I'm having difficulties using ROI using opencv c++.
I have a sequence of images which are stored in a vector. The vector image contained big blob and small blob. I want to remove the small blobs for every vector image. However, there is something wrong with the output result where if the small blobs in current vector image was removed, it will affect the blobs region for the next vector image (and previous vector image). Is there something wrong with ROI opencv c++? Below is sample code:
vector<Mat> finalImg;
for(unsigned int i = 0 ; i < srcImg.size(); i++) {
vector<vector<Point> > contoursFinal;
vector<Vec4i> hierarchyFinal;
Mat tempV_img;
srcImg[i].copyTo(tempV_img);
cv::findContours( tempV_img, contoursFinal, hierarchyFinal, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_NONE, Point(0,0) );
for(unsigned int j = 0; j < contoursFinal.size(); j++) {
Rect r = cv::boundingRect( contoursFinal[j] );
int heightChar = r.height;
/// Set image region of interest
cv::Rect ROI(r.x-1, r.y-1, r.width+2, r.height+2);
Mat srcImg_crop = srcImg[i](ROI);
cv::namedWindow("cropImg (bf)", 0);
cv::imshow("cropImg (bf)", srcImg_crop);
if(heightChar < srcImg[i].rows*0.90){
srcImg_crop.setTo(0);
}
cv::namedWindow("cropImg (af)", 0);
cv::imshow("cropImg (af)", srcImg_crop);
cv::waitKey(0);
if(cv::countNonZero(srcImg_crop) != 0) {
finalImg.push_back(srcImg_crop);
}
srcImg_crop.release();
}
cv::namedWindow("Sorted Final", 0);
cv::imshow("Sorted Final", finalImg[i]);
cv::waitKey(0);
contoursFinal.clear();
hierarchyFinal.clear();
}
Sorry all,
I just figured it out. Below shows the trick.
vector<Mat> tempV;
tempV.clear();
for(unsigned int i = 0 ; i < srcImg.size(); i++) {
Mat temp;
srcImg[i].copyTo(temp);
tempV.push_back(temp);
temp.release();
}
Instead of using srcImg[i]. I replace it with a new vector tempV[i]. Then it will not affect the previous as well as next vector image.
Related
I am trying to run the followin code (based on this page) on an image, but it doesn't work:
Mat src=imread("img.jpg",1);
Mat tmp,thr;
cvtColor(src,tmp,CV_BGR2GRAY);
threshold(tmp,thr,200,255,THRESH_BINARY_INV);
vector< vector <Point> > contours;
vector< Vec4i > hierarchy;
Mat dst(src.rows,src.cols,CV_8UC1,Scalar::all(0));//Ceate Mat to draw contour
int box_w=10; // Define box width here
int box_h=10; // Define box height here
int threshold_perc=25; //perceantage value for eliminating the box according to pixel count inside the box
int threshold=(box_w*box_h*threshold_perc)/100;
findContours( thr, contours, hierarchy,CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE ); //Find contour
for( int i = 0; i< contours.size(); i++ ){
drawContours( dst,contours, i, Scalar(255,255,255),CV_FILLED, 8, hierarchy ); // Draw contour with thickness = filled
Rect r= boundingRect(contours[i]); // Find bounding rect
// Scan the image with in bounding box
for(int j=r.x;j<r.x+r.width;j=j+box_w){
for(int k=r.y;k<r.y+r.height;k=k+box_h){
Rect roi_rect(j,k,box_w,box_h);
Mat roi = dst(roi_rect);
int count = countNonZero(roi);
if(count > threshold)
rectangle(src, roi_rect, Scalar(255,0,0),1,8,0 );
}
}
}
imshow("src",src);
waitKey();
It works fine for any normal image, but for the images below, it either breaks or doesn't find the contour and draws boxes all over the image.
It says:
Unhandled exception at 0x00007FF9A72DA388 in test2.exe: Microsoft C++ exception: cv::Exception at memory location 0x000000FECC9DEAC0.
It breaks and points to here:
inline
Mat Mat::operator()( const Rect& roi ) const
{
return Mat(*this, roi);
}
in mat.inl.hpp.
