private static int Main(string[] args)
{
if (args.Length < 1)
{
Console.WriteLine("No test image here.");
Console.WriteLine("Usage: ./image-classification-predict apple.jpg");
return(0);
}
var testFile = args[0];
// Models path for your model, you have to modify it
var jsonFile = "model/Inception/Inception-BN-symbol.json";
var paramFile = "model/Inception/Inception-BN-0126.params";
var synsetFile = "model/Inception/synset.txt";
var ndFile = "model/Inception/mean_224.nd";
if (!File.Exists(jsonFile))
{
Console.WriteLine($"{jsonFile} is not found.");
return(0);
}
if (!File.Exists(paramFile))
{
Console.WriteLine($"{paramFile} is not found.");
return(0);
}
if (!File.Exists(synsetFile))
{
Console.WriteLine($"{synsetFile} is not found.");
return(0);
}
var jsonData = new BufferFile(jsonFile);
var paramData = new BufferFile(paramFile);
// Parameters
var devType = 1; // 1: cpu, 2: gpu
const int devId = 0; // arbitrary.
var numInputNodes = 1u; // 1 for feedforward
const string inputKey = "data";
string[] inputKeys = { inputKey };
// Image size and channels
const int width = 224;
const int height = 224;
const int channels = 3;
var inputShapeIndptr = new[] { 0u, 4u };
var inputShapeData = new[] { (uint)1,
(uint)channels,
(uint)height,
(uint)width };
if (jsonData.GetLength() == 0 || paramData.GetLength() == 0)
{
return(-1);
}
MXNet.MXPredCreate(File.ReadAllText(jsonFile),
paramData.GetBuffer(),
paramData.GetLength(),
devType,
devId,
numInputNodes,
inputKeys,
inputShapeIndptr,
inputShapeData,
out var predHnd);
var imageSize = (uint)(width * height * channels);
// Read Mean Data
float[] ndData = null;
NDListHandle ndHnd = null;
var ndBuf = new BufferFile(ndFile);
if (ndBuf.GetLength() > 0)
{
const uint nd_index = 0u;
var ndLen = 0u;
uint[] ndShape = null;
string ndKey = null;
var ndNdim = 0u;
MXNet.MXNDListCreate(ndBuf.GetBuffer(), ndBuf.GetLength(), out ndHnd, out ndLen);
MXNet.MXNDListGet(ndHnd, nd_index, out ndKey, out ndData, out ndShape, out ndNdim);
}
// Read Image Data
var imageData = new float[imageSize];
GetImageFile(testFile, imageData, channels, new Size(width, height), ndData);
// Set Input Image
MXNet.MXPredSetInput(predHnd, "data", imageData, imageSize);
// Do Predict Forward
MXNet.MXPredForward(predHnd);
var outputIndex = 0u;
// Get Output Result
MXNet.MXPredGetOutputShape(predHnd, outputIndex, out var shape, out var shape_len);
var size = 1u;
for (var i = 0u; i < shape_len; ++i)
{
size *= shape[i];
}
var data = new float[size];
MXNet.MXPredGetOutput(predHnd, outputIndex, data, size);
// Release NDList
ndHnd?.Dispose();
// Release Predictor
predHnd?.Dispose();
// Synset path for your model, you have to modify it
var synset = LoadSynset(synsetFile);
// Print Output Data
PrintOutputResult(data, synset);
return(0);
}
public float[] Detect(string img)
{
if (!File.Exists(img))
{
throw new FileNotFoundException($"{img} is not found.");
}
using (var image = Cv2.ImRead(img))
{
if (image.Empty())
{
throw new ArgumentException($"Unable to load image file: {img}");
}
if (image.Channels() != 3)
{
throw new ArgumentException($"RGB image required");
}
using (var resized = image.Resize(new Size(this._Width, this._Height)))
{
var size = resized.Channels() * resized.Rows * resized.Cols;
var inData = new float[size];
// de-interleave and minus means
unsafe
{
var ptr = (byte *)resized.Ptr();
fixed(float *dataPtr = &inData[0])
{
var tmp = dataPtr;
for (var i = 0; i < size; i += 3)
{
*tmp = ptr[i] - this._MeanR;
tmp++;
}
for (var i = 1; i < size; i += 3)
{
*tmp = ptr[i] - this._MeanG;
tmp++;
}
for (var i = 2; i < size; i += 3)
{
*tmp = ptr[i] - this._MeanB;
tmp++;
}
}
}
// usr model to forwad
MXNet.MXPredSetInput(this._Predictor, "data", inData, (uint)size);
var sw = new Stopwatch();
sw.Start();
MXNet.MXPredForward(this._Predictor);
MXNet.MXPredGetOutputShape(this._Predictor, 0, out var shape, out var shapeLen);
var ttSize = 1u;
for (var i = 0u; i < shapeLen; ++i)
{
ttSize *= shape[i];
}
var outputs = new float[ttSize];
MXNet.MXPredGetOutput(this._Predictor, 0, outputs, ttSize);
sw.Stop();
Console.WriteLine($"Forward elapsed time: {sw.ElapsedMilliseconds} ms");
return(outputs);
}
}
}
