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opencv/modules/calib3d/test/test_homography.cpp
Vadim Pisarevsky 481f786fe7 added simple HAL test; added RHO homography test
2015-05-28 17:05:43 +03:00

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/*M///////////////////////////////////////////////////////////////////////////////////////
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#include "test_precomp.hpp"
#include <time.h>
#define CALIB3D_HOMOGRAPHY_ERROR_MATRIX_SIZE 1
#define CALIB3D_HOMOGRAPHY_ERROR_MATRIX_DIFF 2
#define CALIB3D_HOMOGRAPHY_ERROR_REPROJ_DIFF 3
#define CALIB3D_HOMOGRAPHY_ERROR_RANSAC_MASK 4
#define CALIB3D_HOMOGRAPHY_ERROR_RANSAC_DIFF 5
#define MESSAGE_MATRIX_SIZE "Homography matrix must have 3*3 sizes."
#define MESSAGE_MATRIX_DIFF "Accuracy of homography transformation matrix less than required."
#define MESSAGE_REPROJ_DIFF_1 "Reprojection error for current pair of points more than required."
#define MESSAGE_REPROJ_DIFF_2 "Reprojection error is not optimal."
#define MESSAGE_RANSAC_MASK_1 "Sizes of inliers/outliers mask are incorrect."
#define MESSAGE_RANSAC_MASK_2 "Mask mustn't have any outliers."
#define MESSAGE_RANSAC_MASK_3 "All values of mask must be 1 (true) or 0 (false)."
#define MESSAGE_RANSAC_MASK_4 "Mask of inliers/outliers is incorrect."
#define MESSAGE_RANSAC_MASK_5 "Inlier in original mask shouldn't be outlier in found mask."
#define MESSAGE_RANSAC_DIFF "Reprojection error for current pair of points more than required."
#define MAX_COUNT_OF_POINTS 303
#define COUNT_NORM_TYPES 3
#define METHODS_COUNT 4
int NORM_TYPE[COUNT_NORM_TYPES] = {cv::NORM_L1, cv::NORM_L2, cv::NORM_INF};
int METHOD[METHODS_COUNT] = {0, cv::RANSAC, cv::LMEDS, cv::RHO};
using namespace cv;
using namespace std;
class CV_HomographyTest: public cvtest::ArrayTest
{
public:
CV_HomographyTest();
~CV_HomographyTest();
void run (int);
protected:
int method;
int image_size;
double reproj_threshold;
double sigma;
private:
float max_diff, max_2diff;
bool check_matrix_size(const cv::Mat& H);
bool check_matrix_diff(const cv::Mat& original, const cv::Mat& found, const int norm_type, double &diff);
int check_ransac_mask_1(const Mat& src, const Mat& mask);
int check_ransac_mask_2(const Mat& original_mask, const Mat& found_mask);
void print_information_1(int j, int N, int method, const Mat& H);
void print_information_2(int j, int N, int method, const Mat& H, const Mat& H_res, int k, double diff);
void print_information_3(int method, int j, int N, const Mat& mask);
void print_information_4(int method, int j, int N, int k, int l, double diff);
void print_information_5(int method, int j, int N, int l, double diff);
void print_information_6(int method, int j, int N, int k, double diff, bool value);
void print_information_7(int method, int j, int N, int k, double diff, bool original_value, bool found_value);
void print_information_8(int method, int j, int N, int k, int l, double diff);
};
CV_HomographyTest::CV_HomographyTest() : max_diff(1e-2f), max_2diff(2e-2f)
{
method = 0;
image_size = 100;
reproj_threshold = 3.0;
sigma = 0.01;
}
CV_HomographyTest::~CV_HomographyTest() {}
