I'm creating a vision algorithm that is implemented in a Simulink S-function( which is c++ code). I accomplished every thing wanted except the alignment of the color and depth image.
My question is how can i make the 2 images correspond to each other. in other words how can i make a 3d image with opencv.
I know my question might be a little vague so i will include my code which will explain the question
#include "opencv2/opencv.hpp"
using namespace cv;
int main(int argc, char** argv)
{
// reading in the color and depth image
Mat color = imread("whitepaint_col.PNG", CV_LOAD_IMAGE_UNCHANGED);
Mat depth = imread("whitepaint_dep.PNG", CV_LOAD_IMAGE_UNCHANGED);
// show bouth the color and depth image
namedWindow("color", CV_WINDOW_AUTOSIZE);
imshow("color", color);
namedWindow("depth", CV_WINDOW_AUTOSIZE);
imshow("depth", depth);
// thershold the color image for the color white
Mat onlywhite;
inRange(color, Scalar(200, 200, 200), Scalar(255, 255, 255), onlywhite);
//display the mask
namedWindow("onlywhite", CV_WINDOW_AUTOSIZE);
imshow("onlywhite", onlywhite);
// apply the mask to the depth image
Mat nocalibration;
depth.copyTo(nocalibration, onlywhite);
//show the result
namedWindow("nocalibration", CV_WINDOW_AUTOSIZE);
imshow("nocalibration", nocalibration);
waitKey(0);
destroyAllWindows;
return 0;
}
output of the program:
As can be seen in the output of my program when i apply the onlywhite mask to the depth image the quad copter body does not consist out of 1 color. The reason for this is that there is a miss match between the 2 images.
I know that i need calibration parameters of my camera and i got these from the last person who worked with this setup. Did the calibration in Matlab and this resulted in the following.
Matlab calibration esults:
I have spent allot of time reading the following opencv page about Camera Calibration and 3D Reconstruction ( cannot include the link because of stack exchange lvl)
But i cannot for the life of me figure out how i could accomplish my goal of adding the correct depth value to each colored pixel.
I tried using reprojectImageTo3D() but i cannot figure out the Q matrix.
i also tried allot of other functions from that page but i cannot seem to get my inputs correct.
As far as I know, Matlab has very good support for Kinect (especially for v1). You may use a function named alignColorToDepth, as follows:
[alignedFlippedImage,flippedDepthImage] = alignColorToDepth(depthImage,colorImage,depthDevice)
The returned values are alignedFlippedImage (the RGB registrated image) and flippedDepthImage (the registrated depth image). These two images are aligned and ready for you to process them.
You can find more at this MathWorks documentation page.
Hope it's what you need :)
As far as I can tell, you are missing the transformation between camera coordinate frames. The Kinect (v1 and v2) uses two separate camera systems to capture the depth and RGB data, and so there is a translation and rotation between them. You may be able to assume no rotation, but you will have to account for the translation to fix the misalignment you are seeing.
Try starting with this thread.
Related
First of all: I'm new to opencv :-)
I want to perform a vignetting correction on a 24bit RGB Image. I used an area scan camera as a line camera and put together an image from 1780x2 px parts to get an complete image with 1780x3000 px. Because of the vignetting, i made a white reference picture with 1780x2 px to calculate a LUT (with correction factor in it) for the vignetting removal. Here is my code idea:
Mat white = imread("WHITE_REF_2L.bmp", 0);
Mat lut(2, 1780, CV_8UC3, Scalar(0));
lut = 255 / white;
imwrite("lut_test.bmp", lut*white);
As i understood, what the second last line will (hopefully) do, is to divide 255 with every intensity value of every channel and store this in the lut matrice.
I want to use that lut then to to calculate the “real” (not distorted) intensity
level of each pixel by multiplying every element of the src img with every element of the lut matrice.
obviously its not working how i want to do it, i get a memory exception.
Can anybody help me with that problem?
edit: i'm using opencv 3.1.0 and i solved the problem like this:
// read white reference image
Mat white = imread("WHITE_REF_2L_D.bmp", IMREAD_COLOR);
white.convertTo(white, CV_32FC3);
// calculate LUT with vignetting correction factors
Mat vLUT(2, 1780, CV_32FC3, Scalar(0.0f));
divide(240.0f, white, vLUT);
of course that's not optimal, i will read in more white references and calculate the mean value to optimize.
Here's the 2 lines white reference, you can see the shadows at the image borders i want to correct
when i multiply vLUT with the white reference i obviously get a homogenous image as the result.
thanks, maybe this can help anyone else ;)
I'm trying to analyse some images which have a lot of noise around the outside of the image, but a clear circular centre with a shape inside. The centre is the part I'm interested in, but the outside noise is affecting my binary thresholding of the image.
