at<cv::Vec3b>(j,i)[0]= 255;
image.at<cv::Vec3b>(j-1,i-1)[1]= 255;
image.at<cv::Vec3b>(j,i-1)[2]= 255;
}
}
}
int main()
{
srand(cv::getTickCount()); // init random number generator
cv::Mat image= cv::imread("space.jpg",0);
salt(image,3000);
cv::namedWindow("Image");
cv::imshow("Image",image);
cv::imwrite("salted.bmp",image);
cv::waitKey(5000);
return 0;
}
I'm a little confused by the question, because the question relates to sobel filters, but you provided a function that adds noise to an image.
To start with, here is the Sobel function, which will call the classic sobel functions (that will calculate dx and dy gradients).
Secondly, there is the more generic filter2D which will let you apply an arbitrary kernel (like the one you created in the question).
Lastly, if you want to apply a different kernel in each channel or band, you can do as the filter2D documentation implies, and call split on an image, and then call filter2D on each channel, and then combine the values into a single band image using the matrix operators.
The most complicated thing I think you could be asking is how to find the locations of that salt you added to the image, and the answer would be to make a kernel for each band like so:
band 0:
[[ 0, 0, 0],
[ 0, 1, 0],
[ 0, 0, 0]]
band 1:
[[ 1, 0, 0],
[ 0, 0, 0],
[ 0, 0, 0]]
band 2:
[[ 0, 1, 0],
[ 0, 0, 0],
[ 0, 0, 0]]
Be sure to put the anchor in the center of the kernel (1,1).