:param weights2: Weights for second image. Must have tha same size as ``img2`` . Supports only ``CV_32F`` type.
:param result: Destination image.
+
+ocl::medianFilter
+--------------------
+Blurs an image using the median filter.
+
+.. ocv:function:: void ocl::medianFilter(const oclMat &src, oclMat &dst, int m)
+
+ :param src: input ```1-``` or ```4```-channel image; the image depth should be ```CV_8U```, ```CV_32F```.
+
+ :param dst: destination array of the same size and type as ```src```.
+
+ :param m: aperture linear size; it must be odd and greater than ```1```. Currently only ```3```, ```5``` are supported.
+
+The function smoothes an image using the median filter with the \texttt{m} \times \texttt{m} aperture. Each channel of a multi-channel image is processed independently. In-place operation is supported.
//! Applies a generic geometrical transformation to an image.
// Supports INTER_NEAREST, INTER_LINEAR.
-
// Map1 supports CV_16SC2, CV_32FC2 types.
-
// Src supports CV_8UC1, CV_8UC2, CV_8UC4.
-
CV_EXPORTS void remap(const oclMat &src, oclMat &dst, oclMat &map1, oclMat &map2, int interpolation, int bordertype, const Scalar &value = Scalar());
//! copies 2D array to a larger destination array and pads borders with user-specifiable constant
CV_EXPORTS void copyMakeBorder(const oclMat &src, oclMat &dst, int top, int bottom, int left, int right, int boardtype, const Scalar &value = Scalar());
//! Smoothes image using median filter
- // The source 1- or 4-channel image. When m is 3 or 5, the image depth should be CV 8U or CV 32F.
+ // The source 1- or 4-channel image. m should be 3 or 5, the image depth should be CV_8U or CV_32F.
CV_EXPORTS void medianFilter(const oclMat &src, oclMat &dst, int m);
//! warps the image using affine transformation
(src.rows == dst.rows));
CV_Assert((src.oclchannels() == dst.oclchannels()));
- int srcStep = src.step1() / src.oclchannels();
- int dstStep = dst.step1() / dst.oclchannels();
- int srcOffset = src.offset / src.elemSize();
- int dstOffset = dst.offset / dst.elemSize();
+ int srcStep = src.step / src.elemSize();
+ int dstStep = dst.step / dst.elemSize();
+ int srcOffset = src.offset / src.elemSize();
+ int dstOffset = dst.offset / dst.elemSize();
int srcOffset_x = srcOffset % srcStep;
int srcOffset_y = srcOffset / srcStep;
sprintf(compile_option, "-D RADIUSX=%d -D RADIUSY=%d -D LSIZE0=%d -D LSIZE1=%d -D ERODE %s %s",
anchor.x, anchor.y, (int)localThreads[0], (int)localThreads[1],
s, rectKernel?"-D RECTKERNEL":"");
+
vector< pair<size_t, const void *> > args;
args.push_back(make_pair(sizeof(cl_mem), (void *)&src.data));
args.push_back(make_pair(sizeof(cl_mem), (void *)&dst.data));
args.push_back(make_pair(sizeof(cl_int), (void *)&src.wholecols));
args.push_back(make_pair(sizeof(cl_int), (void *)&src.wholerows));
args.push_back(make_pair(sizeof(cl_int), (void *)&dstOffset));
+
openCLExecuteKernel(clCxt, &filtering_morph, kernelName, globalThreads, localThreads, args, -1, -1, compile_option);
}
};
CV_Assert(op == MORPH_ERODE || op == MORPH_DILATE);
- CV_Assert(type == CV_8UC1 || type == CV_8UC3 || type == CV_8UC4 || type == CV_32FC1 || type == CV_32FC1 || type == CV_32FC4);
+ CV_Assert(type == CV_8UC1 || type == CV_8UC3 || type == CV_8UC4 || type == CV_32FC1 || type == CV_32FC3 || type == CV_32FC4);
oclMat gpu_krnl;
normalizeKernel(kernel, gpu_krnl);
for(int i = 0; i < kernel.rows * kernel.cols; ++i)
if(kernel.data[i] != 1)
noZero = false;
- MorphFilter_GPU* mfgpu=new MorphFilter_GPU(ksize, anchor, gpu_krnl, GPUMorfFilter_callers[op][CV_MAT_CN(type)]);
+
+ MorphFilter_GPU* mfgpu = new MorphFilter_GPU(ksize, anchor, gpu_krnl, GPUMorfFilter_callers[op][CV_MAT_CN(type)]);
if(noZero)
mfgpu->rectKernel = true;
+
return Ptr<BaseFilter_GPU>(mfgpu);
}
iterations = 1;
}
else
- {
kernel = _kernel;
- }
Ptr<FilterEngine_GPU> f = createMorphologyFilter_GPU(op, src.type(), kernel, anchor, iterations);
for (int i = 0; i < kernel.rows * kernel.cols; ++i)
if (kernel.data[i] != 0)
- {
allZero = false;
- }
if (allZero)
- {
kernel.data[0] = 1;
- }
morphOp(MORPH_ERODE, src, dst, kernel, anchor, iterations, borderType, borderValue);
}
Context *clCxt = src.clCxt;
int filterWidth = ksize.width;
- bool ksize_3x3 = filterWidth == 3 && src.type() != CV_32FC4; // CV_32FC4 is not tuned up with filter2d_3x3 kernel
+ bool ksize_3x3 = filterWidth == 3 && src.type() != CV_32FC4 && src.type() != CV_32FC3; // CV_32FC4 is not tuned up with filter2d_3x3 kernel
string kernelName = ksize_3x3 ? "filter2D_3x3" : "filter2D";
Ptr<FilterEngine_GPU> cv::ocl::createLinearFilter_GPU(int srcType, int dstType, const Mat &kernel, const Point &anchor,
int borderType)
{
-
Size ksize = kernel.size();
-
Ptr<BaseFilter_GPU> linearFilter = getLinearFilter_GPU(srcType, dstType, kernel, ksize, anchor, borderType);
return createFilter2D_GPU(linearFilter);
void cv::ocl::filter2D(const oclMat &src, oclMat &dst, int ddepth, const Mat &kernel, Point anchor, int borderType)
{
-
if (ddepth < 0)
- {
ddepth = src.depth();
- }
dst.create(src.size(), CV_MAKETYPE(ddepth, src.channels()));
int depth = CV_MAT_DEPTH(type);
if (sigma2 <= 0)
- {
sigma2 = sigma1;
- }
// automatic detection of kernel size from sigma
if (ksize.width <= 0 && sigma1 > 0)
void medianFilter(const oclMat &src, oclMat &dst, int m)
{
CV_Assert( m % 2 == 1 && m > 1 );
- CV_Assert( m <= 5 || src.depth() == CV_8U );
- CV_Assert( src.cols <= dst.cols && src.rows <= dst.rows );
+ CV_Assert( (src.depth() == CV_8U || src.depth() == CV_32F) && (src.channels() == 1 || src.channels() == 4));
+ dst.create(src.size(), src.type());
- if (src.data == dst.data)
- {
- oclMat src1;
- src.copyTo(src1);
- return medianFilter(src1, dst, m);
- }
-
- int srcStep = src.step1() / src.oclchannels();
- int dstStep = dst.step1() / dst.oclchannels();
- int srcOffset = src.offset / src.oclchannels() / src.elemSize1();
- int dstOffset = dst.offset / dst.oclchannels() / dst.elemSize1();
+ int srcStep = src.step / src.elemSize(), dstStep = dst.step / dst.elemSize();
+ int srcOffset = src.offset / src.elemSize(), dstOffset = dst.offset / dst.elemSize();
Context *clCxt = src.clCxt;
float *color_weight = &_color_weight[0];
float *space_weight = &_space_weight[0];
int *space_ofs = &_space_ofs[0];
+
int dst_step_in_pixel = dst.step / dst.elemSize();
int dst_offset_in_pixel = dst.offset / dst.elemSize();
int temp_step_in_pixel = temp.step / temp.elemSize();
if ((dst.type() == CV_8UC1) && ((dst.offset & 3) == 0) && ((dst.cols & 3) == 0))
{
kernelName = "bilateral2";
- globalThreads[0] = dst.cols / 4;
+ globalThreads[0] = dst.cols >> 2;
}
vector<pair<size_t , const void *> > args;
args.push_back( make_pair( sizeof(cl_mem), (void *)&oclcolor_weight.data ));
args.push_back( make_pair( sizeof(cl_mem), (void *)&oclspace_weight.data ));
args.push_back( make_pair( sizeof(cl_mem), (void *)&oclspace_ofs.data ));
+
openCLExecuteKernel(src.clCxt, &imgproc_bilateral, kernelName, globalThreads, localThreads, args, dst.oclchannels(), dst.depth());
}
+
void bilateralFilter(const oclMat &src, oclMat &dst, int radius, double sigmaclr, double sigmaspc, int borderType)
{
dst.create( src.size(), src.type() );
if ( src.depth() == CV_8U )
oclbilateralFilter_8u( src, dst, radius, sigmaclr, sigmaspc, borderType );
else
- CV_Error( CV_StsUnsupportedFormat, "Bilateral filtering is only implemented for 8uimages" );
