I am traying to convert a cv::Mat to IplImage in pc with this caracteristcs:
opencv: 3.4.14
OS: Win 10
code: c++
An example of the differents options:
cv::Mat MBin = cv::Mat::zeros(cv::Size(64, 64), CV_32FC1);
IplImage* image0= new IplImage(MBin);
IplImage image1 = MBin;
IplImage* image2 = cvCloneImage(&(IplImage)MBin);
IplImage* image3;
image3 = cvCreateImage(cvSize(MBin.cols, MBin.rows), 8, 3);
IplImage image4 = MBin;
cvCopy(&image4, image3);
Where imageX appears produces the title error.
This is the only solution, which doesn't generate compiler error:
#include <opencv2/core/types_c.h>
Mat Img = imread("1.jpg");
IplImage IBin_2 = cvIplImage(MBin);
IplImage* IBin = &IBin_2;
Before opencv3.x, Mat has a constructor Mat(const IplImage* img, bool copyData=false);. But in opencv3.x, Mat(const IplImage* img, bool copyData=false); constructor is canceled.
So, you could refer to the following example to convert Mat to IplImage.
//Mat—>IplImage
//EXAMPLE:
//shallow copy：
Mat Img=imread("1.jpg");
IplImage* pBinary = &IplImage(Img);
//For a deep copy, just add another copy of the data:
IplImage *input = cvCloneImage(pBinary)
Also, you could refer to this link for more information.
//opencv 4.5.2
IplImage* IplImage_img = cvCreateImage(cvSize(img.cols, img.rows), 8, 1);
cv::Mat MatImg(img.rows, img.cols, CV_8U, cv::Scalar(0));
MatImg = cv::cvarrToMat(IplImage_img);
img.copyTo(MatImg);
Related
I have to transform QImage to cv::Mat, if I use technique described in similar topics, I receive different numbers of contours (7--8) and strange result matrix, but if I do
QImage im;
im.save ("tmp.bmp");
cv::Mat rImage;
rImage = cv::imread ("tmp.bmp", CV_LOAD_IMAGE_GRAYSCALE);
function findContours works fine and properly. What is the difference between these techniques and which way I can archive equal results between these approaches ?
Your code works for me.
int main(int argc, char *argv[]){
QImage img(QString("lena.bmp"));
QImage img2 = img.convertToFormat(QImage::Format_RGB32);
cv::Mat imageMat = qimage_to_cvmat_copy(img2, CV_8UC4);
cv::namedWindow("lena");
cv::imshow("lena", imageMat);
cv::waitKey(0);
}
cv::Mat qimage_to_cvmat_copy(const QImage &img, int format)
{
uchar* b = const_cast<uchar*> (img.bits ());
int c = img.bytesPerLine();
return cv::Mat(img.height(), img.width(), format, b, c).clone();
}
Make sure your Mat format is CV_8UC4 if your QImage format is Format_RGB32. You don't have to do a cvtColor or mixChannels.
All !
As mentioned above I used conversion QImage to cv::Mat as described here. My source code became something like this
QImage srcIm (argv[1]);
QImage img2 = srcIm.convertToFormat(QImage::Format_ARGB32);
Mat src_gray = QImageToCvMat (img2);
cvtColor (src_gray, src_gray1, CV_RGB2GRAY);
Mat bwimg = src_gray1.clone();// > 127;
vector<vector<Point> > contours;
findContours( bwimg, contours, CV_RETR_LIST, CV_CHAIN_APPROX_SIMPLE );
All works fine.
I want to get an image in which only a region has color given a color image.
Mat img = imread("lena.jpg");
Rect roi = Rect(100, 100, 300, 300);// only this should be in color in output
Mat img_yuv;
cvtColor(img, img_yuv, CV_RGB2YUV);
vector<Mat> channels(3);
split(img_yuv, channels);
Mat Y = channels[0];
Mat U = channels[1];
Mat V = channels[2];
// create mask
Mat mask = Mat::zeros(Y.size(), Y.type());
rectangle(mask, roi, Scalar(1), CV_FILLED);
// merging channels
channels[0] = Y;
channels[1] = U.mul(mask)+(Scalar::all(1)-mask).mul(Y);
channels[2] = V.mul(mask)+(Scalar::all(1)-mask).mul(Y);
Mat img_yuv_out, img_out;
merge(channels, img_yuv_out);
cvtColor(img_yuv_out, img_out, CV_YUV2RGB);
imshow("masked_color", img_out);
imshow("lena", img);
with the above opencv code here are my imput and output images respectively.
In the roi it works fine but rest of image doesn't look like a grayscale image(not exactly as we still have 3 channels.
