So I am very new to OpenCV (2.1), so please keep that in mind.
So I managed to calibrate my cheap web camera that I am using (with a wide angle attachment), using the checkerboard calibration method to produce the intrinsic and distortion coefficients.
I then have no trouble feeding these values back in and producing image maps, which I then apply to a video feed to correct the incoming images.
I run into an issue however. I know when it is warping/correcting the image, it creates several skewed sections, and then formats the image to crop out any black areas. My question then is can I view the complete warped image, including some regions that have black areas? Below is an example of the black regions with skewed sections I was trying to convey if my terminology was off:
An image better conveying the regions I am talking about can be found here! This image was discovered in this post.
Currently: The cvRemap() returns basically the yellow box in the image linked above, but I want to see the whole image as there is relevant data I am looking to get out of it.
What I've tried: Applying a scale conversion to the image map to fit the complete image (including stretched parts) into frame
CvMat *intrinsic = (CvMat*)cvLoad( "Intrinsics.xml" );
CvMat *distortion = (CvMat*)cvLoad( "Distortion.xml" );
cvInitUndistortMap( intrinsic, distortion, mapx, mapy );
cvConvertScale(mapx, mapx, 1.25, -shift_x); // Some sort of scale conversion
cvConvertScale(mapy, mapy, 1.25, -shift_y); // applied to the image map
cvRemap(distorted,undistorted,mapx,mapy);
The cvConvertScale, when I think I have aligned the x/y shift correctly (guess/checking), is somehow distorting the image map making the correction useless. There might be some math involved here I am not correctly following/understanding.
Does anyone have any other suggestions to solve this problem, or what I might be doing wrong? I've also tried trying to write my own code to fix distortion issues, but lets just say OpenCV knows already how to do it well.
From memory, you need to use InitUndistortRectifyMap(cameraMatrix,distCoeffs,R,newCameraMatrix,map1,map2), of which InitUndistortMap is a simplified version.
cvInitUndistortMap( intrinsic, distort, map1, map2 )
is equivalent to:
cvInitUndistortRectifyMap( intrinsic, distort, Identity matrix, intrinsic,
map1, map2 )
The new parameters are R and newCameraMatrix. R species an additional transformation (e.g. rotation) to perform (just set it to the identity matrix).
The parameter of interest to you is newCameraMatrix. In InitUndistortMap this is the same as the original camera matrix, but you can use it to get that scaling effect you're talking about.
You get the new camera matrix with GetOptimalNewCameraMatrix(cameraMat, distCoeffs, imageSize, alpha,...). You basically feed in intrinsic, distort, your original image size, and a parameter alpha (along with containers to hold the result matrix, see documentation). The parameter alpha will achieve what you want.
I quote from the documentation:
The function computes the optimal new camera matrix based on the free
scaling parameter. By varying this parameter the user may retrieve
only sensible pixels alpha=0, keep all the original image pixels if
there is valuable information in the corners alpha=1, or get something
in between. When alpha>0, the undistortion result will likely have
some black pixels corresponding to “virtual” pixels outside of the
captured distorted image. The original camera matrix, distortion
coefficients, the computed new camera matrix and the newImageSize
should be passed to InitUndistortRectifyMap to produce the maps for
Remap.
So for the extreme example with all the black bits showing you want alpha=1.
In summary:
call cvGetOptimalNewCameraMatrix with alpha=1 to obtain newCameraMatrix.
use cvInitUndistortRectifymap with R being identity matrix and newCameraMatrix set to the one you just calculated
feed the new maps into cvRemap.
Related
I have question regrading undistortion of a single point using either Scaramuzza or Mei's opencv
I have done the calibration on a dataset and extracted camera matrix and distortion coefficient (for mei) and the necessary parameters for Scaramuzza, after getting mapx (map1) and mapy (map2) I want to apply the undistortion on a single point.
