Update Region class.

src/LineSegmentation.cpp

@@ -477,11 +477,36 @@ Valley::comp(const Valley *a, const Valley *b) {
     return a->position < b->position;
 }
 
-Region::Region(cv::Mat a, vector<int> ro) {
-    region = a.clone();
-    row_offset = ro;
+Region::Region(Line *top, Line *bottom) {
+    this->top = top;
+    this->bottom = bottom;
+}
+
+bool
+Region::update_region(Mat &binary_image, int region_id) {
+    this->region_id = region_id;
+
+    int min_region_row = row_offset = (top == nullptr) ? 0 : top->min_row_position;
+    int max_region_row = (bottom == nullptr) ? binary_image.rows : bottom->max_row_position;
+
+    region = Mat::ones(max_region_row - min_region_row, binary_image.cols, CV_8U) * 255;
+
+    // Fill region.
+    if (bottom != nullptr) {
+        for (int c = 0; c < binary_image.cols; c++) {
+            int start = ((top == nullptr) ? 0 : top->points[c].x);
+            for (int i = start; i < bottom->points[c].x; i++) {
+                region.at<uchar>(i - min_region_row, c) = binary_image.at<uchar>(i, c);
+            }
+        }
+    }
     calculate_mean();
     calculate_covariance();
+
+    imwrite("out/" + string("Region") + to_string(region_id) + ".jpg",
+            region);
+
+    return countNonZero(region) == region.cols * region.rows;
 }
 
 void
@@ -494,9 +519,9 @@ Region::calculate_mean() {
             if (region.at<uchar>(i, j) == 255) continue;
             if (n == 0) {
                 n = n + 1;
-                mean = Vec2f(i + row_offset[j], j);
+                mean = Vec2f(i + row_offset, j);
             } else {
-                mean = (n - 1.0) / n * mean + 1.0 / n * Vec2f(i + row_offset[j], j);
+                mean = (n - 1.0) / n * mean + 1.0 / n * Vec2f(i + row_offset, j);
                 n = n + 1;
             }
         }
@@ -507,26 +532,31 @@ Region::calculate_mean() {
 void
 Region::calculate_covariance() {
     Mat covariance = Mat::zeros(2, 2, CV_32F);
-    int n = 0;
-    float sum_x_squared = 0, sum_y_squared = 0, sum_x_y = 0;
+
+    int n = 0; // Total number of considered points so far.
+    float sum_i_squared = 0, sum_j_squared = 0, sum_i_j = 0;
 
     for (int i = 0; i < region.rows; i++) {
         for (int j = 0; j < region.cols; j++) {
             // if white pixel continue.
             if ((int) region.at<uchar>(i, j) == 255) continue;
 
-            float new_i = i + row_offset[j] - mean[0];
+            float new_i = i + row_offset - mean[0];
             float new_j = j - mean[1];
-            sum_x_squared += new_i * new_i;
-            sum_x_y += new_i * new_j;
-            sum_y_squared += new_j * new_j;
+
+            sum_i_squared += new_i * new_i;
+            sum_i_j += new_i * new_j;
+            sum_j_squared += new_j * new_j;
             n++;
         }
     }
-    covariance.at<float>(0, 0) = sum_x_squared / n;
-    covariance.at<float>(0, 1) = sum_x_y / n;
-    covariance.at<float>(1, 0) = sum_x_y / n;
-    covariance.at<float>(1, 1) = sum_y_squared / n;
+    if (n) {
+        covariance.at<float>(0, 0) = sum_i_squared / n;
+        covariance.at<float>(0, 1) = sum_i_j / n;
+        covariance.at<float>(1, 0) = sum_i_j / n;
+        covariance.at<float>(1, 1) = sum_j_squared / n;
+
+    }
 
     this->covariance = covariance.clone();
 }
@@ -539,23 +569,9 @@ Region::bi_variate_gaussian_density(Mat point) {
     Mat point_transpose;
     transpose(point, point_transpose);
 
-
     Mat ret = ((point * this->covariance.inv() * point_transpose));
     ret *= sqrt(determinant(this->covariance * 2 * M_PI));
 
-//    cout << "COVARIANCE " << covariance << endl;
-//    cout << "INVERSE COVARIANCE " << this->covariance.inv() << endl;
-//    cout << "DETERMIN. " << determinant(this->covariance * 2 * M_PI) << endl;
-//    cout << "SQRT DETERMIN. " <<1.0/sqrt(determinant(this->covariance) *  2 * M_PI) << endl;
-//    cout << "MEAN " << mean << endl;
-
-    // cout << "RET " << ret << endl;
-// Probability above: 6192 below: 3888
-//    Mat m = point * this->covariance.inv() * point_transpose;
-//    cout << "POINT " << point<<endl;
-//    cout << "M" << (m.at<float>(0,0)) << endl;
-//    double ret2 = (1.0 / (2 * M_PI * sqrt(determinant(this->covariance)))) * exp(-0.5 * (m.at<float>(0, 0)));
-//    cout << "RET 2 " << ret2 << endl << endl << endl << endl << endl;
     return ret.at<float>(0, 0);
 }
 

src/LineSegmentation.hpp

@@ -96,24 +96,39 @@ class Region {
     friend class LineSegmentation;
 
 private:
+    int region_id;
+    ///< Region id.
     cv::Mat region;
     ///< 2D matrix representing the region.
+    Line *top;
+    ///< Region top boundaries;
+    Line *bottom;
+    ///< Region bottom boundaries;
     int start_row;
     ///<
     int end_row;
     ///<
-    vector<int> row_offset;
-    ///< A vector containing the offset of each col to the original image matrix.
+    int row_offset;
+    ///< The offset of each col to the original image matrix.
     cv::Mat covariance;
     ///< The covariance of the matrix.
     cv::Vec2f mean;
     ///< The mean of the matrix.
 
-    Region(cv::Mat a, vector<int> ro);
+    Region(Line *top, Line *bottom);
 
+    /// Get the region matrix
+    /// \param img
+    /// \param region_id
+    /// \return
+    bool
+    update_region(Mat &img, int);
+
+    /// Calculate region black pixels mean relative to the whole image dimensions
     void
     calculate_mean();
 
+    /// Calculate region black pixels covariance relative to the whole image dimensions
     void
     calculate_covariance();