diff --git a/src/phg/sift/sift.cpp b/src/phg/sift/sift.cpp
index 7204771..b340b46 100755
--- a/src/phg/sift/sift.cpp
+++ b/src/phg/sift/sift.cpp
@@ -16,6 +16,9 @@
 // 3) https://github.com/opencv/opencv/blob/1834eed8098aa2c595f4d1099eeaa0992ce8b321/modules/features2d/src/sift.dispatch.cpp
 // 4) https://github.com/opencv/opencv/blob/1834eed8098aa2c595f4d1099eeaa0992ce8b321/modules/features2d/src/sift.simd.hpp
 
+#define ENABLE_EXACT_NFEATURES_RECOVERY 1
+#define ENABLE_INCREMENTAL_OCTAVE_GAUSSIAN_BLUR 0
+
 namespace {
 
 cv::Mat upsample2x(const cv::Mat& src)
@@ -108,16 +111,22 @@ std::vector<phg::SIFT::Octave> phg::buildOctaves(const cv::Mat& img, const phg::
         //  можно подумать, как сделать эффективнее - для построения n+1 слоя доблюревать уже поблюренный n-ый слой, так чтобы в итоге получилась такая же сигма
         //  это будет немного быстрее, тк нужно более маленькое ядро свертки на каждый шаг
         for (int i = 1; i < n_layers; i++) {
-            //            TODO double sigma_layer = sigma0 * корень из двух нужной степени, чтобы при i==s получали удвоение базового блюра;
-            //            // вычтем sigma0 чтобы размыть ровно до нужной суммарной сигмы
-            //            TODO sigma_layer = ... (вычитаем как в sigma base);
-            //            cv::GaussianBlur(oct.layers[0], oct.layers[i], cv::Size(), sigma_layer, sigma_layer);
+            double sigma_layer = sigma0 * std::pow(2.0, (double)i / s);
+#if ENABLE_INCREMENTAL_OCTAVE_GAUSSIAN_BLUR
+            double prev_sigma_layer = sigma0 * std::pow(2.0, (double)(i - 1) / s);
+            sigma_layer = std::sqrt(sigma_layer * sigma_layer - prev_sigma_layer * prev_sigma_layer);
+            cv::GaussianBlur(oct.layers[i - 1], oct.layers[i], cv::Size(), sigma_layer, sigma_layer);
+#else
+            // вычтем sigma0 чтобы размыть ровно до нужной суммарной сигмы
+            sigma_layer = std::sqrt(sigma_layer * sigma_layer - sigma0 * sigma0);
+            cv::GaussianBlur(oct.layers[0], oct.layers[i], cv::Size(), sigma_layer, sigma_layer);
+#endif
         }
 
         // подготавливаем базовый слой для следующей октавы
         if (o + 1 < n_octaves) {
             // используется в opencv, формула для пересчета ключевых точек: pt_upscaled = 2^o * pt_downscaled
-            //            TODO cv::resize(даунскейлим текущий слой в два раза, без интерполяции, просто сабсепмлинг);
+            base = downsample2x(oct.layers[s]);
 
             // можно использовать и downsample2x_avg(oct.layers[s]), это позволяет потом заапскейлить слои обратно до оригинального разрешения без сдвига
             // но потребуется везде изменить формулу для пересчета ключевых точек: pt_upscaled = (pt_downscaled + 0.5) * 2^o - 0.5
@@ -138,7 +147,9 @@ std::vector<phg::SIFT::Octave> phg::buildDoG(const std::vector<phg::SIFT::Octave
         const phg::SIFT::Octave& octave = octaves[o];
         dog[o].layers.resize(octave.layers.size() - 1);
 
-        // TODO каждый слой дога это разница n+1 и n-й гауссианы
+        for (size_t i = 0; i + 1 < octave.layers.size(); i++) {
+            dog[o].layers[i] = octave.layers[i + 1] - octave.layers[i];
+        }
     }
 
     return dog;
@@ -207,17 +218,40 @@ std::vector<cv::KeyPoint> phg::findScaleSpaceExtrema(const std::vector<phg::SIFT
                             is_min = false;
                     };
 
-                    // TODO проверить локальный максимум на текущем скейле
+                    check(cp[x - 1]);
+                    check(cp[x]);
+                    check(cp[x + 1]);
+                    check(c[x - 1]);
+                    check(c[x + 1]);
+                    check(cn[x - 1]);
+                    check(cn[x]);
+                    check(cn[x + 1]);
 
                     if (!is_max && !is_min)
                         continue;
 
-                    // TODO проверить локальный максимум на предыдущем скейле
+                    check(pp[x - 1]);
+                    check(pp[x]);
+                    check(pp[x + 1]);
+                    check(p[x - 1]);
+                    check(p[x]);
+                    check(p[x + 1]);
+                    check(pn[x - 1]);
+                    check(pn[x]);
+                    check(pn[x + 1]);
 
                     if (!is_max && !is_min)
                         continue;
 
-                    // TODO проверить локальный максимум на следующем скейле
+                    check(np[x - 1]);
+                    check(np[x]);
+                    check(np[x + 1]);
+                    check(n[x - 1]);
+                    check(n[x]);
+                    check(n[x + 1]);
+                    check(nn[x - 1]);
+                    check(nn[x]);
+                    check(nn[x + 1]);
 
                     if (!is_max && !is_min)
                         continue;
@@ -238,13 +272,13 @@ std::vector<cv::KeyPoint> phg::findScaleSpaceExtrema(const std::vector<phg::SIFT
 
                         // гессиан
                         float dxx, dxy, dyy, dxs, dys, dss;
-//                        float dxx = cL.at<float>(yi, xi + 1) + cL.at<float>(yi, xi - 1) - 2.f * resp_center;
-//                        float dyy = TODO;
-//                        float dss = TODO;
-//
-//                        float dxy = (cL.at<float>(yi + 1, xi + 1) - cL.at<float>(yi + 1, xi - 1) - cL.at<float>(yi - 1, xi + 1) + cL.at<float>(yi - 1, xi - 1)) * 0.25f;
-//                        float dxs = TODO;
-//                        float dys = TODO;
+                        dxx = cL.at<float>(yi, xi + 1) + cL.at<float>(yi, xi - 1) - 2.f * resp_center;
+                        dyy = cL.at<float>(yi + 1, xi) + cL.at<float>(yi - 1, xi) - 2.f * resp_center;
+                        dss = nL.at<float>(yi, xi) + pL.at<float>(yi, xi) - 2.f * resp_center;
+
+                        dxy = (cL.at<float>(yi + 1, xi + 1) - cL.at<float>(yi + 1, xi - 1) - cL.at<float>(yi - 1, xi + 1) + cL.at<float>(yi - 1, xi - 1)) * 0.25f;
+                        dxs = (nL.at<float>(yi, xi + 1) - nL.at<float>(yi, xi - 1) - pL.at<float>(yi, xi + 1) + pL.at<float>(yi, xi - 1)) * 0.25f;
+                        dys = (nL.at<float>(yi + 1, xi) - nL.at<float>(yi - 1, xi) - pL.at<float>(yi + 1, xi) + pL.at<float>(yi - 1, xi)) * 0.25f;
 
                         cv::Matx33f H(dxx, dxy, dxs, dxy, dyy, dys, dxs, dys, dss);
 
@@ -273,21 +307,21 @@ std::vector<cv::KeyPoint> phg::findScaleSpaceExtrema(const std::vector<phg::SIFT
                                 // из линейной алгебры, сумма диагональных элементов матрицы (след) равна сумме собственных чисел
                                 // определитель матрицы равен произведению собственных чисел
                                 // в случае гессиана (пространственной части: (dxx dxy, dxy, dyy)), собственные числа lambda1, lambda2 - силы кривизны в направлении максимальной кривизны и в ортогональном
-//                                float trace = //TODO ; // = lambda1 + lambda2
-//                                float det = // TODO ; // = lambda1 * lambda2
-//                                if (det <= 0)
-//                                    break; // если произведение кривизн отрицательное, то мы находимся в седловой точке, а не в максимуме/минимуме. если нулевое, то это ровная граница вообще
-//
-//                                // если граница незацепистая = грань, то одна кривизна сильно больше чем другая. хотим, чтобы обе кривизны были примерно сопоставимы
-//                                // тогда их отношение r = lambda1/lambda2 будет не очень большим
-//                                // если расписать trace * trace / det через r, то получится (r + 1) ^ 2 / r
-//                                // функция растущая по r, так что если наше фактическое значение trace * trace / det выше (r + 1) ^ 2 / r, то и наше отношение кривизн больше порога, значит плохая зацепистость
-//                                // и просто как интуиция, при больших r это выражение просто до r сокращается
-//
-//                                // в итоге получается что порог edge_threshold в отличие от response_threshold наоборот, чем больше тем расслабленнее
-//                                float r = edge_threshold;
-//                                if (TODO)
-//                                    break;
+                                float trace = dxx + dyy; // = lambda1 + lambda2
+                                float det = dxx * dyy - dxy * dxy; // = lambda1 * lambda2
+                                if (det <= 0)
+                                    break; // если произведение кривизн отрицательное, то мы находимся в седловой точке, а не в максимуме/минимуме. если нулевое, то это ровная граница вообще
+
+                                // если граница незацепистая = грань, то одна кривизна сильно больше чем другая. хотим, чтобы обе кривизны были примерно сопоставимы
+                                // тогда их отношение r = lambda1/lambda2 будет не очень большим
+                                // если расписать trace * trace / det через r, то получится (r + 1) ^ 2 / r
+                                // функция растущая по r, так что если наше фактическое значение trace * trace / det выше (r + 1) ^ 2 / r, то и наше отношение кривизн больше порога, значит плохая зацепистость
+                                // и просто как интуиция, при больших r это выражение просто до r сокращается
+
+                                // в итоге получается что порог edge_threshold в отличие от response_threshold наоборот, чем больше тем расслабленнее
+                                float r = edge_threshold;
+                                if (trace * trace / det > (r + 1.f) * (r + 1.f) / r)
+                                    break;
                             }
 
