I would like to find the median color in a masked area in OpevCV. Does OpenCV have a function that takes an image and a mask, and puts only the pixels from the image where mask != 0 into an array or Mat?
I don't know of any OpenCV function that creates a vector from masked values, I have written my own function to do that in the past, which you could do.
Alternatively you could calculate the histogram and find the median off of that, if your data is uint8.
You should use the following function of the Mat class to copy all the pixels into another Mat by using Mask:
Mat rst;
img.copyTo(rst, mask);
Post is quite old now, but - as there is still no function available in OpenCV - I implemented it for my app. Maybe will be useful for anyone...
cv::Mat extractMaskedData(cv::Mat data, cv::Mat mask)
{
CV_Assert(mask.size()==data.size());
CV_Assert(mask.type()==CV_8UC1);
const bool isContinuous = data.isContinuous() && mask.isContinuous();
const int nRows = isContinuous ? 1 : data.rows;
const int nCols = isContinuous ? data.rows * data.cols : data.cols;
const size_t pixelBitsize = data.channels() * (data.depth() < 2 ? 1 : data.depth() < 4 ? 2 : data.depth() < 6 ? 4 : 8);
cv::Mat extractedData(0, 1, data.type());
uint8_t* m;
uint8_t* d;
for (size_t i = 0; i < nRows; ++i) {
m = mask.ptr<uint8_t>(i);
d = data.ptr(i);
for (size_t j = 0; j < nCols; ++j) {
if(m[j]) {
const cv::Mat pixelData(1, 1, data.type(), d + j * pixelBitsize);
extractedData.push_back(pixelData);
}
}
}
return extractedData;
}
It returns cv::Mat(1,n,data.type()) where n is the number of non-zero elements in mask.
May be optimised by using image-type-specific d pointer (e.g. cv::Vec3f for CV_32FC3 instead of generic uint8_t* d together with const cv::Mat pixelData(1, 1, data.type(), d + j * pixelBitsize);.
Related
I used opencv to read in a picture and split the image.
The divided matrix:
Odd rows are odd columns as A matrix;
The odd-numbered rows are evenly listed as B-matrices;
The odd-numbered rows are evenly listed as C-matrices;
Even-numbered even-numbered columns are D-matrices;
My code:
void SplitMat(Mat& src, Mat& objA, Mat& objB, Mat& objC, Mat& objD) {
// src: Input image, CV_16UC1
// objA: Output image, CV_16UC1
// objB: Output image, CV_16UC1
// objC: Output image, CV_16UC1
// objD: Output image, CV_16UC1
Mat dst;
src.copyTo(dst);
int row, col, i, j;
for (row = 0, i = 0; row < dst.rows - 1; row = row + 2, ++i) {
ushort* temp0 = dst.ptr<ushort>(row);
ushort* temp1 = dst.ptr<ushort>(row + 1);
ushort* obja = objA.ptr<ushort>(i);
ushort* objb = objB.ptr<ushort>(i);
ushort* objc = objC.ptr<ushort>(i);
ushort* objd = objD.ptr<ushort>(i);
for (col = 0, j = 0; col < dst.cols - 1; col = col + 2, ++j) {
obja[j] = temp0[col];
objb[j] = temp0[col + 1];
objc[j] = temp1[col];
objd[j] = temp1[col + 1];
}
}
}
Test result:
I don't know why the image in the x direction has become two images.
Please guide us. Is this a logical error or something else? thanks
I know the reason for this problem. Since the picture read is 8 bits, and the program uses 16 bits.
uchar -> ushort have a question.
I wrote down an openCV code .I tried to embed a 64X64 pix watermark image in a 512X512 image.
my code has 5 parts:
reading two pictures( watermark and original image that I want to
embed watermark in it)
resize 2 readed images to specified size.(64X64 for watermark image
and 512X512 for original image)
devide original resized image to 8X8 blocks and transform them with
DCT.
embedding each pixel of watermark in each block of original image.
applying inverse DCT on each block.
