I have a 200x200 pixels image and I want to keep only the data for a certain region inside it.
Check the following image:
The whole out square is 200x200 pixels.I want to remove from it the smaller square(white).So ,keep only the information that is included in the blue area.But , I want to keep the 200x200 dimensions.
I tried:
Mat whiteArea;
whiteArea = ImageInitial( Range(50,200) , Range(50,200) );
Size size(200,200);
Mat dst;
resize(whiteArea,dst,size);
Mat FinalImage;
subtract(ImageInitial,dst,FinalImage);
I am resizing the white area because I want to substract it from the initial image.
My problem is that it gives me the initial image.
Maybe the resize is the problem .but then how to substract 2 different sized images?
try to use subimages or use a mask:
// use a roi (nice if your target area is rectangular and you know the position)
Rect whiteArea = Rect(50,50, 200,200); // creates a roi of the inner rect
Mat FinalImage = ImageInitial.clone();
// now set the roi area to zero:
FinalImage (whiteArea).setTo(Scalar(0,0,0));
// or FinalImage(whiteArea) = FinalImage(whiteArea) - FinalImage(whiteArea);
imshow("version 1 with subimage", FinalImage);
waitkey(0);
// or use a mask (nice if that region can has arbitrary shape etc and you have to extract it first):
Scalar lowerColorBound = Scalar(x,y,z); //some BGR values to find the color you want to eliminate
Scalar upperColorBound = Scalar(a,b,c); //some BGR values to find the color you want to eliminate
Mat mask;
inRange(ImageInitial, lowerColorBound, upperColorBound mask)
// use the mask for subtraction:
subtract(ImageInitial, ImageInitial, FinalImage , mask);
imshow("version 2 with mask", FinalImage);
waitkey(0);
Related
I've got a Mat image which is a binary mask that I segmented and a cv::Rect that identifies an specific region. When I get the contours of the binary mask the image is like this:
Binary Mask
Contours generated
I would like to fill in the mask the region that intersects with the rectangle. How would I do that?
Thanks in advance.
There is way simpler than #ZdaR's solution: using Regions Of Interest (ROI) which directly selects the bounding rectangle region to process.
cv::Rect boundingRect(/* x, y, width, height */);
contours_image(boundingRect).setTo(255, binary_image(boundingRect));
Here, I select each region with operator parenthesis contours_image(boundingRect) and binary_image(boundingRect), and use the binary image part as a mask to set all corresponding pixels to 255.
A good choice would be to use cv::min() with the binary image and another cv::Mat() with the area under cv::Rect() painted as white. It will filter out the desired portion under the Rect as:
// Create a grayscale canvas with black background
cv::Mat canvas = cv::Mat(binary_img.size(), CV_8UC1, cv::Scalar(0));
// I created a dummy rect replace it with original rect coordinates.
cv::Rect boundingRect = cv::Rect(100, 100, 200, 200);
// Draw filled rect onto the black canvas with white color
cv::rectangle(binary_image, boundingRect, cv::Scalar(255), -1);
// Take the min of binary image and canvas to filter out the contours
cv::min(binary_image, canvas, binary_image);
EDIT:
If you want to filter the contours which intersect the cv::Rect, then you need to iterate over each contour, calculate the boundingRect and check if it intersects the given rect.
for (int i=0; i<contours.size(); i++) {
if ((cv::boundingRect(contours[i]) & boundingRect).area() > 0) {
// Your desired contours found.
}
}
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)
So far i have managed to use masks and get the second image from the first. But what i want is the black area in second image to be transparent (i.e the output i an trying to get is the third image) Here is the code so far. Please advice me on this.
EDIT: Third one is from photoshop
//imwrite parameters
compression_params.push_back(CV_IMWRITE_JPEG_QUALITY);
compression_params.push_back(100);
//reading image to be masked
image = imread(main_img, -1);
//CV_LOAD_IMAGE_COLOR
namedWindow("output", WINDOW_NORMAL);
//imshow("output", image);
//Creating mask image with same size as original image
Mat mask(image.rows, image.cols, CV_8UC1, Scalar(0));
// Create Polygon from vertices
ROI_Vertices.push_back(Point2f(float(3112),float(58)));
ROI_Vertices.push_back(Point2f(float(3515),float(58)));
ROI_Vertices.push_back(Point2f(float(3515),float(1332)));
ROI_Vertices.push_back(Point2f(float(3112),float(958)));
approxPolyDP(ROI_Vertices, ROI_Poly, 1, true);
// Fill polygon white
fillConvexPoly(mask, &ROI_Poly[0] , ROI_Poly.size(), 255, 8, 0);
//imshow("output", mask);
// Create new image for result storage
imageDest = cvCreateMat(image.rows, image.cols, CV_8UC4);
// Cut out ROI and store it in imageDest
image.copyTo(imageDest, mask);
imwrite("masked.jpeg", imageDest, compression_params);
imshow("output", imageDest);
cvWaitKey(0);
This can be done by first setting its alpha value to 0 of the regions that you want to make them fully transparent (255 for others), and then save it to PNG.
