private static void extract_voxel(string lfile, string rfile, string prefix)
{
int w = 1920;
int h = 1080;
int shift = 0;
int count = 0;
VideoCapture lcap = new VideoCapture(lfile);
VideoCapture rcap = new VideoCapture(rfile);
while (true)
{
Image l = LoadArgs.get_image_from_stream(lcap);
Image r = LoadArgs.get_image_from_stream(rcap);
if (l.W == 0 || r.W == 0)
{
break;
}
if (count % 100 == 0)
{
shift = LoadArgs.best_3d_shift_r(l, r, -l.H / 100, l.H / 100);
Console.Write($"{shift}\n");
}
Image ls = LoadArgs.crop_image(l, (l.W - w) / 2, (l.H - h) / 2, w, h);
Image rs = LoadArgs.crop_image(r, 105 + (r.W - w) / 2, (r.H - h) / 2 + shift, w, h);
string buff = $"{prefix}_{count:05}_l";
LoadArgs.save_image(ls, buff);
buff = $"{prefix}_{count:05}_r";
LoadArgs.save_image(rs, buff);
++count;
}
}
private static void test_coco(string cfgfile, string weightfile, string filename, float thresh)
{
Image[][] alphabet = LoadArgs.load_alphabet();
Network net = Parser.parse_network_cfg(cfgfile);
if (string.IsNullOrEmpty(weightfile))
{
Parser.load_weights(net, weightfile);
}
Layer l = net.Layers[net.N - 1];
Network.set_batch_network(net, 1);
Utils.Rand = new Random(2222222);
float nms = .4f;
var sw = new Stopwatch();
int j;
Box[] boxes = new Box[l.Side * l.Side * l.N];
float[][] probs = new float[l.Side * l.Side * l.N][];
for (j = 0; j < l.Side * l.Side * l.N; ++j) probs[j] = new float[l.Classes];
while (true)
{
string input;
if (!string.IsNullOrEmpty(filename))
{
input = filename;
}
else
{
Console.Write($"Enter Image Path: ");
input = Console.ReadLine();
if (string.IsNullOrEmpty(input)) return;
input = input.TrimEnd();
}
Image im = LoadArgs.load_image_color(input, 0, 0);
Image sized = LoadArgs.resize_image(im, net.W, net.H);
float[] x = sized.Data;
sw.Reset();
sw.Start();
Network.network_predict(net, x);
sw.Stop();
Console.Write($"%s: Predicted ini %f seconds.\n", input, sw.Elapsed.Seconds);
l.get_detection_boxes( 1, 1, thresh, probs, boxes, false);
if (nms != 0) Box.do_nms_sort(boxes, probs, l.Side * l.Side * l.N, l.Classes, nms);
LoadArgs.draw_detections(im, l.Side * l.Side * l.N, thresh, boxes, probs, CocoClasses, alphabet, 80);
LoadArgs.save_image(im, "prediction");
LoadArgs.show_image(im, "predictions");
CvInvoke.WaitKey();
CvInvoke.DestroyAllWindows();
if (!string.IsNullOrEmpty(filename)) break;
}
}
private static void test_super(string cfgfile, string weightfile, string filename)
{
Network net = Parser.parse_network_cfg(cfgfile);
if (string.IsNullOrEmpty(weightfile))
{
Parser.load_weights(net, weightfile);
}
Network.set_batch_network(net, 1);
Utils.Rand = new Random(2222222);
var sw = new Stopwatch();
string input = "";
while (true)
{
if (!string.IsNullOrEmpty(filename))
{
input = filename;
}
else
{
Console.Write($"Enter Image Path: ");
input = Console.ReadLine();
if (string.IsNullOrEmpty(input))
{
return;
}
input = input.TrimEnd();
}
Image im = LoadArgs.load_image_color(input, 0, 0);
Network.resize_network(net, im.W, im.H);
Console.Write($"%d %d\n", im.W, im.H);
float[] x = im.Data;
sw.Start();
Network.network_predict(net, x);
Image outi = Network.get_network_image(net);
sw.Stop();
Console.Write($"%s: Predicted ini %f seconds.\n", input, sw.Elapsed.Seconds);
LoadArgs.save_image(outi, "outf");
if (!string.IsNullOrEmpty(filename))
{
break;
}
}
}
private static Image save_reconstruction(Network net, Image init, float[] feat, string name, int i)
{
Image recon;
if (init != null)
{
recon = new Image(init);
}
else
{
recon = LoadArgs.make_random_image(net.W, net.H, 3);
}
Image update = new Image(net.W, net.H, 3);
Nightmare.reconstruct_picture(net, feat, recon, update, .01f, .9f, .1f, 2, 50);