+ CV_Error( CV_StsUnsupportedFormat, "Bilateral filtering is only implemented for CV_8U images" );
}
}
int globalRow = groupStartRow + localRow;
const int src_offset = mad24(src_offset_y, src_step, src_offset_x);
const int dst_offset = mad24(dst_offset_y, dst_step, dst_offset_x);
+
#ifdef BORDER_CONSTANT
for(int i = localRow; i < LOCAL_HEIGHT; i += get_local_size(1))
{
}
}
#endif
+
barrier(CLK_LOCAL_MEM_FENCE);
if(globalRow < rows && globalCol < cols)
{
//////////////////////////////////////////////////////////////////////////////////////////////////////
/////////////////////////////Macro for define elements number per thread/////////////////////////////
////////////////////////////////////////////////////////////////////////////////////////////////////
+
#define ANX 1
#define ANY 1
///////////////////////////////////////////////////////////////////////////////////////////////////
/////////////////////////////////////////8uC1////////////////////////////////////////////////////////
////////////////////////////////////////////////////////////////////////////////////////////////////
+
__kernel void filter2D_3x3(
__global T_IMG *src,
__global T_IMG *dst,
}
}
}
+
if(dst_rows_index < dst_rows_end)
{
T_IMGx4 tmp_dst = CONVERT_TYPEx4(sum);
//BORDER_CONSTANT: iiiiii|abcdefgh|iiiiiii
#define ELEM(i,l_edge,r_edge,elem1,elem2) (i)<(l_edge) | (i) >= (r_edge) ? (elem1) : (elem2)
#ifndef GENTYPE
+
__kernel void morph_C1_D0(__global const uchar * restrict src,
__global uchar *dst,
int src_offset_x, int src_offset_y,
}
}
}
+
#else
+
__kernel void morph(__global const GENTYPE * restrict src,
__global GENTYPE *dst,
int src_offset_x, int src_offset_y,
dst[out_addr] = res;
}
}
+
#endif
__constant float *space_weight,
__constant int *space_ofs)
{
- int gidx = get_global_id(0);
- int gidy = get_global_id(1);
- if((gidy<dst_rows) && (gidx<dst_cols))
+ int x = get_global_id(0);
+ int y = get_global_id(1);
+
+ if (y < dst_rows && x < dst_cols)
{
- int src_addr = mad24(gidy+radius,src_step,gidx+radius);
- int dst_addr = mad24(gidy,dst_step,gidx+dst_offset);
+ int src_index = mad24(y + radius, src_step, x + radius);
+ int dst_index = mad24(y, dst_step, x + dst_offset);
float sum = 0.f, wsum = 0.f;
- int val0 = (int)src[src_addr];
+ int val0 = (int)src[src_index];
for(int k = 0; k < maxk; k++ )
{
- int val = (int)src[src_addr + space_ofs[k]];
- float w = space_weight[k]*color_weight[abs(val - val0)];
- sum += (float)(val)*w;
+ int val = (int)src[src_index + space_ofs[k]];
+ float w = space_weight[k] * color_weight[abs(val - val0)];
+ sum += (float)(val) * w;
wsum += w;
}
- dst[dst_addr] = convert_uchar_rtz(sum/wsum+0.5f);
+ dst[dst_index] = convert_uchar_rtz(sum / wsum + 0.5f);
}
}
+
__kernel void bilateral2_C1_D0(__global uchar *dst,
__global const uchar *src,
const int dst_rows,
__constant float *space_weight,
__constant int *space_ofs)
{
- int gidx = get_global_id(0)<<2;
- int gidy = get_global_id(1);
- if((gidy<dst_rows) && (gidx<dst_cols))
+ int x = get_global_id(0) << 2;
+ int y = get_global_id(1);
+
+ if (y < dst_rows && x < dst_cols)
{
- int src_addr = mad24(gidy+radius,src_step,gidx+radius);
- int dst_addr = mad24(gidy,dst_step,gidx+dst_offset);
+ int src_index = mad24(y + radius, src_step, x + radius);
+ int dst_index = mad24(y, dst_step, x + dst_offset);
float4 sum = (float4)(0.f), wsum = (float4)(0.f);
- int4 val0 = convert_int4(vload4(0,src+src_addr));
+ int4 val0 = convert_int4(vload4(0,src + src_index));
for(int k = 0; k < maxk; k++ )
{
- int4 val = convert_int4(vload4(0,src+src_addr + space_ofs[k]));
- float4 w = (float4)(space_weight[k])*(float4)(color_weight[abs(val.x - val0.x)],color_weight[abs(val.y - val0.y)],color_weight[abs(val.z - val0.z)],color_weight[abs(val.w - val0.w)]);
- sum += convert_float4(val)*w;
+ int4 val = convert_int4(vload4(0,src+src_index + space_ofs[k]));
+ float4 w = (float4)(space_weight[k]) * (float4)(color_weight[abs(val.x - val0.x)], color_weight[abs(val.y - val0.y)],
+ color_weight[abs(val.z - val0.z)], color_weight[abs(val.w - val0.w)]);
+ sum += convert_float4(val) * w;
wsum += w;
}
- *(__global uchar4*)(dst+dst_addr) = convert_uchar4_rtz(sum/wsum+0.5f);
+ *(__global uchar4*)(dst+dst_index) = convert_uchar4_rtz(sum/wsum+0.5f);
}
}
+
__kernel void bilateral_C4_D0(__global uchar4 *dst,
__global const uchar4 *src,
const int dst_rows,
__constant float *space_weight,
__constant int *space_ofs)
{
- int gidx = get_global_id(0);
- int gidy = get_global_id(1);
- if((gidy<dst_rows) && (gidx<dst_cols))
+ int x = get_global_id(0);
+ int y = get_global_id(1);
+
+ if (y < dst_rows && x < dst_cols)
{
- int src_addr = mad24(gidy+radius,src_step,gidx+radius);
- int dst_addr = mad24(gidy,dst_step,gidx+dst_offset);
+ int src_index = mad24(y + radius, src_step, x + radius);
+ int dst_index = mad24(y, dst_step, x + dst_offset);
float4 sum = (float4)0.f;
float wsum = 0.f;
- int4 val0 = convert_int4(src[src_addr]);
+ int4 val0 = convert_int4(src[src_index]);
for(int k = 0; k < maxk; k++ )
{
- int4 val = convert_int4(src[src_addr + space_ofs[k]]);
- float w = space_weight[k]*color_weight[abs(val.x - val0.x)+abs(val.y - val0.y)+abs(val.z - val0.z)];
- sum += convert_float4(val)*(float4)w;
+ int4 val = convert_int4(src[src_index + space_ofs[k]]);
+ float w = space_weight[k] * color_weight[abs(val.x - val0.x) + abs(val.y - val0.y) + abs(val.z - val0.z)];
+ sum += convert_float4(val) * (float4)w;
wsum += w;
}
- wsum=1.f/wsum;
- dst[dst_addr] = convert_uchar4_rtz(sum*(float4)wsum+(float4)0.5f);
+
+ wsum = 1.f / wsum;
+ dst[dst_index] = convert_uchar4_rtz(sum * (float4)wsum + (float4)0.5f);
}
}
#ifdef HAVE_OPENCL
-using namespace cvtest;
using namespace testing;
using namespace std;
+using namespace cv;
-
-PARAM_TEST_CASE(FilterTestBase,
- MatType,
- cv::Size, // kernel size
- cv::Size, // dx,dy
- int // border type, or iteration
- )
+PARAM_TEST_CASE(FilterTestBase, MatType,
+ int, // kernel size
+ Size, // dx, dy
+ int, // border type, or iteration
+ bool) // roi or not
{
- //src mat
- cv::Mat mat1;
- cv::Mat dst;
-
- // set up roi
- int roicols;
- int roirows;
- int src1x;
- int src1y;
- int dstx;
- int dsty;
-
- //src mat with roi
- cv::Mat mat1_roi;
- cv::Mat dst_roi;
-
- //ocl dst mat for testing
- cv::ocl::oclMat gdst_whole;
+ int type, borderType;
+ int ksize;
+ bool useRoi;
- //ocl mat with roi
- cv::ocl::oclMat gmat1;
- cv::ocl::oclMat gdst;
+ Mat src, dst_whole, src_roi, dst_roi;
+ ocl::oclMat gsrc_whole, gsrc_roi, gdst_whole, gdst_roi;
- void random_roi()
+ virtual void SetUp()
{
-#ifdef RANDOMROI
- //randomize ROI
- roicols = rng.uniform(2, mat1.cols);
- roirows = rng.uniform(2, mat1.rows);
- src1x = rng.uniform(0, mat1.cols - roicols);
- src1y = rng.uniform(0, mat1.rows - roirows);
- dstx = rng.uniform(0, dst.cols - roicols);
- dsty = rng.uniform(0, dst.rows - roirows);
-#else
- roicols = mat1.cols;
- roirows = mat1.rows;
- src1x = 0;
- src1y = 0;
- dstx = 0;
- dsty = 0;
-#endif
-
- mat1_roi = mat1(Rect(src1x, src1y, roicols, roirows));
- dst_roi = dst(Rect(dstx, dsty, roicols, roirows));
-
- gdst_whole = dst;
- gdst = gdst_whole(Rect(dstx, dsty, roicols, roirows));
-
- gmat1 = mat1_roi;
+ type = GET_PARAM(0);
+ ksize = GET_PARAM(1);
+ borderType = GET_PARAM(3);
+ useRoi = GET_PARAM(4);
}
- void Init(int mat_type)
+ void random_roi()
{
- cv::Size size(MWIDTH, MHEIGHT);
