I'm using an industrial camera which is capable of grabbing images with 2592x1944 pixels. To improve image processing speed, I'm setting an smaller AOI(area/region of interest), for example, a region of 2200x1400 pixel starting at 100,100, please note the center of AOI may not match the center of the full frame. Now I grab a few images of a chessboard pattern under this AOI setting and pass them to OpenCV functions findChessboardCorners and calibrateCamera. Can it recognize the real frame center and output the correct camera instrict parameters, for undistorting images under the same AOI setting?
what I've learned about the camera calibration, is that you have to cover the whole frame meaning 2592x1944 to get a gut calibration, if you cropped you frame or change the resolution you'll get wrong coordinates of the chessboard and calculating the position of the the image plane need the full resolution size if you give the calibration function the wrong size you'll get a wrong calibration and if you the give it the right size with the wrong or just a part of the coverage you'll also get the wrong calibration! I hope that answer you question
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I am working with Turtlebots and ROS, and using a camera to find the pixel positions of a marker in the camera. I've moved over from simulations to a physical system. The issue I'm having is that the pixel positions in my physical system did not match the pixel positions in the physical system despite the marker and everything else being in the same position as in the simulations. There was a shift in the vertical pixel position by about 40 pixels when everything else like the height between the camera and marker, the marker position, and the distance between the marker and camera were the same in both the physical and simulated system. The simulated system does not need a camera calibration matrix, it is assumed to be ideal.
The resolution I'm using is 640x480, so the center pixels should be cx=320 and cy=240, but what I noticed in the camera calibration matrix I was using in the physical system was that the cx was around 318, which is pretty accurate, but the cy was around 202, which is far from what it should be. This also made me think that the shift in pixel positions in the vertical direction is shifted with about the same amount of pixels that I'm getting as an error.
So is it right to assume that the error in the center pixel in the calibration could be causing the error in the pixel positions?
I have been trying to calibrate a USB camera (Logitech C920 I think) and I've been using the camera_calibrator ROS package found here http://wiki.ros.org/camera_calibration to calibrate the camera. I think the camera calibration did not go that well, seeing as I always have a pretty big error in either cx or cy. Here are the calibration matrices.
First calibration matrix, used 15x10 vertices with size 0.25
Recalibrated but did not actually use this yet, calibrated with 8x6 size 0.25
Same as previous, some difference between the two
The checkerboards were on A4 papers.
Thanks in advance.
I believe the answer to your question is to answer how to perform a better camera calibration.
Quoting from Calib.io enter link description here:
Choose the right size calibration target.
Perform calibration at the approximate working distance (WD) of your final application.
The target should have a high feature count.
Collect images from different areas and tilts.
Use good lighting.
Calibration is only as accurate as the calibration target used. Use laser or inkjet printed targets only to validate and test.
Per sample, proper mounting of calibration target and camera.
Remove bad observations. Carefully inspect reprojection errors.
Obtaining a low re-projection error does not equal a good camera calibration. Be careful of over fitting.
I have done some 6D Pose estimation, and have the Oriented Bounding Box (OBB) of the detected object. So, now I would like to know the ground truth and get the error of the estimated orientation and the position. The detected object is a rectangular box as shown in the image.
How can I know the distance and the rotation of a marker (black box over the red one) to the camera? The red box in the image is the object and the black one is the estimated 6DPose.
So would like to do image postprocessing to get the error of the position and the orientation. So, how can get the error from this image, knowing the real dimensions of the box, camera parameters, and the distance from the camera to the object? So the triangulation formula can give me the distance but then how to get the orientation?
Would be OpenCV to look after?
Any help?
Thanks
find the marker correspondings and solvePnp is what you are looking for
I have a stereo image pair of say 100x100 resolution. I did calibration and I am able to rectify it properly and calculate disparity for the same. Now I have the cropped image of size 50x50 with ROI based on center. If I have to use the same calibration matrices, what should I do? Rescaling the principle point in camera matrix is enough or do we need to do anything else?
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!
After to calibrated a camera using Jean- Yves Bouget's Camera Calibration Toolbox and checkerboard-patterns printed on cardboard, I´ve obtained extrinsic and intrinsic parameters, I can use the informations to find camera coordinates:
Pc = R * Pw + T
After that, how to obtain the world coordinates of an image using the Pc and calibration parametesr?
thanks in advance.
EDIT
The goal is to use the calibrated camera parameters to measure planar objects with a calibrated Camera). To perform this task i dont know to use the camera parameters. in other words i have to convert the pixels coordinates of the image to world coordinates using the calibrated parameters. I already have the parameters and the new image. How can i do this convertion?
thanks in advance.
I was thinking about problem, and came to the result:
You can't find the object size. The problem is by a single shot, when you have no idea how far the Object is from your camera you can't say something about the size of the object. The calibration just say how far is the image plane from the camera (focal length) and the open angles of the lense. When the focal length changes the calbriation changes too.
But there are some possibiltys:
How to get the real life size of an object from an image, when not knowing the distance between object and the camera?
So how I understand you can approximate the size of the objects.
Your problem can be solved if (and only if) you can express the plane of your object in calibrated camera coordinates.
The calibration procedure outputs, along with the camera intrinsic parameters K, a coordinate transform matrix for every calibration image Qwc_i = [Rwc_i |Twc_i] matrix, that expresses the location and pose of a particular scene coordinate frame in the camera coordinates at that calibration image. IIRC, in Jean-Yves toolbox this is the frame attached to the top-left corner of the calibration checkerboard.
So, if your planar object is on the same plane as the checkerboard in one of the calibration images, all you have to do in order to find its location in space is intersect the checkerboard plane with camera rays cast from the camera center (0,0,0) to the pixels into which the object is imaged.
If your object is NOT in one of those planes, all you can do is infer the object's own plane from additional information, if available, e.g. from a feature of known size and shape.