public static string GetCaptcha(byte[] image)
{
// Get the prediction result handle
int poolHandle = Caffe.ForwardByTaskPool(taskPool, image, image.Length, "premuted_fc");
// Get the tensor handle
float[] permute_fc = new float[Caffe.GetBlobLength(poolHandle)];
// Copy the tensor data
CpyBlobData(permute_fc, poolHandle);
string code = string.Empty;
if (permute_fc.Length > 0)
{
int o = 0;
float acc = 0F;
int emptyLabel = alphabetSize - 1;
int prev = emptyLabel;
for (int i = 1; i < timeStep; i++)
{
o = Argmax(permute_fc, (i - 1) * alphabetSize + 1, i * alphabetSize, ref acc);
if (o != emptyLabel && prev != o)
{
code += map[o + 1];
}
prev = o;
}
code = code.Replace("_", "").Trim();
}
ReleaseBlobData(poolHandle);
return(code);
}
public static bool InitCaptcha(string prototxtPath, string modelPath, string mapPath, int gpuId, int batchSize)
{
byte[] deploy = Util.GetFileStream(prototxtPath);
byte[] model = Util.GetFileStream(modelPath);
Caffe.taskPool = Caffe.CreateTaskPoolByData(deploy, deploy.Length, model, model.Length, 1F, "", 0, 0F, gpuId, batchSize);
Caffe.prototxt = System.Text.Encoding.Default.GetString(deploy);
string[] mapFile = Util.LoadStringFromFile(mapPath).Trim().Split("/r/n".ToArray());
Caffe.map = new ArrayList();
for (int i = 0; i < mapFile.Length; i++)
{
if (mapFile[i].Length > 0)
{
Caffe.map.Add(mapFile[i]);
}
}
string time_step = Util.GetMiddleString(Caffe.prototxt, "time_step:", "/r/n");
string layer = Util.GetMiddleString(Caffe.prototxt, "inner_product_param {", "{");
string alphabet_size = Util.GetMiddleString(layer, "num_output:", "/r/n");
Caffe.timeStep = int.Parse(time_step);
Caffe.alphabetSize = int.Parse(alphabet_size);
return(Caffe.taskPool != 0);
}
