public Dictionary <int, Dictionary <string, double> > ClassifyImage(string path)
{
Bitmap bitmapImage = new Bitmap(path);
WatsonAnalizer myFilter = (WatsonAnalizer)this.tools[18];
Dictionary <int, Dictionary <string, double> > resultadoClasificacion = myFilter.FindClassifiers(bitmapImage);
return(resultadoClasificacion);
}
public Dictionary <string, int> SexAndAgeRecognition(Image image)
{
Dictionary <string, int> result = new Dictionary <string, int>()
{
};
WatsonAnalizer WA = new WatsonAnalizer();
Dictionary <int, Dictionary <string, object> > facesDict = WA.FindFaces(image.BitmapImage);
double ageScore = 0;
int maleScore = 0;
int femaleScore = 0;
int cont = 0;
foreach (KeyValuePair <int, Dictionary <string, object> > pair in facesDict)
{
Dictionary <string, object> value = pair.Value;
foreach (KeyValuePair <string, object> Scores in value)
{
string String = Scores.Key;
object Object = Scores.Key;
switch (String)
{
case "age":
Age ageObj = (Age)Object;
ageScore += (ageObj.MaxAge - ageObj.MinAge) / 2;
break;
case "gender":
Gender genderObj = (Gender)Object;
if (genderObj.GenderLabel == "male")
{
maleScore++;
}
else
{
femaleScore++;
}
break;
}
}
cont++;
}
if (maleScore > femaleScore)
{
result.Add("Male", Convert.ToInt32(Math.Floor(ageScore / cont)));
}
else if (maleScore < femaleScore)
{
result.Add("Female", Convert.ToInt32(Math.Floor(ageScore / cont)));
}
else
{
Random rnd = new Random();
string[] genders = new string[2];
genders[0] = "Male";
genders[1] = "Female";
result.Add(genders[rnd.Next(0, 1)], Convert.ToInt32(Math.Floor(ageScore / cont)));
}
return(result);
} // => ESTE METODO SE DEBE VOLVER A HACER
