public void InsertRays()
{
int no_of_stereo_features = 2000;
int image_width = 640;
int image_height = 480;
int no_of_stereo_cameras = 1;
int localisationRadius_mm = 16000;
int maxMappingRange_mm = 16000;
int cellSize_mm = 32;
int dimension_cells = 16000 / cellSize_mm;
int dimension_cells_vertical = dimension_cells/2;
float vacancyWeighting = 0.5f;
float FOV_horizontal = 78 * (float)Math.PI / 180.0f;
// create a grid
Console.WriteLine("Creating grid");
occupancygridSimple grid =
new occupancygridSimple(
dimension_cells,
dimension_cells_vertical,
cellSize_mm,
localisationRadius_mm,
maxMappingRange_mm,
vacancyWeighting);
Assert.AreNotEqual(grid, null, "object occupancygridSimple was not created");
Console.WriteLine("Creating sensor models");
stereoModel inverseSensorModel = new stereoModel();
inverseSensorModel.FOV_horizontal = FOV_horizontal;
inverseSensorModel.FOV_vertical = FOV_horizontal * image_height / image_width;
inverseSensorModel.createLookupTable(cellSize_mm, image_width, image_height);
//Assert.AreNotEqual(0, inverseSensorModel.ray_model.probability[1][5], "Ray model probabilities not updated");
// observer parameters
int pan_angle_degrees = 0;
pos3D observer = new pos3D(0,0,0);
observer.pan = pan_angle_degrees * (float)Math.PI / 180.0f;
float stereo_camera_baseline_mm = 100;
pos3D left_camera_location = new pos3D(stereo_camera_baseline_mm*0.5f,0,0);
pos3D right_camera_location = new pos3D(-stereo_camera_baseline_mm*0.5f,0,0);
left_camera_location = left_camera_location.rotate(observer.pan, observer.tilt, observer.roll);
right_camera_location = right_camera_location.rotate(observer.pan, observer.tilt, observer.roll);
left_camera_location = left_camera_location.translate(observer.x, observer.y, observer.z);
right_camera_location = right_camera_location.translate(observer.x, observer.y, observer.z);
float FOV_degrees = 78;
float[] stereo_features = new float[no_of_stereo_features * 3];
byte[,] stereo_features_colour = new byte[no_of_stereo_features, 3];
float[] stereo_features_uncertainties = new float[no_of_stereo_features];
// create some stereo disparities within the field of view
Console.WriteLine("Adding disparities");
//MersenneTwister rnd = new MersenneTwister(0);
Random rnd = new Random(0);
for (int correspondence = 0; correspondence < no_of_stereo_features; correspondence++)
{
float x = rnd.Next(image_width-1);
float y = rnd.Next(image_height/50) + (image_height/2);
float disparity = 7;
if ((x < image_width/5) || (x > image_width * 4/5))
{
disparity = 7; //15;
}
byte colour_red = (byte)rnd.Next(255);
byte colour_green = (byte)rnd.Next(255);
byte colour_blue = (byte)rnd.Next(255);
stereo_features[correspondence*3] = x;
stereo_features[(correspondence*3)+1] = y;
stereo_features[(correspondence*3)+2] = disparity;
stereo_features_colour[correspondence, 0] = colour_red;
stereo_features_colour[correspondence, 1] = colour_green;
stereo_features_colour[correspondence, 2] = colour_blue;
stereo_features_uncertainties[correspondence] = 0;
}
// create an observation as a set of rays from the stereo correspondence results
List<evidenceRay>[] stereo_rays = new List<evidenceRay>[no_of_stereo_cameras];
for (int cam = 0; cam < no_of_stereo_cameras; cam++)
{
Console.WriteLine("Creating rays");
stereo_rays[cam] =
inverseSensorModel.createObservation(
observer,
stereo_camera_baseline_mm,
image_width,
image_height,
FOV_degrees,
stereo_features,
stereo_features_colour,
stereo_features_uncertainties,
true);
// insert rays into the grid
Console.WriteLine("Throwing rays");
for (int ray = 0; ray < stereo_rays[cam].Count; ray++)
{
grid.Insert(stereo_rays[cam][ray], inverseSensorModel.ray_model, left_camera_location, right_camera_location, false);
}
}
// save the result as an image
Console.WriteLine("Saving grid");
int debug_img_width = 640;
int debug_img_height = 480;
byte[] debug_img = new byte[debug_img_width * debug_img_height * 3];
Bitmap bmp = new Bitmap(debug_img_width, debug_img_height, System.Drawing.Imaging.PixelFormat.Format24bppRgb);
grid.Show(debug_img, debug_img_width, debug_img_height, false, false);
BitmapArrayConversions.updatebitmap_unsafe(debug_img, bmp);
bmp.Save("tests_occupancygrid_simple_InsertRays_overhead.jpg", System.Drawing.Imaging.ImageFormat.Jpeg);
grid.ShowFront(debug_img, debug_img_width, debug_img_height, true);
BitmapArrayConversions.updatebitmap_unsafe(debug_img, bmp);
bmp.Save("tests_occupancygrid_simple_InsertRays_front.jpg", System.Drawing.Imaging.ImageFormat.Jpeg);
// side view of the probabilities
float max_prob = -1;
float min_prob = 1;
float[] probs = new float[dimension_cells/2];
float[] mean_colour = new float[3];
for (int y = dimension_cells / 2; y < dimension_cells; y++)
{
float p = grid.GetProbability(dimension_cells / 2, y, mean_colour);
probs[y - (dimension_cells / 2)] = p;
if (p != occupancygridSimple.NO_OCCUPANCY_EVIDENCE)
{
if (p < min_prob) min_prob = p;
if (p > max_prob) max_prob = p;
}
}
for (int i = 0; i < debug_img.Length; i++) debug_img[i] = 255;
int prev_x = -1;
int prev_y = debug_img_height / 2;
for (int i = 0; i < probs.Length; i++)
{
if (probs[i] != occupancygridSimple.NO_OCCUPANCY_EVIDENCE)
{
int x = i * (debug_img_width - 1) / probs.Length;
int y = debug_img_height - 1 - (int)((probs[i] - min_prob) / (max_prob - min_prob) * (debug_img_height - 1));
int n = ((y * debug_img_width) + x) * 3;
if (prev_x > -1)
{
int r = 255;
int g = 0;
int b = 0;
if (probs[i] > 0.5f)
{
r = 0;
g = 255;
b = 0;
}
drawing.drawLine(debug_img, debug_img_width, debug_img_height, prev_x, prev_y, x, y, r, g, b, 0, false);
}
prev_x = x;
prev_y = y;
}
}
int y_zero = debug_img_height - 1 - (int)((0.5f-min_prob) / (max_prob - min_prob) * (debug_img_height - 1));
drawing.drawLine(debug_img, debug_img_width, debug_img_height, 0, y_zero, debug_img_width - 1, y_zero, 0, 0, 0, 0, false);
BitmapArrayConversions.updatebitmap_unsafe(debug_img, bmp);
bmp.Save("tests_occupancygrid_simple_InsertRays_probs.jpg", System.Drawing.Imaging.ImageFormat.Jpeg);
}