bool CV_HomographyTest::check_matrix_size(const cv::Mat& H)
{
return (H.rows == 3) && (H.cols == 3);
}
bool CV_HomographyTest::check_matrix_diff(const cv::Mat& original, const cv::Mat& found, const int norm_type, double &diff)
{
diff = cvtest::norm(original, found, norm_type);
return diff <= max_diff;
}
int CV_HomographyTest::check_ransac_mask_1(const Mat& src, const Mat& mask)
{
if (!(mask.cols == 1) && (mask.rows == src.cols)) return 1;
if (countNonZero(mask) < mask.rows) return 2;
for (int i = 0; i < mask.rows; ++i) if (mask.at<uchar>(i, 0) > 1) return 3;
return 0;
}
int CV_HomographyTest::check_ransac_mask_2(const Mat& original_mask, const Mat& found_mask)
{
if (!(found_mask.cols == 1) && (found_mask.rows == original_mask.rows)) return 1;
for (int i = 0; i < found_mask.rows; ++i) if (found_mask.at<uchar>(i, 0) > 1) return 2;
return 0;
}
void CV_HomographyTest::print_information_1(int j, int N, int _method, const Mat& H)
{
cout << endl; cout << "Checking for homography matrix sizes..." << endl; cout << endl;
cout << "Type of srcPoints: "; if ((j>-1) && (j<2)) cout << "Mat of CV_32FC2"; else cout << "vector <Point2f>";
cout << " Type of dstPoints: "; if (j % 2 == 0) cout << "Mat of CV_32FC2"; else cout << "vector <Point2f>"; cout << endl;
cout << "Count of points: " << N << endl; cout << endl;
cout << "Method: "; if (_method == 0) cout << 0; else if (_method == 8) cout << "RANSAC"; else if (_method == cv::RHO) cout << "RHO"; else cout << "LMEDS"; cout << endl;
cout << "Homography matrix:" << endl; cout << endl;
cout << H << endl; cout << endl;
cout << "Number of rows: " << H.rows << " Number of cols: " << H.cols << endl; cout << endl;
}
void CV_HomographyTest::print_information_2(int j, int N, int _method, const Mat& H, const Mat& H_res, int k, double diff)
{
cout << endl; cout << "Checking for accuracy of homography matrix computing..." << endl; cout << endl;
cout << "Type of srcPoints: "; if ((j>-1) && (j<2)) cout << "Mat of CV_32FC2"; else cout << "vector <Point2f>";
cout << " Type of dstPoints: "; if (j % 2 == 0) cout << "Mat of CV_32FC2"; else cout << "vector <Point2f>"; cout << endl;
cout << "Count of points: " << N << endl; cout << endl;
cout << "Method: "; if (_method == 0) cout << 0; else if (_method == 8) cout << "RANSAC"; else if (_method == cv::RHO) cout << "RHO"; else cout << "LMEDS"; cout << endl;
cout << "Original matrix:" << endl; cout << endl;
cout << H << endl; cout << endl;
cout << "Found matrix:" << endl; cout << endl;
cout << H_res << endl; cout << endl;
cout << "Norm type using in criteria: "; if (NORM_TYPE[k] == 1) cout << "INF"; else if (NORM_TYPE[k] == 2) cout << "L1"; else cout << "L2"; cout << endl;
cout << "Difference between matrices: " << diff << endl;
cout << "Maximum allowed difference: " << max_diff << endl; cout << endl;
}
void CV_HomographyTest::print_information_3(int _method, int j, int N, const Mat& mask)
{
cout << endl; cout << "Checking for inliers/outliers mask..." << endl; cout << endl;
cout << "Type of srcPoints: "; if ((j>-1) && (j<2)) cout << "Mat of CV_32FC2"; else cout << "vector <Point2f>";