for mei:
we have a position for a point (an intersection in a chess board) in a fisheye image, I was able to find its position using findchessboardcoreners (I know this can be used for calibration but I want to know a position for a well-known point in the image), now I have the undistorted image and I want to know the position of that point after the distortion correction,
I have read many links, suggesting to use undistortpoints method, or by using remap method, and I read links describing that dst(x,y)=src(mapx(x,y),mapy(x,y)) and I applied them all but when I draw the resulted point it wasn't on the same intersection of the chessboard it was even out of the board closer to its position in the fisheye
for Scaramuzza:
I tried to understand world2cam and cam2world methods but still I can't get it right
so
is there a method to know the position of a single point after the distortion correction if we have its position before the distortion? also can someone explain in deep way mapx and mapy .. I have read examples about them and how they can be used but whenever I wanted to implement the mapping between the distorted point and the undistorted one I got confused, for example: mapx and mapy should have the size of the src (in my case it is a point) so how can I use remap method here? or I should get them form the camera matrix and distortion coefficient and use dst(x,y)=src(map1(x,y),map2(x,y) ?
note
I have applied estimateNewCameraMatrixForUndistortRectify, initUndistortRectifyMap and remap successfully on images (for mei's) and I have also applied the undistortion method which was implemented by Scaramuzza on images with a very satisfying result (better than mei)
I was able to solve it by undistortpoints openCV function, the problem was I did not use the fisheye::undistortPoints but I was using the original one, still the surrounded points are not in their right position but the result was kind of acceptable
So I have been tinkering a little bit with opencv and I want to be able to use a camera image to get the position of certain objects that are lying flat on plane. These objects are simple shapes such as circles squares etc. They all have the same height of 5cm. To be able to relate real world points to pixels on the camera I painted 4 white squares on the plane with known distances between them.
So the steps I have been taking are:
Initialization:
Calibrate my camera using a checkerboard image and save the calibration data.
Get the input image. call cv::undistort with the calibration data for my camera.
Find the center points of the 4 squares in the image and pass that data and the real world coordinates of the squares to the cv::solvePnP function. Save the rvec and tvec return parameters.
Warp the perspective of the image so you can get a top down view from the image. This is essentially following this tutorial: https://docs.opencv.org/3.4.1/d9/dab/tutorial_homography.html
Use the resulting image to again find the 4 white squares and then calculate a "pixels per meter" translation constant which can relate a certain amount of difference in pixels between points to the real world distance on the plane where the 4 squares are.
Finding object, This is done after initialization:
Get the input image. call cv::undistort with the calibration data for my camera.
Warp the perspective of the image so you can get a top down view from the image. This is the same as step 4 during initialisation.
Find the centerpoint of the object to detect.
Since the centerpoint of the object is on a higher plane then where I calibrated I use the following formula to correct this(d = is the pixel offset from the center of the image. camHeight is the cameraHeight I measured by using a tape measure. h is height of the object):
d = x - (h * (x / camHeight))
So here for an illustration how I got this formule:
But still the coordinates are not matching up...
So I am wondering at all if this is the correct. Specifically I have the following questions:
Is using cv::undistort before using cv::solvenPnP correct? cv::solvePnP also takes the camera calibration data as input so I'm not sure if I have to pass an undistorted image to it or not.
Similar to 1. During Finding object I call cv::undistort -> cv::warpPerspective. Is this undistort necessary here?
Is my calculation to correct for the parallel planes in step 4 correct? I feel like I am missing something but I can't see what. One thing I am wondering is whether I can get the camera height from opencv once solvePnp is done.
I am a newbie to CV so If anything else is totally wrong please also point it out to me.
Thank you for reading this wall of text!
I need find edges of document that in user hands.
1) Original image from camera:
2) Then i convert image to BG:
3) Then i make blur:
3) Finds edges in an image using the Canny:
4) And use dilate :
As you can see on the last image the contour around the map is torn and the contour is not determined. What is my error and how to solve the problem in order to determine the outline of the document completely?
This is code how i to do it:
final Mat mat = new Mat();
sourceMat.copyTo(mat);
//convert the image to black and white
Imgproc.cvtColor(mat, mat, Imgproc.COLOR_BGR2GRAY);
//blur to enhance edge detection
Imgproc.GaussianBlur(mat, mat, new Size(5, 5), 0);
if (isClicked) saveImageFromMat(mat, "blur", "blur");
//convert the image to black and white does (8 bit)
int thresh = 128;
Imgproc.Canny(mat, mat, thresh, thresh * 2);
//dilate helps to connect nearby line segments
Imgproc.dilate(mat, mat,
Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(3, 3)),
new Point(-1, -1),
2,
1,
new Scalar(1));
This answer is based on my above comment. If someone is holding the document, you cannot see the edge that is behind the user's hand. So, any method for detecting the outline of the document must be robust to some missing parts of the edge.
