Skip to content

O
opencv
Commit 19173665 authored by marina.kolpakova's avatar marina.kolpakova
Browse files
Options

empty cascade

parent 4881205b
Hide whitespace changes
Inline Side-by-side
Showing with 705 additions and 708 deletions
modules/gpu/src/cuda/isf-sc.cu
View file @ 19173665
......@@ -40,221 +40,221 @@
//
//M*/
#include <icf.hpp>
#include <opencv2/gpu/device/saturate_cast.hpp>
#include <stdio.h>
#include <float.h>
//#define LOG_CUDA_CASCADE
#if defined LOG_CUDA_CASCADE
# define dprintf(format, ...) \
do { printf(format, __VA_ARGS__); } while (0)
#else
# define dprintf(format, ...)
#endif
namespace cv { namespace gpu { namespace device {
namespace icf {
enum {
HOG_BINS = 6,
HOG_LUV_BINS = 10,
WIDTH = 640,
HEIGHT = 480,
GREY_OFFSET = HEIGHT * HOG_LUV_BINS
};
__global__ void magToHist(const uchar* __restrict__ mag,
const float* __restrict__ angle, const int angPitch,
uchar* __restrict__ hog, const int hogPitch)
{
const int y = blockIdx.y * blockDim.y + threadIdx.y;
const int x = blockIdx.x * blockDim.x + threadIdx.x;
const int bin = (int)(angle[y * angPitch + x]);
const uchar val = mag[y * angPitch + x];
hog[((HEIGHT * bin) + y) * hogPitch + x] = val;
}
// #include <icf.hpp>
// #include <opencv2/gpu/device/saturate_cast.hpp>
// #include <stdio.h>
// #include <float.h>
// //#define LOG_CUDA_CASCADE
// #if defined LOG_CUDA_CASCADE
// # define dprintf(format, ...) \
// do { printf(format, __VA_ARGS__); } while (0)
// #else
// # define dprintf(format, ...)
// #endif
// namespace cv { namespace gpu { namespace device {
// namespace icf {
// enum {
// HOG_BINS = 6,
// HOG_LUV_BINS = 10,
// WIDTH = 640,
// HEIGHT = 480,
// GREY_OFFSET = HEIGHT * HOG_LUV_BINS
// };
// __global__ void magToHist(const uchar* __restrict__ mag,
// const float* __restrict__ angle, const int angPitch,
// uchar* __restrict__ hog, const int hogPitch)
// {
// const int y = blockIdx.y * blockDim.y + threadIdx.y;
// const int x = blockIdx.x * blockDim.x + threadIdx.x;
// const int bin = (int)(angle[y * angPitch + x]);
// const uchar val = mag[y * angPitch + x];
// hog[((HEIGHT * bin) + y) * hogPitch + x] = val;
// }
void fillBins(cv::gpu::PtrStepSzb hogluv, const cv::gpu::PtrStepSzf& nangle)
{
const uchar* mag = (const uchar*)hogluv.ptr(HEIGHT * HOG_BINS);
uchar* hog = (uchar*)hogluv.ptr();
const float* angle = (const float*)nangle.ptr();
// void fillBins(cv::gpu::PtrStepSzb hogluv, const cv::gpu::PtrStepSzf& nangle)
// {
// const uchar* mag = (const uchar*)hogluv.ptr(HEIGHT * HOG_BINS);
// uchar* hog = (uchar*)hogluv.ptr();
// const float* angle = (const float*)nangle.ptr();
dim3 block(32, 8);
dim3 grid(WIDTH / 32, HEIGHT / 8);
// dim3 block(32, 8);
// dim3 grid(WIDTH / 32, HEIGHT / 8);
magToHist<<<grid, block>>>(mag, angle, nangle.step / sizeof(float), hog, hogluv.step);
cudaSafeCall( cudaGetLastError() );
cudaSafeCall( cudaDeviceSynchronize() );
}
}
__global__ void detect(const cv::gpu::icf::Cascade cascade, const int* __restrict__ hogluv, const int pitch,
PtrStepSz<uchar4> objects)
{
cascade.detectAt(hogluv, pitch, objects);
}
// magToHist<<<grid, block>>>(mag, angle, nangle.step / sizeof(float), hog, hogluv.step);
// cudaSafeCall( cudaGetLastError() );
// cudaSafeCall( cudaDeviceSynchronize() );
// }
// }
// __global__ void detect(const cv::gpu::icf::Cascade cascade, const int* __restrict__ hogluv, const int pitch,
// PtrStepSz<uchar4> objects)
// {
// cascade.detectAt(hogluv, pitch, objects);
// }
}
// }
float __device icf::Cascade::rescale(const icf::Level& level, uchar4& scaledRect,
const int channel, const float threshold) const
{
dprintf("feature %d box %d %d %d %d\n", channel, scaledRect.x, scaledRect.y, scaledRect.z, scaledRect.w);
dprintf("rescale: %f [%f %f]\n",level.relScale, level.scaling[0], level.scaling[1]);
// float __device icf::Cascade::rescale(const icf::Level& level, uchar4& scaledRect,
// const int channel, const float threshold) const
// {
// dprintf("feature %d box %d %d %d %d\n", channel, scaledRect.x, scaledRect.y, scaledRect.z, scaledRect.w);
// dprintf("rescale: %f [%f %f]\n",level.relScale, level.scaling[0], level.scaling[1]);
float relScale = level.relScale;
float farea = (scaledRect.z - scaledRect.x) * (scaledRect.w - scaledRect.y);
// float relScale = level.relScale;
// float farea = (scaledRect.z - scaledRect.x) * (scaledRect.w - scaledRect.y);
// 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);
// // 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.x) * (scaledRect.w - scaledRect.y);
// float sarea = (scaledRect.z - scaledRect.x) * (scaledRect.w - scaledRect.y);
float approx = 1.f;
if (fabs(farea - 0.f) > FLT_EPSILON && fabs(farea - 0.f) > FLT_EPSILON)
{
const float expected_new_area = farea * relScale * relScale;
approx = expected_new_area / sarea;
}
// float approx = 1.f;
// if (fabs(farea - 0.f) > FLT_EPSILON && fabs(farea - 0.f) > FLT_EPSILON)
// {
// const float expected_new_area = farea * relScale * relScale;
// approx = expected_new_area / sarea;
// }
dprintf("new rect: %d box %d %d %d %d rel areas %f %f\n", channel,
scaledRect.x, scaledRect.y, scaledRect.z, scaledRect.w, farea * relScale * relScale, sarea);
// dprintf("new rect: %d box %d %d %d %d rel areas %f %f\n", channel,
// scaledRect.x, scaledRect.y, scaledRect.z, scaledRect.w, farea * relScale * relScale, sarea);
// compensation areas rounding
float rootThreshold = threshold / approx;
// printf(" approx %f\n", rootThreshold);
rootThreshold *= level.scaling[(int)(channel > 6)];
// // compensation areas rounding
// float rootThreshold = threshold / approx;
// // printf(" approx %f\n", rootThreshold);
// rootThreshold *= level.scaling[(int)(channel > 6)];
