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308 lines (257 loc) · 10.4 KB
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/*******************************************************
* Copyright (c) 2014, ArrayFire
* All rights reserved.
*
* This file is distributed under 3-clause BSD license.
* The complete license agreement can be obtained at:
* http://arrayfire.com/licenses/BSD-3-Clause
********************************************************/
#include <gtest/gtest.h>
#include <arrayfire.h>
#include <af/dim4.hpp>
#include <af/traits.hpp>
#include <af/compatible.h>
#include <string>
#include <vector>
#include <cmath>
#include <testHelpers.hpp>
#include <typeinfo>
using std::string;
using std::vector;
using af::dim4;
typedef struct
{
float f[5];
unsigned d[8];
} feat_desc_t;
typedef struct
{
float f[5];
} feat_t;
typedef struct
{
unsigned d[8];
} desc_t;
bool feat_cmp(feat_desc_t i, feat_desc_t j)
{
for (int k = 0; k < 5; k++)
if (i.f[k] != j.f[k])
return (i.f[k] < j.f[k]);
return true;
}
void array_to_feat_desc(vector<feat_desc_t>& feat, float* x, float* y, float* score, float* ori, float* size, unsigned* desc, unsigned nfeat)
{
feat.resize(nfeat);
for (size_t i = 0; i < feat.size(); i++) {
feat[i].f[0] = x[i];
feat[i].f[1] = y[i];
feat[i].f[2] = score[i];
feat[i].f[3] = ori[i];
feat[i].f[4] = size[i];
for (unsigned j = 0; j < 8; j++)
feat[i].d[j] = desc[i * 8 + j];
}
}
void array_to_feat_desc(vector<feat_desc_t>& feat, float* x, float* y, float* score, float* ori, float* size, vector<vector<unsigned> >& desc, unsigned nfeat)
{
feat.resize(nfeat);
for (size_t i = 0; i < feat.size(); i++) {
feat[i].f[0] = x[i];
feat[i].f[1] = y[i];
feat[i].f[2] = score[i];
feat[i].f[3] = ori[i];
feat[i].f[4] = size[i];
for (unsigned j = 0; j < 8; j++)
feat[i].d[j] = desc[i][j];
}
}
void array_to_feat(vector<feat_t>& feat, float *x, float *y, float *score, float *ori, float *size, unsigned nfeat)
{
feat.resize(nfeat);
for (unsigned i = 0; i < feat.size(); i++) {
feat[i].f[0] = x[i];
feat[i].f[1] = y[i];
feat[i].f[2] = score[i];
feat[i].f[3] = ori[i];
feat[i].f[4] = size[i];
}
}
void split_feat_desc(vector<feat_desc_t>& fd, vector<feat_t>& f, vector<desc_t>& d)
{
f.resize(fd.size());
d.resize(fd.size());
for (size_t i = 0; i < fd.size(); i++) {
f[i].f[0] = fd[i].f[0];
f[i].f[1] = fd[i].f[1];
f[i].f[2] = fd[i].f[2];
f[i].f[3] = fd[i].f[3];
f[i].f[4] = fd[i].f[4];
for (unsigned j = 0; j < 8; j++)
d[i].d[j] = fd[i].d[j];
}
}
unsigned popcount(unsigned x)
{
x = x - ((x >> 1) & 0x55555555);
x = (x & 0x33333333) + ((x >> 2) & 0x33333333);
x = (x + (x >> 4)) & 0x0F0F0F0F;
x = x + (x >> 8);
x = x + (x >> 16);
return x & 0x0000003F;
}
bool compareHamming(int data_size, unsigned *cpu, unsigned *gpu, unsigned thr = 1)
{
bool ret = true;
for(int i=0;i<data_size;i++)
{
unsigned x = (cpu[i] ^ gpu[i]);
if(popcount(x) > thr) {
ret = false;
std::cout<<std::endl<<"@compareHamming: first mismatch."<<std::endl;
std::cout<<"(cpu,gpu,cpu-gpu)["<<i<<"] : {"<<cpu[i]<<","<<gpu[i]<<","<<cpu[i]-gpu[i]<<"}"<<std::endl;
std::cout<<std::endl;
break;
}
}
return ret;
}
template<typename T>
class ORB : public ::testing::Test
{
public:
virtual void SetUp() {}