- mat1 = randomMat(size, mat_type, 5, 16);
- dst = randomMat(size, mat_type, 5, 16);
+ Size roiSize = randomSize(1, MAX_VALUE);
+ Border srcBorder = randomBorder(0, useRoi ? MAX_VALUE : 0);
+ randomSubMat(src, src_roi, roiSize, srcBorder, type, 5, 256);
+
+ Border dstBorder = randomBorder(0, useRoi ? MAX_VALUE : 0);
+ randomSubMat(dst_whole, dst_roi, roiSize, dstBorder, type, 5, 16);
+
+ generateOclMat(gsrc_whole, gsrc_roi, src, roiSize, srcBorder);
+ generateOclMat(gdst_whole, gdst_roi, dst_whole, roiSize, dstBorder);
}
- void Near(double threshold)
+ void Near(double threshold = 0.0)
{
- EXPECT_MAT_NEAR(dst, Mat(gdst_whole), threshold);
+ EXPECT_MAT_NEAR(dst_whole, Mat(gdst_whole), threshold);
+ EXPECT_MAT_NEAR(dst_roi, Mat(gdst_roi), threshold);
}
};
/////////////////////////////////////////////////////////////////////////////////////////////////
// blur
-struct Blur : FilterTestBase
-{
- int type;
- cv::Size ksize;
- int bordertype;
- virtual void SetUp()
- {
- type = GET_PARAM(0);
- ksize = GET_PARAM(1);
- bordertype = GET_PARAM(3);
- Init(type);
- }
-};
+typedef FilterTestBase Blur;
OCL_TEST_P(Blur, Mat)
{
- for(int j = 0; j < LOOP_TIMES; j++)
+ Size kernelSize(ksize, ksize);
+
+ for (int j = 0; j < LOOP_TIMES; j++)
{
random_roi();
- cv::blur(mat1_roi, dst_roi, ksize, Point(-1, -1), bordertype);
- cv::ocl::blur(gmat1, gdst, ksize, Point(-1, -1), bordertype);
+
+ blur(src_roi, dst_roi, kernelSize, Point(-1, -1), borderType);
+ ocl::blur(gsrc_roi, gdst_roi, kernelSize, Point(-1, -1), borderType); // TODO anchor
+
Near(1.0);
}
}
-
/////////////////////////////////////////////////////////////////////////////////////////////////
-//Laplacian
-struct Laplacian : FilterTestBase
-{
- int type;
- cv::Size ksize;
+// Laplacian
- virtual void SetUp()
- {
- type = GET_PARAM(0);
- ksize = GET_PARAM(1);
- Init(type);
- }
-};
+typedef FilterTestBase LaplacianTest;
-OCL_TEST_P(Laplacian, Accuracy)
+OCL_TEST_P(LaplacianTest, Accuracy)
{
- for(int j = 0; j < LOOP_TIMES; j++)
+ for (int j = 0; j < LOOP_TIMES; j++)
{
random_roi();
- cv::Laplacian(mat1_roi, dst_roi, -1, ksize.width, 1);
- cv::ocl::Laplacian(gmat1, gdst, -1, ksize.width, 1);
+
+ Laplacian(src_roi, dst_roi, -1, ksize, 1);
+ ocl::Laplacian(gsrc_roi, gdst_roi, -1, ksize, 1); // TODO scale
+
Near(1e-5);
}
}
-
-
/////////////////////////////////////////////////////////////////////////////////////////////////
// erode & dilate
-struct ErodeDilate : FilterTestBase
+
+struct ErodeDilate :
+ public FilterTestBase
{
- int type;
int iterations;
- //erode or dilate kernel
- cv::Mat kernel;
-
virtual void SetUp()
{
type = GET_PARAM(0);
iterations = GET_PARAM(3);
- Init(type);
- kernel = randomMat(Size(3, 3), CV_8UC1, 0, 3);
+ useRoi = GET_PARAM(4);
}
-
};
-OCL_TEST_P(ErodeDilate, Mat)
+typedef ErodeDilate Erode;
+
+OCL_TEST_P(Erode, Mat)
{
- for(int j = 0; j < LOOP_TIMES; j++)
+ // erode or dilate kernel
+ Size kernelSize(ksize, ksize);
+ Mat kernel;
+
+ for (int j = 0; j < LOOP_TIMES; j++)
{
+ kernel = randomMat(kernelSize, CV_8UC1, 0, 3);
+
random_roi();
- cv::erode(mat1_roi, dst_roi, kernel, Point(-1, -1), iterations);
- cv::ocl::erode(gmat1, gdst, kernel, Point(-1, -1), iterations);
+
+ cv::erode(src_roi, dst_roi, kernel, Point(-1, -1), iterations);
+ ocl::erode(gsrc_roi, gdst_roi, kernel, Point(-1, -1), iterations); // TODO iterations, borderType
+
Near(1e-5);
}
- for(int j = 0; j < LOOP_TIMES; j++)
+}
+
+typedef ErodeDilate Dilate;
+
+OCL_TEST_P(Dilate, Mat)
+{
+ // erode or dilate kernel
+ Mat kernel;
+
+ for (int j = 0; j < LOOP_TIMES; j++)
{
+ kernel = randomMat(Size(3, 3), CV_8UC1, 0, 3);
+
random_roi();
- cv::dilate(mat1_roi, dst_roi, kernel, Point(-1, -1), iterations);
- cv::ocl::dilate(gmat1, gdst, kernel, Point(-1, -1), iterations);
+
+ cv::dilate(src_roi, dst_roi, kernel, Point(-1, -1), iterations);
+ ocl::dilate(gsrc_roi, gdst_roi, kernel, Point(-1, -1), iterations); // TODO iterations, borderType
+
Near(1e-5);
}
}
-
/////////////////////////////////////////////////////////////////////////////////////////////////
// Sobel
-struct Sobel : FilterTestBase
+
+struct SobelTest :
+ public FilterTestBase
{
- int type;
- int dx, dy, ksize, bordertype;
+ int dx, dy;
virtual void SetUp()
{
type = GET_PARAM(0);
- Size s = GET_PARAM(1);
- ksize = s.width;
- s = GET_PARAM(2);
- dx = s.width;
- dy = s.height;
- bordertype = GET_PARAM(3);
- Init(type);
+ ksize = GET_PARAM(1);
+ borderType = GET_PARAM(3);
+ useRoi = GET_PARAM(4);
+
+ Size d = GET_PARAM(2);
+ dx = d.width, dy = d.height;
}
};
-OCL_TEST_P(Sobel, Mat)
+OCL_TEST_P(SobelTest, Mat)
{
- for(int j = 0; j < LOOP_TIMES; j++)
+ for (int j = 0; j < LOOP_TIMES; j++)
{
random_roi();
- cv::Sobel(mat1_roi, dst_roi, -1, dx, dy, ksize, /*scale*/0.00001,/*delta*/0, bordertype);
- cv::ocl::Sobel(gmat1, gdst, -1, dx, dy, ksize,/*scale*/0.00001,/*delta*/0, bordertype);
+
+ Sobel(src_roi, dst_roi, -1, dx, dy, ksize, /* scale */ 0.00001, /* delta */0, borderType);
+ ocl::Sobel(gsrc_roi, gdst_roi, -1, dx, dy, ksize, /* scale */ 0.00001, /* delta */ 0, borderType);
+
Near(1);
}
}
-
/////////////////////////////////////////////////////////////////////////////////////////////////
// Scharr
-struct Scharr : FilterTestBase
-{
- int type;
- int dx, dy, bordertype;
- virtual void SetUp()
- {
- type = GET_PARAM(0);
- Size s = GET_PARAM(2);
- dx = s.width;
- dy = s.height;
- bordertype = GET_PARAM(3);
- Init(type);
- }
-};
+typedef SobelTest ScharrTest;
-OCL_TEST_P(Scharr, Mat)
+OCL_TEST_P(ScharrTest, Mat)
{
- for(int j = 0; j < LOOP_TIMES; j++)
+ for (int j = 0; j < LOOP_TIMES; j++)
{
random_roi();
- cv::Scharr(mat1_roi, dst_roi, -1, dx, dy, /*scale*/1,/*delta*/0, bordertype);
- cv::ocl::Scharr(gmat1, gdst, -1, dx, dy,/*scale*/1,/*delta*/0, bordertype);
+
+ Scharr(src_roi, dst_roi, -1, dx, dy, /* scale */ 1, /* delta */ 0, borderType);
+ ocl::Scharr(gsrc_roi, gdst_roi, -1, dx, dy, /* scale */ 1, /* delta */ 0, borderType);
+
Near(1);
}
-
}
-
/////////////////////////////////////////////////////////////////////////////////////////////////
// GaussianBlur
-struct GaussianBlur : FilterTestBase
+
+struct GaussianBlurTest :
+ public FilterTestBase
{
- int type;
- cv::Size ksize;
- int bordertype;
double sigma1, sigma2;
virtual void SetUp()
{
type = GET_PARAM(0);
ksize = GET_PARAM(1);
- bordertype = GET_PARAM(3);
- Init(type);
+ borderType = GET_PARAM(3);
+
sigma1 = rng.uniform(0.1, 1.0);
sigma2 = rng.uniform(0.1, 1.0);
}
};
-OCL_TEST_P(GaussianBlur, Mat)
+OCL_TEST_P(GaussianBlurTest, Mat)
{
- for(int j = 0; j < LOOP_TIMES; j++)
+ for (int j = 0; j < LOOP_TIMES; j++)
{
random_roi();
- cv::GaussianBlur(mat1_roi, dst_roi, ksize, sigma1, sigma2, bordertype);
- cv::ocl::GaussianBlur(gmat1, gdst, ksize, sigma1, sigma2, bordertype);
+
+ GaussianBlur(src_roi, dst_roi, Size(ksize, ksize), sigma1, sigma2, borderType);
+ ocl::GaussianBlur(gsrc_roi, gdst_roi, Size(ksize, ksize), sigma1, sigma2, borderType);
+
Near(1);
}
-
}
-
-
////////////////////////////////////////////////////////////////////////////////////////////////////
// Filter2D
-struct Filter2D : FilterTestBase
-{
- int type;
- cv::Size ksize;
- int bordertype;
- Point anchor;
- virtual void SetUp()
- {
- type = GET_PARAM(0);
- ksize = GET_PARAM(1);
- bordertype = GET_PARAM(3);
- Init(type);
- anchor = Point(-1,-1);
- }
-};
+
+typedef FilterTestBase Filter2D;
OCL_TEST_P(Filter2D, Mat)
{