                             // скейлим координаты точек обратно до родных размеров картинки
@@ -379,39 +413,42 @@ std::vector<cv::KeyPoint> phg::computeOrientations(const std::vector<cv::KeyPoin
 
         for (int dy = -radius; dy <= radius; dy++) {
             for (int dx = -radius; dx <= radius; dx++) {
-//                int px = xi + dx;
-//                int py = yi + dy;
-//
-//                // градиент
-//                float gx = img.at<float>(py, px + 1) - img.at<float>(py, px - 1);
-//                float gy = img.at<float>(py + 1, px) - img.at<float>(py - 1, px);
-//
-//                float mag = TODO;
-//                float angle = std::atan2(TODO); // [-pi, pi]
-//
-//                float angle_deg = angle * 180.f / (float) CV_PI;
-//                if (angle_deg < 0.f) angle_deg += 360.f;
-//
-//                // гауссово взвешивание голоса точки с затуханием к краям
-//                float weight = std::exp(-(TODO) / (2.f * sigma_win * sigma_win));
-//                if (!params.enable_orientation_gaussian_weighting) {
-//                    weight = 1.f;
-//                }
-//
-//                // голосуем в гистограмме направлений. находим два ближайших бина и гладко распределяем голос между ними
-//                //  в таком случае, голос попавший близко к границе между бинами, проголосует поровну за оба бина
-//                float bin = TODO;
-//                if (bin >= n_bins) bin -= n_bins;
-//                int bin0 = (int) bin;
-//                int bin1 = (bin0 + 1) % n_bins;
-//
-//                float frac = bin - bin0;
-//                if (!params.enable_orientation_bin_interpolation) {
-//                    frac = 0.f;
-//                }
-//
-//                histogram[bin0] += TODO;
-//                histogram[bin1] += TODO;
+                int px = xi + dx;
+                int py = yi + dy;
+
+                // градиент
+                float gx = img.at<float>(py, px + 1) - img.at<float>(py, px - 1);
+                float gy = img.at<float>(py + 1, px) - img.at<float>(py - 1, px);
+
+                float mag = std::sqrt(gx * gx + gy * gy);
+                float angle = std::atan2(gy, gx); // [-pi, pi]
+
+                float angle_deg = angle * 180.f / (float) CV_PI;
+                if (angle_deg < 0.f)
+                    angle_deg += 360.f;
+
+                // гауссово взвешивание голоса точки с затуханием к краям
+                float weight = std::exp(-(dx * dx + dy * dy) / (2.f * sigma_win * sigma_win));
+                if (!params.enable_orientation_gaussian_weighting) {
+                    weight = 1.f;
+                }
+
+                // голосуем в гистограмме направлений. находим два ближайших бина и гладко распределяем голос между ними
+                //  в таком случае, голос попавший близко к границе между бинами, проголосует поровну за оба бина
+                float bin = angle_deg * n_bins / 360.f;
+                if (bin >= n_bins)
+                    bin -= n_bins;
+                int bin0 = (int) bin;
+                int bin1 = (bin0 + 1) % n_bins;
+
+                float frac = bin - bin0;
+                if (!params.enable_orientation_bin_interpolation) {
+                    frac = 0.f;
+                }
+
+                float weighted_mag = mag * weight;
+                histogram[bin0] += weighted_mag * (1.f - frac);
+                histogram[bin1] += weighted_mag * frac;
             }
         }
 
@@ -450,20 +487,24 @@ std::vector<cv::KeyPoint> phg::computeOrientations(const std::vector<cv::KeyPoin
                 //  f(1) + f(-1) = 2a + 2c -> a = (left + right - 2 * center) / 2
                 //  f(1) - f(-1) = 2b -> b = (right - left) / 2
 
-//                float offset = TODO;
-//                if (!params.enable_orientation_subpixel_localization) {
-//                    offset = 0.f;
-//                }
-//
-//                float bin_real = i + offset;
-//                if (bin_real < 0.f) bin_real += n_bins;
-//                if (bin_real >= n_bins) bin_real -= n_bins;
-//
-//                float angle = bin_real * 360.f / n_bins;
-//
-//                cv::KeyPoint new_kp = kp;
-//                new_kp.angle = angle;
-//                oriented_kpts.push_back(new_kp);
+                float offset = 0.f;
+                float denom = 2.f * (left + right - 2.f * center);
+                if (std::abs(denom) > 1e-7f) {
+                    offset = (left - right) / denom;
+                }
+                if (!params.enable_orientation_subpixel_localization) {
+                    offset = 0.f;
+                }
+
+                float bin_real = i + offset;
+                if (bin_real < 0.f) bin_real += n_bins;
+                if (bin_real >= n_bins) bin_real -= n_bins;
+
+                float angle = bin_real * 360.f / n_bins;
+
+                cv::KeyPoint new_kp = kp;
+                new_kp.angle = angle;
+                oriented_kpts.push_back(new_kp);
             }
         }
     }
@@ -574,11 +615,11 @@ std::pair<cv::Mat, std::vector<cv::KeyPoint>> phg::computeDescriptors(const std:
                     bin_o -= n_orient_bins;
 
                 // семплы вблизи края патча взвешиваем с меньшим весом
-//                float weight = std::exp(-(TODO) / (2.f * sigma_desc * sigma_desc));
-//                if (!params.enable_descriptor_gaussian_weighting) {
-//                    weight = 1.f;
-//                }
-//                float weighted_mag = mag * weight;
+                float weight = std::exp(-(rot_x * rot_x + rot_y * rot_y) / (2.f * sigma_desc * sigma_desc));
+                if (!params.enable_descriptor_gaussian_weighting) {
+                    weight = 1.f;
+                }
+                float weighted_mag = mag * weight;
 
                 if (params.enable_descriptor_bin_interpolation) {
                     // размажем вклад weighted_mag по пространственным бинам и по бинам гистограммок трилинейной интерполяцией
@@ -609,8 +650,8 @@ std::pair<cv::Mat, std::vector<cv::KeyPoint>> phg::computeDescriptors(const std:
                                     io += n_orient_bins;
                                 float wo = (dio == 0) ? (1.f - fo) : fo;
 
-//                                int idx = TODO;
-//                                desc[idx] += TODO;
+                                int idx = (iy * n_spatial_bins + ix) * n_orient_bins + io;
+                                desc[idx] += weighted_mag * wx * wy * wo;
                             }
                         }
                     }
@@ -620,9 +661,8 @@ std::pair<cv::Mat, std::vector<cv::KeyPoint>> phg::computeDescriptors(const std:
                     int io_nearest = (int)std::round(bin_o) % n_orient_bins;
 
                     if (ix_nearest >= 0 && ix_nearest < n_spatial_bins && iy_nearest >= 0 && iy_nearest < n_spatial_bins) {
-                        // TODO uncomment
-//                        int idx = (iy_nearest * n_spatial_bins + ix_nearest) * n_orient_bins + io_nearest;
-//                        desc[idx] += weighted_mag;
+                        int idx = (iy_nearest * n_spatial_bins + ix_nearest) * n_orient_bins + io_nearest;
+                        desc[idx] += weighted_mag;
                     }
                 }
             }
@@ -712,6 +752,75 @@ void phg::SIFT::detectAndCompute(const cv::Mat& img, const cv::Mat& mask, std::v
     savePyramid("pyramidDoG/03_dog_octave", dog, true);
 
     kpts = findScaleSpaceExtrema(dog, p, verbose_level);
+
+#if ENABLE_EXACT_NFEATURES_RECOVERY
+    // Почему раньше могли вернуть меньше чем nfeatures:
+    // 1) в computeOrientations() часть точек может отброситься по условию в строке 408 (если окно вышло за границы слоя),
+    // 2) в computeDescriptors() часть точек может отброситься по условию в строке 564 (патч дескриптора вышел за границы слоя),
+    // 3) а у нас пайплайн был такой: 
+    //      - находим space-scale экстремумы;
+    //      - в selectTopKeypoints() берём топ-nfeatures из них;
+    //      - делаем computeOrientations(), после чего у нас уже может стать меньше чем nfeatures точек (но могло стать и больше,
+    //        если пиков ориентаций было взято несколько для одного и того же кандидата);
+    //      - делаем selectTopKeypoints() - опять имеем <= nfeatures точек;
+    //      - делаем computeDescriptors(), после которого у нас также может стать меньше чем nfeatures точек.
+    // 
+    // Получаем, что если заранее жестко отрезать до nfeatures, "резервных" кандидатов не остается и финальный набор недобирается.
+    //
+    // Что делаем: не отрезаем :)
+    // Но гоняем computeOrientations() и computeDescriptors() батчами, начиная с самых сильных, чтобы не просесть по производительности.
+    // Если после очередного батча все еще недобор, добираем следующую порцию.
+    // Так мы обычно считаем почти столько же, сколько раньше, но при необходимости можем добрать до ровно nfeatures
+    // (если в принципе хватит валидных точек после всех фильтраций).
+    
+    std::vector<cv::KeyPoint> extrema_kpts = std::move(kpts);
+    if (p.nfeatures > 0) {
+        std::vector<int> ranked_idx(extrema_kpts.size());
+        std::iota(ranked_idx.begin(), ranked_idx.end(), 0);
+        std::sort(ranked_idx.begin(), ranked_idx.end(), [&extrema_kpts](const int& a, const int& b) {
+            float ra = std::abs(extrema_kpts[a].response);
+            float rb = std::abs(extrema_kpts[b].response);
+            if (ra != rb)
+                return ra > rb;
+            return a < b;
+        });
+        kpts.clear();
+        desc.release();
+
+        size_t processed = 0;
+        size_t batch_size = std::max(32, p.nfeatures);
+        while (kpts.size() < p.nfeatures && processed < ranked_idx.size()) {
+            size_t next_processed = std::min(ranked_idx.size(), processed + batch_size);
+            std::vector<cv::KeyPoint> batch_extrema;
+            batch_extrema.reserve(next_processed - processed);
+            for (size_t i = processed; i < next_processed; ++i) {
+                batch_extrema.push_back(extrema_kpts[ranked_idx[i]]);
+            }
+
+            std::vector<cv::KeyPoint> batch_oriented = computeOrientations(batch_extrema, octaves, p, verbose_level);
+            cv::Mat batch_desc;
+            std::vector<cv::KeyPoint> batch_valid_kpts;
+            std::tie(batch_desc, batch_valid_kpts) = computeDescriptors(batch_oriented, octaves, p, verbose_level);
+            if (!batch_desc.empty()) {
+                desc.push_back(batch_desc);
+            }
+
+            kpts.insert(kpts.end(), batch_valid_kpts.begin(), batch_valid_kpts.end());
+            processed = next_processed;
+            if (kpts.size() < p.nfeatures) {
+                batch_size = std::max<size_t>(32, 2 * (p.nfeatures - kpts.size()));
+            }
+        }
+
+        if (kpts.size() > p.nfeatures) {
+            kpts.resize(p.nfeatures);
+            desc = desc.rowRange(0, p.nfeatures).clone();
+        }
+    } else {
+        kpts = computeOrientations(extrema_kpts, octaves, p, verbose_level);
+        std::tie(desc, kpts) = computeDescriptors(kpts, octaves, p, verbose_level);
+    }
+#else
     // ориентация ключевых точек это довольно дорогая операция
     // в случае если пользователь просит малое количество лучших точек (например, 1000, а без порога нашлось 20000),
     // то по производительности очень оправдано сразу их здесь и выбрать, чтобы не тащить до самого конца где все равно отбросим
@@ -721,6 +830,7 @@ void phg::SIFT::detectAndCompute(const cv::Mat& img, const cv::Mat& mask, std::v
     // после подсчета ориентаций количество могло возрасти (и скорее всего возросло)
     // нужно снова выбрать лучшие точки чтобы уложиться в бюджет
     kpts = selectTopKeypoints(kpts, p, verbose_level);
+#endif
 
     if (verbose_level >= 2) {
         cv::Mat kpts_img;
@@ -728,10 +838,10 @@ void phg::SIFT::detectAndCompute(const cv::Mat& img, const cv::Mat& mask, std::v
         saveImg("04_keypoints.jpg", kpts_img);
     }
 
+#if !ENABLE_EXACT_NFEATURES_RECOVERY
     std::tie(desc, kpts) = computeDescriptors(kpts, octaves, p, verbose_level);
+#endif
 
