Eigen3 JacobiSVD different singular values depending on compiler flags - c++
I'm using Eigen3 version 3.3.1 and g++ version (Ubuntu 7.3.0-27ubuntu1~18.04) 7.3.0. I'm finding that I get different results from JacobiSVD::singularValues(), depending on whether -march=native is part of the compile command. It seems as though the actual significant flag within the "-march=native" umbrella is -mavx. Here is a test case:
using dictionary_t = Eigen::Matrix<float, Eigen::Dynamic, Eigen::Dynamic, Eigen::ColMajor>;
const float halfroot = std::sqrt(2.0f)/2.0f;
Eigen::Matrix<float, 37, 38, Eigen::ColMajor> m;
m << 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, halfroot, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, -halfroot, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, halfroot, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -halfroot, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, halfroot, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -halfroot, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, halfroot, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -halfroot, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, halfroot, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -halfroot, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, halfroot, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -halfroot, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, halfroot, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -halfroot, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, halfroot, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -halfroot, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, halfroot, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -halfroot, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, halfroot, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -halfroot, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, halfroot, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -halfroot, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, halfroot, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -halfroot, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, halfroot, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -halfroot, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, halfroot, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -halfroot, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, halfroot, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -halfroot, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, halfroot, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -halfroot, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, halfroot, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -halfroot, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, halfroot,
0, 0, 0, 0, 0, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -halfroot,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1;
Eigen::JacobiSVD<dictionary_t> svdDi{m, Eigen::ComputeFullU|Eigen::ComputeFullV};
Eigen::VectorXf singVals = svdDi.singularValues();
Eigen::IOFormat fmt{Eigen::StreamPrecision, Eigen::DontAlignCols, ", "};
std::cout << "singular values of m: \n" << std::setprecision(10)
<< singVals.format(fmt) << std::endl;
And here is its output without -march=native set:
singular values of m:
1.84775877
1.000000238
1.000000119
1.000000119
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0.9999999404
0.9999999404
0.9999999404
0.9999999404
0.9999999404
0.9999999404
0.9999999404
0.9999999404
0.9999999404
0.9999999404
0.9999999404
0.9999999404
0.9999998808
0.9999998212
0.7653669715
If I compile with -march=native, the first couple of singular values are different:
singular values of m:
1.847759128
1.000000119
1.000000119
1.000000119
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0.9999999404
0.9999999404
0.9999999404
0.9999999404
0.9999999404
0.9999999404
0.9999999404
0.9999999404
0.9999999404
0.7653669715
Sorry about the bulkiness of my example. So, is this expected behavior? If so, is there a reason to prefer one result over the other?
These Eigen values are close enough that they can be considered as identical (especially for float).
Eigen can use a different set of intrinsics depending on the flags, so the computation order can be different, and of course floating point math is broken.
All these numbers are close enough compared to machine precision and the size and type of your matrix.
Related
openCV's EMD-L1 algorithm computes distances that are zeroes
In the following code, I have two histograms, that for this simple example, I hard-coded in the source. Each histogram have 128 bins, where the 64 firsts bins correspond to one histogram, and the 64 other correspond to another histogram. However the resultant distance is 0, even though there are clear differences in the latter 64 bins of the 128 bins of each vector. I don't understand how it's possible why two different vectors have a null distance.
