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/*
 * Copyright (c) 2011-2020, 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.disparity.block.select;

import boofcv.alg.disparity.block.SelectDisparityWithChecksWta;
import boofcv.alg.disparity.block.SelectSparseStandardWta;
import boofcv.alg.disparity.block.score.DisparitySparseRectifiedScoreBM;

/**
 * 
 * Implementation of {@link SelectSparseStandardWta} for score arrays of type S32.
 * 
 *
 * 
 * DO NOT MODIFY. Generated by GenerateSelectSparseStandardWta.
 * 
 *
 * @author Peter Abeles
 */
public class SelectSparseErrorWithChecksWta_S32 extends SelectSparseStandardWta {

	// texture threshold, use an integer value for speed.
	protected int textureThreshold;
	protected static final int discretizer = SelectDisparityWithChecksWta.DISCRETIZER;

	public SelectSparseErrorWithChecksWta_S32( int maxError, double texture, int tolRightToLeft ) {
		super(maxError, texture, tolRightToLeft);
	}

	@Override
	protected void setTexture( double texture ) {
		this.textureThreshold = (int)(discretizer*texture);
	}

	@Override
	public boolean select( DisparitySparseRectifiedScoreBM scorer, int x, int y ) {
		// First compute the error in the normal left to right direction
		if (!scorer.processLeftToRight(x, y))
			return false;
		int[] scores = scorer.getScoreLtoR();
		int disparityRange = scorer.getLocalRangeLtoR();

		// Select the disparity with the best error
		int bestDisparity = 0;
		int scoreBest = scores[0];

		for (int i = 1; i < disparityRange; i++) {
			if (scores[i] < scoreBest) {
				scoreBest = scores[i];
				bestDisparity = i;
			}
		}

		// See if the best match is within tolerance
		if (scoreBest > maxError) {
			return false;
		}
		// test to see if the region lacks sufficient texture if:
		// 1) not already eliminated 2) sufficient disparities to check, 3) it's activated
		if (textureThreshold > 0 && disparityRange >= 3) {
			// find the second best disparity value and exclude its neighbors
			int secondBest = Integer.MAX_VALUE;
			for (int i = 0; i < bestDisparity - 1; i++) {
				if (scores[i] < secondBest)
					secondBest = scores[i];
			}
			for (int i = bestDisparity + 2; i < disparityRange; i++) {
				if (scores[i] < secondBest)
					secondBest = scores[i];
			}

			// similar scores indicate lack of texture
			// C = (C2-C1)/C1
			if (discretizer*(secondBest - scoreBest) <= textureThreshold*scoreBest)
				return false;
		}

		// if requested perform right to left validation. Ideally the two disparities will be identical
		if (tolRightToLeft >= 0) {
			if (!scorer.processRightToLeft(x - bestDisparity - scorer.getDisparityMin(), y))
				return false;
			final int[] scoresRtoL = scorer.getScoreRtoL();
			final int localRangeRtoL = scorer.getLocalRangeRtoL();
			int bestDisparityRtoL = 0;
			int scoreBestRtoL = scoresRtoL[0];

			for (int i = 1; i < localRangeRtoL; i++) {
				if (scoresRtoL[i] < scoreBestRtoL) {
					scoreBestRtoL = scoresRtoL[i];
					bestDisparityRtoL = i;
				}
			}
			if (Math.abs(bestDisparityRtoL - bestDisparity) > tolRightToLeft)
				return false;
		}

		this.disparity = bestDisparity;

		return true;
	}
}