To ignore the noise, I'm trying to set up a circular mask of known centre position and radius whereby all pixels outside this circle are changed to black. I figure that everything inside the circle will now be easy to analyse with binary thresholding.
I'm just wondering if someone might be able to point me in the right direction for this sort of problem please? I've had a look at this solution: How to black out everything outside a circle in Open CV but some of my constraints are different and I'm confused by the method in which source images are loaded.
Thank you in advance!
//First load your source image, here load as gray scale
cv::Mat srcImage = cv::imread("sourceImage.jpg", CV_LOAD_IMAGE_GRAYSCALE);
//Then define your mask image
cv::Mat mask = cv::Mat::zeros(srcImage.size(), srcImage.type());
//Define your destination image
cv::Mat dstImage = cv::Mat::zeros(srcImage.size(), srcImage.type());
//I assume you want to draw the circle at the center of your image, with a radius of 50
cv::circle(mask, cv::Point(mask.cols/2, mask.rows/2), 50, cv::Scalar(255, 0, 0), -1, 8, 0);
//Now you can copy your source image to destination image with masking
srcImage.copyTo(dstImage, mask);
Then do your further processing on your dstImage. Assume this is your source image:
Then the above code gives you this as gray scale input:
And this is the binary mask you created:
And this is your final result after masking operation:
Since you are looking for a clear circular center with a shape inside, you could use Hough Transform to get that area- a careful selection of parameters will help you get this area perfectly.
A detailed tutorial is here:
http://docs.opencv.org/doc/tutorials/imgproc/imgtrans/hough_circle/hough_circle.html
For setting pixels outside a region black:
Create a mask image :
cv::Mat mask(img_src.size(),img_src.type());
Mark the points inside with white color :
cv::circle( mask, center, radius, cv::Scalar(255,255,255),-1, 8, 0 );
You can now use bitwise_AND and thus get an output image with only the pixels enclosed in mask.
cv::bitwise_and(mask,img_src,output);
I am pretty new to OpenCV and I am stuck at the moment. I am dealing with images, not a video. Since I will have same background in my project, I thought it would be easier to work, if I could remove my background. But first, I have to ask one thing. Can I use BackgroundSubtractorMOG2 for images? Because it is under video analysis/motion analysis title.
I read the documentation on opencv.org and looked through countless examples/tutorials but I am still having difficulty understanding how MOG2 works.
Quick question: What is history that in parameters?
So, I have written a simple code. I get a foreground mask. So, what is the next step? How can I remove the background and left with my object only? Shouldn't I load my background first, then the actual image, so that MOG2 could do the background subtraction?
I am using OpenCV 2.4.11.
Code:
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/video/background_segm.hpp>
using namespace cv;
using namespace std;
//global variables
int history = 1;
float varThreshold = 16;
bool bShadowDetection = true;
Mat src; //source image
Mat fgMaskMOG2; //fg mask generated by MOG2 method
Ptr<BackgroundSubtractor> pMOG2; //MOG2 Background subtractor
int main(int argc, char* argv[])
{
//create GUI windows
namedWindow("Source");
namedWindow("FG Mask MOG 2");
src = imread("bluePaper1.png", 1);
//create Background Subtractor objects
pMOG2 = new BackgroundSubtractorMOG2(history, varThreshold, bShadowDetection); //MOG2 approach
pMOG2->setInt("nmixtures", 3);
pMOG2->setDouble("fTau", 0.5);
pMOG2->operator()(src, fgMaskMOG2);
imshow("Source", src);
imshow("FG Mask MOG 2", fgMaskMOG2);
waitKey(0);
return 0;
}
Source image:
fgMask that I get from MOG2:
Mixture of Gaussian method learns background according to history of frames in a fixed camera and so you can not use it for only one image. The history parameter shows how many frames would have effect on construction of the background.
Shadow detection is not a process which depends on BGS method and should be implemented alongside.
for example in MOG2 documentation we have:
The shadow is detected if the pixel is a darker version of the background. Tau is a threshold defining how much darker the shadow can be. Tau= 0.5 means that if a pixel is more than twice darker then it is not shadow
In case of your example the foreground could easily be obtained by a simple frame difference and you can easily remove shadows by the mentioned solution.
you can have the foreground by the following steps:
Subtract given image from known background and threshold the result to obtain the foreground mask
Apply AND operation on foreground mask and the given image to get your object with possible shadows.
Remove pixels which is darker (amount of it should be adjust) than their corresponding pixel in background.
Do some post processing like morphological and connected-component-labeling to have a better result.