#include <iostream>
#include <opencv2/core.hpp>
#include <opencv2/shape/emdL1.hpp>
using Vec128f = cv::Vec<float, 128>;
float sum_of_emd_dists(const Vec128f& a, const Vec128f& b)
{
const cv::Mat a_color(cv::Size{64, 1}, CV_32FC1, (void*)(&a.val[0]));
const cv::Mat a_label(cv::Size{64, 1}, CV_32FC1, (void*)(&a.val[64]));
const cv::Mat b_color(cv::Size{64, 1}, CV_32FC1, (void*)(&b.val[0]));
const cv::Mat b_label(cv::Size{64, 1}, CV_32FC1, (void*)(&b.val[64]));
float dist = cv::EMDL1(a_color, b_color) + cv::EMDL1(a_label, b_label);
return dist;
}
int main()
{
Vec128f a = {64, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.265625, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.734375, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
Vec128f b = {64, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.109375, 0, 0, 0, 0.109375, 0, 0, 0.09375, 0, 0, 0, 0, 0, 0, 0, 0, 0.0625, 0, 0, 0.09375, 0, 0, 0, 0.046875, 0.046875, 0, 0, 0, 0, 0, 0, 0, 0.078125, 0.140625, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.09375, 0, 0, 0.0625, 0, 0, 0, 0.0625, 0, 0, 0, 0, 0, 0};
std::cerr << "dist = " << sum_of_emd_dists(a, b) << std::endl;
return 0;
}
Result:
dist = 0
Thank you for any help explaining why theEMD-L1 distance is 0.
Thats because your matrix size is 1 row and 64 cols and you need single col martix.
How can I rewrite this warp-affine using OpenCV?
I'm trying to optimize this code, in particular:
bool interpolate(const Mat &im, float ofsx, float ofsy, float a11, float a12, float a21, float a22, Mat &res)
{
bool ret = false;
// input size (-1 for the safe bilinear interpolation)
const int width = im.cols-1;
const int height = im.rows-1;
// output size
const int halfWidth = res.cols >> 1;
const int halfHeight = res.rows >> 1;
float *out = res.ptr<float>(0);
for (int j=-halfHeight; j<=halfHeight; ++j)
{
const float rx = ofsx + j * a12;
const float ry = ofsy + j * a22;
for(int i=-halfWidth; i<=halfWidth; ++i)
{
float wx = rx + i * a11;
float wy = ry + i * a21;
const int x = (int) floor(wx);
const int y = (int) floor(wy);
if (x >= 0 && y >= 0 && x < width && y < height)
{
// compute weights
wx -= x; wy -= y;
// bilinear interpolation
*out++ =
(1.0f - wy) * ((1.0f - wx) * im.at<float>(y,x) + wx * im.at<float>(y,x+1)) +
( wy) * ((1.0f - wx) * im.at<float>(y+1,x) + wx * im.at<float>(y+1,x+1));
} else {
*out++ = 0;
ret = true; // touching boundary of the input
}
}
}
return ret;
}
According to Intel Advisor, this is a very time consuming function. In this question I asked how I could optimize this, and someone made me notice that this is warp-affine transformation.
Now, since I'm not the image processing guy, I had to read this article to understand what a warp-affine transformation is.
To my understanding, given a point p=(x,y), you apply a transformation A (a 2x2 matrix) and then translate it by a vector b. So the obtained point after the transformation p' can be expressed as p' = A*p+b. So far so good.
However, I'm a little bit confused on how to apply cv::warpAffine() to this case. First of all, from the function above interpolate() I can see only the 4 A components (a11, a12, a21, a22), while I can't see the 2 b components...Are they ofsx and ofy?
In addition notice that this function returns a bool value, which is not returned by warpAffine (this boolean value is used here at line 126), so I don't know I could this with the OpenCV function.
But most of all I'm so confused by for (int j=-halfHeight; j<=halfHeight; ++j) and for(int i=-halfWidth; i<=halfWidth; ++i) and all the crap that happens inside.
I understand that:
// bilinear interpolation
*out++ =
(1.0f - wy) * ((1.0f - wx) * im.at<float>(y,x) + wx * im.at<float>(y,x+1)) +
( wy) * ((1.0f - wx) * im.at<float>(y+1,x) + wx * im.at<float>(y+1,x+1));
Is what INTER_LINEAR does, but apart from that I'm totally lost.
