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opencv/modules/core/test/test_rand.cpp
Adil Ibragimov 8a4a1bb018 Several type of formal refactoring: 
1. someMatrix.data -> someMatrix.prt()
2. someMatrix.data + someMatrix.step * lineIndex -> someMatrix.ptr( lineIndex )
3. (SomeType*) someMatrix.data -> someMatrix.ptr<SomeType>()
4. someMatrix.data -> !someMatrix.empty() ( or !someMatrix.data -> someMatrix.empty() ) in logical expressions
2014-08-13 15:21:35 +04:00

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#include "test_precomp.hpp"
using namespace cv;
using namespace std;
class Core_RandTest : public cvtest::BaseTest
{
public:
Core_RandTest();
protected:
void run(int);
bool check_pdf(const Mat& hist, double scale, int dist_type,
double& refval, double& realval);
};
Core_RandTest::Core_RandTest()
{
}
static double chi2_p95(int n)
{
static float chi2_tab95[] = {
3.841f, 5.991f, 7.815f, 9.488f, 11.07f, 12.59f, 14.07f, 15.51f,
16.92f, 18.31f, 19.68f, 21.03f, 21.03f, 22.36f, 23.69f, 25.00f,
26.30f, 27.59f, 28.87f, 30.14f, 31.41f, 32.67f, 33.92f, 35.17f,
36.42f, 37.65f, 38.89f, 40.11f, 41.34f, 42.56f, 43.77f };
static const double xp = 1.64;
CV_Assert(n >= 1);
if( n <= 30 )
return chi2_tab95[n-1];
return n + sqrt((double)2*n)*xp + 0.6666666666666*(xp*xp - 1);
}
bool Core_RandTest::check_pdf(const Mat& hist, double scale,
int dist_type, double& refval, double& realval)
{
Mat hist0(hist.size(), CV_32F);
const int* H = hist.ptr<int>();
float* H0 = hist0.ptr<float>();
int i, hsz = hist.cols;
double sum = 0;
for( i = 0; i < hsz; i++ )
sum += H[i];
CV_Assert( fabs(1./sum - scale) < FLT_EPSILON );
if( dist_type == CV_RAND_UNI )
{
float scale0 = (float)(1./hsz);
for( i = 0; i < hsz; i++ )
H0[i] = scale0;
}
else
{
double sum2 = 0, r = (hsz-1.)/2;
double alpha = 2*sqrt(2.)/r, beta = -alpha*r;
for( i = 0; i < hsz; i++ )
{
double x = i*alpha + beta;
H0[i] = (float)exp(-x*x);
sum2 += H0[i];
}
sum2 = 1./sum2;
for( i = 0; i < hsz; i++ )
H0[i] = (float)(H0[i]*sum2);
}
double chi2 = 0;
for( i = 0; i < hsz; i++ )
{
double a = H0[i];
double b = H[i]*scale;
if( a > DBL_EPSILON )
chi2 += (a - b)*(a - b)/(a + b);
}
realval = chi2;
double chi2_pval = chi2_p95(hsz - 1 - (dist_type == CV_RAND_NORMAL ? 2 : 0));
refval = chi2_pval*0.01;
return realval <= refval;
}
void Core_RandTest::run( int )
{
static int _ranges[][2] =
{{ 0, 256 }, { -128, 128 }, { 0, 65536 }, { -32768, 32768 },
{ -1000000, 1000000 }, { -1000, 1000 }, { -1000, 1000 }};
const int MAX_SDIM = 10;
const int N = 2000000;
const int maxSlice = 1000;
const int MAX_HIST_SIZE = 1000;
int progress = 0;
RNG& rng = ts->get_rng();
RNG tested_rng = theRNG();
test_case_count = 200;
for( int idx = 0; idx < test_case_count; idx++ )
{
progress = update_progress( progress, idx, test_case_count, 0 );
ts->update_context( this, idx, false );
int depth = cvtest::randInt(rng) % (CV_64F+1);
int c, cn = (cvtest::randInt(rng) % 4) + 1;
int type = CV_MAKETYPE(depth, cn);
int dist_type = cvtest::randInt(rng) % (CV_RAND_NORMAL+1);
int i, k, SZ = N/cn;
Scalar A, B;
double eps = 1.e-4;
if (depth == CV_64F)
eps = 1.e-7;
bool do_sphere_test = dist_type == CV_RAND_UNI;
Mat arr[2], hist[4];
int W[] = {0,0,0,0};
arr[0].create(1, SZ, type);
arr[1].create(1, SZ, type);
bool fast_algo = dist_type == CV_RAND_UNI && depth < CV_32F;
for( c = 0; c < cn; c++ )
{
int a, b, hsz;
