/// <summary>
/// Parses the result string to an output string for the Text-To-Speech algorithm.
/// Customizes the output string in dependency of the number of list elements.
/// It is differentiated between no result, one result element, more than one result elements and occuring errors.s
/// </summary>
/// <param name="result">The result string from object detection which contains the values and the probabilities.</param>
/// <returns>Parsed string that contains the prediction with value(s), the probability/ies and the number of elements.</returns>
private string ParseResult(string result)
{
if (!string.IsNullOrEmpty(result))
{
var parsedPrediction = PythonOutputParser.ParseToListOfPredictions(result);
//bool isNumber = Directory.GetDirectories(Properties.Resources.PythonModelPath).Any(d => d.Contains(Properties.Resources.ModelNumber));
bool isNumber = new SettingsController().GetTfModelMode() == Properties.Resources.ModelNumber;
StringBuilder stringBuilder = new StringBuilder();
if (parsedPrediction.Count == 0)
{
stringBuilder.Append(Properties.Resources.NoResultFound);
return(stringBuilder.ToString());
}
if (parsedPrediction.Count == 1)
{
stringBuilder.Append(isNumber ? Properties.Resources.FoundNumber : Properties.Resources.FoundLetter);
var number = parsedPrediction[0].PredictedValue;
var probability = parsedPrediction[0].PredictionPercentage;
stringBuilder.Append(number);
stringBuilder.Append(Properties.Resources.Probability);
stringBuilder.Append(probability);
stringBuilder.Append(Properties.Resources.Percent);
stringBuilder.Append(Properties.Resources.Comma);
}
else if (parsedPrediction.Count >= 2)
{
stringBuilder.Append(Properties.Resources.FoundTheFollowing);
string numberOfChunksFound = parsedPrediction.Count.ToString();
stringBuilder.Append(numberOfChunksFound);
stringBuilder.Append(isNumber ? Properties.Resources.FoundNumbers : Properties.Resources.FoundLetters);
foreach (var pred in parsedPrediction)
{
var number = pred.PredictedValue;
var probability = pred.PredictionPercentage;
stringBuilder.Append(number);
stringBuilder.Append(Properties.Resources.Probability);
stringBuilder.Append(probability);
stringBuilder.Append(Properties.Resources.Percent);
stringBuilder.Append(Properties.Resources.Comma);
}
}
stringBuilder.Append(Properties.Resources.ThatIsAll);
return(stringBuilder.ToString());
}
return(Properties.Resources.ThereHasBeenAnError);
}
/// <summary>
/// Initializes the ProcessStartInfo that is used for the object detection process by setting the command as script to execute
/// and the arguments as additional information for the python script to process.
/// </summary>
/// <param name="command">The python script that is executed as own process.</param>
/// <param name="args">Additional (console) arguments that are passed to the process.</param>
/// <returns>ProcessStartInfo that is used when starting the process.</returns>
private ProcessStartInfo InitProcessStartInfo(string command, string args)
{
var tfModelMode = new SettingsController().GetTfModelMode();
ProcessStartInfo processStartInfo = new ProcessStartInfo
{
FileName = new SettingsController().GetPythonInterpreterPath(), //custom path from settings file
Arguments = $"\"{command}\" \"{args}\" \"{tfModelMode}\"", //arguments (image path and tfmodelmode)
UseShellExecute = false, // don't use windows cmd
CreateNoWindow = true,
RedirectStandardOutput = true, // Any output, generated by application will be redirected back
RedirectStandardError = true // Any error in standard output will be redirected back (for example exceptions)
};
return(processStartInfo);
}