-    // TODO всегда ли мы получаем ровно столько точек сколько запросили в параметре nfeatures? в каких случаях это не так и в какую сторону?
-    //   как подкрутить алгоритм, чтобы всегда выдавать ровно запрошенное количество точек (когда это в принципе возможно) но не сильно просесть в производительности?
 }
 
 void phg::SIFT::saveImg(const std::string& name, const cv::Mat& img) const
diff --git a/tests/logs/0999.log b/tests/logs/0999.log
new file mode 100644
index 0000000..9d1a5bd
--- /dev/null
+++ b/tests/logs/0999.log
@@ -0,0 +1,273 @@
+Note: Google Test filter = SIFT.*-SIFT.DetectionDescriptionSteps:SIFT.PairMatching:SIFT.DetectionSmokeTest
+[==========] Running 22 tests from 1 test suite.
+[----------] Global test environment set-up.
+[----------] 22 tests from SIFT
+[ RUN      ] SIFT.MovedTheSameImage
+[ORB_OCV] Points detected: 500 -> 500 (in 0.038195 sec)
+[ORB_OCV] 493/500 (recall=0.986) with average error=0.0520289
+[ORB_OCV] average size ratio between matched points: 1.00185
+[ORB_OCV] average angle difference between matched points: 0.145135 degrees
+[ORB_OCV] average descriptor distance between matched points: 8.8073 (random distance: 127.325) => differentiability=0.0691721
+[SIFTOCV] Points detected: 385 -> 394 (in 0.116707 sec)
+[SIFTOCV] 368/385 (recall=0.955844) with average error=0.046925
+[SIFTOCV] average size ratio between matched points: 0.998047
+[SIFTOCV] average angle difference between matched points: 0.405249 degrees
+[SIFTOCV] average descriptor distance between matched points: 17.7114 (random distance: 518.844) => differentiability=0.0341363
+[       OK ] SIFT.MovedTheSameImage (210 ms)
+[ RUN      ] SIFT.MovedImageRight
+[ORB_OCV] Points detected: 500 -> 500 (in 0.007648 sec)
+[ORB_OCV] 471/500 (recall=0.942) with average error=0.502929
+[ORB_OCV] average size ratio between matched points: 0.992385
+[ORB_OCV] average angle difference between matched points: -0.67963 degrees
+[ORB_OCV] average descriptor distance between matched points: 31.7219 (random distance: 127.724) => differentiability=0.248363
+[SIFTOCV] Points detected: 385 -> 385 (in 0.091896 sec)
+[SIFTOCV] 359/377 (recall=0.952255) with average error=0.109341
+[SIFTOCV] average size ratio between matched points: 1.01202
+[SIFTOCV] average angle difference between matched points: 0.37287 degrees
+[SIFTOCV] average descriptor distance between matched points: 28.0423 (random distance: 515.705) => differentiability=0.0543767
+[       OK ] SIFT.MovedImageRight (159 ms)
+[ RUN      ] SIFT.MovedImageLeft
+[ORB_OCV] Points detected: 500 -> 500 (in 0.007286 sec)
+[ORB_OCV] 466/498 (recall=0.935743) with average error=0.485198
+[ORB_OCV] average size ratio between matched points: 1.0061
+[ORB_OCV] average angle difference between matched points: -2.01597 degrees
+[ORB_OCV] average descriptor distance between matched points: 30.279 (random distance: 126.457) => differentiability=0.239441
+[SIFTOCV] Points detected: 385 -> 383 (in 0.088907 sec)
+[SIFTOCV] 351/373 (recall=0.941019) with average error=0.0804017
+[SIFTOCV] average size ratio between matched points: 0.99759
+[SIFTOCV] average angle difference between matched points: 0.287732 degrees
+[SIFTOCV] average descriptor distance between matched points: 27.8077 (random distance: 521.273) => differentiability=0.0533457
+[       OK ] SIFT.MovedImageLeft (148 ms)
+[ RUN      ] SIFT.MovedImageUpHalfPixel
+[ORB_OCV] Points detected: 500 -> 500 (in 0.007319 sec)
+[ORB_OCV] 460/497 (recall=0.925553) with average error=0.710802
+[ORB_OCV] average size ratio between matched points: 1.01167
+[ORB_OCV] average angle difference between matched points: 0.857323 degrees
+[ORB_OCV] average descriptor distance between matched points: 42.9087 (random distance: 123.602) => differentiability=0.347152
+[SIFTOCV] Points detected: 385 -> 424 (in 0.092558 sec)
+[SIFTOCV] 312/378 (recall=0.825397) with average error=0.38935
+[SIFTOCV] average size ratio between matched points: 1.04734
+[SIFTOCV] average angle difference between matched points: 0.874912 degrees
+[SIFTOCV] average descriptor distance between matched points: 96.2549 (random distance: 523.982) => differentiability=0.183699
+[       OK ] SIFT.MovedImageUpHalfPixel (156 ms)
+[ RUN      ] SIFT.MovedImageDownHalfPixel
+[ORB_OCV] Points detected: 500 -> 500 (in 0.007282 sec)
+[ORB_OCV] 442/478 (recall=0.924686) with average error=0.648609
+[ORB_OCV] average size ratio between matched points: 0.971097
+[ORB_OCV] average angle difference between matched points: -1.00358 degrees
+[ORB_OCV] average descriptor distance between matched points: 42.586 (random distance: 125.477) => differentiability=0.339392
+[SIFTOCV] Points detected: 385 -> 425 (in 0.08923 sec)
+[SIFTOCV] 317/376 (recall=0.843085) with average error=0.433961
+[SIFTOCV] average size ratio between matched points: 1.05648
+[SIFTOCV] average angle difference between matched points: 0.600276 degrees
+[SIFTOCV] average descriptor distance between matched points: 106.539 (random distance: 519.07) => differentiability=0.20525
+[       OK ] SIFT.MovedImageDownHalfPixel (151 ms)
+[ RUN      ] SIFT.Rotate10
+[ORB_OCV] Points detected: 500 -> 500 (in 0.008445 sec)
+[ORB_OCV] 475/500 (recall=0.95) with average error=0.932881
+[ORB_OCV] average size ratio between matched points: 1.00527
+[ORB_OCV] average angle difference between matched points: 8.46336 degrees
+[ORB_OCV] average descriptor distance between matched points: 56.6295 (random distance: 122.783) => differentiability=0.461215
+[SIFTOCV] Points detected: 385 -> 457 (in 0.086789 sec)
+[SIFTOCV] 308/383 (recall=0.804178) with average error=0.478137
+[SIFTOCV] average size ratio between matched points: 1.0446
+[SIFTOCV] average angle difference between matched points: 8.28068 degrees
+[SIFTOCV] average descriptor distance between matched points: 113.55 (random distance: 522.932) => differentiability=0.217141
+[       OK ] SIFT.Rotate10 (148 ms)
+[ RUN      ] SIFT.Rotate20
+[ORB_OCV] Points detected: 500 -> 500 (in 0.007524 sec)
+[ORB_OCV] 456/491 (recall=0.928717) with average error=0.860867
+[ORB_OCV] average size ratio between matched points: 0.968872
+[ORB_OCV] average angle difference between matched points: 19.4252 degrees
+[ORB_OCV] average descriptor distance between matched points: 58.3224 (random distance: 125.59) => differentiability=0.464387
+[SIFTOCV] Points detected: 385 -> 456 (in 0.118033 sec)
+[SIFTOCV] 294/376 (recall=0.781915) with average error=0.517421
+[SIFTOCV] average size ratio between matched points: 1.06356
+[SIFTOCV] average angle difference between matched points: 16.4794 degrees
+[SIFTOCV] average descriptor distance between matched points: 128.095 (random distance: 525.902) => differentiability=0.243572
+[       OK ] SIFT.Rotate20 (199 ms)
+[ RUN      ] SIFT.Rotate30
+[ORB_OCV] Points detected: 500 -> 500 (in 0.00767 sec)
+[ORB_OCV] 446/479 (recall=0.931106) with average error=0.954674
+[ORB_OCV] average size ratio between matched points: 0.971588
+[ORB_OCV] average angle difference between matched points: 25.313 degrees
+[ORB_OCV] average descriptor distance between matched points: 56.7668 (random distance: 125.177) => differentiability=0.453492
+[SIFTOCV] Points detected: 385 -> 428 (in 0.093021 sec)
+[SIFTOCV] 301/374 (recall=0.804813) with average error=0.62647
+[SIFTOCV] average size ratio between matched points: 1.0552
+[SIFTOCV] average angle difference between matched points: 24.1679 degrees
+[SIFTOCV] average descriptor distance between matched points: 139.444 (random distance: 517.49) => differentiability=0.269462
+[       OK ] SIFT.Rotate30 (153 ms)
+[ RUN      ] SIFT.Rotate40
+[ORB_OCV] Points detected: 500 -> 500 (in 0.010826 sec)
+[ORB_OCV] 445/475 (recall=0.936842) with average error=0.927974
+[ORB_OCV] average size ratio between matched points: 0.992927
+[ORB_OCV] average angle difference between matched points: 35.1473 degrees
+[ORB_OCV] average descriptor distance between matched points: 57.0315 (random distance: 125.292) => differentiability=0.455188
+[SIFTOCV] Points detected: 385 -> 412 (in 0.106487 sec)
+[SIFTOCV] 279/370 (recall=0.754054) with average error=0.726889
+[SIFTOCV] average size ratio between matched points: 1.07965
+[SIFTOCV] average angle difference between matched points: 28.8403 degrees
+[SIFTOCV] average descriptor distance between matched points: 148.753 (random distance: 518.131) => differentiability=0.287094
+[       OK ] SIFT.Rotate40 (176 ms)
+[ RUN      ] SIFT.Rotate45
+[ORB_OCV] Points detected: 500 -> 500 (in 0.006953 sec)
+[ORB_OCV] 427/475 (recall=0.898947) with average error=0.913293
+[ORB_OCV] average size ratio between matched points: 0.999695
+[ORB_OCV] average angle difference between matched points: 40.7936 degrees
+[ORB_OCV] average descriptor distance between matched points: 57.0351 (random distance: 126.834) => differentiability=0.449684
+[SIFTOCV] Points detected: 385 -> 443 (in 0.087779 sec)
+[SIFTOCV] 295/366 (recall=0.806011) with average error=0.722539
+[SIFTOCV] average size ratio between matched points: 1.06195
+[SIFTOCV] average angle difference between matched points: 37.5812 degrees
+[SIFTOCV] average descriptor distance between matched points: 142.709 (random distance: 531.264) => differentiability=0.268622
+[       OK ] SIFT.Rotate45 (143 ms)
+[ RUN      ] SIFT.Rotate90
+[ORB_OCV] Points detected: 500 -> 500 (in 0.006638 sec)
+[ORB_OCV] 406/475 (recall=0.854737) with average error=0.794018
+[ORB_OCV] average size ratio between matched points: 0.926186
+[ORB_OCV] average angle difference between matched points: 81.2045 degrees
+[ORB_OCV] average descriptor distance between matched points: 52.2069 (random distance: 126.239) => differentiability=0.413556
+[SIFTOCV] Points detected: 385 -> 372 (in 0.084346 sec)
+[SIFTOCV] 341/367 (recall=0.929155) with average error=0.589335
+[SIFTOCV] average size ratio between matched points: 0.9972
+[SIFTOCV] average angle difference between matched points: 86.1901 degrees
+[SIFTOCV] average descriptor distance between matched points: 49.631 (random distance: 522.565) => differentiability=0.0949758
+[       OK ] SIFT.Rotate90 (141 ms)
+[ RUN      ] SIFT.Scale50
+[ORB_OCV] Points detected: 500 -> 500 (in 0.006697 sec)
+[ORB_OCV] 463/500 (recall=0.926) with average error=0.927987
+[ORB_OCV] average size ratio between matched points: 0.829127
+[ORB_OCV] average angle difference between matched points: 2.9143 degrees
+[ORB_OCV] average descriptor distance between matched points: 89.7322 (random distance: 124.84) => differentiability=0.718776
+[SIFTOCV] Points detected: 385 -> 162 (in 0.079069 sec)
+[SIFTOCV] 202/385 (recall=0.524675) with average error=1.79627
+[SIFTOCV] average size ratio between matched points: 0.919287
+[SIFTOCV] average angle difference between matched points: 1.52171 degrees
+[SIFTOCV] average descriptor distance between matched points: 344.742 (random distance: 531.506) => differentiability=0.648614
+[       OK ] SIFT.Scale50 (134 ms)
+[ RUN      ] SIFT.Scale70
+[ORB_OCV] Points detected: 500 -> 500 (in 0.006747 sec)
+[ORB_OCV] 488/500 (recall=0.976) with average error=0.902066
+[ORB_OCV] average size ratio between matched points: 0.884398
+[ORB_OCV] average angle difference between matched points: 1.14689 degrees
+[ORB_OCV] average descriptor distance between matched points: 70.2889 (random distance: 124.695) => differentiability=0.563688
+[SIFTOCV] Points detected: 385 -> 278 (in 0.079333 sec)
+[SIFTOCV] 255/385 (recall=0.662338) with average error=1.18739
+[SIFTOCV] average size ratio between matched points: 0.937014
+[SIFTOCV] average angle difference between matched points: -1.32973 degrees
+[SIFTOCV] average descriptor distance between matched points: 235.34 (random distance: 525.863) => differentiability=0.447532
