/**
* Using previously computed results it efficiently finds the disparity in the remaining rows.
* When a new block is processes the last row/column is subtracted and the new row/column is
* added.
*/
private void computeRemainingRows(ImageUInt8 left, ImageUInt8 right) {
for (int row = regionHeight; row < left.height; row++, activeVerticalScore++) {
int oldRow = row % regionHeight;
int previous[] = verticalScore[(activeVerticalScore - 1) % regionHeight];
int active[] = verticalScore[activeVerticalScore % regionHeight];
// subtract first row from vertical score
int scores[] = horizontalScore[oldRow];
for (int i = 0; i < lengthHorizontal; i++) {
active[i] = previous[i] - scores[i];
}
UtilDisparityScore.computeScoreRow(
left, right, row, scores, minDisparity, maxDisparity, regionWidth, elementScore);
// add the new score
for (int i = 0; i < lengthHorizontal; i++) {
active[i] += scores[i];
}
if (activeVerticalScore >= regionHeight - 1) {
int top[] = verticalScore[(activeVerticalScore - 2 * radiusY) % regionHeight];
int middle[] = verticalScore[(activeVerticalScore - radiusY) % regionHeight];
int bottom[] = verticalScore[activeVerticalScore % regionHeight];
computeScoreFive(top, middle, bottom, fiveScore, left.width);
computeDisparity.process(row - (1 + 4 * radiusY) + 2 * radiusY + 1, fiveScore);
}
}
}
@Override
public void _process(ImageUInt8 left, ImageUInt8 right, Disparity disparity) {
if (horizontalScore == null || verticalScore.length < lengthHorizontal) {
horizontalScore = new int[regionHeight][lengthHorizontal];
verticalScore = new int[regionHeight][lengthHorizontal];
elementScore = new int[left.width];
fiveScore = new int[lengthHorizontal];
}
computeDisparity.configure(disparity, minDisparity, maxDisparity, radiusX * 2);
// initialize computation
computeFirstRow(left, right);
// efficiently compute rest of the rows using previous results to avoid repeat computations
computeRemainingRows(left, right);
}
@Override
public Class<Disparity> getDisparityType() {
return computeDisparity.getDisparityType();
}
