Calibration Pattern As we said earlier we are going to need some sort of pattern that the program can recognize in order to make the calibration work. The pattern that we are going to use is a chessboard image. The reason why we use this image is because there are some OpenCV functions that can recognize this pattern and draw a scheme which highlights the intersections between each block. To make the calibration work you need to print the chessboard image and show it to the cam; it is important to maintain the sheet still, better if stick to a surface. In order to make a good calibration, we need to have about 20 samples of the pattern taken from different angles and distances. image Source chessboard view. It must be an 8-bit grayscale or color image.

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• Opencv Chessboard Image

patternSize Number of inner corners per a chessboard row and column. corners Output array of detected corners. flags Various operation flags that can be zero or a combination of the following values:. CVCALIBCBADAPTIVETHRESH Use adaptive thresholding to convert the image to black and white, rather than a fixed threshold level (computed from the average image brightness). CVCALIBCBNORMALIZEIMAGE Normalize the image gamma with “equalizeHist” before applying fixed or adaptive thresholding. CVCALIBCBFILTERQUADS Use additional criteria (like contour area, perimeter, square-like shape) to filter out false quads extracted at the contour retrieval stage. CALIBCBFASTCHECK Run a fast check on the image that looks for chessboard corners, and shortcut the call if none is found.

This can drastically speed up the call in the degenerate condition when no chessboard is observed. List rvecs = new ArrayList ; List tvecs = new ArrayList ; intrinsic.

Put ( 0, 0, 1 ); intrinsic. Put ( 1, 1, 1 ); Calib3d. CalibrateCamera ( objectPoints, imagePoints, savedImage.

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Size , intrinsic, distCoeffs, rvecs, tvecs ); The calibrateCamera function estimates the intrinsic camera parameters and extrinsic parameters for each of the views. The algorithm is based on Zhang2000 and BouguetMCT. The coordinates of 3D object points and their corresponding 2D projections in each view must be specified. Its parameters are. objectPoints In the new interface it is a vector of vectors of calibration pattern points in the calibration pattern coordinate space.

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The outer vector contains as many elements as the number of the pattern views. 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. imagePoints It is a vector of vectors of the projections of calibration pattern points. imageSize Size of the image used only to initialize the intrinsic camera matrix. cameraMatrix Output 3x3 floating-point camera matrix A = fx 0 cx 0 fy cy 0 0 1. If CVCALIBUSEINTRINSICGUESS and/or CVCALIBFIXASPECTRATIO are specified, some or all of fx, fy, cx, cy must be initialized before calling the function. distCoeffs Output vector of distortion coefficients of 4, 5, or 8 elements.

Opencv Chessboard Image

rvecs Output vector of rotation vectors estimated for each pattern view. That is, each k-th rotation vector together with the corresponding k-th translation vector. tvecs Output vector of translation vectors estimated for each pattern view.

We ran calibration and got camera’s matrix with the distortion coefficients we may want to correct the image using undistort function.

BACKGROUND So I'm creating a program that recognizes chess moves. So far, I have implemented a fair number of algorithms to come up with the best results possible. What I've found so far is that the combination of undistorting an image (using undistort ), then applying a histogram equalization algorithm, and finally the goodFeaturesToTrack algorithm (I've found this to be better than the harris corner detection) yields pretty decent results.

The goal here is to have every corner of every square accounted for with a point. That way, when I apply canny edge detection, I can process individual squares. EXAMPLE WHAT I'VE CONSIDERED To summarize the link above, the idea is to find the upper-leftmost, upper-rightmost, lower-leftmost, and lower-rightmost points and divide the distance between them by eight. From there you would come up with probable points and compare them to the points that are actually on the board. If one of the points doesn't match, simply replace the point. I've also considered some sort of mode, like finding the distance between neighboring points and storing them in a list.

Then I would perform a mode operation to figure out the most probable distance and use that to draw points. QUESTION As you can see, the points are fairly accurate over most of the squares (though there are random points that do not do what I want). My question is what do you think the best way to find all corners on the chessboard (I'm open to all ideas) and could you give me a somewhat detailed description (just enough to steer me in the right direction or more if you choose:)?

Also, (and this is a secondary question) do you have any recommendations on how to proceed in order to best recognize a move? I'm attempting to implement multiple ways of doing so and am going to compare methods to obtain best results!