I have this problem that all of three imshows have same results.
thank you for your help :)
here is my code :
int _tmain(int argc, _TCHAR* argv[])
{
int index=0;
int iindex=0;
vector<Mat> blocks(4096);
/////////////Part1:reading images
Mat originalImage;
originalImage = imread("C:\\MGC.jpg",CV_LOAD_IMAGE_GRAYSCALE);
Mat watermarkImage;
watermarkImage = imread("C:\\ivp_lg.bmp" , CV_LOAD_IMAGE_GRAYSCALE);
/// show original image
namedWindow("Original");
int x = 0; int y = 0;
moveWindow("Original", x, y);
imshow("Original", originalImage);
x += 100; y += 100;
//////Part 2:Leave originals alone, work on a copys. resize readed images
Mat dctImage = originalImage.clone();
Mat wmrk = watermarkImage.clone();
Mat tmp1(512, 512, CV_8UC1);
Mat tmp2(64, 64, CV_8UC1);
resize(dctImage, dctImage, tmp1.size());
resize(wmrk, wmrk , tmp2.size());
/////Part 3:break dctImage into 8X8 blocks and applying DCT on each block
for (int i = 0; i < 512; i += 8)
{
for (int j = 0; j < 512; j+= 8)
{
Mat block = dctImage(Rect(i, j, 8, 8));
block.convertTo(block, CV_32FC1);
dct(block,blocks[index]);
blocks[index].convertTo(blocks[index], CV_8UC1);
index++;
}
}
/// show transformed image
namedWindow("TransformedImage");
moveWindow("TransformedImage", x, y);
imshow("TransformedImage",dctImage );
x += 100; y += 100;
//////Part 4: embeding watermark. if corresponding pixel of watermark was 255 then element (5,5) in the block increase 200 otherwise do nothing
for(int idx=0 ; idx<4096 ; idx++)
{
int i=idx/64;
int j=idx%64;
float elem=(float) wmrk.at<uchar>(i,j);
if (elem>=128)
{
float tmp=(float) blocks[idx].at<uchar>(5,5);
float temp=tmp +200;
uchar ch=(uchar) temp;
blocks[idx].at<uchar>(5,5)=ch;
}
}
//////Part 5:applying iDCT on each block
for (int i = 0; i < 512; i += 8)
{
for (int j = 0; j < 512; j+= 8)
{
Mat block = dctImage(Rect(i, j, 8, 8));
block.convertTo(block, CV_32FC1);
idct(block,blocks[iindex]);
blocks[iindex].convertTo(blocks[iindex], CV_8UC1);
iindex++;
}
}
/// show watermarked image
namedWindow("WatermarkedImage");
moveWindow("WatermarkedImage", x, y);
imshow("WatermarkedImage",dctImage );
cvWaitKey(80000);
destroyAllWindows();
return 0;
}
#N_Kh As far as I have seen ur code in hurry, You are executing IMSHOW Command over the Matrix dctImage while you are performing operation on different Matrix and vector Block and Blocks respectively.
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;
}
as input data I have a 24 bit RGB image and a palette with 2..20 fixed colours. These colours are in no way spread regularly over the full colour range.
Now I have to modify the colours of input image so that only the colours of the given palette are used - using the colour out of the palette that is closest to the original colour (not closest mathematically but for human's visual impression). So what I need is an algorithm that uses an input colour and finds the colour in target palette that visually fits best to this colour. Please note: I'm not looking for a stupid comparison/difference algorithm but for something that really incorporates the impression a colour has on humans!
Since this is something that already should have been done and because I do not want to re-invent the wheel again: is there some example source code out there that does this job? In best case it is really a piece of code and not a link to a desastrous huge library ;-)
(I'd guess OpenCV does not provide such a function?)
Thanks
You should look at the Lab color space. It was designed so that the distance in the colour space equals the perceptual distance. So once you have converted your image you can compute the distances as you would have done earlier, but should get a better result from a perceptual point of view. In OpenCV you can use the cvtColor(source, destination, CV_BGR2Lab) function.
Another Idea would be to use dithering. The idea is to mix missing colours using neighbouring pixels. A popular algorithm for this is Floyd-Steinberg dithering.
