/// <summary>
/// update the disparity values for FAST corners
/// </summary>
private void updateFASTCornerDisparities()
{
if (line_detector != null)
{
if (line_detector.corners != null)
{
for (int i = 0; i < line_detector.corners.Length; i++)
{
FASTcorner c = line_detector.corners[i];
c.disparity = stereovision_contours.getDisparityMapPoint(c.x, c.y);
}
}
}
}
public void update(Byte[] left_bmp, Byte[] right_bmp,
int wdth, int hght, int bytes_per_pixel,
float calibration_offset_x, float calibration_offset_y,
ref int threshold)
{
Byte[] left_img;
Byte[] right_img;
int max_disparity_pixels = wdth * max_disparity / 100;
if (bytes_per_pixel == 1)
{
left_img = left_bmp;
right_img = right_bmp;
}
else
{
left_img = new Byte[wdth * hght];
right_img = new Byte[wdth * hght];
int n = 0;
for (int i = 0; i < wdth * hght * bytes_per_pixel; i += bytes_per_pixel)
{
int left_tot = 0;
int right_tot = 0;
for (int c = 0; c < bytes_per_pixel; c++)
{
left_tot += left_bmp[i + c];
right_tot += right_bmp[i + c];
}
left_img[n] = (Byte)(left_tot / bytes_per_pixel);
right_img[n] = (Byte)(right_tot / bytes_per_pixel);
n++;
}
}
// set a default threshold value if none is specified
if (threshold == 0)
{
threshold = 50;
}
// extract features from the left image
FASTcorner[] left_corners_all = FAST.fast_corner_detect_10(left_img, wdth, hght, threshold);
FASTcorner[] left_corners = FAST.fast_nonmax(left_img, wdth, hght, left_corners_all, threshold * 2, 0, 0);
// only continue if there aren't too many features
no_of_selected_features = 0;
if (left_corners != null)
{
if (left_corners.Length < required_features * 2)
{
// extract features from the right image
FASTcorner[] right_corners_all = FAST.fast_corner_detect_10(right_img, wdth, hght, threshold);
FASTcorner[] right_corners = FAST.fast_nonmax(right_img, wdth, hght, right_corners_all, threshold * 2, -calibration_offset_x, -calibration_offset_y);
if (right_corners != null)
{
// update feature properties used for matching
FAST.fast_update(left_corners, wdth / 5, hght / 5);
FAST.fast_update(right_corners, wdth / 5, hght / 5);
// adjust the threshold
int no_of_feats = (left_corners.Length + right_corners.Length) / 2;
if (no_of_feats < required_features / 2)
{
threshold -= 4;
}
if (no_of_feats > required_features)
{
threshold += 4;
}
// this is a test
int n = 0;
/*
* for (int i = 0; i < left_corners.Length; i++)
* {
* FASTcorner corner = left_corners[i];
* selected_features[(n * 3)] = corner.x;
* selected_features[(n * 3) + 1] = corner.y;
* selected_features[(n * 3) + 2] = 3;
* n++;
* }
* for (int i = 0; i < right_corners.Length; i++)
* {
* FASTcorner corner = right_corners[i];
* selected_features[(n * 3)] = corner.x;
* selected_features[(n * 3) + 1] = corner.y;
* selected_features[(n * 3) + 2] = 3;
* n++;
* }
*/
no_of_selected_features = n;
// bucket the data into rows
// this helps to make matching more efficient
ArrayList[] left_row = new ArrayList[hght];
ArrayList[] right_row = new ArrayList[hght];
for (int i = 0; i < left_corners.Length; i++)
{
int index = left_corners[i].y;
if (left_row[index] == null)
{
left_row[index] = new ArrayList();
}
left_row[index].Add(left_corners[i]);
}
for (int i = 0; i < right_corners.Length; i++)
{
int index = right_corners[i].y;
if (right_row[index] == null)
{
right_row[index] = new ArrayList();
}
right_row[index].Add(right_corners[i]);
}
// match rows
int vertical_search = 0;
for (int y = 0; y < hght; y++)
{
ArrayList row = left_row[y];
if (row != null)
{
for (int f1 = 0; f1 < row.Count; f1++)
{
FASTcorner corner1 = (FASTcorner)row[f1];
int min_score = 60;
float disp = -1;
for (int yy = y - vertical_search; yy <= y + vertical_search; yy++)
{
if ((yy > -1) && (yy < hght))
{
ArrayList row2 = right_row[yy];
if (row2 != null)
{
for (int f2 = 0; f2 < row2.Count; f2++)
{
FASTcorner corner2 = (FASTcorner)row2[f2];
int dx = corner1.x - corner2.x;
if ((dx >= 0) && (dx < max_disparity_pixels))
{
int dy = yy - y;
int score = corner1.matching_score(corner2) * (Math.Abs(dy) + 1);
//int score = Math.Abs(dy) + dx;
if ((score > -1) && ((min_score == -1) || (score < min_score)))
{
min_score = score;
float left_fx = 0, right_fx = 0, fy = 0;
subPixelLocation(left_img, wdth, hght, corner1.x, corner1.y, 5, 1, ref left_fx, ref fy);
subPixelLocation(right_img, wdth, hght, corner2.x, corner2.y, 5, 1, ref right_fx, ref fy);
disp = left_fx - right_fx;
}
}
}
}
}
}
if (disp > -1)
{
if ((corner1.x > -1) && (corner1.x < 320))
{
selected_features[(no_of_selected_features * 3)] = corner1.x;
selected_features[(no_of_selected_features * 3) + 1] = corner1.y;
selected_features[(no_of_selected_features * 3) + 2] = disp;
no_of_selected_features++;
}
}
}
}
}
}
else
{
threshold -= 4;
}
}
else
{
threshold += 4;
}
}
else
{
threshold -= 4;
}
if (threshold < 10)
{
threshold = 10;
}
}
private FASTcorner[] local_nonmax(FASTcorner[] corners, int radius_x, int radius_y)
{
FASTcorner[] corners2 = null;
if (corners != null)
{
for (int i = 0; i < corners.Length - 1; i++)
{
FASTcorner c1 = corners[i];
if (c1 != null)
{
for (int j = i + 1; j < corners.Length; j++)
{
if (i != j)
{
FASTcorner c2 = corners[j];
if (c2 != null)
{
int dx = c1.x - c2.x;
if ((dx > -radius_x) && (dx < radius_x))
{
int dy = c1.y - c2.y;
if ((dy > -radius_y) && (dy < radius_y))
{
if (c2.score < c1.score)
{
corners[j] = null;
}
else
{
corners[i] = null;
j = corners.Length;
}
}
}
}
}
}
}
}
int count = 0;
for (int i = 0; i < corners.Length; i++)
{
if (corners[i] != null)
{
count++;
}
}
corners2 = new FASTcorner[count];
int n = 0;
for (int i = 0; i < corners.Length; i++)
{
if (corners[i] != null)
{
corners2[n] = corners[i];
n++;
}
}
}
return(corners2);
}
/// <summary>
/// get the point features returned from stereo matching
/// </summary>
/// <returns>The number of features found</returns>
public int getSelectedPointFeatures(float[] features)
{
int no_of_features = 0;
int max = 0;
switch (currentAlgorithmType)
{
case CORRESPONDENCE_SIMPLE:
{
// get features from non-maximal suppression
max = stereovision.no_of_selected_features * 3;
for (int i = 0; i < max; i++)
{
features[i] = stereovision.selected_features[i];
}
break;
}
case CORRESPONDENCE_CONTOURS:
{
// get features from the disparity map
max = stereovision_contours.no_of_selected_features * 3;
if (max > features.Length)
{
max = features.Length;
}
for (int i = 0; i < max; i++)
{
features[i] = stereovision_contours.selected_features[i];
}
break;
}
case CORRESPONDENCE_FAST:
{
// get FAST corner features from non-maximal suppression
if (line_detector != null)
{
if (line_detector.corners != null)
{
max = line_detector.corners.Length * 3;
if (max > features.Length)
{
max = features.Length;
}
int n = 0;
for (int i = 0; i < max; i += 3)
{
FASTcorner c = line_detector.corners[n];
features[i] = c.x;
features[i + 1] = c.y;
features[i + 2] = c.disparity;
n++;
}
}
}
break;
}
case CORRESPONDENCE_LINES:
{
// get features from the disparity map
max = stereovision_contours.no_of_selected_features * 3;
if (max > features.Length)
{
max = features.Length;
}
for (int i = 0; i < max; i++)
{
features[i] = stereovision_contours.selected_features[i];
}
break;
}
}
no_of_features = max / 3;
return(no_of_features);
}
