matSize = 100;
truth = new DMatch[] {
- new DMatch(0, 0, 0, 0.6211397f),
+ new DMatch(0, 0, 0, 0.6159003f),
new DMatch(1, 1, 0, 0.9177120f),
new DMatch(2, 1, 0, 0.3112163f),
new DMatch(3, 1, 0, 0.2925074f),
- new DMatch(4, 1, 0, 0.9309178f)
+ new DMatch(4, 1, 0, 0.26520672f)
};
}
matSize = 100;
truth = new DMatch[] {
- new DMatch(0, 0, 0, 3.0975165f),
- new DMatch(1, 1, 0, 3.5680308f),
- new DMatch(2, 1, 0, 1.3722466f),
- new DMatch(3, 1, 0, 1.3041023f),
- new DMatch(4, 1, 0, 3.5970376f)
+ new DMatch(0, 0, 0, 3.0710702f),
+ new DMatch(1, 1, 0, 3.562016f),
+ new DMatch(2, 1, 0, 1.3682679f),
+ new DMatch(3, 1, 0, 1.3012862f),
+ new DMatch(4, 1, 0, 1.1852086f)
};
}
matSize = 100;
truth = new DMatch[] {
- new DMatch(0, 0, 0, 0.3858146f),
+ new DMatch(0, 0, 0, 0.37933317f),
new DMatch(1, 1, 0, 0.8421953f),
new DMatch(2, 1, 0, 0.0968556f),
new DMatch(3, 1, 0, 0.0855606f),
- new DMatch(4, 1, 0, 0.8666080f)
+ new DMatch(4, 1, 0, 0.07033461f)
};
}
matcher = DescriptorMatcher.create(DescriptorMatcher.FLANNBASED);
matSize = 100;
truth = new DMatch[] {
- new DMatch(0, 0, 0, 0.6211397f),
+ new DMatch(0, 0, 0, 0.6159003f),
new DMatch(1, 1, 0, 0.9177120f),
new DMatch(2, 1, 0, 0.3112163f),
new DMatch(3, 1, 0, 0.2925075f),
- new DMatch(4, 1, 0, 0.9309179f)
+ new DMatch(4, 1, 0, 0.26520672f)
};
}
Mat truth = new Mat(1, 32, CvType.CV_8UC1) {
{
put(0, 0,
- 6, 74, 6, 129, 2, 130, 56, 0, 36, 132, 66, 165, 172, 6, 3, 72, 102, 61, 163, 214, 0, 144, 65, 232, 4, 32, 138, 129, 4, 21, 37, 88);
+ 6, 74, 6, 129, 2, 130, 56, 0, 44, 132, 66, 165, 172, 6, 3, 72, 102, 61, 171, 214, 0, 144, 65, 232, 4, 32, 138, 131, 4, 21, 37, 217);
}
};
assertDescriptorsClose(truth, descriptors, 1);
Mat truth = new Mat(1, 32, CvType.CV_8UC1) {
{
put(0, 0,
- 6, 10, 22, 5, 2, 130, 56, 0, 44, 164, 66, 165, 140, 6, 1, 72, 38, 61, 163, 210, 0, 208, 1, 104, 4, 32, 10, 131, 0, 37, 37, 67);
+ 6, 10, 22, 5, 2, 130, 56, 0, 44, 164, 66, 165, 140, 6, 1, 72, 38, 61, 163, 210, 0, 208, 1, 104, 4, 32, 74, 131, 0, 37, 37, 67);
}
};
assertDescriptorsClose(truth, descriptors, 1);
//M*/
#include "precomp.hpp"
+
+#include <opencv2/core/utils/logger.defines.hpp>
+#undef CV_LOG_STRIP_LEVEL
+#define CV_LOG_STRIP_LEVEL CV_LOG_LEVEL_DEBUG + 1
+#include <opencv2/core/utils/logger.hpp>
+
#include "opencv2/core/opencl/ocl_defs.hpp"
#include "opencl_kernels_imgproc.hpp"
#include "hal_replacement.hpp"
CV_CPU_DISPATCH_MODES_ALL);
}
+static bool createBitExactKernel_32S(const Mat& kernel, Mat& kernel_dst, int bits)
+{
+ kernel.convertTo(kernel_dst, CV_32S, (1 << bits));
+ Mat_<double> kernel_64f;
+ kernel.convertTo(kernel_64f, CV_64F, (1 << bits));
+ int ksize = (int)kernel.total();
+ const double eps = 10 * FLT_EPSILON * (1 << bits);
+ for (int i = 0; i < ksize; i++)
+ {
+ int bitExactValue = kernel_dst.at<int>(i);