cout << " Type of dstPoints: "; if (j % 2 == 0) cout << "Mat of CV_32FC2"; else cout << "vector <Point2f>"; cout << endl;
cout << "Count of points: " << N << endl; cout << endl;
cout << "Method: "; if (_method == RANSAC) cout << "RANSAC" << endl; else if (_method == cv::RHO) cout << "RHO" << endl; else cout << _method << endl;
cout << "Found mask:" << endl; cout << endl;
cout << mask << endl; cout << endl;
cout << "Number of rows: " << mask.rows << " Number of cols: " << mask.cols << endl; cout << endl;
}
void CV_HomographyTest::print_information_4(int _method, int j, int N, int k, int l, double diff)
{
cout << endl; cout << "Checking for accuracy of reprojection error computing..." << endl; cout << endl;
cout << "Method: "; if (_method == 0) cout << 0 << endl; else cout << "CV_LMEDS" << endl;
cout << "Type of srcPoints: "; if ((j>-1) && (j<2)) cout << "Mat of CV_32FC2"; else cout << "vector <Point2f>";
cout << " Type of dstPoints: "; if (j % 2 == 0) cout << "Mat of CV_32FC2"; else cout << "vector <Point2f>"; cout << endl;
cout << "Sigma of normal noise: " << sigma << endl;
cout << "Count of points: " << N << endl;
cout << "Number of point: " << k << endl;
cout << "Norm type using in criteria: "; if (NORM_TYPE[l] == 1) cout << "INF"; else if (NORM_TYPE[l] == 2) cout << "L1"; else cout << "L2"; cout << endl;
cout << "Difference with noise of point: " << diff << endl;
cout << "Maxumum allowed difference: " << max_2diff << endl; cout << endl;
}
void CV_HomographyTest::print_information_5(int _method, int j, int N, int l, double diff)
{
cout << endl; cout << "Checking for accuracy of reprojection error computing..." << endl; cout << endl;
cout << "Method: "; if (_method == 0) cout << 0 << endl; else cout << "CV_LMEDS" << endl;
cout << "Type of srcPoints: "; if ((j>-1) && (j<2)) cout << "Mat of CV_32FC2"; else cout << "vector <Point2f>";
cout << " Type of dstPoints: "; if (j % 2 == 0) cout << "Mat of CV_32FC2"; else cout << "vector <Point2f>"; cout << endl;
cout << "Sigma of normal noise: " << sigma << endl;
cout << "Count of points: " << N << endl;
cout << "Norm type using in criteria: "; if (NORM_TYPE[l] == 1) cout << "INF"; else if (NORM_TYPE[l] == 2) cout << "L1"; else cout << "L2"; cout << endl;
cout << "Difference with noise of points: " << diff << endl;
cout << "Maxumum allowed difference: " << max_diff << endl; cout << endl;
}
void CV_HomographyTest::print_information_6(int _method, int j, int N, int k, double diff, bool value)
{
cout << endl; cout << "Checking for inliers/outliers mask..." << endl; cout << endl;
cout << "Method: "; if (_method == RANSAC) cout << "RANSAC" << endl; else if (_method == cv::RHO) cout << "RHO" << endl; else cout << _method << endl;
cout << "Type of srcPoints: "; if ((j>-1) && (j<2)) cout << "Mat of CV_32FC2"; else cout << "vector <Point2f>";
cout << " Type of dstPoints: "; if (j % 2 == 0) cout << "Mat of CV_32FC2"; else cout << "vector <Point2f>"; cout << endl;
cout << "Count of points: " << N << " " << endl;
cout << "Number of point: " << k << " " << endl;
cout << "Reprojection error for this point: " << diff << " " << endl;
