boofcv.alg.fiducial.calib.chess.DetectChessboardFiducial Maven / Gradle / Ivy

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BoofCV is an open source Java library for real-time computer vision and robotics applications.
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/*
 * Copyright (c) 2011-2016, Peter Abeles. All Rights Reserved.
 *
 * This file is part of BoofCV (http://boofcv.org).
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *   http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package boofcv.alg.fiducial.calib.chess;

import boofcv.abst.filter.binary.InputToBinary;
import boofcv.alg.filter.binary.BinaryImageOps;
import boofcv.alg.shapes.polygon.BinaryPolygonDetector;
import boofcv.alg.shapes.polygon.RefineBinaryPolygon;
import boofcv.struct.image.GrayU8;
import boofcv.struct.image.ImageGray;
import georegression.struct.point.Point2D_F64;

import java.util.List;

/**
 * 
 * Detects calibration points inside a chessboard calibration target.  The image is first the image
 * is thresholded to create a binary image for square detection, then the binary image is eroded to make sure
 * the squares don't touch.  After that {@link DetectChessSquarePoints} is called and it detects and sorts
 * the squares.
 * 
 * 
 * The found control points are ensured to be returned in a row-major format with the correct number of rows and columns,
 * with a counter clockwise ordering.  Note that when viewed on the monitor this will appear to be clockwise because
 * the y-axis points down.  If there are multiple valid solution then the solution with the (0,0) grid point closest
 * top the origin is selected.
 * 
 * 
 * 
 * 
 * Example of a 7 by 5 grid; row, column.
 *
 * @author Peter Abeles
 */
public class DetectChessboardFiducial {

	// detects the chess board
	private DetectChessSquarePoints findSeeds;
	// binary images used to detect chess board
	private GrayU8 binary = new GrayU8(1, 1);
	private GrayU8 eroded = new GrayU8(1, 1);

	// description of the grid its detecting
	private int numRows,numCols;


	InputToBinary inputToBinary;

	/**
	 * Configures detection parameters
	 * @param numRows Number of rows in the grid.  Target dependent.
	 * @param numCols Number of columns in the grid.  Target dependent.
	 * @param maxCornerDistance The maximum distance two square corners can be from each other in pixels
	 */
	public DetectChessboardFiducial(int numRows, int numCols, double maxCornerDistance,
									BinaryPolygonDetector detectorSquare,
									RefineBinaryPolygon refineLine,
									RefineBinaryPolygon refineCorner,
									InputToBinary inputToBinary)
	{
		this.numRows = numRows;
		this.numCols = numCols;

		this.inputToBinary = inputToBinary;

		findSeeds = new DetectChessSquarePoints(numRows,numCols,maxCornerDistance, detectorSquare);

		detectorSquare.setHelper(new ChessboardPolygonHelper(detectorSquare,refineLine,refineCorner));

		reset();
	}

	/**
	 * Forgets any past history and resets the detector
	 */
	public void reset() {
	}

	public boolean process(T gray) {
		binary.reshape(gray.width, gray.height);
		eroded.reshape(gray.width, gray.height);

		inputToBinary.process(gray,binary);

		// erode to make the squares separated
		BinaryImageOps.erode8(binary, 1, eroded);

		return findSeeds.process(gray, eroded);
	}

	public DetectChessSquarePoints getFindSeeds() {
		return findSeeds;
	}

	public List getCalibrationPoints() {
		return findSeeds.getCalibrationPoints().toList();
	}

	public GrayU8 getBinary() {
			return eroded;
	}

	public int getColumns() {
		return numCols;
	}

	public int getRows() {
		return numRows;
	}
}