What is wrong with my image? I have changed it from Gray-scale to RGB, but didn't help.
On the following image, it works fine:
As I commented, you're trying to access a region of the image that doesn't exist by using a rectangle of fixed size.
By intersecting the roi with the rectangle, you can avoid this problem:
Mat roi = dst(roi_rect & r);
The problem was that in the first images, the contour gets close to the boundaries of the image and in the bottom for loop of the program, it exceeds the coordinates. It was fixed with this:
// Scan the image with in bounding box
for (int j = r.x;j<r.x + r.width;j = j + box_w) {
for (int k = r.y;k<r.y + r.height;k = k + box_h) {
Rect roi_rect(j, k, box_w, box_h);
if (j + box_w < dst.cols && k + box_h < dst.rows)
{
Mat roi = dst(roi_rect);
int count = countNonZero(roi);
if (count > threshold)
rectangle(src, roi_rect, Scalar(0,0,255), 1, 8, 0);
}
}
}
I'm using OpenCV2.4.8.2 on Mac OS 10.9.5.
I have the following snippet of code:
static void compute_weights(const vector<Mat>& images, vector<Mat>& weights)
{
weights.clear();
for (int i = 0; i < images.size(); i++) {
Mat image = images[i];
Mat mask = Mat::zeros(image.size(), CV_32F);
int x_start = (i == 0) ? 0 : image.cols/2;
int y_start = 0;
int width = image.cols/2;
int height = image.rows;
Mat roi = mask(Rect(x_start,y_start,width,height)); // Set Roi
roi.setTo(1);
weights.push_back(mask);
}
}
static void blend(const vector<Mat>& inputImages, Mat& outputImage)
{
int maxPyrIndex = 6;
vector<Mat> weights;
compute_weights(inputImages, weights);
// Find the fused pyramid:
vector<Mat> fused_pyramid;
for (int i = 0; i < inputImages.size(); i++) {
Mat image = inputImages[i];
// Build Gaussian Pyramid for Weights
vector<Mat> weight_gaussian_pyramid;
buildPyramid(weights[i], weight_gaussian_pyramid, maxPyrIndex);
// Build Laplacian Pyramid for original image
Mat float_image;
inputImages[i].convertTo(float_image, CV_32FC3, 1.0/255.0);
vector<Mat> orig_guassian_pyramid;
vector<Mat> orig_laplacian_pyramid;
buildPyramid(float_image, orig_guassian_pyramid, maxPyrIndex);
for (int j = 0; j < orig_guassian_pyramid.size() - 1; j++) {
Mat sized_up;
pyrUp(orig_guassian_pyramid[j+1], sized_up, Size(orig_guassian_pyramid[j].cols, orig_guassian_pyramid[j].rows));
orig_laplacian_pyramid.push_back(orig_guassian_pyramid[j] - sized_up);
}
// Last Lapalcian layer is the same as the Gaussian layer
orig_laplacian_pyramid.push_back(orig_guassian_pyramid[orig_guassian_pyramid.size()-1]);
// Convolve laplacian original with guassian weights
vector<Mat> convolved;
for (int j = 0; j < maxPyrIndex + 1; j++) {
// Create 3 channels for weight gaussian pyramid as well
vector<Mat> gaussian_3d_vec;
for (int k = 0; k < 3; k++) {
gaussian_3d_vec.push_back(weight_gaussian_pyramid[j]);
}
Mat gaussian_3d;
merge(gaussian_3d_vec, gaussian_3d);
//Mat convolved_result = weight_gaussian_pyramid[j].clone();
Mat convolved_result = gaussian_3d.clone();
multiply(gaussian_3d, orig_laplacian_pyramid[j], convolved_result);
convolved.push_back(convolved_result);
}
if (i == 0) {
fused_pyramid = convolved;
} else {
for (int j = 0; j < maxPyrIndex + 1; j++) {
fused_pyramid[j] += convolved[j];
}
}
}
// Blending
for (int i = (int)fused_pyramid.size()-1; i > 0; i--) {
Mat sized_up;
pyrUp(fused_pyramid[i], sized_up, Size(fused_pyramid[i-1].cols, fused_pyramid[i-1].rows));
fused_pyramid[i-1] += sized_up;
}
Mat final_color_bgr;
fused_pyramid[0].convertTo(final_color_bgr, CV_32F, 255);
final_color_bgr.copyTo(outputImage);
imshow("final", outputImage);
waitKey(0);
imwrite(outputImagePath, outputImage);
}
This code is doing some basic pyramid blending for 2 images. The key issues are related to imshow and imwrite in the last line. They gave me drastically different results. I apologize for displaying such a long/messy code, but I am afraid this difference is coming from some other parts of the code that can subsequently affect the imshow and imwrite.
