In c++, Visual Studio, I have image
cv::Mat EnergyImage;
which is 2D with dimension of 2x6000000
I want to convert it to 2 image with size of 200x300x100(=6000000), better to say two 3d images.
how can I reshape it and access to 2 image seprately?
OpenCV provides the cv::Mat constructor API where you can create an n-dimensional Matrix with arbitrary bins in each dimension as:
cv::Mat m = cv::Mat(1, 6000000, CV_8UC1, cv::Scalar(100));
int sizes[3] = {200, 300, 100};
cv::Mat multiDimMat = cv::Mat(3, sizes, CV_8UC1, &m.data[0]);
The above code would suffice if you had linear cv::Mat with only one row, but since your input cv::Mat has 2 rows and you want 2 separate cv::Mat objects for each row, you may try:
for(int i = 0; i < m.rows; i++)
{
const uchar* mi = m.ptr<uchar>(i);
// Perform the same routine as above code
}
Related
I am learning image processing with OpenCV in C++. To implement a basic down-sampling algorithm I need to work on the pixel level -to remove rows and columns. However, when I assign values with mat.at<>(i,j) other values are assign - things like 1e-38.
Here is the code :
Mat src, dst;
src = imread("diw3.jpg", CV_32F);//src is a 479x359 grayscale image
//dst will contain src low-pass-filtered I checked by displaying it works fine
Mat kernel;
kernel = Mat::ones(3, 3, CV_32F) / (float)(9);
filter2D(src, dst, -1, kernel, Point(-1, -1), 0, BORDER_DEFAULT);
// Now I try to remove half the rows/columns result is stored in downsampled
Mat downsampled = Mat::zeros(240, 180, CV_32F);
for (int i =0; i<downsampled.rows; i ++){
for (int j=0; j<downsampled.cols; j ++){
downsampled.at<uchar>(i,j) = dst.at<uchar>(2*i,2*j);
}
}
Since I read here OpenCV outputing odd pixel values that for cout I needed to cast, I wrote downsampled.at<uchar>(i,j) = (int) before dst.at<uchar> but it does not work also.
The second argument to cv::imread is cv::ImreadModes, so the line:
src = imread("diw3.jpg", CV_32F);
is not correct; it should probably be:
cv::Mat src_8u = imread("diw3.jpg", cv::IMREAD_GRAYSCALE);
src_8u.convertTo(src, CV_32FC1);
which will read the image as 8-bit grayscale image, and will convert it to floating point values.
The loop should look something like this:
Mat downsampled = Mat::zeros(240, 180, CV_32FC1);
for (int i = 0; i < downsampled.rows; i++) {
for (int j = 0; j < downsampled.cols; j++) {
downsampled.at<float>(i,j) = dst.at<float>(2*i,2*j);
}
}
note that the argument to cv::Mat::zeros is CV_32FC1 (1 channel, with 32-bit floating values), so Mat::at<float> method should be used.
I'm trying to implement color conversion from RGB-LMS and LMS-RGB back and using reshape for multiplication matrix, following answer from this question : Fastest way to apply color matrix to RGB image using OpenCV 3.0?
My ori Mat object is from an image with 3 channel (RGB), and I need to multiply them with matrix of 1 channel (lms), it seems like I have an issue with the matrix type. I've read reshape docs and questions related to this issue, like Issues multiplying Mat matrices, and I believe I have followed the instructions.
Here's my code : [UPDATED : Convert into flat image]
void test(const Mat &forreshape, Mat &output, Mat &pic, int rows, int cols)
{
Mat lms(3, 3, CV_32FC3);
Mat rgb(3, 3, CV_32FC3);
Mat intolms(rows, cols, CV_32F);
lms = (Mat_<float>(3, 3) << 1.4671, 0.1843, 0.0030,
3.8671, 27.1554, 3.4557,
4.1194, 45.5161 , 17.884 );
/* switch the order of the matrix according to the BGR order of color on OpenCV */
Mat transpose = (3, 3, CV_32F, lms).t(); // this will do transpose from matrix lms
pic = forreshape.reshape(1, rows*cols);
Mat flatFloatImage;
pic.convertTo(flatFloatImage, CV_32F);
rgb = flatFloatImag*transpose;
output = rgb.reshape(3, cols);
}
I define my Mat object, and I have converted it into float using convertTo
Mat ori = imread("ori.png", CV_LOAD_IMAGE_COLOR);
int rows = ori.rows;
int cols = ori.cols;
Mat forreshape;
ori.convertTo(forreshape, CV_32F);
Mat pic(rows, cols, CV_32FC3);
Mat output(rows, cols, CV_32FC3);
Error is :
OpenCV Error: Assertion failed (type == B.type() && (type == CV_32FC1 || type == CV_64FC1 || type == CV_32FC2 || type == CV_64FC2)) ,
so it's the type issue.