if( dist_type == CV_RAND_UNI )
{
a = (int)(cvtest::randInt(rng) % (_ranges[depth][1] -
_ranges[depth][0])) + _ranges[depth][0];
do
{
b = (int)(cvtest::randInt(rng) % (_ranges[depth][1] -
_ranges[depth][0])) + _ranges[depth][0];
}
while( abs(a-b) <= 1 );
if( a > b )
std::swap(a, b);
unsigned r = (unsigned)(b - a);
fast_algo = fast_algo && r <= 256 && (r & (r-1)) == 0;
hsz = min((unsigned)(b - a), (unsigned)MAX_HIST_SIZE);
do_sphere_test = do_sphere_test && b - a >= 100;
}
else
{
int vrange = _ranges[depth][1] - _ranges[depth][0];
int meanrange = vrange/16;
int mindiv = MAX(vrange/20, 5);
int maxdiv = MIN(vrange/8, 10000);
a = cvtest::randInt(rng) % meanrange - meanrange/2 +
(_ranges[depth][0] + _ranges[depth][1])/2;
b = cvtest::randInt(rng) % (maxdiv - mindiv) + mindiv;
hsz = min((unsigned)b*9, (unsigned)MAX_HIST_SIZE);
}
A[c] = a;
B[c] = b;
hist[c].create(1, hsz, CV_32S);
}
cv::RNG saved_rng = tested_rng;
int maxk = fast_algo ? 0 : 1;
for( k = 0; k <= maxk; k++ )
{
tested_rng = saved_rng;
int sz = 0, dsz = 0, slice;
for( slice = 0; slice < maxSlice; slice++, sz += dsz )
{
dsz = slice+1 < maxSlice ? (int)(cvtest::randInt(rng) % (SZ - sz + 1)) : SZ - sz;
Mat aslice = arr[k].colRange(sz, sz + dsz);
tested_rng.fill(aslice, dist_type, A, B);
}
}
if( maxk >= 1 && cvtest::norm(arr[0], arr[1], NORM_INF) > eps)
{
ts->printf( cvtest::TS::LOG, "RNG output depends on the array lengths (some generated numbers get lost?)" );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
return;
}
for( c = 0; c < cn; c++ )
{
const uchar* data = arr[0].ptr();
int* H = hist[c].ptr<int>();
int HSZ = hist[c].cols;
double minVal = dist_type == CV_RAND_UNI ? A[c] : A[c] - B[c]*4;
double maxVal = dist_type == CV_RAND_UNI ? B[c] : A[c] + B[c]*4;
double scale = HSZ/(maxVal - minVal);
double delta = -minVal*scale;
hist[c] = Scalar::all(0);
for( i = c; i < SZ*cn; i += cn )
{
double val = depth == CV_8U ? ((const uchar*)data)[i] :
depth == CV_8S ? ((const schar*)data)[i] :
depth == CV_16U ? ((const ushort*)data)[i] :
depth == CV_16S ? ((const short*)data)[i] :
depth == CV_32S ? ((const int*)data)[i] :
depth == CV_32F ? ((const float*)data)[i] :
((const double*)data)[i];
int ival = cvFloor(val*scale + delta);
if( (unsigned)ival < (unsigned)HSZ )
{
H[ival]++;
W[c]++;
}
else if( dist_type == CV_RAND_UNI )
{
if( (minVal <= val && val < maxVal) || (depth >= CV_32F && val == maxVal) )
{
H[ival < 0 ? 0 : HSZ-1]++;
W[c]++;
}
else
{
putchar('^');
}
}
}
if( dist_type == CV_RAND_UNI && W[c] != SZ )
{
ts->printf( cvtest::TS::LOG, "Uniform RNG gave values out of the range [%g,%g) on channel %d/%d\n",
A[c], B[c], c, cn);
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
return;
}
if( dist_type == CV_RAND_NORMAL && W[c] < SZ*.90)
{
ts->printf( cvtest::TS::LOG, "Normal RNG gave too many values out of the range (%g+4*%g,%g+4*%g) on channel %d/%d\n",
A[c], B[c], A[c], B[c], c, cn);
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
return;
}
double refval = 0, realval = 0;
if( !check_pdf(hist[c], 1./W[c], dist_type, refval, realval) )
{
ts->printf( cvtest::TS::LOG, "RNG failed Chi-square test "
"(got %g vs probable maximum %g) on channel %d/%d\n",
realval, refval, c, cn);
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
return;
}
}
// Monte-Carlo test. Compute volume of SDIM-dimensional sphere
// inscribed in [-1,1]^SDIM cube.