- cv::Mat kernel = randomMat(cv::Size(ksize.width, ksize.height), CV_32FC1, 0.0, 1.0);
- for(int j = 0; j < LOOP_TIMES; j++)
+ const Size kernelSize(ksize, ksize);
+ Mat kernel;
+
+ for (int j = 0; j < LOOP_TIMES; j++)
{
+ kernel = randomMat(kernelSize, CV_32FC1, 0.0, 1.0);
+
random_roi();
- cv::filter2D(mat1_roi, dst_roi, -1, kernel, anchor, 0.0, bordertype);
- cv::ocl::filter2D(gmat1, gdst, -1, kernel, anchor, bordertype);
+
+ cv::filter2D(src_roi, dst_roi, -1, kernel, Point(-1, -1), 0.0, borderType); // TODO anchor
+ ocl::filter2D(gsrc_roi, gdst_roi, -1, kernel, Point(-1, -1), borderType);
+
Near(1);
}
}
+
////////////////////////////////////////////////////////////////////////////////////////////////////
// Bilateral
-struct Bilateral : FilterTestBase
-{
- int type;
- cv::Size ksize;
- int bordertype;
- double sigmacolor, sigmaspace;
- virtual void SetUp()
- {
- type = GET_PARAM(0);
- ksize = GET_PARAM(1);
- bordertype = GET_PARAM(3);
- Init(type);
- sigmacolor = rng.uniform(20, 100);
- sigmaspace = rng.uniform(10, 40);
- }
-};
+typedef FilterTestBase Bilateral;
OCL_TEST_P(Bilateral, Mat)
{
- for(int j = 0; j < LOOP_TIMES; j++)
+ for (int j = 0; j < LOOP_TIMES; j++)
{
random_roi();
- cv::bilateralFilter(mat1_roi, dst_roi, ksize.width, sigmacolor, sigmaspace, bordertype);
- cv::ocl::bilateralFilter(gmat1, gdst, ksize.width, sigmacolor, sigmaspace, bordertype);
+
+ double sigmacolor = rng.uniform(20, 100);
+ double sigmaspace = rng.uniform(10, 40);
+
+ cv::bilateralFilter(src_roi, dst_roi, ksize, sigmacolor, sigmaspace, borderType);
+ ocl::bilateralFilter(gsrc_roi, gdst_roi, ksize, sigmacolor, sigmaspace, borderType);
+
Near(1);
}
-
}
////////////////////////////////////////////////////////////////////////////////////////////////////
// AdaptiveBilateral
-struct AdaptiveBilateral : FilterTestBase
-{
- int type;
- cv::Size ksize;
- int bordertype;
- Point anchor;
- virtual void SetUp()
- {
- type = GET_PARAM(0);
- ksize = GET_PARAM(1);
- bordertype = GET_PARAM(3);
- Init(type);
- anchor = Point(-1,-1);
- }
-};
+
+typedef FilterTestBase AdaptiveBilateral;
OCL_TEST_P(AdaptiveBilateral, Mat)
{
- for(int j = 0; j < LOOP_TIMES; j++)
+ const Size kernelSize(ksize, ksize);
+
+ for (int j = 0; j < LOOP_TIMES; j++)
{
random_roi();
- cv::adaptiveBilateralFilter(mat1_roi, dst_roi, ksize, 5, anchor, bordertype);
- cv::ocl::adaptiveBilateralFilter(gmat1, gdst, ksize, 5, anchor, bordertype);
+
+ adaptiveBilateralFilter(src_roi, dst_roi, kernelSize, 5, Point(-1, -1), borderType); // TODO anchor
+ ocl::adaptiveBilateralFilter(gsrc_roi, gdst_roi, kernelSize, 5, Point(-1, -1), borderType);
+
Near(1);
}
-
}
-INSTANTIATE_TEST_CASE_P(Filter, Blur, Combine(
- Values(CV_8UC1, CV_8UC3, CV_8UC4, CV_32FC1, CV_32FC4),
- Values(cv::Size(3, 3), cv::Size(5, 5), cv::Size(7, 7)),
- Values(Size(0, 0)), //not use
- Values((MatType)cv::BORDER_CONSTANT, (MatType)cv::BORDER_REPLICATE, (MatType)cv::BORDER_REFLECT, (MatType)cv::BORDER_REFLECT_101)));
-
-
-INSTANTIATE_TEST_CASE_P(Filter, Laplacian, Combine(
- Values(CV_8UC1, CV_8UC3, CV_8UC4, CV_32FC1, CV_32FC3, CV_32FC4),
- Values(Size(3, 3)),
- Values(Size(0, 0)), //not use
- Values(0))); //not use
-
-INSTANTIATE_TEST_CASE_P(Filter, ErodeDilate, Combine(
- Values(CV_8UC1, CV_8UC4, CV_32FC1, CV_32FC4),
- Values(Size(0, 0)), //not use
- Values(Size(0, 0)), //not use
- Values(1)));
-
+/////////////////////////////////////////////////////////////////////////////////////////////////////
+// MedianFilter
-INSTANTIATE_TEST_CASE_P(Filter, Sobel, Combine(
- Values(CV_8UC1, CV_8UC3, CV_8UC4, CV_32FC1, CV_32FC3, CV_32FC4),
- Values(Size(3, 3), Size(5, 5)),
- Values(Size(1, 0), Size(1, 1), Size(2, 0), Size(2, 1)),
- Values((MatType)cv::BORDER_CONSTANT, (MatType)cv::BORDER_REPLICATE)));
+typedef FilterTestBase MedianFilter;
+OCL_TEST_P(MedianFilter, Mat)
+{
+ for (int i = 0; i < LOOP_TIMES; ++i)
+ {
+ random_roi();
-INSTANTIATE_TEST_CASE_P(Filter, Scharr, Combine(
- Values(CV_8UC1, CV_8UC3, CV_8UC4, CV_32FC1, CV_32FC4),
- Values(Size(0, 0)), //not use
- Values(Size(0, 1), Size(1, 0)),
- Values((MatType)cv::BORDER_CONSTANT, (MatType)cv::BORDER_REPLICATE)));
+ medianBlur(src_roi, dst_roi, ksize);
+ ocl::medianFilter(gsrc_roi, gdst_roi, ksize);
-INSTANTIATE_TEST_CASE_P(Filter, GaussianBlur, Combine(
- Values(CV_8UC1, CV_8UC3, CV_8UC4, CV_32FC1, CV_32FC4),
- Values(Size(3, 3), Size(5, 5)),
- Values(Size(0, 0)), //not use
- Values((MatType)cv::BORDER_CONSTANT, (MatType)cv::BORDER_REPLICATE)));
+ Near();
+ }
+}
+//////////////////////////////////////////////////////////////////////////////////////////////////////////////
+INSTANTIATE_TEST_CASE_P(Filter, Blur, Combine(
+ Values(CV_8UC1, CV_8UC3, CV_8UC4, CV_32FC1, CV_32FC4),
+ Values(3, 5, 7),
+ Values(Size(0, 0)), // not used
+ Values((int)BORDER_CONSTANT, (int)BORDER_REPLICATE, (int)BORDER_REFLECT, (int)BORDER_REFLECT_101),
+ Bool()));
+
+INSTANTIATE_TEST_CASE_P(Filter, LaplacianTest, Combine(
+ Values(CV_8UC1, CV_8UC3, CV_8UC4, CV_32FC1, CV_32FC3, CV_32FC4),
+ Values(1, 3),
+ Values(Size(0, 0)), // not used
+ Values(0), // not used
+ Bool()));
+
+INSTANTIATE_TEST_CASE_P(Filter, Erode, Combine(
+ Values(CV_8UC1, CV_8UC3, CV_8UC4, CV_32FC1, CV_32FC3, CV_32FC4),
+ Values(3, 5, 7),
+ Values(Size(0, 0)), // not used
+ testing::Range(1, 2),
+ Bool()));
+
+INSTANTIATE_TEST_CASE_P(Filter, Dilate, Combine(
+ Values(CV_8UC1, CV_8UC3, CV_8UC4, CV_32FC1, CV_32FC3, CV_32FC4),
+ Values(3, 5, 7),
+ Values(Size(0, 0)), // not used
+ testing::Range(1, 2),
+ Bool()));
+
+INSTANTIATE_TEST_CASE_P(Filter, SobelTest, Combine(
+ Values(CV_8UC1, CV_8UC3, CV_8UC4, CV_32FC1, CV_32FC3, CV_32FC4),
+ Values(3, 5),
+ Values(Size(1, 0), Size(1, 1), Size(2, 0), Size(2, 1)),
+ Values((int)BORDER_CONSTANT, (int)BORDER_REFLECT101,
+ (int)BORDER_REPLICATE, (int)BORDER_REFLECT),
+ Bool()));
+
+INSTANTIATE_TEST_CASE_P(Filter, ScharrTest, Combine(
+ Values(CV_8UC1, CV_8UC3, CV_8UC4, CV_32FC1, CV_32FC4),
+ Values(0), // not used
+ Values(Size(0, 1), Size(1, 0)),
+ Values((int)BORDER_CONSTANT, (int)BORDER_REFLECT101,
+ (int)BORDER_REPLICATE, (int)BORDER_REFLECT),
+ Bool()));
+
+INSTANTIATE_TEST_CASE_P(Filter, GaussianBlurTest, Combine(
+ Values(CV_8UC1, CV_8UC3, CV_8UC4, CV_32FC1, CV_32FC4),
+ Values(3, 5),
+ Values(Size(0, 0)), // not used
+ Values((int)BORDER_CONSTANT, (int)BORDER_REFLECT101,
+ (int)BORDER_REPLICATE, (int)BORDER_REFLECT),
+ Bool()));
INSTANTIATE_TEST_CASE_P(Filter, Filter2D, testing::Combine(
- Values(CV_8UC1, CV_32FC1, CV_32FC4),
- Values(Size(3, 3), Size(15, 15), Size(25, 25)),
- Values(Size(0, 0)), //not use
- Values((MatType)cv::BORDER_CONSTANT, (MatType)cv::BORDER_REFLECT101, (MatType)cv::BORDER_REPLICATE, (MatType)cv::BORDER_REFLECT)));
+ Values(CV_8UC1, CV_32FC1, CV_32FC4),
+ Values(3, 15, 25),
+ Values(Size(0, 0)), // not used
+ Values((int)BORDER_CONSTANT, (int)BORDER_REFLECT101,
+ (int)BORDER_REPLICATE, (int)BORDER_REFLECT),
+ Bool()));
INSTANTIATE_TEST_CASE_P(Filter, Bilateral, Combine(
- Values(CV_8UC1, CV_8UC3),
- Values(Size(5, 5), Size(9, 9)),
- Values(Size(0, 0)), //not use
- Values((MatType)cv::BORDER_CONSTANT, (MatType)cv::BORDER_REPLICATE,
- (MatType)cv::BORDER_REFLECT, (MatType)cv::BORDER_WRAP, (MatType)cv::BORDER_REFLECT_101)));
+ Values(CV_8UC1, CV_8UC3),
+ Values(5, 9),
+ Values(Size(0, 0)), // not used
+ Values((int)BORDER_CONSTANT, (int)BORDER_REPLICATE,