+ double approxValue = kernel_64f.at<double>(i);
+ if (fabs(approxValue - bitExactValue) > eps)
+ return false;
+ }
+ return true;
+}
Ptr<FilterEngine> createSeparableLinearFilter(
int _srcType, int _dstType,
_columnKernel.rows == 1 ? Point(_anchor.y, 0) : Point(0, _anchor.y));
Mat rowKernel, columnKernel;
+ bool isBitExactMode = false;
int bdepth = std::max(CV_32F,std::max(sdepth, ddepth));
int bits = 0;
(rtype & ctype & KERNEL_INTEGER) &&
ddepth == CV_16S)) )
{
- bdepth = CV_32S;
- bits = ddepth == CV_8U ? 8 : 0;
- _rowKernel.convertTo( rowKernel, CV_32S, 1 << bits );
- _columnKernel.convertTo( columnKernel, CV_32S, 1 << bits );
- bits *= 2;
- _delta *= (1 << bits);
+ int bits_ = ddepth == CV_8U ? 8 : 0;
+ bool isValidBitExactRowKernel = createBitExactKernel_32S(_rowKernel, rowKernel, bits_);
+ bool isValidBitExactColumnKernel = createBitExactKernel_32S(_columnKernel, columnKernel, bits_);
+ if (!isValidBitExactRowKernel)
+ {
+ CV_LOG_DEBUG(NULL, "createSeparableLinearFilter: bit-exact row-kernel can't be applied: ksize=" << _rowKernel.total());
+ }
+ else if (!isValidBitExactColumnKernel)
+ {
+ CV_LOG_DEBUG(NULL, "createSeparableLinearFilter: bit-exact column-kernel can't be applied: ksize=" << _columnKernel.total());
+ }
+ else
+ {
+ bdepth = CV_32S;
+ bits = bits_;
+ bits *= 2;
+ _delta *= (1 << bits);
+ isBitExactMode = true;
+ }
}
- else
+ if (!isBitExactMode)
{
if( _rowKernel.type() != bdepth )
_rowKernel.convertTo( rowKernel, bdepth );
CV_ALWAYS_INLINE bool isZero() { return val == 0; }
static CV_ALWAYS_INLINE ufixedpoint16 zero() { return ufixedpoint16(); }
static CV_ALWAYS_INLINE ufixedpoint16 one() { return ufixedpoint16((uint16_t)(1 << fixedShift)); }
+
+ static CV_ALWAYS_INLINE ufixedpoint16 fromRaw(uint16_t v) { return ufixedpoint16(v); }
+ CV_ALWAYS_INLINE ufixedpoint16 raw() { return val; }
};
}
#include "precomp.hpp"
+#include <opencv2/core/utils/logger.hpp>
+
+#include <opencv2/core/utils/configuration.private.hpp>
+
#include <vector>
#include "opencv2/core/hal/intrin.hpp"
Gaussian Blur
\****************************************************************************************/
-Mat getGaussianKernel(int n, double sigma, int ktype)
+/**
+ * Bit-exact in terms of softfloat computations
+ *
+ * returns sum of kernel values. Should be equal to 1.0
+ */
+static
+softdouble getGaussianKernelBitExact(std::vector<softdouble>& result, int n, double sigma)
{
CV_Assert(n > 0);
- const int SMALL_GAUSSIAN_SIZE = 7;
- static const float small_gaussian_tab[][SMALL_GAUSSIAN_SIZE] =
- {
- {1.f},
- {0.25f, 0.5f, 0.25f},
- {0.0625f, 0.25f, 0.375f, 0.25f, 0.0625f},
- {0.03125f, 0.109375f, 0.21875f, 0.28125f, 0.21875f, 0.109375f, 0.03125f}
- };
-
- const float* fixed_kernel = n % 2 == 1 && n <= SMALL_GAUSSIAN_SIZE && sigma <= 0 ?