if( do_sphere_test )
{
int SDIM = cvtest::randInt(rng) % (MAX_SDIM-1) + 2;
int N0 = (SZ*cn/SDIM), n = 0;
double r2 = 0;
const uchar* data = arr[0].ptr();
double scale[4], delta[4];
for( c = 0; c < cn; c++ )
{
scale[c] = 2./(B[c] - A[c]);
delta[c] = -A[c]*scale[c] - 1;
}
for( i = k = c = 0; i <= SZ*cn - SDIM; i++, k++, c++ )
{
double val = depth == CV_8U ? ((const uchar*)data)[i] :
depth == CV_8S ? ((const schar*)data)[i] :
depth == CV_16U ? ((const ushort*)data)[i] :
depth == CV_16S ? ((const short*)data)[i] :
depth == CV_32S ? ((const int*)data)[i] :
depth == CV_32F ? ((const float*)data)[i] : ((const double*)data)[i];
c &= c < cn ? -1 : 0;
val = val*scale[c] + delta[c];
r2 += val*val;
if( k == SDIM-1 )
{
n += r2 <= 1;
r2 = 0;
k = -1;
}
}
double V = ((double)n/N0)*(1 << SDIM);
// the theoretically computed volume
int sdim = SDIM % 2;
double V0 = sdim + 1;
for( sdim += 2; sdim <= SDIM; sdim += 2 )
V0 *= 2*CV_PI/sdim;
if( fabs(V - V0) > 0.3*fabs(V0) )
{
ts->printf( cvtest::TS::LOG, "RNG failed %d-dim sphere volume test (got %g instead of %g)\n",
SDIM, V, V0);
ts->printf( cvtest::TS::LOG, "depth = %d, N0 = %d\n", depth, N0);
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
return;
}
}
}
}
TEST(Core_Rand, quality) { Core_RandTest test; test.safe_run(); }
class Core_RandRangeTest : public cvtest::BaseTest
{
public:
Core_RandRangeTest() {}
~Core_RandRangeTest() {}
protected:
void run(int)
{
Mat a(Size(1280, 720), CV_8U, Scalar(20));
Mat af(Size(1280, 720), CV_32F, Scalar(20));
theRNG().fill(a, RNG::UNIFORM, -DBL_MAX, DBL_MAX);
theRNG().fill(af, RNG::UNIFORM, -DBL_MAX, DBL_MAX);
int n0 = 0, n255 = 0, nx = 0;
int nfmin = 0, nfmax = 0, nfx = 0;
for( int i = 0; i < a.rows; i++ )
for( int j = 0; j < a.cols; j++ )
{
int v = a.at<uchar>(i,j);
double vf = af.at<float>(i,j);
if( v == 0 ) n0++;
else if( v == 255 ) n255++;
else nx++;
if( vf < FLT_MAX*-0.999f ) nfmin++;
else if( vf > FLT_MAX*0.999f ) nfmax++;
else nfx++;
}
CV_Assert( n0 > nx*2 && n255 > nx*2 );
CV_Assert( nfmin > nfx*2 && nfmax > nfx*2 );
}
};
TEST(Core_Rand, range) { Core_RandRangeTest test; test.safe_run(); }
TEST(Core_RNG_MT19937, regression)
{
cv::RNG_MT19937 rng;
int actual[61] = {0, };
const size_t length = (sizeof(actual) / sizeof(actual[0]));
for (int i = 0; i < 10000; ++i )
{
actual[(unsigned)(rng.next() ^ i) % length]++;
}
int expected[length] = {
177, 158, 180, 177, 160, 179, 143, 162,
177, 144, 170, 174, 165, 168, 168, 156,
177, 157, 159, 169, 177, 182, 166, 154,
144, 180, 168, 152, 170, 187, 160, 145,
139, 164, 157, 179, 148, 183, 159, 160,
196, 184, 149, 142, 162, 148, 163, 152,
168, 173, 160, 181, 172, 181, 155, 153,
158, 171, 138, 150, 150 };
for (size_t i = 0; i < length; ++i)
{
ASSERT_EQ(expected[i], actual[i]);
}
}
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