You could try this:
get a copy of the image, and convert it to 3-channel grayscale (I don't know if you need to convert the grayscale explicitly back to (colored) RGB...)
get a Mat for the ROI you want to have the colors in, once for the grayscale copy and once for the original color image
assign/copy the color image ROI to the grayscale image ROI
Indeed, it's exactly as #AndreyKamaev suggests:
#include <opencv2/core/core.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/highgui/highgui.hpp>
int main() {
char const * const fname_in = "lena.jpg";
char const * const fname_out = "lena_out.jpg";
cv::Mat img = cv::imread(fname_in, CV_LOAD_IMAGE_COLOR);
cv::Mat tmp;
cv::cvtColor(img, tmp, CV_BGR2GRAY);
cv::cvtColor(tmp, tmp, CV_GRAY2BGR);
cv::Rect roi(100, 100, 300, 300);
img(roi).copyTo(tmp(roi));
img = tmp;
cv::imwrite(fname_out, img);
}
Output image:
Basically the same as #moooeeeep suggests:
Mat tmp;
cvtColor(img, tmp, COLOR_BGR2GRAY);
cvtColor(tmp, tmp, COLOR_GRAY2BGR);
img(roi).copyTo(tmp(roi));
img = tmp;
To convert roi into grayscale in python you can
Convert the roi to gray
Replace roi by merging the gray, such that [gray, gray, gray] inplace of BGR
Now the roi is gray.
image = cv2.imread(image.jpg')
h, w, _ = image.shape
r, c, s = h//4, w//4, min(h,w)//2
gray_portion = cv2.bitwise_not(cv2.cvtColor(image[r:r+s, c:c+s], cv2.COLOR_BGR2GRAY))
merged = cv2.merge([gray_portion, gray_portion, gray_portion]) #IMPORTANT
image[r:r+s, c:c+s] = merged
cv2.imshow(image)
For those who want to the other way round :-)
Mat tmp;
cvtColor(img, tmp, COLOR_BGR2GRAY);
cvtColor(tmp, tmp, COLOR_GRAY2BGR);
tmp(roi).copyTo(img(roi));
tmp = img;
void doCorrectIntensityVariation(Mat& image)
{
Mat kernel = getStructuringElement(MORPH_ELLIPSE, Size(19,19));
Mat closed;
morphologyEx(image, closed, MORPH_CLOSE, kernel);
image.convertTo(image, CV_32F); // divide requires floating-point
divide(image, closed, image, 1, CV_32F);
normalize(image, image, 0, 255, NORM_MINMAX);
image.convertTo(image, CV_8UC1); // convert back to unsigned int
}
inline void correctIntensityVariation(IplImage *img)
{
//Mat imgMat(img); copy the img
Mat imgMat;
imgMat = img; //no copy is done, imgMat is a header of img
doCorrectIntensityVariation(imgMat);
imshow("gamma corrected",imgMat); cvWaitKey(0);
}
When I call
cvShowImage ("normal", n_im); cvWaitKey (0);
correctIntensityVariation(n_im);//here n_im is IplImage*
cvShowImage ("After processed", n_im); cvWaitKey (0);
// here I require n_im for further processing
I wanted "After processed" to be same as that of "gamma corrected" but what I found "After processed" was not the same as that of "gamma corrected" but same as that of "normal" . Why?? What is going wrong??
A very simple wrapper should do the job
Cheetsheet of openCV
I rarely use the old api, because Mat are much more easier to deal with, and
they do not have performance penalty when compare with the old c api.Like the openCV
tutorial page say The main downside of the C++ interface is that many embedded development systems at the moment support only C. Therefore, unless you are targeting embedded platforms, there’s no point to using the old methods (unless you’re a masochist programmer and you’re asking for trouble).
openCV tutorial
cv::Mat to Ipl
Ipl to cv::Mat and Mat to Ipl
IplImage* pImg = cvLoadImage(“lena.jpg”);
cv::Mat img(pImg,0); //transform Ipl to Mat, 0 means do not copy
IplImage qImg; //not pointer, it is impossible to overload the operator of raw pointer
qImg = IplImage(img); //transform Mat to Ipl
Edit : I did a mistake earlier, if the Mat would be reallocated in the function, you need
to copy or try to steal the resource(I don't know how to do it yet) from the Mat.