dprintf("approximation %f %f -> %f %f\n", approx, threshold, rootThreshold, level.scaling[(int)(channel > 6)]);
// dprintf("approximation %f %f -> %f %f\n", approx, threshold, rootThreshold, level.scaling[(int)(channel > 6)]);
return rootThreshold;
}
// return rootThreshold;
// }
typedef unsigned char uchar;
float __device get(const int* __restrict__ hogluv, const int pitch,
const int x, const int y, int channel, uchar4 area)
{
dprintf("feature box %d %d %d %d ", area.x, area.y, area.z, area.w);
dprintf("get for channel %d\n", channel);
dprintf("extract feature for: [%d %d] [%d %d] [%d %d] [%d %d]\n",
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("at point %d %d with offset %d\n", x, y, 0);
// typedef unsigned char uchar;
// float __device get(const int* __restrict__ hogluv, const int pitch,
// const int x, const int y, int channel, uchar4 area)
// {
// dprintf("feature box %d %d %d %d ", area.x, area.y, area.z, area.w);
// dprintf("get for channel %d\n", channel);
// dprintf("extract feature for: [%d %d] [%d %d] [%d %d] [%d %d]\n",
// 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("at point %d %d with offset %d\n", x, y, 0);
const int* curr = hogluv + ((channel * 121) + y) * pitch;
// const int* curr = hogluv + ((channel * 121) + y) * pitch;
int a = curr[area.y * pitch + x + area.x];
int b = curr[area.y * pitch + x + area.z];
int c = curr[area.w * pitch + x + area.z];
int d = curr[area.w * pitch + x + area.x];
// int a = curr[area.y * pitch + x + area.x];
// int b = curr[area.y * pitch + x + area.z];
// int c = curr[area.w * pitch + x + area.z];
// int d = curr[area.w * pitch + x + area.x];
dprintf(" retruved integral values: %d %d %d %d\n", a, b, c, d);
// dprintf(" retruved integral values: %d %d %d %d\n", a, b, c, d);
return (a - b + c - d);
}
// return (a - b + c - d);
// }
void __device icf::Cascade::detectAt(const int* __restrict__ hogluv, const int pitch,
PtrStepSz<uchar4>& objects) const
{
const icf::Level* lls = (const icf::Level*)levels.ptr();
// void __device icf::Cascade::detectAt(const int* __restrict__ hogluv, const int pitch,
// PtrStepSz<uchar4>& objects) const
// {
// const icf::Level* lls = (const icf::Level*)levels.ptr();
const int y = blockIdx.y * blockDim.y + threadIdx.y;
const int x = blockIdx.x * blockDim.x + threadIdx.x;
// if (x > 0 || y > 0) return;
// const int y = blockIdx.y * blockDim.y + threadIdx.y;
// const int x = blockIdx.x * blockDim.x + threadIdx.x;
// // if (x > 0 || y > 0) return;
Level level = lls[blockIdx.z];
if (x >= level.workRect.x || y >= level.workRect.y) return;
// Level level = lls[blockIdx.z];
// if (x >= level.workRect.x || y >= level.workRect.y) return;
dprintf("level: %d (%f %f) [%f %f] (%d %d) (%d %d)\n", level.octave, level.relScale, level.shrScale,
level.scaling[0], level.scaling[1], level.workRect.x, level.workRect.y, level.objSize.x, level.objSize.y);
// dprintf("level: %d (%f %f) [%f %f] (%d %d) (%d %d)\n", level.octave, level.relScale, level.shrScale,
// level.scaling[0], level.scaling[1], level.workRect.x, level.workRect.y, level.objSize.x, level.objSize.y);
const Octave octave = ((const Octave*)octaves.ptr())[level.octave];
// printf("Octave: %d %d %d (%d %d) %f\n", octave.index, octave.stages,
// octave.shrinkage, octave.size.x, octave.size.y, octave.scale);
// const Octave octave = ((const Octave*)octaves.ptr())[level.octave];
// // printf("Octave: %d %d %d (%d %d) %f\n", octave.index, octave.stages,
// // octave.shrinkage, octave.size.x, octave.size.y, octave.scale);
const int stBegin = octave.index * octave.stages, stEnd = stBegin + octave.stages;
// const int stBegin = octave.index * octave.stages, stEnd = stBegin + octave.stages;
float detectionScore = 0.f;
// float detectionScore = 0.f;
int st = stBegin;
for(; st < stEnd; ++st)
{
const float stage = stages(0, st);
dprintf("Stage: %f\n", stage);
{
const int nId = st * 3;
// int st = stBegin;
// for(; st < stEnd; ++st)
// {
// const float stage = stages(0, st);
// dprintf("Stage: %f\n", stage);
// {
// const int nId = st * 3;
// work with root node
const Node node = ((const Node*)nodes.ptr())[nId];
// // work with root node
// const Node node = ((const Node*)nodes.ptr())[nId];
dprintf("Node: %d %f\n", node.feature, node.threshold);
// dprintf("Node: %d %f\n", node.feature, node.threshold);
const Feature feature = ((const Feature*)features.ptr())[node.feature];
// const Feature feature = ((const Feature*)features.ptr())[node.feature];
uchar4 scaledRect = feature.rect;
float threshold = rescale(level, scaledRect, feature.channel, node.threshold);
// uchar4 scaledRect = feature.rect;
// float threshold = rescale(level, scaledRect, feature.channel, node.threshold);
float sum = get(hogluv,pitch, x, y, feature.channel, scaledRect);
// float sum = get(hogluv,pitch, x, y, feature.channel, scaledRect);
dprintf("root feature %d %f\n",feature.channel, sum);
// dprintf("root feature %d %f\n",feature.channel, sum);
int next = 1 + (int)(sum >= threshold);
// int next = 1 + (int)(sum >= threshold);
dprintf("go: %d (%f >= %f)\n\n" ,next, sum, threshold);
// dprintf("go: %d (%f >= %f)\n\n" ,next, sum, threshold);
// leaves
const Node leaf = ((const Node*)nodes.ptr())[nId + next];
const Feature fLeaf = ((const Feature*)features.ptr())[leaf.feature];
// // leaves
// const Node leaf = ((const Node*)nodes.ptr())[nId + next];
// const Feature fLeaf = ((const Feature*)features.ptr())[leaf.feature];
scaledRect = fLeaf.rect;
threshold = rescale(level, scaledRect, fLeaf.channel, leaf.threshold);
sum = get(hogluv, pitch, x, y, fLeaf.channel, scaledRect);
// scaledRect = fLeaf.rect;
// threshold = rescale(level, scaledRect, fLeaf.channel, leaf.threshold);