To set the alpha value of pixel-(x,y), it can be done:
image.at<cv::Vec4b>(y, x)[3] = 0;
PS: you need to convert it to 4-channel format first if the image is not currently. For example:
cv::cvtColor(image, image, CV_BGR2BGRA);
Updated: It will be easier if you have already computed the mask for the ROI region, where you can simply merge it with the original image (assume having 3 channels) to get the final result. Like:
cv::Mat mask; // 0 for transparent regions, 255 otherwise (serve as the alpha channel)
std::vector<cv::Mat> channels;
cv::split(image, channels);
channels.push_back(mask);
cv::Mat result;
cv::merge(channels, result);
The problem is solved....I used cvGet2D,below is the sample code
CvScalar s;
s=cvGet2D(src_Image,pixel[i].x,pixel[i].y);
cvSet2D(dst_Image,pixel[i].x,pixel[i].y,s);
Where src_Iamge and dst_Image is the source and destination image correspondingly and pixel[i] is the selected pixel i wanted to draw in the dst image. I have include the real out image below.
have an source Ipl image, I want to copy some of the part of the image to a new destination image pixel by pixel. can any body tell me how can do it? I use c,c++ in opencv. For example if the below image is source image,
The real output image
EDIT:
I can see the comments suggesting cvGet2d. I think, if you just want to show "points", it is best to show them with a small neighbourhood so they can be seen where they are. For that you can draw white filled circles with origins at (x,y), on a mask, then you do the copyTo.
using namespace cv;
Mat m(input_iplimage);
Mat mask=Mat::zeros(m.size(), CV_8UC1);
p1 = Point(x,y);
r = 3;
circle(mask,p1,r, 1); // draws the circle around your point.
floodFill(mask, p1, 1); // fills the circle.
//p2, p3, ...
Mat output = Mat::zeros(m.size(),m.type()); // output starts with a black background.
m.copyTo(output, mask); // copies the selected parts of m to output
OLD post:
Create a mask and copy those pixels:
#include<opencv2/opencv.hpp>
using namespace cv;
Mat m(input_iplimage);
Mat mask=Mat::zeros(m.size(), CV_8UC1); // set mask 1 for every pixel you wanna copy.
Rect roi=Rect(x,y,width,height); // create a rectangle
mask(roi) = 1; // set it to 0.
roi = Rect(x2,y2,w2,h2);
mask(roi)=1; // set the second rectangular area for copying...
Mat output = 100*Mat::ones(m.size(),m.type()); // output with a gray background.
m.copyTo(output, mask); // copy selected areas of m to output
Alternatively you can copy Rect-by-Rect:
Mat m(input_iplimage);
Mat output = 100*Mat::ones(m.size(),m.type()); // output with a gray background.
Rect roi=Rect(x,y,width,height);
Mat m_temp, out_temp;
m_temp=m(roi);
out_temp = output(roi);
m_temp.copyTo(out_temp);
roi=Rect(x2,y2,w2,h2);
Mat m_temp, out_temp;
m_temp=m(roi);
out_temp = output(roi);
m_temp.copyTo(out_temp);
The answer to your question only requires to have look at the OpenCV documentation or just to search in your favourite search engine.
Here you've an answer for Ipl images and for newer Mat data.
For having an output as I see in your images, I'd do it setting ROI's, it's more efficient.
I am developing some image processing tools in iOS. Currently, I have a contour of features computed, which is of type InputArrayOfArrays.
Declared as:
std::vector<std::vector<cv::Point> > contours_final( temp_contours.size() );
Now, I would like to extract areas of the original RGB picture circled by contours and may further store sub-image as cv::Mat format. How can I do that?
Thanks in advance!
I'm guessing what you want to do is just extract the regions in the the detected contours. Here is a possible solution:
using namespace cv;
int main(void)
{
vector<Mat> subregions;
// contours_final is as given above in your code
for (int i = 0; i < contours_final.size(); i++)
{
// Get bounding box for contour
Rect roi = boundingRect(contours_final[i]); // This is a OpenCV function
// Create a mask for each contour to mask out that region from image.
Mat mask = Mat::zeros(image.size(), CV_8UC1);
drawContours(mask, contours_final, i, Scalar(255), CV_FILLED); // This is a OpenCV function
// At this point, mask has value of 255 for pixels within the contour and value of 0 for those not in contour.
// Extract region using mask for region
Mat contourRegion;
Mat imageROI;
image.copyTo(imageROI, mask); // 'image' is the image you used to compute the contours.
contourRegion = imageROI(roi);
// Mat maskROI = mask(roi); // Save this if you want a mask for pixels within the contour in contourRegion.
// Store contourRegion. contourRegion is a rectangular image the size of the bounding rect for the contour
// BUT only pixels within the contour is visible. All other pixels are set to (0,0,0).
subregions.push_back(contourRegion);
}
return 0;
}
You might also want to consider saving the individual masks to optionally use as a alpha channel in case you want to save the subregions in a format that supports transparency (e.g. png).
NOTE: I'm NOT extracting ALL the pixels in the bounding box for each contour, just those within the contour. Pixels that are not within the contour but in the bounding box are set to 0. The reason is that your Mat object is an array and that makes it rectangular.
Lastly, I don't see any reason for you to just save the pixels in the contour in a specially created data structure because you would then need to store the position for each pixel in order to recreate the image. If your concern is saving space, that would not save you much space if at all. Saving the tightest bounding box would suffice. If instead you wish to just analyze the pixels in the contour region, then save a copy of the mask for each contour so that you can use it to check which pixels are within the contour.
You are looking for the cv::approxPolyDP() function to connect the points.
I shared a similar use of the overall procedure in this post. Check the for loop after the findContours() call.
I think what you're looking for is cv::boundingRect().
Something like this:
using namespace cv;
Mat img = ...;
...
vector<Mat> roiVector;
for(vector<vector<Point> >::iterator it=contours.begin(); it<contours.end(); it++) {
if (boundingRect( (*it)).area()>minArea) {
roiVector.push_back(img(boundingRect(*it)));
}
}
cv::boundingRect() takes a vector of Points and returns a cv::Rect. Initializing a Mat myRoi = img(myRect) gives you a pointer to that part of the image (so modifying myRoi will ALSO modify img).
See more here.