Speeding up access to a array with pointers in C++ - c++

I am trying to make a fast image threshold function. Currently what I do is:
void threshold(const cv::Mat &input, cv::Mat &output, uchar threshold) {
int rows = input.rows;
int cols = input.cols;
// cv::Mat for result
output.create(rows, cols, CV_8U);
if(input.isContinuous()) { //we have to make sure that we are dealing with a continues memory chunk
const uchar* p;
for (int r = 0; r < rows; ++r) {
p = input.ptr<uchar>(r);
for (int c = 0; c < cols; ++c) {
if(p[c] >= threshold)
//how to access output faster??
output.at<uchar>(r,c) = 255;
else
output.at<uchar>(r,c) = 0;
}
}
}
}
I know that the at() function is quite slow. How can I set the output faster, or in other words how to relate the pointer which I get from the input to the output?

You are thinking of at as the C++ standard library documents it for a few containers, performing a range check and throwing if out of bounds, however this is not the standard library but OpenCV.
According to the cv::Mat::at documentation:
The template methods return a reference to the specified array element. For the sake of higher performance, the index range checks are only performed in the Debug configuration.
So there's no range check as you may be thinking.
Comparing both cv::Mat::at and cv::Mat::ptr in the source code we can see they are almost identical.
So cv::Mat::ptr<>(row) is as expensive as
return (_Tp*)(data + step.p[0] * y);
While cv::Mat::at<>(row, column) is as expensive as:
return ((_Tp*)(data + step.p[0] * i0))[i1];
You might want to take cv::Mat::ptr directly instead of calling cv::Mat::at every column to avoid further repetition of the data + step.p[0] * i0 operation, doing [i1] by yourself.
So you would do:
/* output.create and stuff */
const uchar* p, o;
for (int r = 0; r < rows; ++r) {
p = input.ptr<uchar>(r);
o = output.ptr<uchar>(r); // <-----
for (int c = 0; c < cols; ++c) {
if(p[c] >= threshold)
o[c] = 255;
else
o[c] = 0;
}
}
As a side note you don't and shouldn't check for cv::Mat::isContinuous here, the gaps are from one row to another, you are taking pointers to a single row, so you don't need to deal with the matrix gaps.

Related

Understanding nested array c++ in image scanning context

I came across this sample code on openCV library. What does the line p[j] = table[p[j]] do? I have come across multi dimensional arrays but not something like this before.
Mat& ScanImageAndReduceC(Mat& I, const uchar* const table)
{
// accept only char type matrices
CV_Assert(I.depth() == CV_8U);
int channels = I.channels();
int nRows = I.rows;
int nCols = I.cols * channels;
if (I.isContinuous())
{
nCols *= nRows;
nRows = 1;
}
int i,j;
uchar* p;
for( i = 0; i < nRows; ++i)
{
p = I.ptr<uchar>(i);
for ( j = 0; j < nCols; ++j)
{
p[j] = table[p[j]];
}
}
return I;
}
It is doing color replacement by using a table where each pixel intensity maps to some other value. Commonly used for techniques like color grading, histogram adjustment, or even thresholding.
Here, the table contains unsigned char values and is being indexed by the value of the pixel. The pixel's intensity p[i] is used as an index into the table, and the value at that index is then written to that pixel, replacing its original value.
It is a lookup table conversion.
The pixels of image(I) would be converted by means of table.
For example, the pixel with value 100 would be changed to 10 if table[100]=10.
Your sample code is introduced in OpenCV tutorial which is well explained of what the code does.
https://docs.opencv.org/master/db/da5/tutorial_how_to_scan_images.html

