Chiebot-Mirror
/
opencv
mirror of https://github.com/opencv/opencv.git
8
0
Fork 0
Code Issues Projects Releases Wiki Activity

Merge pull request #1545 from alalek:ocl_test_fix_rng

Browse Source
pull/1538/merge
Roman Donchenko 11 years ago committed by OpenCV Buildbot
parent 95b3a344d9 de0f310e81
commit ec409a436d
19 changed files with 312 additions and 482 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 330
      modules/ocl/test/test_arithm.cpp
  2. 14
      modules/ocl/test/test_bgfg.cpp
  3. 2
      modules/ocl/test/test_brute_force_matcher.cpp
  4. 4
      modules/ocl/test/test_calib3d.cpp
  5. 4
      modules/ocl/test/test_filters.cpp
  6. 80
      modules/ocl/test/test_imgproc.cpp
  7. 6
      modules/ocl/test/test_kalman.cpp
  8. 7
      modules/ocl/test/test_kmeans.cpp
  9. 8
      modules/ocl/test/test_match_template.cpp
  10. 26
      modules/ocl/test/test_matrix_operation.cpp
  11. 13
      modules/ocl/test/test_ml.cpp
  12. 6
      modules/ocl/test/test_moments.cpp
  13. 2
      modules/ocl/test/test_objdetect.cpp
  14. 8
      modules/ocl/test/test_optflow.cpp
  15. 2
      modules/ocl/test/test_precomp.hpp
  16. 4
      modules/ocl/test/test_pyramids.cpp
  17. 12
      modules/ocl/test/test_split_merge.cpp
  18. 73
      modules/ocl/test/utility.cpp
  19. 193
      modules/ocl/test/utility.hpp