cout << "Reprojection error threshold: " << reproj_threshold << " " << endl;
cout << "Value of found mask: "<< value << endl; cout << endl;
}
void CV_HomographyTest::print_information_7(int _method, int j, int N, int k, double diff, bool original_value, bool found_value)
{
cout << endl; cout << "Checking for inliers/outliers mask..." << endl; cout << endl;
cout << "Method: "; if (_method == RANSAC) cout << "RANSAC" << endl; else if (_method == cv::RHO) cout << "RHO" << endl; else cout << _method << endl;
cout << "Type of srcPoints: "; if ((j>-1) && (j<2)) cout << "Mat of CV_32FC2"; else cout << "vector <Point2f>";
cout << " Type of dstPoints: "; if (j % 2 == 0) cout << "Mat of CV_32FC2"; else cout << "vector <Point2f>"; cout << endl;
cout << "Count of points: " << N << " " << endl;
cout << "Number of point: " << k << " " << endl;
cout << "Reprojection error for this point: " << diff << " " << endl;
cout << "Reprojection error threshold: " << reproj_threshold << " " << endl;
cout << "Value of original mask: "<< original_value << " Value of found mask: " << found_value << endl; cout << endl;
}
void CV_HomographyTest::print_information_8(int _method, int j, int N, int k, int l, double diff)
{
cout << endl; cout << "Checking for reprojection error of inlier..." << endl; cout << endl;
cout << "Method: "; if (_method == RANSAC) cout << "RANSAC" << endl; else if (_method == cv::RHO) cout << "RHO" << endl; else cout << _method << endl;
cout << "Sigma of normal noise: " << sigma << endl;
cout << "Type of srcPoints: "; if ((j>-1) && (j<2)) cout << "Mat of CV_32FC2"; else cout << "vector <Point2f>";
cout << " Type of dstPoints: "; if (j % 2 == 0) cout << "Mat of CV_32FC2"; else cout << "vector <Point2f>"; cout << endl;
cout << "Count of points: " << N << " " << endl;
cout << "Number of point: " << k << " " << endl;
cout << "Norm type using in criteria: "; if (NORM_TYPE[l] == 1) cout << "INF"; else if (NORM_TYPE[l] == 2) cout << "L1"; else cout << "L2"; cout << endl;
cout << "Difference with noise of point: " << diff << endl;
cout << "Maxumum allowed difference: " << max_2diff << endl; cout << endl;
}
void CV_HomographyTest::run(int)
{
for (int N = 4; N <= MAX_COUNT_OF_POINTS; ++N)
{
RNG& rng = ts->get_rng();
float *src_data = new float [2*N];
for (int i = 0; i < N; ++i)
{
src_data[2*i] = (float)cvtest::randReal(rng)*image_size;
src_data[2*i+1] = (float)cvtest::randReal(rng)*image_size;
}
cv::Mat src_mat_2f(1, N, CV_32FC2, src_data),
src_mat_2d(2, N, CV_32F, src_data),
src_mat_3d(3, N, CV_32F);
cv::Mat dst_mat_2f, dst_mat_2d, dst_mat_3d;
vector <Point2f> src_vec, dst_vec;
for (int i = 0; i < N; ++i)
{
float *tmp = src_mat_2d.ptr<float>()+2*i;
src_mat_3d.at<float>(0, i) = tmp[0];
src_mat_3d.at<float>(1, i) = tmp[1];
src_mat_3d.at<float>(2, i) = 1.0f;
src_vec.push_back(Point2f(tmp[0], tmp[1]));
}
double fi = cvtest::randReal(rng)*2*CV_PI;
double t_x = cvtest::randReal(rng)*sqrt(image_size*1.0),
t_y = cvtest::randReal(rng)*sqrt(image_size*1.0);
double Hdata[9] = { cos(fi), -sin(fi), t_x,
sin(fi), cos(fi), t_y,
0.0f, 0.0f, 1.0f };
cv::Mat H_64(3, 3, CV_64F, Hdata), H_32;
H_64.convertTo(H_32, CV_32F);
dst_mat_3d = H_32*src_mat_3d;
dst_mat_2d.create(2, N, CV_32F); dst_mat_2f.create(1, N, CV_32FC2);
for (int i = 0; i < N; ++i)
{
float *tmp_2f = dst_mat_2f.ptr<float>()+2*i;
tmp_2f[0] = dst_mat_2d.at<float>(0, i) = dst_mat_3d.at<float>(0, i) /= dst_mat_3d.at<float>(2, i);
tmp_2f[1] = dst_mat_2d.at<float>(1, i) = dst_mat_3d.at<float>(1, i) /= dst_mat_3d.at<float>(2, i);
dst_mat_3d.at<float>(2, i) = 1.0f;
dst_vec.push_back(Point2f(tmp_2f[0], tmp_2f[1]));
}
for (int i = 0; i < METHODS_COUNT; ++i)
{
method = METHOD[i];
switch (method)
{
case 0:
case LMEDS:
{
Mat H_res_64 [4] = { cv::findHomography(src_mat_2f, dst_mat_2f, method),
cv::findHomography(src_mat_2f, dst_vec, method),
cv::findHomography(src_vec, dst_mat_2f, method),
cv::findHomography(src_vec, dst_vec, method) };
for (int j = 0; j < 4; ++j)
{
if (!check_matrix_size(H_res_64[j]))
{
print_information_1(j, N, method, H_res_64[j]);
CV_Error(CALIB3D_HOMOGRAPHY_ERROR_MATRIX_SIZE, MESSAGE_MATRIX_SIZE);
return;
}
double diff;
for (int k = 0; k < COUNT_NORM_TYPES; ++k)
if (!check_matrix_diff(H_64, H_res_64[j], NORM_TYPE[k], diff))
{
print_information_2(j, N, method, H_64, H_res_64[j], k, diff);
CV_Error(CALIB3D_HOMOGRAPHY_ERROR_MATRIX_DIFF, MESSAGE_MATRIX_DIFF);
return;
}
}
continue;
}
case cv::RHO:
case RANSAC:
{
cv::Mat mask [4]; double diff;
Mat H_res_64 [4] = { cv::findHomography(src_mat_2f, dst_mat_2f, method, reproj_threshold, mask[0]),
cv::findHomography(src_mat_2f, dst_vec, method, reproj_threshold, mask[1]),
cv::findHomography(src_vec, dst_mat_2f, method, reproj_threshold, mask[2]),
cv::findHomography(src_vec, dst_vec, method, reproj_threshold, mask[3]) };
for (int j = 0; j < 4; ++j)
{
if (!check_matrix_size(H_res_64[j]))
{
print_information_1(j, N, method, H_res_64[j]);
CV_Error(CALIB3D_HOMOGRAPHY_ERROR_MATRIX_SIZE, MESSAGE_MATRIX_SIZE);
return;
}
for (int k = 0; k < COUNT_NORM_TYPES; ++k)
if (!check_matrix_diff(H_64, H_res_64[j], NORM_TYPE[k], diff))
{
print_information_2(j, N, method, H_64, H_res_64[j], k, diff);
CV_Error(CALIB3D_HOMOGRAPHY_ERROR_MATRIX_DIFF, MESSAGE_MATRIX_DIFF);
return;
}
int code = check_ransac_mask_1(src_mat_2f, mask[j]);
if (code)
{
print_information_3(method, j, N, mask[j]);
switch (code)
{
case 1: { CV_Error(CALIB3D_HOMOGRAPHY_ERROR_RANSAC_MASK, MESSAGE_RANSAC_MASK_1); break; }
case 2: { CV_Error(CALIB3D_HOMOGRAPHY_ERROR_RANSAC_MASK, MESSAGE_RANSAC_MASK_2); break; }
case 3: { CV_Error(CALIB3D_HOMOGRAPHY_ERROR_RANSAC_MASK, MESSAGE_RANSAC_MASK_3); break; }
default: break;
}
return;
}
}
continue;
}
default: continue;
}
}
Mat noise_2f(1, N, CV_32FC2);
rng.fill(noise_2f, RNG::NORMAL, Scalar::all(0), Scalar::all(sigma));
cv::Mat mask(N, 1, CV_8UC1);
for (int i = 0; i < N; ++i)
{
float *a = noise_2f.ptr<float>()+2*i, *_2f = dst_mat_2f.ptr<float>()+2*i;
_2f[0] += a[0]; _2f[1] += a[1];
mask.at<bool>(i, 0) = !(sqrt(a[0]*a[0]+a[1]*a[1]) > reproj_threshold);
}
for (int i = 0; i < METHODS_COUNT; ++i)
{
method = METHOD[i];
switch (method)
{
case 0:
case LMEDS:
{
Mat H_res_64 [4] = { cv::findHomography(src_mat_2f, dst_mat_2f),
cv::findHomography(src_mat_2f, dst_vec),
cv::findHomography(src_vec, dst_mat_2f),
cv::findHomography(src_vec, dst_vec) };
for (int j = 0; j < 4; ++j)
{
if (!check_matrix_size(H_res_64[j]))
{
print_information_1(j, N, method, H_res_64[j]);
CV_Error(CALIB3D_HOMOGRAPHY_ERROR_MATRIX_SIZE, MESSAGE_MATRIX_SIZE);
return;
}
Mat H_res_32; H_res_64[j].convertTo(H_res_32, CV_32F);
cv::Mat dst_res_3d(3, N, CV_32F), noise_2d(2, N, CV_32F);
for (int k = 0; k < N; ++k)
{
Mat tmp_mat_3d = H_res_32*src_mat_3d.col(k);
dst_res_3d.at<float>(0, k) = tmp_mat_3d.at<float>(0, 0) /= tmp_mat_3d.at<float>(2, 0);
dst_res_3d.at<float>(1, k) = tmp_mat_3d.at<float>(1, 0) /= tmp_mat_3d.at<float>(2, 0);
dst_res_3d.at<float>(2, k) = tmp_mat_3d.at<float>(2, 0) = 1.0f;
float *a = noise_2f.ptr<float>()+2*k;
noise_2d.at<float>(0, k) = a[0]; noise_2d.at<float>(1, k) = a[1];
for (int l = 0; l < COUNT_NORM_TYPES; ++l)
if (cv::norm(tmp_mat_3d, dst_mat_3d.col(k), NORM_TYPE[l]) - cv::norm(noise_2d.col(k), NORM_TYPE[l]) > max_2diff)
{
print_information_4(method, j, N, k, l, cv::norm(tmp_mat_3d, dst_mat_3d.col(k), NORM_TYPE[l]) - cv::norm(noise_2d.col(k), NORM_TYPE[l]));
CV_Error(CALIB3D_HOMOGRAPHY_ERROR_REPROJ_DIFF, MESSAGE_REPROJ_DIFF_1);
return;
}
}
for (int l = 0; l < COUNT_NORM_TYPES; ++l)
if (cv::norm(dst_res_3d, dst_mat_3d, NORM_TYPE[l]) - cv::norm(noise_2d, NORM_TYPE[l]) > max_diff)
{
print_information_5(method, j, N, l, cv::norm(dst_res_3d, dst_mat_3d, NORM_TYPE[l]) - cv::norm(noise_2d, NORM_TYPE[l]));
CV_Error(CALIB3D_HOMOGRAPHY_ERROR_REPROJ_DIFF, MESSAGE_REPROJ_DIFF_2);
return;
}
}
continue;
}
case cv::RHO:
case RANSAC:
{
cv::Mat mask_res [4];
Mat H_res_64 [4] = { cv::findHomography(src_mat_2f, dst_mat_2f, method, reproj_threshold, mask_res[0]),
cv::findHomography(src_mat_2f, dst_vec, method, reproj_threshold, mask_res[1]),
cv::findHomography(src_vec, dst_mat_2f, method, reproj_threshold, mask_res[2]),
cv::findHomography(src_vec, dst_vec, method, reproj_threshold, mask_res[3]) };
for (int j = 0; j < 4; ++j)
{
if (!check_matrix_size(H_res_64[j]))
{
print_information_1(j, N, method, H_res_64[j]);
CV_Error(CALIB3D_HOMOGRAPHY_ERROR_MATRIX_SIZE, MESSAGE_MATRIX_SIZE);
return;
}
int code = check_ransac_mask_2(mask, mask_res[j]);
if (code)
{
print_information_3(method, j, N, mask_res[j]);
switch (code)
{
case 1: { CV_Error(CALIB3D_HOMOGRAPHY_ERROR_RANSAC_MASK, MESSAGE_RANSAC_MASK_1); break; }
case 2: { CV_Error(CALIB3D_HOMOGRAPHY_ERROR_RANSAC_MASK, MESSAGE_RANSAC_MASK_3); break; }
default: break;
}
return;
}
cv::Mat H_res_32; H_res_64[j].convertTo(H_res_32, CV_32F);
cv::Mat dst_res_3d = H_res_32*src_mat_3d;
for (int k = 0; k < N; ++k)
{
dst_res_3d.at<float>(0, k) /= dst_res_3d.at<float>(2, k);
dst_res_3d.at<float>(1, k) /= dst_res_3d.at<float>(2, k);
dst_res_3d.at<float>(2, k) = 1.0f;
float *p = dst_mat_2f.ptr<float>()+2*k;
dst_mat_3d.at<float>(0, k) = p[0];
dst_mat_3d.at<float>(1, k) = p[1];
double diff = cv::norm(dst_res_3d.col(k), dst_mat_3d.col(k), NORM_L2);
if (mask_res[j].at<bool>(k, 0) != (diff <= reproj_threshold))
{
print_information_6(method, j, N, k, diff, mask_res[j].at<bool>(k, 0));
CV_Error(CALIB3D_HOMOGRAPHY_ERROR_RANSAC_MASK, MESSAGE_RANSAC_MASK_4);
return;
}
if (mask.at<bool>(k, 0) && !mask_res[j].at<bool>(k, 0))
{
print_information_7(method, j, N, k, diff, mask.at<bool>(k, 0), mask_res[j].at<bool>(k, 0));
CV_Error(CALIB3D_HOMOGRAPHY_ERROR_RANSAC_MASK, MESSAGE_RANSAC_MASK_5);
return;
}
if (mask_res[j].at<bool>(k, 0))
{
float *a = noise_2f.ptr<float>()+2*k;
dst_mat_3d.at<float>(0, k) -= a[0];
dst_mat_3d.at<float>(1, k) -= a[1];
cv::Mat noise_2d(2, 1, CV_32F);
noise_2d.at<float>(0, 0) = a[0]; noise_2d.at<float>(1, 0) = a[1];
for (int l = 0; l < COUNT_NORM_TYPES; ++l)
{
diff = cv::norm(dst_res_3d.col(k), dst_mat_3d.col(k), NORM_TYPE[l]);
if (diff - cv::norm(noise_2d, NORM_TYPE[l]) > max_2diff)
{
print_information_8(method, j, N, k, l, diff - cv::norm(noise_2d, NORM_TYPE[l]));
CV_Error(CALIB3D_HOMOGRAPHY_ERROR_RANSAC_DIFF, MESSAGE_RANSAC_DIFF);
return;
}
}
}
}
}
continue;
}
default: continue;
}
}
}
}
TEST(Calib3d_Homography, accuracy) { CV_HomographyTest test; test.safe_run(); }
TEST(Calib3d_Homography, EKcase)
{
float pt1data[] =
{
2.80073029e+002f, 2.39591217e+002f, 2.21912201e+002f, 2.59783997e+002f,
2.16053192e+002f, 2.78826569e+002f, 2.22782532e+002f, 2.82330383e+002f,
2.09924820e+002f, 2.89122559e+002f, 2.11077698e+002f, 2.89384674e+002f,
2.25287689e+002f, 2.88795532e+002f, 2.11180801e+002f, 2.89653503e+002f,
2.24126404e+002f, 2.90466064e+002f, 2.10914429e+002f, 2.90886963e+002f,
2.23439362e+002f, 2.91657715e+002f, 2.24809387e+002f, 2.91891602e+002f,
2.09809082e+002f, 2.92891113e+002f, 2.08771164e+002f, 2.93093231e+002f,
2.23160095e+002f, 2.93259460e+002f, 2.07874023e+002f, 2.93989990e+002f,
2.08963638e+002f, 2.94209839e+002f, 2.23963165e+002f, 2.94479645e+002f,
2.23241791e+002f, 2.94887817e+002f, 2.09438782e+002f, 2.95233337e+002f,
2.08901886e+002f, 2.95762878e+002f, 2.21867981e+002f, 2.95747711e+002f,
2.24195511e+002f, 2.98270905e+002f, 2.09331345e+002f, 3.05958191e+002f,
2.24727875e+002f, 3.07186035e+002f, 2.26718842e+002f, 3.08095795e+002f,
2.25363953e+002f, 3.08200226e+002f, 2.19897797e+002f, 3.13845093e+002f,
2.25013474e+002f, 3.15558777e+002f
};
float pt2data[] =
{
1.84072723e+002f, 1.43591202e+002f, 1.25912483e+002f, 1.63783859e+002f,
2.06439407e+002f, 2.20573929e+002f, 1.43801437e+002f, 1.80703903e+002f,
9.77904129e+000f, 2.49660202e+002f, 1.38458405e+001f, 2.14502701e+002f,
1.50636337e+002f, 2.15597183e+002f, 6.43103180e+001f, 2.51667648e+002f,
1.54952499e+002f, 2.20780014e+002f, 1.26638412e+002f, 2.43040924e+002f,
3.67568909e+002f, 1.83624954e+001f, 1.60657944e+002f, 2.21794052e+002f,
-1.29507828e+000f, 3.32472443e+002f, 8.51442242e+000f, 4.15561554e+002f,
1.27161377e+002f, 1.97260361e+002f, 5.40714645e+000f, 4.90978302e+002f,
2.25571690e+001f, 3.96912415e+002f, 2.95664978e+002f, 7.36064959e+000f,
1.27241104e+002f, 1.98887573e+002f, -1.25569367e+000f, 3.87713226e+002f,
1.04194012e+001f, 4.31495758e+002f, 1.25868874e+002f, 1.99751617e+002f,
1.28195480e+002f, 2.02270355e+002f, 2.23436356e+002f, 1.80489182e+002f,
1.28727692e+002f, 2.11185410e+002f, 2.03336639e+002f, 2.52182083e+002f,
1.29366486e+002f, 2.12201904e+002f, 1.23897598e+002f, 2.17847351e+002f,
1.29015259e+002f, 2.19560623e+002f
};