+[       OK ] SIFT.Scale70 (139 ms)
+[ RUN      ] SIFT.Scale90
+[ORB_OCV] Points detected: 500 -> 500 (in 0.00678 sec)
+[ORB_OCV] 489/500 (recall=0.978) with average error=0.823308
+[ORB_OCV] average size ratio between matched points: 0.970324
+[ORB_OCV] average angle difference between matched points: -0.183516 degrees
+[ORB_OCV] average descriptor distance between matched points: 60.3333 (random distance: 125.059) => differentiability=0.482438
+[SIFTOCV] Points detected: 385 -> 407 (in 0.078391 sec)
+[SIFTOCV] 301/385 (recall=0.781818) with average error=0.642545
+[SIFTOCV] average size ratio between matched points: 1.01085
+[SIFTOCV] average angle difference between matched points: -1.59491 degrees
+[SIFTOCV] average descriptor distance between matched points: 149.916 (random distance: 517.016) => differentiability=0.289965
+[       OK ] SIFT.Scale90 (131 ms)
+[ RUN      ] SIFT.Scale110
+[ORB_OCV] Points detected: 500 -> 500 (in 0.006782 sec)
+[ORB_OCV] 462/500 (recall=0.924) with average error=0.912118
+[ORB_OCV] average size ratio between matched points: 1.02967
+[ORB_OCV] average angle difference between matched points: -0.905967 degrees
+[ORB_OCV] average descriptor distance between matched points: 59.7468 (random distance: 125.225) => differentiability=0.477115
+[SIFTOCV] Points detected: 385 -> 500 (in 0.085135 sec)
+[SIFTOCV] 293/372 (recall=0.787634) with average error=0.55879
+[SIFTOCV] average size ratio between matched points: 1.16432
+[SIFTOCV] average angle difference between matched points: -1.39771 degrees
+[SIFTOCV] average descriptor distance between matched points: 133.248 (random distance: 526.285) => differentiability=0.253187
+[       OK ] SIFT.Scale110 (139 ms)
+[ RUN      ] SIFT.Scale130
+[ORB_OCV] Points detected: 500 -> 500 (in 0.006849 sec)
+[ORB_OCV] 400/475 (recall=0.842105) with average error=1.02537
+[ORB_OCV] average size ratio between matched points: 1.10237
+[ORB_OCV] average angle difference between matched points: -4.78444 degrees
+[ORB_OCV] average descriptor distance between matched points: 67.4875 (random distance: 126.4) => differentiability=0.53392
+[SIFTOCV] Points detected: 385 -> 500 (in 0.082103 sec)
+[SIFTOCV] 242/351 (recall=0.689459) with average error=0.538303
+[SIFTOCV] average size ratio between matched points: 1.37952
+[SIFTOCV] average angle difference between matched points: -0.631644 degrees
+[SIFTOCV] average descriptor distance between matched points: 120.25 (random distance: 522.019) => differentiability=0.230356
+[       OK ] SIFT.Scale130 (135 ms)
+[ RUN      ] SIFT.Scale150
+[ORB_OCV] Points detected: 500 -> 500 (in 0.006822 sec)
+[ORB_OCV] 325/445 (recall=0.730337) with average error=1.18695
+[ORB_OCV] average size ratio between matched points: 1.16446
+[ORB_OCV] average angle difference between matched points: -1.92861 degrees
+[ORB_OCV] average descriptor distance between matched points: 75.4954 (random distance: 125.905) => differentiability=0.599624
+[SIFTOCV] Points detected: 385 -> 500 (in 0.082087 sec)
+[SIFTOCV] 193/330 (recall=0.584848) with average error=0.514564
+[SIFTOCV] average size ratio between matched points: 1.58428
+[SIFTOCV] average angle difference between matched points: 1.65215 degrees
+[SIFTOCV] average descriptor distance between matched points: 120.89 (random distance: 528.77) => differentiability=0.228625
+[       OK ] SIFT.Scale150 (138 ms)
+[ RUN      ] SIFT.Scale175
+[ORB_OCV] Points detected: 500 -> 500 (in 0.006867 sec)
+[ORB_OCV] 235/380 (recall=0.618421) with average error=1.3217
+[ORB_OCV] average size ratio between matched points: 1.25586
+[ORB_OCV] average angle difference between matched points: 4.13008 degrees
+[ORB_OCV] average descriptor distance between matched points: 84.1617 (random distance: 124.894) => differentiability=0.673867
+[SIFTOCV] Points detected: 385 -> 500 (in 0.081887 sec)
+[SIFTOCV] 170/300 (recall=0.566667) with average error=0.536976
+[SIFTOCV] average size ratio between matched points: 1.81545
+[SIFTOCV] average angle difference between matched points: -0.0531282 degrees
+[SIFTOCV] average descriptor distance between matched points: 122.891 (random distance: 526.429) => differentiability=0.233443
+[       OK ] SIFT.Scale175 (137 ms)
+[ RUN      ] SIFT.Scale200
+[ORB_OCV] Points detected: 500 -> 500 (in 0.007116 sec)
+[ORB_OCV] 174/333 (recall=0.522523) with average error=1.36043
+[ORB_OCV] average size ratio between matched points: 1.34374
+[ORB_OCV] average angle difference between matched points: -3.78969 degrees
+[ORB_OCV] average descriptor distance between matched points: 86.1954 (random distance: 126.011) => differentiability=0.684028
+[SIFTOCV] Points detected: 385 -> 500 (in 0.082443 sec)
+[SIFTOCV] 162/274 (recall=0.591241) with average error=0.556679
+[SIFTOCV] average size ratio between matched points: 2.0394
+[SIFTOCV] average angle difference between matched points: -4.26259 degrees
+[SIFTOCV] average descriptor distance between matched points: 99.0094 (random distance: 526.638) => differentiability=0.188003
+[       OK ] SIFT.Scale200 (139 ms)
+[ RUN      ] SIFT.Rotate10Scale90
+[ORB_OCV] Points detected: 500 -> 500 (in 0.006753 sec)
+[ORB_OCV] 486/500 (recall=0.972) with average error=0.862656
+[ORB_OCV] average size ratio between matched points: 1.00909
+[ORB_OCV] average angle difference between matched points: 12.1602 degrees
+[ORB_OCV] average descriptor distance between matched points: 61.9033 (random distance: 126.477) => differentiability=0.489442
+[SIFTOCV] Points detected: 385 -> 394 (in 0.079139 sec)
+[SIFTOCV] 290/385 (recall=0.753247) with average error=0.652913
+[SIFTOCV] average size ratio between matched points: 0.972783
+[SIFTOCV] average angle difference between matched points: 8.41556 degrees
+[SIFTOCV] average descriptor distance between matched points: 154.724 (random distance: 527.209) => differentiability=0.293477
+[       OK ] SIFT.Rotate10Scale90 (132 ms)
+[ RUN      ] SIFT.Rotate30Scale75
+[ORB_OCV] Points detected: 500 -> 500 (in 0.006706 sec)
+[ORB_OCV] 471/500 (recall=0.942) with average error=0.873322
+[ORB_OCV] average size ratio between matched points: 0.882831
+[ORB_OCV] average angle difference between matched points: 32.7295 degrees
+[ORB_OCV] average descriptor distance between matched points: 68.9915 (random distance: 123.987) => differentiability=0.55644
+[SIFTOCV] Points detected: 385 -> 302 (in 0.079743 sec)
+[SIFTOCV] 265/385 (recall=0.688312) with average error=0.995207
+[SIFTOCV] average size ratio between matched points: 0.979378
+[SIFTOCV] average angle difference between matched points: 19.5607 degrees
+[SIFTOCV] average descriptor distance between matched points: 207.53 (random distance: 525.691) => differentiability=0.394776
+[       OK ] SIFT.Rotate30Scale75 (130 ms)
+[ RUN      ] SIFT.HerzJesu19RotateM40
+[ORB_OCV] Points detected: 500 -> 500 (in 0.080186 sec)
+[ORB_OCV] 494/500 (recall=0.988) with average error=1.25431
+[ORB_OCV] average size ratio between matched points: 1.00708
+[ORB_OCV] average angle difference between matched points: -36.7101 degrees
+[ORB_OCV] average descriptor distance between matched points: 48.3603 (random distance: 121.69) => differentiability=0.397405
+[SIFTOCV] Points detected: 500 -> 500 (in 3.52565 sec)
+[SIFTOCV] 384/429 (recall=0.895105) with average error=2.40066
+[SIFTOCV] average size ratio between matched points: 1.04042
+[SIFTOCV] average angle difference between matched points: -33.5724 degrees
+[SIFTOCV] average descriptor distance between matched points: 146.801 (random distance: 530.239) => differentiability=0.276858
+[       OK ] SIFT.HerzJesu19RotateM40 (5061 ms)
+[----------] 22 tests from SIFT (8200 ms total)
+
+[----------] Global test environment tear-down
+[==========] 22 tests from 1 test suite ran. (8200 ms total)
+[  PASSED  ] 22 tests.
diff --git a/tests/logs/descriptor_matching.log b/tests/logs/descriptor_matching.log
new file mode 100644
index 0000000..806b3a5
--- /dev/null
+++ b/tests/logs/descriptor_matching.log
@@ -0,0 +1,282 @@
+[==========] Running 25 tests from 1 test suite.
+[----------] Global test environment set-up.
+[----------] 25 tests from SIFT
+[ RUN      ] SIFT.MovedTheSameImage
+[ORB_OCV] Points detected: 500 -> 500 (in 0.03743 sec)
+[ORB_OCV] 493/500 (recall=0.986) with average error=0.0520289
+[ORB_OCV] average size ratio between matched points: 1.00185
+[ORB_OCV] average angle difference between matched points: 0.526519 degrees
+[ORB_OCV] average descriptor distance between matched points: 8.8073 (random distance: 127.325) => differentiability=0.0691721
+[SIFTOCV] Points detected: 453 -> 465 (in 0.092094 sec)
+[SIFTOCV] 436/453 (recall=0.962472) with average error=0.0435686
+[SIFTOCV] average size ratio between matched points: 0.998252
+[SIFTOCV] average angle difference between matched points: -0.128614 degrees
+[SIFTOCV] average descriptor distance between matched points: 90.3355 (random distance: 523.008) => differentiability=0.172723
+[       OK ] SIFT.MovedTheSameImage (188 ms)
+[ RUN      ] SIFT.MovedImageRight
+[ORB_OCV] Points detected: 500 -> 500 (in 0.007709 sec)
+[ORB_OCV] 471/500 (recall=0.942) with average error=0.502929
+[ORB_OCV] average size ratio between matched points: 0.992385
+[ORB_OCV] average angle difference between matched points: -0.711678 degrees
+[ORB_OCV] average descriptor distance between matched points: 31.7219 (random distance: 127.724) => differentiability=0.248363
+[SIFTOCV] Points detected: 453 -> 447 (in 0.082962 sec)
+[SIFTOCV] 421/442 (recall=0.952489) with average error=0.107924
+[SIFTOCV] average size ratio between matched points: 1.01134
+[SIFTOCV] average angle difference between matched points: 0.361078 degrees
+[SIFTOCV] average descriptor distance between matched points: 90.1555 (random distance: 527.86) => differentiability=0.170794
+[       OK ] SIFT.MovedImageRight (143 ms)
+[ RUN      ] SIFT.MovedImageLeft
+[ORB_OCV] Points detected: 500 -> 500 (in 0.007142 sec)
+[ORB_OCV] 466/498 (recall=0.935743) with average error=0.485198
+[ORB_OCV] average size ratio between matched points: 1.0061
+[ORB_OCV] average angle difference between matched points: -1.04915 degrees
+[ORB_OCV] average descriptor distance between matched points: 30.279 (random distance: 126.457) => differentiability=0.239441
+[SIFTOCV] Points detected: 453 -> 447 (in 0.082649 sec)
+[SIFTOCV] 417/441 (recall=0.945578) with average error=0.0855277
+[SIFTOCV] average size ratio between matched points: 0.997577
+[SIFTOCV] average angle difference between matched points: -0.461582 degrees
+[SIFTOCV] average descriptor distance between matched points: 97.5722 (random distance: 523.97) => differentiability=0.186217
+[       OK ] SIFT.MovedImageLeft (141 ms)
+[ RUN      ] SIFT.MovedImageUpHalfPixel
+[ORB_OCV] Points detected: 500 -> 500 (in 0.007244 sec)
+[ORB_OCV] 460/497 (recall=0.925553) with average error=0.710802
+[ORB_OCV] average size ratio between matched points: 1.01167
+[ORB_OCV] average angle difference between matched points: -0.290587 degrees
+[ORB_OCV] average descriptor distance between matched points: 42.9087 (random distance: 123.602) => differentiability=0.347152
+[SIFTOCV] Points detected: 453 -> 500 (in 0.079778 sec)
+[SIFTOCV] 367/445 (recall=0.824719) with average error=0.398716
+[SIFTOCV] average size ratio between matched points: 1.0449
+[SIFTOCV] average angle difference between matched points: 0.281815 degrees
+[SIFTOCV] average descriptor distance between matched points: 152.166 (random distance: 530.014) => differentiability=0.287097
+[       OK ] SIFT.MovedImageUpHalfPixel (139 ms)
+[ RUN      ] SIFT.MovedImageDownHalfPixel
+[ORB_OCV] Points detected: 500 -> 500 (in 0.00709 sec)
+[ORB_OCV] 442/478 (recall=0.924686) with average error=0.648609
