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updated normalization routine in the matchTemplate to avoid division by zero on black images (ticket #798), added test

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This commit is contained in:
Alexey Spizhevoy 2011-01-11 09:36:21 +00:00
parent a961cfe135
commit dc763e0250
3 changed files with 72 additions and 67 deletions
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3
modules/gpu/include/opencv2/gpu/gpu.hpp
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@ -1180,7 +1180,6 @@ namespace cv
size_t getBlockHistogramSize() const;
void setSVMDetector(const vector<float>& detector);
bool checkDetectorSize() const;
static vector<float> getDefaultPeopleDetector();
static vector<float> getPeopleDetector_48x96();
@ -1212,7 +1211,9 @@ namespace cv
protected:
void computeBlockHistograms(const GpuMat& img);
void computeGradient(const GpuMat& img, GpuMat& grad, GpuMat& qangle);
double getWinSigma() const;
bool checkDetectorSize() const;
static int numPartsWithin(int size, int part_size, int stride);
static Size numPartsWithin(Size size, Size part_size, Size stride);

10
modules/gpu/src/cuda/match_template.cu
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@ -560,7 +560,7 @@ __global__ void matchTemplatePreparedKernel_CCOFF_NORMED_8U(
(image_sqsum.ptr(y + h)[x + w] - image_sqsum.ptr(y)[x + w]) -
(image_sqsum.ptr(y + h)[x] - image_sqsum.ptr(y)[x]));
result.ptr(y)[x] = min(1.f, (ccorr - image_sum_ * templ_sum_scale) *
rsqrtf(templ_sqsum_scale * (image_sqsum_ - weight * image_sum_ * image_sum_)));
rsqrtf(templ_sqsum_scale * (image_sqsum_ - weight * image_sum_ * image_sum_ + 1.f)));
}
}
@ -611,7 +611,7 @@ __global__ void matchTemplatePreparedKernel_CCOFF_NORMED_8UC2(
(image_sqsum_g.ptr(y + h)[x] - image_sqsum_g.ptr(y)[x]));
float ccorr = result.ptr(y)[x];
float rdenom = rsqrtf(templ_sqsum_scale * (image_sqsum_r_ - weight * image_sum_r_ * image_sum_r_
+ image_sqsum_g_ - weight * image_sum_g_ * image_sum_g_));
+ image_sqsum_g_ - weight * image_sum_g_ * image_sum_g_ + 1.f));
result.ptr(y)[x] = min(1.f, (ccorr - image_sum_r_ * templ_sum_scale_r
- image_sum_g_ * templ_sum_scale_g) * rdenom);
}
@ -680,7 +680,7 @@ __global__ void matchTemplatePreparedKernel_CCOFF_NORMED_8UC3(
float ccorr = result.ptr(y)[x];
float rdenom = rsqrtf(templ_sqsum_scale * (image_sqsum_r_ - weight * image_sum_r_ * image_sum_r_
+ image_sqsum_g_ - weight * image_sum_g_ * image_sum_g_
+ image_sqsum_b_ - weight * image_sum_b_ * image_sum_b_));
+ image_sqsum_b_ - weight * image_sum_b_ * image_sum_b_ + 1.f));
result.ptr(y)[x] = min(1.f, (ccorr - image_sum_r_ * templ_sum_scale_r
- image_sum_g_ * templ_sum_scale_g
- image_sum_b_ * templ_sum_scale_b) * rdenom);
@ -763,7 +763,7 @@ __global__ void matchTemplatePreparedKernel_CCOFF_NORMED_8UC4(
float rdenom = rsqrtf(templ_sqsum_scale * (image_sqsum_r_ - weight * image_sum_r_ * image_sum_r_
+ image_sqsum_g_ - weight * image_sum_g_ * image_sum_g_
+ image_sqsum_b_ - weight * image_sum_b_ * image_sum_b_
+ image_sqsum_a_ - weight * image_sum_a_ * image_sum_a_));
+ image_sqsum_a_ - weight * image_sum_a_ * image_sum_a_ + 1.f));
result.ptr(y)[x] = min(1.f, (ccorr - image_sum_r_ * templ_sum_scale_r
- image_sum_g_ * templ_sum_scale_g
- image_sum_b_ * templ_sum_scale_b
@ -822,7 +822,7 @@ __global__ void normalizeKernel_8U(
float image_sqsum_ = (float)(
(image_sqsum.ptr(y + h)[(x + w) * cn] - image_sqsum.ptr(y)[(x + w) * cn]) -
(image_sqsum.ptr(y + h)[x * cn] - image_sqsum.ptr(y)[x * cn]));
result.ptr(y)[x] = min(1.f, result.ptr(y)[x] * rsqrtf(image_sqsum_ * templ_sqsum));
result.ptr(y)[x] = min(1.f, result.ptr(y)[x] * rsqrtf((image_sqsum_ + 1.f) * templ_sqsum));
}
}