+ (int)BORDER_REFLECT, (int)BORDER_WRAP, (int)BORDER_REFLECT_101),
+ Values(false))); // TODO does not work with ROI
INSTANTIATE_TEST_CASE_P(Filter, AdaptiveBilateral, Combine(
- Values(CV_8UC1, CV_8UC3),
- Values(Size(5, 5), Size(9, 9)),
- Values(Size(0, 0)), //not use
- Values((MatType)cv::BORDER_CONSTANT, (MatType)cv::BORDER_REPLICATE,
- (MatType)cv::BORDER_REFLECT, (MatType)cv::BORDER_REFLECT_101)));
+ Values(CV_8UC1, CV_8UC3),
+ Values(5, 9),
+ Values(Size(0, 0)), // not used
+ Values((int)BORDER_CONSTANT, (int)BORDER_REPLICATE,
+ (int)BORDER_REFLECT, (int)BORDER_REFLECT_101),
+ Bool()));
+
+INSTANTIATE_TEST_CASE_P(Filter, MedianFilter, Combine(
+ Values((MatType)CV_8UC1, (MatType)CV_8UC4, (MatType)CV_32FC1, (MatType)CV_32FC4),
+ Values(3, 5),
+ Values(Size(0, 0)), // not used
+ Values(0), // not used
+ Bool()));
+
#endif // HAVE_OPENCL
#ifdef HAVE_OPENCL
-using namespace cvtest;
-using namespace testing;
+using namespace cv;
using namespace std;
+using namespace testing;
MatType nulltype = -1;
short y;
} COOR;
-COOR do_meanShift(int x0, int y0, uchar *sptr, uchar *dptr, int sstep, cv::Size size, int sp, int sr, int maxIter, float eps, int *tab)
+COOR do_meanShift(int x0, int y0, uchar *sptr, uchar *dptr, int sstep, Size size, int sp, int sr, int maxIter, float eps, int *tab)
{
int isr2 = sr * sr;
return coor;
}
-void meanShiftFiltering_(const Mat &src_roi, Mat &dst_roi, int sp, int sr, cv::TermCriteria crit)
+void meanShiftFiltering_(const Mat &src_roi, Mat &dst_roi, int sp, int sr, TermCriteria crit)
{
if( src_roi.empty() )
CV_Error( CV_StsBadArg, "The input image is empty" );
CV_Assert( (src_roi.cols == dst_roi.cols) && (src_roi.rows == dst_roi.rows) );
CV_Assert( !(dst_roi.step & 0x3) );
- if( !(crit.type & cv::TermCriteria::MAX_ITER) )
+ if( !(crit.type & TermCriteria::MAX_ITER) )
crit.maxCount = 5;
int maxIter = std::min(std::max(crit.maxCount, 1), 100);
float eps;
- if( !(crit.type & cv::TermCriteria::EPS) )
+ if( !(crit.type & TermCriteria::EPS) )
eps = 1.f;
eps = (float)std::max(crit.epsilon, 0.0);
uchar *dptr = dst_roi.data;
int sstep = (int)src_roi.step;
int dstep = (int)dst_roi.step;
- cv::Size size = src_roi.size();
+ Size size = src_roi.size();
for(int i = 0; i < size.height; i++, sptr += sstep - (size.width << 2),
dptr += dstep - (size.width << 2))
}
}
-void meanShiftProc_(const Mat &src_roi, Mat &dst_roi, Mat &dstCoor_roi, int sp, int sr, cv::TermCriteria crit)
+void meanShiftProc_(const Mat &src_roi, Mat &dst_roi, Mat &dstCoor_roi, int sp, int sr, TermCriteria crit)
{
if( src_roi.empty() )
(src_roi.cols == dstCoor_roi.cols) && (src_roi.rows == dstCoor_roi.rows));
CV_Assert( !(dstCoor_roi.step & 0x3) );
- if( !(crit.type & cv::TermCriteria::MAX_ITER) )
+ if( !(crit.type & TermCriteria::MAX_ITER) )
crit.maxCount = 5;
int maxIter = std::min(std::max(crit.maxCount, 1), 100);
float eps;
- if( !(crit.type & cv::TermCriteria::EPS) )
+ if( !(crit.type & TermCriteria::EPS) )
eps = 1.f;
eps = (float)std::max(crit.epsilon, 0.0);
int sstep = (int)src_roi.step;
int dstep = (int)dst_roi.step;
int dCoorstep = (int)dstCoor_roi.step >> 1;
- cv::Size size = src_roi.size();
+ Size size = src_roi.size();
for(int i = 0; i < size.height; i++, sptr += sstep - (size.width << 2),
dptr += dstep - (size.width << 2), dCoorptr += dCoorstep - (size.width << 1))
PARAM_TEST_CASE(ImgprocTestBase, MatType, MatType, MatType, MatType, MatType, bool)
{
int type1, type2, type3, type4, type5;
- cv::Scalar val;
+ Scalar val;
// set up roi
int roicols;
int roirows;
int masky;
//mat
- cv::Mat mat1;
- cv::Mat mat2;
- cv::Mat mask;
- cv::Mat dst;
- cv::Mat dst1; //bak, for two outputs
+ Mat mat1;
+ Mat mat2;
+ Mat mask;
+ Mat dst;
+ Mat dst1; //bak, for two outputs
//mat with roi
- cv::Mat mat1_roi;
- cv::Mat mat2_roi;
- cv::Mat mask_roi;
- cv::Mat dst_roi;
- cv::Mat dst1_roi; //bak
+ Mat mat1_roi;
+ Mat mat2_roi;
+ Mat mask_roi;
+ Mat dst_roi;
+ Mat dst1_roi; //bak
//ocl mat
- cv::ocl::oclMat clmat1;
- cv::ocl::oclMat clmat2;
- cv::ocl::oclMat clmask;
- cv::ocl::oclMat cldst;
- cv::ocl::oclMat cldst1; //bak
+ ocl::oclMat clmat1;
+ ocl::oclMat clmat2;
+ ocl::oclMat clmask;
+ ocl::oclMat cldst;
+ ocl::oclMat cldst1; //bak
//ocl mat with roi
- cv::ocl::oclMat clmat1_roi;
- cv::ocl::oclMat clmat2_roi;
- cv::ocl::oclMat clmask_roi;
- cv::ocl::oclMat cldst_roi;
- cv::ocl::oclMat cldst1_roi;
+ ocl::oclMat clmat1_roi;
+ ocl::oclMat clmat2_roi;
+ ocl::oclMat clmask_roi;
+ ocl::oclMat cldst_roi;
+ ocl::oclMat cldst1_roi;
virtual void SetUp()
{
type3 = GET_PARAM(2);
type4 = GET_PARAM(3);
type5 = GET_PARAM(4);
- cv::Size size(MWIDTH, MHEIGHT);
+ Size size(MWIDTH, MHEIGHT);
double min = 1, max = 20;
if(type1 != nulltype)
if(type5 != nulltype)
{
mask = randomMat(size, CV_8UC1, 0, 2, false);
- cv::threshold(mask, mask, 0.5, 255., type5);
+ threshold(mask, mask, 0.5, 255., type5);
clmask = mask;
}
- val = cv::Scalar(rng.uniform(-10.0, 10.0), rng.uniform(-10.0, 10.0), rng.uniform(-10.0, 10.0), rng.uniform(-10.0, 10.0));
+ val = Scalar(rng.uniform(-10.0, 10.0), rng.uniform(-10.0, 10.0), rng.uniform(-10.0, 10.0), rng.uniform(-10.0, 10.0));
}
void random_roi()
void Near(double threshold)
{
- cv::Mat cpu_cldst;
+ Mat cpu_cldst;
cldst.download(cpu_cldst);
EXPECT_MAT_NEAR(dst, cpu_cldst, threshold);
}
};
////////////////////////////////equalizeHist//////////////////////////////////////////
-struct equalizeHist : ImgprocTestBase {};
+typedef ImgprocTestBase EqualizeHist;
-OCL_TEST_P(equalizeHist, Mat)
+OCL_TEST_P(EqualizeHist, Mat)
{
if (mat1.type() != CV_8UC1 || mat1.type() != dst.type())
{
for(int j = 0; j < LOOP_TIMES; j++)
{
random_roi();
- cv::equalizeHist(mat1_roi, dst_roi);
- cv::ocl::equalizeHist(clmat1_roi, cldst_roi);
+ equalizeHist(mat1_roi, dst_roi);
+ ocl::equalizeHist(clmat1_roi, cldst_roi);
Near(1.1);
}
}
////////////////////////////////copyMakeBorder////////////////////////////////////////////
-struct CopyMakeBorder : ImgprocTestBase {};
+typedef ImgprocTestBase CopyMakeBorder;
OCL_TEST_P(CopyMakeBorder, Mat)
{
- int bordertype[] = {cv::BORDER_CONSTANT, cv::BORDER_REPLICATE, cv::BORDER_REFLECT, cv::BORDER_WRAP, cv::BORDER_REFLECT_101};
+ int bordertype[] = {BORDER_CONSTANT, BORDER_REPLICATE, BORDER_REFLECT, BORDER_WRAP, BORDER_REFLECT_101};
int top = rng.uniform(0, 10);
int bottom = rng.uniform(0, 10);
int left = rng.uniform(0, 10);
{
random_roi();
#ifdef RANDOMROI
- if(((bordertype[i] != cv::BORDER_CONSTANT) && (bordertype[i] != cv::BORDER_REPLICATE)) && (mat1_roi.cols <= left) || (mat1_roi.cols <= right) || (mat1_roi.rows <= top) || (mat1_roi.rows <= bottom))
+ if(((bordertype[i] != BORDER_CONSTANT) && (bordertype[i] != BORDER_REPLICATE)) && (mat1_roi.cols <= left) || (mat1_roi.cols <= right) || (mat1_roi.rows <= top) || (mat1_roi.rows <= bottom))
{
continue;
}
continue;
}
#endif
- cv::copyMakeBorder(mat1_roi, dst_roi, top, bottom, left, right, bordertype[i] | cv::BORDER_ISOLATED, cv::Scalar(1.0));
- cv::ocl::copyMakeBorder(clmat1_roi, cldst_roi, top, bottom, left, right, bordertype[i] | cv::BORDER_ISOLATED, cv::Scalar(1.0));
+ cv::copyMakeBorder(mat1_roi, dst_roi, top, bottom, left, right, bordertype[i] | BORDER_ISOLATED, Scalar(1.0));
+ ocl::copyMakeBorder(clmat1_roi, cldst_roi, top, bottom, left, right, bordertype[i] | BORDER_ISOLATED, Scalar(1.0));
- cv::Mat cpu_cldst;
+ Mat cpu_cldst;
#ifndef RANDOMROI