+[ORB_OCV] average size ratio between matched points: 0.971097
+[ORB_OCV] average angle difference between matched points: 1.08285 degrees
+[ORB_OCV] average descriptor distance between matched points: 42.586 (random distance: 125.477) => differentiability=0.339392
+[SIFTOCV] Points detected: 453 -> 500 (in 0.081573 sec)
+[SIFTOCV] 374/439 (recall=0.851936) with average error=0.445461
+[SIFTOCV] average size ratio between matched points: 1.05024
+[SIFTOCV] average angle difference between matched points: 1.44735 degrees
+[SIFTOCV] average descriptor distance between matched points: 165.511 (random distance: 529.613) => differentiability=0.312513
+[       OK ] SIFT.MovedImageDownHalfPixel (137 ms)
+[ RUN      ] SIFT.Rotate10
+[ORB_OCV] Points detected: 500 -> 500 (in 0.007111 sec)
+[ORB_OCV] 475/500 (recall=0.95) with average error=0.932881
+[ORB_OCV] average size ratio between matched points: 1.00527
+[ORB_OCV] average angle difference between matched points: 8.9931 degrees
+[ORB_OCV] average descriptor distance between matched points: 56.6295 (random distance: 122.783) => differentiability=0.461215
+[SIFTOCV] Points detected: 453 -> 500 (in 0.075131 sec)
+[SIFTOCV] 350/451 (recall=0.776053) with average error=0.501799
+[SIFTOCV] average size ratio between matched points: 1.03657
+[SIFTOCV] average angle difference between matched points: 9.41985 degrees
+[SIFTOCV] average descriptor distance between matched points: 178.858 (random distance: 521.738) => differentiability=0.342812
+[       OK ] SIFT.Rotate10 (131 ms)
+[ RUN      ] SIFT.Rotate20
+[ORB_OCV] Points detected: 500 -> 500 (in 0.007128 sec)
+[ORB_OCV] 456/491 (recall=0.928717) with average error=0.860867
+[ORB_OCV] average size ratio between matched points: 0.968872
+[ORB_OCV] average angle difference between matched points: 19.8609 degrees
+[ORB_OCV] average descriptor distance between matched points: 58.3224 (random distance: 125.59) => differentiability=0.464387
+[SIFTOCV] Points detected: 453 -> 500 (in 0.07548 sec)
+[SIFTOCV] 337/443 (recall=0.760722) with average error=0.556804
+[SIFTOCV] average size ratio between matched points: 1.04965
+[SIFTOCV] average angle difference between matched points: 13.8479 degrees
+[SIFTOCV] average descriptor distance between matched points: 186.303 (random distance: 531.487) => differentiability=0.350531
+[       OK ] SIFT.Rotate20 (132 ms)
+[ RUN      ] SIFT.Rotate30
+[ORB_OCV] Points detected: 500 -> 500 (in 0.007378 sec)
+[ORB_OCV] 446/479 (recall=0.931106) with average error=0.954674
+[ORB_OCV] average size ratio between matched points: 0.971588
+[ORB_OCV] average angle difference between matched points: 28.3797 degrees
+[ORB_OCV] average descriptor distance between matched points: 56.7668 (random distance: 125.177) => differentiability=0.453492
+[SIFTOCV] Points detected: 453 -> 500 (in 0.076006 sec)
+[SIFTOCV] 356/441 (recall=0.807256) with average error=0.635313
+[SIFTOCV] average size ratio between matched points: 1.04861
+[SIFTOCV] average angle difference between matched points: 22.6383 degrees
+[SIFTOCV] average descriptor distance between matched points: 194.029 (random distance: 529.795) => differentiability=0.366233
+[       OK ] SIFT.Rotate30 (131 ms)
+[ RUN      ] SIFT.Rotate40
+[ORB_OCV] Points detected: 500 -> 500 (in 0.007174 sec)
+[ORB_OCV] 445/475 (recall=0.936842) with average error=0.927974
+[ORB_OCV] average size ratio between matched points: 0.992927
+[ORB_OCV] average angle difference between matched points: 37.8999 degrees
+[ORB_OCV] average descriptor distance between matched points: 57.0315 (random distance: 125.292) => differentiability=0.455188
+[SIFTOCV] Points detected: 453 -> 470 (in 0.079921 sec)
+[SIFTOCV] 335/436 (recall=0.768349) with average error=0.722947
+[SIFTOCV] average size ratio between matched points: 1.07134
+[SIFTOCV] average angle difference between matched points: 28.1711 degrees
+[SIFTOCV] average descriptor distance between matched points: 189.937 (random distance: 524.78) => differentiability=0.361937
+[       OK ] SIFT.Rotate40 (138 ms)
+[ RUN      ] SIFT.Rotate45
+[ORB_OCV] Points detected: 500 -> 500 (in 0.007143 sec)
+[ORB_OCV] 427/475 (recall=0.898947) with average error=0.913293
+[ORB_OCV] average size ratio between matched points: 0.999695
+[ORB_OCV] average angle difference between matched points: 44.5806 degrees
+[ORB_OCV] average descriptor distance between matched points: 57.0351 (random distance: 126.834) => differentiability=0.449684
+[SIFTOCV] Points detected: 453 -> 500 (in 0.079777 sec)
+[SIFTOCV] 344/431 (recall=0.798144) with average error=0.735095
+[SIFTOCV] average size ratio between matched points: 1.04736
+[SIFTOCV] average angle difference between matched points: 38.6116 degrees
+[SIFTOCV] average descriptor distance between matched points: 189.73 (random distance: 526.815) => differentiability=0.360145
+[       OK ] SIFT.Rotate45 (139 ms)
+[ RUN      ] SIFT.Rotate90
+[ORB_OCV] Points detected: 500 -> 500 (in 0.007141 sec)
+[ORB_OCV] 406/475 (recall=0.854737) with average error=0.794018
+[ORB_OCV] average size ratio between matched points: 0.926186
+[ORB_OCV] average angle difference between matched points: 87.3904 degrees
+[ORB_OCV] average descriptor distance between matched points: 52.2069 (random distance: 126.239) => differentiability=0.413556
+[SIFTOCV] Points detected: 453 -> 429 (in 0.074407 sec)
+[SIFTOCV] 397/428 (recall=0.92757) with average error=0.597669
+[SIFTOCV] average size ratio between matched points: 0.994223
+[SIFTOCV] average angle difference between matched points: 85.9039 degrees
+[SIFTOCV] average descriptor distance between matched points: 109.39 (random distance: 522.253) => differentiability=0.209459
+[       OK ] SIFT.Rotate90 (132 ms)
+[ RUN      ] SIFT.Scale50
+[ORB_OCV] Points detected: 500 -> 500 (in 0.006664 sec)
+[ORB_OCV] 463/500 (recall=0.926) with average error=0.927987
+[ORB_OCV] average size ratio between matched points: 0.829127
+[ORB_OCV] average angle difference between matched points: 0.243979 degrees
+[ORB_OCV] average descriptor distance between matched points: 89.7322 (random distance: 124.84) => differentiability=0.718776
+[SIFTOCV] Points detected: 453 -> 191 (in 0.072397 sec)
+[SIFTOCV] 235/453 (recall=0.518764) with average error=1.81213
+[SIFTOCV] average size ratio between matched points: 0.90594
+[SIFTOCV] average angle difference between matched points: 0.126219 degrees
+[SIFTOCV] average descriptor distance between matched points: 362.817 (random distance: 526.31) => differentiability=0.68936
+[       OK ] SIFT.Scale50 (127 ms)
+[ RUN      ] SIFT.Scale70
+[ORB_OCV] Points detected: 500 -> 500 (in 0.006592 sec)
+[ORB_OCV] 488/500 (recall=0.976) with average error=0.902066
+[ORB_OCV] average size ratio between matched points: 0.884398
+[ORB_OCV] average angle difference between matched points: 1.67147 degrees
+[ORB_OCV] average descriptor distance between matched points: 70.2889 (random distance: 124.695) => differentiability=0.563688
+[SIFTOCV] Points detected: 453 -> 335 (in 0.072242 sec)
+[SIFTOCV] 307/453 (recall=0.677704) with average error=1.1649
+[SIFTOCV] average size ratio between matched points: 0.92551
+[SIFTOCV] average angle difference between matched points: 0.682746 degrees
+[SIFTOCV] average descriptor distance between matched points: 272.272 (random distance: 530.603) => differentiability=0.513138
+[       OK ] SIFT.Scale70 (125 ms)
+[ RUN      ] SIFT.Scale90
+[ORB_OCV] Points detected: 500 -> 500 (in 0.006778 sec)
+[ORB_OCV] 489/500 (recall=0.978) with average error=0.823308
+[ORB_OCV] average size ratio between matched points: 0.970324
+[ORB_OCV] average angle difference between matched points: -1.10786 degrees
+[ORB_OCV] average descriptor distance between matched points: 60.3333 (random distance: 125.059) => differentiability=0.482438
+[SIFTOCV] Points detected: 453 -> 469 (in 0.07171 sec)
+[SIFTOCV] 354/453 (recall=0.781457) with average error=0.59984
+[SIFTOCV] average size ratio between matched points: 1.00052
+[SIFTOCV] average angle difference between matched points: 1.23403 degrees
+[SIFTOCV] average descriptor distance between matched points: 193.204 (random distance: 523.29) => differentiability=0.369209
+[       OK ] SIFT.Scale90 (126 ms)
+[ RUN      ] SIFT.Scale110
+[ORB_OCV] Points detected: 500 -> 500 (in 0.006687 sec)
+[ORB_OCV] 462/500 (recall=0.924) with average error=0.912118
+[ORB_OCV] average size ratio between matched points: 1.02967
+[ORB_OCV] average angle difference between matched points: -2.21074 degrees
+[ORB_OCV] average descriptor distance between matched points: 59.7468 (random distance: 125.225) => differentiability=0.477115
+[SIFTOCV] Points detected: 453 -> 500 (in 0.073027 sec)
+[SIFTOCV] 307/434 (recall=0.707373) with average error=0.561683
+[SIFTOCV] average size ratio between matched points: 1.15716
+[SIFTOCV] average angle difference between matched points: 0.0277976 degrees
+[SIFTOCV] average descriptor distance between matched points: 182.143 (random distance: 530.492) => differentiability=0.343347
+[       OK ] SIFT.Scale110 (361 ms)
+[ RUN      ] SIFT.Scale130
+[ORB_OCV] Points detected: 500 -> 500 (in 0.00871 sec)
+[ORB_OCV] 400/475 (recall=0.842105) with average error=1.02537
+[ORB_OCV] average size ratio between matched points: 1.10237
+[ORB_OCV] average angle difference between matched points: -0.0928462 degrees
+[ORB_OCV] average descriptor distance between matched points: 67.4875 (random distance: 126.4) => differentiability=0.53392
+[SIFTOCV] Points detected: 453 -> 500 (in 0.081611 sec)
+[SIFTOCV] 253/409 (recall=0.618582) with average error=0.533529
+[SIFTOCV] average size ratio between matched points: 1.37601
+[SIFTOCV] average angle difference between matched points: -0.510379 degrees
+[SIFTOCV] average descriptor distance between matched points: 175.173 (random distance: 529.43) => differentiability=0.33087
+[       OK ] SIFT.Scale130 (158 ms)
+[ RUN      ] SIFT.Scale150
+[ORB_OCV] Points detected: 500 -> 500 (in 0.007242 sec)
+[ORB_OCV] 325/445 (recall=0.730337) with average error=1.18695
+[ORB_OCV] average size ratio between matched points: 1.16446
+[ORB_OCV] average angle difference between matched points: -0.709934 degrees
+[ORB_OCV] average descriptor distance between matched points: 75.4954 (random distance: 125.905) => differentiability=0.599624
+[SIFTOCV] Points detected: 453 -> 500 (in 0.081615 sec)
+[SIFTOCV] 208/387 (recall=0.537468) with average error=0.519869
+[SIFTOCV] average size ratio between matched points: 1.56537
+[SIFTOCV] average angle difference between matched points: -1.0201 degrees
+[SIFTOCV] average descriptor distance between matched points: 178.951 (random distance: 530.33) => differentiability=0.337433
+[       OK ] SIFT.Scale150 (144 ms)
+[ RUN      ] SIFT.Scale175
+[ORB_OCV] Points detected: 500 -> 500 (in 0.007775 sec)
+[ORB_OCV] 235/380 (recall=0.618421) with average error=1.3217
+[ORB_OCV] average size ratio between matched points: 1.25586
+[ORB_OCV] average angle difference between matched points: 3.49119 degrees
+[ORB_OCV] average descriptor distance between matched points: 84.1617 (random distance: 124.894) => differentiability=0.673867
+[SIFTOCV] Points detected: 453 -> 501 (in 0.079356 sec)
+[SIFTOCV] 189/354 (recall=0.533898) with average error=0.530545
+[SIFTOCV] average size ratio between matched points: 1.79649
+[SIFTOCV] average angle difference between matched points: 0.375372 degrees
+[SIFTOCV] average descriptor distance between matched points: 182.373 (random distance: 522.122) => differentiability=0.349291
+[       OK ] SIFT.Scale175 (141 ms)
+[ RUN      ] SIFT.Scale200
+[ORB_OCV] Points detected: 500 -> 500 (in 0.007598 sec)
+[ORB_OCV] 174/333 (recall=0.522523) with average error=1.36043
+[ORB_OCV] average size ratio between matched points: 1.34374
