Hi, When I complete a camera calibration this seems to re-orientate the pixels that could be displayed to the real pixel that holds the data. This I guess is a remap of the data that correlates to the image size. So if I want to measure intensity values in the rows and columns it would resolve itself to the corrected image.

Im trying to understand a 3D measurement system where I get "Y" as the distance to the target and "X" is the position across a laser line. I'm trying to make a 3D measurement system. Can someone explain this any better. Thank You. Asked: Why does camera calibration work on one image but not on a very similar other image? How to verify the correctness dj tv channel calibration of a webcam.

OpenCV Camera Calibration for telecentric lenses. Calibration of a high resolution camera. Resolution and camera calibration. Results of camera calibration vary. How to decrease the number of processed frames from a live video camera? First time here? Check out the FAQ! Hi there! Please sign in help. Question Tools Follow. Related questions Why does camera calibration work on one image but not on a very similar other image?

How to verify the correctness of calibration of a webcam OpenCV Camera Calibration for telecentric lenses Calibration of a high resolution camera Resolution and camera calibration camera calibration problem Results of camera calibration vary cvCalibrateCamera2 How to decrease the number of processed frames from a live video camera? Capture Properties. Copyright OpenCV foundation Powered by Askbot version 0. Please note: OpenCV answers requires javascript to work properly, please enable javascript in your browser, here is how.

Ask Your Question.The functions in this section use a so-called pinhole camera model. In this model, a scene view is formed by projecting 3D points into the image plane using a perspective transformation.

The matrix of intrinsic parameters does not depend on the scene viewed.

**Laser Tracking System -using OpenCV 3.1 and Raspberry Pi 3**

So, once estimated, it can be re-used as long as the focal length is fixed in case of zoom lens. The joint rotation-translation matrix is called a matrix of extrinsic parameters.

It is used to describe the camera motion around a static scene, or vice versa, rigid motion of an object in front of a still camera.

That is, translates coordinates of a point to a coordinate system, fixed with respect to the camera. The transformation above is equivalent to the following when :.

Real lenses usually have some distortion, mostly radial distortion and slight tangential distortion. So, the above model is extended as:. Higher-order coefficients are not considered in OpenCV. The next figure shows two common types of radial distortion: barrel distortion typically and pincushion distortion typically. That is, if the vector contains four elements, it means that. The distortion coefficients do not depend on the scene viewed.

Thus, they also belong to the intrinsic camera parameters. And they remain the same regardless of the captured image resolution. If, for example, a camera has been calibrated on images of x resolution, absolutely the same distortion coefficients can be used for x images from the same camera while,and need to be scaled appropriately. In the new interface it is a vector of vectors of calibration pattern points in the calibration pattern coordinate space e. The outer vector contains as many elements as the number of the pattern views.

If the same calibration pattern is shown in each view and it is fully visible, all the vectors will be the same. Although, it is possible to use partially occluded patterns, or even different patterns in different views. Then, the vectors will be different. The points are 3D, but since they are in a pattern coordinate system, then, if the rig is planar, it may make sense to put the model to a XY coordinate plane so that Z-coordinate of each input object point is 0.

In the old interface all the vectors of object points from different views are concatenated together. In the new interface it is a vector of vectors of the projections of calibration pattern points e. The function estimates the intrinsic camera parameters and extrinsic parameters for each of the views. The coordinates of 3D object points and their corresponding 2D projections in each view must be specified. That may be achieved by using an object with a known geometry and easily detectable feature points.

Such an object is called a calibration rig or calibration pattern, and OpenCV has built-in support for a chessboard as a calibration rig see findChessboardCorners. The function computes various useful camera characteristics from the previously estimated camera matrix.

See Rodrigues for details. Also, the functions can compute the derivatives of the output vectors with regards to the input vectors see matMulDeriv. The functions are used inside stereoCalibrate but can also be used in your own code where Levenberg-Marquardt or another gradient-based solver is used to optimize a function that contains a matrix multiplication.

For every point in one of the two images of a stereo pair, the function finds the equation of the corresponding epipolar line in the other image. Line coefficients are defined up to a scale. They are normalized so that. That is, each point x1, x2, The function converts points homogeneous to Euclidean space using perspective projection.Cameras have been around for a long-long time. However, with the introduction of the cheap pinhole cameras in the late 20th century, they became a common occurrence in our everyday life.