I have done a stereo calibration and I got the validPixROI1 and 2 (green border). Now I want to use StereoSGBM but the rois from calibration (from stereoRectify) are not the same size. Anyone know how to solve this?
Actually I do somethine linke this:
Rect roiLeft(...);
Rect roiRight(...);
Mat cLeft(rLeft, roiLeft);
//Mat cRight(rRight, roiRight); // not same size...
Mat cRight(cRight, roiLeft);
stereoBM(cLeft,cRight, dst);
If I crop my images with that roi, will be the picture middle point be the same?
Here it works.
Why not run stereoBM on the (uncropped)calibrated images, then you can use those ROIs after to mask out the invalid bits of the result...
stereoBM(rLeft,rRight, disp);
//get intersection of both rois or use target image roi, if you know the target image
cv::Rect visibleRoi = roiLeft & roiRight;
cv::Mat cDisp(disp,visibleRoi);
Now you have no issues with different size inputs, or different centers and such.
Cheers
According to wiki
A point R at the intersection of the optical axis and the image plane. This point is referred to as the principal point or image center.
So I don't think the center will be same.
Refer to this site . Here in one of the examples the principal point is 302.71656,242.33386 for a 640x480 pixel camera which shows that the principal point and the image center are not the same.
Run the block matcher on the uncropped rectified images and then use.
cv::getValidDisparityROI(roi1, roi2, minDisparity, numberOfDisparities, SADWindowSize);
That call returns a cv::Rect that will be a bounding box for all the valid pixels in the left image and the disparity map. The valid pixels are only pixels that both cameras can "see" (caveat on occluded edges).
Once you have the disparity map the right image becomes useless.
Be aware that the roi's returned from stereoRectify are just valid pixels after the remap from the cameras intrinsics.
I've an image (.jpg image), and I want to extract the background from the original image. I've googled a lot but have only found tutorials of extracting foreground image.
I've taken the code from another stackoverflow question. The code is working fine for me, and I've successfully extracted the foreground (as per my requirements). Now I want to completely remove this foreground from the original image. I want it to be something like this:-
Background = Original Image - Foreground
The empty space can be filled with black or white color. How can I achieve this?
I've tried using this technique:-
Mat background = image2 - foreground;
but it gives a complete black image.
Code:-
#include <opencv2/opencv.hpp>
#include <iostream>
using namespace cv;
using namespace std;
int main( )
{
// Open another image
Mat image;
image= cv::imread("images/abc.jpg");
Mat image2 = image.clone();
// define bounding rectangle
cv::Rect rectangle(40,90,image.cols-80,image.rows-170);
cv::Mat result; // segmentation result (4 possible values)
cv::Mat bgModel,fgModel; // the models (internally used)
// GrabCut segmentation
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
cout << "oks pa dito" <<endl;
// Get the pixels marked as likely foreground
cv::compare(result,cv::GC_PR_FGD,result,cv::CMP_EQ);
// Generate output image
cv::Mat foreground(image.size(),CV_8UC3,cv::Scalar(255,255,255));
//cv::Mat background(image.size(),CV_8UC3,cv::Scalar(255,255,255));
image.copyTo(foreground,result); // bg pixels not copied
// draw rectangle on original image
cv::rectangle(image, rectangle, cv::Scalar(255,255,255),1);
imwrite("img_1.jpg",image);
imwrite("Foreground.jpg",foreground);
Mat background = image2 - foreground;
imwrite("Background.jpg",background);
return 0;
}
Note: I'm an opencv beginner and don't have much knowledge of it right now. I shall be very thankful to you if you can either post the complete code (as required by me) or just post the lines of code and tell me where these lines of code be placed. Thanks.
P.S. This is my second question at StackOverflow.com. apologies ... if not following any convention.
Instead of copying all the pixels that are foreground, it copies all pixels which are not foreground. You can do this by using ~, which negates the mask:
image.copyTo(background,~result);
What if you //Get the pixels marked as likely background:
// Get the pixels marked as likely background
cv::compare(result,cv::GC_PR_BGD,result,cv::CMP_EQ);
Edit: The above code is missing GC_BGD pixels. Despite a more efficient answer was given, let's finish what we started:
// Get the pixels marked as background
cv::compare(result,cv::GC_BGD,result_a,cv::CMP_EQ);
// Get the pixels marked as likely background
cv::compare(result,cv::GC_PR_BGD,result_b,cv::CMP_EQ);
// Final results
result=result_a+result_b;
Just a small suggestion，#William's
answer can be written more concisely as：
result = result & 1;
in order to get the binary mask.
Maybe another example helps, in which I assumed that the middle portion of the image is definitely foreground.
So try this link.
Example