MATLAB Tensor Indexing in C++

I am attempting to load in a .mat file containing a tensor of known dimensions in C++; 144x192x256.
I have adjusted the linear index for the read operation to be column major as in MATLAB. However I am still getting memory access issues.
void FeatureLoader::readMat(const std::string &fname, Image< std::vector<float> > *out) {
//Read MAT file.
const char mode = 'r';
MATFile *matFile = matOpen(fname.c_str(), &mode);
if (matFile == NULL) {
throw std::runtime_error("Cannot read MAT file.");
}
//Copy the data from column major to row major storage.
float *newData = newImage->GetData();
const mxArray *arr = matGetVariable(matFile, "map");
if (arr == NULL) {
throw std::runtime_error("Cannot read variable.");
}
double *arrData = (double*)mxGetPr(arr);
#pragma omp parallel for
for (int i = 0; i < 144; i++) {
#pragma omp parallel for
for (int j = 0; j < 192; j++) {
for (int k = 0; k < 256; k++) {
int rowMajIdx = (i * 192 + j) * 256 + k;
int colMajIdx = (j * 144 + i) * 256 + k;
newData[rowMajIdx] = static_cast<float>(arrData[colMajIdx]);
}
}
}
}
In the above snippet, am I right to be accessing the data linearly as with a flattened 3D array in C++? For example:-
idx_row_major = (x*WIDTH + y)*DEPTH + z
idx_col_major = (y*HEIGHT + x)*DEPTH + z
Is this the underlying representation that MATLAB uses?
You have some errors in the indexing of the row mayor and column mayor Idx. Additionally, naively accessing the data can lead to very slow times due to random memory access (memory latency is key! Read more here).
The best way to pass from MATLAB to C++ types (From 3D to 1D) is following the example below.
In this example we illustrate how to take a double real-type 3D matrix from MATLAB, and pass it to a C double* array.
The main objectives of this example are showing how to obtain data from MATLAB MEX arrays and to highlight some small details in matrix storage and handling.
matrixIn.cpp
#include "mex.h"
void mexFunction(int nlhs , mxArray *plhs[],
int nrhs, mxArray const *prhs[]){
// check amount of inputs
if (nrhs!=1) {
mexErrMsgIdAndTxt("matrixIn:InvalidInput", "Invalid number of inputs to MEX file.");
}
// check type of input
if( !mxIsDouble(prhs[0]) || mxIsComplex(prhs[0])){
mexErrMsgIdAndTxt("matrixIn:InvalidType", "Input matrix must be a double, non-complex array.");
}
// extract the data
double const * const matrixAux= static_cast<double const *>(mxGetData(prhs[0]));
// Get matrix size
const mwSize *sizeInputMatrix= mxGetDimensions(prhs[0]);
// allocate array in C. Note: its 1D array, not 3D even if our input is 3D
double* matrixInC= (double*)malloc(sizeInputMatrix[0] *sizeInputMatrix[1] *sizeInputMatrix[2]* sizeof(double));
// MATLAB is column major, not row major (as C). We need to reorder the numbers
// Basically permutes dimensions
// NOTE: the ordering of the loops is optimized for fastest memory access!
// This improves the speed in about 300%
const int size0 = sizeInputMatrix[0]; // Const makes compiler optimization kick in
const int size1 = sizeInputMatrix[1];
const int size2 = sizeInputMatrix[2];
for (int j = 0; j < size2; j++)
{
int jOffset = j*size0*size1; // this saves re-computation time
for (int k = 0; k < size0; k++)
{
int kOffset = k*size1; // this saves re-computation time
for (int i = 0; i < size1; i++)
{
int iOffset = i*size0;
matrixInC[i + jOffset + kOffset] = matrixAux[iOffset + jOffset + k];
}
}
}
// we are done!
// Use your C matrix here
// free memory
free(matrixInC);
return;
}
The relevant concepts to be aware of:
MATLAB matrices are all 1D in memory, no matter how many dimensions they have when used in MATLAB. This is also true for most (if not all) main matrix representation in C/C++ libraries, as allows optimization and faster execution.
You need to explicitly copy matrices from MATLAB to C in a loop.
MATLAB matrices are stored in column major order, as in Fortran, but C/C++ and most modern languages are row major. It is important to permute the input matrix , or else the data will look completely different.
The relevant function in this example are:
mxIsDouble checks if input is double type.
mxIsComplex checks if input is real or imaginary.
mxGetData returns a pointer to the real data in the input array. NULL if there is no real data.
mxGetDimensions returns an pointer to a mwSize array, with the size of the dimension in each index.

How to save to vector coordinates of white pixels?