// sum = get(hogluv, pitch, x, y, fLeaf.channel, scaledRect);
const int lShift = (next - 1) * 2 + (int)(sum >= threshold);
float impact = leaves(0, (st * 4) + lShift);
// const int lShift = (next - 1) * 2 + (int)(sum >= threshold);
// float impact = leaves(0, (st * 4) + lShift);
detectionScore += impact;
// detectionScore += impact;
dprintf("decided: %d (%f >= %f) %d %f\n\n" ,next, sum, threshold, lShift, impact);
dprintf("extracted stage:\n");
dprintf("ct %f\n", stage);
dprintf("computed score %f\n\n", detectionScore);
dprintf("\n\n");
}
// dprintf("decided: %d (%f >= %f) %d %f\n\n" ,next, sum, threshold, lShift, impact);
// dprintf("extracted stage:\n");
// dprintf("ct %f\n", stage);
// dprintf("computed score %f\n\n", detectionScore);
// dprintf("\n\n");
// }
if (detectionScore <= stage || st - stBegin == 100) break;
}
// if (detectionScore <= stage || st - stBegin == 100) break;
// }
dprintf("x %d y %d: %d\n", x, y, st - stBegin);
// dprintf("x %d y %d: %d\n", x, y, st - stBegin);
if (st == stEnd)
{
uchar4 a;
a.x = level.workRect.x;
a.y = level.workRect.y;
objects(0, threadIdx.x) = a;
}
}
// if (st == stEnd)
// {
// uchar4 a;
// a.x = level.workRect.x;
// a.y = level.workRect.y;
// objects(0, threadIdx.x) = a;
// }
// }
void icf::Cascade::detect(const cv::gpu::PtrStepSzi& hogluv, PtrStepSz<uchar4> objects, cudaStream_t stream) const
{
dim3 block(32, 8, 1);
dim3 grid(ChannelStorage::FRAME_WIDTH / 32, ChannelStorage::FRAME_HEIGHT / 8, 47);
device::detect<<<grid, block, 0, stream>>>(*this, hogluv, hogluv.step / sizeof(int), objects);
cudaSafeCall( cudaGetLastError() );
if (!stream)
cudaSafeCall( cudaDeviceSynchronize() );
}
// void icf::Cascade::detect(const cv::gpu::PtrStepSzi& hogluv, PtrStepSz<uchar4> objects, cudaStream_t stream) const
// {
// dim3 block(32, 8, 1);
// dim3 grid(ChannelStorage::FRAME_WIDTH / 32, ChannelStorage::FRAME_HEIGHT / 8, 47);
// device::detect<<<grid, block, 0, stream>>>(*this, hogluv, hogluv.step / sizeof(int), objects);
// cudaSafeCall( cudaGetLastError() );
// if (!stream)
// cudaSafeCall( cudaDeviceSynchronize() );
// }
}}
\ No newline at end of file
// }}
\ No newline at end of file
modules/gpu/src/icf.hpp
View file @ 19173665
......@@ -40,127 +40,127 @@
//
//M*/
#include <opencv2/gpu/device/common.hpp>
#ifndef __OPENCV_ICF_HPP__
#define __OPENCV_ICF_HPP__
#if defined __CUDACC__
# define __device __device__ __forceinline__
#else
# define __device
#endif
namespace cv { namespace gpu { namespace icf {
using cv::gpu::PtrStepSzb;
using cv::gpu::PtrStepSzf;
typedef unsigned char uchar;
struct __align__(16) Octave
{
ushort index;
ushort stages;
ushort shrinkage;
ushort2 size;
float scale;
Octave(const ushort i, const ushort s, const ushort sh, const ushort2 sz, const float sc)
: index(i), stages(s), shrinkage(sh), size(sz), scale(sc) {}
};
struct __align__(8) Level //is actually 24 bytes
{
int octave;
// float origScale; //not actually used
float relScale;
float shrScale; // used for marking detection
float scaling[2]; // calculated according to Dollal paper
// for 640x480 we can not get overflow
uchar2 workRect;
uchar2 objSize;
Level(int idx, const Octave& oct, const float scale, const int w, const int h)
: octave(idx), relScale(scale / oct.scale), shrScale (relScale / (float)oct.shrinkage)
{
workRect.x = round(w / (float)oct.shrinkage);
workRect.y = round(h / (float)oct.shrinkage);
objSize.x = round(oct.size.x * relScale);
objSize.y = round(oct.size.y * relScale);
}
};
struct Cascade
{
Cascade() {}
Cascade(const cv::gpu::PtrStepSzb& octs, const cv::gpu::PtrStepSzf& sts, const cv::gpu::PtrStepSzb& nds,
const cv::gpu::PtrStepSzf& lvs, const cv::gpu::PtrStepSzb& fts, const cv::gpu::PtrStepSzb& lls)
: octaves(octs), stages(sts), nodes(nds), leaves(lvs), features(fts), levels(lls) {}
void detect(const cv::gpu::PtrStepSzi& hogluv, cv::gpu::PtrStepSz<uchar4> objects, cudaStream_t stream) const;
void __device detectAt(const int* __restrict__ hogluv, const int pitch, PtrStepSz<uchar4>& objects) const;
float __device rescale(const icf::Level& level, uchar4& scaledRect,
const int channel, const float threshold) const;
PtrStepSzb octaves;
PtrStepSzf stages;
PtrStepSzb nodes;
PtrStepSzf leaves;
PtrStepSzb features;
PtrStepSzb levels;
};
struct ChannelStorage
{
ChannelStorage(){}
ChannelStorage(const cv::gpu::PtrStepSzb& buff, const cv::gpu::PtrStepSzb& shr,
const cv::gpu::PtrStepSzb& itg, const int s)
: dmem (buff), shrunk(shr), hogluv(itg), shrinkage(s) {}
void frame(const cv::gpu::PtrStepSz<uchar3>& rgb, cudaStream_t stream){}
PtrStepSzb dmem;
PtrStepSzb shrunk;
PtrStepSzb hogluv;
enum
{
FRAME_WIDTH = 640,
FRAME_HEIGHT = 480,
TOTAL_SCALES = 55,
CLASSIFIERS = 5,
ORIG_OBJECT_WIDTH = 64,
ORIG_OBJECT_HEIGHT = 128,
HOG_BINS = 6,
HOG_LUV_BINS = 10
};
int shrinkage;
static const float magnitudeScaling = 1.f ;// / sqrt(2);
};
struct __align__(8) Node
{
int feature;
float threshold;
Node(const int f, const float t) : feature(f), threshold(t) {}
};
struct __align__(8) Feature
{
int channel;
uchar4 rect;
Feature(const int c, const uchar4 r) : channel(c), rect(r) {}
};
}}}
#endif
\ No newline at end of file
// #include <opencv2/gpu/device/common.hpp>
// #ifndef __OPENCV_ICF_HPP__
// #define __OPENCV_ICF_HPP__
// #if defined __CUDACC__
// # define __device __device__ __forceinline__
// #else
// # define __device
// #endif
// namespace cv { namespace gpu { namespace icf {
// using cv::gpu::PtrStepSzb;
// using cv::gpu::PtrStepSzf;
// typedef unsigned char uchar;
// struct __align__(16) Octave
// {
// ushort index;
// ushort stages;
// ushort shrinkage;
// ushort2 size;
// float scale;
// Octave(const ushort i, const ushort s, const ushort sh, const ushort2 sz, const float sc)
