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removed ABF from 2.4 because of low quality #2299

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removed ABF from 2.4 because of low quality #2299

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23 changes: 0 additions & 23 deletions modules/imgproc/doc/filtering.rst
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Expand Up @@ -412,29 +412,6 @@ http://www.dai.ed.ac.uk/CVonline/LOCAL\_COPIES/MANDUCHI1/Bilateral\_Filtering.ht
This filter does not work inplace.


adaptiveBilateralFilter
-----------------------
Applies the adaptive bilateral filter to an image.

.. 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: The source image

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

: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 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.

: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
----
Blurs an image using the normalized box filter.
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4 changes: 0 additions & 4 deletions modules/imgproc/include/opencv2/imgproc/imgproc.hpp
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Expand Up @@ -398,10 +398,6 @@ CV_EXPORTS_W void GaussianBlur( InputArray src,
CV_EXPORTS_W void bilateralFilter( InputArray src, OutputArray dst, int d,
double sigmaColor, double sigmaSpace,
int borderType=BORDER_DEFAULT );
//! smooths the image using adaptive bilateral filter
CV_EXPORTS_W void adaptiveBilateralFilter( InputArray src, OutputArray dst, Size ksize,
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,
Size ksize, Point anchor=Point(-1,-1),
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279 changes: 0 additions & 279 deletions modules/imgproc/src/smooth.cpp
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Expand Up @@ -2272,285 +2272,6 @@ void cv::bilateralFilter( InputArray _src, OutputArray _dst, int d,
"Bilateral filtering is only implemented for 8u and 32f images" );
}


/****************************************************************************************\
Adaptive Bilateral Filtering
\****************************************************************************************/

namespace cv
{
#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, 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;
CV_Assert((ksize.width & 1) && (ksize.height & 1));
space_weight.resize(ksize.width * ksize.height);
double sigma2 = sigma_space * sigma_space;
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++)
{
#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
{
int cn = dest->channels();
int anX = anchor.x;

const uchar *tptr;

for(int i = range.start;i < range.end; i++)
{
int startY = i;
if(cn == 1)
{
float var;
int currVal;
int sumVal = 0;
int sumValSqr = 0;
int currValCenter;
int currWRTCenter;
float weight;
float totalWeight = 0.;
float tmpSum = 0.;

for(int j = 0;j < dest->cols *cn; j+=cn)
{
sumVal = 0;
sumValSqr= 0;
totalWeight = 0.;
tmpSum = 0.;

// Top row: don't sum the very last element
int startLMJ = 0;
int endLMJ = ksize.width - 1;
int howManyAll = (anX *2 +1)*(ksize.width );
#if ABF_CALCVAR
for(int x = startLMJ; x< endLMJ; x++)
{
tptr = temp->ptr(startY + x) +j;
for(int y=-anX; y<=anX; y++)
{
currVal = tptr[cn*(y+anX)];
sumVal += currVal;
sumValSqr += (currVal *currVal);
}
}
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 = maxSigmaColor*maxSigmaColor;
#endif
startLMJ = 0;
endLMJ = ksize.width;
tptr = temp->ptr(startY + (startLMJ+ endLMJ)/2);
currValCenter =tptr[j+cn*anX];
for(int x = startLMJ; x< endLMJ; x++)
{
tptr = temp->ptr(startY + x) +j;
for(int y=-anX; y<=anX; y++)
{
#if ABF_FIXED_WEIGHT
weight = 1.0;
#else
currVal = tptr[cn*(y+anX)];
currWRTCenter = currVal - currValCenter;

#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;
}
}
tmpSum /= totalWeight;

dest->at<uchar>(startY ,j)= static_cast<uchar>(tmpSum);
}
}
else
{
assert(cn == 3);
float var_b, var_g, var_r;
int currVal_b, currVal_g, currVal_r;
int sumVal_b= 0, sumVal_g= 0, sumVal_r= 0;
int sumValSqr_b= 0, sumValSqr_g= 0, sumValSqr_r= 0;
int currValCenter_b= 0, currValCenter_g= 0, currValCenter_r= 0;
int currWRTCenter_b, currWRTCenter_g, currWRTCenter_r;
float weight_b, weight_g, weight_r;
float totalWeight_b= 0., totalWeight_g= 0., totalWeight_r= 0.;
float tmpSum_b = 0., tmpSum_g= 0., tmpSum_r = 0.;

for(int j = 0;j < dest->cols *cn; j+=cn)
{
sumVal_b= 0, sumVal_g= 0, sumVal_r= 0;
sumValSqr_b= 0, sumValSqr_g= 0, sumValSqr_r= 0;
totalWeight_b= 0., totalWeight_g= 0., totalWeight_r= 0.;
tmpSum_b = 0., tmpSum_g= 0., tmpSum_r = 0.;

// Top row: don't sum the very last element
int startLMJ = 0;
int endLMJ = ksize.width - 1;
int howManyAll = (anX *2 +1)*(ksize.width);
#if ABF_CALCVAR
float max_var = (float)( maxSigma_Color*maxSigma_Color);
for(int x = startLMJ; x< endLMJ; x++)
{
tptr = temp->ptr(startY + x) +j;
for(int y=-anX; y<=anX; y++)
{
currVal_b = tptr[cn*(y+anX)], currVal_g = tptr[cn*(y+anX)+1], currVal_r =tptr[cn*(y+anX)+2];
sumVal_b += currVal_b;
sumVal_g += currVal_g;
sumVal_r += currVal_r;
sumValSqr_b += (currVal_b *currVal_b);
sumValSqr_g += (currVal_g *currVal_g);
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));

