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fixed bug in gpu::matchTemplate (added normalization routine to make the GPU version consistent with the CPU one), added test cases from the ticket #1341

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This commit is contained in:
Alexey Spizhevoy
2011-08-29 08:06:45 +00:00
parent 8bf5c0e7ae
commit 50b72197ab
2 changed files with 117 additions and 48 deletions
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84
modules/gpu/src/cuda/match_template.cu
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@@ -313,6 +313,29 @@ void matchTemplatePrepared_SQDIFF_8U(
}
// normAcc* are accurate normalization routines which make GPU matchTemplate
// consistent with CPU one
__device__ float normAcc(float num, float denum)
{
if (fabs(num) < denum)
return num / denum;
if (fabs(num) < denum * 1.125f)
return num > 0 ? 1 : -1;
return 0;
}
__device__ float normAcc_SQDIFF(float num, float denum)
{
if (fabs(num) < denum)
return num / denum;
if (fabs(num) < denum * 1.125f)
return num > 0 ? 1 : -1;
return 1;
}
template <int cn>
__global__ void matchTemplatePreparedKernel_SQDIFF_NORMED_8U(
int w, int h, const PtrStep_<unsigned long long> image_sqsum,
@@ -327,8 +350,8 @@ __global__ void matchTemplatePreparedKernel_SQDIFF_NORMED_8U(
(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]));
float ccorr = result.ptr(y)[x];
result.ptr(y)[x] = min(1.f, (image_sqsum_ - 2.f * ccorr + templ_sqsum) *
rsqrtf(image_sqsum_ * templ_sqsum));
result.ptr(y)[x] = normAcc_SQDIFF(image_sqsum_ - 2.f * ccorr + templ_sqsum,
sqrtf(image_sqsum_ * templ_sqsum));
}
}
@@ -440,7 +463,7 @@ void matchTemplatePrepared_CCOFF_8UC2(
__global__ void matchTemplatePreparedKernel_CCOFF_8UC3(
int w, int h,
float templ_sum_scale_r,
float templ_sum_scale_r,
float templ_sum_scale_g,
float templ_sum_scale_b,
const PtrStep_<unsigned int> image_sum_r,
@@ -463,7 +486,7 @@ __global__ void matchTemplatePreparedKernel_CCOFF_8UC3(
(image_sum_b.ptr(y + h)[x + w] - image_sum_b.ptr(y)[x + w]) -
(image_sum_b.ptr(y + h)[x] - image_sum_b.ptr(y)[x]));
float ccorr = result.ptr(y)[x];
result.ptr(y)[x] = ccorr - image_sum_r_ * templ_sum_scale_r
result.ptr(y)[x] = ccorr - image_sum_r_ * templ_sum_scale_r
- image_sum_g_ * templ_sum_scale_g
- image_sum_b_ * templ_sum_scale_b;
}
@@ -484,8 +507,8 @@ void matchTemplatePrepared_CCOFF_8UC3(
dim3 grid(divUp(result.cols, threads.x), divUp(result.rows, threads.y));
matchTemplatePreparedKernel_CCOFF_8UC3<<<grid, threads>>>(
w, h,
(float)templ_sum_r / (w * h),
(float)templ_sum_g / (w * h),
(float)templ_sum_r / (w * h),
(float)templ_sum_g / (w * h),
(float)templ_sum_b / (w * h),
image_sum_r, image_sum_g, image_sum_b, result);
cudaSafeCall( cudaGetLastError() );
@@ -579,8 +602,8 @@ __global__ void matchTemplatePreparedKernel_CCOFF_NORMED_8U(
float image_sqsum_ = (float)(
(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] = (ccorr - image_sum_ * templ_sum_scale) *
rsqrtf(templ_sqsum_scale * max(1e-3f, image_sqsum_ - weight * image_sum_ * image_sum_));
result.ptr(y)[x] = normAcc(ccorr - image_sum_ * templ_sum_scale,
sqrtf(templ_sqsum_scale * (image_sqsum_ - weight * image_sum_ * image_sum_)));
}
}
@@ -631,11 +654,12 @@ __global__ void matchTemplatePreparedKernel_CCOFF_NORMED_8UC2(
float image_sqsum_g_ = (float)(
(image_sqsum_g.ptr(y + h)[x + w] - image_sqsum_g.ptr(y)[x + w]) -
(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 * max(1e-3f, image_sqsum_r_ - weight * image_sum_r_ * image_sum_r_
+ image_sqsum_g_ - weight * image_sum_g_ * image_sum_g_));
result.ptr(y)[x] = (ccorr - image_sum_r_ * templ_sum_scale_r
- image_sum_g_ * templ_sum_scale_g) * rdenom;
float num = result.ptr(y)[x] - image_sum_r_ * templ_sum_scale_r
- image_sum_g_ * templ_sum_scale_g;
float denum = sqrtf(templ_sqsum_scale * (image_sqsum_r_ - weight * image_sum_r_ * image_sum_r_
+ image_sqsum_g_ - weight * image_sum_g_ * image_sum_g_));
result.ptr(y)[x] = normAcc(num, denum);
}
}
@@ -701,13 +725,14 @@ __global__ void matchTemplatePreparedKernel_CCOFF_NORMED_8UC3(
float image_sqsum_b_ = (float)(
(image_sqsum_b.ptr(y + h)[x + w] - image_sqsum_b.ptr(y)[x + w]) -
(image_sqsum_b.ptr(y + h)[x] - image_sqsum_b.ptr(y)[x]));
float ccorr = result.ptr(y)[x];
float rdenom = rsqrtf(templ_sqsum_scale * max(1e-3f, 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_));
result.ptr(y)[x] = (ccorr - image_sum_r_ * templ_sum_scale_r
- image_sum_g_ * templ_sum_scale_g
- image_sum_b_ * templ_sum_scale_b) * rdenom;
float num = result.ptr(y)[x] - image_sum_r_ * templ_sum_scale_r
- image_sum_g_ * templ_sum_scale_g
- image_sum_b_ * templ_sum_scale_b;
float denum = sqrtf(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_));
result.ptr(y)[x] = normAcc(num, denum);
}
}
@@ -785,15 +810,14 @@ __global__ void matchTemplatePreparedKernel_CCOFF_NORMED_8UC4(
float image_sqsum_a_ = (float)(
(image_sqsum_a.ptr(y + h)[x + w] - image_sqsum_a.ptr(y)[x + w]) -
(image_sqsum_a.ptr(y + h)[x] - image_sqsum_a.ptr(y)[x]));
float ccorr = result.ptr(y)[x];
float rdenom = rsqrtf(templ_sqsum_scale * max(1e-3f, 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_));
result.ptr(y)[x] = (ccorr - image_sum_r_ * templ_sum_scale_r
- image_sum_g_ * templ_sum_scale_g
- image_sum_b_ * templ_sum_scale_b
- image_sum_a_ * templ_sum_scale_a) * rdenom;
float num = result.ptr(y)[x] - image_sum_r_ * templ_sum_scale_r - image_sum_g_ * templ_sum_scale_g
- image_sum_b_ * templ_sum_scale_b - image_sum_a_ * templ_sum_scale_a;
float denum = sqrtf(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_));
result.ptr(y)[x] = normAcc(num, denum);
}
}
@@ -850,7 +874,7 @@ __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] = result.ptr(y)[x] * rsqrtf(max(1.f, image_sqsum_) * templ_sqsum);
result.ptr(y)[x] = normAcc(result.ptr(y)[x], sqrtf(image_sqsum_ * templ_sqsum));
}
}