The first image shows the result from imwrite and the second image shows the result from imshow, based on the code given. I'm quite confused about why this is the case.
I also noticed that when I do these:
Mat float_image;
inputImages[i].convertTo(float_image, CV_32FC3, 1.0/255.0);
imshow("float image", float_image);
imshow("orig image", image);
They show exactly the same thing, that is they both show the same picture in the original rgb image (in image).
IMWRITE functionality
By default, imwrite, converts the input image into Only 8-bit (or 16-bit unsigned (CV_16U) in case of PNG, JPEG 2000, and TIFF) single-channel or 3-channel (with ‘BGR’ channel order) images can be saved using this function.
So whatever format you feed in for imwrite, it blindly converts into CV_8U with a range 0(black) - 255(white) in BGR format.
IMSHOW - problem
So when noticed your function, fused_pyramid[0].convertTo(final_color_bgr, CV_32F, 255); fused_pyramid is already under mat type 21 (floating point CV_32F). You tried to convert into floating point with a scale factor 255. This scaling factor 255 caused the problem # imshow. Instead to visualize, you can directly feed in fused_pyramid without conversion as already it is scaled to floating point between 0.0(black) - 1.0(white).
Hope it helps.
I need to find the number of inner holes in the below image.i.e my ultimate requirement is to detect and find the area of round shape black holes alone using contour hierarchy in opencv.No need to use any other algorithms.
Based on this link Using hierarchy in findContours () in OpenCV? i tried but it won't worked.
is there any other method to find the no of holes in the image?
here with i have attached the sample image and code.Can anybody give idea to find the inner black holes alone using hierarchy.I don't have a much experience in contour hierarchy.Thanks in advance.
i used opencv c++ lib.
cv::Mat InputImage = imread("New Image.jpg");
int Err;
if(InputImage.empty() == 1)
{
InputImage.release();
cout<<"Error:Input Image Not Loaded"<<endl;
return 1;
}
cv::Mat greenTargetImage;
std::vector<cv::Mat> Planes;
cv::split(InputImage,Planes);
greenTargetImage = Planes[1];
cv::Mat thresholdImage = cv::Mat (greenTargetImage.size(),greenTargetImage.type());
cv::threshold(greenTargetImage,thresholdImage,128,255,THRESH_OTSU);
imwrite("thresholdImage.jpg",thresholdImage);
std::vector<std::vector<cv::Point>> contours;
std::vector<cv::Vec4i> hierarchy;
cv::findContours(thresholdImage,contours,hierarchy,cv::RETR_CCOMP,cv::CHAIN_APPROX_SIMPLE,cv::Point(-1,-1));
cout<<contours.size()<<endl;
cout<<hierarchy.size()<<endl;
int count = 0;
if (!contours.empty() && !hierarchy.empty())
{
for (int i = 0;i<contours.size();i++ )
{
if ( hierarchy[i][3] != -1)
{
cv::drawContours(InputImage,contours,i,CV_RGB(0,255,0),3);
count = count+1;
}
}
}
cout<<count<<endl; //No of inner holes in same level
imwrite("ContourImage.jpg",InputImage);
After applying this code i got the output count value is 11.But my requirement is count value should be 10 and also i need to draw only inner black holes alone not all boundaries of outer contours.Sorry for my english.
Try this code works fine for me using hierarchy.
The idea is simple, just consider the contour which doesn’t have child.