cldst_roi.download(cpu_cldst);
EXPECT_MAT_NEAR(dst_roi, cpu_cldst, 0.0);
////////////////////////////////cornerMinEigenVal//////////////////////////////////////////
-struct cornerMinEigenVal : ImgprocTestBase {};
+struct CornerMinEigenVal : ImgprocTestBase {};
-OCL_TEST_P(cornerMinEigenVal, Mat)
+OCL_TEST_P(CornerMinEigenVal, Mat)
{
for(int j = 0; j < LOOP_TIMES; j++)
{
random_roi();
int blockSize = 3, apertureSize = 3;//1 + 2 * (rand() % 4);
- //int borderType = cv::BORDER_CONSTANT;
- //int borderType = cv::BORDER_REPLICATE;
- int borderType = cv::BORDER_REFLECT;
- cv::cornerMinEigenVal(mat1_roi, dst_roi, blockSize, apertureSize, borderType);
- cv::ocl::cornerMinEigenVal(clmat1_roi, cldst_roi, blockSize, apertureSize, borderType);
+ //int borderType = BORDER_CONSTANT;
+ //int borderType = BORDER_REPLICATE;
+ int borderType = BORDER_REFLECT;
+ cornerMinEigenVal(mat1_roi, dst_roi, blockSize, apertureSize, borderType);
+ ocl::cornerMinEigenVal(clmat1_roi, cldst_roi, blockSize, apertureSize, borderType);
Near(1.);
}
}
////////////////////////////////cornerHarris//////////////////////////////////////////
-struct cornerHarris : ImgprocTestBase {};
+typedef ImgprocTestBase CornerHarris;
-OCL_TEST_P(cornerHarris, Mat)
+OCL_TEST_P(CornerHarris, Mat)
{
for(int j = 0; j < LOOP_TIMES; j++)
{
random_roi();
int blockSize = 3, apertureSize = 3; //1 + 2 * (rand() % 4);
double k = 2;
- //int borderType = cv::BORDER_CONSTANT;
- //int borderType = cv::BORDER_REPLICATE;
- int borderType = cv::BORDER_REFLECT;
- cv::cornerHarris(mat1_roi, dst_roi, blockSize, apertureSize, k, borderType);
- cv::ocl::cornerHarris(clmat1_roi, cldst_roi, blockSize, apertureSize, k, borderType);
+ //int borderType = BORDER_CONSTANT;
+ //int borderType = BORDER_REPLICATE;
+ int borderType = BORDER_REFLECT;
+ cornerHarris(mat1_roi, dst_roi, blockSize, apertureSize, k, borderType);
+ ocl::cornerHarris(clmat1_roi, cldst_roi, blockSize, apertureSize, k, borderType);
Near(1.);
}
}
////////////////////////////////integral/////////////////////////////////////////////////
-struct integral : ImgprocTestBase {};
+typedef ImgprocTestBase Integral;
-OCL_TEST_P(integral, Mat1)
+OCL_TEST_P(Integral, Mat1)
{
for(int j = 0; j < LOOP_TIMES; j++)
{
random_roi();
- cv::ocl::integral(clmat1_roi, cldst_roi);
- cv::integral(mat1_roi, dst_roi);
+ ocl::integral(clmat1_roi, cldst_roi);
+ integral(mat1_roi, dst_roi);
Near(0);
}
}
-OCL_TEST_P(integral, Mat2)
+OCL_TEST_P(Integral, Mat2)
{
for(int j = 0; j < LOOP_TIMES; j++)
{
random_roi();
- cv::ocl::integral(clmat1_roi, cldst_roi, cldst1_roi);
- cv::integral(mat1_roi, dst_roi, dst1_roi);
+ ocl::integral(clmat1_roi, cldst_roi, cldst1_roi);
+ integral(mat1_roi, dst_roi, dst1_roi);
Near(0);
- cv::Mat cpu_cldst1;
+ Mat cpu_cldst1;
cldst1.download(cpu_cldst1);
EXPECT_MAT_NEAR(dst1, cpu_cldst1, 0.0);
}
PARAM_TEST_CASE(WarpTestBase, MatType, int)
{
int type;
- cv::Size size;
+ Size size;
int interpolation;
//src mat
- cv::Mat mat1;
- cv::Mat dst;
+ Mat mat1;
+ Mat dst;
// set up roi
int src_roicols;
//src mat with roi
- cv::Mat mat1_roi;
- cv::Mat dst_roi;
+ Mat mat1_roi;
+ Mat dst_roi;
//ocl dst mat for testing
- cv::ocl::oclMat gdst_whole;
+ ocl::oclMat gdst_whole;
//ocl mat with roi
- cv::ocl::oclMat gmat1;
- cv::ocl::oclMat gdst;
+ ocl::oclMat gmat1;
+ ocl::oclMat gdst;
virtual void SetUp()
{
type = GET_PARAM(0);
interpolation = GET_PARAM(1);
- size = cv::Size(MWIDTH, MHEIGHT);
+ size = Size(MWIDTH, MHEIGHT);
mat1 = randomMat(size, type, 5, 16, false);
dst = randomMat(size, type, 5, 16, false);
/////warpAffine
-struct WarpAffine : WarpTestBase {};
+typedef WarpTestBase WarpAffine;
OCL_TEST_P(WarpAffine, Mat)
{
{
random_roi();
- cv::warpAffine(mat1_roi, dst_roi, M, size, interpolation);
- cv::ocl::warpAffine(gmat1, gdst, M, size, interpolation);
+ warpAffine(mat1_roi, dst_roi, M, size, interpolation);
+ ocl::warpAffine(gmat1, gdst, M, size, interpolation);
- cv::Mat cpu_dst;
+ Mat cpu_dst;
gdst_whole.download(cpu_dst);
EXPECT_MAT_NEAR(dst, cpu_dst, 1.0);
}
// warpPerspective
-struct WarpPerspective : WarpTestBase {};
+typedef WarpTestBase WarpPerspective;
OCL_TEST_P(WarpPerspective, Mat)
{
{
random_roi();
- cv::warpPerspective(mat1_roi, dst_roi, M, size, interpolation);
- cv::ocl::warpPerspective(gmat1, gdst, M, size, interpolation);
+ warpPerspective(mat1_roi, dst_roi, M, size, interpolation);
+ ocl::warpPerspective(gmat1, gdst, M, size, interpolation);
- cv::Mat cpu_dst;
+ Mat cpu_dst;
gdst_whole.download(cpu_dst);
EXPECT_MAT_NEAR(dst, cpu_dst, 1.0);
}
int srcType;
int map1Type;
int map2Type;
- cv::Scalar val;
+ Scalar val;
int interpolation;
int bordertype;
- cv::Mat src;
- cv::Mat dst;
- cv::Mat map1;
- cv::Mat map2;
+ Mat src;
+ Mat dst;
+ Mat map1;
+ Mat map2;
- //std::vector<cv::ocl::Info> oclinfo;
+ //std::vector<ocl::Info> oclinfo;
int src_roicols;
int src_roirows;
int map2x;
int map2y;
- cv::Mat src_roi;
- cv::Mat dst_roi;
- cv::Mat map1_roi;
- cv::Mat map2_roi;
+ Mat src_roi;
+ Mat dst_roi;
+ Mat map1_roi;
+ Mat map2_roi;
//ocl mat for testing
- cv::ocl::oclMat gdst;
+ ocl::oclMat gdst;
//ocl mat with roi
- cv::ocl::oclMat gsrc_roi;
- cv::ocl::oclMat gdst_roi;
- cv::ocl::oclMat gmap1_roi;
- cv::ocl::oclMat gmap2_roi;
+ ocl::oclMat gsrc_roi;
+ ocl::oclMat gdst_roi;
+ ocl::oclMat gmap1_roi;
+ ocl::oclMat gmap2_roi;
virtual void SetUp()
{
interpolation = GET_PARAM(3);
bordertype = GET_PARAM(4);
- cv::Size srcSize = cv::Size(MWIDTH, MHEIGHT);
- cv::Size map1Size = cv::Size(MWIDTH, MHEIGHT);
+ Size srcSize = Size(MWIDTH, MHEIGHT);
+ Size map1Size = Size(MWIDTH, MHEIGHT);
double min = 5, max = 16;
if(srcType != nulltype)
switch (src.channels())
{
case 1:
- val = cv::Scalar(rng.uniform(0.0, 10.0), 0, 0, 0);
+ val = Scalar(rng.uniform(0.0, 10.0), 0, 0, 0);
break;
case 2:
- val = cv::Scalar(rng.uniform(0.0, 10.0), rng.uniform(0.0, 10.0), 0, 0);
+ val = Scalar(rng.uniform(0.0, 10.0), rng.uniform(0.0, 10.0), 0, 0);
break;
case 3:
- val = cv::Scalar(rng.uniform(0.0, 10.0), rng.uniform(0.0, 10.0), rng.uniform(0.0, 10.0), 0);
+ val = Scalar(rng.uniform(0.0, 10.0), rng.uniform(0.0, 10.0), rng.uniform(0.0, 10.0), 0);
break;
case 4:
- val = cv::Scalar(rng.uniform(0.0, 10.0), rng.uniform(0.0, 10.0), rng.uniform(0.0, 10.0), rng.uniform(0.0, 10.0));
+ val = Scalar(rng.uniform(0.0, 10.0), rng.uniform(0.0, 10.0), rng.uniform(0.0, 10.0), rng.uniform(0.0, 10.0));
break;
}
cout << "Don't support the dataType" << endl;
return;
}
- int bordertype[] = {cv::BORDER_CONSTANT, cv::BORDER_REPLICATE/*,BORDER_REFLECT,BORDER_WRAP,BORDER_REFLECT_101*/};
+ int bordertype[] = {BORDER_CONSTANT, BORDER_REPLICATE/*,BORDER_REFLECT,BORDER_WRAP,BORDER_REFLECT_101*/};
for(int j = 0; j < LOOP_TIMES; j++)
{
random_roi();
- cv::remap(src_roi, dst_roi, map1_roi, map2_roi, interpolation, bordertype[0], val);
- cv::ocl::remap(gsrc_roi, gdst_roi, gmap1_roi, gmap2_roi, interpolation, bordertype[0], val);
- cv::Mat cpu_dst;
+ remap(src_roi, dst_roi, map1_roi, map2_roi, interpolation, bordertype[0], val);
+ ocl::remap(gsrc_roi, gdst_roi, gmap1_roi, gmap2_roi, interpolation, bordertype[0], val);
+ Mat cpu_dst;
gdst.download(cpu_dst);
if(interpolation == 0)
/////////////////////////////////////////////////////////////////////////////////////////////////
// resize
-PARAM_TEST_CASE(Resize, MatType, cv::Size, double, double, int)
+PARAM_TEST_CASE(Resize, MatType, Size, double, double, int)