330
modules/ocl/test/test_arithm.cpp
Unescape View File

@ -80,12 +80,14 @@ PARAM_TEST_CASE(Lut, int, int, bool, bool)
cv::Mat dst_roi;
// ocl dst mat for testing
cv::ocl::oclMat gsrc_whole;
cv::ocl::oclMat glut_whole;
cv::ocl::oclMat gdst_whole;
// ocl mat with roi
cv::ocl::oclMat gsrc;
cv::ocl::oclMat glut;
cv::ocl::oclMat gdst;
cv::ocl::oclMat gsrc_roi;
cv::ocl::oclMat glut_roi;
cv::ocl::oclMat gdst_roi;
virtual void SetUp()
{
@ -93,66 +95,34 @@ PARAM_TEST_CASE(Lut, int, int, bool, bool)
cn = GET_PARAM(1);
same_cn = GET_PARAM(2);
use_roi = GET_PARAM(3);
const int src_type = CV_MAKE_TYPE(CV_8U, cn);
const int lut_type = CV_MAKE_TYPE(lut_depth, same_cn ? cn : 1);
const int dst_type = CV_MAKE_TYPE(lut_depth, cn);
cv::RNG &rng = TS::ptr()->get_rng();
src = randomMat(rng, randomSize(MIN_VALUE, MAX_VALUE), src_type, 0, 256, false);
lut = randomMat(rng, use_roi ? randomSize(260, 300) : Size(256, 1), lut_type, 5, 16, false);
dst = randomMat(rng, use_roi ? randomSize(MIN_VALUE, MAX_VALUE) : src.size(), dst_type, 5, 16, false);
}
void random_roi()
{
// set up roi
int roicols, roirows;
int srcx, srcy;
int lutx, luty;
int dstx, dsty;
if (use_roi)
{
// randomize ROI
cv::RNG &rng = TS::ptr()->get_rng();
roicols = rng.uniform(1, MIN_VALUE);
roirows = rng.uniform(1, MIN_VALUE);
srcx = rng.uniform(0, src.cols - roicols);
srcy = rng.uniform(0, src.rows - roirows);
lutx = rng.uniform(0, lut.cols - 256);
luty = rng.uniform(0, lut.rows - 1);
const int src_type = CV_MAKE_TYPE(CV_8U, cn);
const int lut_type = CV_MAKE_TYPE(lut_depth, same_cn ? cn : 1);
const int dst_type = CV_MAKE_TYPE(lut_depth, cn);
dstx = rng.uniform(0, dst.cols - roicols);
dsty = rng.uniform(0, dst.rows - roirows);
}
else
{
roicols = src.cols;
roirows = src.rows;
srcx = srcy = 0;
lutx = luty = 0;
dstx = dsty = 0;
}
Size roiSize = randomSize(1, MAX_VALUE);
Border srcBorder = randomBorder(0, use_roi ? MAX_VALUE : 0);
randomSubMat(src, src_roi, roiSize, srcBorder, src_type, 0, 256);
src_roi = src(Rect(srcx, srcy, roicols, roirows));
lut_roi = lut(Rect(lutx, luty, 256, 1));
dst_roi = dst(Rect(dstx, dsty, roicols, roirows));
Size lutRoiSize = Size(256, 1);
Border lutBorder = randomBorder(0, use_roi ? MAX_VALUE : 0);
randomSubMat(lut, lut_roi, lutRoiSize, lutBorder, lut_type, 5, 16);
gdst_whole = dst;
gdst = gdst_whole(Rect(dstx, dsty, roicols, roirows));
Border dstBorder = randomBorder(0, use_roi ? MAX_VALUE : 0);
randomSubMat(dst, dst_roi, roiSize, dstBorder, dst_type, 5, 16);
gsrc = src_roi;
glut = lut_roi;
generateOclMat(gsrc_whole, gsrc_roi, src, roiSize, srcBorder);
generateOclMat(glut_whole, glut_roi, lut, lutRoiSize, lutBorder);
generateOclMat(gdst_whole, gdst_roi, dst, roiSize, dstBorder);
}
void Near(double threshold = 0.)
{
EXPECT_MAT_NEAR(dst, Mat(gdst_whole), threshold);
EXPECT_MAT_NEAR(dst_roi, Mat(gdst), threshold);
EXPECT_MAT_NEAR(dst_roi, Mat(gdst_roi), threshold);
}
};
@ -163,7 +133,7 @@ TEST_P(Lut, Mat)
random_roi();
cv::LUT(src_roi, lut_roi, dst_roi);
cv::ocl::LUT(gsrc, glut, gdst);
cv::ocl::LUT(gsrc_roi, glut_roi, gdst_roi);
Near();
}
@ -183,50 +153,34 @@ PARAM_TEST_CASE(ArithmTestBase, int, int, bool)
cv::Mat src2;
cv::Mat mask;
cv::Mat dst1;
cv::Mat dst2; // for two outputs
// set up roi
int roicols, roirows;
int src1x, src1y;
int src2x, src2y;
int dst1x, dst1y;
int dst2x, dst2y;
int maskx, masky;
cv::Mat dst2;
// src mat with roi
cv::Mat src1_roi;
cv::Mat src2_roi;
cv::Mat mask_roi;
cv::Mat dst1_roi;
cv::Mat dst2_roi; // for two outputs
cv::Mat dst2_roi;
// ocl dst mat for testing
cv::ocl::oclMat gsrc1_whole;
cv::ocl::oclMat gsrc2_whole;
cv::ocl::oclMat gdst1_whole;
cv::ocl::oclMat gdst2_whole; // for two outputs
cv::ocl::oclMat gdst2_whole;
cv::ocl::oclMat gmask_whole;
// ocl mat with roi
cv::ocl::oclMat gsrc1;
cv::ocl::oclMat gsrc2;
cv::ocl::oclMat gdst1;
cv::ocl::oclMat gdst2; // for two outputs
cv::ocl::oclMat gmask;
cv::ocl::oclMat gsrc1_roi;
cv::ocl::oclMat gsrc2_roi;
cv::ocl::oclMat gdst1_roi;
cv::ocl::oclMat gdst2_roi;
cv::ocl::oclMat gmask_roi;
virtual void SetUp()
{
depth = GET_PARAM(0);
cn = GET_PARAM(1);
use_roi = GET_PARAM(2);
const int type = CV_MAKE_TYPE(depth, cn);
cv::RNG &rng = TS::ptr()->get_rng();
src1 = randomMat(rng, randomSize(MIN_VALUE, MAX_VALUE), type, 2, 11, false);
src2 = randomMat(rng, !use_roi ? src1.size() : randomSize(MIN_VALUE, MAX_VALUE), type, -1540, 1740, false);
dst1 = randomMat(rng, !use_roi ? src1.size() : randomSize(MIN_VALUE, MAX_VALUE), type, 5, 16, false);
dst2 = randomMat(rng, !use_roi ? src1.size() : randomSize(MIN_VALUE, MAX_VALUE), type, 5, 16, false);
mask = randomMat(rng, !use_roi ? src1.size() : randomSize(MIN_VALUE, MAX_VALUE), CV_8UC1, 0, 2, false);
cv::threshold(mask, mask, 0.5, 255., CV_8UC1);
val = cv::Scalar(rng.uniform(-100.0, 100.0), rng.uniform(-100.0, 100.0),
rng.uniform(-100.0, 100.0), rng.uniform(-100.0, 100.0));
@ -234,65 +188,43 @@ PARAM_TEST_CASE(ArithmTestBase, int, int, bool)
void random_roi()
{
if (use_roi)
{
// randomize ROI
cv::RNG &rng = TS::ptr()->get_rng();
roicols = rng.uniform(1, MIN_VALUE);
roirows = rng.uniform(1, MIN_VALUE);
const int type = CV_MAKE_TYPE(depth, cn);
src1x = rng.uniform(0, src1.cols - roicols);
src1y = rng.uniform(0, src1.rows - roirows);
src2x = rng.uniform(0, src2.cols - roicols);
src2y = rng.uniform(0, src2.rows - roirows);
Size roiSize = randomSize(1, MAX_VALUE);
Border srcBorder = randomBorder(0, use_roi ? MAX_VALUE : 0);
randomSubMat(src1, src1_roi, roiSize, srcBorder, type, 2, 11);
dst1x = rng.uniform(0, dst1.cols - roicols);
dst1y = rng.uniform(0, dst1.rows - roirows);
dst2x = rng.uniform(0, dst2.cols - roicols);
dst2y = rng.uniform(0, dst2.rows - roirows);
Border src2Border = randomBorder(0, use_roi ? MAX_VALUE : 0);
randomSubMat(src2, src2_roi, roiSize, src2Border, type, -1540, 1740);
maskx = rng.uniform(0, mask.cols - roicols);
masky = rng.uniform(0, mask.rows - roirows);
}
else
{
roicols = src1.cols;
roirows = src1.rows;
src1x = src1y = 0;
src2x = src2y = 0;
dst1x = dst1y = 0;
dst2x = dst2y = 0;
maskx = masky = 0;
}
Border dst1Border = randomBorder(0, use_roi ? MAX_VALUE : 0);
randomSubMat(dst1, dst1_roi, roiSize, dst1Border, type, 5, 16);
src1_roi = src1(Rect(src1x, src1y, roicols, roirows));
src2_roi = src2(Rect(src2x, src2y, roicols, roirows));
mask_roi = mask(Rect(maskx, masky, roicols, roirows));
dst1_roi = dst1(Rect(dst1x, dst1y, roicols, roirows));
dst2_roi = dst2(Rect(dst2x, dst2y, roicols, roirows));
Border dst2Border = randomBorder(0, use_roi ? MAX_VALUE : 0);
randomSubMat(dst2, dst2_roi, roiSize, dst2Border, type, 5, 16);
gdst1_whole = dst1;
gdst1 = gdst1_whole(Rect(dst1x, dst1y, roicols, roirows));
Border maskBorder = randomBorder(0, use_roi ? MAX_VALUE : 0);
randomSubMat(mask, mask_roi, roiSize, maskBorder, CV_8UC1, 0, 2);
cv::threshold(mask, mask, 0.5, 255., CV_8UC1);
gdst2_whole = dst2;
gdst2 = gdst2_whole(Rect(dst2x, dst2y, roicols, roirows));
gsrc1 = src1_roi;
gsrc2 = src2_roi;
gmask = mask_roi;
generateOclMat(gsrc1_whole, gsrc1_roi, src1, roiSize, srcBorder);
generateOclMat(gsrc2_whole, gsrc2_roi, src2, roiSize, src2Border);
generateOclMat(gdst1_whole, gdst1_roi, dst1, roiSize, dst1Border);
generateOclMat(gdst2_whole, gdst2_roi, dst2, roiSize, dst2Border);
generateOclMat(gmask_whole, gmask_roi, mask, roiSize, maskBorder);
}
void Near(double threshold = 0.)
{
EXPECT_MAT_NEAR(dst1, Mat(gdst1_whole), threshold);
EXPECT_MAT_NEAR(dst1_roi, Mat(gdst1), threshold);
EXPECT_MAT_NEAR(dst1_roi, Mat(gdst1_roi), threshold);
}
void Near1(double threshold = 0.)
{
EXPECT_MAT_NEAR(dst2, Mat(gdst2_whole), threshold);
EXPECT_MAT_NEAR(dst2_roi, Mat(gdst2), threshold);
EXPECT_MAT_NEAR(dst2_roi, Mat(gdst2_roi), threshold);
}
};
@ -307,7 +239,7 @@ TEST_P(Exp, Mat)
random_roi();
cv::exp(src1_roi, dst1_roi);
cv::ocl::exp(gsrc1, gdst1);
cv::ocl::exp(gsrc1_roi, gdst1_roi);
Near(2);
}
@ -324,7 +256,7 @@ TEST_P(Log, Mat)
random_roi();
cv::log(src1_roi, dst1_roi);
cv::ocl::log(gsrc1, gdst1);
cv::ocl::log(gsrc1_roi, gdst1_roi);
Near(1);
}
}
@ -340,7 +272,7 @@ TEST_P(Add, Mat)
random_roi();
cv::add(src1_roi, src2_roi, dst1_roi);
cv::ocl::add(gsrc1, gsrc2, gdst1);
cv::ocl::add(gsrc1_roi, gsrc2_roi, gdst1_roi);
Near(0);
}
}
@ -352,7 +284,7 @@ TEST_P(Add, Mat_Mask)
random_roi();
cv::add(src1_roi, src2_roi, dst1_roi, mask_roi);
cv::ocl::add(gsrc1, gsrc2, gdst1, gmask);
cv::ocl::add(gsrc1_roi, gsrc2_roi, gdst1_roi, gmask_roi);
Near(0);
}
}
@ -364,7 +296,7 @@ TEST_P(Add, Scalar)
random_roi();
cv::add(src1_roi, val, dst1_roi);
cv::ocl::add(gsrc1, val, gdst1);
cv::ocl::add(gsrc1_roi, val, gdst1_roi);
Near(1e-5);
}
}
@ -376,7 +308,7 @@ TEST_P(Add, Scalar_Mask)
random_roi();
cv::add(src1_roi, val, dst1_roi, mask_roi);
cv::ocl::add(gsrc1, val, gdst1, gmask);
cv::ocl::add(gsrc1_roi, val, gdst1_roi, gmask_roi);
Near(1e-5);
}
}
@ -392,7 +324,7 @@ TEST_P(Sub, Mat)
random_roi();
cv::subtract(src1_roi, src2_roi, dst1_roi);
cv::ocl::subtract(gsrc1, gsrc2, gdst1);
cv::ocl::subtract(gsrc1_roi, gsrc2_roi, gdst1_roi);
Near(0);
}
@ -405,7 +337,7 @@ TEST_P(Sub, Mat_Mask)
random_roi();
cv::subtract(src1_roi, src2_roi, dst1_roi, mask_roi);
cv::ocl::subtract(gsrc1, gsrc2, gdst1, gmask);
cv::ocl::subtract(gsrc1_roi, gsrc2_roi, gdst1_roi, gmask_roi);
Near(0);
}
}
@ -417,7 +349,7 @@ TEST_P(Sub, Scalar)
random_roi();
cv::subtract(src1_roi, val, dst1_roi);
cv::ocl::subtract(gsrc1, val, gdst1);
cv::ocl::subtract(gsrc1_roi, val, gdst1_roi);
Near(1e-5);
}
@ -430,7 +362,7 @@ TEST_P(Sub, Scalar_Mask)
random_roi();
cv::subtract(src1_roi, val, dst1_roi, mask_roi);
cv::ocl::subtract(gsrc1, val, gdst1, gmask);
cv::ocl::subtract(gsrc1_roi, val, gdst1_roi, gmask_roi);
Near(1e-5);
}
}
@ -446,7 +378,7 @@ TEST_P(Mul, Mat)
random_roi();
cv::multiply(src1_roi, src2_roi, dst1_roi);
cv::ocl::multiply(gsrc1, gsrc2, gdst1);
cv::ocl::multiply(gsrc1_roi, gsrc2_roi, gdst1_roi);
Near(0);
}
}
@ -458,9 +390,9 @@ TEST_P(Mul, Scalar)
random_roi();
cv::multiply(val[0], src1_roi, dst1_roi);
cv::ocl::multiply(val[0], gsrc1, gdst1);
cv::ocl::multiply(val[0], gsrc1_roi, gdst1_roi);
Near(gdst1.depth() >= CV_32F ? 1e-3 : 1);
Near(gdst1_roi.depth() >= CV_32F ? 1e-3 : 1);
}
}
@ -471,9 +403,9 @@ TEST_P(Mul, Mat_Scalar)
random_roi();
cv::multiply(src1_roi, src2_roi, dst1_roi, val[0]);
cv::ocl::multiply(gsrc1, gsrc2, gdst1, val[0]);
cv::ocl::multiply(gsrc1_roi, gsrc2_roi, gdst1_roi, val[0]);
Near(gdst1.depth() >= CV_32F ? 1e-3 : 1);
Near(gdst1_roi.depth() >= CV_32F ? 1e-3 : 1);
}
}
@ -488,7 +420,7 @@ TEST_P(Div, Mat)
random_roi();
cv::divide(src1_roi, src2_roi, dst1_roi);
cv::ocl::divide(gsrc1, gsrc2, gdst1);
cv::ocl::divide(gsrc1_roi, gsrc2_roi, gdst1_roi);
Near(1);
}
}
@ -500,9 +432,9 @@ TEST_P(Div, Scalar)
random_roi();
cv::divide(val[0], src1_roi, dst1_roi);
cv::ocl::divide(val[0], gsrc1, gdst1);
cv::ocl::divide(val[0], gsrc1_roi, gdst1_roi);
Near(gdst1.depth() >= CV_32F ? 1e-3 : 1);
Near(gdst1_roi.depth() >= CV_32F ? 1e-3 : 1);
}
}
@ -513,9 +445,9 @@ TEST_P(Div, Mat_Scalar)
random_roi();
cv::divide(src1_roi, src2_roi, dst1_roi, val[0]);
cv::ocl::divide(gsrc1, gsrc2, gdst1, val[0]);
cv::ocl::divide(gsrc1_roi, gsrc2_roi, gdst1_roi, val[0]);
Near(gdst1.depth() >= CV_32F ? 1e-3 : 1);
Near(gdst1_roi.depth() >= CV_32F ? 1e-3 : 1);
}
}
@ -530,7 +462,7 @@ TEST_P(Absdiff, Mat)
random_roi();
cv::absdiff(src1_roi, src2_roi, dst1_roi);
cv::ocl::absdiff(gsrc1, gsrc2, gdst1);
cv::ocl::absdiff(gsrc1_roi, gsrc2_roi, gdst1_roi);