So, to test my approach, I tried to do the equivalent of line 131 of this as:
bool touchesBoundary = interpolate(smoothed, (float)(patchImageSize>>1), (float)(patchImageSize>>1), imageToPatchScale, 0, 0, imageToPatchScale, patch);
Mat warp_mat( 2, 3, CV_32FC1 );
float a_11 = imageToPatchScale;
float a_12 = 0;
float a_21 = 0;
float a_22 = imageToPatchScale;
float ofx = (float)(patchImageSize>>1);
float ofy = (float)(patchImageSize>>1);
float ofx_new = ofx - a12*halfHeight - a11*halfWidth;
float ofy_new = ofy - a22*halfHeight - a21*halfWidth;
warp_mat.at<float>(0,0) = imageToPatchScale;
warp_mat.at<float>(0,1) = 0;
warp_mat.at<float>(0,2) = ofx_new;
warp_mat.at<float>(1,0) = 0;
warp_mat.at<float>(1,1) = imageToPatchScale;
warp_mat.at<float>(1,2) = ofy_new;
cv::Mat myPatch;
std::cout<<"Applying warpAffine"<<std::endl;
warpAffine(smoothed, myPatch, warp_mat, patch.size());
std::cout<<"WarpAffineApplied patch size="<<patch.size()<<" myPatch size="<<myPatch.size()<<std::endl;
cv::Mat diff = patch!=myPatch;
if(cv::countNonZero(diff) != 0){
throw std::runtime_error("Warp affine doesn't work!");
}
else{
std::cout<<"It's working!"<<std::endl;
}
And of course at the first time the this is executed, the exception is thrown (so the two methods are not equivalent)...How can I solve this?
Can someone help me please?
As I already written in the comments, the resulting matrix by using the code above is a zero matrix. While this is maPatch obtained by using ofx and ofy instead of ofx_new and ofy_new, while patch has all the values different from zero:
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0;
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0;
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0;
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0;
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0;
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0;
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0;
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0;
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0;
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0;
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0;
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 229.78679, 229.5752, 229.11732, 229.09612, 229.84615, 230.28633, 230.35257, 230.70955, 230.99368, 231.00777, 231.20511, 231.63196, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0;
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 230.60367, 230.16417, 230.07034, 230.06793, 230.02016, 230.14925, 230.60413, 230.84822, 230.92368, 231.02249, 230.99162, 230.9149, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0;
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 232.76547, 231.39716, 231.26674, 231.34512, 230.746, 230.25253, 229.65276, 227.83998, 225.43642, 229.57695, 230.31363, 230.16011, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0;
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 234.01663, 232.88118, 232.15475, 231.40129, 223.21553, 208.22626, 205.58975, 214.53882, 220.32681, 228.11552, 229.31509, 228.86545, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0;
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 234.04565, 233.00443, 231.9902, 230.14912, 198.0849, 114.86175, 97.901344, 160.0218, 217.38528, 231.07045, 231.13109, 231.10185, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0;
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 233.293, 232.69095, 217.03873, 190.56714, 167.61592, 94.968391, 81.302032, 150.72263, 194.79535, 215.15564, 230.01717, 232.37894, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0;
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 231.70988, 227.81319, 207.59377, 173.35149, 113.88276, 73.171112, 71.523285, 103.05875, 160.05588, 194.65132, 226.4287, 231.45871, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0;
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 231.93924, 224.24269, 199.1693, 150.65695, 103.33984, 79.489555, 77.509094, 87.893059, 122.01918, 168.37506, 219.22086, 231.05161, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0;
