Sample files for image scanning, basic Mat, file I/O. Added Victors removed tutorials back in the system. Some typo fixing.Expanded conf.py with new global links.

2011-07-22 15:11:59 +00:00
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+.. _CameraCalibrationSquareChessBoardTutorial:
+
+Camera calibration with square chessboard
+*****************************************
+
+.. highlight:: cpp
+
+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 a code that detects a chessboard in a new image and finds its distance from the camera. You can apply the same method to any object with known 3D geometry that you can 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], CV_LOAD_IMAGE_GRAYSCALE);
+
+#.
+    Detect a chessboard in this image using findChessboard function. ::
+
+        bool found = findChessboardCorners( img, boardSize, ptvec, CV_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 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 to calculate the distance from the camera origin to any of the corners? 
@@ -5,8 +5,32 @@

 Although we got most of our images in a 2D format they do come from a 3D world. Here you will learn how to find out from the 2D images information about the 3D world. 

-.. include:: ../../definitions/noContent.rst
+.. include:: ../../definitions/tocDefinitions.rst 
+
+ 
+  .. tabularcolumns:: m{100pt} m{300pt}
+  .. cssclass:: toctableopencv
+
+  ===================== ==============================================
+   |CameraCalSqChess|   **Title:** :ref:`CameraCalibrationSquareChessBoardTutorial`
+
+                        *Compatibility:* > OpenCV 2.0
+
+                        *Author:* |Author_VictorE|
+
+                        You will use some chessboard images to calibrate your camera.
+
+  ===================== ==============================================
+
+  .. |CameraCalSqChess| image:: images/camera_calibration_square_chess.jpg
+     :height: 90pt
+     :width:  90pt

 .. raw:: latex

   \pagebreak
+
+.. toctree::
+   :hidden:
+
+   ../camera_calibration_square_chess/camera_calibration_square_chess