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implement different behaviour for up- and down-scaling

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pull/158/head
marina.kolpakova 13 years ago
parent 56c7ef06e7
commit 672cf1f445
3 changed files with 95 additions and 18 deletions
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  1. 98
      modules/gpu/src/cuda/isf-sc.cu
  2. 13
      modules/gpu/src/softcascade.cpp
  3. 2
      modules/gpu/test/test_softcascade.cpp

98
modules/gpu/src/cuda/isf-sc.cu
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@ -86,8 +86,11 @@ namespace icf {
}
texture<int, cudaTextureType2D, cudaReadModeElementType> thogluv;
__device__ __forceinline__ float rescale(const Level& level, uchar4& scaledRect, const Node& node)
template<bool isUp>
__device__ __forceinline__ float rescale(const Level& level, Node& node)
{
uchar4& scaledRect = node.rect;
float relScale = level.relScale;
float farea = (scaledRect.z - scaledRect.x) * (scaledRect.w - scaledRect.y);
@ -119,7 +122,44 @@ namespace icf {
return rootThreshold;
}
__device__ __forceinline__ int get(const int x, int y, uchar4 area)
template<>
__device__ __forceinline__ float rescale<true>(const Level& level, Node& node)
{
uchar4& scaledRect = node.rect;
float relScale = level.relScale;
float farea = scaledRect.z * scaledRect.w;
dprintf("%d: feature %d box %d %d %d %d\n",threadIdx.x, (node.threshold >> 28), scaledRect.x, scaledRect.y,
scaledRect.z, scaledRect.w);
dprintf("%d: rescale: %f [%f %f] selected %f\n",threadIdx.x, level.relScale, level.scaling[0], level.scaling[1],
level.scaling[(node.threshold >> 28) > 6]);
// rescale
scaledRect.x = __float2int_rn(relScale * scaledRect.x);
scaledRect.y = __float2int_rn(relScale * scaledRect.y);
scaledRect.z = __float2int_rn(relScale * scaledRect.z);
scaledRect.w = __float2int_rn(relScale * scaledRect.w);
float sarea = scaledRect.z * scaledRect.w;
const float expected_new_area = farea * relScale * relScale;
float approx = __fdividef(sarea, expected_new_area);
dprintf("%d: new rect: %d box %d %d %d %d rel areas %f %f\n",threadIdx.x, (node.threshold >> 28),
scaledRect.x, scaledRect.y, scaledRect.z, scaledRect.w, farea * relScale * relScale, sarea);
float rootThreshold = (node.threshold & 0x0FFFFFFFU) * approx;
rootThreshold *= level.scaling[(node.threshold >> 28) > 6];
dprintf("%d: approximation %f %d -> %f %f\n",threadIdx.x, approx, (node.threshold & 0x0FFFFFFFU), rootThreshold,
level.scaling[(node.threshold >> 28) > 6]);
return rootThreshold;
}
template<bool isUp>
__device__ __forceinline__ int get(int x, int y, uchar4 area)
{
dprintf("%d: feature box %d %d %d %d\n",threadIdx.x, area.x, area.y, area.z, area.w);
@ -138,7 +178,30 @@ namespace icf {
return (a - b + c - d);
}
template<>
__device__ __forceinline__ int get<true>(int x, int y, uchar4 area)
{
dprintf("%d: feature box %d %d %d %d\n",threadIdx.x, area.x, area.y, area.z, area.w);
dprintf("%d: extract feature for: [%d %d] [%d %d] [%d %d] [%d %d]\n",threadIdx.x,
x + area.x, y + area.y, x + area.z, y + area.y, x + area.z,y + area.w,
x + area.x, y + area.w);
dprintf("%d: at point %d %d with offset %d\n", x, y, 0);
x += area.x;
y += area.y;
int a = tex2D(thogluv, x, y);
int b = tex2D(thogluv, x + area.z, y);
int c = tex2D(thogluv, x + area.z, y + area.w);
int d = tex2D(thogluv, x, y + area.w);
dprintf("%d retruved integral values: %d %d %d %d\n",threadIdx.x, a, b, c, d);
return (a - b + c - d);
}
#if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300
template<bool isUp>
__global__ void test_kernel_warp(const Level* levels, const Octave* octaves, const float* stages,
const Node* nodes, const float* leaves, Detection* objects, const uint ndetections, uint* ctr,
const int downscales)
@ -163,15 +226,15 @@ namespace icf {
