boofcv.alg.filter.derivative.GradientSobel Maven / Gradle / Ivy

/*
 * 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.filter.derivative;

import boofcv.alg.InputSanityCheck;
import boofcv.alg.filter.convolve.border.ConvolveJustBorder_General;
import boofcv.alg.filter.derivative.impl.GradientSobel_Outer;
import boofcv.alg.filter.derivative.impl.GradientSobel_UnrolledOuter;
import boofcv.core.image.border.ImageBorder_F32;
import boofcv.core.image.border.ImageBorder_S32;
import boofcv.struct.convolve.Kernel2D;
import boofcv.struct.convolve.Kernel2D_F32;
import boofcv.struct.convolve.Kernel2D_I32;
import boofcv.struct.image.GrayF32;
import boofcv.struct.image.GrayS16;
import boofcv.struct.image.GrayU8;

/**
 * 
 * Computes the image's first derivative along the x and y axises using the Sobel operator.
 * 
 * 
 * The Sobel kernel weights the inner most pixels more than ones farther away.  This tends to produce better results,
 * but not as good as a gaussian kernel with larger kernel.  However, it can be optimized so that it is much faster than a Gaussian.
 * 
 * 
 * For integer images, the derivatives in the x and y direction are computed by convolving the following kernels:

 * 

 * y-axis

 * 
 * 
 * 
 * 
 * 
 *   -0.25   -0.5   -0.25  
  0   0   0  
  0.25   0.5   0.25  
 * 
 * 
 * 
 * 
 *
 * @author Peter Abeles
 */
public class GradientSobel {

	public static Kernel2D_I32 kernelDerivX_I32 = new Kernel2D_I32(3, new int[]{-1,0,1,-2,0,2,-1,0,1});
	public static Kernel2D_I32 kernelDerivY_I32 = new Kernel2D_I32(3, new int[]{-1,-2,-1,0,0,0,1,2,1});
	public static Kernel2D_F32 kernelDerivX_F32 = new Kernel2D_F32(
			3, new float[]{-0.25f,0,0.25f,-0.5f,0,0.5f,-0.25f,0,0.25f});
	public static Kernel2D_F32 kernelDerivY_F32 = new Kernel2D_F32(
			3, new float[]{-0.25f,-0.5f,-0.25f,0,0,0,0.25f,0.5f,0.25f});

	/**
	 * Returns the kernel for computing the derivative along the x-axis.
	 */
	public static Kernel2D getKernelX( boolean isInteger ) {
		if( isInteger )
			return kernelDerivX_I32;
		else
			return kernelDerivX_F32;
	}

	/**
	 * Computes the derivative in the X and Y direction using an integer Sobel edge detector.
	 *
	 * @param orig   Input image.  Not modified.
	 * @param derivX Storage for image derivative along the x-axis. Modified.
	 * @param derivY Storage for image derivative along the y-axis. Modified.
	 * @param border Specifies how the image border is handled. If null the border is not processed.
	 */
	public static void process(GrayU8 orig, GrayS16 derivX, GrayS16 derivY, ImageBorder_S32 border ) {
		InputSanityCheck.checkSameShape(orig, derivX, derivY);
		GradientSobel_Outer.process_I8_sub(orig, derivX, derivY);

		if( border != null ) {
			border.setImage(orig);
			ConvolveJustBorder_General.convolve(kernelDerivX_I32, border,derivX);
			ConvolveJustBorder_General.convolve(kernelDerivY_I32, border,derivY);
		}
	}

	/**
	 * Computes the derivative in the X and Y direction using an integer Sobel edge detector.
	 *
	 * @param orig   Input image.  Not modified.
	 * @param derivX Storage for image derivative along the x-axis. Modified.
	 * @param derivY Storage for image derivative along the y-axis. Modified.
	 * @param border Specifies how the image border is handled. If null the border is not processed.
	 */
	public static void process(GrayS16 orig, GrayS16 derivX, GrayS16 derivY, ImageBorder_S32 border ) {
		InputSanityCheck.checkSameShape(orig, derivX, derivY);
		GradientSobel_Outer.process_I8_sub(orig, derivX, derivY);

		if( border != null ) {
			border.setImage(orig);
			ConvolveJustBorder_General.convolve(kernelDerivX_I32, border,derivX);
			ConvolveJustBorder_General.convolve(kernelDerivY_I32, border,derivY);
		}
	}

	/**
	 * Computes the derivative in the X and Y direction using an integer Sobel edge detector.
	 *
	 * @param orig   Input image.  Not modified.
	 * @param derivX Storage for image derivative along the x-axis. Modified.
	 * @param derivY Storage for image derivative along the y-axis. Modified.
	 * @param border Specifies how the image border is handled. If null the border is not processed.
	 */
	public static void process(GrayF32 orig, GrayF32 derivX, GrayF32 derivY, ImageBorder_F32 border) {
		InputSanityCheck.checkSameShape(orig, derivX, derivY);

//		GradientSobel_Outer.process_F32(orig, derivX, derivY);
		GradientSobel_UnrolledOuter.process_F32_sub(orig, derivX, derivY);

		if( border != null ) {
			border.setImage(orig);
			ConvolveJustBorder_General.convolve(kernelDerivX_F32, border,derivX);
			ConvolveJustBorder_General.convolve(kernelDerivY_F32, border,derivY);
		}
	}
}
 













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