+[ORB_OCV] average angle difference between matched points: 0.387991 degrees
+[ORB_OCV] average descriptor distance between matched points: 86.1954 (random distance: 126.011) => differentiability=0.684028
+[SIFTOCV] Points detected: 453 -> 500 (in 0.077906 sec)
+[SIFTOCV] 178/324 (recall=0.549383) with average error=0.536323
+[SIFTOCV] average size ratio between matched points: 2.03968
+[SIFTOCV] average angle difference between matched points: -3.44031 degrees
+[SIFTOCV] average descriptor distance between matched points: 167.42 (random distance: 528.219) => differentiability=0.316952
+[       OK ] SIFT.Scale200 (138 ms)
+[ RUN      ] SIFT.Rotate10Scale90
+[ORB_OCV] Points detected: 500 -> 500 (in 0.007465 sec)
+[ORB_OCV] 486/500 (recall=0.972) with average error=0.862656
+[ORB_OCV] average size ratio between matched points: 1.00909
+[ORB_OCV] average angle difference between matched points: 10.8416 degrees
+[ORB_OCV] average descriptor distance between matched points: 61.9033 (random distance: 126.477) => differentiability=0.489442
+[SIFTOCV] Points detected: 453 -> 464 (in 0.077476 sec)
+[SIFTOCV] 351/453 (recall=0.774834) with average error=0.660494
+[SIFTOCV] average size ratio between matched points: 0.970697
+[SIFTOCV] average angle difference between matched points: 10.0653 degrees
+[SIFTOCV] average descriptor distance between matched points: 210.278 (random distance: 528.178) => differentiability=0.39812
+[       OK ] SIFT.Rotate10Scale90 (136 ms)
+[ RUN      ] SIFT.Rotate30Scale75
+[ORB_OCV] Points detected: 500 -> 500 (in 0.007044 sec)
+[ORB_OCV] 471/500 (recall=0.942) with average error=0.873322
+[ORB_OCV] average size ratio between matched points: 0.882831
+[ORB_OCV] average angle difference between matched points: 29.9983 degrees
+[ORB_OCV] average descriptor distance between matched points: 68.9915 (random distance: 123.987) => differentiability=0.55644
+[SIFTOCV] Points detected: 453 -> 373 (in 0.078585 sec)
+[SIFTOCV] 318/453 (recall=0.701987) with average error=0.961622
+[SIFTOCV] average size ratio between matched points: 0.958296
+[SIFTOCV] average angle difference between matched points: 21.1272 degrees
+[SIFTOCV] average descriptor distance between matched points: 241.741 (random distance: 528.552) => differentiability=0.457364
+[       OK ] SIFT.Rotate30Scale75 (138 ms)
+[ RUN      ] SIFT.HerzJesu19RotateM40
+[ORB_OCV] Points detected: 500 -> 500 (in 0.077833 sec)
+[ORB_OCV] 494/500 (recall=0.988) with average error=1.25431
+[ORB_OCV] average size ratio between matched points: 1.00708
+[ORB_OCV] average angle difference between matched points: -29.9254 degrees
+[ORB_OCV] average descriptor distance between matched points: 48.3603 (random distance: 121.69) => differentiability=0.397405
+[SIFTOCV] Points detected: 500 -> 500 (in 3.40811 sec)
+[SIFTOCV] 377/435 (recall=0.866667) with average error=2.85298
+[SIFTOCV] average size ratio between matched points: 1.00886
+[SIFTOCV] average angle difference between matched points: -29.3651 degrees
+[SIFTOCV] average descriptor distance between matched points: 212.589 (random distance: 537.412) => differentiability=0.395579
+[       OK ] SIFT.HerzJesu19RotateM40 (5121 ms)
+[ RUN      ] SIFT.DetectionSmokeTest
+ENABLE_MY_SIFT_TESTING is disabled, test skipped
+[       OK ] SIFT.DetectionSmokeTest (0 ms)
+[ RUN      ] SIFT.DetectionDescriptionSteps
+ENABLE_MY_SIFT_TESTING is disabled, test skipped
+[       OK ] SIFT.DetectionDescriptionSteps (0 ms)
+[ RUN      ] SIFT.PairMatching
+ENABLE_MY_SIFT_TESTING is disabled, test skipped
+Final score: UNKNOWN
+[       OK ] SIFT.PairMatching (0 ms)
+[----------] 25 tests from SIFT (8268 ms total)
+
+[----------] Global test environment tear-down
+[==========] 25 tests from 1 test suite ran. (8268 ms total)
+[  PASSED  ] 25 tests.
diff --git a/tests/logs/original.log b/tests/logs/original.log
new file mode 100644
index 0000000..d5e55f1
--- /dev/null
+++ b/tests/logs/original.log
@@ -0,0 +1,282 @@
+[==========] Running 25 tests from 1 test suite.
+[----------] Global test environment set-up.
+[----------] 25 tests from SIFT
+[ RUN      ] SIFT.MovedTheSameImage
+[ORB_OCV] Points detected: 500 -> 500 (in 0.038255 sec)
+[ORB_OCV] 493/500 (recall=0.986) with average error=0.0520289
+[ORB_OCV] average size ratio between matched points: 1.00185
+[ORB_OCV] average angle difference between matched points: 0.145135 degrees
+[ORB_OCV] average descriptor distance between matched points: 8.8073 (random distance: 127.325) => differentiability=0.0691721
+[SIFTOCV] Points detected: 453 -> 465 (in 0.084385 sec)
+[SIFTOCV] 436/453 (recall=0.962472) with average error=0.0435686
+[SIFTOCV] average size ratio between matched points: 0.998252
+[SIFTOCV] average angle difference between matched points: -4.7478 degrees
+[SIFTOCV] average descriptor distance between matched points: 90.3355 (random distance: 523.008) => differentiability=0.172723
+[       OK ] SIFT.MovedTheSameImage (176 ms)
+[ RUN      ] SIFT.MovedImageRight
+[ORB_OCV] Points detected: 500 -> 500 (in 0.007215 sec)
+[ORB_OCV] 471/500 (recall=0.942) with average error=0.502929
+[ORB_OCV] average size ratio between matched points: 0.992385
+[ORB_OCV] average angle difference between matched points: -0.67963 degrees
+[ORB_OCV] average descriptor distance between matched points: 31.7219 (random distance: 127.724) => differentiability=0.248363
+[SIFTOCV] Points detected: 453 -> 447 (in 0.07736 sec)
+[SIFTOCV] 421/442 (recall=0.952489) with average error=0.107924
+[SIFTOCV] average size ratio between matched points: 1.01134
+[SIFTOCV] average angle difference between matched points: -3.58242 degrees
+[SIFTOCV] average descriptor distance between matched points: 90.1555 (random distance: 527.86) => differentiability=0.170794
+[       OK ] SIFT.MovedImageRight (138 ms)
+[ RUN      ] SIFT.MovedImageLeft
+[ORB_OCV] Points detected: 500 -> 500 (in 0.007141 sec)
+[ORB_OCV] 466/498 (recall=0.935743) with average error=0.485198
+[ORB_OCV] average size ratio between matched points: 1.0061
+[ORB_OCV] average angle difference between matched points: -2.01597 degrees
+[ORB_OCV] average descriptor distance between matched points: 30.279 (random distance: 126.457) => differentiability=0.239441
+[SIFTOCV] Points detected: 453 -> 447 (in 0.077483 sec)
+[SIFTOCV] 417/441 (recall=0.945578) with average error=0.0855277
+[SIFTOCV] average size ratio between matched points: 0.997577
+[SIFTOCV] average angle difference between matched points: -6.43682 degrees
+[SIFTOCV] average descriptor distance between matched points: 97.5722 (random distance: 523.97) => differentiability=0.186217
+[       OK ] SIFT.MovedImageLeft (132 ms)
+[ RUN      ] SIFT.MovedImageUpHalfPixel
+[ORB_OCV] Points detected: 500 -> 500 (in 0.007232 sec)
+[ORB_OCV] 460/497 (recall=0.925553) with average error=0.710802
+[ORB_OCV] average size ratio between matched points: 1.01167
+[ORB_OCV] average angle difference between matched points: 0.857323 degrees
+[ORB_OCV] average descriptor distance between matched points: 42.9087 (random distance: 123.602) => differentiability=0.347152
+[SIFTOCV] Points detected: 453 -> 500 (in 0.076186 sec)
+[SIFTOCV] 367/445 (recall=0.824719) with average error=0.398716
+[SIFTOCV] average size ratio between matched points: 1.0449
+[SIFTOCV] average angle difference between matched points: 0.114179 degrees
+[SIFTOCV] average descriptor distance between matched points: 152.166 (random distance: 530.014) => differentiability=0.287097
+[       OK ] SIFT.MovedImageUpHalfPixel (132 ms)
+[ RUN      ] SIFT.MovedImageDownHalfPixel
+[ORB_OCV] Points detected: 500 -> 500 (in 0.007206 sec)
+[ORB_OCV] 442/478 (recall=0.924686) with average error=0.648609
+[ORB_OCV] average size ratio between matched points: 0.971097
+[ORB_OCV] average angle difference between matched points: -1.00358 degrees
+[ORB_OCV] average descriptor distance between matched points: 42.586 (random distance: 125.477) => differentiability=0.339392
+[SIFTOCV] Points detected: 453 -> 500 (in 0.076776 sec)
+[SIFTOCV] 374/439 (recall=0.851936) with average error=0.445461
+[SIFTOCV] average size ratio between matched points: 1.05024
+[SIFTOCV] average angle difference between matched points: 0.589903 degrees
+[SIFTOCV] average descriptor distance between matched points: 165.511 (random distance: 529.613) => differentiability=0.312513
+[       OK ] SIFT.MovedImageDownHalfPixel (134 ms)
+[ RUN      ] SIFT.Rotate10
+[ORB_OCV] Points detected: 500 -> 500 (in 0.007204 sec)
+[ORB_OCV] 475/500 (recall=0.95) with average error=0.932881
+[ORB_OCV] average size ratio between matched points: 1.00527
+[ORB_OCV] average angle difference between matched points: 8.46336 degrees
+[ORB_OCV] average descriptor distance between matched points: 56.6295 (random distance: 122.783) => differentiability=0.461215
+[SIFTOCV] Points detected: 453 -> 500 (in 0.080769 sec)
+[SIFTOCV] 350/451 (recall=0.776053) with average error=0.501799
+[SIFTOCV] average size ratio between matched points: 1.03657
+[SIFTOCV] average angle difference between matched points: 6.5729 degrees
+[SIFTOCV] average descriptor distance between matched points: 178.858 (random distance: 521.738) => differentiability=0.342812
+[       OK ] SIFT.Rotate10 (138 ms)
+[ RUN      ] SIFT.Rotate20
+[ORB_OCV] Points detected: 500 -> 500 (in 0.00735 sec)
+[ORB_OCV] 456/491 (recall=0.928717) with average error=0.860867
+[ORB_OCV] average size ratio between matched points: 0.968872
+[ORB_OCV] average angle difference between matched points: 19.4252 degrees
+[ORB_OCV] average descriptor distance between matched points: 58.3224 (random distance: 125.59) => differentiability=0.464387
+[SIFTOCV] Points detected: 453 -> 500 (in 0.079375 sec)
+[SIFTOCV] 337/443 (recall=0.760722) with average error=0.556804
+[SIFTOCV] average size ratio between matched points: 1.04965
+[SIFTOCV] average angle difference between matched points: 1.4678 degrees
+[SIFTOCV] average descriptor distance between matched points: 186.303 (random distance: 531.487) => differentiability=0.350531
+[       OK ] SIFT.Rotate20 (135 ms)
+[ RUN      ] SIFT.Rotate30
+[ORB_OCV] Points detected: 500 -> 500 (in 0.007226 sec)
+[ORB_OCV] 446/479 (recall=0.931106) with average error=0.954674
+[ORB_OCV] average size ratio between matched points: 0.971588
+[ORB_OCV] average angle difference between matched points: 25.313 degrees
+[ORB_OCV] average descriptor distance between matched points: 56.7668 (random distance: 125.177) => differentiability=0.453492
+[SIFTOCV] Points detected: 453 -> 500 (in 0.080099 sec)
+[SIFTOCV] 356/441 (recall=0.807256) with average error=0.635313
+[SIFTOCV] average size ratio between matched points: 1.04861
+[SIFTOCV] average angle difference between matched points: 9.58379 degrees
+[SIFTOCV] average descriptor distance between matched points: 194.029 (random distance: 529.795) => differentiability=0.366233
+[       OK ] SIFT.Rotate30 (138 ms)
+[ RUN      ] SIFT.Rotate40
+[ORB_OCV] Points detected: 500 -> 500 (in 0.007173 sec)
+[ORB_OCV] 445/475 (recall=0.936842) with average error=0.927974
+[ORB_OCV] average size ratio between matched points: 0.992927
+[ORB_OCV] average angle difference between matched points: 35.1473 degrees
+[ORB_OCV] average descriptor distance between matched points: 57.0315 (random distance: 125.292) => differentiability=0.455188
+[SIFTOCV] Points detected: 453 -> 470 (in 0.084055 sec)
+[SIFTOCV] 335/436 (recall=0.768349) with average error=0.722947
+[SIFTOCV] average size ratio between matched points: 1.07134
+[SIFTOCV] average angle difference between matched points: 12.1292 degrees
+[SIFTOCV] average descriptor distance between matched points: 189.937 (random distance: 524.78) => differentiability=0.361937
+[       OK ] SIFT.Rotate40 (142 ms)
