/// <summary>
/// Finds and classifies detections for all frames in a video.
/// </summary>
/// <param name="detect_path">Path to detect model file.</param>
/// <param name="classify_path">Path to classify model file.</param>
/// <param name="roi">Region of interest output from FindVideoRoi.</param>
/// <param name="endpoints">Ruler endpoints output from FindVideoRoi.</param>
/// <param name="detections">Detections for each frame.</param>
/// <param name="scores">Cover and species scores for each detection.</param>
static void DetectAndClassify(
string detect_path,
string classify_path,
string vid_path,
Rect roi,
PointPair endpoints,
out VectorVectorDetection detections,
out VectorVectorClassification scores)
{
// Determined by experimentation with GPU having 8GB memory.
const int kMaxImg = 32;
// Initialize the outputs.
detections = new VectorVectorDetection();
scores = new VectorVectorClassification();
// Create and initialize the detector.
Detector detector = new Detector();
ErrorCode status = detector.Init(detect_path, 0.5);
if (status != ErrorCode.kSuccess)
{
throw new Exception("Failed to initialize detector!");
}
// Create and initialize the classifier.
Classifier classifier = new Classifier();
status = classifier.Init(classify_path, 0.5);
if (status != ErrorCode.kSuccess)
{
throw new Exception("Failed to initialize classifier!");
}
// Initialize the video reader.
VideoReader reader = new VideoReader();
status = reader.Init(vid_path);
if (status != ErrorCode.kSuccess)
{
throw new Exception("Failed to open video!");
}
// Iterate through frames.
bool vid_end = false;
while (true)
{
// Find detections.
VectorVectorDetection dets = new VectorVectorDetection();
VectorImage imgs = new VectorImage();
for (int i = 0; i < kMaxImg; ++i)
{
Image img = new Image();
status = reader.GetFrame(img);
if (status != ErrorCode.kSuccess)
{
vid_end = true;
break;
}
img = openem.Rectify(img, endpoints);
img = openem.Crop(img, roi);
imgs.Add(img);
status = detector.AddImage(img);
if (status != ErrorCode.kSuccess)
{
throw new Exception("Failed to add frame to detector!");
}
}
status = detector.Process(dets);
if (status != ErrorCode.kSuccess)
{
throw new Exception("Failed to process detector!");
}
for (int i = 0; i < dets.Count; ++i)
{
}
detections.AddRange(dets);
for (int i = 0; i < detections.Count; ++i)
{
}
// Classify detections.
for (int i = 0; i < dets.Count; ++i)
{
VectorClassification score_batch = new VectorClassification();
for (int j = 0; j < dets[i].Count; ++j)
{
Image det_img = openem.GetDetImage(imgs[i], dets[i][j].location);
status = classifier.AddImage(det_img);
if (status != ErrorCode.kSuccess)
{
throw new Exception("Failed to add frame to classifier!");
}
}
status = classifier.Process(score_batch);
if (status != ErrorCode.kSuccess)
{
throw new Exception("Failed to process classifier!");
}
scores.Add(score_batch);
}
if (vid_end)
{
break;
}
}
}
/// <summary>
/// Main program.
/// </summary>
static int Main(string[] args)
{
// Check input arguments.
if (args.Length < 2)
{
Console.WriteLine("Expected at least two arguments:");
Console.WriteLine(" Path to protobuf file containing model");
Console.WriteLine(" Paths to one or more image files");
return(-1);
}
// Create and initialize classifier.
Classifier classifier = new Classifier();
ErrorCode status = classifier.Init(args[0]);
if (status != ErrorCode.kSuccess)
{
Console.WriteLine("Failed to initialize classifier!");
return(-1);
}
// Load in images.
VectorImage imgs = new VectorImage();
PairIntInt img_size = classifier.ImageSize();
for (int i = 1; i < args.Length; i++)
{
Image img = new Image();
status = img.FromFile(args[i]);
if (status != ErrorCode.kSuccess)
{
Console.WriteLine("Failed to load image {0}!", args[i]);
return(-1);
}
img.Resize(img_size.first, img_size.second);
imgs.Add(img);
}
// Add images to processing queue.
foreach (var img in imgs)
{
status = classifier.AddImage(img);
if (status != ErrorCode.kSuccess)
{
Console.WriteLine("Failed to add image for processing!");
return(-1);
}
}
// Process the loaded images.
VectorClassification scores = new VectorClassification();
status = classifier.Process(scores);
if (status != ErrorCode.kSuccess)
{
Console.WriteLine("Failed to process images!");
return(-1);
}
// Display the images and print scores to console.
for (int i = 0; i < scores.Count; ++i)
{
Console.WriteLine("*******************************************");
Console.WriteLine("Fish cover scores:");
Console.WriteLine("No fish: {0}", scores[i].cover[0]);
Console.WriteLine("Hand over fish: {0}", scores[i].cover[1]);
Console.WriteLine("Fish clear: {0}", scores[i].cover[2]);
Console.WriteLine("*******************************************");
Console.WriteLine("Fish species scores:");
Console.WriteLine("Fourspot: {0}", scores[i].species[0]);
Console.WriteLine("Grey sole: {0}", scores[i].species[1]);
Console.WriteLine("Other: {0}", scores[i].species[2]);
Console.WriteLine("Plaice: {0}", scores[i].species[3]);
Console.WriteLine("Summer: {0}", scores[i].species[4]);
Console.WriteLine("Windowpane: {0}", scores[i].species[5]);
Console.WriteLine("Winter: {0}", scores[i].species[6]);
Console.WriteLine("");
imgs[i].Show();
}
return(0);
}