Unfortunately, this cheapness comes with its price: significant distortion. Luckily, these are constants and with a calibration and some remapping we can correct this. For the distortion OpenCV takes into account the radial and tangential factors. For the radial factor one uses the following formula:. So for an old pixel point at coordinates in the input image, its position on the corrected output image will be. Tangential distortion occurs because the image taking lenses are not perfectly parallel to the imaging plane.

It can be corrected via the formulas:. So we have five distortion parameters which in OpenCV are presented as one row matrix with 5 columns:. Here the presence of is explained by the use of homography coordinate system and. The unknown parameters are and camera focal lengths and which are the optical centers expressed in pixels coordinates.

If for both axes a common focal length is used with a given aspect ratio usually 1then and in the upper formula we will have a single focal length. The matrix containing these four parameters is referred to as the camera matrix. While the distortion coefficients are the same regardless of the camera resolutions used, these should be scaled along with the current resolution from the calibrated resolution.

The process of determining these two matrices is the calibration. Calculation of these parameters is done through basic geometrical equations.

The equations used depend on the chosen calibrating objects. Currently OpenCV supports three types of objects for calibration:. Basically, you need to take snapshots of these patterns with your camera and let OpenCV find them.

Each found pattern results in a new equation.A camera, when used as a visual sensor, is an integral part of several domains like robotics, surveillance, space exploration, social media, industrial automation, and even the entertainment industry.

For many applications, it is essential to know the parameters of a camera to use it effectively as a visual sensor. In this post, you will understand the steps involved in camera calibration and their significance.

This means we have all the information parameters or coefficients about the camera required to determine an accurate relationship between a 3D point in the real world and its corresponding 2D projection pixel in the image captured by that calibrated camera. In the image below, the parameters of the lens estimated using geometric calibration were used to un-distort the image.

To understand the process of calibration we first need to understand the geometry of image formation. Click on the link below for a detailed explanation. Geometry of Image Formation. As explained in the blog post, to find the projection of a 3D point onto the image plane, we first need to transform the point from world coordinate system to the camera coordinate system using the extrinsic parameters Rotation and Translation.

Next, using the intrinsic parameters of the camera, we project the point onto the image plane. The equations that relate 3D point in world coordinates to its projection in the image coordinates are shown below.

As mentioned in the previous post, the intrinsic matrix is upper triangular. Using the center of the image is usually a good enough approximation. It is usually 1. When we get the values of intrinsic and extrinsic parameters the camera is said to be calibrated. Note : In OpenCV the camera intrinsic matrix does not have the skew parameter. So the matrix is of the form. In the process of calibration we calculate the camera parameters by a set of know 3D points and their corresponding pixel location in the image.

For the 3D points we photograph a checkerboard pattern with known dimensions at many different orientations. The world coordinate is attached to the checkerboard and since all the corner points lie on a plane, we can arbitrarily choose for every point to be 0. Since points are equally spaced in the checkerboard, the coordinates of each 3D point are easily defined by taking one point as reference 0, 0 and defining remaining with respect to that reference point.

Checkerboard patterns are distinct and easy to detect in an image. Not only that, the corners of squares on the checkerboard are ideal for localizing them because they have sharp gradients in two directions.

In addition, these corners are also related by the fact that they are at the intersection of checkerboard lines. All these facts are used to robustly locate the corners of the squares in a checkerboard pattern.

Next, we keep the checkerboard static and take multiple images of the checkerboard by moving the camera.Some pinhole cameras introduce significant distortion to images. Two major kinds of distortion are radial distortion and tangential distortion. Radial distortion causes straight lines to appear curved. Radial distortion becomes larger the farther points are from the center of the image. For example, one image is shown below in which two edges of a chess board are marked with red lines. But, you can see that the border of the chess board is not a straight line and doesn't match with the red line.

All the expected straight lines are bulged out. Visit Distortion optics for more details. Similarly, tangential distortion occurs because the image-taking lense is not aligned perfectly parallel to the imaging plane. So, some areas in the image may look nearer than expected. The amount of tangential distortion can be represented as below:. In addition to this, we need to some other information, like the intrinsic and extrinsic parameters of the camera.

Intrinsic parameters are specific to a camera.

The focal length and optical centers can be used to create a camera matrix, which can be used to remove distortion due to the lenses of a specific camera. The camera matrix is unique to a specific camera, so once calculated, it can be reused on other images taken by the same camera. It is expressed as a 3x3 matrix:.