That is
hierarchy[i][2]= -1
code:-
Mat tmp,thr;
Mat src=imread("img.jpg",1);
cvtColor(src,tmp,CV_BGR2GRAY);
threshold(tmp,thr,200,255,THRESH_BINARY_INV);
namedWindow("thr",0);
imshow("thr",thr);
vector< vector <Point> > contours; // Vector for storing contour
vector< Vec4i > hierarchy;
Mat dst(src.rows,src.cols,CV_8UC1,Scalar::all(0)); //create destination image
int count=0;
findContours( thr, contours, hierarchy,CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE ); // Find the contours in the image
for( int i = 0; i< contours.size(); i=hierarchy[i][0] ) // iterate through each contour.
{
Rect r= boundingRect(contours[i]);
if(hierarchy[i][2]<0){
rectangle(src,Point(r.x,r.y), Point(r.x+r.width,r.y+r.height), Scalar(0,0,255),3,8,0);
count++;
}
}
cout<<"Numeber of contour = "<<count<<endl;
imshow("src",src);
imshow("contour",dst);
waitKey();
Result:-
I'm trying to rewrite my very slow naive segmentation using floodFill to something faster. I ruled out meanShiftFiltering a year ago because of the difficulty in labelling the colours and then finding their contours.
The current version of opencv seems to have a fast new function that labels segments using mean shift: gpu::meanShiftSegmentation(). It produces images like the following:
(source: ekran.org)
So this looks to me pretty close to being able to generating contours. How can I run findContours to generate segments?
Seems to me, this would be done by extracting the labelled colours from the image, and then testing which pixel values in the image match each label colour to make a boolean image suitable for findContours. This is what I have done in the following (but its a bit slow and strikes me there should be a better way):
Mat image = imread("test.png");
...
// gpu operations on image resulting in gpuOpen
...
// Mean shift
TermCriteria iterations = TermCriteria(CV_TERMCRIT_ITER, 2, 0);
gpu::meanShiftSegmentation(gpuOpen, segments, 10, 20, 300, iterations);
// convert to greyscale (HSV image)
vector<Mat> channels;
split(segments, channels);
// get labels from histogram of image.
int size = 256;
labels = Mat(256, 1, CV_32SC1);
calcHist(&channels.at(2), 1, 0, Mat(), labels, 1, &size, 0);
// Loop through hist bins
for (int i=0; i<256; i++) {
float count = labels.at<float>(i);
// Does this bin represent a label in the image?
if (count > 0) {
// find areas of the image that match this label and findContours on the result.
Mat label = Mat(channels.at(2).rows, channels.at(2).cols, CV_8UC1, Scalar::all(i)); // image filled with label colour.
Mat boolImage = (channels.at(2) == label); // which pixels in labeled image are identical to this label?
vector<vector<Point>> labelContours;
findContours(boolImage, labelContours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);
// Loop through contours.
for (int idx = 0; idx < labelContours.size(); idx++) {
// get bounds for this contour.
bounds = boundingRect(labelContours[idx]);
// create ROI for bounds to extract this region
Mat patchROI = image(bounds);
Mat maskROI = boolImage(bounds);
}
}
}
Is this the best approach or is there a better way to get the label colours? Seems it would be logical for meanShiftSegmentation to provide this information? (vector of colour values, or vector of masks for each label, etc.)
Thank you.
Following is another way of doing this without thowing away the colour information in the meanShiftSegmentation results. I did not compare the two for performance.
// Loop through whole image, pixel and pixel and then use the colour to index an array of bools indicating presence.
vector<Scalar> colours;
vector<Scalar>::iterator colourIter;
vector< vector< vector<bool> > > colourSpace;
vector< vector< vector<bool> > >::iterator colourSpaceBIter;
vector< vector<bool> >::iterator colourSpaceGIter;
vector<bool>::iterator colourSpaceRIter;
// Initialize 3D Vector
colourSpace.resize(256);
for (int i = 0; i < 256; i++) {
colourSpace[i].resize(256);
for (int j = 0; j < 256; j++) {
colourSpace[i][j].resize(256);
}
}
// Loop through pixels in the image (should be fastish, look into LUT for faster)
uchar r, g, b;
for (int i = 0; i < segments.rows; i++)
{
Vec3b* pixel = segments.ptr<Vec3b>(i); // point to first pixel in row
for (int j = 0; j < segments.cols; j++)
{
b = pixel[j][0];
g = pixel[j][1];
r = pixel[j][2];
colourSpace[b][g][r] = true; // this colour is in the image.