dprintf("\n\n%d: stage: %d %d\n",threadIdx.x, st, nId);
Node node = nodes[nId];
float threshold = rescale(level, node.rect, node);
int sum = get(x, y + (node.threshold >> 28) * 121, node.rect);
float threshold = rescale<isUp>(level, node);
int sum = get<isUp>(x, y + (node.threshold >> 28) * 121, node.rect);
int next = 1 + (int)(sum >= threshold);
dprintf("%d: go: %d (%d >= %f)\n\n" ,threadIdx.x, next, sum, threshold);
node = nodes[nId + next];
threshold = rescale(level, node.rect, node);
sum = get(x, y + (node.threshold >> 28) * 121, node.rect);
threshold = rescale<isUp>(level, node);
sum = get<isUp>(x, y + (node.threshold >> 28) * 121, node.rect);
const int lShift = (next - 1) * 2 + (int)(sum >= threshold);
float impact = leaves[(st + threadIdx.x) * 4 + lShift];
@ -192,7 +255,7 @@ namespace icf {
dprintf("%d: impact scaned %f\n" ,threadIdx.x, impact);
confidence += impact;
if(__any((confidence <= stages[(st + threadIdx.x)]))) break;
if(__any((confidence <= stages[(st + threadIdx.x)]))) st += stEnd;
}
if(st == stEnd && !threadIdx.x)
@ -204,6 +267,7 @@ namespace icf {
}
}
#else
template<bool isUp>
__global__ void test_kernel_warp(const Level* levels, const Octave* octaves, const float* stages,
const Node* nodes, const float* leaves, Detection* objects, const uint ndetections, uint* ctr,
const int downscales)
@ -231,8 +295,8 @@ namespace icf {
dprintf("Node: [%d %d %d %d] %d %d\n", node.rect.x, node.rect.y, node.rect.z, node.rect.w,
node.threshold >> 28, node.threshold & 0x0FFFFFFFU);
float threshold = rescale(level, node.rect, node);
int sum = get(x, y + (node.threshold >> 28) * 121, node.rect);
float threshold = rescale<isUp>(level, node);
int sum = get<isUp>(x, y + (node.threshold >> 28) * 121, node.rect);
dprintf("Node: [%d %d %d %d] %f\n", node.rect.x, node.rect.y, node.rect.z,
node.rect.w, threshold);
@ -241,8 +305,8 @@ namespace icf {
dprintf("go: %d (%d >= %f)\n\n" ,next, sum, threshold);
node = nodes[nId + next];
threshold = rescale(level, node.rect, node);
sum = get(x, y + (node.threshold >> 28) * 121, node.rect);
threshold = rescale<isUp>(level, node);
sum = get<isUp>(x, y + (node.threshold >> 28) * 121, node.rect);
const int lShift = (next - 1) * 2 + (int)(sum >= threshold);
float impact = leaves[st * 4 + lShift];
@ -286,18 +350,18 @@ namespace icf {
cudaChannelFormatDesc desc = cudaCreateChannelDesc<int>();
cudaSafeCall( cudaBindTexture2D(0, thogluv, hogluv.data, desc, hogluv.cols, hogluv.rows, hogluv.step));
test_kernel_warp<<<grid, block>>>(l, oct, st, nd, lf, det, max_det, ctr, 0);
test_kernel_warp<false><<<grid, block>>>(l, oct, st, nd, lf, det, max_det, ctr, 0);
cudaSafeCall( cudaGetLastError());
grid = dim3(fw, fh / 8, 47 - downscales);
test_kernel_warp<<<grid, block>>>(l, oct, st, nd, lf, det, max_det, ctr, downscales);
test_kernel_warp<true><<<grid, block>>>(l, oct, st, nd, lf, det, max_det, ctr, downscales);
cudaSafeCall( cudaGetLastError());
cudaSafeCall( cudaDeviceSynchronize());
}
void detectAtScale(const int scale, const PtrStepSzb& levels, const PtrStepSzb& octaves, const PtrStepSzf& stages,
const PtrStepSzb& nodes, const PtrStepSzf& leaves, const PtrStepSzi& hogluv, PtrStepSz<uchar4> objects,
PtrStepSzi counter)
PtrStepSzi counter, const int downscales)
{
int fw = 160;
int fh = 120;
@ -317,7 +381,11 @@ namespace icf {
cudaChannelFormatDesc desc = cudaCreateChannelDesc<int>();
cudaSafeCall( cudaBindTexture2D(0, thogluv, hogluv.data, desc, hogluv.cols, hogluv.rows, hogluv.step));
test_kernel_warp<<<grid, block>>>(l, oct, st, nd, lf, det, max_det, ctr, scale);
if (scale >= downscales)
test_kernel_warp<true><<<grid, block>>>(l, oct, st, nd, lf, det, max_det, ctr, scale);
else
test_kernel_warp<false><<<grid, block>>>(l, oct, st, nd, lf, det, max_det, ctr, scale);
cudaSafeCall( cudaGetLastError());
cudaSafeCall( cudaDeviceSynchronize());
}