};
typedef ::testing::Types<float, double> TestTypes;
TYPED_TEST_CASE(ORB, TestTypes);
template<typename T>
void orbTest(string pTestFile)
{
if (noDoubleTests<T>()) return;
vector<dim4> inDims;
vector<string> inFiles;
vector<vector<float> > goldFeat;
vector<vector<unsigned> > goldDesc;
readImageFeaturesDescriptors<unsigned>(pTestFile, inDims, inFiles, goldFeat, goldDesc);
size_t testCount = inDims.size();
for (size_t testId=0; testId<testCount; ++testId) {
af_array inArray_f32 = 0;
af_array inArray = 0;
af_array desc = 0;
af_features feat;
inFiles[testId].insert(0,string(TEST_DIR"/orb/"));
ASSERT_EQ(AF_SUCCESS, af_load_image(&inArray_f32, inFiles[testId].c_str(), false));
ASSERT_EQ(AF_SUCCESS, conv_image<T>(&inArray, inArray_f32));
ASSERT_EQ(AF_SUCCESS, af_orb(&feat, &desc, inArray, 20.0f, 400, 1.2f, 8, true));
float * outX = new float[feat.n];
float * outY = new float[feat.n];
float * outScore = new float[feat.n];
float * outOrientation = new float[feat.n];
float * outSize = new float[feat.n];
dim_t descSize;
ASSERT_EQ(AF_SUCCESS, af_get_elements(&descSize, desc));
unsigned * outDesc = new unsigned[descSize];
ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outX, feat.x));
ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outY, feat.y));
ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outScore, feat.score));
ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outOrientation, feat.orientation));
ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outSize, feat.size));
ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outDesc, desc));
vector<feat_desc_t> out_feat_desc;
array_to_feat_desc(out_feat_desc, outX, outY, outScore, outOrientation, outSize, outDesc, feat.n);
vector<feat_desc_t> gold_feat_desc;
array_to_feat_desc(gold_feat_desc, &goldFeat[0].front(), &goldFeat[1].front(), &goldFeat[2].front(), &goldFeat[3].front(), &goldFeat[4].front(), goldDesc, goldFeat[0].size());
std::sort(out_feat_desc.begin(), out_feat_desc.end(), feat_cmp);
std::sort(gold_feat_desc.begin(), gold_feat_desc.end(), feat_cmp);
vector<feat_t> out_feat;
vector<desc_t> v_out_desc;
vector<feat_t> gold_feat;
vector<desc_t> v_gold_desc;
split_feat_desc(out_feat_desc, out_feat, v_out_desc);
split_feat_desc(gold_feat_desc, gold_feat, v_gold_desc);
for (int elIter = 0; elIter < (int)feat.n; elIter++) {
ASSERT_EQ(out_feat[elIter].f[0], gold_feat[elIter].f[0]) << "at: " << elIter << std::endl;
ASSERT_EQ(out_feat[elIter].f[1], gold_feat[elIter].f[1]) << "at: " << elIter << std::endl;
ASSERT_LE(fabs(out_feat[elIter].f[2] - gold_feat[elIter].f[2]), 1e-3) << "at: " << elIter << std::endl;
ASSERT_LE(fabs(out_feat[elIter].f[3] - gold_feat[elIter].f[3]), 1e-3) << "at: " << elIter << std::endl;
ASSERT_LE(fabs(out_feat[elIter].f[4] - gold_feat[elIter].f[4]), 1e-3) << "at: " << elIter << std::endl;
}
// TODO: improve distance for single/double-precision interchangeability
EXPECT_TRUE(compareHamming(descSize, (unsigned*)&v_out_desc[0], (unsigned*)&v_gold_desc[0], 3));
ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
ASSERT_EQ(AF_SUCCESS, af_release_array(inArray_f32));
ASSERT_EQ(AF_SUCCESS, af_release_array(feat.x));