//cout << "BGR: " << int(b) << " " << int(g) << " " << int(r) << endl;
}
}
// Get all the unique colours from colourSpace
// loop through colourSpace
int bi=0;
for (colourSpaceBIter = colourSpace.begin(); colourSpaceBIter != colourSpace.end(); colourSpaceBIter++) {
int gi=0;
for (colourSpaceGIter = colourSpaceBIter->begin(); colourSpaceGIter != colourSpaceBIter->end(); colourSpaceGIter++) {
int ri=0;
for (colourSpaceRIter = colourSpaceGIter->begin(); colourSpaceRIter != colourSpaceGIter->end(); colourSpaceRIter++) {
if (*colourSpaceRIter)
colours.push_back( Scalar(bi,gi,ri) );
ri++;
}
gi++;
}
bi++;
}
// For each colour
int segmentCount = 0;
for (colourIter = colours.begin(); colourIter != colours.end(); colourIter++) {
Mat label = Mat(segments.rows, segments.cols, CV_8UC3, *colourIter); // image filled with label colour.
Mat boolImage = Mat(segments.rows, segments.cols, CV_8UC3);
inRange(segments, *colourIter, *colourIter, boolImage); // which pixels in labeled image are identical to this label?
vector<vector<Point> > labelContours;
findContours(boolImage, labelContours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);
// Loop through contours.
for (int idx = 0; idx < labelContours.size(); idx++) {
// get bounds for this contour.
Rect bounds = boundingRect(labelContours[idx]);
float area = contourArea(labelContours[idx]);
// Draw this contour on a new blank image
Mat maskImage = Mat::zeros(boolImage.rows, boolImage.cols, boolImage.type());
drawContours(maskImage, labelContours, idx, Scalar(255,255,255), CV_FILLED);
Mat patchROI = frame(bounds);
Mat maskROI = maskImage(bounds);
}
segmentCount++;
}
I'm working on a face recognition project and I am having problems when projecting on PCA subspace.
When I pass a mat vector to my funcion with the resized images, I project them, and then I reconstruct them to verify it's working well, but all I have in "Cam" window is a grey image (all same color).
I don't know what I am doing bad.
This is the function:
void doPCA (const vector<Mat>& images)
{
int nEigens = images.size()-1;
Mat data (images.size(), images[0].rows*images[0].cols, images[0].type() );
for (int i = 0; i < images.size(); i++)
{
Mat aux = data.row(i);
images[i].reshape(1,1).copyTo(aux);
}
PCA pca(data,Mat(),CV_PCA_DATA_AS_ROW,nEigens);
//Project images
Mat dataprojected(data.rows, nEigens, CV_32FC1) ;
for(int i=0; i<images.size(); i++)
{
pca.project(data.row(i), dataprojected.row(i));
}
//Backproject to reconstruct images
Mat datareconstructed (data.rows, data.cols, data.type());
for(int i=0; i<images.size(); i++)
{
pca.backProject (dataprojected.row(i), datareconstructed.row(i) );
}
for(int i=0; i<images.size(); i++)
{
imshow ("Cam", datareconstructed.row(i).reshape(1,images[0].rows) );
waitKey();
}
}
I think this post is a duplicate of:
PCA + SVM using C++ Syntax in OpenCV 2.2
Ah, I have found the error in your code. When you create the data matrix you do:
images[i].reshape(1,1).copyTo(aux);
You have to use convertTo to convert the data into the correct type and copy it to your data matrix:
images[i].reshape(1,1).convertTo(aux, CV_32FC1, 1/255.);
Then the normalized eigenvectors should be ok. And don't forget to to normalize the values between 0 and 255 before displaying them, you can use cv::normalize to do this, here's a simple function for turning it into grayscale:
Mat toGrayscale(const Mat& src) {
Mat srcnorm;
cv::normalize(src, srcnorm, 0, 255, NORM_MINMAX, CV_8UC1);
return srcnorm;
}
You may want to look at the example in my blog:
http://bytefish.de/blog/pca_in_opencv#simple_example