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42
43 #include "test_precomp.hpp"
44 #include <time.h>
45
46 #define CALIB3D_HOMOGRAPHY_ERROR_MATRIX_SIZE 1
47 #define CALIB3D_HOMOGRAPHY_ERROR_MATRIX_DIFF 2
48 #define CALIB3D_HOMOGRAPHY_ERROR_REPROJ_DIFF 3
49 #define CALIB3D_HOMOGRAPHY_ERROR_RANSAC_MASK 4
50 #define CALIB3D_HOMOGRAPHY_ERROR_RANSAC_DIFF 5
51
52 #define MESSAGE_MATRIX_SIZE "Homography matrix must have 3*3 sizes."
53 #define MESSAGE_MATRIX_DIFF "Accuracy of homography transformation matrix less than required."
54 #define MESSAGE_REPROJ_DIFF_1 "Reprojection error for current pair of points more than required."
55 #define MESSAGE_REPROJ_DIFF_2 "Reprojection error is not optimal."
56 #define MESSAGE_RANSAC_MASK_1 "Sizes of inliers/outliers mask are incorrect."
57 #define MESSAGE_RANSAC_MASK_2 "Mask mustn't have any outliers."
58 #define MESSAGE_RANSAC_MASK_3 "All values of mask must be 1 (true) or 0 (false)."
59 #define MESSAGE_RANSAC_MASK_4 "Mask of inliers/outliers is incorrect."
60 #define MESSAGE_RANSAC_MASK_5 "Inlier in original mask shouldn't be outlier in found mask."
61 #define MESSAGE_RANSAC_DIFF "Reprojection error for current pair of points more than required."
62
63 #define MAX_COUNT_OF_POINTS 303
64 #define COUNT_NORM_TYPES 3
65 #define METHODS_COUNT 4
66
67 int NORM_TYPE[COUNT_NORM_TYPES] = {cv::NORM_L1, cv::NORM_L2, cv::NORM_INF};
68 int METHOD[METHODS_COUNT] = {0, cv::RANSAC, cv::LMEDS, cv::RHO};
69
70 using namespace cv;
71 using namespace std;
72
73 class CV_HomographyTest: public cvtest::ArrayTest
74 {
75 public:
76 CV_HomographyTest();
77 ~CV_HomographyTest();
78
79 void run (int);
80
81 protected:
82
83 int method;
84 int image_size;
85 double reproj_threshold;
86 double sigma;
87
88 private:
89 float max_diff, max_2diff;
90 bool check_matrix_size(const cv::Mat& H);
91 bool check_matrix_diff(const cv::Mat& original, const cv::Mat& found, const int norm_type, double &diff);
92 int check_ransac_mask_1(const Mat& src, const Mat& mask);
93 int check_ransac_mask_2(const Mat& original_mask, const Mat& found_mask);
94
95 void print_information_1(int j, int N, int method, const Mat& H);
96 void print_information_2(int j, int N, int method, const Mat& H, const Mat& H_res, int k, double diff);
97 void print_information_3(int method, int j, int N, const Mat& mask);
98 void print_information_4(int method, int j, int N, int k, int l, double diff);
99 void print_information_5(int method, int j, int N, int l, double diff);
100 void print_information_6(int method, int j, int N, int k, double diff, bool value);
101 void print_information_7(int method, int j, int N, int k, double diff, bool original_value, bool found_value);
102 void print_information_8(int method, int j, int N, int k, int l, double diff);
103 };
104
CV_HomographyTest()105 CV_HomographyTest::CV_HomographyTest() : max_diff(1e-2f), max_2diff(2e-2f)
106 {
107 method = 0;
108 image_size = 100;
109 reproj_threshold = 3.0;
110 sigma = 0.01;
111 }
112
~CV_HomographyTest()113 CV_HomographyTest::~CV_HomographyTest() {}
114
check_matrix_size(const cv::Mat & H)115 bool CV_HomographyTest::check_matrix_size(const cv::Mat& H)
116 {
117 return (H.rows == 3) && (H.cols == 3);
118 }
119
check_matrix_diff(const cv::Mat & original,const cv::Mat & found,const int norm_type,double & diff)120 bool CV_HomographyTest::check_matrix_diff(const cv::Mat& original, const cv::Mat& found, const int norm_type, double &diff)
