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Merge pull request opencv#1758 from apavlenko:adaptive_bilateral_filter
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Andrey Pavlenko authored and OpenCV Buildbot committed Nov 6, 2013
2 parents bca6308 + a1de91a commit 9afe65e
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17 changes: 8 additions & 9 deletions modules/imgproc/doc/filtering.rst
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Expand Up @@ -416,24 +416,23 @@ adaptiveBilateralFilter
-----------------------
Applies the adaptive bilateral filter to an image.

.. ocv:function:: void adaptiveBilateralFilter( InputArray src, OutputArray dst, Size ksize, double sigmaSpace, Point anchor=Point(-1, -1), int borderType=BORDER_DEFAULT )
.. ocv:function:: void adaptiveBilateralFilter( InputArray src, OutputArray dst, Size ksize, double sigmaSpace, double maxSigmaColor = 20.0, Point anchor=Point(-1, -1), int borderType=BORDER_DEFAULT )
.. ocv:pyfunction:: cv2.adaptiveBilateralFilter(src, ksize, sigmaSpace[, dst[, anchor[, borderType]]]) -> dst
:param src: Source 8-bit, 1-channel or 3-channel image.

:param dst: Destination image of the same size and type as ``src`` .
:param src: The source image

:param ksize: filter kernel size.
:param dst: The destination image; will have the same size and the same type as src

:param sigmaSpace: Filter sigma in the coordinate space. It has similar meaning with ``sigmaSpace`` in ``bilateralFilter``.
:param ksize: The kernel size. This is the neighborhood where the local variance will be calculated, and where pixels will contribute (in a weighted manner).

:param anchor: anchor point; default value ``Point(-1,-1)`` means that the anchor is at the kernel center. Only default value is supported now.
:param sigmaSpace: Filter sigma in the coordinate space. Larger value of the parameter means that farther pixels will influence each other (as long as their colors are close enough; see sigmaColor). Then d>0, it specifies the neighborhood size regardless of sigmaSpace, otherwise d is proportional to sigmaSpace.

:param borderType: border mode used to extrapolate pixels outside of the image.
:param maxSigmaColor: Maximum allowed sigma color (will clamp the value calculated in the ksize neighborhood. Larger value of the parameter means that more dissimilar pixels will influence each other (as long as their colors are close enough; see sigmaColor). Then d>0, it specifies the neighborhood size regardless of sigmaSpace, otherwise d is proportional to sigmaSpace.

The function applies adaptive bilateral filtering to the input image. This filter is similar to ``bilateralFilter``, in that dissimilarity from and distance to the center pixel is punished. Instead of using ``sigmaColor``, we employ the variance of pixel values in the neighbourhood.
:param borderType: Pixel extrapolation method.

A main part of our strategy will be to load each raw pixel once, and reuse it to calculate all pixels in the output (filtered) image that need this pixel value. The math of the filter is that of the usual bilateral filter, except that the sigma color is calculated in the neighborhood, and clamped by the optional input value.


blur
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2 changes: 1 addition & 1 deletion modules/imgproc/include/opencv2/imgproc/imgproc.hpp
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Expand Up @@ -400,7 +400,7 @@ CV_EXPORTS_W void bilateralFilter( InputArray src, OutputArray dst, int d,
int borderType=BORDER_DEFAULT );
//! smooths the image using adaptive bilateral filter
CV_EXPORTS_W void adaptiveBilateralFilter( InputArray src, OutputArray dst, Size ksize,
double sigmaSpace, Point anchor=Point(-1, -1),
double sigmaSpace, double maxSigmaColor = 20.0, Point anchor=Point(-1, -1),
int borderType=BORDER_DEFAULT );
//! smooths the image using the box filter. Each pixel is processed in O(1) time
CV_EXPORTS_W void boxFilter( InputArray src, OutputArray dst, int ddepth,
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85 changes: 67 additions & 18 deletions modules/imgproc/src/smooth.cpp
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Expand Up @@ -2279,15 +2279,24 @@ void cv::bilateralFilter( InputArray _src, OutputArray _dst, int d,

namespace cv
{
#define CALCVAR 1
#define FIXED_WEIGHT 0
#ifndef ABF_CALCVAR
#define ABF_CALCVAR 1
#endif