I tried to change all type into either 32FC3 of 32FC1, but doesn't seem to work. Any suggestion ?
I believe what you need is to convert your input to a flat image and than multiply them
float lms [] = {1.4671, 0.1843, 0.0030,
3.8671, 27.1554, 3.4557,
4.1194, 45.5161 , 17.884};
Mat lmsMat(3, 3, CV_32F, lms );
Mat flatImage = ori.reshape(1, ori.rows * ori.cols);
Mat flatFloatImage;
flatImage.convertTo(flatFloatImage, CV_32F);
Mat mixedImage = flatFloatImage * lmsMat;
Mat output = mixedImage.reshape(3, imData.rows);
I might have messed up with lms matrix there, but I guess you will catch up from here.
Also see 3D matrix multiplication in opencv for RGB color mixing
EDIT:
Problem with distortion is that you got overflow after float to 8U conversion. This would do the trick:
rgb = flatFloatImage*transpose;
rgb.convertTo(pic, CV_32S);
output = pic.reshape(3, rows)
Output:
;
Also I'm not sure but quick google search gives me different matrix for LMS see here. Also note that opencv stores colors in B-G-R format instead of RGB so change your mix mtraixes recordingly.
I created a 3D matrix as cv::mat which contain on each axis (X, Y and Z) avalue from 0 to 255, as follow:
int sizes[] = { 100, 100, 100 };
Mat *matrix;
matrix = new Mat(3,sizes, CV_32FC1, cv::Scalar(0));
for(int i=0;i<100;i++)
for(int j=0;j<100;j++)
for(int k=0;k<100;k++){
//some values are 255
matrix->at<float>(i,j,k) = 0;
// and some of them are 255 : (TODO)
}
And now I want smooth the whole 3D matrix, how I can do that with opencv lib?
Any help will be appreciated,
I would not use new Mat() to initialize an OpenCV Mat. There is no need for this. You also run the risk of forgeting to free your Matrix once finished with it.
Simply use:
Mat matrix(3,sizes, CV_32FC1, cv::Scalar(0));
As for creating a 3D Matrix you can find a good example here
If you wish to filter the 3D Matrix with a Gaussian filter you will find a simple example here
I'm beginner in opencv. I have not gotten main concepts of opencv in details.
So maybe my code it's too dumb;
Out of my curiosity I want to try machine learning functions like a KNN, ANN.
I have the set of images with size 28*28 pixels. I want to do train cassifier for digit recognition. So first I need to assemble train set and train classes;
Mat train_data = Mat(rows, cols, CV_32FC1);
Mat train_classes = Mat(rows, 1, CV_32SC1);
Mat img = imread(image);
Mat float_data(1, cols, CV_32FC1);
img.convertTo(float_data, CV_32FC1);
How to fill train_data with float_data ?
I thought It was smth like this:
for (int i = 0; i < train_data.rows; ++i)
{
... // image is a string which contains next image path
image = imread(image);
img.convertTo(float_data, CV_32FC1);
for( int x = 0; x < train_data.cols; x++ ){
train_data.at<float> (i, x) = float_data.at<float>( x);;
}
}
KNearest knn;
knn.train(train_data, train_classes);
but it's of course doesn't work . . .
Please, tell me how to do it right. Or at least suggest the books for dummies :)
Mat train_data; // initially empty
Mat train_labels; // empty, too.
// for each img in the train set :
Mat img = imread("image_path");
Mat float_data;
img.convertTo(float_data, CV_32FC1); // to float
train_data.push_back( float_data.reshape(1,1) ); // add 1 row (flattened image)
train_labels.push_back( label_for_image ); // add 1 item
KNearest knn;
knn.train(train_data, train_labels);
it's all the same for other ml algos !
I'm having problems getting PCA and Eigenfaces working using the latest C++ syntax with the Mat and PCA classes. The older C syntax took an array of IplImage* as a parameter to perform its processing and the current API only takes a Mat that is formatted by Column or Row. I took the Row approach using the reshape function to fit my image's matrix to fit in a single row. I eventually want to take this data and then use the SVM algorithm to perform detection, but when I do that all my data is just a stream of 0s. Can someone please help me out? What am I doing wrong? Thanks!
I saw this question and it's somewhat related, but I'm not sure what the solution is.
This is basically what I have:
vector<Mat> images; //This variable will be loaded with a set of images to perform PCA on.