121 {
122 diff = cvtest::norm(original, found, norm_type);
123 return diff <= max_diff;
124 }
125
check_ransac_mask_1(const Mat & src,const Mat & mask)126 int CV_HomographyTest::check_ransac_mask_1(const Mat& src, const Mat& mask)
127 {
128 if (!(mask.cols == 1) && (mask.rows == src.cols)) return 1;
129 if (countNonZero(mask) < mask.rows) return 2;
130 for (int i = 0; i < mask.rows; ++i) if (mask.at<uchar>(i, 0) > 1) return 3;
131 return 0;
132 }
133
check_ransac_mask_2(const Mat & original_mask,const Mat & found_mask)134 int CV_HomographyTest::check_ransac_mask_2(const Mat& original_mask, const Mat& found_mask)
135 {
136 if (!(found_mask.cols == 1) && (found_mask.rows == original_mask.rows)) return 1;
137 for (int i = 0; i < found_mask.rows; ++i) if (found_mask.at<uchar>(i, 0) > 1) return 2;
138 return 0;
139 }
140
print_information_1(int j,int N,int _method,const Mat & H)141 void CV_HomographyTest::print_information_1(int j, int N, int _method, const Mat& H)
142 {
143 cout << endl; cout << "Checking for homography matrix sizes..." << endl; cout << endl;
144 cout << "Type of srcPoints: "; if ((j>-1) && (j<2)) cout << "Mat of CV_32FC2"; else cout << "vector <Point2f>";
145 cout << " Type of dstPoints: "; if (j % 2 == 0) cout << "Mat of CV_32FC2"; else cout << "vector <Point2f>"; cout << endl;
146 cout << "Count of points: " << N << endl; cout << endl;
147 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;
148 cout << "Homography matrix:" << endl; cout << endl;
149 cout << H << endl; cout << endl;
150 cout << "Number of rows: " << H.rows << " Number of cols: " << H.cols << endl; cout << endl;
151 }
152
print_information_2(int j,int N,int _method,const Mat & H,const Mat & H_res,int k,double diff)153 void CV_HomographyTest::print_information_2(int j, int N, int _method, const Mat& H, const Mat& H_res, int k, double diff)
154 {
155 cout << endl; cout << "Checking for accuracy of homography matrix computing..." << endl; cout << endl;
156 cout << "Type of srcPoints: "; if ((j>-1) && (j<2)) cout << "Mat of CV_32FC2"; else cout << "vector <Point2f>";
157 cout << " Type of dstPoints: "; if (j % 2 == 0) cout << "Mat of CV_32FC2"; else cout << "vector <Point2f>"; cout << endl;
158 cout << "Count of points: " << N << endl; cout << endl;
159 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;
160 cout << "Original matrix:" << endl; cout << endl;
161 cout << H << endl; cout << endl;
162 cout << "Found matrix:" << endl; cout << endl;
163 cout << H_res << endl; cout << endl;
164 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;
165 cout << "Difference between matrices: " << diff << endl;
166 cout << "Maximum allowed difference: " << max_diff << endl; cout << endl;
167 }
168
print_information_3(int _method,int j,int N,const Mat & mask)169 void CV_HomographyTest::print_information_3(int _method, int j, int N, const Mat& mask)
170 {
171 cout << endl; cout << "Checking for inliers/outliers mask..." << endl; cout << endl;
172 cout << "Type of srcPoints: "; if ((j>-1) && (j<2)) cout << "Mat of CV_32FC2"; else cout << "vector <Point2f>";
173 cout << " Type of dstPoints: "; if (j % 2 == 0) cout << "Mat of CV_32FC2"; else cout << "vector <Point2f>"; cout << endl;
174 cout << "Count of points: " << N << endl; cout << endl;
175 cout << "Method: "; if (_method == RANSAC) cout << "RANSAC" << endl; else if (_method == cv::RHO) cout << "RHO" << endl; else cout << _method << endl;
