init - 初始化项目

doc/tutorials/calib3d/camera_calibration_square_chess/camera_calibration_square_chess.markdown

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+Camera calibration with square chessboard {#tutorial_camera_calibration_square_chess}
+=========================================
+
+@tableofcontents
+
+@prev_tutorial{tutorial_camera_calibration_pattern}
+@next_tutorial{tutorial_camera_calibration}
+
+|    |    |
+| -: | :- |
+| Original author | Victor Eruhimov |
+| Compatibility | OpenCV >= 4.0 |
+
+
+The goal of this tutorial is to learn how to calibrate a camera given a set of chessboard images.
+
+*Test data*: use images in your data/chess folder.
+
+-   Compile OpenCV with samples by setting BUILD_EXAMPLES to ON in cmake configuration.
+
+-   Go to bin folder and use imagelist_creator to create an XML/YAML list of your images.
+
+-   Then, run calibration sample to get camera parameters. Use square size equal to 3cm.
+
+Pose estimation
+---------------
+
+Now, let us write code that detects a chessboard in an image and finds its distance from the
+camera. You can apply this method to any object with known 3D geometry; which you detect in an
+image.
+
+*Test data*: use chess_test\*.jpg images from your data folder.
+
+-   Create an empty console project. Load a test image :
+
+        Mat img = imread(argv[1], IMREAD_GRAYSCALE);
+
+-   Detect a chessboard in this image using findChessboard function :
+
+        bool found = findChessboardCorners( img, boardSize, ptvec, CALIB_CB_ADAPTIVE_THRESH );
+
+-   Now, write a function that generates a vector\<Point3f\> array of 3d coordinates of a chessboard
+    in any coordinate system. For simplicity, let us choose a system such that one of the chessboard
+    corners is in the origin and the board is in the plane *z = 0*
+
+-   Read camera parameters from XML/YAML file :
+
+        FileStorage fs( filename, FileStorage::READ );
+        Mat intrinsics, distortion;
+        fs["camera_matrix"] >> intrinsics;
+        fs["distortion_coefficients"] >> distortion;
+
+-   Now we are ready to find a chessboard pose by running \`solvePnP\` :
+
+        vector<Point3f> boardPoints;
+        // fill the array
+        ...
+
+        solvePnP(Mat(boardPoints), Mat(foundBoardCorners), cameraMatrix,
+                             distCoeffs, rvec, tvec, false);
+
+-   Calculate reprojection error like it is done in calibration sample (see
+    opencv/samples/cpp/calibration.cpp, function computeReprojectionErrors).
+
+Question: how would you calculate distance from the camera origin to any one of the corners?
+Answer: As our image lies in a 3D space, firstly we would calculate the relative camera pose. This would give us 3D to 2D correspondences. Next, we can apply a simple L2 norm to calculate distance between any point (end point for corners).