// : index(i), stages(s), shrinkage(sh), size(sz), scale(sc) {}
// };
// struct __align__(8) Level //is actually 24 bytes
// {
// int octave;
// // float origScale; //not actually used
// float relScale;
// float shrScale; // used for marking detection
// float scaling[2]; // calculated according to Dollal paper
// // for 640x480 we can not get overflow
// uchar2 workRect;
// uchar2 objSize;
// Level(int idx, const Octave& oct, const float scale, const int w, const int h)
// : octave(idx), relScale(scale / oct.scale), shrScale (relScale / (float)oct.shrinkage)
// {
// workRect.x = round(w / (float)oct.shrinkage);
// workRect.y = round(h / (float)oct.shrinkage);
// objSize.x = round(oct.size.x * relScale);
// objSize.y = round(oct.size.y * relScale);
// }
// };
// struct Cascade
// {
// Cascade() {}
// Cascade(const cv::gpu::PtrStepSzb& octs, const cv::gpu::PtrStepSzf& sts, const cv::gpu::PtrStepSzb& nds,
// const cv::gpu::PtrStepSzf& lvs, const cv::gpu::PtrStepSzb& fts, const cv::gpu::PtrStepSzb& lls)
// : octaves(octs), stages(sts), nodes(nds), leaves(lvs), features(fts), levels(lls) {}
// void detect(const cv::gpu::PtrStepSzi& hogluv, cv::gpu::PtrStepSz<uchar4> objects, cudaStream_t stream) const;
// void __device detectAt(const int* __restrict__ hogluv, const int pitch, PtrStepSz<uchar4>& objects) const;
// float __device rescale(const icf::Level& level, uchar4& scaledRect,
// const int channel, const float threshold) const;
// PtrStepSzb octaves;
// PtrStepSzf stages;
// PtrStepSzb nodes;
// PtrStepSzf leaves;
// PtrStepSzb features;
// PtrStepSzb levels;
// };
// struct ChannelStorage
// {
// ChannelStorage(){}
// ChannelStorage(const cv::gpu::PtrStepSzb& buff, const cv::gpu::PtrStepSzb& shr,
// const cv::gpu::PtrStepSzb& itg, const int s)
// : dmem (buff), shrunk(shr), hogluv(itg), shrinkage(s) {}
// void frame(const cv::gpu::PtrStepSz<uchar3>& rgb, cudaStream_t stream){}
// PtrStepSzb dmem;
// PtrStepSzb shrunk;
// PtrStepSzb hogluv;
// enum
// {
// FRAME_WIDTH = 640,
// FRAME_HEIGHT = 480,
// TOTAL_SCALES = 55,
// CLASSIFIERS = 5,
// ORIG_OBJECT_WIDTH = 64,
// ORIG_OBJECT_HEIGHT = 128,
// HOG_BINS = 6,
// HOG_LUV_BINS = 10
// };
// int shrinkage;
// static const float magnitudeScaling = 1.f ;// / sqrt(2);
// };
// struct __align__(8) Node
// {
// int feature;
// float threshold;
// Node(const int f, const float t) : feature(f), threshold(t) {}
// };
// struct __align__(8) Feature
// {
// int channel;
// uchar4 rect;
// Feature(const int c, const uchar4 r) : channel(c), rect(r) {}
// };
// }}}
// #endif
\ No newline at end of file
modules/gpu/src/softcascade.cpp
View file @ 19173665
......@@ -41,361 +41,365 @@
//M*/
#include <precomp.hpp>
#include "opencv2/highgui/highgui.hpp"
#include <opencv2/highgui/highgui.hpp>
#if !defined (HAVE_CUDA)
cv::gpu::SoftCascade::SoftCascade() : filds(0) { throw_nogpu(); }
cv::gpu::SoftCascade::SoftCascade( const string&, const float, const float) : filds(0) { throw_nogpu(); }
cv::gpu::SoftCascade::~SoftCascade() { throw_nogpu(); }
bool cv::gpu::SoftCascade::load( const string&, const float, const float) { throw_nogpu(); }
bool cv::gpu::SoftCascade::load( const string&, const float, const float) { throw_nogpu(); return false; }
void cv::gpu::SoftCascade::detectMultiScale(const GpuMat&, const GpuMat&, GpuMat&, const int, Stream) { throw_nogpu(); }
#else
#include <icf.hpp>
// #include <icf.hpp>
namespace cv { namespace gpu { namespace device {
namespace icf {
void fillBins(cv::gpu::PtrStepSzb hogluv,const cv::gpu::PtrStepSzf& nangle);
}
}}}
// namespace cv { namespace gpu { namespace device {
// namespace icf {
// void fillBins(cv::gpu::PtrStepSzb hogluv,const cv::gpu::PtrStepSzf& nangle);
// }
// }}}
// namespace {
// char *itoa(long i, char* s, int /*dummy_radix*/)
// {
// sprintf(s, "%ld", i);
// return s;
// }
// }
struct cv::gpu::SoftCascade::Filds
{
// scales range
float minScale;
float maxScale;
int origObjWidth;
int origObjHeight;
GpuMat octaves;
GpuMat stages;
GpuMat nodes;
GpuMat leaves;
GpuMat features;
GpuMat levels;
// preallocated buffer 640x480x10 + 640x480
GpuMat dmem;
// 160x120x10
GpuMat shrunk;
// 161x121x10
GpuMat hogluv;
// will be removed in final version
// temporial mat for cvtColor
GpuMat luv;
// temporial mat for integrall
GpuMat integralBuffer;
// temp matrix for sobel and cartToPolar
GpuMat dfdx, dfdy, angle, mag, nmag, nangle;
std::vector<float> scales;
icf::Cascade cascade;
icf::ChannelStorage storage;
enum { BOOST = 0 };
enum
{
FRAME_WIDTH = 640,
FRAME_HEIGHT = 480,
TOTAL_SCALES = 55,
CLASSIFIERS = 5,
ORIG_OBJECT_WIDTH = 64,
ORIG_OBJECT_HEIGHT = 128,
HOG_BINS = 6,
HOG_LUV_BINS = 10
};
bool fill(const FileNode &root, const float mins, const float maxs);
void detect(cv::gpu::GpuMat objects, cudaStream_t stream) const
{
cascade.detect(hogluv, objects, stream);
}
private:
void calcLevels(const std::vector<icf::Octave>& octs,
int frameW, int frameH, int nscales);
typedef std::vector<icf::Octave>::const_iterator octIt_t;
int fitOctave(const std::vector<icf::Octave>& octs, const float& logFactor) const
{
float minAbsLog = FLT_MAX;
int res = 0;
for (int oct = 0; oct < (int)octs.size(); ++oct)
{
const icf::Octave& octave =octs[oct];
float logOctave = ::log(octave.scale);
float logAbsScale = ::fabs(logFactor - logOctave);
if(logAbsScale < minAbsLog)
{
res = oct;
minAbsLog = logAbsScale;
}
}
return res;
}
// // scales range
// float minScale;
// float maxScale;
// int origObjWidth;
// int origObjHeight;
// GpuMat octaves;
// GpuMat stages;
// GpuMat nodes;
// GpuMat leaves;
// GpuMat features;
// GpuMat levels;
// // preallocated buffer 640x480x10 + 640x480
// GpuMat dmem;
// // 160x120x10
// GpuMat shrunk;