176 cout << "Found mask:" << endl; cout << endl;
177 cout << mask << endl; cout << endl;
178 cout << "Number of rows: " << mask.rows << " Number of cols: " << mask.cols << endl; cout << endl;
179 }
180
print_information_4(int _method,int j,int N,int k,int l,double diff)181 void CV_HomographyTest::print_information_4(int _method, int j, int N, int k, int l, double diff)
182 {
183 cout << endl; cout << "Checking for accuracy of reprojection error computing..." << endl; cout << endl;
184 cout << "Method: "; if (_method == 0) cout << 0 << endl; else cout << "CV_LMEDS" << endl;
185 cout << "Type of srcPoints: "; if ((j>-1) && (j<2)) cout << "Mat of CV_32FC2"; else cout << "vector <Point2f>";
186 cout << " Type of dstPoints: "; if (j % 2 == 0) cout << "Mat of CV_32FC2"; else cout << "vector <Point2f>"; cout << endl;
187 cout << "Sigma of normal noise: " << sigma << endl;
188 cout << "Count of points: " << N << endl;
189 cout << "Number of point: " << k << endl;
190 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;
191 cout << "Difference with noise of point: " << diff << endl;
192 cout << "Maxumum allowed difference: " << max_2diff << endl; cout << endl;
193 }
194
print_information_5(int _method,int j,int N,int l,double diff)195 void CV_HomographyTest::print_information_5(int _method, int j, int N, int l, double diff)
196 {
197 cout << endl; cout << "Checking for accuracy of reprojection error computing..." << endl; cout << endl;
198 cout << "Method: "; if (_method == 0) cout << 0 << endl; else cout << "CV_LMEDS" << endl;
199 cout << "Type of srcPoints: "; if ((j>-1) && (j<2)) cout << "Mat of CV_32FC2"; else cout << "vector <Point2f>";
200 cout << " Type of dstPoints: "; if (j % 2 == 0) cout << "Mat of CV_32FC2"; else cout << "vector <Point2f>"; cout << endl;
201 cout << "Sigma of normal noise: " << sigma << endl;
202 cout << "Count of points: " << N << endl;
203 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;
204 cout << "Difference with noise of points: " << diff << endl;
205 cout << "Maxumum allowed difference: " << max_diff << endl; cout << endl;
206 }
207
print_information_6(int _method,int j,int N,int k,double diff,bool value)208 void CV_HomographyTest::print_information_6(int _method, int j, int N, int k, double diff, bool value)
209 {
210 cout << endl; cout << "Checking for inliers/outliers mask..." << endl; cout << endl;
211 cout << "Method: "; if (_method == RANSAC) cout << "RANSAC" << endl; else if (_method == cv::RHO) cout << "RHO" << endl; else cout << _method << endl;
212 cout << "Type of srcPoints: "; if ((j>-1) && (j<2)) cout << "Mat of CV_32FC2"; else cout << "vector <Point2f>";
213 cout << " Type of dstPoints: "; if (j % 2 == 0) cout << "Mat of CV_32FC2"; else cout << "vector <Point2f>"; cout << endl;
214 cout << "Count of points: " << N << " " << endl;
215 cout << "Number of point: " << k << " " << endl;
216 cout << "Reprojection error for this point: " << diff << " " << endl;
217 cout << "Reprojection error threshold: " << reproj_threshold << " " << endl;
218 cout << "Value of found mask: "<< value << endl; cout << endl;
219 }
220
print_information_7(int _method,int j,int N,int k,double diff,bool original_value,bool found_value)221 void CV_HomographyTest::print_information_7(int _method, int j, int N, int k, double diff, bool original_value, bool found_value)
222 {