int npoints = (int)(sizeof(pt1data)/sizeof(pt1data[0])/2);
Mat p1(1, npoints, CV_32FC2, pt1data);
Mat p2(1, npoints, CV_32FC2, pt2data);
Mat mask;
Mat h = findHomography(p1, p2, RANSAC, 0.01, mask);
ASSERT_TRUE(!h.empty());
transpose(mask, mask);
Mat p3, mask2;
int ninliers = countNonZero(mask);
Mat nmask[] = { mask, mask };
merge(nmask, 2, mask2);
perspectiveTransform(p1, p3, h);
mask2 = mask2.reshape(1);
p2 = p2.reshape(1);
p3 = p3.reshape(1);
double err = norm(p2, p3, NORM_INF, mask2);
printf("ninliers: %d, inliers err: %.2g\n", ninliers, err);
ASSERT_GE(ninliers, 10);
ASSERT_LE(err, 0.01);
}
TEST(Calib3d_Homography, fromImages)
{
Mat img_1 = imread(cvtest::TS::ptr()->get_data_path() + "cv/optflow/image1.png", 0);
Mat img_2 = imread(cvtest::TS::ptr()->get_data_path() + "cv/optflow/image2.png", 0);
Ptr<ORB> orb = ORB::create();
vector<KeyPoint> keypoints_1, keypoints_2;
Mat descriptors_1, descriptors_2;
orb->detectAndCompute( img_1, Mat(), keypoints_1, descriptors_1, false );
orb->detectAndCompute( img_2, Mat(), keypoints_2, descriptors_2, false );
//-- Step 3: Matching descriptor vectors using Brute Force matcher
BFMatcher matcher(NORM_HAMMING,false);
std::vector< DMatch > matches;
matcher.match( descriptors_1, descriptors_2, matches );
double max_dist = 0; double min_dist = 100;
//-- Quick calculation of max and min distances between keypoints
for( int i = 0; i < descriptors_1.rows; i++ )
{
double dist = matches[i].distance;
if( dist < min_dist ) min_dist = dist;
if( dist > max_dist ) max_dist = dist;
}
//-- Draw only "good" matches (i.e. whose distance is less than 3*min_dist )
std::vector< DMatch > good_matches;
for( int i = 0; i < descriptors_1.rows; i++ )
{
if( matches[i].distance <= 100 )
good_matches.push_back( matches[i]);
}
//-- Localize the model
std::vector<Point2f> pointframe1;
std::vector<Point2f> pointframe2;
for( int i = 0; i < (int)good_matches.size(); i++ )
{
//-- Get the keypoints from the good matches
pointframe1.push_back( keypoints_1[ good_matches[i].queryIdx ].pt );
pointframe2.push_back( keypoints_2[ good_matches[i].trainIdx ].pt );
}
Mat H0, H1, inliers0, inliers1;
double min_t0 = DBL_MAX, min_t1 = DBL_MAX;
for( int i = 0; i < 10; i++ )
{
double t = (double)getTickCount();
H0 = findHomography( pointframe1, pointframe2, RANSAC, 3.0, inliers0 );
t = (double)getTickCount() - t;
min_t0 = std::min(min_t0, t);
}
int ninliers0 = countNonZero(inliers0);
for( int i = 0; i < 10; i++ )
{
double t = (double)getTickCount();
H1 = findHomography( pointframe1, pointframe2, RHO, 3.0, inliers1 );
t = (double)getTickCount() - t;
min_t1 = std::min(min_t1, t);
}
int ninliers1 = countNonZero(inliers1);
double freq = getTickFrequency();
printf("nfeatures1 = %d, nfeatures2=%d, matches=%d, ninliers(RANSAC)=%d, "
"time(RANSAC)=%.2fmsec, ninliers(RHO)=%d, time(RHO)=%.2fmsec\n",
(int)keypoints_1.size(), (int)keypoints_2.size(),
(int)good_matches.size(), ninliers0, min_t0*1000./freq, ninliers1, min_t1*1000./freq);
ASSERT_TRUE(!H0.empty());
ASSERT_GE(ninliers0, 80);
ASSERT_TRUE(!H1.empty());
ASSERT_GE(ninliers1, 80);
}
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