// // 161x121x10
// GpuMat hogluv;
// // will be removed in final version
// // temporial mat for cvtColor
// GpuMat luv;
// // temporial mat for integrall
// GpuMat integralBuffer;
// // temp matrix for sobel and cartToPolar
// GpuMat dfdx, dfdy, angle, mag, nmag, nangle;
// std::vector<float> scales;
// icf::Cascade cascade;
// icf::ChannelStorage storage;
// enum { BOOST = 0 };
// enum
// {
// FRAME_WIDTH = 640,
// FRAME_HEIGHT = 480,
// TOTAL_SCALES = 55,
// CLASSIFIERS = 5,
// ORIG_OBJECT_WIDTH = 64,
// ORIG_OBJECT_HEIGHT = 128,
// HOG_BINS = 6,
// HOG_LUV_BINS = 10
// };
// bool fill(const FileNode &root, const float mins, const float maxs);
// void detect(cv::gpu::GpuMat objects, cudaStream_t stream) const
// {
// cascade.detect(hogluv, objects, stream);
// }
// private:
// void calcLevels(const std::vector<icf::Octave>& octs,
// int frameW, int frameH, int nscales);
// typedef std::vector<icf::Octave>::const_iterator octIt_t;
// int fitOctave(const std::vector<icf::Octave>& octs, const float& logFactor) const
// {
// float minAbsLog = FLT_MAX;
// int res = 0;
// for (int oct = 0; oct < (int)octs.size(); ++oct)
// {
// const icf::Octave& octave =octs[oct];
// float logOctave = ::log(octave.scale);
// float logAbsScale = ::fabs(logFactor - logOctave);
// if(logAbsScale < minAbsLog)
// {
// res = oct;
// minAbsLog = logAbsScale;
// }
// }
// return res;
// }
};
inline bool cv::gpu::SoftCascade::Filds::fill(const FileNode &root, const float mins, const float maxs)
{
minScale = mins;
maxScale = maxs;
// cascade properties
static const char *const SC_STAGE_TYPE = "stageType";
static const char *const SC_BOOST = "BOOST";
static const char *const SC_FEATURE_TYPE = "featureType";
static const char *const SC_ICF = "ICF";
static const char *const SC_ORIG_W = "width";
static const char *const SC_ORIG_H = "height";
static const char *const SC_OCTAVES = "octaves";
static const char *const SC_STAGES = "stages";
static const char *const SC_FEATURES = "features";
static const char *const SC_WEEK = "weakClassifiers";
static const char *const SC_INTERNAL = "internalNodes";
static const char *const SC_LEAF = "leafValues";
static const char *const SC_OCT_SCALE = "scale";
static const char *const SC_OCT_STAGES = "stageNum";
static const char *const SC_OCT_SHRINKAGE = "shrinkingFactor";
static const char *const SC_STAGE_THRESHOLD = "stageThreshold";
static const char * const SC_F_CHANNEL = "channel";
static const char * const SC_F_RECT = "rect";
// only Ada Boost supported
std::string stageTypeStr = (string)root[SC_STAGE_TYPE];
CV_Assert(stageTypeStr == SC_BOOST);
// only HOG-like integral channel features cupported
string featureTypeStr = (string)root[SC_FEATURE_TYPE];
CV_Assert(featureTypeStr == SC_ICF);
origObjWidth = (int)root[SC_ORIG_W];
CV_Assert(origObjWidth == ORIG_OBJECT_WIDTH);
origObjHeight = (int)root[SC_ORIG_H];
CV_Assert(origObjHeight == ORIG_OBJECT_HEIGHT);
FileNode fn = root[SC_OCTAVES];
if (fn.empty()) return false;
std::vector<icf::Octave> voctaves;
std::vector<float> vstages;
std::vector<icf::Node> vnodes;
std::vector<float> vleaves;
std::vector<icf::Feature> vfeatures;
scales.clear();
// std::vector<Level> levels;
FileNodeIterator it = fn.begin(), it_end = fn.end();
int feature_offset = 0;
ushort octIndex = 0;
ushort shrinkage = 1;
for (; it != it_end; ++it)
{
FileNode fns = *it;
float scale = (float)fns[SC_OCT_SCALE];
scales.push_back(scale);
ushort nstages = saturate_cast<ushort>((int)fns[SC_OCT_STAGES]);
ushort2 size;
size.x = cvRound(ORIG_OBJECT_WIDTH * scale);
size.y = cvRound(ORIG_OBJECT_HEIGHT * scale);
shrinkage = saturate_cast<ushort>((int)fns[SC_OCT_SHRINKAGE]);
icf::Octave octave(octIndex, nstages, shrinkage, size, scale);
CV_Assert(octave.stages > 0);
voctaves.push_back(octave);
FileNode ffs = fns[SC_FEATURES];
if (ffs.empty()) return false;
fns = fns[SC_STAGES];
if (fn.empty()) return false;
// for each stage (~ decision tree with H = 2)
FileNodeIterator st = fns.begin(), st_end = fns.end();
for (; st != st_end; ++st )
{
fns = *st;
vstages.push_back((float)fns[SC_STAGE_THRESHOLD]);
fns = fns[SC_WEEK];
FileNodeIterator ftr = fns.begin(), ft_end = fns.end();
for (; ftr != ft_end; ++ftr)
{
fns = (*ftr)[SC_INTERNAL];
FileNodeIterator inIt = fns.begin(), inIt_end = fns.end();
for (; inIt != inIt_end;)
{
int feature = (int)(*(inIt +=2)++) + feature_offset;
float th = (float)(*(inIt++));
vnodes.push_back(icf::Node(feature, th));
}
fns = (*ftr)[SC_LEAF];
inIt = fns.begin(), inIt_end = fns.end();
for (; inIt != inIt_end; ++inIt)
vleaves.push_back((float)(*inIt));
}
}
st = ffs.begin(), st_end = ffs.end();
for (; st != st_end; ++st )
{
cv::FileNode rn = (*st)[SC_F_RECT];
cv::FileNodeIterator r_it = rn.begin();
uchar4 rect;
rect.x = saturate_cast<uchar>((int)*(r_it++));
rect.y = saturate_cast<uchar>((int)*(r_it++));
rect.z = saturate_cast<uchar>((int)*(r_it++));
rect.w = saturate_cast<uchar>((int)*(r_it++));
vfeatures.push_back(icf::Feature((int)(*st)[SC_F_CHANNEL], rect));
}
feature_offset += octave.stages * 3;
++octIndex;
}
// upload in gpu memory
octaves.upload(cv::Mat(1, voctaves.size() * sizeof(icf::Octave), CV_8UC1, (uchar*)&(voctaves[0]) ));
CV_Assert(!octaves.empty());
stages.upload(cv::Mat(vstages).reshape(1,1));
CV_Assert(!stages.empty());
nodes.upload(cv::Mat(1, vnodes.size() * sizeof(icf::Node), CV_8UC1, (uchar*)&(vnodes[0]) ));
CV_Assert(!nodes.empty());
leaves.upload(cv::Mat(vleaves).reshape(1,1));
CV_Assert(!leaves.empty());
features.upload(cv::Mat(1, vfeatures.size() * sizeof(icf::Feature), CV_8UC1, (uchar*)&(vfeatures[0]) ));
CV_Assert(!features.empty());
// compute levels
calcLevels(voctaves, FRAME_WIDTH, FRAME_HEIGHT, TOTAL_SCALES);