+[ RUN      ] SIFT.Rotate45
+[ORB_OCV] Points detected: 500 -> 500 (in 0.007422 sec)
+[ORB_OCV] 427/475 (recall=0.898947) with average error=0.913293
+[ORB_OCV] average size ratio between matched points: 0.999695
+[ORB_OCV] average angle difference between matched points: 40.7936 degrees
+[ORB_OCV] average descriptor distance between matched points: 57.0351 (random distance: 126.834) => differentiability=0.449684
+[SIFTOCV] Points detected: 453 -> 500 (in 0.082223 sec)
+[SIFTOCV] 344/431 (recall=0.798144) with average error=0.735095
+[SIFTOCV] average size ratio between matched points: 1.04736
+[SIFTOCV] average angle difference between matched points: 25.7374 degrees
+[SIFTOCV] average descriptor distance between matched points: 189.73 (random distance: 526.815) => differentiability=0.360145
+[       OK ] SIFT.Rotate45 (140 ms)
+[ RUN      ] SIFT.Rotate90
+[ORB_OCV] Points detected: 500 -> 500 (in 0.006792 sec)
+[ORB_OCV] 406/475 (recall=0.854737) with average error=0.794018
+[ORB_OCV] average size ratio between matched points: 0.926186
+[ORB_OCV] average angle difference between matched points: 81.2045 degrees
+[ORB_OCV] average descriptor distance between matched points: 52.2069 (random distance: 126.239) => differentiability=0.413556
+[SIFTOCV] Points detected: 453 -> 429 (in 0.105262 sec)
+[SIFTOCV] 397/428 (recall=0.92757) with average error=0.597669
+[SIFTOCV] average size ratio between matched points: 0.994223
+[SIFTOCV] average angle difference between matched points: 68.2509 degrees
+[SIFTOCV] average descriptor distance between matched points: 109.39 (random distance: 522.253) => differentiability=0.209459
+[       OK ] SIFT.Rotate90 (176 ms)
+[ RUN      ] SIFT.Scale50
+[ORB_OCV] Points detected: 500 -> 500 (in 0.008428 sec)
+[ORB_OCV] 463/500 (recall=0.926) with average error=0.927987
+[ORB_OCV] average size ratio between matched points: 0.829127
+[ORB_OCV] average angle difference between matched points: 2.9143 degrees
+[ORB_OCV] average descriptor distance between matched points: 89.7322 (random distance: 124.84) => differentiability=0.718776
+[SIFTOCV] Points detected: 453 -> 191 (in 0.076658 sec)
+[SIFTOCV] 235/453 (recall=0.518764) with average error=1.81213
+[SIFTOCV] average size ratio between matched points: 0.90594
+[SIFTOCV] average angle difference between matched points: -0.218442 degrees
+[SIFTOCV] average descriptor distance between matched points: 362.817 (random distance: 526.31) => differentiability=0.68936
+[       OK ] SIFT.Scale50 (134 ms)
+[ RUN      ] SIFT.Scale70
+[ORB_OCV] Points detected: 500 -> 500 (in 0.006651 sec)
+[ORB_OCV] 488/500 (recall=0.976) with average error=0.902066
+[ORB_OCV] average size ratio between matched points: 0.884398
+[ORB_OCV] average angle difference between matched points: 1.14689 degrees
+[ORB_OCV] average descriptor distance between matched points: 70.2889 (random distance: 124.695) => differentiability=0.563688
+[SIFTOCV] Points detected: 453 -> 335 (in 0.071259 sec)
+[SIFTOCV] 307/453 (recall=0.677704) with average error=1.1649
+[SIFTOCV] average size ratio between matched points: 0.92551
+[SIFTOCV] average angle difference between matched points: -8.88537 degrees
+[SIFTOCV] average descriptor distance between matched points: 272.272 (random distance: 530.603) => differentiability=0.513138
+[       OK ] SIFT.Scale70 (124 ms)
+[ RUN      ] SIFT.Scale90
+[ORB_OCV] Points detected: 500 -> 500 (in 0.00671 sec)
+[ORB_OCV] 489/500 (recall=0.978) with average error=0.823308
+[ORB_OCV] average size ratio between matched points: 0.970324
+[ORB_OCV] average angle difference between matched points: -0.183516 degrees
+[ORB_OCV] average descriptor distance between matched points: 60.3333 (random distance: 125.059) => differentiability=0.482438
+[SIFTOCV] Points detected: 453 -> 469 (in 0.076825 sec)
+[SIFTOCV] 354/453 (recall=0.781457) with average error=0.59984
+[SIFTOCV] average size ratio between matched points: 1.00052
+[SIFTOCV] average angle difference between matched points: -4.5203 degrees
+[SIFTOCV] average descriptor distance between matched points: 193.204 (random distance: 523.29) => differentiability=0.369209
+[       OK ] SIFT.Scale90 (132 ms)
+[ RUN      ] SIFT.Scale110
+[ORB_OCV] Points detected: 500 -> 500 (in 0.006781 sec)
+[ORB_OCV] 462/500 (recall=0.924) with average error=0.912118
+[ORB_OCV] average size ratio between matched points: 1.02967
+[ORB_OCV] average angle difference between matched points: -0.905967 degrees
+[ORB_OCV] average descriptor distance between matched points: 59.7468 (random distance: 125.225) => differentiability=0.477115
+[SIFTOCV] Points detected: 453 -> 500 (in 0.074938 sec)
+[SIFTOCV] 307/434 (recall=0.707373) with average error=0.561683
+[SIFTOCV] average size ratio between matched points: 1.15716
+[SIFTOCV] average angle difference between matched points: 1.13854 degrees
+[SIFTOCV] average descriptor distance between matched points: 182.143 (random distance: 530.492) => differentiability=0.343347
+[       OK ] SIFT.Scale110 (130 ms)
+[ RUN      ] SIFT.Scale130
+[ORB_OCV] Points detected: 500 -> 500 (in 0.006976 sec)
+[ORB_OCV] 400/475 (recall=0.842105) with average error=1.02537
+[ORB_OCV] average size ratio between matched points: 1.10237
+[ORB_OCV] average angle difference between matched points: -4.78444 degrees
+[ORB_OCV] average descriptor distance between matched points: 67.4875 (random distance: 126.4) => differentiability=0.53392
+[SIFTOCV] Points detected: 453 -> 500 (in 0.076654 sec)
+[SIFTOCV] 253/409 (recall=0.618582) with average error=0.533529
+[SIFTOCV] average size ratio between matched points: 1.37601
+[SIFTOCV] average angle difference between matched points: -2.24977 degrees
+[SIFTOCV] average descriptor distance between matched points: 175.173 (random distance: 529.43) => differentiability=0.33087
+[       OK ] SIFT.Scale130 (135 ms)
+[ RUN      ] SIFT.Scale150
+[ORB_OCV] Points detected: 500 -> 500 (in 0.00682 sec)
+[ORB_OCV] 325/445 (recall=0.730337) with average error=1.18695
+[ORB_OCV] average size ratio between matched points: 1.16446
+[ORB_OCV] average angle difference between matched points: -1.92861 degrees
+[ORB_OCV] average descriptor distance between matched points: 75.4954 (random distance: 125.905) => differentiability=0.599624
+[SIFTOCV] Points detected: 453 -> 500 (in 0.0792 sec)
+[SIFTOCV] 208/387 (recall=0.537468) with average error=0.519869
+[SIFTOCV] average size ratio between matched points: 1.56537
+[SIFTOCV] average angle difference between matched points: 2.7434 degrees
+[SIFTOCV] average descriptor distance between matched points: 178.951 (random distance: 530.33) => differentiability=0.337433
+[       OK ] SIFT.Scale150 (134 ms)
+[ RUN      ] SIFT.Scale175
+[ORB_OCV] Points detected: 500 -> 500 (in 0.00707 sec)
+[ORB_OCV] 235/380 (recall=0.618421) with average error=1.3217
+[ORB_OCV] average size ratio between matched points: 1.25586
+[ORB_OCV] average angle difference between matched points: 4.13008 degrees
+[ORB_OCV] average descriptor distance between matched points: 84.1617 (random distance: 124.894) => differentiability=0.673867
+[SIFTOCV] Points detected: 453 -> 501 (in 0.079173 sec)
+[SIFTOCV] 189/354 (recall=0.533898) with average error=0.530545
+[SIFTOCV] average size ratio between matched points: 1.79649
+[SIFTOCV] average angle difference between matched points: -2.33097 degrees
+[SIFTOCV] average descriptor distance between matched points: 182.373 (random distance: 522.122) => differentiability=0.349291
+[       OK ] SIFT.Scale175 (136 ms)
+[ RUN      ] SIFT.Scale200
+[ORB_OCV] Points detected: 500 -> 500 (in 0.007015 sec)
+[ORB_OCV] 174/333 (recall=0.522523) with average error=1.36043
+[ORB_OCV] average size ratio between matched points: 1.34374
+[ORB_OCV] average angle difference between matched points: -3.78969 degrees
+[ORB_OCV] average descriptor distance between matched points: 86.1954 (random distance: 126.011) => differentiability=0.684028
+[SIFTOCV] Points detected: 453 -> 500 (in 0.077291 sec)
+[SIFTOCV] 178/324 (recall=0.549383) with average error=0.536323
+[SIFTOCV] average size ratio between matched points: 2.03968
+[SIFTOCV] average angle difference between matched points: -1.02503 degrees
+[SIFTOCV] average descriptor distance between matched points: 167.42 (random distance: 528.219) => differentiability=0.316952
+[       OK ] SIFT.Scale200 (132 ms)
+[ RUN      ] SIFT.Rotate10Scale90
+[ORB_OCV] Points detected: 500 -> 500 (in 0.006837 sec)
+[ORB_OCV] 486/500 (recall=0.972) with average error=0.862656
+[ORB_OCV] average size ratio between matched points: 1.00909
+[ORB_OCV] average angle difference between matched points: 12.1602 degrees
+[ORB_OCV] average descriptor distance between matched points: 61.9033 (random distance: 126.477) => differentiability=0.489442
+[SIFTOCV] Points detected: 453 -> 464 (in 0.077303 sec)
+[SIFTOCV] 351/453 (recall=0.774834) with average error=0.660494
+[SIFTOCV] average size ratio between matched points: 0.970697
+[SIFTOCV] average angle difference between matched points: 0.36928 degrees
+[SIFTOCV] average descriptor distance between matched points: 210.278 (random distance: 528.178) => differentiability=0.39812
+[       OK ] SIFT.Rotate10Scale90 (131 ms)
+[ RUN      ] SIFT.Rotate30Scale75
+[ORB_OCV] Points detected: 500 -> 500 (in 0.006681 sec)
+[ORB_OCV] 471/500 (recall=0.942) with average error=0.873322
+[ORB_OCV] average size ratio between matched points: 0.882831
+[ORB_OCV] average angle difference between matched points: 32.7295 degrees
+[ORB_OCV] average descriptor distance between matched points: 68.9915 (random distance: 123.987) => differentiability=0.55644
+[SIFTOCV] Points detected: 453 -> 373 (in 0.074213 sec)
+[SIFTOCV] 318/453 (recall=0.701987) with average error=0.961622
+[SIFTOCV] average size ratio between matched points: 0.958296
+[SIFTOCV] average angle difference between matched points: 6.75176 degrees
+[SIFTOCV] average descriptor distance between matched points: 241.741 (random distance: 528.552) => differentiability=0.457364
+[       OK ] SIFT.Rotate30Scale75 (126 ms)
+[ RUN      ] SIFT.HerzJesu19RotateM40
+[ORB_OCV] Points detected: 500 -> 500 (in 0.078168 sec)
+[ORB_OCV] 494/500 (recall=0.988) with average error=1.25431
+[ORB_OCV] average size ratio between matched points: 1.00708
+[ORB_OCV] average angle difference between matched points: -36.7101 degrees
+[ORB_OCV] average descriptor distance between matched points: 48.3603 (random distance: 121.69) => differentiability=0.397405
+[SIFTOCV] Points detected: 500 -> 500 (in 3.69454 sec)
+[SIFTOCV] 377/435 (recall=0.866667) with average error=2.85298
+[SIFTOCV] average size ratio between matched points: 1.00886
+[SIFTOCV] average angle difference between matched points: -16.179 degrees
+[SIFTOCV] average descriptor distance between matched points: 212.589 (random distance: 537.412) => differentiability=0.395579
+[       OK ] SIFT.HerzJesu19RotateM40 (5429 ms)
+[ RUN      ] SIFT.DetectionSmokeTest
+ENABLE_MY_SIFT_TESTING is disabled, test skipped
+[       OK ] SIFT.DetectionSmokeTest (0 ms)
+[ RUN      ] SIFT.DetectionDescriptionSteps
+ENABLE_MY_SIFT_TESTING is disabled, test skipped
+[       OK ] SIFT.DetectionDescriptionSteps (0 ms)
+[ RUN      ] SIFT.PairMatching
+ENABLE_MY_SIFT_TESTING is disabled, test skipped
+Final score: UNKNOWN
+[       OK ] SIFT.PairMatching (0 ms)
+[----------] 25 tests from SIFT (8324 ms total)
+
+[----------] Global test environment tear-down
+[==========] 25 tests from 1 test suite ran. (8324 ms total)
+[  PASSED  ] 25 tests.
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diff --git a/tests/test_sift.cpp b/tests/test_sift.cpp
index cf3bd7d..ba8bdc0 100755
--- a/tests/test_sift.cpp
+++ b/tests/test_sift.cpp
@@ -25,10 +25,12 @@
 