223 cout << endl; cout << "Checking for inliers/outliers mask..." << endl; cout << endl;
224 cout << "Method: "; if (_method == RANSAC) cout << "RANSAC" << endl; else if (_method == cv::RHO) cout << "RHO" << endl; else cout << _method << endl;
225 cout << "Type of srcPoints: "; if ((j>-1) && (j<2)) cout << "Mat of CV_32FC2"; else cout << "vector <Point2f>";
226 cout << " Type of dstPoints: "; if (j % 2 == 0) cout << "Mat of CV_32FC2"; else cout << "vector <Point2f>"; cout << endl;
227 cout << "Count of points: " << N << " " << endl;
228 cout << "Number of point: " << k << " " << endl;
229 cout << "Reprojection error for this point: " << diff << " " << endl;
230 cout << "Reprojection error threshold: " << reproj_threshold << " " << endl;
231 cout << "Value of original mask: "<< original_value << " Value of found mask: " << found_value << endl; cout << endl;
232 }
233
print_information_8(int _method,int j,int N,int k,int l,double diff)234 void CV_HomographyTest::print_information_8(int _method, int j, int N, int k, int l, double diff)
235 {
236 cout << endl; cout << "Checking for reprojection error of inlier..." << endl; cout << endl;
237 cout << "Method: "; if (_method == RANSAC) cout << "RANSAC" << endl; else if (_method == cv::RHO) cout << "RHO" << endl; else cout << _method << endl;
238 cout << "Sigma of normal noise: " << sigma << endl;
239 cout << "Type of srcPoints: "; if ((j>-1) && (j<2)) cout << "Mat of CV_32FC2"; else cout << "vector <Point2f>";
240 cout << " Type of dstPoints: "; if (j % 2 == 0) cout << "Mat of CV_32FC2"; else cout << "vector <Point2f>"; cout << endl;
241 cout << "Count of points: " << N << " " << endl;
242 cout << "Number of point: " << k << " " << endl;
243 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;
244 cout << "Difference with noise of point: " << diff << endl;
245 cout << "Maxumum allowed difference: " << max_2diff << endl; cout << endl;
246 }
247
run(int)248 void CV_HomographyTest::run(int)
249 {
250 for (int N = 4; N <= MAX_COUNT_OF_POINTS; ++N)
251 {
252 RNG& rng = ts->get_rng();
253
254 float *src_data = new float [2*N];
255
256 for (int i = 0; i < N; ++i)
257 {
258 src_data[2*i] = (float)cvtest::randReal(rng)*image_size;
259 src_data[2*i+1] = (float)cvtest::randReal(rng)*image_size;
260 }
261
262 cv::Mat src_mat_2f(1, N, CV_32FC2, src_data),
263 src_mat_2d(2, N, CV_32F, src_data),
264 src_mat_3d(3, N, CV_32F);
265 cv::Mat dst_mat_2f, dst_mat_2d, dst_mat_3d;
266
267 vector <Point2f> src_vec, dst_vec;
268
269 for (int i = 0; i < N; ++i)
270 {
271 float *tmp = src_mat_2d.ptr<float>()+2*i;
272 src_mat_3d.at<float>(0, i) = tmp[0];
273 src_mat_3d.at<float>(1, i) = tmp[1];
274 src_mat_3d.at<float>(2, i) = 1.0f;
275
276 src_vec.push_back(Point2f(tmp[0], tmp[1]));
277 }
278
279 double fi = cvtest::randReal(rng)*2*CV_PI;
280
281 double t_x = cvtest::randReal(rng)*sqrt(image_size*1.0),
282 t_y = cvtest::randReal(rng)*sqrt(image_size*1.0);
283
284 double Hdata[9] = { cos(fi), -sin(fi), t_x,
285 sin(fi), cos(fi), t_y,
286 0.0f, 0.0f, 1.0f };
287
288 cv::Mat H_64(3, 3, CV_64F, Hdata), H_32;
289
290 H_64.convertTo(H_32, CV_32F);
291
292 dst_mat_3d = H_32*src_mat_3d;
293
294 dst_mat_2d.create(2, N, CV_32F); dst_mat_2f.create(1, N, CV_32FC2);
295
296 for (int i = 0; i < N; ++i)
297 {
298 float *tmp_2f = dst_mat_2f.ptr<float>()+2*i;
299 tmp_2f[0] = dst_mat_2d.at<float>(0, i) = dst_mat_3d.at<float>(0, i) /= dst_mat_3d.at<float>(2, i);