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 232.2706, 232.12926, 206.97635, 127.69308, 92.714355, 81.512207, 74.89402, 75.968353, 84.518105, 157.07962, 223.18773, 229.92766, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0;
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 232.64882, 222.16704, 161.95021, 92.577881, 83.757164, 76.764214, 67.041054, 66.195595, 71.112335, 131.66878, 188.27278, 217.6635, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0;
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 234.77202, 231.75511, 178.64326, 104.27015, 95.664223, 82.791382, 67.68969, 72.78054, 72.355469, 104.77696, 172.32361, 204.92691, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0;
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 236.49684, 235.5802, 185.34337, 115.96995, 106.85963, 82.980408, 61.703068, 69.540627, 76.200562, 82.429321, 101.46993, 119.75877, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0;
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0;
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0;
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0;
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0;
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0;
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0;
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0;
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0;
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0;
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0;
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0;
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0;
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0;
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0;
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0;
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0;
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0;
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
Example of smoothed:
[229.78679, 229.67955, 229.56825, 229.40576, 229.08748, 228.90848, 229.13086, 229.53154, 229.91875, 230.1864, 230.31964, 230.34709, 230.35471, 230.51445, 230.81174, 230.97459, 231.00513, 231.00487, 231.01001, 231.08649, 231.30977, 231.55736, 231.71651;
229.71237, 229.63612, 229.65092, 229.72298, 229.65163, 229.58559, 229.68594, 229.8093, 229.91052, 230.0466, 230.22325, 230.43683, 230.67668, 230.87794, 230.98672, 231.02505, 231.03383, 231.03091, 231.02097, 231.03201, 231.09761, 231.17659, 231.23175;
230.66309, 230.37627, 230.1866, 230.1675, 230.09061, 230.03766, 230.10495, 230.09256, 230.01401, 230.03775, 230.18376, 230.42041, 230.67554, 230.82742, 230.84885, 230.87372, 230.94225, 231.01442, 231.02843, 231.00027, 230.97455, 230.9254, 230.86211;
232.00514, 231.33768, 230.82791, 230.77686, 230.84599, 230.88741, 230.84238, 230.58279, 230.27737, 230.22282, 230.2531, 230.28053, 230.33743, 230.24406, 229.8969, 229.53674, 229.66661, 230.42201, 230.86761, 230.84827, 230.7677, 230.72296, 230.69333;
232.84413, 232.07454, 231.4113, 231.24339, 231.31792, 231.42, 231.39203, 231.09439, 230.71797, 230.52229, 230.16359, 229.71872, 229.5307, 228.81219, 226.98767, 224.92525, 225.05101, 228.29745, 230.37059, 230.39821, 230.14323, 230.08817, 230.12051;
233.69714, 233.27977, 232.63216, 231.97507, 231.61856, 231.50835, 231.37958, 230.94897, 230.22003, 229.17024, 227.78331, 226.92528, 227.3483, 226.49516, 223.07671, 219.54231, 220.02966, 225.84485, 229.56601, 229.69946, 229.2941, 228.91028, 228.47911;
234.07579, 233.56334, 232.87689, 232.33269, 232.23909, 232.26355, 231.24196, 227.51971, 220.59465, 210.97746, 202.39467, 198.75334, 202.68945, 209.23911, 214.57399, 218.0966, 221.80714, 226.69366, 229.27985, 229.35699, 229.21922, 229.04704, 228.72176;
234.02943, 233.1526, 232.62421, 232.68416, 232.63794, 232.74126, 230.84375, 220.47586, 197.81956, 164.03839, 136.08931, 125.05849, 134.9079, 158.19888, 186.67014, 209.67909, 223.89606, 229.51706, 230.72685, 230.50046, 230.31461, 230.29973, 230.30855;