{
int type;
- cv::Size dsize;
+ Size dsize;
double fx, fy;
int interpolation;
//src mat
- cv::Mat mat1;
- cv::Mat dst;
+ Mat mat1;
+ Mat dst;
// set up roi
int src_roicols;
int dsty;
//src mat with roi
- cv::Mat mat1_roi;
- cv::Mat dst_roi;
+ Mat mat1_roi;
+ Mat dst_roi;
//ocl dst mat for testing
- cv::ocl::oclMat gdst_whole;
+ ocl::oclMat gdst_whole;
//ocl mat with roi
- cv::ocl::oclMat gmat1;
- cv::ocl::oclMat gdst;
+ ocl::oclMat gmat1;
+ ocl::oclMat gdst;
virtual void SetUp()
{
fy = GET_PARAM(3);
interpolation = GET_PARAM(4);
- cv::Size size(MWIDTH, MHEIGHT);
+ Size size(MWIDTH, MHEIGHT);
- if(dsize == cv::Size() && !(fx > 0 && fy > 0))
+ if(dsize == Size() && !(fx > 0 && fy > 0))
{
cout << "invalid dsize and fx fy" << endl;
return;
}
- if(dsize == cv::Size())
+ if(dsize == Size())
{
dsize.width = (int)(size.width * fx);
dsize.height = (int)(size.height * fy);
{
random_roi();
- // cv::resize(mat1_roi, dst_roi, dsize, fx, fy, interpolation);
- // cv::ocl::resize(gmat1, gdst, dsize, fx, fy, interpolation);
+ // resize(mat1_roi, dst_roi, dsize, fx, fy, interpolation);
+ // ocl::resize(gmat1, gdst, dsize, fx, fy, interpolation);
if(dst_roicols < 1 || dst_roirows < 1) continue;
- cv::resize(mat1_roi, dst_roi, dsize, fx, fy, interpolation);
- cv::ocl::resize(gmat1, gdst, dsize, fx, fy, interpolation);
+ resize(mat1_roi, dst_roi, dsize, fx, fy, interpolation);
+ ocl::resize(gmat1, gdst, dsize, fx, fy, interpolation);
- cv::Mat cpu_dst;
+ Mat cpu_dst;
gdst_whole.download(cpu_dst);
EXPECT_MAT_NEAR(dst, cpu_dst, 1.0);
}
int threshOp;
//src mat
- cv::Mat mat1;
- cv::Mat dst;
+ Mat mat1;
+ Mat dst;
// set up roi
int roicols;
int dsty;
//src mat with roi
- cv::Mat mat1_roi;
- cv::Mat dst_roi;
+ Mat mat1_roi;
+ Mat dst_roi;
//ocl dst mat for testing
- cv::ocl::oclMat gdst_whole;
+ ocl::oclMat gdst_whole;
//ocl mat with roi
- cv::ocl::oclMat gmat1;
- cv::ocl::oclMat gdst;
+ ocl::oclMat gmat1;
+ ocl::oclMat gdst;
virtual void SetUp()
{
type = GET_PARAM(0);
threshOp = GET_PARAM(1);
- cv::Size size(MWIDTH, MHEIGHT);
+ Size size(MWIDTH, MHEIGHT);
mat1 = randomMat(size, type, 5, 16, false);
dst = randomMat(size, type, 5, 16, false);
double maxVal = randomDouble(20.0, 127.0);
double thresh = randomDouble(0.0, maxVal);
- cv::threshold(mat1_roi, dst_roi, thresh, maxVal, threshOp);
- cv::ocl::threshold(gmat1, gdst, thresh, maxVal, threshOp);
+ threshold(mat1_roi, dst_roi, thresh, maxVal, threshOp);
+ ocl::threshold(gmat1, gdst, thresh, maxVal, threshOp);
- cv::Mat cpu_dst;
+ Mat cpu_dst;
gdst_whole.download(cpu_dst);
EXPECT_MAT_NEAR(dst, cpu_dst, 1);
}
}
-PARAM_TEST_CASE(meanShiftTestBase, MatType, MatType, int, int, cv::TermCriteria)
+PARAM_TEST_CASE(MeanShiftTestBase, MatType, MatType, int, int, TermCriteria)
{
int type, typeCoor;
int sp, sr;
- cv::TermCriteria crit;
+ TermCriteria crit;
//src mat
- cv::Mat src;
- cv::Mat dst;
- cv::Mat dstCoor;
+ Mat src;
+ Mat dst;
+ Mat dstCoor;
//set up roi
int roicols;
int dsty;
//src mat with roi
- cv::Mat src_roi;
- cv::Mat dst_roi;
- cv::Mat dstCoor_roi;
+ Mat src_roi;
+ Mat dst_roi;
+ Mat dstCoor_roi;
//ocl dst mat
- cv::ocl::oclMat gdst;
- cv::ocl::oclMat gdstCoor;
+ ocl::oclMat gdst;
+ ocl::oclMat gdstCoor;
//ocl mat with roi
- cv::ocl::oclMat gsrc_roi;
- cv::ocl::oclMat gdst_roi;
- cv::ocl::oclMat gdstCoor_roi;
+ ocl::oclMat gsrc_roi;
+ ocl::oclMat gdst_roi;
+ ocl::oclMat gdstCoor_roi;
virtual void SetUp()
{
crit = GET_PARAM(4);
// MWIDTH=256, MHEIGHT=256. defined in utility.hpp
- cv::Size size = cv::Size(MWIDTH, MHEIGHT);
+ Size size = Size(MWIDTH, MHEIGHT);
src = randomMat(size, type, 5, 16, false);
dst = randomMat(size, type, 5, 16, false);
};
/////////////////////////meanShiftFiltering/////////////////////////////
-struct meanShiftFiltering : meanShiftTestBase {};
-OCL_TEST_P(meanShiftFiltering, Mat)
+typedef MeanShiftTestBase MeanShiftFiltering;
+
+OCL_TEST_P(MeanShiftFiltering, Mat)
{
for(int j = 0; j < LOOP_TIMES; j++)
{
random_roi();
- cv::Mat cpu_gdst;
+ Mat cpu_gdst;
gdst.download(cpu_gdst);
- meanShiftFiltering_(src_roi, dst_roi, sp, sr, crit);
- cv::ocl::meanShiftFiltering(gsrc_roi, gdst_roi, sp, sr, crit);
+ ::meanShiftFiltering_(src_roi, dst_roi, sp, sr, crit);
+ ocl::meanShiftFiltering(gsrc_roi, gdst_roi, sp, sr, crit);
gdst.download(cpu_gdst);
EXPECT_MAT_NEAR(dst, cpu_gdst, 0.0);
}
///////////////////////////meanShiftProc//////////////////////////////////
-struct meanShiftProc : meanShiftTestBase {};
-OCL_TEST_P(meanShiftProc, Mat)
+typedef MeanShiftTestBase MeanShiftProc;
+
+OCL_TEST_P(MeanShiftProc, Mat)
{
for(int j = 0; j < LOOP_TIMES; j++)
{
random_roi();
- cv::Mat cpu_gdst;
- cv::Mat cpu_gdstCoor;
+ Mat cpu_gdst;
+ Mat cpu_gdstCoor;
meanShiftProc_(src_roi, dst_roi, dstCoor_roi, sp, sr, crit);
- cv::ocl::meanShiftProc(gsrc_roi, gdst_roi, gdstCoor_roi, sp, sr, crit);
+ ocl::meanShiftProc(gsrc_roi, gdst_roi, gdstCoor_roi, sp, sr, crit);
gdst.download(cpu_gdst);
gdstCoor.download(cpu_gdstCoor);
///////////////////////////////////////////////////////////////////////////////////////
//hist
-void calcHistGold(const cv::Mat &src, cv::Mat &hist)
+
+void calcHistGold(const Mat &src, Mat &hist)
{
hist.create(1, 256, CV_32SC1);
- hist.setTo(cv::Scalar::all(0));
+ hist.setTo(Scalar::all(0));
int *hist_row = hist.ptr<int>();
for (int y = 0; y < src.rows; ++y)
}
}
-PARAM_TEST_CASE(histTestBase, MatType, MatType)
+PARAM_TEST_CASE(HistTestBase, MatType, MatType)
{
int type_src;
//src mat
- cv::Mat src;
- cv::Mat dst_hist;
+ Mat src;
+ Mat dst_hist;
//set up roi
int roicols;
int roirows;
int srcx;
int srcy;
//src mat with roi
- cv::Mat src_roi;
+ Mat src_roi;
//ocl dst mat, dst_hist and gdst_hist don't have roi
- cv::ocl::oclMat gdst_hist;
+ ocl::oclMat gdst_hist;
//ocl mat with roi
- cv::ocl::oclMat gsrc_roi;
+ ocl::oclMat gsrc_roi;
virtual void SetUp()
{
type_src = GET_PARAM(0);
- cv::Size size = cv::Size(MWIDTH, MHEIGHT);
+ Size size = Size(MWIDTH, MHEIGHT);
src = randomMat(size, type_src, 0, 256, false);
gsrc_roi = src_roi;
}
};
+
///////////////////////////calcHist///////////////////////////////////////
-struct calcHist : histTestBase {};
-OCL_TEST_P(calcHist, Mat)
+typedef HistTestBase CalcHist;
+
+OCL_TEST_P(CalcHist, Mat)
{
for(int j = 0; j < LOOP_TIMES; j++)
{
random_roi();
- cv::Mat cpu_hist;
+ Mat cpu_hist;
calcHistGold(src_roi, dst_hist);
- cv::ocl::calcHist(gsrc_roi, gdst_hist);
+ ocl::calcHist(gsrc_roi, gdst_hist);
gdst_hist.download(cpu_hist);
EXPECT_MAT_NEAR(dst_hist, cpu_hist, 0.0);
}
}
+
///////////////////////////////////////////////////////////////////////////////////////////////////////
// CLAHE
-PARAM_TEST_CASE(CLAHE, cv::Size, double)
+PARAM_TEST_CASE(CLAHE_Test, Size, double)
{
- cv::Size gridSize;
+ Size gridSize;
double clipLimit;
- cv::Mat src;
- cv::Mat dst_gold;
+ Mat src;
+ Mat dst_gold;
- cv::ocl::oclMat g_src;
- cv::ocl::oclMat g_dst;
+ ocl::oclMat g_src;
+ ocl::oclMat g_dst;
virtual void SetUp()
{
gridSize = GET_PARAM(0);
clipLimit = GET_PARAM(1);
- src = randomMat(cv::Size(MWIDTH, MHEIGHT), CV_8UC1, 0, 256, false);
+ src = randomMat(Size(MWIDTH, MHEIGHT), CV_8UC1, 0, 256, false);
g_src.upload(src);
}
};
-OCL_TEST_P(CLAHE, Accuracy)
+OCL_TEST_P(CLAHE_Test, Accuracy)
{
- cv::Ptr<cv::CLAHE> clahe = cv::ocl::createCLAHE(clipLimit, gridSize);
+ Ptr<CLAHE> clahe = ocl::createCLAHE(clipLimit, gridSize);