300 tmp_2f[1] = dst_mat_2d.at<float>(1, i) = dst_mat_3d.at<float>(1, i) /= dst_mat_3d.at<float>(2, i);
301 dst_mat_3d.at<float>(2, i) = 1.0f;
302
303 dst_vec.push_back(Point2f(tmp_2f[0], tmp_2f[1]));
304 }
305
306 for (int i = 0; i < METHODS_COUNT; ++i)
307 {
308 method = METHOD[i];
309 switch (method)
310 {
311 case 0:
312 case LMEDS:
313 {
314 Mat H_res_64 [4] = { cv::findHomography(src_mat_2f, dst_mat_2f, method),
315 cv::findHomography(src_mat_2f, dst_vec, method),
316 cv::findHomography(src_vec, dst_mat_2f, method),
317 cv::findHomography(src_vec, dst_vec, method) };
318
319 for (int j = 0; j < 4; ++j)
320 {
321
322 if (!check_matrix_size(H_res_64[j]))
323 {
324 print_information_1(j, N, method, H_res_64[j]);
325 CV_Error(CALIB3D_HOMOGRAPHY_ERROR_MATRIX_SIZE, MESSAGE_MATRIX_SIZE);
326 return;
327 }
328
329 double diff;
330
331 for (int k = 0; k < COUNT_NORM_TYPES; ++k)
332 if (!check_matrix_diff(H_64, H_res_64[j], NORM_TYPE[k], diff))
333 {
334 print_information_2(j, N, method, H_64, H_res_64[j], k, diff);
335 CV_Error(CALIB3D_HOMOGRAPHY_ERROR_MATRIX_DIFF, MESSAGE_MATRIX_DIFF);
336 return;
337 }
338 }
339
340 continue;
341 }
342 case cv::RHO:
343 case RANSAC:
344 {
345 cv::Mat mask [4]; double diff;
346
347 Mat H_res_64 [4] = { cv::findHomography(src_mat_2f, dst_mat_2f, method, reproj_threshold, mask[0]),
348 cv::findHomography(src_mat_2f, dst_vec, method, reproj_threshold, mask[1]),
349 cv::findHomography(src_vec, dst_mat_2f, method, reproj_threshold, mask[2]),
350 cv::findHomography(src_vec, dst_vec, method, reproj_threshold, mask[3]) };
351
352 for (int j = 0; j < 4; ++j)
353 {
354
355 if (!check_matrix_size(H_res_64[j]))
356 {
357 print_information_1(j, N, method, H_res_64[j]);
358 CV_Error(CALIB3D_HOMOGRAPHY_ERROR_MATRIX_SIZE, MESSAGE_MATRIX_SIZE);
359 return;
360 }
361
362 for (int k = 0; k < COUNT_NORM_TYPES; ++k)
363 if (!check_matrix_diff(H_64, H_res_64[j], NORM_TYPE[k], diff))
364 {
365 print_information_2(j, N, method, H_64, H_res_64[j], k, diff);
366 CV_Error(CALIB3D_HOMOGRAPHY_ERROR_MATRIX_DIFF, MESSAGE_MATRIX_DIFF);
367 return;
368 }
369
370 int code = check_ransac_mask_1(src_mat_2f, mask[j]);
371
372 if (code)
373 {
374 print_information_3(method, j, N, mask[j]);
375
376 switch (code)
377 {
378 case 1: { CV_Error(CALIB3D_HOMOGRAPHY_ERROR_RANSAC_MASK, MESSAGE_RANSAC_MASK_1); break; }
379 case 2: { CV_Error(CALIB3D_HOMOGRAPHY_ERROR_RANSAC_MASK, MESSAGE_RANSAC_MASK_2); break; }
380 case 3: { CV_Error(CALIB3D_HOMOGRAPHY_ERROR_RANSAC_MASK, MESSAGE_RANSAC_MASK_3); break; }
381
382 default: break;
383 }
384
385 return;
386 }
387
388 }
389
390 continue;
391 }
392
393 default: continue;
394 }
395 }
396
397 Mat noise_2f(1, N, CV_32FC2);
398 rng.fill(noise_2f, RNG::NORMAL, Scalar::all(0), Scalar::all(sigma));
399
400 cv::Mat mask(N, 1, CV_8UC1);
401
402 for (int i = 0; i < N; ++i)
403 {
404 float *a = noise_2f.ptr<float>()+2*i, *_2f = dst_mat_2f.ptr<float>()+2*i;
405 _2f[0] += a[0]; _2f[1] += a[1];
406 mask.at<bool>(i, 0) = !(sqrt(a[0]*a[0]+a[1]*a[1]) > reproj_threshold);
407 }
408
409 for (int i = 0; i < METHODS_COUNT; ++i)
410 {
411 method = METHOD[i];
412 switch (method)
413 {
414 case 0:
415 case LMEDS:
416 {
417 Mat H_res_64 [4] = { cv::findHomography(src_mat_2f, dst_mat_2f),
418 cv::findHomography(src_mat_2f, dst_vec),
419 cv::findHomography(src_vec, dst_mat_2f),
420 cv::findHomography(src_vec, dst_vec) };
421
422 for (int j = 0; j < 4; ++j)
423 {
424
425 if (!check_matrix_size(H_res_64[j]))
426 {
427 print_information_1(j, N, method, H_res_64[j]);
428 CV_Error(CALIB3D_HOMOGRAPHY_ERROR_MATRIX_SIZE, MESSAGE_MATRIX_SIZE);
429 return;
430 }
431
432 Mat H_res_32; H_res_64[j].convertTo(H_res_32, CV_32F);
433
434 cv::Mat dst_res_3d(3, N, CV_32F), noise_2d(2, N, CV_32F);
435
436 for (int k = 0; k < N; ++k)
437 {
438
439 Mat tmp_mat_3d = H_res_32*src_mat_3d.col(k);
440
441 dst_res_3d.at<float>(0, k) = tmp_mat_3d.at<float>(0, 0) /= tmp_mat_3d.at<float>(2, 0);
442 dst_res_3d.at<float>(1, k) = tmp_mat_3d.at<float>(1, 0) /= tmp_mat_3d.at<float>(2, 0);
443 dst_res_3d.at<float>(2, k) = tmp_mat_3d.at<float>(2, 0) = 1.0f;
444
445 float *a = noise_2f.ptr<float>()+2*k;
446 noise_2d.at<float>(0, k) = a[0]; noise_2d.at<float>(1, k) = a[1];
447
448 for (int l = 0; l < COUNT_NORM_TYPES; ++l)
449 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)
450 {
451 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]));
452 CV_Error(CALIB3D_HOMOGRAPHY_ERROR_REPROJ_DIFF, MESSAGE_REPROJ_DIFF_1);
453 return;
454 }
455
456 }
457
458 for (int l = 0; l < COUNT_NORM_TYPES; ++l)
459 if (cv::norm(dst_res_3d, dst_mat_3d, NORM_TYPE[l]) - cv::norm(noise_2d, NORM_TYPE[l]) > max_diff)
460 {
461 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]));
462 CV_Error(CALIB3D_HOMOGRAPHY_ERROR_REPROJ_DIFF, MESSAGE_REPROJ_DIFF_2);
463 return;
464 }
465
466 }
467
468 continue;
469 }
470 case cv::RHO:
471 case RANSAC:
472 {
473 cv::Mat mask_res [4];
474
475 Mat H_res_64 [4] = { cv::findHomography(src_mat_2f, dst_mat_2f, method, reproj_threshold, mask_res[0]),
476 cv::findHomography(src_mat_2f, dst_vec, method, reproj_threshold, mask_res[1]),
477 cv::findHomography(src_vec, dst_mat_2f, method, reproj_threshold, mask_res[2]),
478 cv::findHomography(src_vec, dst_vec, method, reproj_threshold, mask_res[3]) };
479
480 for (int j = 0; j < 4; ++j)
481 {
482 if (!check_matrix_size(H_res_64[j]))
483 {
484 print_information_1(j, N, method, H_res_64[j]);
485 CV_Error(CALIB3D_HOMOGRAPHY_ERROR_MATRIX_SIZE, MESSAGE_MATRIX_SIZE);
486 return;
487 }
488
489 int code = check_ransac_mask_2(mask, mask_res[j]);
490
491 if (code)
492 {
493 print_information_3(method, j, N, mask_res[j]);
494
495 switch (code)
496 {
497 case 1: { CV_Error(CALIB3D_HOMOGRAPHY_ERROR_RANSAC_MASK, MESSAGE_RANSAC_MASK_1); break; }
498 case 2: { CV_Error(CALIB3D_HOMOGRAPHY_ERROR_RANSAC_MASK, MESSAGE_RANSAC_MASK_3); break; }
499
500 default: break;
501 }
502
503 return;
504 }
505
506 cv::Mat H_res_32; H_res_64[j].convertTo(H_res_32, CV_32F);
507
508 cv::Mat dst_res_3d = H_res_32*src_mat_3d;
509
510 for (int k = 0; k < N; ++k)
511 {
512 dst_res_3d.at<float>(0, k) /= dst_res_3d.at<float>(2, k);
513 dst_res_3d.at<float>(1, k) /= dst_res_3d.at<float>(2, k);
514 dst_res_3d.at<float>(2, k) = 1.0f;
515
516 float *p = dst_mat_2f.ptr<float>()+2*k;
517
518 dst_mat_3d.at<float>(0, k) = p[0];
519 dst_mat_3d.at<float>(1, k) = p[1];
520
521 double diff = cv::norm(dst_res_3d.col(k), dst_mat_3d.col(k), NORM_L2);
522
523 if (mask_res[j].at<bool>(k, 0) != (diff <= reproj_threshold))
524 {
525 print_information_6(method, j, N, k, diff, mask_res[j].at<bool>(k, 0));
526 CV_Error(CALIB3D_HOMOGRAPHY_ERROR_RANSAC_MASK, MESSAGE_RANSAC_MASK_4);
527 return;
528 }
529
530 if (mask.at<bool>(k, 0) && !mask_res[j].at<bool>(k, 0))
531 {
532 print_information_7(method, j, N, k, diff, mask.at<bool>(k, 0), mask_res[j].at<bool>(k, 0));
533 CV_Error(CALIB3D_HOMOGRAPHY_ERROR_RANSAC_MASK, MESSAGE_RANSAC_MASK_5);
534 return;
535 }
536
537 if (mask_res[j].at<bool>(k, 0))
538 {
539 float *a = noise_2f.ptr<float>()+2*k;
540 dst_mat_3d.at<float>(0, k) -= a[0];
541 dst_mat_3d.at<float>(1, k) -= a[1];
542
543 cv::Mat noise_2d(2, 1, CV_32F);
544 noise_2d.at<float>(0, 0) = a[0]; noise_2d.at<float>(1, 0) = a[1];
545
546 for (int l = 0; l < COUNT_NORM_TYPES; ++l)
547 {
548 diff = cv::norm(dst_res_3d.col(k), dst_mat_3d.col(k), NORM_TYPE[l]);
549
550 if (diff - cv::norm(noise_2d, NORM_TYPE[l]) > max_2diff)
551 {
552 print_information_8(method, j, N, k, l, diff - cv::norm(noise_2d, NORM_TYPE[l]));
553 CV_Error(CALIB3D_HOMOGRAPHY_ERROR_RANSAC_DIFF, MESSAGE_RANSAC_DIFF);
554 return;
555 }
556 }
557 }
558 }
559 }
560
561 continue;
562 }
563
564 default: continue;
565 }
566 }
567 }
568 }
569
TEST(Calib3d_Homography,accuracy)570 TEST(Calib3d_Homography, accuracy) { CV_HomographyTest test; test.safe_run(); }
571
TEST(Calib3d_Homography,EKcase)572 TEST(Calib3d_Homography, EKcase)
573 {
574 float pt1data[] =
575 {
576 2.80073029e+002f, 2.39591217e+002f, 2.21912201e+002f, 2.59783997e+002f,
577 2.16053192e+002f, 2.78826569e+002f, 2.22782532e+002f, 2.82330383e+002f,
578 2.09924820e+002f, 2.89122559e+002f, 2.11077698e+002f, 2.89384674e+002f,
579 2.25287689e+002f, 2.88795532e+002f, 2.11180801e+002f, 2.89653503e+002f,
580 2.24126404e+002f, 2.90466064e+002f, 2.10914429e+002f, 2.90886963e+002f,
581 2.23439362e+002f, 2.91657715e+002f, 2.24809387e+002f, 2.91891602e+002f,
582 2.09809082e+002f, 2.92891113e+002f, 2.08771164e+002f, 2.93093231e+002f,
583 2.23160095e+002f, 2.93259460e+002f, 2.07874023e+002f, 2.93989990e+002f,
584 2.08963638e+002f, 2.94209839e+002f, 2.23963165e+002f, 2.94479645e+002f,
585 2.23241791e+002f, 2.94887817e+002f, 2.09438782e+002f, 2.95233337e+002f,
586 2.08901886e+002f, 2.95762878e+002f, 2.21867981e+002f, 2.95747711e+002f,
587 2.24195511e+002f, 2.98270905e+002f, 2.09331345e+002f, 3.05958191e+002f,
588 2.24727875e+002f, 3.07186035e+002f, 2.26718842e+002f, 3.08095795e+002f,
589 2.25363953e+002f, 3.08200226e+002f, 2.19897797e+002f, 3.13845093e+002f,
590 2.25013474e+002f, 3.15558777e+002f
591 };
592
593 float pt2data[] =
594 {
595 1.84072723e+002f, 1.43591202e+002f, 1.25912483e+002f, 1.63783859e+002f,
596 2.06439407e+002f, 2.20573929e+002f, 1.43801437e+002f, 1.80703903e+002f,
597 9.77904129e+000f, 2.49660202e+002f, 1.38458405e+001f, 2.14502701e+002f,
598 1.50636337e+002f, 2.15597183e+002f, 6.43103180e+001f, 2.51667648e+002f,
599 1.54952499e+002f, 2.20780014e+002f, 1.26638412e+002f, 2.43040924e+002f,
600 3.67568909e+002f, 1.83624954e+001f, 1.60657944e+002f, 2.21794052e+002f,
601 -1.29507828e+000f, 3.32472443e+002f, 8.51442242e+000f, 4.15561554e+002f,
602 1.27161377e+002f, 1.97260361e+002f, 5.40714645e+000f, 4.90978302e+002f,
603 2.25571690e+001f, 3.96912415e+002f, 2.95664978e+002f, 7.36064959e+000f,
604 1.27241104e+002f, 1.98887573e+002f, -1.25569367e+000f, 3.87713226e+002f,
605 1.04194012e+001f, 4.31495758e+002f, 1.25868874e+002f, 1.99751617e+002f,
606 1.28195480e+002f, 2.02270355e+002f, 2.23436356e+002f, 1.80489182e+002f,
607 1.28727692e+002f, 2.11185410e+002f, 2.03336639e+002f, 2.52182083e+002f,
608 1.29366486e+002f, 2.12201904e+002f, 1.23897598e+002f, 2.17847351e+002f,
609 1.29015259e+002f, 2.19560623e+002f
610 };
611
612 int npoints = (int)(sizeof(pt1data)/sizeof(pt1data[0])/2);
613