Mat values(images.size(), 1, CV_32SC1); //Values are the corresponding values to each of my images.
int nEigens = images.size() - 1; //Number of Eigen Vectors.
//Load the images into a Matrix
Mat desc_mat(images.size(), images[0].rows * images[0].cols, CV_32FC1);
for (int i=0; i<images.size(); i++) {
desc_mat.row(i) = images[i].reshape(1, 1);
}
Mat average;
PCA pca(desc_mat, average, CV_PCA_DATA_AS_ROW, nEigens);
Mat data(desc_mat.rows, nEigens, CV_32FC1); //This Mat will contain all the Eigenfaces that will be used later with SVM for detection
//Project the images onto the PCA subspace
for(int i=0; i<images.size(); i++) {
Mat projectedMat(1, nEigens, CV_32FC1);
pca.project(desc_mat.row(i), projectedMat);
data.row(i) = projectedMat.row(0);
}
CvMat d1 = (CvMat)data;
CvMat d2 = (CvMat)values;
CvSVM svm;
svm.train(&d1, &d2);
svm.save("svmdata.xml");
What etarion said is correct.
To copy a column or row you always have to write:
Mat B = mat.col(i);
A.copyTo(B);
The following program shows how to perform a PCA in OpenCV. It'll show the mean image and the first three Eigenfaces. The images I used in there are available from http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html:
#include "cv.h"
#include "highgui.h"
using namespace std;
using namespace cv;
Mat normalize(const Mat& src) {
Mat srcnorm;
normalize(src, srcnorm, 0, 255, NORM_MINMAX, CV_8UC1);
return srcnorm;
}
int main(int argc, char *argv[]) {
vector<Mat> db;
// load greyscale images (these are from http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html)
db.push_back(imread("s1/1.pgm",0));
db.push_back(imread("s1/2.pgm",0));
db.push_back(imread("s1/3.pgm",0));
db.push_back(imread("s2/1.pgm",0));
db.push_back(imread("s2/2.pgm",0));
db.push_back(imread("s2/3.pgm",0));
db.push_back(imread("s3/1.pgm",0));
db.push_back(imread("s3/2.pgm",0));
db.push_back(imread("s3/3.pgm",0));
db.push_back(imread("s4/1.pgm",0));
db.push_back(imread("s4/2.pgm",0));
db.push_back(imread("s4/3.pgm",0));
int total = db[0].rows * db[0].cols;
// build matrix (column)
Mat mat(total, db.size(), CV_32FC1);
for(int i = 0; i < db.size(); i++) {
Mat X = mat.col(i);
db[i].reshape(1, total).col(0).convertTo(X, CV_32FC1, 1/255.);
}
// Change to the number of principal components you want:
int numPrincipalComponents = 12;
// Do the PCA:
PCA pca(mat, Mat(), CV_PCA_DATA_AS_COL, numPrincipalComponents);
// Create the Windows:
namedWindow("avg", 1);
namedWindow("pc1", 1);
namedWindow("pc2", 1);
namedWindow("pc3", 1);
// Mean face:
imshow("avg", pca.mean.reshape(1, db[0].rows));
// First three eigenfaces:
imshow("pc1", normalize(pca.eigenvectors.row(0)).reshape(1, db[0].rows));
imshow("pc2", normalize(pca.eigenvectors.row(1)).reshape(1, db[0].rows));
imshow("pc3", normalize(pca.eigenvectors.row(2)).reshape(1, db[0].rows));
// Show the windows:
waitKey(0);
}
and if you want to build the matrix by row (like in your original question above) use this instead:
// build matrix
Mat mat(db.size(), total, CV_32FC1);
for(int i = 0; i < db.size(); i++) {
Mat X = mat.row(i);
db[i].reshape(1, 1).row(0).convertTo(X, CV_32FC1, 1/255.);
}
and set the flag in the PCA to:
CV_PCA_DATA_AS_ROW
Regarding machine learning. I wrote a document on machine learning with the OpenCV C++ API that has examples for most of the classifiers, including Support Vector Machines. Maybe you can get some inspiration there: http://www.bytefish.de/pdf/machinelearning.pdf.
data.row(i) = projectedMat.row(0);
This will not work. operator= is a shallow copy, meaning no data is actually copied. Use
cv::Mat sample = data.row(i); // also a shallow copy, points to old data!
projectedMat.row(0).copyTo(sample);
The same also for:
desc_mat.row(i) = images[i].reshape(1, 1);
I would suggest looking at the newly checked in tests in svn head
modules/core/test/test_mat.cpp
online here : https://code.ros.org/svn/opencv/trunk/opencv/modules/core/test/test_mat.cpp
has examples for PCA in the old c and new c++
Hope that helps!