Near(0);
}
}
@ -542,7 +474,7 @@ TEST_P(Absdiff, Scalar)
random_roi();
cv::absdiff(src1_roi, val, dst1_roi);
cv::ocl::absdiff(gsrc1, val, gdst1);
cv::ocl::absdiff(gsrc1_roi, val, gdst1_roi);
Near(1e-5);
}
}
@ -558,7 +490,7 @@ TEST_P(CartToPolar, angleInDegree)
random_roi();
cv::cartToPolar(src1_roi, src2_roi, dst1_roi, dst2_roi, true);
cv::ocl::cartToPolar(gsrc1, gsrc2, gdst1, gdst2, true);
cv::ocl::cartToPolar(gsrc1_roi, gsrc2_roi, gdst1_roi, gdst2_roi, true);
Near(.5);
Near1(.5);
}
@ -571,7 +503,7 @@ TEST_P(CartToPolar, angleInRadians)
random_roi();
cv::cartToPolar(src1_roi, src2_roi, dst1_roi, dst2_roi);
cv::ocl::cartToPolar(gsrc1, gsrc2, gdst1, gdst2);
cv::ocl::cartToPolar(gsrc1_roi, gsrc2_roi, gdst1_roi, gdst2_roi);
Near(.5);
Near1(.5);
}
@ -588,7 +520,7 @@ TEST_P(PolarToCart, angleInDegree)
random_roi();
cv::polarToCart(src1_roi, src2_roi, dst1_roi, dst2_roi, true);
cv::ocl::polarToCart(gsrc1, gsrc2, gdst1, gdst2, true);
cv::ocl::polarToCart(gsrc1_roi, gsrc2_roi, gdst1_roi, gdst2_roi, true);
Near(.5);
Near1(.5);
@ -602,7 +534,7 @@ TEST_P(PolarToCart, angleInRadians)
random_roi();
cv::polarToCart(src1_roi, src2_roi, dst1_roi, dst2_roi);
cv::ocl::polarToCart(gsrc1, gsrc2, gdst1, gdst2);
cv::ocl::polarToCart(gsrc1_roi, gsrc2_roi, gdst1_roi, gdst2_roi);
Near(.5);
Near1(.5);
@ -620,7 +552,7 @@ TEST_P(Magnitude, Mat)
random_roi();
cv::magnitude(src1_roi, src2_roi, dst1_roi);
cv::ocl::magnitude(gsrc1, gsrc2, gdst1);
cv::ocl::magnitude(gsrc1_roi, gsrc2_roi, gdst1_roi);
Near(depth == CV_64F ? 1e-5 : 1e-2);
}
}
@ -636,7 +568,7 @@ TEST_P(Transpose, Mat)
random_roi();
cv::transpose(src1_roi, dst1_roi);
cv::ocl::transpose(gsrc1, gdst1);
cv::ocl::transpose(gsrc1_roi, gdst1_roi);
Near(1e-5);
}
@ -644,35 +576,23 @@ TEST_P(Transpose, Mat)
TEST_P(Transpose, SquareInplace)
{
cv::RNG &rng = TS::ptr()->get_rng();
int value = randomInt(MIN_VALUE, MAX_VALUE);
src1 = randomMat(rng, Size(value, value), CV_MAKE_TYPE(depth, cn), 5, 16, false);
const int type = CV_MAKE_TYPE(depth, cn);
if (use_roi)
for (int j = 0; j < LOOP_TIMES; j++)
{
roirows = roicols = randomInt(1, src1.cols);
Size roiSize = randomSize(1, MAX_VALUE);
roiSize.height = roiSize.width; // make it square
src1x = randomInt(0, src1.cols - roicols);
src1y = randomInt(0, src1.rows - roirows);
}
else
{
roicols = roirows = src1.cols;
src1x = src1y = 0;
}
Border srcBorder = randomBorder(0, use_roi ? MAX_VALUE : 0);
randomSubMat(src1, src1_roi, roiSize, srcBorder, type, 5, 16);
Rect r(src1x, src1y, roicols, roirows);
src1_roi = src1(r);
gdst1_whole = src1;
gdst1 = gdst1_whole(r);
generateOclMat(gsrc1_whole, gsrc1_roi, src1, roiSize, srcBorder);
for (int j = 0; j < LOOP_TIMES; j++)
{
cv::transpose(src1_roi, src1_roi);
cv::ocl::transpose(gdst1, gdst1);
cv::ocl::transpose(gsrc1_roi, gsrc1_roi);
EXPECT_MAT_NEAR(src1, Mat(gdst1_whole), 0.0);
EXPECT_MAT_NEAR(src1_roi, Mat(gdst1), 0.0);
EXPECT_MAT_NEAR(src1, Mat(gsrc1_whole), 0.0);
EXPECT_MAT_NEAR(src1_roi, Mat(gsrc1_roi), 0.0);
}
}
@ -687,7 +607,7 @@ TEST_P(Flip, X)
random_roi();
cv::flip(src1_roi, dst1_roi, 0);
cv::ocl::flip(gsrc1, gdst1, 0);
cv::ocl::flip(gsrc1_roi, gdst1_roi, 0);
Near(1e-5);
}
}
@ -699,7 +619,7 @@ TEST_P(Flip, Y)
random_roi();
cv::flip(src1_roi, dst1_roi, 1);
cv::ocl::flip(gsrc1, gdst1, 1);
cv::ocl::flip(gsrc1_roi, gdst1_roi, 1);
Near(1e-5);
}
}
@ -711,7 +631,7 @@ TEST_P(Flip, BOTH)
random_roi();
cv::flip(src1_roi, dst1_roi, -1);
cv::ocl::flip(gsrc1, gdst1, -1);
cv::ocl::flip(gsrc1_roi, gdst1_roi, -1);
Near(1e-5);
}
}
@ -739,12 +659,12 @@ TEST_P(MinMax, MAT)
{
signed char val = src1_roi.at<signed char>(i, j);
if (val < minVal) minVal = val;
else if (val > maxVal) maxVal = val;
if (val > maxVal) maxVal = val;
}
}
double minVal_, maxVal_;
cv::ocl::minMax(gsrc1, &minVal_, &maxVal_);
cv::ocl::minMax(gsrc1_roi, &minVal_, &maxVal_);
EXPECT_DOUBLE_EQ(minVal_, minVal);
EXPECT_DOUBLE_EQ(maxVal_, maxVal);
@ -777,7 +697,7 @@ TEST_P(MinMax, MASK)
}
double minVal_, maxVal_;
cv::ocl::minMax(gsrc1, &minVal_, &maxVal_, gmask);
cv::ocl::minMax(gsrc1_roi, &minVal_, &maxVal_, gmask_roi);
EXPECT_DOUBLE_EQ(minVal, minVal_);
EXPECT_DOUBLE_EQ(maxVal, maxVal_);
@ -825,7 +745,7 @@ TEST_P(MinMaxLoc, MAT)
double minVal_, maxVal_;
cv::Point minLoc_, maxLoc_;
cv::ocl::minMaxLoc(gsrc1, &minVal_, &maxVal_, &minLoc_, &maxLoc_, cv::ocl::oclMat());
cv::ocl::minMaxLoc(gsrc1_roi, &minVal_, &maxVal_, &minLoc_, &maxLoc_, cv::ocl::oclMat());
double error0 = 0., error1 = 0., minlocVal = 0., minlocVal_ = 0., maxlocVal = 0., maxlocVal_ = 0.;
if (depth == 0)
@ -938,7 +858,7 @@ TEST_P(MinMaxLoc, MASK)
double minVal_, maxVal_;
cv::Point minLoc_, maxLoc_;
cv::ocl::minMaxLoc(gsrc1, &minVal_, &maxVal_, &minLoc_, &maxLoc_, gmask);
cv::ocl::minMaxLoc(gsrc1_roi, &minVal_, &maxVal_, &minLoc_, &maxLoc_, gmask_roi);
double error0 = 0., error1 = 0., minlocVal = 0., minlocVal_ = 0., maxlocVal = 0., maxlocVal_ = 0.;
if (minLoc_.x == -1 || minLoc_.y == -1 || maxLoc_.x == -1 || maxLoc_.y == -1) continue;
@ -1027,7 +947,7 @@ TEST_P(Sum, MAT)
random_roi();
Scalar cpures = cv::sum(src1_roi);
Scalar gpures = cv::ocl::sum(gsrc1);
Scalar gpures = cv::ocl::sum(gsrc1_roi);
// check results
EXPECT_NEAR(cpures[0], gpures[0], 0.1);
@ -1085,7 +1005,7 @@ TEST_P(SqrSum, MAT)
CV_Assert(func != 0);
Scalar cpures = func(src1_roi);
Scalar gpures = cv::ocl::sqrSum(gsrc1);
Scalar gpures = cv::ocl::sqrSum(gsrc1_roi);
// check results
EXPECT_NEAR(cpures[0], gpures[0], 1.0);
@ -1141,7 +1061,7 @@ TEST_P(AbsSum, MAT)
CV_Assert(func != 0);
Scalar cpures = func(src1_roi);
Scalar gpures = cv::ocl::absSum(gsrc1);
Scalar gpures = cv::ocl::absSum(gsrc1_roi);
// check results
EXPECT_NEAR(cpures[0], gpures[0], 0.1);
@ -1161,7 +1081,7 @@ TEST_P(CountNonZero, MAT)
{
random_roi();
int cpures = cv::countNonZero(src1_roi);
int gpures = cv::ocl::countNonZero(gsrc1);
int gpures = cv::ocl::countNonZero(gsrc1_roi);
EXPECT_DOUBLE_EQ((double)cpures, (double)gpures);
}
@ -1177,7 +1097,7 @@ TEST_P(Phase, angleInDegrees)
{
random_roi();
cv::phase(src1_roi, src2_roi, dst1_roi, true);
cv::ocl::phase(gsrc1, gsrc2, gdst1, true);
cv::ocl::phase(gsrc1_roi, gsrc2_roi, gdst1_roi, true);
Near(1e-2);
}
@ -1189,7 +1109,7 @@ TEST_P(Phase, angleInRadians)
{
random_roi();
cv::phase(src1_roi, src2_roi, dst1_roi);
cv::ocl::phase(gsrc1, gsrc2, gdst1);
cv::ocl::phase(gsrc1_roi, gsrc2_roi, gdst1_roi);
Near(1e-2);
}
@ -1206,7 +1126,7 @@ TEST_P(Bitwise_and, Mat)
random_roi();
cv::bitwise_and(src1_roi, src2_roi, dst1_roi);
cv::ocl::bitwise_and(gsrc1, gsrc2, gdst1);
cv::ocl::bitwise_and(gsrc1_roi, gsrc2_roi, gdst1_roi);
Near(0);
}
}
@ -1218,7 +1138,7 @@ TEST_P(Bitwise_and, Mat_Mask)
random_roi();
cv::bitwise_and(src1_roi, src2_roi, dst1_roi, mask_roi);
cv::ocl::bitwise_and(gsrc1, gsrc2, gdst1, gmask);
cv::ocl::bitwise_and(gsrc1_roi, gsrc2_roi, gdst1_roi, gmask_roi);
Near(0);
}
}
@ -1230,7 +1150,7 @@ TEST_P(Bitwise_and, Scalar)
random_roi();
cv::bitwise_and(src1_roi, val, dst1_roi);
cv::ocl::bitwise_and(gsrc1, val, gdst1);
cv::ocl::bitwise_and(gsrc1_roi, val, gdst1_roi);
Near(1e-5);
}
}
@ -1242,7 +1162,7 @@ TEST_P(Bitwise_and, Scalar_Mask)
random_roi();
cv::bitwise_and(src1_roi, val, dst1_roi, mask_roi);
cv::ocl::bitwise_and(gsrc1, val, gdst1, gmask);
cv::ocl::bitwise_and(gsrc1_roi, val, gdst1_roi, gmask_roi);
Near(1e-5);
}
}
@ -1258,7 +1178,7 @@ TEST_P(Bitwise_or, Mat)
random_roi();
cv::bitwise_or(src1_roi, src2_roi, dst1_roi);
cv::ocl::bitwise_or(gsrc1, gsrc2, gdst1);
cv::ocl::bitwise_or(gsrc1_roi, gsrc2_roi, gdst1_roi);
Near(0);
}
}
@ -1270,7 +1190,7 @@ TEST_P(Bitwise_or, Mat_Mask)
random_roi();
cv::bitwise_or(src1_roi, src2_roi, dst1_roi, mask_roi);
cv::ocl::bitwise_or(gsrc1, gsrc2, gdst1, gmask);
cv::ocl::bitwise_or(gsrc1_roi, gsrc2_roi, gdst1_roi, gmask_roi);
Near(0);
}
}
@ -1282,7 +1202,7 @@ TEST_P(Bitwise_or, Scalar)
random_roi();
cv::bitwise_or(src1_roi, val, dst1_roi);
cv::ocl::bitwise_or(gsrc1, val, gdst1);
cv::ocl::bitwise_or(gsrc1_roi, val, gdst1_roi);
Near(1e-5);
}
}
@ -1294,7 +1214,7 @@ TEST_P(Bitwise_or, Scalar_Mask)
random_roi();
cv::bitwise_or(src1_roi, val, dst1_roi, mask_roi);
cv::ocl::bitwise_or(gsrc1, val, gdst1, gmask);
cv::ocl::bitwise_or(gsrc1_roi, val, gdst1_roi, gmask_roi);
Near(1e-5);
}
}
@ -1310,7 +1230,7 @@ TEST_P(Bitwise_xor, Mat)
random_roi();
cv::bitwise_xor(src1_roi, src2_roi, dst1_roi);
cv::ocl::bitwise_xor(gsrc1, gsrc2, gdst1);
cv::ocl::bitwise_xor(gsrc1_roi, gsrc2_roi, gdst1_roi);
Near(0);
}
}
@ -1322,7 +1242,7 @@ TEST_P(Bitwise_xor, Mat_Mask)
random_roi();
cv::bitwise_xor(src1_roi, src2_roi, dst1_roi, mask_roi);
cv::ocl::bitwise_xor(gsrc1, gsrc2, gdst1, gmask);
cv::ocl::bitwise_xor(gsrc1_roi, gsrc2_roi, gdst1_roi, gmask_roi);
Near(0);
}
}
@ -1334,7 +1254,7 @@ TEST_P(Bitwise_xor, Scalar)
random_roi();
cv::bitwise_xor(src1_roi, val, dst1_roi);
cv::ocl::bitwise_xor(gsrc1, val, gdst1);
cv::ocl::bitwise_xor(gsrc1_roi, val, gdst1_roi);
Near(1e-5);
}
}
@ -1346,7 +1266,7 @@ TEST_P(Bitwise_xor, Scalar_Mask)
random_roi();
cv::bitwise_xor(src1_roi, val, dst1_roi, mask_roi);
cv::ocl::bitwise_xor(gsrc1, val, gdst1, gmask);
cv::ocl::bitwise_xor(gsrc1_roi, val, gdst1_roi, gmask_roi);
Near(1e-5);
}
}
@ -1362,7 +1282,7 @@ TEST_P(Bitwise_not, Mat)
random_roi();
cv::bitwise_not(src1_roi, dst1_roi);
cv::ocl::bitwise_not(gsrc1, gdst1);
cv::ocl::bitwise_not(gsrc1_roi, gdst1_roi);
Near(0);
}
}
@ -1382,7 +1302,7 @@ TEST_P(Compare, Mat)
random_roi();
cv::compare(src1_roi, src2_roi, dst1_roi, cmp_codes[i]);
cv::ocl::compare(gsrc1, gsrc2, gdst1, cmp_codes[i]);
cv::ocl::compare(gsrc1_roi, gsrc2_roi, gdst1_roi, cmp_codes[i]);
Near(0);
}
@ -1399,7 +1319,7 @@ TEST_P(Pow, Mat)
random_roi();
double p = 4.5;
cv::pow(src1_roi, p, dst1_roi);
cv::ocl::pow(gsrc1, p, gdst1);
cv::ocl::pow(gsrc1_roi, p, gdst1_roi);
Near(1);
}
}
@ -1417,7 +1337,7 @@ TEST_P(AddWeighted, Mat)
const double alpha = 2.0, beta = 1.0, gama = 3.0;
cv::addWeighted(src1_roi, alpha, src2_roi, beta, gama, dst1_roi);
cv::ocl::addWeighted(gsrc1, alpha, gsrc2, beta, gama, gdst1);
cv::ocl::addWeighted(gsrc1_roi, alpha, gsrc2_roi, beta, gama, gdst1_roi);
Near(1e-5);
}
@ -1434,7 +1354,7 @@ TEST_P(SetIdentity, Mat)
random_roi();
cv::setIdentity(dst1_roi, val);
cv::ocl::setIdentity(gdst1, val);
cv::ocl::setIdentity(gdst1_roi, val);
Near(0);
}
@ -1454,7 +1374,7 @@ TEST_P(MeanStdDev, Mat)
Scalar gpu_mean, gpu_stddev;
cv::meanStdDev(src1_roi, cpu_mean, cpu_stddev);
cv::ocl::meanStdDev(gsrc1, gpu_mean, gpu_stddev);
cv::ocl::meanStdDev(gsrc1_roi, gpu_mean, gpu_stddev);
for (int i = 0; i < 4; ++i)
{
@ -1480,7 +1400,7 @@ TEST_P(Norm, NORM_INF)
type |= NORM_RELATIVE;
const double cpuRes = cv::norm(src1_roi, src2_roi, type);
const double gpuRes = cv::ocl::norm(gsrc1, gsrc2, type);
const double gpuRes = cv::ocl::norm(gsrc1_roi, gsrc2_roi, type);
EXPECT_NEAR(cpuRes, gpuRes, 0.1);
}
@ -1498,7 +1418,7 @@ TEST_P(Norm, NORM_L1)
type |= NORM_RELATIVE;
const double cpuRes = cv::norm(src1_roi, src2_roi, type);
const double gpuRes = cv::ocl::norm(gsrc1, gsrc2, type);
const double gpuRes = cv::ocl::norm(gsrc1_roi, gsrc2_roi, type);
EXPECT_NEAR(cpuRes, gpuRes, 0.1);
}
@ -1516,7 +1436,7 @@ TEST_P(Norm, NORM_L2)
type |= NORM_RELATIVE;
const double cpuRes = cv::norm(src1_roi, src2_roi, type);
const double gpuRes = cv::ocl::norm(gsrc1, gsrc2, type);
const double gpuRes = cv::ocl::norm(gsrc1_roi, gsrc2_roi, type);
EXPECT_NEAR(cpuRes, gpuRes, 0.1);
}