Extrinsic parameters corresponds to rotation and translation vectors which translates a coordinates of a 3D point to a coordinate system. For stereo applications, these distortions need to be corrected first.

To find these parameters, we must provide some sample images of a well defined pattern e. We find some specific points of which we already know the relative positions e. We know the coordinates of these points in real world space and we know the coordinates in the image, so we can solve for the distortion coefficients. For better results, we need at least 10 test patterns. As mentioned above, we need at least 10 test patterns for camera calibration. Consider an image of a chess board.

The important input data needed for calibration of the camera is the set of 3D real world points and the corresponding 2D coordinates of these points in the image. These image points are locations where two black squares touch each other in chess boards. What about the 3D points from real world space? Those images are taken from a static camera and chess boards are placed at different locations and orientations.

This consideration helps us to find only X,Y values.

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Now for X,Y values, we can simply pass the points as 0,01,02,0In this case, the results we get will be in the scale of size of chess board square. But if we know the square size, say 30 mmwe can pass the values as 0,030,060,0Thus, we get the results in mm.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service. The dark mode beta is finally here. Change your preferences any time.

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Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I'm trying to use OpenCV 2. I've used the data below in matlab and the calibration worked, but I can't seem to get it to work in OpenCV. The camera matrix I setup as an initial guess is very close to the answer calculated from the matlab toolbox. First off, your camera matrix is wrong. If you read the documentationit should look like:.

Next, I ran your code and I see that "can't seem to get it to work" means the following error by the way - always say what you mean by "can't seem to get it to work" in your questions - if it's an error, post the error. If it runs but gives you weirdo numbers, say so :. I tried to run calibration. Moral of the story - read the OpenCV documentation carefully, and use the newest version i.

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With these two things you should be able to work out most problems. You can see that last element in the vector K3 will be zero.

Learn more. OpenCV 2. Asked 8 years ago. Active 2 years, 4 months ago. Viewed 10k times. Eric Leschinski k 47 47 gold badges silver badges bronze badges.Cameras have been around for a long-long time. However, with the introduction of the cheap pinhole cameras in the late 20th century, they became a common occurrence in our everyday life. Unfortunately, this cheapness comes with its price: significant distortion.

Luckily, these are constants and with a calibration and some remapping we can correct this. Furthermore, with calibration you may also determine the relation between the camera's natural units pixels and the real world units for example millimeters. For the distortion OpenCV takes into account the radial and tangential factors. For the radial factor one uses the following formula:. The presence of the radial distortion manifests in form of the "barrel" or "fish-eye" effect.

### Camera Calibration using OpenCV

Tangential distortion occurs because the image taking lenses are not perfectly parallel to the imaging plane. It can be represented via the formulas:. So we have five distortion parameters which in OpenCV are presented as one row matrix with 5 columns:. The matrix containing these four parameters is referred to as the camera matrix.

While the distortion coefficients are the same regardless of the camera resolutions used, these should be scaled along with the current resolution from the calibrated resolution. The process of determining these two matrices is the calibration. Calculation of these parameters is done through basic geometrical equations.

The equations used depend on the chosen calibrating objects. Currently OpenCV supports three types of objects for calibration:. Basically, you need to take snapshots of these patterns with your camera and let OpenCV find them.

Each found pattern results in a new equation. To solve the equation you need at least a predetermined number of pattern snapshots to form a well-posed equation system. This number is higher for the chessboard pattern and less for the circle ones. For example, in theory the chessboard pattern requires at least two snapshots. However, in practice we have a good amount of noise present in our input images, so for good results you will probably need at least 10 good snapshots of the input pattern in different positions.

The program has a single argument: the name of its configuration file. If none is given then it will try to open the one named "default. Here's a sample configuration file in XML format. In the configuration file you may choose to use camera as an input, a video file or an image list.

If you opt for the last one, you will need to create a configuration file where you enumerate the images to use. Here's an example of this. The important part to remember is that the images need to be specified using the absolute path or the relative one from your application's working directory.

You may find all this in the samples directory mentioned above. The application starts up with reading the settings from the configuration file. Although, this is an important part of it, it has nothing to do with the subject of this tutorial: camera calibration. Therefore, I've chosen not to post the code for that part here.

After this we have a big loop where we do the following operations: get the next image from the image list, camera or video file. If this fails or we have enough images then we run the calibration process. The formation of the equations I mentioned above aims to finding major patterns in the input: in case of the chessboard this are corners of the squares and for the circles, well, the circles themselves.

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