boofcv.alg.feature.orientation.impl.ImplOrientationAverageGradientIntegral Maven / Gradle / Ivy

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/*
 * Copyright (c) 2021, 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.feature.orientation.impl;

import boofcv.abst.feature.orientation.RegionOrientation;
import boofcv.alg.feature.describe.SurfDescribeOps;
import boofcv.alg.feature.orientation.OrientationIntegralBase;
import boofcv.struct.image.ImageGray;
import boofcv.struct.sparse.GradientValue;
import boofcv.struct.sparse.SparseGradientSafe;
import boofcv.struct.sparse.SparseImageGradient;

/**
 * 
 * Estimates the orientation of a region by computing the image derivative from an integral image.
 * The derivative along each axis is summed up and the angle computed from that.
 * 
 *
 * @author Peter Abeles
 */
public class ImplOrientationAverageGradientIntegral, G extends GradientValue>
		extends OrientationIntegralBase {
	/**
	 * @param sampleRadius Radius of the region being considered in terms of Wavelet samples. Typically 6.
	 * @param weightSigma Sigma for weighting distribution. Zero for unweighted.
	 */
	public ImplOrientationAverageGradientIntegral( double radiusToScale,
												   int sampleRadius, double period,
												   int kernelWidth, double weightSigma,
												   Class imageType ) {
		super(radiusToScale, sampleRadius, period, kernelWidth, weightSigma, true, imageType);
	}

	@Override
	public double compute( double c_x, double c_y ) {

		double period = scale*this.period;
		double tl_x = c_x - sampleRadius*period;
		double tl_y = c_y - sampleRadius*period;

		SparseImageGradient g;
		// use a faster algorithm if it is entirely inside
		if (!SurfDescribeOps.isInside(ii.width, ii.height, tl_x, tl_y, sampleWidth*period, kernelWidth*scale)) {
			g = new SparseGradientSafe<>(this.g);
		} else {
			g = this.g;
		}

		if (weights == null)
			return computeUnweighted(tl_x, tl_y, period, g);
		else
			return computeWeighted(tl_x, tl_y, period, g);
	}

	/**
	 * Compute the gradient while checking for border conditions
	 */
	protected double computeUnweighted( double tl_x, double tl_y,
										double samplePeriod,
										SparseImageGradient g ) {
		// add 0.5 to c_x and c_y to have it round
		tl_x += 0.5;
		tl_y += 0.5;

		double Dx = 0, Dy = 0;
		for (int y = 0; y < sampleWidth; y++) {
			int pixelsY = (int)(tl_y + y*samplePeriod);

			for (int x = 0; x < sampleWidth; x++) {
				int pixelsX = (int)(tl_x + x*samplePeriod);

				GradientValue v = g.compute(pixelsX, pixelsY);
				Dx += v.getX();
				Dy += v.getY();
			}
		}
		return Math.atan2(Dy, Dx);
	}

	/**
	 * Compute the gradient while checking for border conditions
	 */
	protected double computeWeighted( double tl_x, double tl_y,
									  double samplePeriod,
									  SparseImageGradient g ) {
		// add 0.5 to c_x and c_y to have it round
		tl_x += 0.5;
		tl_y += 0.5;

		double Dx = 0, Dy = 0;
		int i = 0;
		for (int y = 0; y < sampleWidth; y++) {
			int pixelsY = (int)(tl_y + y*samplePeriod);

			for (int x = 0; x < sampleWidth; x++, i++) {
				int pixelsX = (int)(tl_x + x*samplePeriod);

				double w = weights.data[i];
				GradientValue v = g.compute(pixelsX, pixelsY);
				Dx += w*v.getX();
				Dy += w*v.getY();
			}
		}

		return Math.atan2(Dy, Dx);
	}

	@Override
	public RegionOrientation copy() {
		return new ImplOrientationAverageGradientIntegral<>(
				objectRadiusToScale, sampleRadius, period, kernelWidth, weightSigma, getImageType());
	}
}