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;
}
}
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;
}
}
}