14
modules/ocl/test/test_bgfg.cpp
Unescape View File

@ -85,9 +85,7 @@ PARAM_TEST_CASE(mog, UseGray, LearningRate, bool)
virtual void SetUp()
{
useGray = GET_PARAM(0);
learningRate = GET_PARAM(1);
useRoi = GET_PARAM(2);
}
};
@ -103,7 +101,7 @@ TEST_P(mog, Update)
ASSERT_FALSE(frame.empty());
cv::ocl::MOG mog;
cv::ocl::oclMat foreground = createMat_ocl(frame.size(), CV_8UC1, useRoi);
cv::ocl::oclMat foreground = createMat_ocl(rng, frame.size(), CV_8UC1, useRoi);
cv::BackgroundSubtractorMOG mog_gold;
cv::Mat foreground_gold;
@ -120,7 +118,7 @@ TEST_P(mog, Update)
cv::swap(temp, frame);
}
mog(loadMat_ocl(frame, useRoi), foreground, (float)learningRate);
mog(loadMat_ocl(rng, frame, useRoi), foreground, (float)learningRate);
mog_gold(frame, foreground_gold, learningRate);
@ -165,7 +163,7 @@ TEST_P(mog2, Update)
cv::ocl::MOG2 mog2;
mog2.bShadowDetection = detectShadow;
cv::ocl::oclMat foreground = createMat_ocl(frame.size(), CV_8UC1, useRoi);
cv::ocl::oclMat foreground = createMat_ocl(rng, frame.size(), CV_8UC1, useRoi);
cv::BackgroundSubtractorMOG2 mog2_gold;
mog2_gold.set("detectShadows", detectShadow);
@ -183,7 +181,7 @@ TEST_P(mog2, Update)
cv::swap(temp, frame);
}
mog2(loadMat_ocl(frame, useRoi), foreground);
mog2(loadMat_ocl(rng, frame, useRoi), foreground);
mog2_gold(frame, foreground_gold);
@ -218,12 +216,12 @@ TEST_P(mog2, getBackgroundImage)
cap >> frame;
ASSERT_FALSE(frame.empty());
mog2(loadMat_ocl(frame, useRoi), foreground);
mog2(loadMat_ocl(rng, frame, useRoi), foreground);
mog2_gold(frame, foreground_gold);
}
cv::ocl::oclMat background = createMat_ocl(frame.size(), frame.type(), useRoi);
cv::ocl::oclMat background = createMat_ocl(rng, frame.size(), frame.type(), useRoi);
mog2.getBackgroundImage(background);
cv::Mat background_gold;

2
modules/ocl/test/test_brute_force_matcher.cpp
Unescape View File

@ -72,8 +72,6 @@ namespace
queryDescCount = 300; // must be even number because we split train data in some cases in two
countFactor = 4; // do not change it
cv::RNG &rng = cvtest::TS::ptr()->get_rng();
cv::Mat queryBuf, trainBuf;
// Generate query descriptors randomly.

4
modules/ocl/test/test_calib3d.cpp
Unescape View File

@ -46,10 +46,10 @@
#include "test_precomp.hpp"
#include <iomanip>
#ifdef HAVE_OPENCL
using namespace cv;
#ifdef HAVE_OPENCL
PARAM_TEST_CASE(StereoMatchBM, int, int)
{
int n_disp;

4
modules/ocl/test/test_filters.cpp
Unescape View File

@ -91,7 +91,6 @@ PARAM_TEST_CASE(FilterTestBase,
{
#ifdef RANDOMROI
//randomize ROI
cv::RNG &rng = TS::ptr()->get_rng();
roicols = rng.uniform(2, mat1.cols);
roirows = rng.uniform(2, mat1.rows);
src1x = rng.uniform(0, mat1.cols - roicols);
@ -201,7 +200,6 @@ struct ErodeDilate : FilterTestBase
type = GET_PARAM(0);
iterations = GET_PARAM(3);
Init(type);
// rng.fill(kernel, cv::RNG::UNIFORM, cv::Scalar::all(0), cv::Scalar::all(3));
kernel = randomMat(Size(3, 3), CV_8UC1, 0, 3);
}
@ -304,7 +302,6 @@ struct GaussianBlur : FilterTestBase
ksize = GET_PARAM(1);
bordertype = GET_PARAM(3);
Init(type);
cv::RNG &rng = TS::ptr()->get_rng();
sigma1 = rng.uniform(0.1, 1.0);
sigma2 = rng.uniform(0.1, 1.0);
}
@ -368,7 +365,6 @@ struct Bilateral : FilterTestBase
ksize = GET_PARAM(1);
bordertype = GET_PARAM(3);
Init(type);
cv::RNG &rng = TS::ptr()->get_rng();
sigmacolor = rng.uniform(20, 100);
sigmaspace = rng.uniform(10, 40);
}