234.04939, 233.55843, 233.05295, 232.52957, 231.76837, 231.33992, 229.65753, 220.00912, 191.89427, 140.79909, 97.534477, 80.921623, 93.553299, 127.26912, 171.24872, 205.13603, 224.29935, 230.74513, 231.68158, 231.38503, 231.22385, 231.26157, 231.31372;
233.67462, 233.69278, 233.09642, 230.73448, 225.79077, 220.33292, 216.52835, 212.6403, 192.7964, 142.2917, 93.74559, 73.776016, 92.972778, 136.18417, 183.40891, 209.98003, 220.25392, 225.67984, 229.14565, 230.97379, 231.68997, 231.87923, 231.80464;
233.16579, 232.95818, 232.5157, 227.84683, 212.53104, 193.47, 179.53844, 171.00941, 154.97589, 118.29485, 82.342369, 67.311531, 83.867973, 119.85723, 158.53325, 180.67912, 191.74194, 203.44008, 216.87592, 227.59789, 231.31285, 232.24002, 232.91658;
232.21611, 231.93192, 231.80423, 227.06053, 208.82571, 183.86725, 160.27481, 136.63663, 112.56454, 89.978371, 73.328209, 66.652176, 73.406273, 90.259987, 113.70027, 138.08961, 159.2791, 178.08627, 201.78604, 223.79007, 230.86775, 231.59146, 232.17819;
231.5118, 230.38042, 225.97289, 217.07312, 205.34308, 192.29631, 174.19812, 142.59843, 105.71719, 80.45845, 68.488274, 67.021088, 73.29406, 86.493896, 110.19484, 145.04185, 174.52554, 187.26851, 202.64322, 221.51042, 229.94238, 231.48595, 231.08746;
231.67564, 229.07423, 217.57478, 197.87076, 181.8385, 167.48799, 148.19232, 124.3977, 100.57513, 83.081154, 73.410683, 71.723045, 77.010704, 85.107651, 98.029099, 121.88382, 145.77963, 161.43314, 184.43152, 212.01347, 227.27411, 231.84755, 231.33319;
232.0773, 231.27109, 227.09813, 218.50165, 206.31781, 182.26494, 144.46196, 115.64604, 99.402679, 87.584351, 79.348366, 76.547188, 79.332504, 82.244148, 86.3069, 100.71764, 122.39668, 147.5081, 179.02258, 210.10269, 226.37909, 231.12947, 230.34335;
232.11732, 231.67418, 231.89207, 229.20001, 213.83904, 180.2238, 134.82561, 107.20949, 97.260231, 88.765694, 80.533333, 75.941055, 76.372505, 77.851997, 78.464508, 81.875244, 96.896721, 131.28108, 175.47084, 213.05406, 227.81297, 230.31032, 229.60373;
232.36255, 232.00981, 232.29773, 226.30051, 199.48029, 156.13557, 112.30969, 91.346344, 88.295509, 85.21006, 79.416222, 74.552238, 73.894844, 75.069275, 74.349594, 72.166176, 85.453522, 128.47208, 180.33452, 218.87312, 229.58446, 229.77406, 230.03587;
232.52425, 231.2455, 226.65468, 210.90804, 174.35748, 128.79022, 92.861343, 79.050415, 78.796555, 76.526512, 71.317635, 67.324234, 67.506172, 69.193619, 68.941025, 67.913399, 82.488945, 124.88449, 171.48178, 203.84958, 215.13747, 221.22523, 228.15715;
232.74571, 229.80283, 217.69687, 189.34862, 145.52664, 104.71513, 84.893997, 83.699814, 88.473457, 86.446617, 77.834595, 68.74688, 65.925613, 65.426163, 63.241882, 61.236107, 69.682426, 97.213646, 131.60564, 160.99944, 180.75278, 202.22523, 223.85883;
233.80923, 232.82767, 227.83594, 209.05493, 166.58002, 120.64989, 94.880188, 89.971268, 93.209671, 90.605591, 80.354561, 69.243584, 67.490875, 70.700516, 72.353569, 70.053764, 70.773293, 86.577957, 121.76624, 160.51776, 182.91074, 203.17424, 224.06786;
235.62155, 235.22169, 234.91901, 223.3783, 181.88362, 132.80327, 104.59508, 97.904762, 98.472153, 91.749123, 79.65731, 69.025223, 66.806007, 70.64135, 75.239159, 74.961838, 73.406227, 83.469612, 118.84832, 161.62743, 181.61127, 192.7933, 203.54196;
236.851, 236.1096, 235.65253, 224.02559, 182.0352, 134.56085, 111.10134, 106.82736, 105.87054, 95.272148, 80.614365, 68.017456, 61.20583, 62.735069, 69.976379, 72.687195, 71.943336, 75.369637, 89.042145, 106.32064, 116.6455, 127.58019, 139.77493;
236.09546, 235.84727, 235.44041, 223.06668, 180.65508, 134.57915, 114.13975, 110.49339, 107.15049, 93.355858, 77.559898, 65.277794, 58.067509, 62.642029, 76.700447, 81.800919, 80.054298, 80.085251, 82.980927, 87.177017, 92.031647, 100.26192, 109.12404]
I'm more familiar with this warpAffine, whose basic statement is
cv::warpAffine (InputArray src, // input mat
OutputArray dst, // output mat
InputArray M, // affine transformation mat
Size dsize) // size of the output mat
where M is the matrix
a11 a12 ofx
a21 a22 ofy
In your term, the first two columns is the linear transformation matrix A, the last is the translation vector b.