Here is an example of mine, where I combined a optimized palette using k-means with the Lab colourspace and floyd steinberg dithering:
#include <opencv2/opencv.hpp>
#include <iostream>
using namespace cv;
using namespace std;
cv::Mat floydSteinberg(cv::Mat img, cv::Mat palette);
cv::Vec3b findClosestPaletteColor(cv::Vec3b color, cv::Mat palette);
int main(int argc, char** argv)
{
// Number of clusters (colors on result image)
int nrColors = 18;
cv::Mat imgBGR = imread(argv[1],1);
cv::Mat img;
cvtColor(imgBGR, img, CV_BGR2Lab);
cv::Mat colVec = img.reshape(1, img.rows*img.cols); // change to a Nx3 column vector
cv::Mat colVecD;
colVec.convertTo(colVecD, CV_32FC3, 1.0); // convert to floating point
cv::Mat labels, centers;
cv::kmeans(colVecD, nrColors, labels,
cv::TermCriteria(CV_TERMCRIT_ITER, 100, 0.1),
3, cv::KMEANS_PP_CENTERS, centers); // compute k mean centers
// replace pixels by there corresponding image centers
cv::Mat imgPosterized = img.clone();
for(int i = 0; i < img.rows; i++ )
for(int j = 0; j < img.cols; j++ )
for(int k = 0; k < 3; k++)
imgPosterized.at<Vec3b>(i,j)[k] = centers.at<float>(labels.at<int>(j+img.cols*i),k);
// convert palette back to uchar
cv::Mat palette;
centers.convertTo(palette,CV_8UC3,1.0);
// call floyd steinberg dithering algorithm
cv::Mat fs = floydSteinberg(img, palette);
cv::Mat imgPosterizedBGR, fsBGR;
cvtColor(imgPosterized, imgPosterizedBGR, CV_Lab2BGR);
cvtColor(fs, fsBGR, CV_Lab2BGR);
imshow("input",imgBGR); // original image
imshow("result",imgPosterizedBGR); // posterized image
imshow("fs",fsBGR); // floyd steinberg dithering
waitKey();
return 0;
}
cv::Mat floydSteinberg(cv::Mat imgOrig, cv::Mat palette)
{
cv::Mat img = imgOrig.clone();
cv::Mat resImg = img.clone();
for(int i = 0; i < img.rows; i++ )
for(int j = 0; j < img.cols; j++ )
{
cv::Vec3b newpixel = findClosestPaletteColor(img.at<Vec3b>(i,j), palette);
resImg.at<Vec3b>(i,j) = newpixel;
for(int k=0;k<3;k++)
{
int quant_error = (int)img.at<Vec3b>(i,j)[k] - newpixel[k];
if(i+1<img.rows)
img.at<Vec3b>(i+1,j)[k] = min(255,max(0,(int)img.at<Vec3b>(i+1,j)[k] + (7 * quant_error) / 16));
if(i-1 > 0 && j+1 < img.cols)
img.at<Vec3b>(i-1,j+1)[k] = min(255,max(0,(int)img.at<Vec3b>(i-1,j+1)[k] + (3 * quant_error) / 16));
if(j+1 < img.cols)
img.at<Vec3b>(i,j+1)[k] = min(255,max(0,(int)img.at<Vec3b>(i,j+1)[k] + (5 * quant_error) / 16));
if(i+1 < img.rows && j+1 < img.cols)
img.at<Vec3b>(i+1,j+1)[k] = min(255,max(0,(int)img.at<Vec3b>(i+1,j+1)[k] + (1 * quant_error) / 16));
}
}
return resImg;
}
float vec3bDist(cv::Vec3b a, cv::Vec3b b)
{
return sqrt( pow((float)a[0]-b[0],2) + pow((float)a[1]-b[1],2) + pow((float)a[2]-b[2],2) );
}
cv::Vec3b findClosestPaletteColor(cv::Vec3b color, cv::Mat palette)
{
int i=0;
int minI = 0;
cv::Vec3b diff = color - palette.at<Vec3b>(0);
float minDistance = vec3bDist(color, palette.at<Vec3b>(0));
for (int i=0;i<palette.rows;i++)
{
float distance = vec3bDist(color, palette.at<Vec3b>(i));
if (distance < minDistance)
{
minDistance = distance;
minI = i;
}
}
return palette.at<Vec3b>(minI);