126
tests/gpu/src/match_template.cpp
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@ -124,7 +124,7 @@ struct CV_GpuMatchTemplateTest: CvTest
F(t = clock();)
gpu::matchTemplate(gpu::GpuMat(image), gpu::GpuMat(templ), dst, CV_TM_CCORR_NORMED);
F(cout << "gpu_block: " << clock() - t << endl;)
if (!check(dst_gold, Mat(dst), h * w * 1e-5f)) return;
if (!check(dst_gold, Mat(dst), h * w * 1e-4f)) return;
gen(image, n, m, CV_8U, cn);
gen(templ, h, w, CV_8U, cn);
@ -146,7 +146,7 @@ struct CV_GpuMatchTemplateTest: CvTest
F(t = clock();)
gpu::matchTemplate(gpu::GpuMat(image), gpu::GpuMat(templ), dst, CV_TM_CCOEFF_NORMED);
F(cout << "gpu_block: " << clock() - t << endl;)
if (!check(dst_gold, Mat(dst), h * w * 1e-6f)) return;
if (!check(dst_gold, Mat(dst), h * w * 1e-4f)) return;
gen(image, n, m, CV_32F, cn);
gen(templ, h, w, CV_32F, cn);
@ -207,66 +207,70 @@ struct CV_GpuMatchTemplateTest: CvTest
return false;
}
//// Debug check
//for (int i = 0; i < a.rows; ++i)
//{
// for (int j = 0; j < a.cols; ++j)
// {
// float v1 = a.at<float>(i, j);
// float v2 = b.at<float>(i, j);
// if (fabs(v1 - v2) > max_err)
// {
// ts->printf(CvTS::CONSOLE, "%d %d %f %f\n", i, j, v1, v2);
// cin.get();
// }
// }
//}
return true;
}
//void match_template_naive_SQDIFF(const Mat& a, const Mat& b, Mat& c)
//{
// c.create(a.rows - b.rows + 1, a.cols - b.cols + 1, CV_32F);
// for (int i = 0; i < c.rows; ++i)
// {
// for (int j = 0; j < c.cols; ++j)
// {
// float delta;
// float sum = 0.f;
// for (int y = 0; y < b.rows; ++y)
// {
// const unsigned char* arow = a.ptr(i + y);
// const unsigned char* brow = b.ptr(y);
// for (int x = 0; x < b.cols; ++x)
// {
// delta = (float)(arow[j + x] - brow[x]);
// sum += delta * delta;
// }
// }
// c.at<float>(i, j) = sum;
// }
// }
//}
//void match_template_naive_CCORR(const Mat& a, const Mat& b, Mat& c)
//{
// c.create(a.rows - b.rows + 1, a.cols - b.cols + 1, CV_32F);
// for (int i = 0; i < c.rows; ++i)
// {
// for (int j = 0; j < c.cols; ++j)
// {
// float sum = 0.f;
// for (int y = 0; y < b.rows; ++y)
// {
// const float* arow = a.ptr<float>(i + y);
// const float* brow = b.ptr<float>(y);
// for (int x = 0; x < b.cols; ++x)
// sum += arow[j + x] * brow[x];
// }
// c.at<float>(i, j) = sum;
// }
// }
//}
} match_template_test;
struct CV_GpuMatchTemplateFindPatternInBlackTest: CvTest
{
CV_GpuMatchTemplateFindPatternInBlackTest()
: CvTest("GPU-MatchTemplateFindPatternInBlackTest", "matchTemplate") {}
void run(int)
{
try
{
Mat image = imread(std::string(ts->get_data_path()) + "matchtemplate/black.jpg");
if (image.empty())
{
ts->printf(CvTS::CONSOLE, "can't open file '%s'", (std::string(ts->get_data_path())
+ "matchtemplate/black.jpg").c_str());
ts->set_failed_test_info(CvTS::FAIL_INVALID_TEST_DATA);
return;
}
Mat pattern = imread(std::string(ts->get_data_path()) + "matchtemplate/cat.jpg");
if (pattern.empty())
{
ts->printf(CvTS::CONSOLE, "can't open file '%s'", (std::string(ts->get_data_path())
+ "matchtemplate/cat.jpg").c_str());
ts->set_failed_test_info(CvTS::FAIL_INVALID_TEST_DATA);
return;
}
gpu::GpuMat d_image(image);
gpu::GpuMat d_pattern(pattern);
gpu::GpuMat d_result;
double maxValue;
Point maxLoc;
Point maxLocGold(284, 12);
gpu::matchTemplate(d_image, d_pattern, d_result, CV_TM_CCOEFF_NORMED);
gpu::minMaxLoc(d_result, NULL, &maxValue, NULL, &maxLoc );
if (maxLoc != maxLocGold)
{
ts->printf(CvTS::CONSOLE, "bad match (CV_TM_CCOEFF_NORMED): %d %d, must be at: %d %d",
maxLoc.x, maxLoc.y, maxLocGold.x, maxLocGold.y);
ts->set_failed_test_info(CvTS::FAIL_INVALID_OUTPUT);
return;
}
gpu::matchTemplate(d_image, d_pattern, d_result, CV_TM_CCORR_NORMED);
gpu::minMaxLoc(d_result, NULL, &maxValue, NULL, &maxLoc );
if (maxLoc != maxLocGold)
{
ts->printf(CvTS::CONSOLE, "bad match (CV_TM_CCORR_NORMED): %d %d, must be at: %d %d",
maxLoc.x, maxLoc.y, maxLocGold.x, maxLocGold.y);
ts->set_failed_test_info(CvTS::FAIL_INVALID_OUTPUT);
return;
}
}
catch (const Exception& e)
{
ts->printf(CvTS::CONSOLE, e.what());
if (!check_and_treat_gpu_exception(e, ts)) throw;
return;
}
}
} match_templet_find_bordered_pattern_test;