CV_Assert(!levels.empty());
//init Cascade
cascade = icf::Cascade(octaves, stages, nodes, leaves, features, levels);
// allocate buffers
dmem.create(FRAME_HEIGHT * (HOG_LUV_BINS + 1), FRAME_WIDTH, CV_8UC1);
shrunk.create(FRAME_HEIGHT / shrinkage * HOG_LUV_BINS, FRAME_WIDTH / shrinkage, CV_8UC1);
// hogluv.create( (FRAME_HEIGHT / shrinkage + 1) * HOG_LUV_BINS, (FRAME_WIDTH / shrinkage + 1), CV_16UC1);
hogluv.create( (FRAME_HEIGHT / shrinkage + 1) * HOG_LUV_BINS, (FRAME_WIDTH / shrinkage + 1), CV_32SC1);
luv.create(FRAME_HEIGHT, FRAME_WIDTH, CV_8UC3);
integralBuffer.create(shrunk.rows + 1 * HOG_LUV_BINS, shrunk.cols + 1, CV_32SC1);
dfdx.create(FRAME_HEIGHT, FRAME_WIDTH, CV_32FC1);
dfdy.create(FRAME_HEIGHT, FRAME_WIDTH, CV_32FC1);
angle.create(FRAME_HEIGHT, FRAME_WIDTH, CV_32FC1);
mag.create(FRAME_HEIGHT, FRAME_WIDTH, CV_32FC1);
nmag.create(FRAME_HEIGHT, FRAME_WIDTH, CV_32FC1);
nangle.create(FRAME_HEIGHT, FRAME_WIDTH, CV_32FC1);
storage = icf::ChannelStorage(dmem, shrunk, hogluv, shrinkage);
return true;
}
namespace {
struct CascadeIntrinsics
{
static const float lambda = 1.099f, a = 0.89f;
static float getFor(int channel, float scaling)
{
CV_Assert(channel < 10);
if (fabs(scaling - 1.f) < FLT_EPSILON)
return 1.f;
// according to R. Benenson, M. Mathias, R. Timofte and L. Van Gool's and Dallal's papers
static const float A[2][2] =
{ //channel <= 6, otherwise
{ 0.89f, 1.f}, // down
{ 1.00f, 1.f} // up
};
static const float B[2][2] =
{ //channel <= 6, otherwise
{ 1.099f / log(2), 2.f}, // down
{ 0.f, 2.f} // up
};
float a = A[(int)(scaling >= 1)][(int)(channel > 6)];
float b = B[(int)(scaling >= 1)][(int)(channel > 6)];
// printf("!!! scaling: %f %f %f -> %f\n", scaling, a, b, a * pow(scaling, b));
return a * pow(scaling, b);
}
};
}
inline void cv::gpu::SoftCascade::Filds::calcLevels(const std::vector<icf::Octave>& octs,
int frameW, int frameH, int nscales)
{
CV_Assert(nscales > 1);
std::vector<icf::Level> vlevels;
float logFactor = (::log(maxScale) - ::log(minScale)) / (nscales -1);
float scale = minScale;
for (int sc = 0; sc < nscales; ++sc)
{
int width = ::std::max(0.0f, frameW - (origObjWidth * scale));
int height = ::std::max(0.0f, frameH - (origObjHeight * scale));
float logScale = ::log(scale);
int fit = fitOctave(octs, logScale);
icf::Level level(fit, octs[fit], scale, width, height);
level.scaling[0] = CascadeIntrinsics::getFor(0, level.relScale);
level.scaling[1] = CascadeIntrinsics::getFor(9, level.relScale);
if (!width || !height)
break;
else
vlevels.push_back(level);
if (::fabs(scale - maxScale) < FLT_EPSILON) break;
scale = ::std::min(maxScale, ::expf(::log(scale) + logFactor));
// printf("level: %d (%f %f) [%f %f] (%d %d) (%d %d)\n", level.octave, level.relScale, level.shrScale,
// level.scaling[0], level.scaling[1], level.workRect.x, level.workRect.y, level.objSize.x, level.objSize.y);
// std::cout << "level " << sc
// << " octeve "
// << vlevels[sc].octave
// << " relScale "
// << vlevels[sc].relScale
// << " " << vlevels[sc].shrScale
// << " [" << (int)vlevels[sc].objSize.x
// << " " << (int)vlevels[sc].objSize.y << "] ["
// << (int)vlevels[sc].workRect.x << " " << (int)vlevels[sc].workRect.y << "]" << std::endl;
}
levels.upload(cv::Mat(1, vlevels.size() * sizeof(icf::Level), CV_8UC1, (uchar*)&(vlevels[0]) ));
}
// inline bool cv::gpu::SoftCascade::Filds::fill(const FileNode &root, const float mins, const float maxs)
// {
// minScale = mins;
// maxScale = maxs;
// // cascade properties
// static const char *const SC_STAGE_TYPE = "stageType";
// static const char *const SC_BOOST = "BOOST";
// static const char *const SC_FEATURE_TYPE = "featureType";
// static const char *const SC_ICF = "ICF";
// static const char *const SC_ORIG_W = "width";
// static const char *const SC_ORIG_H = "height";
// static const char *const SC_OCTAVES = "octaves";
// static const char *const SC_STAGES = "stages";
// static const char *const SC_FEATURES = "features";
// static const char *const SC_WEEK = "weakClassifiers";
// static const char *const SC_INTERNAL = "internalNodes";
// static const char *const SC_LEAF = "leafValues";
// static const char *const SC_OCT_SCALE = "scale";
// static const char *const SC_OCT_STAGES = "stageNum";
// static const char *const SC_OCT_SHRINKAGE = "shrinkingFactor";
// static const char *const SC_STAGE_THRESHOLD = "stageThreshold";
// static const char * const SC_F_CHANNEL = "channel";
// static const char * const SC_F_RECT = "rect";
// // only Ada Boost supported
// std::string stageTypeStr = (string)root[SC_STAGE_TYPE];
// CV_Assert(stageTypeStr == SC_BOOST);
// // only HOG-like integral channel features cupported
// string featureTypeStr = (string)root[SC_FEATURE_TYPE];
// CV_Assert(featureTypeStr == SC_ICF);
// origObjWidth = (int)root[SC_ORIG_W];
// CV_Assert(origObjWidth == ORIG_OBJECT_WIDTH);
// origObjHeight = (int)root[SC_ORIG_H];
// CV_Assert(origObjHeight == ORIG_OBJECT_HEIGHT);
// FileNode fn = root[SC_OCTAVES];
// if (fn.empty()) return false;
// std::vector<icf::Octave> voctaves;
// std::vector<float> vstages;
// std::vector<icf::Node> vnodes;
// std::vector<float> vleaves;
// std::vector<icf::Feature> vfeatures;
// scales.clear();
// // std::vector<Level> levels;
// FileNodeIterator it = fn.begin(), it_end = fn.end();
// int feature_offset = 0;
// ushort octIndex = 0;
// ushort shrinkage = 1;
// for (; it != it_end; ++it)
// {
// FileNode fns = *it;
// float scale = (float)fns[SC_OCT_SCALE];
// scales.push_back(scale);
// ushort nstages = saturate_cast<ushort>((int)fns[SC_OCT_STAGES]);
// ushort2 size;
// size.x = cvRound(ORIG_OBJECT_WIDTH * scale);
// size.y = cvRound(ORIG_OBJECT_HEIGHT * scale);
// shrinkage = saturate_cast<ushort>((int)fns[SC_OCT_SHRINKAGE]);
