I am creating a stitching program and it works well. My next task is to crop the image so the image doesn't get too large and only contains the stitched areas. I am able to crop the image using the threshold() and findContours() functions. I then parse through the contours to determine where to crop. Another issue I wish to tackle in this function is to set the background to be transparent or alpha(255). My logic is take the base image to crop and do this step in conjunction with finding the area to crop. My code follows with portions that were taken from these two resources:
http://answers.opencv.org/question/37441/how-to-set-transparent-background-to-grabcut-output-image-in-opencv-c/
http://answers.opencv.org/question/24463/how-to-remove-black-background-from-grabcut-output-image-in-opencv-android/#24508
Mat crop(Mat image){
Mat cropped, grayed, thresh, transparent, result;
vector<vector<Point>> contours;
vector<Vec4i> hierarchy;
int largest_contour_index=0;
int largest_area=0;
Mat alpha(image.size(),CV_8UC1,Scalar(0));
cvtColor(image, grayed, CV_BGR2GRAY);
threshold( grayed, thresh, 1, 255,THRESH_BINARY);
findContours( thresh, contours, hierarchy, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);
vector<int> x,y;
for(int i=0; i < contours.size(); i++){
for(int j = 0; j < contours.at(i).size();j++){
double a=contourArea( contours[i],false); // Find the area of contour
if(a>largest_area){
largest_area=a;
largest_contour_index=i; //Store the index of largest contour
}
x.push_back(contours[i][j].x);
y.push_back(contours[i][j].y);
}
}
drawContours( alpha,contours, largest_contour_index, Scalar(255),CV_FILLED, 8, hierarchy );
Mat rgb[3];
split(image,rgb);
Mat rgba[4]={rgb[0],rgb[1],rgb[2],alpha};
merge(rgba,4,transparent);
imshow("Transparent",transparent);
waitKey();
destroyAllWindows();
auto xVals = std::minmax_element(x.begin(), x.end());
auto yVals = std::minmax_element(y.begin(), y.end());
Rect rect (*xVals.first,*yVals.first,(*xVals.second)-(*xVals.first),(*yVals.second)-(*yVals.first));
cropped = image(rect);
return cropped;
}
The following image is the result of this code. As you can see the crop work but the image background is not transparent
Any help is appreciated!
Related
I have a question which i am unable to resolve. I am taking difference of two images using OpenCV. I am getting output in a seperate Mat. Difference method used is the AbsDiff method. Here is the code.
char imgName[15];
Mat img1 = imread(image_path1, COLOR_BGR2GRAY);
Mat img2 = imread(image_path2, COLOR_BGR2GRAY);
/*cvtColor(img1, img1, CV_BGR2GRAY);
cvtColor(img2, img2, CV_BGR2GRAY);*/
cv::Mat diffImage;
cv::absdiff(img2, img1, diffImage);
cv::Mat foregroundMask = cv::Mat::zeros(diffImage.rows, diffImage.cols, CV_8UC3);
float threshold = 30.0f;
float dist;
for(int j=0; j<diffImage.rows; ++j)
{
for(int i=0; i<diffImage.cols; ++i)
{
cv::Vec3b pix = diffImage.at<cv::Vec3b>(j,i);
dist = (pix[0]*pix[0] + pix[1]*pix[1] + pix[2]*pix[2]);
dist = sqrt(dist);
if(dist>threshold)
{
foregroundMask.at<unsigned char>(j,i) = 255;
}
}
}
sprintf(imgName,"D:/outputer/d.jpg");
imwrite(imgName, diffImage);
I want to bound the difference part in a rectangle. findContours is drawing too many contours. but i only need a particular portion. My diff image is
I want to draw a single rectangle around all the five dials.
Please point me to right direction.
Regards,
I would search for the highest value for i index giving a non black pixel; that's the right border.
The lowest non black i is the left border. Similar for j.
You can:
binarize the image with a threshold. Background will be 0.
Use findNonZero to retrieve all points that are not 0, i.e. all foreground points.
use boundingRect on the retrieved points.
