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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 std::abs;
using af::dim4;
template<typename T>
class Homography : public ::testing::Test
{
public:
virtual void SetUp() {}
};
typedef ::testing::Types<float, double> TestTypes;
TYPED_TEST_CASE(Homography, TestTypes);
template<typename T>
af::array perspectiveTransform(af::dim4 inDims, af::array H)
{
T d0 = (T)inDims[0];
T d1 = (T)inDims[1];
af::dim4 dims(4, 3);
T h_in[4*3] = { (T)0, (T)0, (T)d1, (T)d1,
(T)0, (T)d0, (T)d0, (T)0,
(T)1, (T)1, (T)1, (T)1 };
af::array in(dims, h_in);
af::array w = 1.f / af::matmul(in, H(af::span, 2));
af::array xt = af::matmul(in, H(af::span, 0)) * w;
af::array yt = af::matmul(in, H(af::span, 1)) * w;
af::array t = join(1, xt, yt);
return t;
}
template<typename T>
void homographyTest(string pTestFile, const af_homography_type htype,
const bool rotate, const float size_ratio)
{
if (noDoubleTests<T>()) return;
if (noImageIOTests()) return;
vector<dim4> inDims;
vector<string> inFiles;
vector<vector<float> > gold;
readImageTests(pTestFile, inDims, inFiles, gold);
inFiles[0].insert(0,string(TEST_DIR"/homography/"));
af_array trainArray_f32 = 0;
af_array trainArray = 0;
af_array train_desc = 0;
af_array train_feat_x = 0;
af_array train_feat_y = 0;
af_features train_feat;
ASSERT_EQ(AF_SUCCESS, af_load_image(&trainArray_f32, inFiles[0].c_str(), false));
ASSERT_EQ(AF_SUCCESS, conv_image<T>(&trainArray, trainArray_f32));
ASSERT_EQ(AF_SUCCESS, af_orb(&train_feat, &train_desc, trainArray, 20.0f, 2000, 1.2f, 8, true));
ASSERT_EQ(AF_SUCCESS, af_get_features_xpos(&train_feat_x, train_feat));
ASSERT_EQ(AF_SUCCESS, af_get_features_ypos(&train_feat_y, train_feat));
af_array queryArray = 0;
af_array query_desc = 0;
af_array idx = 0;
af_array dist = 0;
af_array const_50 = 0;
af_array dist_thr = 0;
af_array train_idx = 0;
af_array query_idx = 0;
af_array query_feat_x = 0;
af_array query_feat_y = 0;
af_array H = 0;
af_array train_feat_x_idx = 0;
af_array train_feat_y_idx = 0;
af_array query_feat_x_idx = 0;
af_array query_feat_y_idx = 0;
af_features query_feat;
const float theta = af::Pi * 0.5f;
const dim_t test_d0 = inDims[0][0] * size_ratio;
const dim_t test_d1 = inDims[0][1] * size_ratio;
if (rotate)
ASSERT_EQ(AF_SUCCESS, af_rotate(&queryArray, trainArray, theta, false, AF_INTERP_NEAREST));
else
ASSERT_EQ(AF_SUCCESS, af_resize(&queryArray, trainArray, test_d0, test_d1, AF_INTERP_BILINEAR));
ASSERT_EQ(AF_SUCCESS, af_orb(&query_feat, &query_desc, queryArray, 20.0f, 2000, 1.2f, 8, true));
ASSERT_EQ(AF_SUCCESS, af_hamming_matcher(&idx, &dist, train_desc, query_desc, 0, 1));
dim_t distDims[4];
ASSERT_EQ(AF_SUCCESS, af_get_dims(&distDims[0], &distDims[1], &distDims[2], &distDims[3], dist));
ASSERT_EQ(AF_SUCCESS, af_constant(&const_50, 50, 2, distDims, u32));
ASSERT_EQ(AF_SUCCESS, af_lt(&dist_thr, dist, const_50, false));