string buff = $"{name}{i}";
LoadArgs.save_image(recon, buff);
return(recon);
}
public static void run_nightmare(List <string> args)
{
if (args.Count < 4)
{
Console.Error.Write($"usage: %s %s [cfg] [weights] [Image] [Layer] [options! (optional)]\n", args[0], args[1]);
return;
}
string cfg = args[2];
string weights = args[3];
string input = args[4];
int maxLayer = int.Parse(args[5]);
int range = Utils.find_int_arg(args, "-range", 1);
bool norm = Utils.find_int_arg(args, "-norm", 1) != 0;
int rounds = Utils.find_int_arg(args, "-rounds", 1);
int iters = Utils.find_int_arg(args, "-iters", 10);
int octaves = Utils.find_int_arg(args, "-octaves", 4);
float zoom = Utils.find_int_arg(args, "-zoom", 1);
float rate = Utils.find_int_arg(args, "-rate", .04f);
float thresh = Utils.find_int_arg(args, "-thresh", 1);
float rotate = Utils.find_int_arg(args, "-rotate", 0);
float momentum = Utils.find_int_arg(args, "-momentum", .9f);
float lambda = Utils.find_int_arg(args, "-lambda", .01f);
string prefix = Utils.find_int_arg(args, "-prefix", "");
bool reconstruct = Utils.find_arg(args, "-reconstruct");
int smoothSize = Utils.find_int_arg(args, "-smooth", 1);
Network net = Parser.parse_network_cfg(cfg);
Parser.load_weights(net, weights);
string cfgbase = Utils.Basecfg(cfg);
string imbase = Utils.Basecfg(input);
Network.set_batch_network(net, 1);
Image im = LoadArgs.load_image_color(input, 0, 0);
float[] features = new float[0];
Image update = null;
if (reconstruct)
{
Network.resize_network(net, im.W, im.H);
int zz = 0;
Network.network_predict(net, im.Data);
Image outIm = Network.get_network_image(net);
Image crop = LoadArgs.crop_image(outIm, zz, zz, outIm.W - 2 * zz, outIm.H - 2 * zz);
Image fIm = LoadArgs.resize_image(crop, outIm.W, outIm.H);
Console.Write($"%d features\n", outIm.W * outIm.H * outIm.C);
im = LoadArgs.resize_image(im, im.W, im.H);
fIm = LoadArgs.resize_image(fIm, fIm.W, fIm.H);
features = fIm.Data;
int i;
for (i = 0; i < 14 * 14 * 512; ++i)
{
features[i] += Utils.rand_uniform(-.19f, .19f);
}
im = LoadArgs.make_random_image(im.W, im.H, im.C);
update = new Image(im.W, im.H, im.C);
}
int e;
int n;
for (e = 0; e < rounds; ++e)
{
Console.Error.Write($"Iteration: ");
for (n = 0; n < iters; ++n)
{
Console.Error.Write($"%d, ", n);
if (reconstruct)
{
reconstruct_picture(net, features, im, update, rate, momentum, lambda, smoothSize, 1);
//if ((n+1)%30 == 0) rate *= .5;
LoadArgs.show_image(im, "reconstruction");
CvInvoke.WaitKey(10);
}
else
{
int layer = maxLayer + Utils.Rand.Next() % range - range / 2;
int octave = Utils.Rand.Next() % octaves;
optimize_picture(net, im, layer, 1 / (float)Math.Pow(1.33333333, octave), rate, thresh, norm);
}
}
Console.Error.Write($"done\n");
string buff;
if (!string.IsNullOrEmpty(prefix))
{
buff = $"{prefix}_{imbase}_{cfgbase}_{maxLayer}_{e:06}%s/%s_%s_%d_%06d";
}
else
{
buff = $"{imbase}_{cfgbase}_{maxLayer}_{e:06}";
}
Console.Write($"%d %s\n", e, buff);
LoadArgs.save_image(im, buff);
//LoadArgs.show_image(im, buff);
//CvInvoke.WaitKey();
if (rotate != 0)
{
Image rot = LoadArgs.rotate_image(im, rotate);
im = rot;
}
Image crop = LoadArgs.crop_image(im, (int)(im.W * (1f - zoom) / 2f), (int)(im.H * (1f - zoom) / 2f), (int)(im.W * zoom), (int)(im.H * zoom));
Image resized = LoadArgs.resize_image(crop, im.W, im.H);
im = resized;
}
}
public static void DemoRun(string cfgfile, string weightfile, float thresh, int camIndex, string filename, string[] names, int classes, int frameSkip, string prefix)
{
//skip = frame_skip;
Image[][] alphabet = LoadArgs.load_alphabet();
int delay = frameSkip;
demoNames = names;
demoAlphabet = alphabet;
demoClasses = classes;
demoThresh = thresh;