80
modules/ocl/test/test_imgproc.cpp
Unescape View File

@ -351,33 +351,32 @@ PARAM_TEST_CASE(ImgprocTestBase, MatType, MatType, MatType, MatType, MatType, bo
type3 = GET_PARAM(2);
type4 = GET_PARAM(3);
type5 = GET_PARAM(4);
cv::RNG &rng = TS::ptr()->get_rng();
cv::Size size(MWIDTH, MHEIGHT);
double min = 1, max = 20;
if(type1 != nulltype)
{
mat1 = randomMat(rng, size, type1, min, max, false);
mat1 = randomMat(size, type1, min, max, false);
clmat1 = mat1;
}
if(type2 != nulltype)
{
mat2 = randomMat(rng, size, type2, min, max, false);
mat2 = randomMat(size, type2, min, max, false);
clmat2 = mat2;
}
if(type3 != nulltype)
{
dst = randomMat(rng, size, type3, min, max, false);
dst = randomMat(size, type3, min, max, false);
cldst = dst;
}
if(type4 != nulltype)
{
dst1 = randomMat(rng, size, type4, min, max, false);
dst1 = randomMat(size, type4, min, max, false);
cldst1 = dst1;
}
if(type5 != nulltype)
{
mask = randomMat(rng, size, CV_8UC1, 0, 2, false);
mask = randomMat(size, CV_8UC1, 0, 2, false);
cv::threshold(mask, mask, 0.5, 255., type5);
clmask = mask;
}
@ -388,7 +387,6 @@ PARAM_TEST_CASE(ImgprocTestBase, MatType, MatType, MatType, MatType, MatType, bo
{
#ifdef RANDOMROI
//randomize ROI
cv::RNG &rng = TS::ptr()->get_rng();
roicols = rng.uniform(1, mat1.cols);
roirows = rng.uniform(1, mat1.rows);
src1x = rng.uniform(0, mat1.cols - roicols);
@ -482,7 +480,6 @@ struct CopyMakeBorder : ImgprocTestBase {};
TEST_P(CopyMakeBorder, Mat)
{
int bordertype[] = {cv::BORDER_CONSTANT, cv::BORDER_REPLICATE, cv::BORDER_REFLECT, cv::BORDER_WRAP, cv::BORDER_REFLECT_101};
cv::RNG &rng = TS::ptr()->get_rng();
int top = rng.uniform(0, 10);
int bottom = rng.uniform(0, 10);
int left = rng.uniform(0, 10);
@ -646,22 +643,17 @@ PARAM_TEST_CASE(WarpTestBase, MatType, int)
virtual void SetUp()
{
type = GET_PARAM(0);
//dsize = GET_PARAM(1);
interpolation = GET_PARAM(1);
cv::RNG &rng = TS::ptr()->get_rng();
size = cv::Size(MWIDTH, MHEIGHT);
mat1 = randomMat(rng, size, type, 5, 16, false);
dst = randomMat(rng, size, type, 5, 16, false);
mat1 = randomMat(size, type, 5, 16, false);
dst = randomMat(size, type, 5, 16, false);
}
void random_roi()
{
#ifdef RANDOMROI
//randomize ROI
cv::RNG &rng = TS::ptr()->get_rng();
src_roicols = rng.uniform(1, mat1.cols);
src_roirows = rng.uniform(1, mat1.rows);
dst_roicols = rng.uniform(1, dst.cols);
@ -810,23 +802,22 @@ PARAM_TEST_CASE(Remap, MatType, MatType, MatType, int, int)
interpolation = GET_PARAM(3);
bordertype = GET_PARAM(4);
cv::RNG &rng = TS::ptr()->get_rng();
cv::Size srcSize = cv::Size(MWIDTH, MHEIGHT);
cv::Size map1Size = cv::Size(MWIDTH, MHEIGHT);
double min = 5, max = 16;
if(srcType != nulltype)
{
src = randomMat(rng, srcSize, srcType, min, max, false);
src = randomMat(srcSize, srcType, min, max, false);
}
if((map1Type == CV_16SC2 && map2Type == nulltype) || (map1Type == CV_32FC2 && map2Type == nulltype))
{
map1 = randomMat(rng, map1Size, map1Type, min, max, false);
map1 = randomMat(map1Size, map1Type, min, max, false);
}
else if (map1Type == CV_32FC1 && map2Type == CV_32FC1)
{
map1 = randomMat(rng, map1Size, map1Type, min, max, false);
map2 = randomMat(rng, map1Size, map1Type, min, max, false);
map1 = randomMat(map1Size, map1Type, min, max, false);
map2 = randomMat(map1Size, map1Type, min, max, false);
}
else
@ -835,7 +826,7 @@ PARAM_TEST_CASE(Remap, MatType, MatType, MatType, int, int)
return;
}
dst = randomMat(rng, map1Size, srcType, min, max, false);
dst = randomMat(map1Size, srcType, min, max, false);
switch (src.channels())
{
case 1:
@ -855,8 +846,6 @@ PARAM_TEST_CASE(Remap, MatType, MatType, MatType, int, int)
}
void random_roi()
{
cv::RNG &rng = TS::ptr()->get_rng();
dst_roicols = rng.uniform(1, dst.cols);
dst_roirows = rng.uniform(1, dst.rows);
@ -966,8 +955,6 @@ PARAM_TEST_CASE(Resize, MatType, cv::Size, double, double, int)
fy = GET_PARAM(3);
interpolation = GET_PARAM(4);
cv::RNG &rng = TS::ptr()->get_rng();
cv::Size size(MWIDTH, MHEIGHT);
if(dsize == cv::Size() && !(fx > 0 && fy > 0))
@ -982,8 +969,8 @@ PARAM_TEST_CASE(Resize, MatType, cv::Size, double, double, int)
dsize.height = (int)(size.height * fy);
}
mat1 = randomMat(rng, size, type, 5, 16, false);
dst = randomMat(rng, dsize, type, 5, 16, false);
mat1 = randomMat(size, type, 5, 16, false);
dst = randomMat(dsize, type, 5, 16, false);
}
@ -991,7 +978,6 @@ PARAM_TEST_CASE(Resize, MatType, cv::Size, double, double, int)
{
#ifdef RANDOMROI
//randomize ROI
cv::RNG &rng = TS::ptr()->get_rng();
src_roicols = rng.uniform(1, mat1.cols);
src_roirows = rng.uniform(1, mat1.rows);
dst_roicols = (int)(src_roicols * fx);
@ -1082,18 +1068,16 @@ PARAM_TEST_CASE(Threshold, MatType, ThreshOp)
type = GET_PARAM(0);
threshOp = GET_PARAM(1);
cv::RNG &rng = TS::ptr()->get_rng();
cv::Size size(MWIDTH, MHEIGHT);
mat1 = randomMat(rng, size, type, 5, 16, false);
dst = randomMat(rng, size, type, 5, 16, false);
mat1 = randomMat(size, type, 5, 16, false);
dst = randomMat(size, type, 5, 16, false);
}
void random_roi()
{
#ifdef RANDOMROI
//randomize ROI
cv::RNG &rng = TS::ptr()->get_rng();
roicols = rng.uniform(1, mat1.cols);
roirows = rng.uniform(1, mat1.rows);
src1x = rng.uniform(0, mat1.cols - roicols);
@ -1179,22 +1163,18 @@ PARAM_TEST_CASE(meanShiftTestBase, MatType, MatType, int, int, cv::TermCriteria)
sr = GET_PARAM(3);
crit = GET_PARAM(4);
cv::RNG &rng = TS::ptr()->get_rng();
// MWIDTH=256, MHEIGHT=256. defined in utility.hpp
cv::Size size = cv::Size(MWIDTH, MHEIGHT);
src = randomMat(rng, size, type, 5, 16, false);
dst = randomMat(rng, size, type, 5, 16, false);
dstCoor = randomMat(rng, size, typeCoor, 5, 16, false);
src = randomMat(size, type, 5, 16, false);
dst = randomMat(size, type, 5, 16, false);
dstCoor = randomMat(size, typeCoor, 5, 16, false);
}
void random_roi()
{
#ifdef RANDOMROI
cv::RNG &rng = TS::ptr()->get_rng();
//randomize ROI
roicols = rng.uniform(1, src.cols);
roirows = rng.uniform(1, src.rows);
@ -1307,18 +1287,15 @@ PARAM_TEST_CASE(histTestBase, MatType, MatType)
{
type_src = GET_PARAM(0);
cv::RNG &rng = TS::ptr()->get_rng();
cv::Size size = cv::Size(MWIDTH, MHEIGHT);
src = randomMat(rng, size, type_src, 0, 256, false);
src = randomMat(size, type_src, 0, 256, false);
}
void random_roi()
{
#ifdef RANDOMROI
cv::RNG &rng = TS::ptr()->get_rng();
//randomize ROI
roicols = rng.uniform(1, src.cols);
roirows = rng.uniform(1, src.rows);
@ -1372,8 +1349,7 @@ PARAM_TEST_CASE(CLAHE, cv::Size, double)
gridSize = GET_PARAM(0);
clipLimit = GET_PARAM(1);
cv::RNG &rng = TS::ptr()->get_rng();
src = randomMat(rng, cv::Size(MWIDTH, MHEIGHT), CV_8UC1, 0, 256, false);
src = randomMat(cv::Size(MWIDTH, MHEIGHT), CV_8UC1, 0, 256, false);
g_src.upload(src);
}
};
@ -1425,19 +1401,15 @@ PARAM_TEST_CASE(ConvolveTestBase, MatType, bool)
{
type = GET_PARAM(0);
cv::RNG &rng = TS::ptr()->get_rng();
cv::Size size(MWIDTH, MHEIGHT);
mat1 = randomMat(rng, size, type, 5, 16, false);
mat2 = randomMat(rng, size, type, 5, 16, false);
dst = randomMat(rng, size, type, 5, 16, false);
dst1 = randomMat(rng, size, type, 5, 16, false);
mat1 = randomMat(size, type, 5, 16, false);
mat2 = randomMat(size, type, 5, 16, false);
dst = randomMat(size, type, 5, 16, false);
dst1 = randomMat(size, type, 5, 16, false);
}
void random_roi()
{
cv::RNG &rng = TS::ptr()->get_rng();
#ifdef RANDOMROI
//randomize ROI
roicols = rng.uniform(1, mat1.cols);
@ -1542,7 +1514,7 @@ PARAM_TEST_CASE(ColumnSum, cv::Size)
TEST_P(ColumnSum, Accuracy)
{
cv::Mat src = randomMat(size, CV_32FC1);
cv::Mat src = randomMat(size, CV_32FC1, 0, 255);
cv::ocl::oclMat d_dst;
cv::ocl::oclMat d_src(src);

6
modules/ocl/test/test_kalman.cpp
Unescape View File

@ -69,8 +69,6 @@ TEST_P(Kalman, Accuracy)
const double max_init = 1;
const double max_noise = 0.1;
cv::RNG &rng = TS::ptr()->get_rng();
Mat sample_mat(Dim, 1, CV_32F), temp_mat;
oclMat Sample(Dim, 1, CV_32F);
oclMat Temp(Dim, 1, CV_32F);
@ -78,7 +76,7 @@ TEST_P(Kalman, Accuracy)
Size size(Sample.cols, Sample.rows);
sample_mat = randomMat(rng, size, Sample.type(), -max_init, max_init, false);
sample_mat = randomMat(size, Sample.type(), -max_init, max_init, false);
Sample.upload(sample_mat);
//ocl start
@ -120,7 +118,7 @@ TEST_P(Kalman, Accuracy)
cv::gemm(kalman_filter_cpu.transitionMatrix, sample_mat, 1, cv::Mat(), 0, Temp_cpu);
Size size1(Temp.cols, Temp.rows);
Mat temp = randomMat(rng, size1, Temp.type(), 0, 0xffff, false);
Mat temp = randomMat(size1, Temp.type(), 0, 0xffff, false);
cv::multiply(2, temp, temp);

7
modules/ocl/test/test_kmeans.cpp
Unescape View File

@ -66,12 +66,11 @@ PARAM_TEST_CASE(Kmeans, int, int, int)
Mat labels, centers;
ocl::oclMat d_labels, d_centers;
cv::RNG rng ;
virtual void SetUp(){
virtual void SetUp()
{
K = GET_PARAM(0);
type = GET_PARAM(1);
flags = GET_PARAM(2);
rng = TS::ptr()->get_rng();
// MWIDTH=256, MHEIGHT=256. defined in utility.hpp
cv::Size size = cv::Size(MWIDTH, MHEIGHT);
@ -92,7 +91,7 @@ PARAM_TEST_CASE(Kmeans, int, int, int)
{
Mat cur_row_header = src.row(row_idx + 1 + j);
center_row_header.copyTo(cur_row_header);
Mat tmpmat = randomMat(rng, cur_row_header.size(), cur_row_header.type(), -200, 200, false);
Mat tmpmat = randomMat(cur_row_header.size(), cur_row_header.type(), -200, 200, false);
cur_row_header += tmpmat;
}
row_idx += 1 + max_neighbour;

8
modules/ocl/test/test_match_template.cpp
Unescape View File

@ -72,8 +72,8 @@ PARAM_TEST_CASE(MatchTemplate8U, cv::Size, TemplateSize, Channels, TemplateMetho
TEST_P(MatchTemplate8U, Accuracy)
{
cv::Mat image = randomMat(size, CV_MAKETYPE(CV_8U, cn));
cv::Mat templ = randomMat(templ_size, CV_MAKETYPE(CV_8U, cn));
cv::Mat image = randomMat(size, CV_MAKETYPE(CV_8U, cn), 0, 255);
cv::Mat templ = randomMat(templ_size, CV_MAKETYPE(CV_8U, cn), 0, 255);
cv::ocl::oclMat dst, ocl_image(image), ocl_templ(templ);
cv::ocl::matchTemplate(ocl_image, ocl_templ, dst, method);
@ -105,8 +105,8 @@ PARAM_TEST_CASE(MatchTemplate32F, cv::Size, TemplateSize, Channels, TemplateMeth
TEST_P(MatchTemplate32F, Accuracy)
{
cv::Mat image = randomMat(size, CV_MAKETYPE(CV_32F, cn));
cv::Mat templ = randomMat(templ_size, CV_MAKETYPE(CV_32F, cn));
cv::Mat image = randomMat(size, CV_MAKETYPE(CV_32F, cn), 0, 255);
cv::Mat templ = randomMat(templ_size, CV_MAKETYPE(CV_32F, cn), 0, 255);
cv::ocl::oclMat dst, ocl_image(image), ocl_templ(templ);
cv::ocl::matchTemplate(ocl_image, ocl_templ, dst, method);

26
modules/ocl/test/test_matrix_operation.cpp
Unescape View File

@ -90,10 +90,8 @@ PARAM_TEST_CASE(ConvertToTestBase, MatType, MatType, int, bool)
use_roi = GET_PARAM(3);
cv::RNG &rng = TS::ptr()->get_rng();
mat = randomMat(rng, randomSize(MIN_VALUE, MAX_VALUE), src_type, 5, 136, false);
dst = randomMat(rng, use_roi ? randomSize(MIN_VALUE, MAX_VALUE) : mat.size(), dst_type, 5, 136, false);
mat = randomMat(randomSize(MIN_VALUE, MAX_VALUE), src_type, 5, 136, false);
dst = randomMat(use_roi ? randomSize(MIN_VALUE, MAX_VALUE) : mat.size(), dst_type, 5, 136, false);
}
void random_roi()
@ -101,7 +99,6 @@ PARAM_TEST_CASE(ConvertToTestBase, MatType, MatType, int, bool)
if (use_roi)
{
// randomize ROI
cv::RNG &rng = TS::ptr()->get_rng();
roicols = rng.uniform(1, MIN_VALUE);
roirows = rng.uniform(1, MIN_VALUE);
srcx = rng.uniform(0, mat.cols - roicols);
@ -178,11 +175,9 @@ PARAM_TEST_CASE(CopyToTestBase, MatType, int, bool)
int type = CV_MAKETYPE(GET_PARAM(0), GET_PARAM(1));
use_roi = GET_PARAM(2);
cv::RNG &rng = TS::ptr()->get_rng();
src = randomMat(rng, randomSize(MIN_VALUE, MAX_VALUE), type, 5, 16, false);
dst = randomMat(rng, use_roi ? randomSize(MIN_VALUE, MAX_VALUE) : src.size(), type, 5, 16, false);
mask = randomMat(rng, use_roi ? randomSize(MIN_VALUE, MAX_VALUE) : src.size(), CV_8UC1, 0, 2, false);
src = randomMat(randomSize(MIN_VALUE, MAX_VALUE), type, 5, 16, false);
dst = randomMat(use_roi ? randomSize(MIN_VALUE, MAX_VALUE) : src.size(), type, 5, 16, false);
mask = randomMat(use_roi ? randomSize(MIN_VALUE, MAX_VALUE) : src.size(), CV_8UC1, 0, 2, false);
cv::threshold(mask, mask, 0.5, 255., CV_8UC1);
}
@ -192,7 +187,6 @@ PARAM_TEST_CASE(CopyToTestBase, MatType, int, bool)
if (use_roi)
{
// randomize ROI
cv::RNG &rng = TS::ptr()->get_rng();
roicols = rng.uniform(1, MIN_VALUE);
roirows = rng.uniform(1, MIN_VALUE);
srcx = rng.uniform(0, src.cols - roicols);
@ -295,11 +289,10 @@ PARAM_TEST_CASE(SetToTestBase, MatType, int, bool)
channels = GET_PARAM(1);
use_roi = GET_PARAM(2);
cv::RNG &rng = TS::ptr()->get_rng();
int type = CV_MAKE_TYPE(depth, channels);
src = randomMat(rng, randomSize(MIN_VALUE, MAX_VALUE), type, 5, 16, false);
mask = randomMat(rng, use_roi ? randomSize(MIN_VALUE, MAX_VALUE) : src.size(), CV_8UC1, 0, 2, false);
src = randomMat(randomSize(MIN_VALUE, MAX_VALUE), type, 5, 16, false);
mask = randomMat(use_roi ? randomSize(MIN_VALUE, MAX_VALUE) : src.size(), CV_8UC1, 0, 2, false);
cv::threshold(mask, mask, 0.5, 255., CV_8UC1);
val = cv::Scalar(rng.uniform(-10.0, 10.0), rng.uniform(-10.0, 10.0),
@ -311,7 +304,6 @@ PARAM_TEST_CASE(SetToTestBase, MatType, int, bool)
if (use_roi)
{
// randomize ROI
cv::RNG &rng = TS::ptr()->get_rng();
roicols = rng.uniform(1, MIN_VALUE);
roirows = rng.uniform(1, MIN_VALUE);
srcx = rng.uniform(0, src.cols - roicols);
@ -401,8 +393,7 @@ PARAM_TEST_CASE(convertC3C4, MatType, bool)
use_roi = GET_PARAM(1);
int type = CV_MAKE_TYPE(depth, 3);
cv::RNG &rng = TS::ptr()->get_rng();
src = randomMat(rng, randomSize(1, MAX_VALUE), type, 0, 40, false);
src = randomMat(randomSize(1, MAX_VALUE), type, 0, 40, false);
}
void random_roi()
@ -410,7 +401,6 @@ PARAM_TEST_CASE(convertC3C4, MatType, bool)
if (use_roi)
{
//randomize ROI
cv::RNG &rng = TS::ptr()->get_rng();
roicols = rng.uniform(1, src.cols);
roirows = rng.uniform(1, src.rows);
srcx = rng.uniform(0, src.cols - roicols);

13
modules/ocl/test/test_ml.cpp
Unescape View File

@ -50,10 +50,9 @@ using namespace cv::ocl;
using namespace cvtest;
using namespace testing;
///////K-NEAREST NEIGHBOR//////////////////////////
static void genTrainData(Mat& trainData, int trainDataRow, int trainDataCol,
static void genTrainData(cv::RNG& rng, Mat& trainData, int trainDataRow, int trainDataCol,
Mat& trainLabel = Mat().setTo(Scalar::all(0)), int nClasses = 0)
{
cv::RNG &rng = TS::ptr()->get_rng();
cv::Size size(trainDataCol, trainDataRow);
trainData = randomMat(rng, size, CV_32FC1, 1.0, 1000.0, false);
if(nClasses != 0)
@ -85,10 +84,10 @@ TEST_P(KNN, Accuracy)
{
Mat trainData, trainLabels;
const int trainDataRow = 500;
genTrainData(trainData, trainDataRow, trainDataCol, trainLabels, nClass);
genTrainData(rng, trainData, trainDataRow, trainDataCol, trainLabels, nClass);
Mat testData, testLabels;
genTrainData(testData, testDataRow, trainDataCol);
genTrainData(rng, testData, testDataRow, trainDataCol);
KNearestNeighbour knn_ocl;
CvKNearest knn_cpu;
@ -130,7 +129,6 @@ PARAM_TEST_CASE(SVM_OCL, int, int, int)
int svm_type;
Mat src, labels, samples, labels_predict;
int K;
cv::RNG rng ;
virtual void SetUp()
{
@ -138,7 +136,6 @@ PARAM_TEST_CASE(SVM_OCL, int, int, int)
kernel_type = GET_PARAM(0);
svm_type = GET_PARAM(1);
K = GET_PARAM(2);
rng = TS::ptr()->get_rng();
cv::Size size = cv::Size(MWIDTH, MHEIGHT);
src.create(size, CV_32FC1);
labels.create(1, size.height, CV_32SC1);
@ -160,7 +157,7 @@ PARAM_TEST_CASE(SVM_OCL, int, int, int)
{
Mat cur_row_header = src.row(row_idx + 1 + j);
center_row_header.copyTo(cur_row_header);
Mat tmpmat = randomMat(rng, cur_row_header.size(), cur_row_header.type(), 1, 100, false);
Mat tmpmat = randomMat(cur_row_header.size(), cur_row_header.type(), 1, 100, false);
cur_row_header += tmpmat;
labels.at<int>(0, row_idx + 1 + j) = i;
}
@ -187,7 +184,7 @@ PARAM_TEST_CASE(SVM_OCL, int, int, int)
{
Mat cur_row_header = samples.row(row_idx + 1 + j);
center_row_header.copyTo(cur_row_header);
Mat tmpmat = randomMat(rng, cur_row_header.size(), cur_row_header.type(), 1, 100, false);
Mat tmpmat = randomMat(cur_row_header.size(), cur_row_header.type(), 1, 100, false);
cur_row_header += tmpmat;
labels_predict.at<int>(0, row_idx + 1 + j) = i;
}

6
modules/ocl/test/test_moments.cpp
Unescape View File

@ -9,7 +9,7 @@ using namespace cv::ocl;
using namespace cvtest;
using namespace testing;
using namespace std;
extern string workdir;
PARAM_TEST_CASE(MomentsTest, MatType, bool)
{
int type;
@ -20,9 +20,8 @@ PARAM_TEST_CASE(MomentsTest, MatType, bool)
{
type = GET_PARAM(0);
test_contours = GET_PARAM(1);
cv::RNG &rng = TS::ptr()->get_rng();
cv::Size size(10*MWIDTH, 10*MHEIGHT);
mat1 = randomMat(rng, size, type, 5, 16, false);
mat1 = randomMat(size, type, 5, 16, false);
}
void Compare(Moments& cpu, Moments& gpu)
@ -39,7 +38,6 @@ PARAM_TEST_CASE(MomentsTest, MatType, bool)
TEST_P(MomentsTest, Mat)
{
bool binaryImage = 0;
SetUp();
for(int j = 0; j < LOOP_TIMES; j++)
{

2
modules/ocl/test/test_objdetect.cpp
Unescape View File

@ -51,8 +51,6 @@ using namespace cv;
using namespace testing;
#ifdef HAVE_OPENCL
extern string workdir;
///////////////////// HOG /////////////////////////////
PARAM_TEST_CASE(HOG, Size, int)
{

8
modules/ocl/test/test_optflow.cpp
Unescape View File

@ -54,9 +54,6 @@ using namespace cvtest;
using namespace testing;
using namespace std;
extern string workdir;
//////////////////////////////////////////////////////
// GoodFeaturesToTrack
namespace
@ -153,9 +150,8 @@ TEST_P(TVL1, Accuracy)
ASSERT_FALSE(frame1.empty());
cv::ocl::OpticalFlowDual_TVL1_OCL d_alg;
cv::RNG &rng = TS::ptr()->get_rng();
cv::Mat flowx = randomMat(rng, frame0.size(), CV_32FC1, 0, 0, useRoi);
cv::Mat flowy = randomMat(rng, frame0.size(), CV_32FC1, 0, 0, useRoi);
cv::Mat flowx = randomMat(frame0.size(), CV_32FC1, 0, 0, useRoi);
cv::Mat flowy = randomMat(frame0.size(), CV_32FC1, 0, 0, useRoi);
cv::ocl::oclMat d_flowx(flowx), d_flowy(flowy);
d_alg(oclMat(frame0), oclMat(frame1), d_flowx, d_flowy);

2
modules/ocl/test/test_precomp.hpp
Unescape View File

@ -73,4 +73,6 @@
#include "utility.hpp"
//#include "add_test_info.h"
using namespace cvtest;
#endif

4
modules/ocl/test/test_pyramids.cpp
Unescape View File

@ -79,7 +79,7 @@ TEST_P(PyrDown, Mat)
for (int j = 0; j < LOOP_TIMES; j++)
{
Size size(MWIDTH, MHEIGHT);
Mat src = randomMat(size, CV_MAKETYPE(depth, channels));
Mat src = randomMat(size, CV_MAKETYPE(depth, channels), 0, 255);
oclMat gsrc(src);
pyrDown(src, dst_cpu);
@ -102,7 +102,7 @@ TEST_P(PyrUp, Accuracy)
for (int j = 0; j < LOOP_TIMES; j++)
{
Size size(MWIDTH, MHEIGHT);
Mat src = randomMat(size, CV_MAKETYPE(depth, channels));
Mat src = randomMat(size, CV_MAKETYPE(depth, channels), 0, 255);
oclMat gsrc(src);
pyrUp(src, dst_cpu);

12
modules/ocl/test/test_split_merge.cpp
Unescape View File

@ -90,12 +90,11 @@ PARAM_TEST_CASE(MergeTestBase, MatType, int, bool)
channels = GET_PARAM(1);
use_roi = GET_PARAM(2);
cv::RNG &rng = TS::ptr()->get_rng();
cv::Size size(MWIDTH, MHEIGHT);
for (int i = 0; i < channels; ++i)
mat[i] = randomMat(rng, size, CV_MAKETYPE(type, 1), 5, 16, false);
dst = randomMat(rng, size, CV_MAKETYPE(type, channels), 5, 16, false);
mat[i] = randomMat(size, CV_MAKETYPE(type, 1), 5, 16, false);
dst = randomMat(size, CV_MAKETYPE(type, channels), 5, 16, false);
}
void random_roi()
@ -103,7 +102,6 @@ PARAM_TEST_CASE(MergeTestBase, MatType, int, bool)
if (use_roi)
{
//randomize ROI
cv::RNG &rng = TS::ptr()->get_rng();
roicols = rng.uniform(1, mat[0].cols);
roirows = rng.uniform(1, mat[0].rows);
@ -191,19 +189,17 @@ PARAM_TEST_CASE(SplitTestBase, MatType, int, bool)
channels = GET_PARAM(1);
use_roi = GET_PARAM(2);
cv::RNG &rng = TS::ptr()->get_rng();
cv::Size size(MWIDTH, MHEIGHT);
mat = randomMat(rng, size, CV_MAKETYPE(type, channels), 5, 16, false);
mat = randomMat(size, CV_MAKETYPE(type, channels), 5, 16, false);
for (int i = 0; i < channels; ++i)
dst[i] = randomMat(rng, size, CV_MAKETYPE(type, 1), 5, 16, false); }
dst[i] = randomMat(size, CV_MAKETYPE(type, 1), 5, 16, false); }
void random_roi()
{
if (use_roi)
{
//randomize ROI
cv::RNG &rng = TS::ptr()->get_rng();
roicols = rng.uniform(1, mat.cols);
roirows = rng.uniform(1, mat.rows);
srcx = rng.uniform(0, mat.cols - roicols);

73
modules/ocl/test/utility.cpp
Unescape View File

@ -46,7 +46,7 @@ using namespace cv;
using namespace cv::gpu;
using namespace cvtest;
namespace cvtest {
//std::string generateVarList(int first,...)
//{
// vector<std::string> varname;
@ -73,41 +73,14 @@ using namespace cvtest;
// return ss.str();
//};
int randomInt(int minVal, int maxVal)
{
RNG &rng = TS::ptr()->get_rng();
return rng.uniform(minVal, maxVal);
}
double randomDouble(double minVal, double maxVal)
{
RNG &rng = TS::ptr()->get_rng();
return rng.uniform(minVal, maxVal);
}
Size randomSize(int minVal, int maxVal)
{
return cv::Size(randomInt(minVal, maxVal), randomInt(minVal, maxVal));
}
Scalar randomScalar(double minVal, double maxVal)
{
return Scalar(randomDouble(minVal, maxVal), randomDouble(minVal, maxVal), randomDouble(minVal, maxVal), randomDouble(minVal, maxVal));
}
Mat randomMat(Size size, int type, double minVal, double maxVal)
{
return randomMat(TS::ptr()->get_rng(), size, type, minVal, maxVal, false);
}
cv::ocl::oclMat createMat_ocl(Size size, int type, bool useRoi)
cv::ocl::oclMat createMat_ocl(cv::RNG& rng, Size size, int type, bool useRoi)
{
Size size0 = size;
if (useRoi)
{
size0.width += randomInt(5, 15);
size0.height += randomInt(5, 15);
size0.width += rng.uniform(5, 15);
size0.height += rng.uniform(5, 15);
}
cv::ocl::oclMat d_m(size0, type);
@ -118,11 +91,11 @@ cv::ocl::oclMat createMat_ocl(Size size, int type, bool useRoi)
return d_m;
}
cv::ocl::oclMat loadMat_ocl(const Mat& m, bool useRoi)
cv::ocl::oclMat loadMat_ocl(cv::RNG& rng, const Mat& m, bool useRoi)
{
CV_Assert(m.type() == CV_8UC1 || m.type() == CV_8UC3);
cv::ocl::oclMat d_m;
d_m = createMat_ocl(m.size(), m.type(), useRoi);
d_m = createMat_ocl(rng, m.size(), m.type(), useRoi);
Size ls;
Point pt;
@ -138,38 +111,6 @@ cv::ocl::oclMat loadMat_ocl(const Mat& m, bool useRoi)
m_ocl.copyTo(d_m);
return d_m;
}
/*
void showDiff(InputArray gold_, InputArray actual_, double eps)
{
Mat gold;
if (gold_.kind() == _InputArray::MAT)
gold = gold_.getMat();
else
gold_.getGpuMat().download(gold);
Mat actual;
if (actual_.kind() == _InputArray::MAT)
actual = actual_.getMat();
else
actual_.getGpuMat().download(actual);
Mat diff;
absdiff(gold, actual, diff);
threshold(diff, diff, eps, 255.0, cv::THRESH_BINARY);
namedWindow("gold", WINDOW_NORMAL);
namedWindow("actual", WINDOW_NORMAL);
namedWindow("diff", WINDOW_NORMAL);
imshow("gold", gold);
imshow("actual", actual);
imshow("diff", diff);
waitKey();
}
*/
vector<MatType> types(int depth_start, int depth_end, int cn_start, int cn_end)
{
@ -289,3 +230,5 @@ double checkRectSimilarity(Size sz, std::vector<Rect>& ob1, std::vector<Rect>& o
}
return final_test_result;
}
} // namespace cvtest