ASSERT_EQ(AF_SUCCESS, af_where(&train_idx, dist_thr));
dim_t tidxDims[4];
ASSERT_EQ(AF_SUCCESS, af_get_dims(&tidxDims[0], &tidxDims[1], &tidxDims[2], &tidxDims[3], train_idx));
af_index_t tindexs;
tindexs.isSeq = false;
tindexs.idx.seq = af_make_seq(0, tidxDims[0]-1, 1);
tindexs.idx.arr = train_idx;
ASSERT_EQ(AF_SUCCESS, af_index_gen(&query_idx, idx, 1, &tindexs));
ASSERT_EQ(AF_SUCCESS, af_get_features_xpos(&query_feat_x, query_feat));
ASSERT_EQ(AF_SUCCESS, af_get_features_ypos(&query_feat_y, query_feat));
dim_t qidxDims[4];
ASSERT_EQ(AF_SUCCESS, af_get_dims(&qidxDims[0], &qidxDims[1], &qidxDims[2], &qidxDims[3], query_idx));
af_index_t qindexs;
qindexs.isSeq = false;
qindexs.idx.seq = af_make_seq(0, qidxDims[0]-1, 1);
qindexs.idx.arr = query_idx;
ASSERT_EQ(AF_SUCCESS, af_index_gen(&train_feat_x_idx, train_feat_x, 1, &tindexs));
ASSERT_EQ(AF_SUCCESS, af_index_gen(&train_feat_y_idx, train_feat_y, 1, &tindexs));
ASSERT_EQ(AF_SUCCESS, af_index_gen(&query_feat_x_idx, query_feat_x, 1, &qindexs));
ASSERT_EQ(AF_SUCCESS, af_index_gen(&query_feat_y_idx, query_feat_y, 1, &qindexs));
int inliers = 0;
ASSERT_EQ(AF_SUCCESS, af_homography(&H, &inliers, train_feat_x_idx, train_feat_y_idx,
query_feat_x_idx, query_feat_y_idx, htype,
3.0f, 1000, (af_dtype) af::dtype_traits<T>::af_type));
af::array HH(H);
af::array t = perspectiveTransform<T>(inDims[0], HH);
T* gold_t = new T[8];
for (int i = 0; i < 8; i++)
gold_t[i] = (T)0;
if (rotate) {
gold_t[1] = test_d0;
gold_t[2] = test_d0;
gold_t[4] = test_d1;
gold_t[5] = test_d1;
} else {
gold_t[2] = test_d1;
gold_t[3] = test_d1;
gold_t[5] = test_d0;
gold_t[6] = test_d0;
}
T* out_t = new T[8];
t.host(out_t);
for (int elIter = 0; elIter < 8; elIter++)
ASSERT_LE(fabs(out_t[elIter] - gold_t[elIter]), 70.f) << "at: " << elIter << std::endl;
delete[] gold_t;
delete[] out_t;
ASSERT_EQ(AF_SUCCESS, af_release_array(queryArray));
ASSERT_EQ(AF_SUCCESS, af_release_array(query_desc));
ASSERT_EQ(AF_SUCCESS, af_release_array(idx));
ASSERT_EQ(AF_SUCCESS, af_release_array(dist));
ASSERT_EQ(AF_SUCCESS, af_release_array(const_50));
ASSERT_EQ(AF_SUCCESS, af_release_array(dist_thr));
ASSERT_EQ(AF_SUCCESS, af_release_array(train_idx));
ASSERT_EQ(AF_SUCCESS, af_release_array(query_idx));
ASSERT_EQ(AF_SUCCESS, af_release_array(query_feat_x));
ASSERT_EQ(AF_SUCCESS, af_release_array(query_feat_y));
ASSERT_EQ(AF_SUCCESS, af_release_array(train_feat_x_idx));
ASSERT_EQ(AF_SUCCESS, af_release_array(train_feat_y_idx));
ASSERT_EQ(AF_SUCCESS, af_release_array(query_feat_x_idx));
ASSERT_EQ(AF_SUCCESS, af_release_array(query_feat_y_idx));
ASSERT_EQ(AF_SUCCESS, af_release_array(trainArray));
ASSERT_EQ(AF_SUCCESS, af_release_array(trainArray_f32));
ASSERT_EQ(AF_SUCCESS, af_release_array(train_desc));
ASSERT_EQ(AF_SUCCESS, af_release_array(train_feat_x));
ASSERT_EQ(AF_SUCCESS, af_release_array(train_feat_y));
