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BoofCV is an open source Java library for real-time computer vision and robotics applications.
/*
* Copyright (c) 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.transform.census;
import boofcv.struct.border.ImageBorder;
import boofcv.struct.border.ImageBorder_F32;
import boofcv.struct.border.ImageBorder_S32;
import boofcv.struct.image.*;
import georegression.struct.point.Point2D_I32;
import org.ddogleg.struct.DogArray_I32;
import org.ddogleg.struct.FastAccess;
import org.jetbrains.annotations.Nullable;
/**
* The Census Transform [1] computes a bit mask for each pixel in the image. If a neighboring pixel is greater than the
* center pixel in a region that bit is set to 1. A 3x3 region 9radius=1) is encoded in 8-bits and a 5x5 region
* (radius=2) in 24-bits. To compute the error between two pixels simply compute the hamming distance. The
* hamming distance for an input can be computed using DescriptorDistance.hamming().
*
* DEVELOPMENT NOTE: See if this can be speed up by only comparing each pixel with another once.
* Code will be complex
*
*
* [1] Zabih, Ramin, and John Woodfill. "Non-parametric local transforms for computing visual correspondence."
* European conference on computer vision. Springer, Berlin, Heidelberg, 1994.
*
*
* @author Peter Abeles
* @see CensusTransform
*/
public class GCensusTransform {
public static > void dense3x3( final T input, final GrayU8 output,
@Nullable ImageBorder border ) {
if (input.getClass() == GrayU8.class) {
CensusTransform.dense3x3((GrayU8)input, output, (ImageBorder_S32)border);
} else if (input.getClass() == GrayU16.class) {
CensusTransform.dense3x3((GrayU16)input, output, (ImageBorder_S32)border);
} else if (input.getClass() == GrayF32.class) {
CensusTransform.dense3x3((GrayF32)input, output, (ImageBorder_F32)border);
} else {
throw new IllegalArgumentException("Unknown input image type. " + input.getClass().getSimpleName());
}
}
public static > void dense5x5( final T input, final GrayS32 output,
@Nullable ImageBorder border ) {
if (input.getClass() == GrayU8.class) {
CensusTransform.dense5x5((GrayU8)input, output, (ImageBorder_S32)border);
} else if (input.getClass() == GrayU16.class) {
CensusTransform.dense5x5((GrayU16)input, output, (ImageBorder_S32)border);
} else if (input.getClass() == GrayF32.class) {
CensusTransform.dense5x5((GrayF32)input, output, (ImageBorder_F32)border);
} else {
throw new IllegalArgumentException("Unknown input image type. " + input.getClass().getSimpleName());
}
}
public static > void sample_S64( final T input, final FastAccess sample,
final GrayS64 output,
@Nullable ImageBorder border, @Nullable DogArray_I32 workSpace ) {
if (input.getClass() == GrayU8.class) {
CensusTransform.sample_S64((GrayU8)input, sample, output, (ImageBorder_S32)border, workSpace);
} else if (input.getClass() == GrayU16.class) {
CensusTransform.sample_S64((GrayU16)input, sample, output, (ImageBorder_S32)border, workSpace);
} else if (input.getClass() == GrayF32.class) {
CensusTransform.sample_S64((GrayF32)input, sample, output, (ImageBorder_F32)border, workSpace);
} else {
throw new IllegalArgumentException("Unknown input image type. " + input.getClass().getSimpleName());
}
}
public static > void sample_IU16( final T input, final FastAccess sample,
final InterleavedU16 output,
@Nullable ImageBorder border, @Nullable DogArray_I32 workSpace ) {
if (input.getClass() == GrayU8.class) {
CensusTransform.sample_IU16((GrayU8)input, sample, output, (ImageBorder_S32)border, workSpace);
} else if (input.getClass() == GrayU16.class) {
CensusTransform.sample_IU16((GrayU16)input, sample, output, (ImageBorder_S32)border, workSpace);
} else if (input.getClass() == GrayF32.class) {
CensusTransform.sample_IU16((GrayF32)input, sample, output, (ImageBorder_F32)border, workSpace);
} else {
throw new IllegalArgumentException("Unknown input image type. " + input.getClass().getSimpleName());
}
}
}