13
modules/gpu/src/softcascade.cpp
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@ -68,7 +68,7 @@ namespace icf {
PtrStepSzi counter, const int downscales);
void detectAtScale(const int scale, const PtrStepSzb& levels, const PtrStepSzb& octaves, const PtrStepSzf& stages,
const PtrStepSzb& nodes, const PtrStepSzf& leaves, const PtrStepSzi& hogluv, PtrStepSz<uchar4> objects,
PtrStepSzi counter);
PtrStepSzi counter, const int downscales);
}
}}}
@ -147,7 +147,7 @@ struct cv::gpu::SoftCascade::Filds
{
cudaMemset(detCounter.data, 0, detCounter.step * detCounter.rows * sizeof(int));
device::icf::detectAtScale(scale, levels, octaves, stages, nodes, leaves, hogluv, objects,
detCounter);
detCounter, downscales);
}
private:
@ -240,6 +240,9 @@ bool cv::gpu::SoftCascade::Filds::fill(const FileNode &root, const float mins, c
{
FileNode fns = *it;
float scale = (float)fns[SC_OCT_SCALE];
bool isUPOctave = scale >= 1;
scales.push_back(scale);
ushort nstages = saturate_cast<ushort>((int)fns[SC_OCT_STAGES]);
ushort2 size;
@ -286,6 +289,12 @@ bool cv::gpu::SoftCascade::Filds::fill(const FileNode &root, const float mins, c
rect.z = saturate_cast<uchar>((int)*(r_it++));
rect.w = saturate_cast<uchar>((int)*(r_it++));
if (isUPOctave)
{
rect.z -= rect.x;
rect.w -= rect.y;
}
uint channel = saturate_cast<uint>((int)(*ftrs)[SC_F_CHANNEL]);
vnodes.push_back(Node(rect, channel, th));
++ftrs;

2
modules/gpu/test/test_softcascade.cpp
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@ -63,7 +63,7 @@ TEST(SoftCascade, detect)
cv::Mat coloredCpu = cv::imread(cvtest::TS::ptr()->get_data_path()
+ "../cv/cascadeandhog/bahnhof/image_00000000_0.png");
ASSERT_FALSE(coloredCpu.empty());
GpuMat colored(coloredCpu), objectBoxes(1, 1000, CV_8UC1), rois;
GpuMat colored(coloredCpu), objectBoxes(1, 100000, CV_8UC1), rois;
// ASSERT_NO_THROW(
// {

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