I suggest using a variant of the Hough transform to detect the document. The Wikipedia article about the Hough transform makes it sound quite scary (as Wikipedia often does with mathematical subjects), but don't be discouraged, actually they are not too difficult to understand or implement.
The original Hough transform detected straight lines in images. As explained in this OpenCV tutorial, any straight line in an image can be defined by 2 parameters: an angle θ and a distance r of the line from the origin. So you quantize these 2 parameters, and create a 2D array with one cell for every possible line that could be present in your image. (The finer the quantization you use, the larger the array you will need, but the more accurate the position of the found lines will be.) Initialize the array to zeros. Then, for every pixel that is part of an edge detected by Canny, you determine every line (θ,r) that the pixel could be part of, and increment the corresponding bin. After processing all pixels, you will have, for each bin, a count of how many pixels were detected on the line corresponding to that bin. Counts which are high enough probably represent real lines in the image, even if parts of the line are missing. So you just scan through the bins to find bins which exceed the threshold.
OpenCV contains Hough detectors for straight lines and circles, but not for rectangles. You could either use the line detector and check for 4 lines that form the edges of your document; or you could write your own Hough detector for rectangles, perhaps using the paper Jung 2004 for inspiration. Rectangles have at least 5 degrees of freedom (2D position, scale, aspect ratio, and rotation angle), and memory requirement for a 5D array obviously goes up pretty fast. But since the range of each parameter is limited (ie, the document's aspect ratio is known, and you can assume the document will be well centered and not rotated much) it is probably feasible.
I've got problem with precise detection of markers using OpenCV.
I've recorded video presenting that issue: http://youtu.be/IeSSW4MdyfU
As you see I'm markers that I'm detecting are slightly moved at some camera angles. I've read on the web that this may be camera calibration problems, so I'll tell you guys how I'm calibrating camera, and maybe you'd be able to tell me what am I doing wrong?
At the beginnig I'm collecting data from various images, and storing calibration corners in _imagePoints vector like this
std::vector<cv::Point2f> corners;
_imageSize = cvSize(image->size().width, image->size().height);
bool found = cv::findChessboardCorners(*image, _patternSize, corners);
if (found) {
cv::Mat *gray_image = new cv::Mat(image->size().height, image->size().width, CV_8UC1);
cv::cvtColor(*image, *gray_image, CV_RGB2GRAY);
cv::cornerSubPix(*gray_image, corners, cvSize(11, 11), cvSize(-1, -1), cvTermCriteria(CV_TERMCRIT_EPS+ CV_TERMCRIT_ITER, 30, 0.1));
cv::drawChessboardCorners(*image, _patternSize, corners, found);
}
_imagePoints->push_back(_corners);
Than, after collecting enough data I'm calculating camera matrix and coefficients with this code:
std::vector< std::vector<cv::Point3f> > *objectPoints = new std::vector< std::vector< cv::Point3f> >();
for (unsigned long i = 0; i < _imagePoints->size(); i++) {
std::vector<cv::Point2f> currentImagePoints = _imagePoints->at(i);
std::vector<cv::Point3f> currentObjectPoints;
for (int j = 0; j < currentImagePoints.size(); j++) {
cv::Point3f newPoint = cv::Point3f(j % _patternSize.width, j / _patternSize.width, 0);
currentObjectPoints.push_back(newPoint);
}
objectPoints->push_back(currentObjectPoints);
}
std::vector<cv::Mat> rvecs, tvecs;
static CGSize size = CGSizeMake(_imageSize.width, _imageSize.height);
cv::Mat cameraMatrix = [_userDefaultsManager cameraMatrixwithCurrentResolution:size]; // previously detected matrix
cv::Mat coeffs = _userDefaultsManager.distCoeffs; // previously detected coeffs
cv::calibrateCamera(*objectPoints, *_imagePoints, _imageSize, cameraMatrix, coeffs, rvecs, tvecs);
Results are like you've seen in the video.