clahe->apply(g_src, g_dst);
- cv::Mat dst(g_dst);
+ Mat dst(g_dst);
- cv::Ptr<cv::CLAHE> clahe_gold = cv::createCLAHE(clipLimit, gridSize);
+ Ptr<CLAHE> clahe_gold = createCLAHE(clipLimit, gridSize);
clahe_gold->apply(src, dst_gold);
EXPECT_MAT_NEAR(dst_gold, dst, 1.0);
}
///////////////////////////Convolve//////////////////////////////////
+
PARAM_TEST_CASE(ConvolveTestBase, MatType, bool)
{
int type;
//src mat
- cv::Mat mat1;
- cv::Mat mat2;
- cv::Mat dst;
- cv::Mat dst1; //bak, for two outputs
+ Mat mat1;
+ Mat mat2;
+ Mat dst;
+ Mat dst1; //bak, for two outputs
// set up roi
int roicols;
int roirows;
int dstx;
int dsty;
//src mat with roi
- cv::Mat mat1_roi;
- cv::Mat mat2_roi;
- cv::Mat dst_roi;
- cv::Mat dst1_roi; //bak
+ Mat mat1_roi;
+ Mat mat2_roi;
+ Mat dst_roi;
+ Mat dst1_roi; //bak
//ocl dst mat for testing
- cv::ocl::oclMat gdst_whole;
- cv::ocl::oclMat gdst1_whole; //bak
+ ocl::oclMat gdst_whole;
+ ocl::oclMat gdst1_whole; //bak
//ocl mat with roi
- cv::ocl::oclMat gmat1;
- cv::ocl::oclMat gmat2;
- cv::ocl::oclMat gdst;
- cv::ocl::oclMat gdst1; //bak
+ ocl::oclMat gmat1;
+ ocl::oclMat gmat2;
+ ocl::oclMat gdst;
+ ocl::oclMat gdst1; //bak
virtual void SetUp()
{
type = GET_PARAM(0);
- cv::Size size(MWIDTH, MHEIGHT);
+ Size size(MWIDTH, MHEIGHT);
mat1 = randomMat(size, type, 5, 16, false);
mat2 = randomMat(size, type, 5, 16, false);
}
};
-struct Convolve : ConvolveTestBase {};
-void conv2( cv::Mat x, cv::Mat y, cv::Mat z)
+typedef ConvolveTestBase Convolve;
+
+void conv2( Mat x, Mat y, Mat z)
{
int N1 = x.rows;
int M1 = x.cols;
dstdata[i * (z.step >> 2) + j] = temp;
}
}
+
OCL_TEST_P(Convolve, Mat)
{
if(mat1.type() != CV_32FC1)
for(int j = 0; j < LOOP_TIMES; j++)
{
random_roi();
- cv::ocl::oclMat temp1;
- cv::Mat kernel_cpu = mat2(Rect(0, 0, 7, 7));
+ ocl::oclMat temp1;
+ Mat kernel_cpu = mat2(Rect(0, 0, 7, 7));
temp1 = kernel_cpu;
conv2(kernel_cpu, mat1_roi, dst_roi);
- cv::ocl::convolve(gmat1, temp1, gdst);
+ ocl::convolve(gmat1, temp1, gdst);
- cv::Mat cpu_dst;
+ Mat cpu_dst;
gdst_whole.download(cpu_dst);
EXPECT_MAT_NEAR(dst, cpu_dst, .1);
}
//////////////////////////////// ColumnSum //////////////////////////////////////
-PARAM_TEST_CASE(ColumnSum, cv::Size)
+
+PARAM_TEST_CASE(ColumnSum, Size)
{
- cv::Size size;
- cv::Mat src;
+ Size size;
+ Mat src;
virtual void SetUp()
{
OCL_TEST_P(ColumnSum, Accuracy)
{
- cv::Mat src = randomMat(size, CV_32FC1, 0, 255);
- cv::ocl::oclMat d_dst;
- cv::ocl::oclMat d_src(src);
+ Mat src = randomMat(size, CV_32FC1, 0, 255);
+ ocl::oclMat d_dst;
+ ocl::oclMat d_src(src);
- cv::ocl::columnSum(d_src, d_dst);
+ ocl::columnSum(d_src, d_dst);
- cv::Mat dst(d_dst);
+ Mat dst(d_dst);
for (int j = 0; j < src.cols; ++j)
{
}
}
}
+
/////////////////////////////////////////////////////////////////////////////////////
-INSTANTIATE_TEST_CASE_P(ImgprocTestBase, equalizeHist, Combine(
+INSTANTIATE_TEST_CASE_P(ImgprocTestBase, EqualizeHist, Combine(
ONE_TYPE(CV_8UC1),
NULL_TYPE,
ONE_TYPE(CV_8UC1),
NULL_TYPE,
Values(false))); // Values(false) is the reserved parameter
-INSTANTIATE_TEST_CASE_P(ImgprocTestBase, cornerMinEigenVal, Combine(
+INSTANTIATE_TEST_CASE_P(ImgprocTestBase, CornerMinEigenVal, Combine(
Values(CV_8UC1, CV_32FC1),
NULL_TYPE,
ONE_TYPE(CV_32FC1),
NULL_TYPE,
Values(false))); // Values(false) is the reserved parameter
-INSTANTIATE_TEST_CASE_P(ImgprocTestBase, cornerHarris, Combine(
+INSTANTIATE_TEST_CASE_P(ImgprocTestBase, CornerHarris, Combine(
Values(CV_8UC1, CV_32FC1),
NULL_TYPE,
ONE_TYPE(CV_32FC1),
Values(false))); // Values(false) is the reserved parameter
-INSTANTIATE_TEST_CASE_P(ImgprocTestBase, integral, Combine(
+INSTANTIATE_TEST_CASE_P(ImgprocTestBase, Integral, Combine(
ONE_TYPE(CV_8UC1),
NULL_TYPE,
ONE_TYPE(CV_32SC1),
INSTANTIATE_TEST_CASE_P(Imgproc, WarpAffine, Combine(
Values(CV_8UC1, CV_8UC3, CV_8UC4, CV_32FC1, CV_32FC3, CV_32FC4),
- Values((MatType)cv::INTER_NEAREST, (MatType)cv::INTER_LINEAR,
- (MatType)cv::INTER_CUBIC, (MatType)(cv::INTER_NEAREST | cv::WARP_INVERSE_MAP),
- (MatType)(cv::INTER_LINEAR | cv::WARP_INVERSE_MAP), (MatType)(cv::INTER_CUBIC | cv::WARP_INVERSE_MAP))));
+ Values((MatType)INTER_NEAREST, (MatType)INTER_LINEAR,
+ (MatType)INTER_CUBIC, (MatType)(INTER_NEAREST | WARP_INVERSE_MAP),
+ (MatType)(INTER_LINEAR | WARP_INVERSE_MAP), (MatType)(INTER_CUBIC | WARP_INVERSE_MAP))));
INSTANTIATE_TEST_CASE_P(Imgproc, WarpPerspective, Combine
(Values(CV_8UC1, CV_8UC3, CV_8UC4, CV_32FC1, CV_32FC3, CV_32FC4),
- Values((MatType)cv::INTER_NEAREST, (MatType)cv::INTER_LINEAR,
- (MatType)cv::INTER_CUBIC, (MatType)(cv::INTER_NEAREST | cv::WARP_INVERSE_MAP),
- (MatType)(cv::INTER_LINEAR | cv::WARP_INVERSE_MAP), (MatType)(cv::INTER_CUBIC | cv::WARP_INVERSE_MAP))));
+ Values((MatType)INTER_NEAREST, (MatType)INTER_LINEAR,
+ (MatType)INTER_CUBIC, (MatType)(INTER_NEAREST | WARP_INVERSE_MAP),
+ (MatType)(INTER_LINEAR | WARP_INVERSE_MAP), (MatType)(INTER_CUBIC | WARP_INVERSE_MAP))));
INSTANTIATE_TEST_CASE_P(Imgproc, Resize, Combine(
- Values(CV_8UC1, CV_8UC3, CV_8UC4, CV_32FC1, CV_32FC3, CV_32FC4), Values(cv::Size()),
- Values(0.5, 1.5, 2), Values(0.5, 1.5, 2), Values((MatType)cv::INTER_NEAREST, (MatType)cv::INTER_LINEAR)));
+ Values(CV_8UC1, CV_8UC3, CV_8UC4, CV_32FC1, CV_32FC3, CV_32FC4), Values(Size()),
+ Values(0.5, 1.5, 2), Values(0.5, 1.5, 2), Values((MatType)INTER_NEAREST, (MatType)INTER_LINEAR)));
INSTANTIATE_TEST_CASE_P(Imgproc, Threshold, Combine(
- Values(CV_8UC1, CV_32FC1), Values(ThreshOp(cv::THRESH_BINARY),
- ThreshOp(cv::THRESH_BINARY_INV), ThreshOp(cv::THRESH_TRUNC),
- ThreshOp(cv::THRESH_TOZERO), ThreshOp(cv::THRESH_TOZERO_INV))));
+ Values(CV_8UC1, CV_32FC1), Values(ThreshOp(THRESH_BINARY),
+ ThreshOp(THRESH_BINARY_INV), ThreshOp(THRESH_TRUNC),
+ ThreshOp(THRESH_TOZERO), ThreshOp(THRESH_TOZERO_INV))));
-INSTANTIATE_TEST_CASE_P(Imgproc, meanShiftFiltering, Combine(
+INSTANTIATE_TEST_CASE_P(Imgproc, MeanShiftFiltering, Combine(
ONE_TYPE(CV_8UC4),
ONE_TYPE(CV_16SC2),
Values(5),
Values(6),
- Values(cv::TermCriteria(cv::TermCriteria::COUNT + cv::TermCriteria::EPS, 5, 1))
+ Values(TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 5, 1))
));
-INSTANTIATE_TEST_CASE_P(Imgproc, meanShiftProc, Combine(
+INSTANTIATE_TEST_CASE_P(Imgproc, MeanShiftProc, Combine(
ONE_TYPE(CV_8UC4),
ONE_TYPE(CV_16SC2),
Values(5),
Values(6),
- Values(cv::TermCriteria(cv::TermCriteria::COUNT + cv::TermCriteria::EPS, 5, 1))
+ Values(TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 5, 1))
));
INSTANTIATE_TEST_CASE_P(Imgproc, Remap, Combine(
Values(CV_8UC1, CV_8UC3, CV_8UC4, CV_32FC1, CV_32FC3, CV_32FC4),
Values(CV_32FC1, CV_16SC2, CV_32FC2), Values(-1, CV_32FC1),
- Values((int)cv::INTER_NEAREST, (int)cv::INTER_LINEAR),
- Values((int)cv::BORDER_CONSTANT)));
+ Values((int)INTER_NEAREST, (int)INTER_LINEAR),
+ Values((int)BORDER_CONSTANT)));
-INSTANTIATE_TEST_CASE_P(histTestBase, calcHist, Combine(
+INSTANTIATE_TEST_CASE_P(histTestBase, CalcHist, Combine(
ONE_TYPE(CV_8UC1),
ONE_TYPE(CV_32SC1) //no use
));
-INSTANTIATE_TEST_CASE_P(Imgproc, CLAHE, Combine(
- Values(cv::Size(4, 4), cv::Size(32, 8), cv::Size(8, 64)),
+INSTANTIATE_TEST_CASE_P(Imgproc, CLAHE_Test, Combine(
+ Values(Size(4, 4), Size(32, 8), Size(8, 64)),
Values(0.0, 10.0, 62.0, 300.0)));
INSTANTIATE_TEST_CASE_P(Imgproc, ColumnSum, DIFFERENT_SIZES);
projects / profile / ivi / opencv.git / commitdiff