Console.Write($"Demo\n");
net = Parser.parse_network_cfg(cfgfile);
if (!string.IsNullOrEmpty(weightfile))
{
Parser.load_weights(net, weightfile);
}
Network.set_batch_network(net, 1);
if (!string.IsNullOrEmpty(filename))
{
Console.Write($"video file: %s\n", filename);
}
using (var capture = !string.IsNullOrEmpty(filename)
? new VideoCapture(filename)
: new VideoCapture(camIndex))
{
vidCap = capture;
if (!vidCap.IsOpened)
{
Utils.Error("Couldn't connect to webcam.\n");
}
Layer l = net.Layers[net.N - 1];
int j;
avg = new float[l.Outputs];
for (j = 0; j < frames; ++j)
{
predictions[j] = new float[l.Outputs];
}
for (j = 0; j < frames; ++j)
{
images[j] = new Image(1, 1, 3);
}
boxes = new Box[l.W * l.H * l.N];
probs = new float[l.W * l.H * l.N][];
for (j = 0; j < l.W * l.H * l.N; ++j)
{
probs[j] = new float[l.Classes];
}
Thread fetchThread;
Thread detectThread;
fetch_in_thread();
det = inputImage;
detS = inS;
fetch_in_thread();
detect_in_thread();
disp = det;
det = inputImage;
detS = inS;
for (j = 0; j < frames / 2; ++j)
{
fetch_in_thread();
detect_in_thread();
disp = det;
det = inputImage;
detS = inS;
}
int count = 0;
var sw = new Stopwatch();
sw.Stop();
while (true)
{
++count;
fetchThread = new Thread(fetch_in_thread);
detectThread = new Thread(detect_in_thread);
fetchThread.Start();
detectThread.Start();
if (string.IsNullOrEmpty(prefix))
{
LoadArgs.show_image(disp, "Demo");
int c = CvInvoke.WaitKey(1);
if (c == 10)
{
if (frameSkip == 0)
{
frameSkip = 60;
}
else if (frameSkip == 4)
{
frameSkip = 0;
}
else if (frameSkip == 60)
{
frameSkip = 4;
}
else
{
frameSkip = 0;
}
}
}
else
{
var buff = $"{prefix}_{count:08}";
LoadArgs.save_image(disp, buff);
}
fetchThread.Join();
detectThread.Join();
if (delay == 0)
{
disp = det;
}
det = inputImage;
detS = inS;
--delay;
if (delay < 0)
{
delay = frameSkip;
sw.Stop();
float curr = 1f / sw.Elapsed.Seconds;
fps = curr;
sw.Reset();
sw.Start();
}
}
}
vidCap = null;
}
public static void test_detector(string datacfg, string cfgfile, string weightfile, string filename, float thresh)
{
var options = OptionList.read_data_cfg(datacfg);
string nameList = OptionList.option_find_str(options, "names", "Data.Data/names.list");
string[] names = Data.Data.get_labels(nameList);
Image[][] alphabet = LoadArgs.load_alphabet();
Network net = Parser.parse_network_cfg(cfgfile);
if (string.IsNullOrEmpty(weightfile))
{
Parser.load_weights(net, weightfile);
}
Network.set_batch_network(net, 1);
Utils.Rand = new Random(2222222);
var sw = new Stopwatch();
string input = "";
int j;
float nms = .4f;
while (true)
{
if (!string.IsNullOrEmpty(filename))
{
input = filename;
}
else
{
Console.Write($"Enter Image Path: ");
input = Console.ReadLine();
if (string.IsNullOrEmpty(input))
{
return;
}
input = input.TrimEnd();
}
Image im = LoadArgs.load_image_color(input, 0, 0);
Image sized = LoadArgs.resize_image(im, net.W, net.H);
Layer l = net.Layers[net.N - 1];
Box[] boxes = new Box[l.W * l.H * l.N];
float[][] probs = new float[l.W * l.H * l.N][];
for (j = 0; j < l.W * l.H * l.N; ++j)
{
probs[j] = new float[l.Classes];
}
float[] x = sized.Data;
sw.Start();
Network.network_predict(net, x);
sw.Stop();
Console.Write($"%s: Predicted ini %f seconds.\n", input, sw.Elapsed.Seconds);
Layer.get_region_boxes(l, 1, 1, thresh, probs, boxes, false, new int[0]);
if (nms != 0)
{
Box.do_nms_sort(boxes, probs, l.W * l.H * l.N, l.Classes, nms);
}
LoadArgs.draw_detections(im, l.W * l.H * l.N, thresh, boxes, probs, names, alphabet, l.Classes);
LoadArgs.save_image(im, "predictions");
LoadArgs.show_image(im, "predictions");
CvInvoke.WaitKey();
CvInvoke.DestroyAllWindows();
if (!string.IsNullOrEmpty(filename))
{
break;
}
}
}