// icf::Octave octave(octIndex, nstages, shrinkage, size, scale);
// CV_Assert(octave.stages > 0);
// voctaves.push_back(octave);
// FileNode ffs = fns[SC_FEATURES];
// if (ffs.empty()) return false;
// fns = fns[SC_STAGES];
// if (fn.empty()) return false;
// // for each stage (~ decision tree with H = 2)
// FileNodeIterator st = fns.begin(), st_end = fns.end();
// for (; st != st_end; ++st )
// {
// fns = *st;
// vstages.push_back((float)fns[SC_STAGE_THRESHOLD]);
// fns = fns[SC_WEEK];
// FileNodeIterator ftr = fns.begin(), ft_end = fns.end();
// for (; ftr != ft_end; ++ftr)
// {
// fns = (*ftr)[SC_INTERNAL];
// FileNodeIterator inIt = fns.begin(), inIt_end = fns.end();
// for (; inIt != inIt_end;)
// {
// int feature = (int)(*(inIt +=2)++) + feature_offset;
// float th = (float)(*(inIt++));
// vnodes.push_back(icf::Node(feature, th));
// }
// fns = (*ftr)[SC_LEAF];
// inIt = fns.begin(), inIt_end = fns.end();
// for (; inIt != inIt_end; ++inIt)
// vleaves.push_back((float)(*inIt));
// }
// }
// st = ffs.begin(), st_end = ffs.end();
// for (; st != st_end; ++st )
// {
// cv::FileNode rn = (*st)[SC_F_RECT];
// cv::FileNodeIterator r_it = rn.begin();
// uchar4 rect;
// rect.x = saturate_cast<uchar>((int)*(r_it++));
// rect.y = saturate_cast<uchar>((int)*(r_it++));
// rect.z = saturate_cast<uchar>((int)*(r_it++));
// rect.w = saturate_cast<uchar>((int)*(r_it++));
// vfeatures.push_back(icf::Feature((int)(*st)[SC_F_CHANNEL], rect));
// }
// feature_offset += octave.stages * 3;
// ++octIndex;
// }
// // upload in gpu memory
// octaves.upload(cv::Mat(1, voctaves.size() * sizeof(icf::Octave), CV_8UC1, (uchar*)&(voctaves[0]) ));
// CV_Assert(!octaves.empty());
// stages.upload(cv::Mat(vstages).reshape(1,1));
// CV_Assert(!stages.empty());
// nodes.upload(cv::Mat(1, vnodes.size() * sizeof(icf::Node), CV_8UC1, (uchar*)&(vnodes[0]) ));
// CV_Assert(!nodes.empty());
// leaves.upload(cv::Mat(vleaves).reshape(1,1));
// CV_Assert(!leaves.empty());
// features.upload(cv::Mat(1, vfeatures.size() * sizeof(icf::Feature), CV_8UC1, (uchar*)&(vfeatures[0]) ));
// CV_Assert(!features.empty());
// // compute levels
// calcLevels(voctaves, FRAME_WIDTH, FRAME_HEIGHT, TOTAL_SCALES);
// CV_Assert(!levels.empty());
// //init Cascade
// cascade = icf::Cascade(octaves, stages, nodes, leaves, features, levels);
// // allocate buffers
// dmem.create(FRAME_HEIGHT * (HOG_LUV_BINS + 1), FRAME_WIDTH, CV_8UC1);
// shrunk.create(FRAME_HEIGHT / shrinkage * HOG_LUV_BINS, FRAME_WIDTH / shrinkage, CV_8UC1);
// // hogluv.create( (FRAME_HEIGHT / shrinkage + 1) * HOG_LUV_BINS, (FRAME_WIDTH / shrinkage + 1), CV_16UC1);
// hogluv.create( (FRAME_HEIGHT / shrinkage + 1) * HOG_LUV_BINS, (FRAME_WIDTH / shrinkage + 1), CV_32SC1);
// luv.create(FRAME_HEIGHT, FRAME_WIDTH, CV_8UC3);
// integralBuffer.create(shrunk.rows + 1 * HOG_LUV_BINS, shrunk.cols + 1, CV_32SC1);
// dfdx.create(FRAME_HEIGHT, FRAME_WIDTH, CV_32FC1);
// dfdy.create(FRAME_HEIGHT, FRAME_WIDTH, CV_32FC1);
// angle.create(FRAME_HEIGHT, FRAME_WIDTH, CV_32FC1);
// mag.create(FRAME_HEIGHT, FRAME_WIDTH, CV_32FC1);
// nmag.create(FRAME_HEIGHT, FRAME_WIDTH, CV_32FC1);
// nangle.create(FRAME_HEIGHT, FRAME_WIDTH, CV_32FC1);
// storage = icf::ChannelStorage(dmem, shrunk, hogluv, shrinkage);
// return true;
// }
// namespace {
// struct CascadeIntrinsics
// {
// static const float lambda = 1.099f, a = 0.89f;
// static float getFor(int channel, float scaling)
// {
// CV_Assert(channel < 10);
// if (fabs(scaling - 1.f) < FLT_EPSILON)
// return 1.f;
// // according to R. Benenson, M. Mathias, R. Timofte and L. Van Gool's and Dallal's papers
// static const float A[2][2] =
// { //channel <= 6, otherwise
// { 0.89f, 1.f}, // down
// { 1.00f, 1.f} // up
// };
// static const float B[2][2] =
// { //channel <= 6, otherwise
// { 1.099f / log(2), 2.f}, // down
// { 0.f, 2.f} // up
// };
// float a = A[(int)(scaling >= 1)][(int)(channel > 6)];
// float b = B[(int)(scaling >= 1)][(int)(channel > 6)];
// // printf("!!! scaling: %f %f %f -> %f\n", scaling, a, b, a * pow(scaling, b));
// return a * pow(scaling, b);
// }
// };
// }
// inline void cv::gpu::SoftCascade::Filds::calcLevels(const std::vector<icf::Octave>& octs,
// int frameW, int frameH, int nscales)
// {
// CV_Assert(nscales > 1);
// std::vector<icf::Level> vlevels;
// float logFactor = (::log(maxScale) - ::log(minScale)) / (nscales -1);
// float scale = minScale;
// for (int sc = 0; sc < nscales; ++sc)
// {
// int width = ::std::max(0.0f, frameW - (origObjWidth * scale));
// int height = ::std::max(0.0f, frameH - (origObjHeight * scale));
// float logScale = ::log(scale);
// int fit = fitOctave(octs, logScale);
// icf::Level level(fit, octs[fit], scale, width, height);
// level.scaling[0] = CascadeIntrinsics::getFor(0, level.relScale);
// level.scaling[1] = CascadeIntrinsics::getFor(9, level.relScale);
// if (!width || !height)
// break;
// else
// vlevels.push_back(level);
// if (::fabs(scale - maxScale) < FLT_EPSILON) break;
// scale = ::std::min(maxScale, ::expf(::log(scale) + logFactor));
// // printf("level: %d (%f %f) [%f %f] (%d %d) (%d %d)\n", level.octave, level.relScale, level.shrScale,
// // level.scaling[0], level.scaling[1], level.workRect.x, level.workRect.y, level.objSize.x, level.objSize.y);
// // std::cout << "level " << sc
// // << " octeve "
// // << vlevels[sc].octave
// // << " relScale "
// // << vlevels[sc].relScale
// // << " " << vlevels[sc].shrScale
// // << " [" << (int)vlevels[sc].objSize.x
// // << " " << (int)vlevels[sc].objSize.y << "] ["
// // << (int)vlevels[sc].workRect.x << " " << (int)vlevels[sc].workRect.y << "]" << std::endl;
// }