- small_gaussian_tab[n>>1] : 0;
+ //TODO: incorrect SURF implementation requests kernel with n = 20 (PATCH_SZ): https://github.com/opencv/opencv/issues/15856
+ //CV_Assert((n & 1) == 1); // odd
- CV_Assert( ktype == CV_32F || ktype == CV_64F );
- Mat kernel(n, 1, ktype);
- float* cf = kernel.ptr<float>();
- double* cd = kernel.ptr<double>();
-
- double sigmaX = sigma > 0 ? sigma : ((n-1)*0.5 - 1)*0.3 + 0.8;
- double scale2X = -0.5/(sigmaX*sigmaX);
- double sum = 0;
-
- int i;
- for( i = 0; i < n; i++ )
+ if (sigma <= 0)
{
- double x = i - (n-1)*0.5;
- double t = fixed_kernel ? (double)fixed_kernel[i] : std::exp(scale2X*x*x);
- if( ktype == CV_32F )
+ if (n == 1)
{
- cf[i] = (float)t;
- sum += cf[i];
+ result = std::vector<softdouble>(1, softdouble::one());
+ return softdouble::one();
}
- else
+ else if (n == 3)
{
- cd[i] = t;
- sum += cd[i];
+ softdouble v3[] = {
+ softdouble::fromRaw(0x3fd0000000000000), // 0.25
+ softdouble::fromRaw(0x3fe0000000000000), // 0.5
+ softdouble::fromRaw(0x3fd0000000000000) // 0.25
+ };
+ result.assign(v3, v3 + 3);
+ return softdouble::one();
}
- }
-
- CV_DbgAssert(fabs(sum) > 0);
- sum = 1./sum;
- for( i = 0; i < n; i++ )
- {
- if( ktype == CV_32F )
- cf[i] = (float)(cf[i]*sum);
- else
- cd[i] *= sum;
- }
-
- return kernel;
-}
-
-template <typename T>
-static std::vector<T> getFixedpointGaussianKernel( int n, double sigma )
-{
- if (sigma <= 0)
- {
- if(n == 1)
- return std::vector<T>(1, softdouble(1.0));
- else if(n == 3)
+ else if (n == 5)
{
- T v3[] = { softdouble(0.25), softdouble(0.5), softdouble(0.25) };
- return std::vector<T>(v3, v3 + 3);
+ softdouble v5[] = {
+ softdouble::fromRaw(0x3fb0000000000000), // 0.0625
+ softdouble::fromRaw(0x3fd0000000000000), // 0.25
+ softdouble::fromRaw(0x3fd8000000000000), // 0.375
+ softdouble::fromRaw(0x3fd0000000000000), // 0.25
+ softdouble::fromRaw(0x3fb0000000000000) // 0.0625
+ };
+ result.assign(v5, v5 + 5);
+ return softdouble::one();
}
- else if(n == 5)
+ else if (n == 7)
{
- T v5[] = { softdouble(0.0625), softdouble(0.25), softdouble(0.375), softdouble(0.25), softdouble(0.0625) };
- return std::vector<T>(v5, v5 + 5);
+ softdouble v7[] = {
+ softdouble::fromRaw(0x3fa0000000000000), // 0.03125
+ softdouble::fromRaw(0x3fbc000000000000), // 0.109375
+ softdouble::fromRaw(0x3fcc000000000000), // 0.21875
+ softdouble::fromRaw(0x3fd2000000000000), // 0.28125
+ softdouble::fromRaw(0x3fcc000000000000), // 0.21875
+ softdouble::fromRaw(0x3fbc000000000000), // 0.109375
+ softdouble::fromRaw(0x3fa0000000000000) // 0.03125
+ };
+ result.assign(v7, v7 + 7);
+ return softdouble::one();
}
- else if(n == 7)
+ else if (n == 9)
{
- T v7[] = { softdouble(0.03125), softdouble(0.109375), softdouble(0.21875), softdouble(0.28125), softdouble(0.21875), softdouble(0.109375), softdouble(0.03125) };
- return std::vector<T>(v7, v7 + 7);
+ softdouble v9[] = {
+ softdouble::fromRaw(0x3f90000000000000), // 4 / 256
+ softdouble::fromRaw(0x3faa000000000000), // 13 / 256
+ softdouble::fromRaw(0x3fbe000000000000), // 30 / 256
+ softdouble::fromRaw(0x3fc9800000000000), // 51 / 256
+ softdouble::fromRaw(0x3fce000000000000), // 60 / 256
+ softdouble::fromRaw(0x3fc9800000000000), // 51 / 256
+ softdouble::fromRaw(0x3fbe000000000000), // 30 / 256
+ softdouble::fromRaw(0x3faa000000000000), // 13 / 256
+ softdouble::fromRaw(0x3f90000000000000) // 4 / 256
+ };
+ result.assign(v9, v9 + 9);
+ return softdouble::one();
}
}
+ softdouble sd_0_15 = softdouble::fromRaw(0x3fc3333333333333); // 0.15
+ softdouble sd_0_35 = softdouble::fromRaw(0x3fd6666666666666); // 0.35
+ softdouble sd_minus_0_125 = softdouble::fromRaw(0xbfc0000000000000); // -0.5*0.25
- softdouble sigmaX = sigma > 0 ? softdouble(sigma) : mulAdd(softdouble(n),softdouble(0.15),softdouble(0.35));// softdouble(((n-1)*0.5 - 1)*0.3 + 0.8)
- softdouble scale2X = softdouble(-0.5*0.25)/(sigmaX*sigmaX);
- std::vector<softdouble> values(n);
- softdouble sum(0.);
- for(int i = 0, x = 1 - n; i < n; i++, x+=2 )
+ softdouble sigmaX = sigma > 0 ? softdouble(sigma) : mulAdd(softdouble(n), sd_0_15, sd_0_35);// softdouble(((n-1)*0.5 - 1)*0.3 + 0.8)
+ softdouble scale2X = sd_minus_0_125/(sigmaX*sigmaX);
+
+ int n2_ = (n - 1) / 2;
+ cv::AutoBuffer<softdouble> values(n2_ + 1);
+ softdouble sum = softdouble::zero();
+ for (int i = 0, x = 1 - n; i < n2_; i++, x+=2)
{
// x = i - (n - 1)*0.5
// t = std::exp(scale2X*x*x)
- values[i] = exp(softdouble(x*x)*scale2X);
- sum += values[i];
+ softdouble t = exp(softdouble(x*x)*scale2X);
+ values[i] = t;
+ sum += t;
+ }
+ sum *= softdouble(2);
+ //values[n2_] = softdouble::one(); // x=0 in exp(softdouble(x*x)*scale2X);
+ sum += softdouble::one();
+ if ((n & 1) == 0)
+ {
+ //values[n2_ + 1] = softdouble::one();
+ sum += softdouble::one();
}
- sum = softdouble::one()/sum;
- std::vector<T> kernel(n);
- for(int i = 0; i < n; i++ )
+ // normalize: sum(k[i]) = 1
+ softdouble mul1 = softdouble::one()/sum;
+
+ result.resize(n);
+
+ softdouble sum2 = softdouble::zero();
+ for (int i = 0; i < n2_; i++ )
+ {
+ softdouble t = values[i] * mul1;
+ result[i] = t;
+ result[n - 1 - i] = t;
+ sum2 += t;
+ }
+ sum2 *= softdouble(2);
+ result[n2_] = /*values[n2_]*/ softdouble::one() * mul1;
+ sum2 += result[n2_];
+ if ((n & 1) == 0)
{
- kernel[i] = values[i] * sum;
+ result[n2_ + 1] = result[n2_];
+ sum2 += result[n2_];
+ }
+
+ return sum2;
+}
+
+Mat getGaussianKernel(int n, double sigma, int ktype)
+{
+ CV_CheckDepth(ktype, ktype == CV_32F || ktype == CV_64F, "");
+ Mat kernel(n, 1, ktype);
+
+ std::vector<softdouble> kernel_bitexact;
+ getGaussianKernelBitExact(kernel_bitexact, n, sigma);
+
+ if (ktype == CV_32F)
+ {
+ for (int i = 0; i < n; i++)
+ kernel.at<float>(i) = (float)kernel_bitexact[i];
+ }
+ else
+ {
+ CV_DbgAssert(ktype == CV_64F);
+ for (int i = 0; i < n; i++)
+ kernel.at<double>(i) = kernel_bitexact[i];
}
return kernel;
-};
+}
+
+static
+softdouble getGaussianKernelFixedPoint_ED(CV_OUT std::vector<int64_t>& result, const std::vector<softdouble> kernel_bitexact, int fractionBits)
+{
+ const int n = (int)kernel_bitexact.size();
+ CV_Assert((n & 1) == 1); // odd
+
+ CV_CheckGT(fractionBits, 0, "");
+ CV_CheckLE(fractionBits, 32, "");