Copy the data
void doCorrectIntensityVariation(cv::Mat& image)
{
cv::Mat kernel = cv::getStructuringElement(cv::MORPH_ELLIPSE, cv::Size(19,19));
cv::Mat closed;
cv::morphologyEx(image, closed, cv::MORPH_CLOSE, kernel);
image.convertTo(image, CV_32F); // divide requires floating-point
cv::divide(image, closed, image, 1, CV_32F);
cv::normalize(image, image, 0, 255, cv::NORM_MINMAX);
image.convertTo(image, CV_8UC1); // convert back to unsigned int
}
//don't need to change the name of the function, the compiler treat
//these as different function in c++
void doCorrectIntensityVariation(IplImage **img)
{
cv::Mat imgMat;
imgMat = *img; //no copy is done, imgMat is a header of img
doCorrectIntensityVariation(imgMat);
IplImage* old = *img;
IplImage src = imgMat;
*img = cvCloneImage(&src);
cvReleaseImage(&old);
}
int main()
{
std::string const name = "onebit_31.png";
cv::Mat mat = cv::imread(name);
if(mat.data){
doCorrectIntensityVariation(mat);
cv::imshow("gamma corrected mat",mat);
cv::waitKey();
}
IplImage* templat = cvLoadImage(name.c_str(), 1);
if(templat){
doCorrectIntensityVariation(&templat);
cvShowImage("mainWin", templat);
// wait for a key
cvWaitKey(0);
cvReleaseImage(&templat);
}
return 0;
}
you could write a small function to alleviate the chores
void copy_mat_Ipl(cv::Mat const &src, IplImage **dst)
{
IplImage* old = *dst;
IplImage temp_src = src;
*dst = cvCloneImage(&temp_src);
cvReleaseImage(&old);
}
and call it in the function
void doCorrectIntensityVariation(IplImage **img)
{
cv::Mat imgMat;
imgMat = *img; //no copy is done, imgMat is a header of img
doCorrectIntensityVariation(imgMat);
copy_mat_to_Ipl(imgMat, img);
}
I will post how to "steal" the resource from Mat rather than copy after
I figure out a solid solution.Anyone know how to do it?
When doing:
IplImage blobimg = image;
IplImage *labelImg=cvCreateImage(cvGetSize(&blobimg), IPL_DEPTH_LABEL, 1);
IplImage *test=cvCreateImage(cvGetSize(&blobimg), IPL_DEPTH_8U, 3);
unsigned int result=cvLabel(&blobimg, labelImg, blobs);
cvRenderBlobs(labelImg, blobs, &blobimg,test,CV_BLOB_RENDER_BOUNDING_BOX);
Mat imgMat(test);
imshow("Depth", imgMat);
I notice that my test variable is empty :
I think I have to do this instead:
cvRenderBlobs(labelImg, blobs, &blobimg,&blobimg,CV_BLOB_RENDER_BOUNDING_BOX);
But cvRenderBlobs destImg has to have 3 channels and IPL_DEPTH_8U and my image has only 1 channel since it's a gray image.
Can someone tell me why this is and how I can fix this ?
Edit
Where image comes from:
Mat *depthImage = new Mat(480, 640, CV_8UC1, Scalar::all(0));
Mat image = *depthImage;
Will guess here but not too many times I've seen instances of IplImages that are not actually pointers. Are you sure that image, wherever it's coming from, isn't also a pointer to an IplImage struct?
IplImage *blobimg = image;
I use this portion of code in my project and it works, see if it can help:
//BYTE* blobMap = ... blobMap holds an image
CvMat mat = cvMat( HEIGHT, WIDTH, CV_8UC1, blobMap);
IplImage *img = cvCreateImage(cvSize(HEIGHT,WIDTH), IPL_DEPTH_8U, 1);
cvGetImage(&mat, img);
cvThreshold(img, img, 10, 255, CV_THRESH_BINARY);
IplImage *labelImg = cvCreateImage(cvGetSize(img),IPL_DEPTH_LABEL,1);
CvBlobs blobs;
unsigned int result = cvLabel(img, labelImg, blobs);
cvFilterByArea(blobs, 1000, 1680*HEIGHT);
IplImage *imgOut = cvCreateImage(cvGetSize(img), IPL_DEPTH_8U, 3);
cvRenderBlobs(labelImg, blobs, img, imgOut);
cvNamedWindow("test", 1);
cvShowImage("test", imgOut);
cvWaitKey(0);
cvDestroyWindow("test");
I also don't like the way you pass the Mat to an IplImage, are you sure that your input image (blobimg) is ok?
i was wondering how to use cvDCT void in opencv c++
if anyone have an example
The manual explains both the parameters and the inner workings of the function.
You can use the following code:
IplImage* src0 = cvLoadImage(strPath, CV_LOAD_IMAGE_GRAYSCALE);
IplImage* src = cvCreateImage(cvGetSize(src0), IPL_DEPTH_32F, 1);
cvConvert(src0, src);
IplImage* dst = cvCreateImage(cvGetSize(src0), IPL_DEPTH_32F, 1);
cvDCT(src, dst, 0);
//cvDCT (dst, src, 1);
//cvConvert(src, src0);
cvShowImage("Source", src0);