193
modules/ocl/test/utility.hpp
Unescape View File

@ -42,7 +42,7 @@
#ifndef __OPENCV_TEST_UTILITY_HPP__
#define __OPENCV_TEST_UTILITY_HPP__
#define LOOP_TIMES 1
#define LOOP_TIMES 10
#define MWIDTH 256
#define MHEIGHT 256
@ -50,16 +50,12 @@
#define MIN_VALUE 171
#define MAX_VALUE 357
//#define RANDOMROI
int randomInt(int minVal, int maxVal);
double randomDouble(double minVal, double maxVal);
//std::string generateVarList(int first,...);
std::string generateVarList(int &p1, int &p2);
cv::Size randomSize(int minVal, int maxVal);
cv::Scalar randomScalar(double minVal, double maxVal);
cv::Mat randomMat(cv::Size size, int type, double minVal = 0.0, double maxVal = 255.0);
namespace cvtest {
void showDiff(cv::InputArray gold, cv::InputArray actual, double eps);
//void showDiff(cv::InputArray gold, cv::InputArray actual, double eps);
cv::ocl::oclMat createMat_ocl(cv::RNG& rng, Size size, int type, bool useRoi);
cv::ocl::oclMat loadMat_ocl(cv::RNG& rng, const Mat& m, bool useRoi);
// This function test if gpu_rst matches cpu_rst.
// If the two vectors are not equal, it will return the difference in vector size
@ -76,10 +72,6 @@ double checkNorm(const cv::Mat &m);
double checkNorm(const cv::Mat &m1, const cv::Mat &m2);
double checkSimilarity(const cv::Mat &m1, const cv::Mat &m2);
//oclMat create
cv::ocl::oclMat createMat_ocl(cv::Size size, int type, bool useRoi = false);
cv::ocl::oclMat loadMat_ocl(const cv::Mat& m, bool useRoi = false);
#define EXPECT_MAT_NORM(mat, eps) \
{ \
EXPECT_LE(checkNorm(cv::Mat(mat)), eps) \
@ -99,13 +91,6 @@ cv::ocl::oclMat loadMat_ocl(const cv::Mat& m, bool useRoi = false);
EXPECT_LE(checkSimilarity(cv::Mat(mat1), cv::Mat(mat2)), eps); \
}
namespace cv
{
namespace ocl
{
// void PrintTo(const DeviceInfo& info, std::ostream* os);
}
}
using perf::MatDepth;
using perf::MatType;
@ -132,79 +117,105 @@ private:
void PrintTo(const Inverse &useRoi, std::ostream *os);
enum {FLIP_BOTH = 0, FLIP_X = 1, FLIP_Y = -1};
CV_ENUM(FlipCode, FLIP_BOTH, FLIP_X, FLIP_Y)
#define OCL_RNG_SEED 123456
CV_ENUM(CmpCode, CMP_EQ, CMP_GT, CMP_GE, CMP_LT, CMP_LE, CMP_NE)
CV_ENUM(NormCode, NORM_INF, NORM_L1, NORM_L2, NORM_TYPE_MASK, NORM_RELATIVE, NORM_MINMAX)
CV_ENUM(ReduceOp, CV_REDUCE_SUM, CV_REDUCE_AVG, CV_REDUCE_MAX, CV_REDUCE_MIN)
CV_ENUM(MorphOp, MORPH_OPEN, MORPH_CLOSE, MORPH_GRADIENT, MORPH_TOPHAT, MORPH_BLACKHAT)
CV_ENUM(ThreshOp, THRESH_BINARY, THRESH_BINARY_INV, THRESH_TRUNC, THRESH_TOZERO, THRESH_TOZERO_INV)
CV_ENUM(Interpolation, INTER_NEAREST, INTER_LINEAR, INTER_CUBIC)
CV_ENUM(Border, BORDER_REFLECT101, BORDER_REPLICATE, BORDER_CONSTANT, BORDER_REFLECT, BORDER_WRAP)
CV_ENUM(TemplateMethod, TM_SQDIFF, TM_SQDIFF_NORMED, TM_CCORR, TM_CCORR_NORMED, TM_CCOEFF, TM_CCOEFF_NORMED)
template <typename T>
struct TSTestWithParam : public ::testing::TestWithParam<T>
{
cv::RNG rng;
CV_FLAGS(GemmFlags, GEMM_1_T, GEMM_2_T, GEMM_3_T);
CV_FLAGS(WarpFlags, INTER_NEAREST, INTER_LINEAR, INTER_CUBIC, WARP_INVERSE_MAP)
CV_FLAGS(DftFlags, DFT_INVERSE, DFT_SCALE, DFT_ROWS, DFT_COMPLEX_OUTPUT, DFT_REAL_OUTPUT)
TSTestWithParam()
{
rng = cv::RNG(OCL_RNG_SEED);
}
void run_perf_test();
int randomInt(int minVal, int maxVal)
{
return rng.uniform(minVal, maxVal);
}
#define PARAM_TEST_CASE(name, ...) struct name : testing::TestWithParam< std::tr1::tuple< __VA_ARGS__ > >
double randomDouble(double minVal, double maxVal)
{
return rng.uniform(minVal, maxVal);
}
#define GET_PARAM(k) std::tr1::get< k >(GetParam())
double randomDoubleLog(double minVal, double maxVal)
{
double logMin = log((double)minVal + 1);
double logMax = log((double)maxVal + 1);
double pow = rng.uniform(logMin, logMax);
double v = exp(pow) - 1;
CV_Assert(v >= minVal && (v < maxVal || (v == minVal && v == maxVal)));
return v;
}
#define ALL_DEVICES testing::ValuesIn(devices())
#define DEVICES(feature) testing::ValuesIn(devices(feature))
Size randomSize(int minVal, int maxVal)
{
#if 1
return cv::Size((int)randomDoubleLog(minVal, maxVal), (int)randomDoubleLog(minVal, maxVal));
#else
return cv::Size(randomInt(minVal, maxVal), randomInt(minVal, maxVal));
#endif
}
#define ALL_TYPES testing::ValuesIn(all_types())
#define TYPES(depth_start, depth_end, cn_start, cn_end) testing::ValuesIn(types(depth_start, depth_end, cn_start, cn_end))
Size randomSize(int minValX, int maxValX, int minValY, int maxValY)
{
#if 1
return cv::Size(randomDoubleLog(minValX, maxValX), randomDoubleLog(minValY, maxValY));
#else
return cv::Size(randomInt(minVal, maxVal), randomInt(minVal, maxVal));
#endif
}
#define DIFFERENT_SIZES testing::Values(cv::Size(128, 128), cv::Size(113, 113), cv::Size(1300, 1300))
Scalar randomScalar(double minVal, double maxVal)
{
return Scalar(randomDouble(minVal, maxVal), randomDouble(minVal, maxVal), randomDouble(minVal, maxVal), randomDouble(minVal, maxVal));
}
#define DIRECT_INVERSE testing::Values(Inverse(false), Inverse(true))
Mat randomMat(Size size, int type, double minVal, double maxVal, bool useRoi = false)
{
RNG dataRng(rng.next());
return cvtest::randomMat(dataRng, size, type, minVal, maxVal, useRoi);
}
#ifndef ALL_DEPTH
#define ALL_DEPTH testing::Values(MatDepth(CV_8U), MatDepth(CV_8S), MatDepth(CV_16U), MatDepth(CV_16S), MatDepth(CV_32S), MatDepth(CV_32F), MatDepth(CV_64F))
#endif
#define REPEAT 1000
#define COUNT_U 0 // count the uploading execution time for ocl mat structures
#define COUNT_D 0
// the following macro section tests the target function (kernel) performance
// upload is the code snippet for converting cv::mat to cv::ocl::oclMat
// downloading is the code snippet for converting cv::ocl::oclMat back to cv::mat
// change COUNT_U and COUNT_D to take downloading and uploading time into account
#define P_TEST_FULL( upload, kernel_call, download ) \
{ \
std::cout<< "\n" #kernel_call "\n----------------------"; \
{upload;} \
R_TEST( kernel_call, 2 ); \
double t = (double)cvGetTickCount(); \
R_T( { \
if( COUNT_U ) {upload;} \
kernel_call; \
if( COUNT_D ) {download;} \
} ); \
t = (double)cvGetTickCount() - t; \
std::cout << "runtime is " << t/((double)cvGetTickFrequency()* 1000.) << "ms" << std::endl; \
}
struct Border
{
int top, bot, lef, rig;
};
#define R_T2( test ) \
{ \
std::cout<< "\n" #test "\n----------------------"; \
R_TEST( test, 15 ) \
clock_t st = clock(); \
R_T( test ) \
std::cout<< clock() - st << "ms\n"; \
}
#define R_T( test ) \
R_TEST( test, REPEAT )
#define R_TEST( test, repeat ) \
try{ \
for( int i = 0; i < repeat; i ++ ) { test; } \
} catch( ... ) { std::cout << "||||| Exception catched! |||||\n"; return; }
Border randomBorder(int minValue = 0, int maxValue = MAX_VALUE)
{
Border border = {
(int)randomDoubleLog(minValue, maxValue),
(int)randomDoubleLog(minValue, maxValue),
(int)randomDoubleLog(minValue, maxValue),
(int)randomDoubleLog(minValue, maxValue)
};
return border;
}
void randomSubMat(Mat& whole, Mat& subMat, const Size& roiSize, const Border& border, int type, double minVal, double maxVal)
{
Size wholeSize = Size(roiSize.width + border.lef + border.rig, roiSize.height + border.top + border.bot);
whole = randomMat(wholeSize, type, minVal, maxVal, false);
subMat = whole(Rect(border.lef, border.top, roiSize.width, roiSize.height));
}
void generateOclMat(cv::ocl::oclMat& whole, cv::ocl::oclMat& subMat, const Mat& wholeMat, const Size& roiSize, const Border& border)
{
whole = wholeMat;
subMat = whole(Rect(border.lef, border.top, roiSize.width, roiSize.height));
}
};
#define PARAM_TEST_CASE(name, ...) struct name : public TSTestWithParam< std::tr1::tuple< __VA_ARGS__ > >
#define GET_PARAM(k) std::tr1::get< k >(GetParam())
#define ALL_TYPES testing::ValuesIn(all_types())
#define TYPES(depth_start, depth_end, cn_start, cn_end) testing::ValuesIn(types(depth_start, depth_end, cn_start, cn_end))
//////// Utility
#define DIFFERENT_SIZES testing::Values(cv::Size(128, 128), cv::Size(113, 113), cv::Size(1300, 1300))
#define IMAGE_CHANNELS testing::Values(Channels(1), Channels(3), Channels(4))
#ifndef IMPLEMENT_PARAM_CLASS
@ -225,4 +236,22 @@ void run_perf_test();
IMPLEMENT_PARAM_CLASS(Channels, int)
#endif // IMPLEMENT_PARAM_CLASS
} // namespace cvtest
enum {FLIP_BOTH = 0, FLIP_X = 1, FLIP_Y = -1};
CV_ENUM(FlipCode, FLIP_BOTH, FLIP_X, FLIP_Y)
CV_ENUM(CmpCode, CMP_EQ, CMP_GT, CMP_GE, CMP_LT, CMP_LE, CMP_NE)
CV_ENUM(NormCode, NORM_INF, NORM_L1, NORM_L2, NORM_TYPE_MASK, NORM_RELATIVE, NORM_MINMAX)
CV_ENUM(ReduceOp, CV_REDUCE_SUM, CV_REDUCE_AVG, CV_REDUCE_MAX, CV_REDUCE_MIN)
CV_ENUM(MorphOp, MORPH_OPEN, MORPH_CLOSE, MORPH_GRADIENT, MORPH_TOPHAT, MORPH_BLACKHAT)
CV_ENUM(ThreshOp, THRESH_BINARY, THRESH_BINARY_INV, THRESH_TRUNC, THRESH_TOZERO, THRESH_TOZERO_INV)
CV_ENUM(Interpolation, INTER_NEAREST, INTER_LINEAR, INTER_CUBIC)
CV_ENUM(Border, BORDER_REFLECT101, BORDER_REPLICATE, BORDER_CONSTANT, BORDER_REFLECT, BORDER_WRAP)
CV_ENUM(TemplateMethod, TM_SQDIFF, TM_SQDIFF_NORMED, TM_CCORR, TM_CCORR_NORMED, TM_CCOEFF, TM_CCOEFF_NORMED)
CV_FLAGS(GemmFlags, GEMM_1_T, GEMM_2_T, GEMM_3_T);
CV_FLAGS(WarpFlags, INTER_NEAREST, INTER_LINEAR, INTER_CUBIC, WARP_INVERSE_MAP)
CV_FLAGS(DftFlags, DFT_INVERSE, DFT_SCALE, DFT_ROWS, DFT_COMPLEX_OUTPUT, DFT_REAL_OUTPUT)
#endif // __OPENCV_TEST_UTILITY_HPP__

Write Preview
Loading…
Cancel
Save