The cv::hal::warpAffine() is just the same, where double M[6] corresponds to the above affine transformation matrix, but I'm not sure in which order it is flatten (most likely, [a11,a12,ofx,a21,a22,ofy]).
In OpenCV, the origin (0,0) is the top-left conner as usual, while in Intel's code, the origin (0,0) is in the middle of the image. That's what the part
for (int j=-halfHeight; j<=halfHeight; ++j)
{
for(int i=-halfWidth; i<=halfWidth; ++i)
{
const int y = (int) floor(wy);
//...
}
}
does: (i,j) is the coordinate in res, j from -halfHeight to halfHeight and i from -halfHeight to halfHeight. So in this case (0,0) is in the center of the res image.
In the provided code, if you want to map src onto res (i guess), you would need to do:
bool touchesBoundary = interpolate(smoothed, (float)(imageSize>>1), (float)(imageSize>>1), imageToPatchScale, 0, 0, imageToPatchScale, patch);
Notice here imageSize>>1 instead of patchImageSize>>1. Why? You want the center of the res (i=0,j=0) maps to the center of src, i.e. the value src.at<float>(src.cols/2, src.rows/2) (why?)
Now to make that work in your example, the equivalent of cv::warpedAffine() would be
warpAffine(smoothed, myPatch, warp_mat, patch.size(),WARP_INVERSE_MAP);
where the warp_mat has ofsx=0,ofsy=0.
Finally, here's an illustration of what I tried:
where diff = mypatch - patch >5 and smoothed is scaled up by OS. Notice the black border in patch, it is because the restrictions x < width and y<height in the code.
Define const array in source file
I want to define a constant array as data member of class in its source file.
The array has 256 elements, and there is no relation between them.
class Foo {
private:
const int myArray[256] = {
0, 0, 0, 0, 0, 0, 0, 9, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 0, 0, 0, 0, 0, 0,
0, 0, 3, 0, 3, 0, 0, 1, 3, 0, 0, 0, 0, 1, 2, 0,
0, 0, 2, 2, 1, 0, 3, 2, 0, 2, 0, 0, 0, 0, 0, 0,
0, 4, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 4, 0,
0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
};
};
My Questions:
1- Is it better to shift the definition of the array to the source file?
2- Is there anyway to define this array in a source file instead of the header that defines the class?
in the header:
static const int myArray[256];
in the cpp file
const int Foo::myArray[256] = {
// numbers
};
I'm assuming here you don't need one per class instance.
Regarding the question of where it's better to put it; I would put it in the cpp file, but that's mostly a matter of style.
How do I loop through Matrix data how it appears?
Situation
I have a matrix which is 300 columns and 1 row. When I cout << it, I get:
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
...which is of the form I expect/want.
Problem
However, when I loop through it, I want to get each single value each iteration. However, instead I get a slightly different order (sometimes it is quite similar, though).
Code
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/core/core.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/imgcodecs/imgcodecs.hpp"
using namespace std;
using namespace cv;
int main(){
Mat test(1,300,CV_8UC1, 255);
cout << test;
Mat frame, grayFrame,threshFrame,smaller;
VideoCapture cap(0);
while(true){
cap.read(frame);
cvtColor(frame, grayFrame, cv::COLOR_RGB2GRAY);
threshold(grayFrame, threshFrame, 160, 255, THRESH_BINARY);
smaller = threshFrame(Rect(0,0,300,1));
cout << smaller;
for(int x=0;x<smaller.cols;x++){
int color = smaller.at<Vec3b>(x,1)[0];
cout << color;
}
break;
}
}
... And the weird output that does not follow the exact same order of 0s and 255s as the original Matrix:
00000000000000000000000000000000000000000000000000000000000000000000000000000000000002552552552552552552552552552552552552552552552552552552552552552552552552552552552552552552552552552552552552552552552552552552552552552552552552552552552552552552552552552552552552552552552552552552552552552552552552552552552552552552550000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
The matrix has many 0s at first, and few 255s, where as the loop output has many 255s, and not so much beginning 0s.