614 Mat p1(1, npoints, CV_32FC2, pt1data);
615 Mat p2(1, npoints, CV_32FC2, pt2data);
616 Mat mask;
617
618 Mat h = findHomography(p1, p2, RANSAC, 0.01, mask);
619 ASSERT_TRUE(!h.empty());
620
621 transpose(mask, mask);
622 Mat p3, mask2;
623 int ninliers = countNonZero(mask);
624 Mat nmask[] = { mask, mask };
625 merge(nmask, 2, mask2);
626 perspectiveTransform(p1, p3, h);
627 mask2 = mask2.reshape(1);
628 p2 = p2.reshape(1);
629 p3 = p3.reshape(1);
630 double err = norm(p2, p3, NORM_INF, mask2);
631
632 printf("ninliers: %d, inliers err: %.2g\n", ninliers, err);
633 ASSERT_GE(ninliers, 10);
634 ASSERT_LE(err, 0.01);
635 }
636
TEST(Calib3d_Homography,fromImages)637 TEST(Calib3d_Homography, fromImages)
638 {
639 Mat img_1 = imread(cvtest::TS::ptr()->get_data_path() + "cv/optflow/image1.png", 0);
640 Mat img_2 = imread(cvtest::TS::ptr()->get_data_path() + "cv/optflow/image2.png", 0);
641 Ptr<ORB> orb = ORB::create();
642 vector<KeyPoint> keypoints_1, keypoints_2;
643 Mat descriptors_1, descriptors_2;
644 orb->detectAndCompute( img_1, Mat(), keypoints_1, descriptors_1, false );
645 orb->detectAndCompute( img_2, Mat(), keypoints_2, descriptors_2, false );
646
647 //-- Step 3: Matching descriptor vectors using Brute Force matcher
648 BFMatcher matcher(NORM_HAMMING,false);
649 std::vector< DMatch > matches;
650 matcher.match( descriptors_1, descriptors_2, matches );
651
652 double max_dist = 0; double min_dist = 100;
653 //-- Quick calculation of max and min distances between keypoints
654 for( int i = 0; i < descriptors_1.rows; i++ )
655 {
656 double dist = matches[i].distance;
657 if( dist < min_dist ) min_dist = dist;
658 if( dist > max_dist ) max_dist = dist;
659 }
660
661 //-- Draw only "good" matches (i.e. whose distance is less than 3*min_dist )
662 std::vector< DMatch > good_matches;
663 for( int i = 0; i < descriptors_1.rows; i++ )
664 {
665 if( matches[i].distance <= 100 )
666 good_matches.push_back( matches[i]);
667 }
668
669 //-- Localize the model
670 std::vector<Point2f> pointframe1;
671 std::vector<Point2f> pointframe2;
672 for( int i = 0; i < (int)good_matches.size(); i++ )
673 {
674 //-- Get the keypoints from the good matches
675 pointframe1.push_back( keypoints_1[ good_matches[i].queryIdx ].pt );
676 pointframe2.push_back( keypoints_2[ good_matches[i].trainIdx ].pt );
677 }
678
679 Mat H0, H1, inliers0, inliers1;
680 double min_t0 = DBL_MAX, min_t1 = DBL_MAX;
681 for( int i = 0; i < 10; i++ )
682 {
683 double t = (double)getTickCount();
684 H0 = findHomography( pointframe1, pointframe2, RANSAC, 3.0, inliers0 );
685 t = (double)getTickCount() - t;
686 min_t0 = std::min(min_t0, t);
687 }
688 int ninliers0 = countNonZero(inliers0);
689 for( int i = 0; i < 10; i++ )
690 {
691 double t = (double)getTickCount();
692 H1 = findHomography( pointframe1, pointframe2, RHO, 3.0, inliers1 );
693 t = (double)getTickCount() - t;
694 min_t1 = std::min(min_t1, t);
695 }
696 int ninliers1 = countNonZero(inliers1);
697 double freq = getTickFrequency();
698 printf("nfeatures1 = %d, nfeatures2=%d, matches=%d, ninliers(RANSAC)=%d, "
699 "time(RANSAC)=%.2fmsec, ninliers(RHO)=%d, time(RHO)=%.2fmsec\n",
700 (int)keypoints_1.size(), (int)keypoints_2.size(),
701 (int)good_matches.size(), ninliers0, min_t0*1000./freq, ninliers1, min_t1*1000./freq);
702
703 ASSERT_TRUE(!H0.empty());
704 ASSERT_GE(ninliers0, 80);
705 ASSERT_TRUE(!H1.empty());
706 ASSERT_GE(ninliers1, 80);
707 }
708