#ifndef ABF_FIXED_WEIGHT
#define ABF_FIXED_WEIGHT 0
#endif

#ifndef ABF_GAUSSIAN
#define ABF_GAUSSIAN 1
#endif

class adaptiveBilateralFilter_8u_Invoker :
public ParallelLoopBody
{
public:
adaptiveBilateralFilter_8u_Invoker(Mat& _dest, const Mat& _temp, Size _ksize, double _sigma_space, Point _anchor) :
temp(&_temp), dest(&_dest), ksize(_ksize), sigma_space(_sigma_space), anchor(_anchor)
adaptiveBilateralFilter_8u_Invoker(Mat& _dest, const Mat& _temp, Size _ksize, double _sigma_space, double _maxSigmaColor, Point _anchor) :
temp(&_temp), dest(&_dest), ksize(_ksize), sigma_space(_sigma_space), maxSigma_Color(_maxSigmaColor), anchor(_anchor)
{
if( sigma_space <= 0 )
sigma_space = 1;
Expand All @@ -2300,7 +2309,11 @@ class adaptiveBilateralFilter_8u_Invoker :
for(int y=-h; y<=h; y++)
for(int x=-w; x<=w; x++)
{
#if ABF_GAUSSIAN
space_weight[idx++] = (float)exp ( -0.5*(x * x + y * y)/sigma2);
#else
space_weight[idx++] = (float)(sigma2 / (sigma2 + x * x + y * y));
#endif
}
}
virtual void operator()(const Range& range) const
Expand Down Expand Up @@ -2336,7 +2349,7 @@ class adaptiveBilateralFilter_8u_Invoker :
int startLMJ = 0;
int endLMJ = ksize.width - 1;
int howManyAll = (anX *2 +1)*(ksize.width );
#if CALCVAR
#if ABF_CALCVAR
for(int x = startLMJ; x< endLMJ; x++)
{
tptr = temp->ptr(startY + x) +j;
Expand All @@ -2348,8 +2361,14 @@ class adaptiveBilateralFilter_8u_Invoker :
}
}
var = ( (sumValSqr * howManyAll)- sumVal * sumVal ) / ( (float)(howManyAll*howManyAll));

if(var < 0.01)
var = 0.01f;
else if(var > (float)(maxSigma_Color*maxSigma_Color) )
var = (float)(maxSigma_Color*maxSigma_Color) ;

#else
var = 900.0;
var = maxSigmaColor*maxSigmaColor;
#endif
startLMJ = 0;
endLMJ = ksize.width;
Expand All @@ -2360,13 +2379,18 @@ class adaptiveBilateralFilter_8u_Invoker :
tptr = temp->ptr(startY + x) +j;
for(int y=-anX; y<=anX; y++)
{
#if FIXED_WEIGHT
#if ABF_FIXED_WEIGHT
weight = 1.0;
#else
currVal = tptr[cn*(y+anX)];
currWRTCenter = currVal - currValCenter;

weight = var / ( var + (currWRTCenter * currWRTCenter) ) * space_weight[x*ksize.width+y+anX];;
#if ABF_GAUSSIAN
weight = exp ( -0.5f * currWRTCenter * currWRTCenter/var ) * space_weight[x*ksize.width+y+anX];
#else
weight = var / ( var + (currWRTCenter * currWRTCenter) ) * space_weight[x*ksize.width+y+anX];
#endif

#endif
tmpSum += ((float)tptr[cn*(y+anX)] * weight);
totalWeight += weight;
Expand Down Expand Up @@ -2401,7 +2425,8 @@ class adaptiveBilateralFilter_8u_Invoker :
int startLMJ = 0;
int endLMJ = ksize.width - 1;
int howManyAll = (anX *2 +1)*(ksize.width);
#if CALCVAR
#if ABF_CALCVAR
float max_var = (float)( maxSigma_Color*maxSigma_Color);
for(int x = startLMJ; x< endLMJ; x++)
{
tptr = temp->ptr(startY + x) +j;
Expand All @@ -2416,11 +2441,27 @@ class adaptiveBilateralFilter_8u_Invoker :
sumValSqr_r += (currVal_r *currVal_r);
}
}
var_b = ( (sumValSqr_b * howManyAll)- sumVal_b * sumVal_b ) / ( (float)(howManyAll*howManyAll));
var_g = ( (sumValSqr_g * howManyAll)- sumVal_g * sumVal_g ) / ( (float)(howManyAll*howManyAll));
var_r = ( (sumValSqr_r * howManyAll)- sumVal_r * sumVal_r ) / ( (float)(howManyAll*howManyAll));
var_b = ( (sumValSqr_b * howManyAll)- sumVal_b * sumVal_b ) / ( (float)(howManyAll*howManyAll));
var_g = ( (sumValSqr_g * howManyAll)- sumVal_g * sumVal_g ) / ( (float)(howManyAll*howManyAll));
var_r = ( (sumValSqr_r * howManyAll)- sumVal_r * sumVal_r ) / ( (float)(howManyAll*howManyAll));

