I got this image and I'd like to fill the upper left black area with white, but all I get is a completely white image … Any ideas what's wrong with my code?
Code:
...
cv::Rect rect;
roi = cv::floodFill(roi, cv::Point(1,1), cv::Scalar(0), &rect, cv::Scalar(0), cv::Scalar(0), 4);
...
Input image:
This is the image I get with the following code:
int main()
{
cv::Mat image = cv::imread("TF2XE.jpg", -1);
cv::imshow("image before filling", image);
int filling = cv::floodFill(image, cv::Point(0,0), 255, (cv::Rect*)0, cv::Scalar(), 200);
cv::imshow("image after filling", image);
cv::waitKey();
return 0;
}
Notice that I used 200 as upDiff parameter, since if you set it to 0 there will be some gray pixels that will not be considered inside the connected component, change that if that is indeed what you want.
Not sure, but according to the documentation I was able to dig up, it says that cv::floodFill() returns an int. So assuming that roi is a matrix and the openCV matrix class defines operator= for int parameters, you could be assigning some int to each element of the matrix.
Related
I am trying to get the difference between two images using the following code:
Mat getWithoutBackground(Mat img, Mat back)
{
_backgroundSubVal = 50;
cv::Mat diff;
cv::absdiff(img, back, diff);
cv::Mat diff1Channel;
cv::cvtColor(diff, diff1Channel, CV_BGRA2GRAY);
cv::Mat mask = diff1Channel > _backgroundSubVal;
cv::Mat data(img.size(), CV_8UC4, Scalar(255,255,255,0));
// copy masked area
img.copyTo(data, mask);
cv::imwrite(""1.png", data);
return similarRegions;
}
first is the background:
second is the actual image:
and here is the result:
and I am almost getting the correct solution but i would have liked to have a transparent background instead of black.
(I dont want to remove all of the picture's black colors)
I thought that the 4'th parameter in the Scalar should have done the trick for having a transparent background but i guess that I am missing something..
cv::Mat data(img.size(), CV_8UC4, Scalar(255,255,255,0));
any ideas what am i doing wrong?
I create a Bird-View-Image with the warpPerspective()-function like this:
warpPerspective(frame, result, H, result.size(), CV_WARP_INVERSE_MAP, BORDER_TRANSPARENT);
The result looks very good and also the border is transparent:
Bird-View-Image
Now I want to put this image on top of another image "out". I try doing this with the function warpAffine like this:
warpAffine(result, out, M, out.size(), CV_INTER_LINEAR, BORDER_TRANSPARENT);
I also converted "out" to a four channel image with alpha channel according to a question which was already asked on stackoverflow:
Convert Image
This is the code: cvtColor(out, out, CV_BGR2BGRA);
I expected to see the chessboard but not the gray background. But in fact, my result looks like this:
Result Image
What am I doing wrong? Do I forget something to do? Is there another way to solve my problem? Any help is appreciated :)
Thanks!
Best regards
DamBedEi
I hope there is a better way, but here it is something you could do:
Do warpaffine normally (without the transparency thing)
Find the contour that encloses the image warped
Use this contour for creating a mask (white values inside the image warped, blacks in the borders)
Use this mask for copy the image warped into the other image
Sample code:
// load images
cv::Mat image2 = cv::imread("lena.png");
cv::Mat image = cv::imread("IKnowOpencv.jpg");
cv::resize(image, image, image2.size());
// perform warp perspective
std::vector<cv::Point2f> prev;
prev.push_back(cv::Point2f(-30,-60));
prev.push_back(cv::Point2f(image.cols+50,-50));
prev.push_back(cv::Point2f(image.cols+100,image.rows+50));
prev.push_back(cv::Point2f(-50,image.rows+50 ));
std::vector<cv::Point2f> post;
post.push_back(cv::Point2f(0,0));
post.push_back(cv::Point2f(image.cols-1,0));
post.push_back(cv::Point2f(image.cols-1,image.rows-1));
post.push_back(cv::Point2f(0,image.rows-1));
cv::Mat homography = cv::findHomography(prev, post);
cv::Mat imageWarped;
cv::warpPerspective(image, imageWarped, homography, image.size());
// find external contour and create mask
std::vector<std::vector<cv::Point> > contours;
cv::Mat imageWarpedCloned = imageWarped.clone(); // clone the image because findContours will modify it
cv::cvtColor(imageWarpedCloned, imageWarpedCloned, CV_BGR2GRAY); //only if the image is BGR
cv::findContours (imageWarpedCloned, contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_NONE);
// create mask
cv::Mat mask = cv::Mat::zeros(image.size(), CV_8U);
cv::drawContours(mask, contours, 0, cv::Scalar(255), -1);
// copy warped image into image2 using the mask
cv::erode(mask, mask, cv::Mat()); // for avoid artefacts
imageWarped.copyTo(image2, mask); // copy the image using the mask
//show images
cv::imshow("imageWarpedCloned", imageWarpedCloned);
cv::imshow("warped", imageWarped);
cv::imshow("image2", image2);
cv::waitKey();
One of the easiest ways to approach this (not necessarily the most efficient) is to warp the image twice, but set the OpenCV constant boundary value to different values each time (i.e. zero the first time and 255 the second time). These constant values should be chosen towards the minimum and maximum values in the image.