What am I doing wrong? is that an issue in the code? How much images should I use to perform calibration (right now I'm trying to obtain 20-30 images before end of calibration).
Should I use images that containg wrongly detected chessboard corners, like this:
or should I use only properly detected chessboards like these:
I've been experimenting with circles grid instead of of chessboards, but results were much worse that now.
In case of questions how I'm detecting marker: I'm using solvepnp function:
solvePnP(modelPoints, imagePoints, [_arEngine currentCameraMatrix], _userDefaultsManager.distCoeffs, rvec, tvec);
with modelPoints specified like this:
markerPoints3D.push_back(cv::Point3d(-kMarkerRealSize / 2.0f, -kMarkerRealSize / 2.0f, 0));
markerPoints3D.push_back(cv::Point3d(kMarkerRealSize / 2.0f, -kMarkerRealSize / 2.0f, 0));
markerPoints3D.push_back(cv::Point3d(kMarkerRealSize / 2.0f, kMarkerRealSize / 2.0f, 0));
markerPoints3D.push_back(cv::Point3d(-kMarkerRealSize / 2.0f, kMarkerRealSize / 2.0f, 0));
and imagePoints are coordinates of marker corners in processing image (I'm using custom algorithm to do that)
In order to properly debug your problem I would need all the code :-)
I assume you are following the approach suggested in the tutorials (calibration and pose) cited by #kobejohn in his comment and so that your code follows these steps:
collect various images of chessboard target
find chessboard corners in images of point 1)
calibrate the camera (with cv::calibrateCamera) and so obtain as a result the intrinsic camera parameters (let's call them intrinsic) and the lens distortion parameters (let's call them distortion)
collect an image of your own custom target (the target is seen at 0:57 in your video) and it is shown in the following figure and find some relevant points in it (let's call the point you found in image image_custom_target_vertices and world_custom_target_vertices the corresponding 3D points).
estimate the rotation matrix (let's call it R) and the translation vector (let's call it t) of the camera from the image of your own custom target you get in point 4), with a call to cv::solvePnP like this one cv::solvePnP(world_custom_target_vertices,image_custom_target_vertices,intrinsic,distortion,R,t)
giving the 8 corners cube in 3D (let's call them world_cube_vertices) you get the 8 2D image points (let's call them image_cube_vertices) by means of a call to cv2::projectPoints like this one cv::projectPoints(world_cube_vertices,R,t,intrinsic,distortion,image_cube_vertices)
draw the cube with your own draw function.
Now, the final result of the draw procedure depends on all the previous computed data and we have to find where the problem lies:
Calibration: as you observed in your answer, in 3) you should discard the images where the corners are not properly detected. You need a threshold for the reprojection error in order to discard "bad" chessboard target images. Quoting from the calibration tutorial:
Re-projection Error
Re-projection error gives a good estimation of just how exact is the
found parameters. This should be as close to zero as possible. Given
the intrinsic, distortion, rotation and translation matrices, we first
transform the object point to image point using cv2.projectPoints().
Then we calculate the absolute norm between what we got with our
transformation and the corner finding algorithm. To find the average
error we calculate the arithmetical mean of the errors calculate for
all the calibration images.
Usually you will find a suitable threshold with some experiments. With this extra step you will get better values for intrinsic and distortion.
Finding you own custom target: it does not seem to me that you explain how you find your own custom target in the step I labeled as point 4). Do you get the expected image_custom_target_vertices? Do you discard images where that results are "bad"?
Pose of the camera: I think that in 5) you use intrinsic found in 3), are you sure nothing is changed in the camera in the meanwhile? Referring to the Callari's Second Rule of Camera Calibration:
Second Rule of Camera Calibration: "Thou shalt not touch the lens
after calibration". In particular, you may not refocus nor change the
f-stop, because both focusing and iris affect the nonlinear lens
distortion and (albeit less so, depending on the lens) the field of
view. Of course, you are completely free to change the exposure time,
as it does not affect the lens geometry at all.
And then there may be some problems in the draw function.
So, I've experimented a lot with my code, and I still haven't fixed the main issue (shifted objects), but I've managed to answer some of calibration questions I've asked.