if(var_b < 0.01)
var_b = 0.01f;
else if(var_b > max_var )
var_b = (float)(max_var) ;

if(var_g < 0.01)
var_g = 0.01f;
else if(var_g > max_var )
var_g = (float)(max_var) ;

if(var_r < 0.01)
var_r = 0.01f;
else if(var_r > max_var )
var_r = (float)(max_var) ;

#else
var_b = 900.0; var_g = 900.0;var_r = 900.0;
var_b = maxSigma_Color*maxSigma_Color; var_g = maxSigma_Color*maxSigma_Color; var_r = maxSigma_Color*maxSigma_Color;
#endif
startLMJ = 0;
endLMJ = ksize.width;
Expand All @@ -2431,7 +2472,7 @@ class adaptiveBilateralFilter_8u_Invoker :
tptr = temp->ptr(startY + x) +j;
for(int y=-anX; y<=anX; y++)
{
#if FIXED_WEIGHT
#if ABF_FIXED_WEIGHT
weight_b = 1.0;
weight_g = 1.0;
weight_r = 1.0;
Expand All @@ -2442,9 +2483,16 @@ class adaptiveBilateralFilter_8u_Invoker :
currWRTCenter_r = currVal_r - currValCenter_r;

float cur_spw = space_weight[x*ksize.width+y+anX];

#if ABF_GAUSSIAN
weight_b = exp( -0.5f * currWRTCenter_b * currWRTCenter_b/ var_b ) * cur_spw;
weight_g = exp( -0.5f * currWRTCenter_g * currWRTCenter_g/ var_g ) * cur_spw;
weight_r = exp( -0.5f * currWRTCenter_r * currWRTCenter_r/ var_r ) * cur_spw;
#else
weight_b = var_b / ( var_b + (currWRTCenter_b * currWRTCenter_b) ) * cur_spw;
weight_g = var_g / ( var_g + (currWRTCenter_g * currWRTCenter_g) ) * cur_spw;
weight_r = var_r / ( var_r + (currWRTCenter_r * currWRTCenter_r) ) * cur_spw;
#endif
#endif
tmpSum_b += ((float)tptr[cn*(y+anX)] * weight_b);
tmpSum_g += ((float)tptr[cn*(y+anX)+1] * weight_g);
Expand All @@ -2468,10 +2516,11 @@ class adaptiveBilateralFilter_8u_Invoker :
Mat *dest;
Size ksize;
double sigma_space;
double maxSigma_Color;
Point anchor;
vector<float> space_weight;
};
static void adaptiveBilateralFilter_8u( const Mat& src, Mat& dst, Size ksize, double sigmaSpace, Point anchor, int borderType )
static void adaptiveBilateralFilter_8u( const Mat& src, Mat& dst, Size ksize, double sigmaSpace, double maxSigmaColor, Point anchor, int borderType )
{
Size size = src.size();