+
+ int64_t fractionMultiplier = CV_BIG_INT(1) << fractionBits;
+ softdouble fractionMultiplier_sd(fractionMultiplier);
+
+ result.resize(n);
+
+ int n2_ = n / 2; // n is odd
+ softdouble err = softdouble::zero();
+ int64_t sum = 0;
+ for (int i = 0; i < n2_; i++)
+ {
+ //softdouble err0 = err;
+ softdouble adj_v = kernel_bitexact[i] * fractionMultiplier_sd + err;
+ int64_t v0 = cvRound(adj_v); // cvFloor() provides bad results
+ err = adj_v - softdouble(v0);
+ //printf("%3d: adj_v=%8.3f(%8.3f+%8.3f) v0=%d ed_err=%8.3f\n", i, (double)adj_v, (double)(kernel_bitexact[i] * fractionMultiplier_sd), (double)err0, (int)v0, (double)err);
+
+ result[i] = v0;
+ result[n - 1 - i] = v0;
+ sum += v0;
+ }
+ sum *= 2;
+ softdouble adj_v_center = kernel_bitexact[n2_] * fractionMultiplier_sd + err;
+ int64_t v_center = fractionMultiplier - sum;
+ result[n2_] = v_center;
+ //printf("center = %g ===> %g ===> %g\n", (double)(kernel_bitexact[n2_] * fractionMultiplier), (double)adj_v_center, (double)v_center);
+ return (adj_v_center - softdouble(v_center));
+}
static void getGaussianKernel(int n, double sigma, int ktype, Mat& res) { res = getGaussianKernel(n, sigma, ktype); }
-template <typename T> static void getGaussianKernel(int n, double sigma, int, std::vector<T>& res) { res = getFixedpointGaussianKernel<T>(n, sigma); }
+template <typename T> static void getGaussianKernel(int n, double sigma, int, std::vector<T>& res);
+//{ res = getFixedpointGaussianKernel<T>(n, sigma); }
+
+template<> void getGaussianKernel<ufixedpoint16>(int n, double sigma, int, std::vector<ufixedpoint16>& res)
+{
+ std::vector<softdouble> res_sd;
+ softdouble s0 = getGaussianKernelBitExact(res_sd, n, sigma);
+ CV_UNUSED(s0);
+
+ std::vector<int64_t> fixed_256;
+ softdouble approx_err = getGaussianKernelFixedPoint_ED(fixed_256, res_sd, 8);
+ CV_UNUSED(approx_err);
+
+ res.resize(n);
+ for (int i = 0; i < n; i++)
+ {
+ res[i] = ufixedpoint16::fromRaw((uint16_t)fixed_256[i]);
+ //printf("%03d: %d\n", i, res[i].raw());
+ }
+}
template <typename T>
static void createGaussianKernels( T & kx, T & ky, int type, Size &ksize,
}
#endif
+template<typename T>
+static bool validateGaussianBlurKernel(std::vector<T>& kernel)
+{
+ softdouble validation_sum = softdouble::zero();
+ for (size_t i = 0; i < kernel.size(); i++)
+ {
+ validation_sum += softdouble((double)kernel[i]);
+ }
+
+ bool isValid = validation_sum == softdouble::one();
+ return isValid;
+}
+
void GaussianBlur(InputArray _src, OutputArray _dst, Size ksize,
double sigma1, double sigma2,
int borderType)
{
std::vector<ufixedpoint16> fkx, fky;
createGaussianKernels(fkx, fky, type, ksize, sigma1, sigma2);
- if (src.data == dst.data)
- src = src.clone();
- CV_CPU_DISPATCH(GaussianBlurFixedPoint, (src, dst, (const uint16_t*)&fkx[0], (int)fkx.size(), (const uint16_t*)&fky[0], (int)fky.size(), borderType),
- CV_CPU_DISPATCH_MODES_ALL);
- return;
+
+ static bool param_check_gaussian_blur_bitexact_kernels = utils::getConfigurationParameterBool("OPENCV_GAUSSIANBLUR_CHECK_BITEXACT_KERNELS", false);