}
#define HOMOGRAPHY_INIT(desc, image, htype, rotate, size_ratio) \
TYPED_TEST(Homography, desc) \
{ \
homographyTest<TypeParam>(string(TEST_DIR"/homography/"#image".test"), \
htype, rotate, size_ratio); \
}
HOMOGRAPHY_INIT(Tux_RANSAC, tux, AF_HOMOGRAPHY_RANSAC, false, 1.0f);
HOMOGRAPHY_INIT(Tux_RANSAC_90degrees, tux, AF_HOMOGRAPHY_RANSAC, true, 1.0f);
HOMOGRAPHY_INIT(Tux_RANSAC_resize, tux, AF_HOMOGRAPHY_RANSAC, false, 1.5f);
//HOMOGRAPHY_INIT(Tux_LMedS, tux, AF_HOMOGRAPHY_LMEDS, false, 1.0f);
//HOMOGRAPHY_INIT(Tux_LMedS_90degrees, tux, AF_HOMOGRAPHY_LMEDS, true, 1.0f);
//HOMOGRAPHY_INIT(Tux_LMedS_resize, tux, AF_HOMOGRAPHY_LMEDS, false, 1.5f);
///////////////////////////////////// CPP ////////////////////////////////
//
TEST(Homography, CPP)
{
if (noImageIOTests()) return;
vector<dim4> inDims;
vector<string> inFiles;
vector<vector<float> > gold;
readImageTests(string(TEST_DIR"/homography/tux.test"), inDims, inFiles, gold);
inFiles[0].insert(0,string(TEST_DIR"/homography/"));
const float size_ratio = 0.5f;
af::array train_img = af::loadImage(inFiles[0].c_str(), false);
af::array query_img = af::resize(size_ratio, train_img);
af::dim4 tDims = train_img.dims();
af::features feat_train, feat_query;
af::array desc_train, desc_query;
orb(feat_train, desc_train, train_img, 20, 2000, 1.2, 8, true);
orb(feat_query, desc_query, query_img, 20, 2000, 1.2, 8, true);
af::array idx, dist;
af::hammingMatcher(idx, dist, desc_train, desc_query, 0, 1);
af::array train_idx = where(dist < 30);
af::array query_idx = idx(train_idx);
af::array feat_train_x = feat_train.getX()(train_idx);
af::array feat_train_y = feat_train.getY()(train_idx);
af::array feat_train_score = feat_train.getScore()(train_idx);
af::array feat_train_orientation = feat_train.getOrientation()(train_idx);
af::array feat_train_size = feat_train.getSize()(train_idx);
af::array feat_query_x = feat_query.getX()(query_idx);
af::array feat_query_y = feat_query.getY()(query_idx);
af::array feat_query_score = feat_query.getScore()(query_idx);
af::array feat_query_orientation = feat_query.getOrientation()(query_idx);
af::array feat_query_size = feat_query.getSize()(query_idx);
af::array H;
int inliers = 0;
af::homography(H, inliers, feat_train_x, feat_train_y, feat_query_x, feat_query_y, AF_HOMOGRAPHY_RANSAC, 3.0f, 1000, f32);
float* gold_t = new float[8];
for (int i = 0; i < 8; i++)
gold_t[i] = 0.f;
gold_t[2] = tDims[1] * size_ratio;
gold_t[3] = tDims[1] * size_ratio;
gold_t[5] = tDims[0] * size_ratio;
gold_t[6] = tDims[0] * size_ratio;
af::array t = perspectiveTransform<float>(train_img.dims(), H);
float* out_t = new float[4*2];
t.host(out_t);
for (int elIter = 0; elIter < 8; elIter++)
ASSERT_LE(fabs(out_t[elIter] - gold_t[elIter]), 70.f) << "at: " << elIter << std::endl;
delete[] gold_t;
delete[] out_t;
}
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