I want to loop through a binarized cv::Mat and save all coordinates of pixels with a value of 255.
cv::Mat bin;
std::vector<cv::Point2i> binVec;
int h = 0;
int white = 254; //Just for comparison with pointer of Matrix value
for (int i = 0; i < bin.rows; i++, h++) {
for (int j = 0; j < bin.cols; j++, h++) {
int* p = bin.ptr<int>(h); //Pointer to bin Data, should loop through Matrix
if (p >= &white) //If a white pixel has been found, push i and j in binVec
binVec.push_back(cv::Point2i(i, j));
}
}
This snippet is not working, and I don't know why.
Exception thrown at 0x76C6C42D in example.exe: Microsoft C++ exception: cv::Exception at memory location 0x0019E4F4.
Unhandled exception at 0x76C6C42D in example.exe: Microsoft C++ exception: cv::Exception at memory location 0x0019E4F4.
So how can I count h and let the pointer work?
You can avoid to scan the image. To save the coordinates of all white pixels in a vector you can do like:
Mat bin;
// fill bin with some value
std::vector<Point> binVec;
findNonZero(bin == 255, binVec);
You can use Point instead of Point2i, since they are the same:
typedef Point2i Point;
If you really want to use a for loop, you should do like:
const uchar white = 255;
for (int r = 0; r < bin.rows; ++r)
{
uchar* ptr = bin.ptr<uchar>(r);
for(int c = 0; c < bin.cols; ++c)
{
if (ptr[c] == 255) {
binVec.push_back(Point(c,r));
}
}
}
Remember that:
you binary image is probably CV_8UC1, and not a CV_32SC1, so you should use uchar instead of int.
bin.ptr<...>(i) gives you a pointer to the start of the i-th row, so you should take it out of the inner loop.
you should compare the values, not the address.
Point take as parameters x (cols) and y (rows), while you are passing i (rows) and j (cols). So you need to swap them.
this loop can be further optimized, but for your task I strongly recommend the findNonZero approach, so I don't show it here.
You should only increment h in the inner loop
You should compare the value pointed at by p with h, not compare p with the address of h.
So
cv::Mat bin;
std::vector<cv::Point2i> binVec;
int h = 0;
int white = 254; //Just for comparison with pointer of Matrix value
for (int i = 0; i < bin.rows; i++) {
for (int j = 0; j < bin.cols; j++) {
int* p = bin.ptr<int>(h++); //Pointer to bin Data, should loop through Matrix
if (*p >= white) //If a white pixel has been found, push i and j in binVec
binVec.push_back(cv::Point2i(i, j));
}
}

Take off pads with OpenCV

I have to translate from Matlab to C this code:
% take off the pads
x = (1 + padSize) : (rows - pad8Size);
y = (1 + padSize) : (cols - padSize);
rpad=rpad(x,y);
1st and 2nd create 2 array, but I don t know how I have to delete it from rpad Mat object It can be something like(subtract every element)
for(int i=1+pad;i<=rows-pad;i++){
for(int j=1+pad;i<=cols-pad;j++){
subtract(rpad,x,rpad);
subtract(rpad,y,rpad);}}
Or something like(delete the external element)
int a=(rows-pad)-(1+pad);
int b=(cols-pad)-(1+pad);
rpad.create(img.rows - a,img.cols - b,original.type());
img.copyTo(rpad);
Try
cv::Rect roi(padSize, padSize, rpad.cols-2*padSize, rpad.rows-2*padSize);
cv::Mat result = rpad(roi);
And depending on whether you want continuous memory, you can choose to directly use result (discontinuous, usually okay for most OpenCV functions) or copy it to back to rpad (continuous)
Is it possible to multiply a Mat object with a bidimensional array? Imfft is obviously the Mat object
for (int i = 0; i < rows; i++){
for (int j = 0; j < cols; j++){
imfft=imfft*filter[i][j]
}
}

Fastest way to extract individual pixel data?

I have to get information about the scalar value of a lot of pixels on a gray-scale image using OpenCV. It will be traversing hundreds of thousands of pixels so I need the fastest possible method. Every other source I've found online has been very cryptic and hard to understand. Is there a simple line of code that should just hand a simple integer value representing the scalar value of the first channel (brightness) of the image?
for (int row=0;row<image.height;row++) {
unsigned char *data = image.ptr(row);
for (int col=0;col<image.width;col++) {
// then use *data for the pixel value, assuming you know the order, RGB etc
// Note 'rgb' is actually stored B,G,R
blue= *data++;
green = *data++;
red = *data++;
}
}
You need to get the data pointer on each new row because opencv will pad the data to 32bit boundary at the start of each row
With regards to Martin's post, you can actually check if the memory is allocated continuously using the isContinuous() method in OpenCV's Mat object. The following is a common idiom for ensuring the outer loop only loops once if possible:
#include <opencv2/core/core.hpp>
using namespace cv;
int main(void)
{
Mat img = imread("test.jpg");
int rows = img.rows;
int cols = img.cols;
if (img.isContinuous())
{
cols = rows * cols; // Loop over all pixels as 1D array.
rows = 1;
}
for (int i = 0; i < rows; i++)
{
Vec3b *ptr = img.ptr<Vec3b>(i);
for (int j = 0; j < cols; j++)
{
Vec3b pixel = ptr[j];
}
}
return 0;
}