Then it is easy to find a binary mask where the two warp values are close to equal.
More importantly, you can also create a transparency effect through simple algebra like the following:
new_image = np.float32((warp_const_255 - warp_const_0) *
preferred_bkg_img) / 255.0 + np.float32(warp_const_0)
The main reason I prefer this method is that openCV seems to interpolate smoothly down (or up) to the constant value at the image edges. A fully binary mask will pick up these dark or light fringe areas as artifacts. The above method acts more like true transparency and blends properly with the preferred background.
Here's a small test program that warps with transparent "border", then copies the warped image to a solid background.
int main()
{
cv::Mat input = cv::imread("../inputData/Lenna.png");
cv::Mat transparentInput, transparentWarped;
cv::cvtColor(input, transparentInput, CV_BGR2BGRA);
//transparentInput = input.clone();
// create sample transformation mat
cv::Mat M = cv::Mat::eye(2,3, CV_64FC1);
// as a sample, just scale down and translate a little:
M.at<double>(0,0) = 0.3;
M.at<double>(0,2) = 100;
M.at<double>(1,1) = 0.3;
M.at<double>(1,2) = 100;
// warp to same size with transparent border:
cv::warpAffine(transparentInput, transparentWarped, M, transparentInput.size(), CV_INTER_LINEAR, cv::BORDER_TRANSPARENT);
// NOW: merge image with background, here I use the original image as background:
cv::Mat background = input;
// create output buffer with same size as input
cv::Mat outputImage = input.clone();
for(int j=0; j<transparentWarped.rows; ++j)
for(int i=0; i<transparentWarped.cols; ++i)
{
cv::Scalar pixWarped = transparentWarped.at<cv::Vec4b>(j,i);
cv::Scalar pixBackground = background.at<cv::Vec3b>(j,i);
float transparency = pixWarped[3] / 255.0f; // pixel value: 0 (0.0f) = fully transparent, 255 (1.0f) = fully solid
outputImage.at<cv::Vec3b>(j,i)[0] = transparency * pixWarped[0] + (1.0f-transparency)*pixBackground[0];
outputImage.at<cv::Vec3b>(j,i)[1] = transparency * pixWarped[1] + (1.0f-transparency)*pixBackground[1];
outputImage.at<cv::Vec3b>(j,i)[2] = transparency * pixWarped[2] + (1.0f-transparency)*pixBackground[2];
}
cv::imshow("warped", outputImage);
cv::imshow("input", input);
cv::imwrite("../outputData/TransparentWarped.png", outputImage);
cv::waitKey(0);
return 0;
}
I use this as input:
and get this output:
which looks like ALPHA channel isn't set to ZERO by warpAffine but to something like 205...
But in general this is the way I would do it (unoptimized)
I just start to learn opencv, I have defined a vector like:
vector<Point2f> cornersB;
and after that i have done some calculations like:goodFeaturesToTrack,cornerSubPix and calcOpticalFlowPyrLK using cornersB.
And now I want to show cornerB to see the points that has been drawn, my code is:
pointmat = Mat(cornersB);
imshow("Window", pointmat);
But I got error said that bad number of channels (Source image must have 1, 3 or 4 channels) in cvConvertImage.
Anyone can teach me how to show the points of cornerB in an image?
I just want to see the points (points in white and the background in black).