ASSERT_EQ(AF_SUCCESS, af_release_array(feat.y));
ASSERT_EQ(AF_SUCCESS, af_release_array(feat.score));
ASSERT_EQ(AF_SUCCESS, af_release_array(feat.orientation));
ASSERT_EQ(AF_SUCCESS, af_release_array(feat.size));
ASSERT_EQ(AF_SUCCESS, af_release_array(desc));
delete[] outX;
delete[] outY;
delete[] outScore;
delete[] outOrientation;
delete[] outSize;
delete[] outDesc;
}
}
#define ORB_INIT(desc, image) \
TYPED_TEST(ORB, desc) \
{ \
orbTest<TypeParam>(string(TEST_DIR"/orb/"#image".test")); \
}
ORB_INIT(square, square);
ORB_INIT(lena, lena);
///////////////////////////////////// CPP ////////////////////////////////
//
TEST(ORB, CPP)
{
if (noDoubleTests<float>()) return;
vector<dim4> inDims;
vector<string> inFiles;
vector<vector<float> > goldFeat;
vector<vector<unsigned> > goldDesc;
readImageFeaturesDescriptors<unsigned>(string(TEST_DIR"/orb/square.test"), inDims, inFiles, goldFeat, goldDesc);
inFiles[0].insert(0,string(TEST_DIR"/orb/"));
af::array in = af::loadimage(inFiles[0].c_str(), false);
af::features feat;
af::array desc;
af::orb(feat, desc, in, 20.0f, 400, 1.2f, 8, true);
float * outX = new float[feat.getNumFeatures()];
float * outY = new float[feat.getNumFeatures()];
float * outScore = new float[feat.getNumFeatures()];
float * outOrientation = new float[feat.getNumFeatures()];
float * outSize = new float[feat.getNumFeatures()];
unsigned * outDesc = new unsigned[desc.elements()];
feat.getX().host(outX);
feat.getY().host(outY);
feat.getScore().host(outScore);
feat.getOrientation().host(outOrientation);
feat.getSize().host(outSize);
desc.host(outDesc);
vector<feat_desc_t> out_feat_desc;
array_to_feat_desc(out_feat_desc, outX, outY, outScore, outOrientation, outSize, outDesc, feat.getNumFeatures());
vector<feat_desc_t> gold_feat_desc;
array_to_feat_desc(gold_feat_desc, &goldFeat[0].front(), &goldFeat[1].front(), &goldFeat[2].front(), &goldFeat[3].front(), &goldFeat[4].front(), goldDesc, goldFeat[0].size());
std::sort(out_feat_desc.begin(), out_feat_desc.end(), feat_cmp);
std::sort(gold_feat_desc.begin(), gold_feat_desc.end(), feat_cmp);
vector<feat_t> out_feat;
vector<desc_t> v_out_desc;
vector<feat_t> gold_feat;
vector<desc_t> v_gold_desc;
split_feat_desc(out_feat_desc, out_feat, v_out_desc);
split_feat_desc(gold_feat_desc, gold_feat, v_gold_desc);
for (int elIter = 0; elIter < (int)feat.getNumFeatures(); elIter++) {
ASSERT_EQ(out_feat[elIter].f[0], gold_feat[elIter].f[0]) << "at: " << elIter << std::endl;
ASSERT_EQ(out_feat[elIter].f[1], gold_feat[elIter].f[1]) << "at: " << elIter << std::endl;
ASSERT_LE(fabs(out_feat[elIter].f[2] - gold_feat[elIter].f[2]), 1e-3) << "at: " << elIter << std::endl;
ASSERT_LE(fabs(out_feat[elIter].f[3] - gold_feat[elIter].f[3]), 1e-3) << "at: " << elIter << std::endl;
ASSERT_LE(fabs(out_feat[elIter].f[4] - gold_feat[elIter].f[4]), 1e-3) << "at: " << elIter << std::endl;
}
// TODO: improve distance for single/double-precision interchangeability
EXPECT_TRUE(compareHamming(desc.elements(), (unsigned*)&v_out_desc[0], (unsigned*)&v_gold_desc[0], 3));
delete[] outX;
delete[] outY;
delete[] outScore;
delete[] outOrientation;
delete[] outSize;
delete[] outDesc;
}
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