+ if (param_check_gaussian_blur_bitexact_kernels && !validateGaussianBlurKernel(fkx))
+ {
+ CV_LOG_INFO(NULL, "GaussianBlur: bit-exact fx kernel can't be applied: ksize=" << ksize << " sigma=" << Size2d(sigma1, sigma2));
+ }
+ else if (param_check_gaussian_blur_bitexact_kernels && !validateGaussianBlurKernel(fky))
+ {
+ CV_LOG_INFO(NULL, "GaussianBlur: bit-exact fy kernel can't be applied: ksize=" << ksize << " sigma=" << Size2d(sigma1, sigma2));
+ }
+ else
+ {
+ if (src.data == dst.data)
+ src = src.clone();
+ CV_CPU_DISPATCH(GaussianBlurFixedPoint, (src, dst, (const uint16_t*)&fkx[0], (int)fkx.size(), (const uint16_t*)&fky[0], (int)fky.size(), borderType),
+ CV_CPU_DISPATCH_MODES_ALL);
+ return;
+ }
}
sepFilter2D(src, dst, sdepth, kx, ky, Point(-1, -1), 0, borderType);
bool fp_kernel;
bool inplace;
int border;
+
+ void dump_test_case(int test_case_idx, std::ostream* out) CV_OVERRIDE
+ {
+ ArrayTest::dump_test_case(test_case_idx, out);
+ *out << "border=" << border << std::endl;
+ }
+
};
void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
double get_success_error_level( int test_case_idx, int i, int j );
const char* smooth_type;
+
+ void dump_test_case(int test_case_idx, std::ostream* out) CV_OVERRIDE
+ {
+ CV_FilterBaseTest::dump_test_case(test_case_idx, out);
+ *out << "smooth_type=" << smooth_type << std::endl;
+ }
};
double get_success_error_level( int /*test_case_idx*/, int /*i*/, int /*j*/ );
double sigma;
int param1, param2;
+
+ void dump_test_case(int test_case_idx, std::ostream* out) CV_OVERRIDE
+ {
+ CV_SmoothBaseTest::dump_test_case(test_case_idx, out);
+ *out << "kernel=(" << param1 << ", " << param2 << ") sigma=" << sigma << std::endl;
+ }
};
// !!! Copied from cvSmooth, if the code is changed in cvSmooth,
// make sure to update this one too.
-#define SMALL_GAUSSIAN_SIZE 7
+#define SMALL_GAUSSIAN_SIZE 9
static void
calcGaussianKernel( int n, double sigma, vector<float>& kernel )
{
{1.f},
{0.25f, 0.5f, 0.25f},
{0.0625f, 0.25f, 0.375f, 0.25f, 0.0625f},
- {0.03125, 0.109375, 0.21875, 0.28125, 0.21875, 0.109375, 0.03125}
+ {0.03125, 0.109375, 0.21875, 0.28125, 0.21875, 0.109375, 0.03125},
+ {4.0 / 256, 13.0 / 256, 30.0 / 256, 51.0 / 256, 60.0 / 256, 51.0 / 256, 30.0 / 256, 13.0 / 256, 4.0 / 256}
};
kernel.resize(n);
if( n <= SMALL_GAUSSIAN_SIZE && sigma <= 0 )
{
- assert( n%2 == 1 );
- memcpy( &kernel[0], small_gaussian_tab[n>>1], n*sizeof(kernel[0]));
+ CV_Assert(n%2 == 1);
+ memcpy(&kernel[0], small_gaussian_tab[n / 2], n*sizeof(kernel[0]));
}
else
{
{ fixedOne >> 2, fixedOne >> 1, fixedOne >> 2 }, // size 3, sigma 0
{ fixedOne >> 4, fixedOne >> 2, 6 * (fixedOne >> 4), fixedOne >> 2, fixedOne >> 4 }, // size 5, sigma 0
{ fixedOne >> 5, 7 * (fixedOne >> 6), 7 * (fixedOne >> 5), 9 * (fixedOne >> 5), 7 * (fixedOne >> 5), 7 * (fixedOne >> 6), fixedOne >> 5 }, // size 7, sigma 0
- { 4, 13, 30, 51, 61, 51, 30, 13, 4 }, // size 9, sigma 0
- { 81, 95, 81 }, // size 3, sigma 1.75
- { 65, 125, 65 }, // size 3, sigma 0.875
+ { 4, 13, 30, 51, 60, 51, 30, 13, 4 }, // size 9, sigma 0
+#if 1
+#define CV_TEST_INACCURATE_GAUSSIAN_BLUR