Result:
Code:
#include <opencv2/opencv.hpp>
using namespace cv;
int main()
{
// Load image (grayscale)
Mat1b img = imread("path_to_image", IMREAD_GRAYSCALE);
// Binarize image
Mat1b bin = img > 70;
// Find non-black points
vector<Point> points;
findNonZero(bin, points);
// Get bounding rect
Rect box = boundingRect(points);
// Draw (in color)
Mat3b out;
cvtColor(img, out, COLOR_GRAY2BGR);
rectangle(out, box, Scalar(0,255,0), 3);
// Show
imshow("Result", out);
waitKey();
return 0;
}
Find contours, it will output a set of contours as std::vector<std::vector<cv::Point> let us call it contours:
std::vector<cv::Point> all_points;
size_t points_count{0};
for(const auto& contour:contours){
points_count+=contour.size();
all_points.reserve(all_points);
std::copy(contour.begin(), contour.end(),
std::back_inserter(all_points));
}
auto bounding_rectnagle=cv::boundingRect(all_points);
I am using OpenCV C++ to find the contours in a video. I want to count the no of contours present in video in a a specified region or in between two lines drawn in a video. For example a stream of contours are moving in a video and I want to count them when they reach to a specific region in a video. I will decrease the count as they leave the specific region in the video. I know some basic stuffs to find contour, calculate the area etc. But I am not getting any programming tips to count the no of contours within specified region. Please help me with the related topics and some tips on programming. (I do not want to use cvBlob.h library)
Basically I am counting the number of cars entered in that region. If car is entered I will increment the count and if it leaves the region then I will decrease the count.
You can approximate the contour you found to a polygon or circle:
for( int i = 0; i < contours.size(); i++ )
{ approxPolyDP( Mat(contours[i]), contours_poly[i], 3, true );
boundRect[i] = boundingRect( Mat(contours_poly[i]) )
}
after that use the line equation y=a and compare the coordinates of the corners of rectangle or the center+radius of the circle to determine if the contour passed the line.
1.Use a part of your Mat image.(If your ROI is rectangle)
Mat src; // suppose this is your source frame
Mat src_of_interest = src(Rect(Point(x1, y1), Point(x2, y2)));
// Do the rest of finding contour..
vector<vector<Point>> contours;
vector<Vec4i> hierarchy;
findContours(src_of_interest.clone(), contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE);
//now you can do your job with contour vector.
int count = contours.size();
Mat dst = Mat::zeros(src.size(), CV_8UC3);
for(int i=0; i< count; i++)
{
drawContours(dst, contours, i, CV_RGB(255, 0, 0)); // draw contour in red
}
2. If your Region of Interest is not rectangle, try this approach:
vector<Point> contour_of_interest; // this contour is where you want to check
vector<vector<Point>> contours; // this is the contours you found
Mat dst = Mat::zeros(src.size(), CV_8U);
for(int i=0; i< count; i++)
{
drawContours(dst, contours, i, Scalar(255)); // set contour area to 255
}
Mat roi = Mat::zeros(src.size(), CV_8U);
vector<vector<Point>> coi_vector;
coi_vector.push_back(contour_of_interest);
drawContours(roi, coi_vector, 0, Scalar(255));
Mat and = dst & roi; // where 2 mats are both TRUE(not zero)
findContours(and.clone(), contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE);
//now you can do your job with contour vector.
int count = contours.size();
3. If you want to count contours between 4 points, try this approach:
vector<Point> contour_of_interest; // this contour is the area where you want to check
Point p1(x1, y1);
Point p2(x1, y2);
Point p3(x2, y2);
Point p4(x2, y1);
contour_of_interest.push_back(p1);
contour_of_interest.push_back(p2);
contour_of_interest.push_back(p3);
contour_of_interest.push_back(p4);
vector<vector<Point>> coi_list;
coi_list.push_back(contour_of_interest);
Mat mask = Mat:zeros(src.size(), CV_8U);
drawContours(mask, coi_list, 0, Scalar(255));
Mat src; // suppose this is your source frame
Mat src_of_interest = src & mask; // remove any pixels outside mask area
// Do the rest of finding contour..
