Example usage for org.opencv.utils Converters vector_int_to_Mat

Introduction

In this page you can find the example usage for org.opencv.utils Converters vector_int_to_Mat.

Prototype

public static Mat vector_int_to_Mat(List<Integer> is) 

Source Link

Usage

From source file:karthik.Barcode.MatrixBarcode.java

License:Open Source License

private Mat calcProbabilityTilings(int tileSize) {
    // calculates probability of each tile being in a 2D barcode region
    // tiles must be square
    assert (searchParams.RECT_HEIGHT == searchParams.RECT_WIDTH) : "RECT_HEIGHT and RECT_WIDTH must be equal in searchParams imageSpecificParams";

    int probMatTileSize = (int) (tileSize * (searchParams.PROB_MAT_TILE_SIZE / (1.0 * searchParams.tileSize)));
    int threshold_min_gradient_edges = (int) (tileSize * tileSize
            * searchParams.THRESHOLD_MIN_GRADIENT_EDGES_MULTIPLIER);

    int right_col, bottom_row;
    int prob_mat_right_col, prob_mat_bottom_row;

    Mat imgWindow; // used to hold sub-matrices from the image that represent the window around the current point
    Mat prob_window; // used to hold sub-matrices into probability matrix that represent window around current point

    int num_edges;
    double prob;/*from   w  w w  .  j a  v  a 2s .  com*/
    int max_angle_idx, second_highest_angle_index, max_angle_count, second_highest_angle_count, angle_diff;

    img_details.probabilities.setTo(ZERO_SCALAR);

    for (int i = 0, row_offset = 0; i < rows; i += tileSize, row_offset += probMatTileSize) {
        // first do bounds checking for bottom right of tiles

        bottom_row = java.lang.Math.min((i + tileSize), rows);
        prob_mat_bottom_row = java.lang.Math.min((row_offset + probMatTileSize), img_details.probMatRows);

        for (int j = 0, col_offset = 0; j < cols; j += tileSize, col_offset += probMatTileSize) {

            // then calculate the column locations of the rectangle and set them to -1 
            // if they are outside the matrix bounds                
            right_col = java.lang.Math.min((j + tileSize), cols);
            prob_mat_right_col = java.lang.Math.min((col_offset + probMatTileSize), img_details.probMatCols);

            // calculate number of edges in the tile using the already calculated integral image 
            num_edges = (int) calc_rect_sum(img_details.edgeDensity, img_details.histNumRows,
                    img_details.histNumCols, i, bottom_row, j, right_col);

            if (num_edges < threshold_min_gradient_edges)
                // if gradient density is below the threshold level, prob of matrix code in this tile is 0
                continue;

            for (int r = 0; r < img_details.bins; r++) {

                img_details.histArray[r] = (int) calc_rect_sum(img_details.histIntegralArrays[r],
                        img_details.histNumRows, img_details.histNumCols, i, bottom_row, j, right_col);
            }

            hist = Converters.vector_int_to_Mat(Arrays.asList(img_details.histArray));
            Core.sortIdx(hist, histIdx, Core.SORT_EVERY_COLUMN + Core.SORT_DESCENDING);

            max_angle_idx = (int) histIdx.get(0, 0)[0];
            max_angle_count = (int) hist.get(max_angle_idx, 0)[0];

            second_highest_angle_index = (int) histIdx.get(1, 0)[0];
            second_highest_angle_count = (int) hist.get(second_highest_angle_index, 0)[0];

            angle_diff = Math.abs(max_angle_idx - second_highest_angle_index);

            // formula below is modified from Szentandrasi, Herout, Dubska paper pp. 4
            prob = 0;
            if (angle_diff != 1) // ignores tiles where there is just noise between adjacent bins in the histogram
                prob = 2.0 * Math.min(max_angle_count, second_highest_angle_count)
                        / (max_angle_count + second_highest_angle_count);

            prob_window = img_details.probabilities.submat(row_offset, prob_mat_bottom_row, col_offset,
                    prob_mat_right_col);
            prob_window.setTo(new Scalar((int) (prob * 255)));

        } // for j
    } // for i

    return img_details.probabilities;

}

---