}
Try this algorithm (it will reduct color number, but it compute palette by itself):
#include <opencv2/opencv.hpp>
#include "opencv2/legacy/legacy.hpp"
#include <vector>
#include <list>
#include <iostream>
using namespace cv;
using namespace std;
void main(void)
{
// Number of clusters (colors on result image)
int NrGMMComponents = 32;
// Source file name
string fname="D:\\ImagesForTest\\tools.jpg";
cv::Mat SampleImg = imread(fname,1);
//cv::GaussianBlur(SampleImg,SampleImg,Size(5,5),3);
int SampleImgHeight = SampleImg.rows;
int SampleImgWidth = SampleImg.cols;
// Pick datapoints
vector<Vec3d> ListSamplePoints;
for (int y=0; y<SampleImgHeight; y++)
{
for (int x=0; x<SampleImgWidth; x++)
{
// Get pixel color at that position
Vec3b bgrPixel = SampleImg.at<Vec3b>(y, x);
uchar b = bgrPixel.val[0];
uchar g = bgrPixel.val[1];
uchar r = bgrPixel.val[2];
if(rand()%25==0) // Pick not every, bu t every 25-th
{
ListSamplePoints.push_back(Vec3d(b,g,r));
}
} // for (x)
} // for (y)
// Form training matrix
Mat labels;
int NrSamples = ListSamplePoints.size();
Mat samples( NrSamples, 3, CV_32FC1 );
for (int s=0; s<NrSamples; s++)
{
Vec3d v = ListSamplePoints.at(s);
samples.at<float>(s,0) = (float) v[0];
samples.at<float>(s,1) = (float) v[1];
samples.at<float>(s,2) = (float) v[2];
}
cout << "Learning to represent the sample distributions with" << NrGMMComponents << "gaussians." << endl;
// Algorithm parameters
CvEMParams params;
params.covs = NULL;
params.means = NULL;
params.weights = NULL;
params.probs = NULL;
params.nclusters = NrGMMComponents;
params.cov_mat_type = CvEM::COV_MAT_GENERIC; // DIAGONAL, GENERIC, SPHERICAL
params.start_step = CvEM::START_AUTO_STEP;
params.term_crit.max_iter = 1500;
params.term_crit.epsilon = 0.001;
params.term_crit.type = CV_TERMCRIT_ITER|CV_TERMCRIT_EPS;
//params.term_crit.type = CV_TERMCRIT_ITER;
// Train
cout << "Started GMM training" << endl;
CvEM em_model;
em_model.train( samples, Mat(), params, &labels );
cout << "Finished GMM training" << endl;
// Result image
Mat img = Mat::zeros( Size( SampleImgWidth, SampleImgHeight ), CV_8UC3 );
// Ask classifier for each pixel
Mat sample( 1, 3, CV_32FC1 );
Mat means;
means=em_model.getMeans();
for(int i = 0; i < img.rows; i++ )
{
for(int j = 0; j < img.cols; j++ )
{
Vec3b v=SampleImg.at<Vec3b>(i,j);
sample.at<float>(0,0) = (float) v[0];
sample.at<float>(0,1) = (float) v[1];
sample.at<float>(0,2) = (float) v[2];
int response = cvRound(em_model.predict( sample ));
img.at<Vec3b>(i,j)[0]=means.at<double>(response,0);
img.at<Vec3b>(i,j)[1]=means.at<double>(response,1);
img.at<Vec3b>(i,j)[2]=means.at<double>(response,2);
}
}
img.convertTo(img,CV_8UC3);
imshow("result",img);
waitKey();
// Save the result
cv::imwrite("result.png", img);
}
PS: For perceptive color distance measurement it's better to use L*a*b color space. There is converter in opencv for this purpose. For clustering you can use k-means with defined cluster centers (your palette entries). After clustering you'll get points with indexes of palette intries.