vector<vector<Point>> contours;
vector<Vec4i> hierarchy;
findContours(src_of_interest.clone(), contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE);
//now you can do your job with contour vector.
int count = contours.size();
Mat dst = Mat::zeros(src.size(), CV_8UC3);
for(int i=0; i< count; i++)
{
drawContours(dst, contours, i, CV_RGB(255, 0, 0)); // draw contour in red
}
I have a photo where a person holds a sheet of paper. I'd like to detect the rectangle of that sheet of paper.
I have tried following different tutorials from OpenCV and various SO answers and sample code for detecting squares / rectangles, but the problem is that they all rely on contours of some kind.
If I follow the squares.cpp example, I get the following results from contours:
As you can see, the fingers are part of the contour, so the algorithm does not find the square.
I, also, tried using HoughLines() approach, but I get similar results to above:
I can detect the corners, reliably though:
There are other corners in the image, but I'm limiting total corners found to < 50 and the corners for the sheet of paper are always found.
Is there some algorithm for finding a rectangle from multiple corners in an image? I can't seem to find an existing approach.
You can apply a morphological filter to close the gaps in your edge image. Then if you find the contours, you can detect an inner closed contour as shown below. Then find the convexhull of this contour to get the rectangle.
Closed edges:
Contour:
Convexhull:
In the code below I've just used an arbitrary kernel size for morphological filter and filtered out the contour of interest using an area ratio threshold. You can use your own criteria instead of those.
Code
Mat im = imread("Sh1Vp.png", 0); // the edge image
Mat kernel = getStructuringElement(MORPH_ELLIPSE, Size(11, 11));
Mat morph;
morphologyEx(im, morph, CV_MOP_CLOSE, kernel);
int rectIdx = 0;
vector<vector<Point>> contours;
vector<Vec4i> hierarchy;
findContours(morph, contours, hierarchy, CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE, Point(0, 0));
for (size_t idx = 0; idx < contours.size(); idx++)
{
RotatedRect rect = minAreaRect(contours[idx]);
double areaRatio = abs(contourArea(contours[idx])) / (rect.size.width * rect.size.height);
if (areaRatio > .95)
{
rectIdx = idx;
break;
}
}
// get the convexhull of the contour
vector<Point> hull;
convexHull(contours[rectIdx], hull, false, true);
// visualization
Mat rgb;
cvtColor(im, rgb, CV_GRAY2BGR);
drawContours(rgb, contours, rectIdx, Scalar(0, 0, 255), 2);
for(size_t i = 0; i < hull.size(); i++)
{
line(rgb, hull[i], hull[(i + 1)%hull.size()], Scalar(0, 255, 0), 2);
}
I am trying to track a custom circular marker in an image, and I need to check that a circle contains a minimum number of other circles/objects. My code for finding circles is below:
void findMarkerContours( int, void* )
{
vector<vector<Point> > contours;
vector<Vec4i> hierarchy;
vector<Point> approx;
cv::Mat dst = src.clone();
cv::Mat src_gray;
cv::cvtColor(src, src_gray, CV_BGR2GRAY);
//Reduce noise with a 3x3 kernel
blur( src_gray, src_gray, Size(3,3));
//Convert to binary using canny
cv::Mat bw;
cv::Canny(src_gray, bw, thresh, 3*thresh, 3);
imshow("bw", bw);
findContours(bw.clone(), contours, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE);
Mat drawing = Mat::zeros( bw.size(), CV_8UC3 );
for (int i = 0; i < contours.size(); i++)
{
Scalar color = Scalar( rng.uniform(0, 255), rng.uniform(0,255), rng.uniform(0,255) );
// contour
drawContours( drawing, contours, i, color, 1, 8, vector<Vec4i>(), 0, Point() );
//Approximate the contour with accuracy proportional to contour perimeter
cv::approxPolyDP(cv::Mat(contours[i]), approx, cv::arcLength(cv::Mat(contours[i]), true) *0.02, true);
//Skip small or non-convex objects
if(fabs(cv::contourArea(contours[i])) < 100 || !cv::isContourConvex(approx))
continue;
if (approx.size() >= 8) //More than 6-8 vertices means its likely a circle
{
drawContours( dst, contours, i, Scalar(0,255,0), 2, 8);
}
imshow("Hopefully we should have circles! Yay!", dst);
}
namedWindow( "Contours", CV_WINDOW_AUTOSIZE );
imshow( "Contours", drawing );
}
As you can see the code to detect circles works quite well:
But now I need to filter out markers that I do not want. My marker is the bottom one. So once I have found a contour that is a circle, I want to check if there are other circular contours that exist within the region of the first circle and finally check the color of the smallest circle.