// levels.upload(cv::Mat(1, vlevels.size() * sizeof(icf::Level), CV_8UC1, (uchar*)&(vlevels[0]) ));
// }
cv::gpu::SoftCascade::SoftCascade() : filds(0) {}
......@@ -419,97 +423,89 @@ bool cv::gpu::SoftCascade::load( const string& filename, const float minScale, c
if (!fs.isOpened()) return false;
filds = new Filds;
Filds& flds = *filds;
if (!flds.fill(fs.getFirstTopLevelNode(), minScale, maxScale)) return false;
// Filds& flds = *filds;
// if (!flds.fill(fs.getFirstTopLevelNode(), minScale, maxScale)) return false;
return true;
}
namespace {
char *itoa(long i, char* s, int /*dummy_radix*/)
{
sprintf(s, "%ld", i);
return s;
}
}
#define USE_REFERENCE_VALUES
// #define USE_REFERENCE_VALUES
void cv::gpu::SoftCascade::detectMultiScale(const GpuMat& colored, const GpuMat& /*rois*/,
GpuMat& objects, const int /*rejectfactor*/, Stream s)
{
// only color images are supperted
CV_Assert(colored.type() == CV_8UC3);
// // only this window size allowed
CV_Assert(colored.cols == 640 && colored.rows == 480);
Filds& flds = *filds;
#if defined USE_REFERENCE_VALUES
cudaMemset(flds.hogluv.data, 0, flds.hogluv.step * flds.hogluv.rows);
cv::FileStorage imgs("/home/kellan/testInts.xml", cv::FileStorage::READ);
char buff[33];
for(int i = 0; i < Filds::HOG_LUV_BINS; ++i)
{
cv::Mat channel;
imgs[std::string("channel") + itoa(i, buff, 10)] >> channel;
GpuMat gchannel(flds.hogluv, cv::Rect(0, 121 * i, 161, 121));
gchannel.upload(channel);
}
#else
GpuMat& dmem = flds.dmem;
cudaMemset(dmem.data, 0, dmem.step * dmem.rows);
GpuMat& shrunk = flds.shrunk;
int w = shrunk.cols;
int h = colored.rows / flds.storage.shrinkage;
// // only color images are supperted
// CV_Assert(colored.type() == CV_8UC3);
// // // only this window size allowed
// CV_Assert(colored.cols == 640 && colored.rows == 480);
// Filds& flds = *filds;
// #if defined USE_REFERENCE_VALUES
// cudaMemset(flds.hogluv.data, 0, flds.hogluv.step * flds.hogluv.rows);
// cv::FileStorage imgs("/home/kellan/testInts.xml", cv::FileStorage::READ);
// char buff[33];
// for(int i = 0; i < Filds::HOG_LUV_BINS; ++i)
// {
// cv::Mat channel;
// imgs[std::string("channel") + itoa(i, buff, 10)] >> channel;
// GpuMat gchannel(flds.hogluv, cv::Rect(0, 121 * i, 161, 121));
// gchannel.upload(channel);
// }
// #else
// GpuMat& dmem = flds.dmem;
// cudaMemset(dmem.data, 0, dmem.step * dmem.rows);
// GpuMat& shrunk = flds.shrunk;
// int w = shrunk.cols;
// int h = colored.rows / flds.storage.shrinkage;
std::vector<GpuMat> splited;
for(int i = 0; i < 3; ++i)
{
splited.push_back(GpuMat(dmem, cv::Rect(0, colored.rows * (7 + i), colored.cols, colored.rows)));
}
// std::vector<GpuMat> splited;
// for(int i = 0; i < 3; ++i)
// {
// splited.push_back(GpuMat(dmem, cv::Rect(0, colored.rows * (7 + i), colored.cols, colored.rows)));
// }
GpuMat gray(dmem, cv::Rect(0, colored.rows * 10, colored.cols, colored.rows) );
// GpuMat gray(dmem, cv::Rect(0, colored.rows * 10, colored.cols, colored.rows) );
cv::gpu::cvtColor(colored, gray, CV_RGB2GRAY);
// cv::gpu::cvtColor(colored, gray, CV_RGB2GRAY);
//create hog
cv::gpu::Sobel(gray, flds.dfdx, CV_32F, 1, 0, 3, 0.25);
cv::gpu::Sobel(gray, flds.dfdy, CV_32F, 0, 1, 3, 0.25);
// //create hog
// cv::gpu::Sobel(gray, flds.dfdx, CV_32F, 1, 0, 3, 0.25);
// cv::gpu::Sobel(gray, flds.dfdy, CV_32F, 0, 1, 3, 0.25);
cv::gpu::cartToPolar(flds.dfdx, flds.dfdy, flds.mag, flds.angle, true);
// cv::gpu::cartToPolar(flds.dfdx, flds.dfdy, flds.mag, flds.angle, true);
cv::gpu::multiply(flds.mag, cv::Scalar::all(1.0 / ::log(2)), flds.nmag);
cv::gpu::multiply(flds.angle, cv::Scalar::all(1.0 / 60.0), flds.nangle);
// cv::gpu::multiply(flds.mag, cv::Scalar::all(1.0 / ::log(2)), flds.nmag);
// cv::gpu::multiply(flds.angle, cv::Scalar::all(1.0 / 60.0), flds.nangle);
GpuMat magCannel(dmem, cv::Rect(0, colored.rows * 6, colored.cols, colored.rows));
flds.nmag.convertTo(magCannel, CV_8UC1);
device::icf::fillBins(dmem, flds.nangle);
// GpuMat magCannel(dmem, cv::Rect(0, colored.rows * 6, colored.cols, colored.rows));
// flds.nmag.convertTo(magCannel, CV_8UC1);
// device::icf::fillBins(dmem, flds.nangle);
// create luv
cv::gpu::cvtColor(colored, flds.luv, CV_BGR2Luv);
cv::gpu::split(flds.luv, splited);
// // create luv
// cv::gpu::cvtColor(colored, flds.luv, CV_BGR2Luv);
// cv::gpu::split(flds.luv, splited);
GpuMat plane(dmem, cv::Rect(0, 0, colored.cols, colored.rows * Filds::HOG_LUV_BINS));
cv::gpu::resize(plane, flds.shrunk, cv::Size(), 0.25, 0.25, CV_INTER_AREA);
// GpuMat plane(dmem, cv::Rect(0, 0, colored.cols, colored.rows * Filds::HOG_LUV_BINS));
// cv::gpu::resize(plane, flds.shrunk, cv::Size(), 0.25, 0.25, CV_INTER_AREA);
// fer debug purpose
// cudaMemset(flds.hogluv.data, 0, flds.hogluv.step * flds.hogluv.rows);
// // fer debug purpose
// // cudaMemset(flds.hogluv.data, 0, flds.hogluv.step * flds.hogluv.rows);
for(int i = 0; i < Filds::HOG_LUV_BINS; ++i)
{
GpuMat channel(shrunk, cv::Rect(0, h * i, w, h ));
GpuMat sum(flds.hogluv, cv::Rect(0, (h + 1) * i, w + 1, h + 1));
cv::gpu::integralBuffered(channel, sum, flds.integralBuffer);
}
// for(int i = 0; i < Filds::HOG_LUV_BINS; ++i)
// {
// GpuMat channel(shrunk, cv::Rect(0, h * i, w, h ));
// GpuMat sum(flds.hogluv, cv::Rect(0, (h + 1) * i, w + 1, h + 1));
// cv::gpu::integralBuffered(channel, sum, flds.integralBuffer);
// }
#endif
// #endif
cudaStream_t stream = StreamAccessor::getStream(s);
// detection
flds.detect(objects, stream);
// cudaStream_t stream = StreamAccessor::getStream(s);
// // detection
// flds.detect(objects, stream);
// flds.storage.frame(colored, stream);
// // flds.storage.frame(colored, stream);
}
#endif
\ No newline at end of file
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment