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;
}
}
public void Update(Byte[] raw_image, int width, int height)
{
mono_image = sluggish.utilities.image.monoImage(raw_image, width, height);
if (line_threshold == 0)
{
line_threshold = 200;
}
if (corner_threshold == 0)
{
corner_threshold = 50;
}
FASTcorner[] corners_all = FAST.fast_corner_detect_10(mono_image, width, height, corner_threshold);
FASTcorner[] corners1 = FAST.fast_nonmax(mono_image, width, height, corners_all, corner_threshold * 2, 0, 0);
corners = local_nonmax(corners1, width / 15, height / 15);
if (corners != null)
{
int no_of_feats = corners1.Length;
if (no_of_feats < required_features / 2)
{
corner_threshold -= 4;
}
if (no_of_feats > required_features)
{
corner_threshold += 4;
}
if ((no_of_feats > 1) && (no_of_feats < required_features * 2))
{
int min_line_length = width / 10;
lines = FAST.fast_lines(mono_image, width, height, corners, line_threshold, min_line_length);
if (lines != null)
{
int no_of_lines = 0;
for (int i = 0; i < lines.Length; i++)
{
FASTline line = lines[i];
if (!(((line.point1.y < height / 10) && (line.point2.y < height / 10)) ||
((line.point1.y > height - (height / 20)) && (line.point2.y > height - (height / 20))) ||
((line.point1.x > width - (width / 20)) && (line.point2.x > width - (width / 20))) ||
((line.point1.x < width / 20) && (line.point2.x < width / 20))
))
{
line.Visible = true;
no_of_lines++;
}
}
// detect the horizon
if (showHorizon)
{
int vertical_position = 0;
float gradient = FAST.horizon_detection(raw_image, width, height, lines, horizon_threshold, ref vertical_position);
if (vertical_position > 0)
{
int tx = (width / 2) + (int)((width / 4) * 1);
int ty = vertical_position - (int)((width / 4) * gradient);
//ty = ty * height / width;
sluggish.utilities.drawing.drawLine(raw_image, width, height, width / 2, vertical_position, tx, ty, 0, 255, 0, 1, false);
}
}
// draw lines
if (drawLines)
{
for (int i = 0; i < lines.Length; i++)
{
FASTline line = lines[i];
if (line.Visible)
{
if (!line.onHorizon)
{
sluggish.utilities.drawing.drawLine(raw_image, width, height,
line.point1.x, line.point1.y, line.point2.x, line.point2.y,
255, 0, 0, 0, false);
}
else
{
sluggish.utilities.drawing.drawLine(raw_image, width, height,
line.point1.x, line.point1.y, line.point2.x, line.point2.y,
0, 255, 0, 0, false);
}
}
}
}
if (no_of_lines < required_lines)
{
line_threshold -= 4;
}
if (no_of_lines > required_lines)
{
line_threshold += 8;
}
if (line_threshold < 100)
{
line_threshold = 100;
}
// detect the gravity angle
if (showGravity)
{
float gravity_angle = FAST.gravity_direction(lines);
if (drawLines)
{
int pendulum_length = width / 8;
int px = (width / 2) + (int)(pendulum_length * Math.Sin(gravity_angle));
int py = (height / 2) + (int)(pendulum_length * Math.Cos(gravity_angle) * height / width);
sluggish.utilities.drawing.drawLine(raw_image, width, height,
width / 2, height / 2, px, py,
0, 255, 0, 1, false);
int arrow_length = pendulum_length / 5;
float angle2 = gravity_angle + (float)(Math.PI * 0.8f);
int px2 = px + (int)(arrow_length * Math.Sin(angle2));
int py2 = py + (int)(arrow_length * Math.Cos(angle2) * height / width);
sluggish.utilities.drawing.drawLine(raw_image, width, height,
px2, py2, px, py,
0, 255, 0, 1, false);
angle2 = gravity_angle - (float)(Math.PI * 0.8f);
px2 = px + (int)(arrow_length * Math.Sin(angle2));
py2 = py + (int)(arrow_length * Math.Cos(angle2) * height / width);
sluggish.utilities.drawing.drawLine(raw_image, width, height,
px2, py2, px, py,
0, 255, 0, 1, false);
}
}
}
}
}
//odometry.update(disp_bmp_data, width, height);
output_image = raw_image;
}