What method can I take to say if (circle contains 3+ smaller circles || smallest circle is [color] ) -> do stuff?
Take a look at the documentation for
findContours(InputOutputArray image, OutputArrayOfArrays contours, OutputArray hierarchy, int mode, int method, Point offset=Point())
You'll see that there's an optional hierarchy output vector which should be handy for your problem.
hierarchy – Optional output vector, containing information about the image topology. It has as many elements as the number of contours.
For each i-th contour contours[i] , the elements hierarchy[i][0] ,
hiearchyi , hiearchyi , and hiearchyi are set to
0-based indices in contours of the next and previous contours at the
same hierarchical level, the first child contour and the parent
contour, respectively. If for the contour i there are no next,
previous, parent, or nested contours, the corresponding elements of
hierarchy[i] will be negative.
When calling findCountours using CV_RETR_TREE you'll be getting the full hierarchy of each contour that was found.
This doc explains the hierarchy format pretty well.
You are already searching for circles of a certain size
//Skip small or non-convex objects
if(fabs(cv::contourArea(contours[i])) < 100 || !cv::isContourConvex(approx))
continue;
So you can use that to look for smaller circles than the one youve got, instead of looking for < 100 look for contours.size
I imagine there is the same for color also...
I want use findContours with the binary image, but the callback function causes an error:
Invalid address specified to RtlFreeHeap
when returning.
When i want to use clear() to free the vector<vector<Point> > value, it causes the same exception and the code crashed in free.c at the line:
if (retval == 0) errno = _get_errno_from_oserr(GetLastError());
For example:
void onChangeContourMode(int, void *)
{
Mat m_frB = imread("3.jpg", 0);
vector<vector<Point>> contours
vector<Vec4i> hierarchy;
findContours(m_frB, contours, hierarchy, g_contour_mode, CV_CHAIN_APPROX_SIMPLE);
for( int idx = 0 ; idx >= 0; idx = hierarchy[idx][0] )
drawContours( m_frB, contours, idx, Scalar(255,255,255),
CV_FILLED, 8, hierarchy );
imshow( "Contours", m_frB );
}
Can anyone help me? Thank you very much!
Mat m_frB = imread("3.jpg", CV_LOAD_IMAGE_GRAYSCALE);
loads 3.jpg as a 8bpp grayscale image, so it's not binary image. It is specific for findContours function that "non-zero pixels are treated as 1’s. Zero pixels remain 0’s, so the image is treated as binary". Also note that this "function modifies the image while extracting the contours".
The actual problem here is that although the destination image is 8bpp, you should make sure that it has 3 channels by using CV_8UC3 before you draw RGB contours into it. Try this:
// find contours:
vector<vector<Point> > contours;
vector<Vec4i> hierarchy;
findContours(m_frB, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE);
// draw contours:
Mat imgWithContours = Mat::zeros(m_frB.rows, m_frB.cols, CV_8UC3);
RNG rng(12345);
for (int i = 0; i < contours.size(); i++)
{
Scalar color = Scalar(rng.uniform(50, 255), rng.uniform(50,255), rng.uniform(50,255));
drawContours(imgWithContours, contours, i, color, 1, 8, hierarchy, 0);
}
imshow("Contours", imgWithContours);