public static RecogReply PredictionFunc(Guid id, int timeBudgetInMS, RecogRequest req)
{
byte[] imgBuf = req.Data;
byte[] imgType = System.Text.Encoding.UTF8.GetBytes("jpg");
Guid imgID = BufferCache.HashBufferAndType(imgBuf, imgType);
string imgFileName = imgID.ToString() + ".jpg";
string filename = Path.Combine(saveImageDir, imgFileName);
if (!File.Exists(filename))
{
FileTools.WriteBytesToFileConcurrent(filename, imgBuf);
}
Stopwatch timer = Stopwatch.StartNew();
CaffeModel.SetDevice(gpu);
string resultString = predictor.Predict(filename);
timer.Stop();
File.Delete(filename);
numImageRecognized++;
Console.WriteLine("Image {0}:{1}:{2}: {3}", numImageRecognized, imgFileName, timer.Elapsed, resultString);
return(VHubRecogResultHelper.FixedClassificationResult(resultString, resultString));
}
public static bool InitializeRecognizer(VHubBackendStartParam pa)
{
var bInitialized = true;
var x = ImageCaptionInstance.Current;
if (!Object.ReferenceEquals(x, null))
{
/// <remarks>
/// To implement your own image recognizer, please obtain a connection Guid by contacting [email protected]
/// </remarks>
x.RegisterAppInfo(new Guid("843EF294-C635-42DA-9AD8-E79E82F9A357"), "0.0.0.1");
Func <Guid, int, RecogRequest, RecogReply> del = PredictionFunc;
/// <remarks>
/// Register your prediction function here.
/// </remarks>
string exeDir = Path.GetDirectoryName(Process.GetCurrentProcess().MainModule.FileName);
x.RegisterClassifierCS("#ImageCaption", Path.Combine(exeDir, "logo.jpg"), 100, del);
string wordDetectorProto = Path.Combine(rootDir, @"Model\wordDetector.proto");
string wordDetectorModel = Path.Combine(rootDir, @"Model\wordDetector.caffemodel");
string featureDetectorProto = Path.Combine(rootDir, @"Model\FeaturesDetector.proto");
string featureDetectorModel = Path.Combine(rootDir, @"Model\FeaturesDetector.caffemodel");
string thresholdFile = Path.Combine(rootDir, @"Model\thresholds.txt");
string wordLabelMapFile = Path.Combine(rootDir, @"Model\labelmap.txt");
string languageModel = Path.Combine(rootDir, @"Model\sentencesGeneration.lblmmodel");
string referenceDssmModelFile = Path.Combine(rootDir, @"Model\reference.dssmmodel");
string candidateDssmModelFile = Path.Combine(rootDir, @"Model\candidate.dssmmodel");
string trigramFile = Path.Combine(rootDir, @"Model\coco.cap.l3g");
CaffeModel.SetDevice(gpu); //This needs to be set before instantiating the net
var lmTestArguments = new LangModel.Arguments
{
NumWorkers = 8,
MaxSentenceLength = 19,
NumSentences = 500,
BeamWidth = 200,
AttributesCoverageBar = 5,
KTopWords = 100
};
predictor = new Predictor(wordDetectorProto, wordDetectorModel, featureDetectorProto, featureDetectorModel,
thresholdFile, wordLabelMapFile,
languageModel, lmTestArguments, referenceDssmModelFile, candidateDssmModelFile, trigramFile);
}
else
{
bInitialized = false;
}
return(bInitialized);
}
public void Init(string protoFile, string modelFile, string meanFile, string labelMapFile, string entityInfoFile, int gpu)
{
// Init caffe model
CaffeModel.SetDevice(gpu);
_caffeModel = new CaffeModel(protoFile, modelFile);
_caffeModel.SetMeanFile(meanFile);
Console.WriteLine("Succeed: Load Model File!\n");
_labelMap = File.ReadAllLines(labelMapFile)
.Select(line => line.Split('\t')[0])
.ToArray();
if (!string.IsNullOrEmpty(entityInfoFile))
{
_entityInfo = File.ReadLines(entityInfoFile)
.Select(line => line.Split('\t'))
.ToDictionary(cols => cols[0], cols => cols[1]);
}
}