The simpler is to use cv::drawKeypoints
drawKeypoints( InputArray image, const std::vector<KeyPoint>& keypoints, InputOutputArray outImage,const Scalar& color=Scalar::all(-1), int flags=DrawMatchesFlags::DEFAULT );
In your case, let define a black image as image:
cv::Mat image(512,512,CV_8U)
image.setTo(0);
Then convert cornersB to cv::KeyPoint kp_cornerB and define the color as white with CV_RGB(255, 255, 255)
std::vector<cv::KeyPoint> kp_cornerB ;
// TODO convert cornersB to kp_cornerB
cv::Mat pointmat;
cv::drawKeypoints(image, kp_cornerB, pointmat, CV_RGB(255, 255, 255));
imshow("Window", pointmat);
The conversion can be done with a for loop on the vector:
for(vector<Point2f>::const_iterator it = cornersB.begin();
it != cornersB.end(); it++) {
cv::KeyPoint kp(*it, 8);
kp_cornerB.push_back(kp);
}
Here, the value '8' is the 'size' of the keypoint.
The implementation of this functionality seems pretty straightforward in Python, as shown here: http://docs.opencv.org/trunk/doc/py_tutorials/py_imgproc/py_grabcut/py_grabcut.html
Yet, when I tried to do exactly the same in C++, I get bad arguments error (for the grabcut function). How to put the mask image in the right format?
I am a newbie at this, so I'd be very thankful if someone could help me understand better. Thank you!
Here's what I have so far:
Mat image;
image= imread(file);
Mat mask;
mask.setTo( GC_BGD );
mask = imread("messi5.png");
Mat image2 = image.clone();
// define bounding rectangle
cv::Rect rectangle(startX, startY, width, height);
cv::Mat result; // segmentation result (4 possible values)
cv::Mat bgModel,fgModel; // the models (internally used)
//// GrabCut segmentation that works, but with a rectangle, not with the mask I need
//cv::grabCut(image, // input image
// result, // segmentation result
// rectangle,// rectangle containing foreground
// bgModel,fgModel, // models
// 1, // number of iterations
// cv::GC_INIT_WITH_RECT); // use rectangle
grabCut( image, mask, rectangle, bgModel, fgModel, 1, GC_INIT_WITH_MASK);
cv::compare(mask,cv::GC_PR_FGD,mask,cv::CMP_EQ);
cv::Mat foreground(image.size(),CV_8UC3,cv::Scalar(255,255,255));
image.copyTo(foreground,mask); // bg pixels not copied
namedWindow( "Display window", WINDOW_AUTOSIZE );
imshow( "Display window", foreground );
waitKey(0);
return 0;
}
It looks like you have misunderstood the guide, repeated here from the linked guide in the question:
# newmask is the mask image I manually labelled
newmask = cv2.imread('newmask.png',0)
# whereever it is marked white (sure foreground), change mask=1
# whereever it is marked black (sure background), change mask=0
mask[newmask == 0] = 0
mask[newmask == 255] = 1
mask, bgdModel, fgdModel = cv2.grabCut(img,mask,None,bgdModel,fgdModel,5,cv2.GC_INIT_WITH_MASK)
mask = np.where((mask==2)|(mask==0),0,1).astype('uint8')
img = img*mask[:,:,np.newaxis]
plt.imshow(img),plt.colorbar(),plt.show()
this is not what you have done i'm afraid. For a start you seem to have set the mask to the rgb image:
mask = imread("messi5.png");
whereas is should be set to the mask image:
mask = imread("newmask.png",CV_LOAD_IMAGE_GRAYSCALE);
EDIT from comments:
from a pure red mask painted over the image (an actual mask would be better).
maskTmp = imread("messi5.png");
std::vector<cv::Mat> channels(3)
split( messi5, channels);
cv::Mat maskRed = channels[2];
now threshold on the red channel to get your binary mask.
I would like to ask which is the most efficient way to set a region of a grayscale Mat image to zeros (or any other constant value, for that matter).
Should I create a zeros image and then use copyTo() or is there a better way?
I would use setTo(), for example:
// load an image
cv::Mat pImage = cv::imread("someimage.jpg", CV_LOAD_IMAGE_COLOR);
// select a region of interest
cv::Mat pRoi = pImage(cv::Rect(10, 10, 20, 20));
// set roi to some rgb colour
pRoi.setTo(cv::Scalar(blue, green, red));
Let's say we paint a black rectangle in a white canvas:
cv::Mat img(100,100,CV_8U,cv::Scalar(255));
img(cv::Rect(15,15,20,40))=0;
cv::imshow("Img",img);
cv::waitKey();
Try the following code
Mat image;
image = imread("images/lena.jpg");
int x=100;int y=100; int w=100; int h=100;
Rect roi = Rect(x,y,w,h);
image(roi).setTo(cv::Scalar(0,0,0));
imshow("display",image);