+ { 81, 94, 81 }, // size 3, sigma 1.75
+ { 65, 126, 65 }, // size 3, sigma 0.875
{ 0, 7, 242, 7, 0 }, // size 5, sigma 0.375
{ 4, 56, 136, 56, 4 } // size 5, sigma 0.75
+#endif
};
template <typename T, int fixedShift>
{ CV_8UC1, Size( 256, 128), Size(5, 5), 0, 0, vector<int64_t>(v[2], v[2]+5), vector<int64_t>(v[2], v[2]+5) },
{ CV_8UC1, Size( 256, 128), Size(7, 7), 0, 0, vector<int64_t>(v[3], v[3]+7), vector<int64_t>(v[3], v[3]+7) },
{ CV_8UC1, Size( 256, 128), Size(9, 9), 0, 0, vector<int64_t>(v[4], v[4]+9), vector<int64_t>(v[4], v[4]+9) },
+#ifdef CV_TEST_INACCURATE_GAUSSIAN_BLUR
{ CV_8UC1, Size( 256, 128), Size(3, 3), 1.75, 0.875, vector<int64_t>(v[5], v[5]+3), vector<int64_t>(v[6], v[6]+3) },
{ CV_8UC2, Size( 256, 128), Size(3, 3), 1.75, 0.875, vector<int64_t>(v[5], v[5]+3), vector<int64_t>(v[6], v[6]+3) },
{ CV_8UC3, Size( 256, 128), Size(3, 3), 1.75, 0.875, vector<int64_t>(v[5], v[5]+3), vector<int64_t>(v[6], v[6]+3) },
{ CV_8UC4, Size( 256, 128), Size(3, 3), 1.75, 0.875, vector<int64_t>(v[5], v[5]+3), vector<int64_t>(v[6], v[6]+3) },
{ CV_8UC1, Size( 256, 128), Size(5, 5), 0.375, 0.75, vector<int64_t>(v[7], v[7]+5), vector<int64_t>(v[8], v[8]+5) }
+#endif
};
int bordermodes[] = {
{
Mat src(100,100,CV_8UC3,Scalar(255,255,255));
Mat dst;
- GaussianBlur(src, dst, Size(5, 5), 9);
+ GaussianBlur(src, dst, Size(5, 5), 0);
ASSERT_EQ(0.0, cvtest::norm(dst, src, NORM_INF));
}
+
+static void checkGaussianBlur_8Uvs32F(const Mat& src8u, const Mat& src32f, int N, double sigma)
+{
+ Mat dst8u; GaussianBlur(src8u, dst8u, Size(N, N), sigma); // through bit-exact path
+ Mat dst8u_32f; dst8u.convertTo(dst8u_32f, CV_32F);
+
+ Mat dst32f; GaussianBlur(src32f, dst32f, Size(N, N), sigma); // without bit-exact computations
+
+ double normINF_32f = cv::norm(dst8u_32f, dst32f, NORM_INF);
+ EXPECT_LE(normINF_32f, 1.0);
+}
+
+TEST(GaussianBlur_Bitexact, regression_9863)
+{
+ Mat src8u = imread(cvtest::findDataFile("shared/lena.png"));
+ Mat src32f; src8u.convertTo(src32f, CV_32F);
+
+ checkGaussianBlur_8Uvs32F(src8u, src32f, 151, 30);
+}
+
}} // namespace
img = self.get_sample( sample)
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
- gray = cv.GaussianBlur(gray, (5, 5), 5.1)
+ gray = cv.GaussianBlur(gray, (5, 5), 0)
rects = detect(gray, cascade)
faces.append(rects)
// updates progress bar
virtual int update_progress( int progress, int test_case_idx, int count, double dt );
+ // dump test case input parameters
+ virtual void dump_test_case(int test_case_idx, std::ostream* out);
+
// finds test parameter
const CvFileNode* find_param( CvFileStorage* fs, const char* param_name );
return;
if( validate_test_results( test_case_idx ) < 0 || ts->get_err_code() < 0 )
+ {
+ std::stringstream ss;
+ dump_test_case(test_case_idx, &ss);
+ std::string s = ss.str();
+ ts->printf( TS::LOG, "%s", s.c_str());
return;
+ }
}
}
}
+void BaseTest::dump_test_case(int test_case_idx, std::ostream* out)
+{
+ *out << "test_case_idx = " << test_case_idx << std::endl;
+}
+
+
BadArgTest::BadArgTest()
{
test_case_idx = -1;
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