 #define GAUSSIAN_NOISE_STDDEV 1.0
 
+#define USE_DESCRIPTOR_MATCHING_FOR_ANGLE_DIFF 0
+
 // TODO ENABLE ME
 // TODO ENABLE ME
 // TODO ENABLE ME
-#define ENABLE_MY_SIFT_TESTING 0
+#define ENABLE_MY_SIFT_TESTING 1
 
 #define DENY_CREATE_REF_DATA 1
 
@@ -239,12 +241,27 @@ void evaluateDetection(const cv::Mat& M, double minRecall, cv::Mat img0 = cv::Ma
 
                 ptrdiff_t closest_j = -1; // будем искать ближайшую точку детектированную на искаженном изображении
                 double min_error = std::numeric_limits<float>::max();
+#if USE_DESCRIPTOR_MATCHING_FOR_ANGLE_DIFF
+                ptrdiff_t best_angle_j = -1; // лучший по дескриптору кандидат среди геометрически допустимых
+                double best_angle_desc_dist = std::numeric_limits<double>::max();
+                cv::Mat angle_d0 = desc0.rowRange(cv::Range(i, i + 1));
+#endif
                 for (ptrdiff_t j = 0; j < kps1.size(); ++j) {
                     double error = cv::norm(kps1[j].pt - p01);
                     if (error < min_error) {
                         min_error = error;
                         closest_j = j;
                     }
+#if USE_DESCRIPTOR_MATCHING_FOR_ANGLE_DIFF
+                    if (error <= MAX_ACCEPTED_PIXEL_ERROR * width) {
+                        cv::Mat angle_d1 = desc1.rowRange(cv::Range(j, j + 1));
+                        double angle_desc_dist = cv::norm(angle_d0, angle_d1, (method_name == "ORB") ? cv::NORM_HAMMING : cv::NORM_L2);
+                        if (angle_desc_dist < best_angle_desc_dist) {
+                            best_angle_desc_dist = angle_desc_dist;
+                            best_angle_j = j;
+                        }
+                    }
+#endif
                 }
                 if (closest_j != -1 && min_error <= MAX_ACCEPTED_PIXEL_ERROR * width) {
                     // мы нашли что-то достаточно близкое - успех!
@@ -258,7 +275,11 @@ void evaluateDetection(const cv::Mat& M, double minRecall, cv::Mat img0 = cv::Ma
                     if (kps0[i].size != 0.0) {
                         size_ratio_sum += kps1[closest_j].size / kps0[i].size;
                     }
+#if USE_DESCRIPTOR_MATCHING_FOR_ANGLE_DIFF
+                    angle_diff_sum += diffAngles(kps0[i].angle, kps1[best_angle_j].angle);
+#else
                     angle_diff_sum += diffAngles(kps0[i].angle, kps1[closest_j].angle);
+#endif
 
                     cv::Mat d0 = desc0.rowRange(cv::Range(i, i + 1));
                     cv::Mat d1 = desc1.rowRange(cv::Range(closest_j, closest_j + 1));