This question is specific to opencv:
The kmeans example given in the opencv documentation has a 2-channel matrix - one channel for each dimension of the feature vector. But, some of the other example seem to say that it should be a one channel matrix with features along the columns with one row for each sample. Which of these is right?
if I have a 5 dimensional feature vector, what should be the input matrix that I use:
This one:
cv::Mat inputSamples(numSamples, 1, CV32FC(numFeatures))
or this one:
cv::Mat inputSamples(numSamples, numFeatures, CV_32F)
The correct answer is cv::Mat inputSamples(numSamples, numFeatures, CV_32F).
The OpenCV Documentation about kmeans says:
samples – Floating-point matrix of input samples, one row per sample
So it is not a Floating-point vector of n-Dimensional floats as in the other option. Which examples suggested such a behaviour?
Here is also a small example by me that shows how kmeans can be used. It clusters the pixels of an image and displays the result:
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/highgui/highgui.hpp"
using namespace cv;
int main( int argc, char** argv )
{
Mat src = imread( argv[1], 1 );
Mat samples(src.rows * src.cols, 3, CV_32F);
for( int y = 0; y < src.rows; y++ )
for( int x = 0; x < src.cols; x++ )
for( int z = 0; z < 3; z++)
samples.at<float>(y + x*src.rows, z) = src.at<Vec3b>(y,x)[z];
int clusterCount = 15;
Mat labels;
int attempts = 5;
Mat centers;
kmeans(samples, clusterCount, labels, TermCriteria(CV_TERMCRIT_ITER|CV_TERMCRIT_EPS, 10000, 0.0001), attempts, KMEANS_PP_CENTERS, centers );
Mat new_image( src.size(), src.type() );
for( int y = 0; y < src.rows; y++ )
for( int x = 0; x < src.cols; x++ )
{
int cluster_idx = labels.at<int>(y + x*src.rows,0);
new_image.at<Vec3b>(y,x)[0] = centers.at<float>(cluster_idx, 0);
new_image.at<Vec3b>(y,x)[1] = centers.at<float>(cluster_idx, 1);
new_image.at<Vec3b>(y,x)[2] = centers.at<float>(cluster_idx, 2);
}
imshow( "clustered image", new_image );
waitKey( 0 );
}
As alternative to reshaping the input matrix manually, you can use OpenCV reshape function to achieve similar result with less code. Here is my working implementation of reducing colors count with K-Means method (in Java):
private final static int MAX_ITER = 10;
private final static int CLUSTERS = 16;
public static Mat colorMapKMeans(Mat img, int K, int maxIterations) {
Mat m = img.reshape(1, img.rows() * img.cols());
m.convertTo(m, CvType.CV_32F);
Mat bestLabels = new Mat(m.rows(), 1, CvType.CV_8U);
Mat centroids = new Mat(K, 1, CvType.CV_32F);
Core.kmeans(m, K, bestLabels,
new TermCriteria(TermCriteria.COUNT | TermCriteria.EPS, maxIterations, 1E-5),
1, Core.KMEANS_RANDOM_CENTERS, centroids);
List<Integer> idx = new ArrayList<>(m.rows());
Converters.Mat_to_vector_int(bestLabels, idx);
Mat imgMapped = new Mat(m.size(), m.type());
for(int i = 0; i < idx.size(); i++) {
Mat row = imgMapped.row(i);
centroids.row(idx.get(i)).copyTo(row);
}
return imgMapped.reshape(3, img.rows());
}
public static void main(String[] args) {
System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
Highgui.imwrite("result.png",
colorMapKMeans(Highgui.imread(args[0], Highgui.CV_LOAD_IMAGE_COLOR),
CLUSTERS, MAX_ITER));
}
OpenCV reads image into 2 dimensional, 3 channel matrix. First call to reshape - img.reshape(1, img.rows() * img.cols()); - essentially unrolls 3 channels into columns. In resulting matrix one row corresponds to one pixel of the input image, and 3 columns corresponds to RGB components.
After K-Means algorithm finished its work, and color mapping has been applied, we call reshape again - imgMapped.reshape(3, img.rows()), but now rolling columns back into channels, and reducing row numbers to the original image row number, thus getting back the original matrix format, but only with reduced colors.