Expand All @@ -2481,12 +2530,12 @@ static void adaptiveBilateralFilter_8u( const Mat& src, Mat& dst, Size ksize, do
Mat temp;
copyMakeBorder(src, temp, anchor.x, anchor.y, anchor.x, anchor.y, borderType);

adaptiveBilateralFilter_8u_Invoker body(dst, temp, ksize, sigmaSpace, anchor);
adaptiveBilateralFilter_8u_Invoker body(dst, temp, ksize, sigmaSpace, maxSigmaColor, anchor);
parallel_for_(Range(0, size.height), body, dst.total()/(double)(1<<16));
}
}
void cv::adaptiveBilateralFilter( InputArray _src, OutputArray _dst, Size ksize,
double sigmaSpace, Point anchor, int borderType )
double sigmaSpace, double maxSigmaColor, Point anchor, int borderType )
{
Mat src = _src.getMat();
_dst.create(src.size(), src.type());
Expand All @@ -2496,7 +2545,7 @@ void cv::adaptiveBilateralFilter( InputArray _src, OutputArray _dst, Size ksize,

anchor = normalizeAnchor(anchor,ksize);
if( src.depth() == CV_8U )
adaptiveBilateralFilter_8u( src, dst, ksize, sigmaSpace, anchor, borderType );
adaptiveBilateralFilter_8u( src, dst, ksize, sigmaSpace, maxSigmaColor, anchor, borderType );
else
CV_Error( CV_StsUnsupportedFormat,
"Adaptive Bilateral filtering is only implemented for 8u images" );
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12 changes: 5 additions & 7 deletions modules/ocl/doc/image_filtering.rst
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Expand Up @@ -497,23 +497,21 @@ ocl::adaptiveBilateralFilter
--------------------------------
Returns void

.. ocv:function:: void ocl::adaptiveBilateralFilter(const oclMat& src, oclMat& dst, Size ksize, double sigmaSpace, Point anchor = Point(-1, -1), int borderType=BORDER_DEFAULT)
.. ocv:function:: void ocl::adaptiveBilateralFilter(const oclMat& src, oclMat& dst, Size ksize, double sigmaSpace, double maxSigmaColor = 20.0, Point anchor = Point(-1, -1), int borderType=BORDER_DEFAULT)
:param src: The source image

:param dst: The destination image; will have the same size and the same type as src

:param ksize: The kernel size
:param ksize: The kernel size. This is the neighborhood where the local variance will be calculated, and where pixels will contribute (in a weighted manner).

:param sigmaSpace: Filter sigma in the coordinate space. Larger value of the parameter means that farther pixels will influence each other (as long as their colors are close enough; see sigmaColor). Then d>0, it specifies the neighborhood size regardless of sigmaSpace, otherwise d is proportional to sigmaSpace.

:param borderType: Pixel extrapolation method.

A main part of our strategy will be to load each raw pixel once, and reuse it to calculate all pixels in the output (filtered) image that need this pixel value.
:param maxSigmaColor: Maximum allowed sigma color (will clamp the value calculated in the ksize neighborhood. Larger value of the parameter means that more dissimilar pixels will influence each other (as long as their colors are close enough; see sigmaColor). Then d>0, it specifies the neighborhood size regardless of sigmaSpace, otherwise d is proportional to sigmaSpace.

.. math::
:param borderType: Pixel extrapolation method.

\emph{O}_i = \frac{1}{W_i}\sum\limits_{j\in{N(i)}}{\frac{1}{1+\frac{(V_i-V_j)^2}{\sigma_{N{'}(i)}^2}}*\frac{1}{1+\frac{d(i,j)^2}{\sum^2}}}V_j
A main part of our strategy will be to load each raw pixel once, and reuse it to calculate all pixels in the output (filtered) image that need this pixel value. The math of the filter is that of the usual bilateral filter, except that the sigma color is calculated in the neighborhood, and clamped by the optional input value.

Local memory organization

Expand Down
9 changes: 5 additions & 4 deletions modules/ocl/include/opencv2/ocl/ocl.hpp
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Original file line number Diff line number Diff line change
Expand Up @@ -553,11 +553,12 @@ namespace cv
CV_EXPORTS void bilateralFilter(const oclMat& src, oclMat& dst, int d, double sigmaColor, double sigmaSpace, int borderType=BORDER_DEFAULT);

//! Applies an adaptive bilateral filter to the input image
// This is not truly a bilateral filter. Instead of using user provided fixed parameters,
// the function calculates a constant at each window based on local standard deviation,
// and use this constant to do filtering.
// Unlike the usual bilateral filter that uses fixed value for sigmaColor,
// the adaptive version calculates the local variance in he ksize neighborhood
// and use this as sigmaColor, for the value filtering. However, the local standard deviation is
// clamped to the maxSigmaColor.
// supports 8UC1, 8UC3
CV_EXPORTS void adaptiveBilateralFilter(const oclMat& src, oclMat& dst, Size ksize, double sigmaSpace, Point anchor = Point(-1, -1), int borderType=BORDER_DEFAULT);
CV_EXPORTS void adaptiveBilateralFilter(const oclMat& src, oclMat& dst, Size ksize, double sigmaSpace, double maxSigmaColor=20.0, Point anchor = Point(-1, -1), int borderType=BORDER_DEFAULT);

//! computes exponent of each matrix element (dst = e**src)
// supports only CV_32FC1, CV_64FC1 type
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30 changes: 23 additions & 7 deletions modules/ocl/src/filtering.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -20,6 +20,7 @@
// Zero Lin, [email protected]
// Zhang Ying, [email protected]
// Yao Wang, [email protected]
// Harris Gasparakis, [email protected]
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
Expand Down Expand Up @@ -1407,7 +1408,7 @@ void cv::ocl::GaussianBlur(const oclMat &src, oclMat &dst, Size ksize, double si
////////////////////////////////////////////////////////////////////////////////////////////////////
// Adaptive Bilateral Filter

void cv::ocl::adaptiveBilateralFilter(const oclMat& src, oclMat& dst, Size ksize, double sigmaSpace, Point anchor, int borderType)
void cv::ocl::adaptiveBilateralFilter(const oclMat& src, oclMat& dst, Size ksize, double sigmaSpace, double maxSigmaColor, Point anchor, int borderType)
{
CV_Assert((ksize.width & 1) && (ksize.height & 1)); // ksize must be odd
CV_Assert(src.type() == CV_8UC1 || src.type() == CV_8UC3); // source must be 8bit RGB image
Expand All @@ -1418,18 +1419,32 @@ void cv::ocl::adaptiveBilateralFilter(const oclMat& src, oclMat& dst, Size ksize
int idx = 0;
int w = ksize.width / 2;
int h = ksize.height / 2;
for(int y=-h; y<=h; y++)
for(int x=-w; x<=w; x++)

int ABF_GAUSSIAN_ocl = 1;

if(ABF_GAUSSIAN_ocl)
{
lut.at<float>(idx++) = sigma2 / (sigma2 + x * x + y * y);
for(int y=-h; y<=h; y++)
for(int x=-w; x<=w; x++)
{
lut.at<float>(idx++) = expf( (float)(-0.5 * (x * x + y * y)/sigma2));
}
}
else
{
for(int y=-h; y<=h; y++)
for(int x=-w; x<=w; x++)
{
lut.at<float>(idx++) = (float) (sigma2 / (sigma2 + x * x + y * y));
}
}

oclMat dlut(lut);
int depth = src.depth();
int cn = src.oclchannels();

normalizeAnchor(anchor, ksize);
const static String kernelName = "edgeEnhancingFilter";
const static String kernelName = "adaptiveBilateralFilter";

dst.create(src.size(), src.type());

Expand Down Expand Up @@ -1478,9 +1493,10 @@ void cv::ocl::adaptiveBilateralFilter(const oclMat& src, oclMat& dst, Size ksize

//LDATATYPESIZE is sizeof local data store. This is to exemplify effect of LDS on kernel performance
sprintf(build_options,
"-D VAR_PER_CHANNEL=1 -D CALCVAR=1 -D FIXED_WEIGHT=0 -D EXTRA=%d"
"-D VAR_PER_CHANNEL=1 -D CALCVAR=1 -D FIXED_WEIGHT=0 -D EXTRA=%d -D MAX_VAR_VAL=%f -D ABF_GAUSSIAN=%d"
" -D THREADS=%d -D anX=%d -D anY=%d -D ksX=%d -D ksY=%d -D %s",
static_cast<int>(EXTRA), static_cast<int>(blockSizeX), anchor.x, anchor.y, ksize.width, ksize.height, btype);
static_cast<int>(EXTRA), static_cast<float>(maxSigmaColor*maxSigmaColor), static_cast<int>(ABF_GAUSSIAN_ocl),
static_cast<int>(blockSizeX), anchor.x, anchor.y, ksize.width, ksize.height, btype);

std::vector<pair<size_t , const void *> > args;
args.push_back(std::make_pair(sizeof(cl_mem), &src.data));
Expand Down
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