First of all - in order to obtain good calibration results you have to use images with properly detected grid elements/circles positions!. Using all captured images in calibration process (even those that aren't properly detected) will result bad calibration.
I've experimented with various calibration patterns:
Asymmetric circles pattern (CALIB_CB_ASYMMETRIC_GRID), give much worse results than any other pattern. By worse results I mean that it produces a lot of wrongly detected corners like these:
I've experimented with CALIB_CB_CLUSTERING and it haven't helped much - in some cases (different light environment) it got better, but not much.
Symmetric circles pattern (CALIB_CB_SYMMETRIC_GRID) - better results than asymmetric grid, but still I've got much worse results than standard grid (chessboard). It often produces errors like these:
Chessboard (found using findChessboardCorners function) - this method is producing best possible results - it doesn't produce misaligned corners very often, and almost every calibration is producing similar results to best-possible results from symmetric circles grid
For every calibration I've been using 20-30 images that were coming from different angles. I've tried even with 100+ images but it haven't produced noticeable change in calibration results than smaller amount of images. It's worth noticing that larger number of test images is increasing time needed to compute camera parameters in non-linear way (100 test images in 480x360 resolution are computing 25 minutes in iPad4, compared with 4 minutes with ~50 images)
I've also experimented with solvePNP parameters - but is also haven't gave me any acceptable results: I've tried all 3 detection methods (ITERATIVE, EPNP and P3P), but I haven't seen aby noticeable change.
Also I've tried with useExtrinsicGuess set to true, and I've used rvec and tvec from previous detection, but this one resulted with complete disapperance of detected cube.
I've ran out of ideas - what else could be affecting these shifting problems?
For those still interested:
this is an old question, but I think your problem is not the bad calibration.
I developed an AR app for iOS, using OpenCV and SceneKit, and I have had your same issue.
I think your problem is the wrong render position of the cube:
OpenCV's solvePnP returns the X, Y, Z coordinates of the marker center, but you wanna render the cube over the marker, at a specific distance along the Z axis of the marker, exactly at one half of the cube side size. So you need to improve the Z coordinate of the marker translation vector of this distance.
In fact, when you see your cube from the top, the cube is render properly.
I have done an image in order to explain the problem, but my reputation prevent to post it.
I am new to Open Cv, I want to transform the two images src and dst image . I am using cv::estimateRigidTransform() to calculate the transformation matrix and after that using cv::warpAffine() to transform from dst to src. when I compare the new transformed image with src image it is almost same (transformed), but when I am getting the abs difference of new transformed image and the src image, there is lot of difference. what should I do as My dst image has some rotation and translation factor as well. here is my code
cv::Mat transformMat = cv::estimateRigidTransform(src, dst, true);
cv::Mat output;
cv::Size dsize = leftImageMat.size(); //This specifies the output image size--change needed
cv::warpAffine(src, output, transformMat, dsize);
Src Image
destination Image
output image
absolute Difference Image
Thanks
You have some misconceptions about the process.
The method cv::estimateRigidTransform takes as input two sets of corresponding points. And then solves set of equations to find the transformation matrix. The output of the transformation matches src points to dst points (exactly or closely, if exact match is not possible - for example float coordinates).
If you apply estimateRigidTransform on two images, OpenCV first find matching pairs of points using some internal method (see opencv docs).
cv::warpAffine then transforms the src image to dst according to given transformation matrix. But any (almost any) transformation is loss operation. The algorithm has to estimate some data, because they aren't available. This process is called interpolation, using known information you calculate the unknown value. Some info regarding image scaling can be found on wiki. Same rules apply to other transformations - rotation, skew, perspective... Obviously this doesn't apply to translation.
Given your test images, I would guess that OpenCV takes the lampshade as reference. From the difference is clear that the lampshade is transformed best. Default the OpenCV uses linear interpolation for warping as it's fastest method. But you can set more advances method for better results - again consult opencv docs.
Conclusion:
The result you got is pretty good, if you bear in mind, it's result of automated process. If you want better results, you'll have to find another method for selecting corresponding points. Or use better interpolation method. Either way, after the transform, the diff will not be 0. It virtually impossible to achieve that, because bitmap is discrete grid of pixels, so there will always be some gaps, which needs to be estimated.