Essentially, I want to loop through the matrix first shown, and for each iteration, get each value. So 0,0,255,255... etc.
You're reading garbage. The at function needs (row, col), and not (x, y). Remember that row = y, and col = x. If your matrix is just a single row, the row index must be 0, not 1. Your matrix is a single channel of unsigned char, so you need to use at<uchar> In practice, use: uchar color = smaller.at<uchar>(0, x); cout << int(color); or using indices: uchar color = smaller.at<uchar>(x);
How to create a Mat on OpenCV for making an histogram
I'm making kind of an histogram stored in a Matrix on OpenCV. So, if I match one result, I will at +1 on some index. My mat is: Mat countFramesMatrix = Mat::zeros(9,9,CV_8U); when I try to access to sum +1 to the already set index (from 0), I do: int valueMatrixFrames = countFramesMatrix.at<int>(sliceMatch.j, sliceMatch.i); valueMatrixFrames++; countFramesMatrix.at<unsigned char>(sliceMatch.j, sliceMatch.i) = (unsigned char)valueMatrixFrames; I tried in other ways, as changing unsigned char for int an other problems I had before, but nothing happens. My results are: Or all the matrix is zeros. Or I get something like: [2.3693558e-38, 0, 0, 0, 0, 0, 0, 0, 0; 2.3693558e-38, 0, 0, 0, 0, 0, 0, 0, 0; 0, 0, 0, 0, 0, 0, 0, 0, 0; 0, 0, 0, 0, 0, 0, 0, 0, 0; 0, 0, 0, 0, 0, 0, 0, 0, 0; 0, 0, 0, 0, 0, 0, 0, 0, 0; 0, 0, 0, 0, 0, 0, 0, 0, 0; 0, 0, 0, 0, 0, 0, 0, 0, 0; 0, 0, 0, 0, 0, 0, 0, 0, 0] And I'm never storing data at (0,0) or (0,1) or (1,0) or (1,1), :( What would you suggest? thank you.
You are misting a very simple mistake, valueMatrixFrames++; will increment value of valueMatrixFrames, not of the matrix location. RIGHT WAY Let's say, if you want to increment at (1,1) you should use, countFramesMatrix.at<uchar>(1,1)++; OUTPUT [0, 0, 0, 0, 0, 0, 0, 0, 0; 0, 1, 0, 0, 0, 0, 0, 0, 0; 0, 0, 0, 0, 0, 0, 0, 0, 0; 0, 0, 0, 0, 0, 0, 0, 0, 0; 0, 0, 0, 0, 0, 0, 0, 0, 0; 0, 0, 0, 0, 0, 0, 0, 0, 0; 0, 0, 0, 0, 0, 0, 0, 0, 0; 0, 0, 0, 0, 0, 0, 0, 0, 0; 0, 0, 0, 0, 0, 0, 0, 0, 0] Running the above command again will increment the value at (1, 1) to 2. [0, 0, 0, 0, 0, 0, 0, 0, 0; 0, 2, 0, 0, 0, 0, 0, 0, 0; 0, 0, 0, 0, 0, 0, 0, 0, 0; 0, 0, 0, 0, 0, 0, 0, 0, 0; 0, 0, 0, 0, 0, 0, 0, 0, 0; 0, 0, 0, 0, 0, 0, 0, 0, 0; 0, 0, 0, 0, 0, 0, 0, 0, 0; 0, 0, 0, 0, 0, 0, 0, 0, 0; 0, 0, 0, 0, 0, 0, 0, 0, 0] So, your histogram is ready!!