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orb.cpp
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328 lines (276 loc) · 11 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 <arrayfire.h>
#include <gtest/gtest.h>
#include <testHelpers.hpp>
#include <af/compatible.h>
#include <af/dim4.hpp>
#include <af/traits.hpp>
#include <cmath>
#include <string>
#include <typeinfo>
#include <vector>
using af::array;
using af::dim4;
using af::features;
using af::loadImage;
using std::abs;
using std::cout;
using std::endl;
using std::string;
using std::vector;
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;
static 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 false;
}
static 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];
}
}
static 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];
}
}
static 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];
}
}
static 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;
cout << endl << "@compareHamming: first mismatch." << endl;
cout << "(cpu,gpu,cpu-gpu)[" << i << "] : {" << cpu[i] << ","
<< gpu[i] << "," << cpu[i] - gpu[i] << "}" << endl;
cout << endl;
break;
}
}
return ret;
}
template<typename T>
class ORB : public ::testing::Test {
public:
virtual void SetUp() {}
};
typedef ::testing::Types<float, double> TestTypes;
TYPED_TEST_SUITE(ORB, TestTypes);
template<typename T>
void orbTest(string pTestFile) {
SUPPORTED_TYPE_CHECK(T);
IMAGEIO_ENABLED_CHECK();
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_SUCCESS(
af_load_image(&inArray_f32, inFiles[testId].c_str(), false));
ASSERT_SUCCESS(conv_image<T>(&inArray, inArray_f32));
ASSERT_SUCCESS(
af_orb(&feat, &desc, inArray, 20.0f, 400, 1.2f, 8, true));
dim_t n = 0;
af_array x, y, score, orientation, size;
ASSERT_SUCCESS(af_get_features_num(&n, feat));
ASSERT_SUCCESS(af_get_features_xpos(&x, feat));
ASSERT_SUCCESS(af_get_features_ypos(&y, feat));
ASSERT_SUCCESS(af_get_features_score(&score, feat));
ASSERT_SUCCESS(af_get_features_orientation(&orientation, feat));
ASSERT_SUCCESS(af_get_features_size(&size, feat));
float* outX = new float[n];
float* outY = new float[n];
float* outScore = new float[n];
float* outOrientation = new float[n];
float* outSize = new float[n];
dim_t descSize;
ASSERT_SUCCESS(af_get_elements(&descSize, desc));
unsigned* outDesc = new unsigned[descSize];
ASSERT_SUCCESS(af_get_data_ptr((void*)outX, x));
ASSERT_SUCCESS(af_get_data_ptr((void*)outY, y));
ASSERT_SUCCESS(af_get_data_ptr((void*)outScore, score));
ASSERT_SUCCESS(af_get_data_ptr((void*)outOrientation, orientation));
ASSERT_SUCCESS(af_get_data_ptr((void*)outSize, size));
ASSERT_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, 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)n; elIter++) {
ASSERT_EQ(out_feat[elIter].f[0], gold_feat[elIter].f[0])
<< "at: " << elIter << endl;
ASSERT_EQ(out_feat[elIter].f[1], gold_feat[elIter].f[1])
<< "at: " << elIter << endl;
ASSERT_LE(fabs(out_feat[elIter].f[2] - gold_feat[elIter].f[2]),
1e-3)
<< "at: " << elIter << endl;
ASSERT_LE(fabs(out_feat[elIter].f[3] - gold_feat[elIter].f[3]),
1e-3)
<< "at: " << elIter << endl;
ASSERT_LE(fabs(out_feat[elIter].f[4] - gold_feat[elIter].f[4]),
1e-3)
<< "at: " << elIter << 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_SUCCESS(af_release_array(inArray));
ASSERT_SUCCESS(af_release_array(inArray_f32));
ASSERT_SUCCESS(af_release_features(feat));
ASSERT_SUCCESS(af_release_array(desc));
delete[] outX;
delete[] outY;
delete[] outScore;
delete[] outOrientation;
delete[] outSize;
delete[] outDesc;
}
}
TYPED_TEST(ORB, Square) {
UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
orbTest<TypeParam>(string(TEST_DIR "/orb/square.test"));
}
TYPED_TEST(ORB, Lena) {
UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
orbTest<TypeParam>(string(TEST_DIR "/orb/lena.test"));
}
///////////////////////////////////// CPP ////////////////////////////////
//
TEST(ORB, CPP) {
UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
IMAGEIO_ENABLED_CHECK();
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/"));
array in = loadImage(inFiles[0].c_str(), false);
features feat;
array desc;
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 << endl;
ASSERT_EQ(out_feat[elIter].f[1], gold_feat[elIter].f[1])
<< "at: " << elIter << endl;
ASSERT_LE(fabs(out_feat[elIter].f[2] - gold_feat[elIter].f[2]), 1e-3)
<< "at: " << elIter << endl;
ASSERT_LE(fabs(out_feat[elIter].f[3] - gold_feat[elIter].f[3]), 1e-3)
<< "at: " << elIter << endl;
ASSERT_LE(fabs(out_feat[elIter].f[4] - gold_feat[elIter].f[4]), 1e-3)
<< "at: " << elIter << 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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