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 = maxSigma_Color*maxSigma_Color; var_g = maxSigma_Color*maxSigma_Color; var_r = maxSigma_Color*maxSigma_Color;
#endif
startLMJ = 0;
endLMJ = ksize.width;
tptr = temp->ptr(startY + (startLMJ+ endLMJ)/2) + j;
currValCenter_b =tptr[cn*anX], currValCenter_g =tptr[cn*anX+1], currValCenter_r =tptr[cn*anX+2];
for(int x = startLMJ; x< endLMJ; x++)
{
tptr = temp->ptr(startY + x) +j;
for(int y=-anX; y<=anX; y++)
{
#if ABF_FIXED_WEIGHT
weight_b = 1.0;
weight_g = 1.0;
weight_r = 1.0;
#else
currVal_b = tptr[cn*(y+anX)];currVal_g=tptr[cn*(y+anX)+1];currVal_r=tptr[cn*(y+anX)+2];
currWRTCenter_b = currVal_b - currValCenter_b;
currWRTCenter_g = currVal_g - currValCenter_g;
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);
tmpSum_r += ((float)tptr[cn*(y+anX)+2] * weight_r);
totalWeight_b += weight_b, totalWeight_g += weight_g, totalWeight_r += weight_r;
}
}
tmpSum_b /= totalWeight_b;
tmpSum_g /= totalWeight_g;
tmpSum_r /= totalWeight_r;

dest->at<uchar>(startY,j )= static_cast<uchar>(tmpSum_b);
dest->at<uchar>(startY,j+1)= static_cast<uchar>(tmpSum_g);
dest->at<uchar>(startY,j+2)= static_cast<uchar>(tmpSum_r);
}
}
}
}
private:
const Mat *temp;
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, double maxSigmaColor, Point anchor, int borderType )
{
Size size = src.size();

CV_Assert( (src.type() == CV_8UC1 || src.type() == CV_8UC3) &&
src.type() == dst.type() && src.size() == dst.size() &&
src.data != dst.data );
Mat temp;
copyMakeBorder(src, temp, anchor.x, anchor.y, anchor.x, anchor.y, borderType);

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, double maxSigmaColor, Point anchor, int borderType )
{
Mat src = _src.getMat();
_dst.create(src.size(), src.type());
Mat dst = _dst.getMat();

CV_Assert(src.type() == CV_8UC1 || src.type() == CV_8UC3);

anchor = normalizeAnchor(anchor,ksize);
if( src.depth() == CV_8U )
adaptiveBilateralFilter_8u( src, dst, ksize, sigmaSpace, maxSigmaColor, anchor, borderType );
else
CV_Error( CV_StsUnsupportedFormat,
"Adaptive Bilateral filtering is only implemented for 8u images" );
}

//////////////////////////////////////////////////////////////////////////////////////////

CV_IMPL void
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31 changes: 0 additions & 31 deletions modules/ocl/doc/image_filtering.rst
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Expand Up @@ -499,37 +499,6 @@ Returns void

Applies bilateral filter to the image. Supports 8UC1 8UC4 data types.

ocl::adaptiveBilateralFilter
--------------------------------
Returns void

.. 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. 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 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.

: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.

Local memory organization


.. image:: images/adaptiveBilateralFilter.jpg
:height: 250pt
:width: 350pt
:alt: Introduction Icon

.. note:: We partition the image to non-overlapping blocks of size (Ux, Uy). Each such block will correspond to the pixel locations where we will calculate the filter result in one workgroup. Considering neighbourhoods of sizes (kx, ky), where kx = 2 dx + 1, and ky = 2 dy + 1 (in image ML, dx = dy = 1, and kx = ky = 3), it is clear that we need to load data of size Wx = Ux + 2 dx, Wy = Uy + 2 dy. Furthermore, if (Sx, Sy) is the top left pixel coordinates for a particular block, and (Sx + Ux - 1, Sy + Uy -1) is to botom right coordinate of the block, we need to load data starting at top left coordinate (PSx, PSy) = (Sx - dx, Sy - dy), and ending at bottom right coordinate (Sx + Ux - 1 + dx, Sy + Uy - 1 + dy). The workgroup layout is (Wx,1). However, to take advantage of the natural hardware properties (preferred wavefront sizes), we restrict Wx to be a multiple of that preferred wavefront size (for current AMD hardware this is typically 64). Each thread in the workgroup will load Wy elements (under the constraint that Wx*Wy*pixel width <= max local memory).

Applies bilateral filter to the image. Supports 8UC1 8UC3 data types.

ocl::copyMakeBorder
-----------------------
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8 changes: 0 additions & 8 deletions modules/ocl/include/opencv2/ocl/ocl.hpp
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Expand Up @@ -550,14 +550,6 @@ namespace cv
// supports 8UC1 8UC4
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
// 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, 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
CV_EXPORTS void exp(const oclMat &src, oclMat &dst);
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