boofcv.alg.filter.derivative.HessianSobel 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.HessianSobel_Shared;
import boofcv.core.image.border.ImageBorder_F32;
import boofcv.core.image.border.ImageBorder_S32;
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 second derivative (Hessian) of an image using. This hessian is derived using the {@link GradientSobel}
* gradient function.
*
*
*
* WARNING: It is computationally more expensive to compute the Hessian with this operation than applying the Sobel
* gradient operator multiple times. However, this does not require the creation additional storage to save
* intermediate results.
*
*
*
* Kernel for ∂ 2f/∂ y2:
*
* 1 4 6 4 1
* 0 0 0 0 0
* -2 -8 -12 -8 -2
* 0 0 0 0 0
* 1 4 6 4 1
* 2f/∂ x∂y is:
*
* 1 2 0 -2 -1
* 2 4 0 -4 -2
* 0 0 0 0 0
* -2 -4 0 4 2
* -1 -2 0 2 1
*
*
* @author Peter Abeles
*/
public class HessianSobel {
public static Kernel2D_I32 kernelYY_I32 = new Kernel2D_I32(5, new int[]
{1, 4, 6 , 4, 1,
0, 0, 0 , 0, 0,
-2,-8,-12,-8,-2,
0, 0, 0 , 0, 0,
1, 4, 6 , 4, 1});
public static Kernel2D_I32 kernelXX_I32 = new Kernel2D_I32(5, new int[]
{1, 0,-2 , 0, 1,
4, 0,-8 , 0, 4,
6, 0,-12, 0, 6,
4, 0,-8 , 0, 4,
1, 0,-2 , 0, 1});
public static Kernel2D_I32 kernelXY_I32 = new Kernel2D_I32(5, new int[]
{1, 2,0,-2,-1,
2, 4,0,-4,-2,
0, 0,0, 0, 0,
-2,-4,0, 4, 2,
-1,-2,0, 2, 1});
public static Kernel2D_F32 kernelYY_F32 = new Kernel2D_F32(5, new float[]
{1, 4, 6 , 4, 1,
0, 0, 0 , 0, 0,
-2,-8,-12,-8,-2,
0, 0, 0 , 0, 0,
1, 4, 6 , 4, 1});
public static Kernel2D_F32 kernelXX_F32 = new Kernel2D_F32(5, new float[]
{1, 0,-2 , 0, 1,
4, 0,-8 , 0, 4,
6, 0,-12, 0, 6,
4, 0,-8 , 0, 4,
1, 0,-2 , 0, 1});
public static Kernel2D_F32 kernelXY_F32 = new Kernel2D_F32(5, new float[]
{1, 2,0,-2,-1,
2, 4,0,-4,-2,
0, 0,0, 0, 0,
-2,-4,0, 4, 2,
-1,-2,0, 2, 1});
/**
* Computes the image's second derivatives.
*
* @param orig Which which is to be differentiated. Not Modified.
* @param derivXX Second derivative along the x-axis. Modified.
* @param derivYY Second derivative along the y-axis. Modified.
* @param derivXY Second cross derivative. Modified.
* @param border Specifies how the image border is handled. If null the border is not processed.
*/
public static void process(GrayU8 orig,
GrayS16 derivXX, GrayS16 derivYY, GrayS16 derivXY ,
ImageBorder_S32 border ) {
InputSanityCheck.checkSameShape(orig, derivXX, derivYY, derivXY);
HessianSobel_Shared.process(orig, derivXX, derivYY, derivXY);
if( border != null ) {
border.setImage(orig);
ConvolveJustBorder_General.convolve(kernelXX_I32, border,derivXX);
ConvolveJustBorder_General.convolve(kernelYY_I32, border,derivYY);
ConvolveJustBorder_General.convolve(kernelXY_I32, border,derivXY);
}
}
/**
* Computes the image's second derivatives.
*
* @param orig Which which is to be differentiated. Not Modified.
* @param derivXX Second derivative along the x-axis. Modified.
* @param derivYY Second derivative along the y-axis. Modified.
* @param derivXY Second cross derivative. Modified.
* @param border Specifies how the image border is handled. If null the border is not processed.
*/
public static void process(GrayF32 orig,
GrayF32 derivXX, GrayF32 derivYY, GrayF32 derivXY ,
ImageBorder_F32 border ) {
InputSanityCheck.checkSameShape(orig, derivXX, derivYY, derivXY);
HessianSobel_Shared.process(orig, derivXX, derivYY, derivXY);
if( border != null ) {
border.setImage(orig);
ConvolveJustBorder_General.convolve(kernelXX_F32, border , derivXX);
ConvolveJustBorder_General.convolve(kernelYY_F32, border , derivYY);
ConvolveJustBorder_General.convolve(kernelXY_F32, border , derivXY);
}
}
}
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