/// <summary>
/// Uses the Tensorflow model to predict the most exciting moment.
/// Returns metadata about the highlight.
/// </summary>
/// <param name="matchMetricGroup"></param>
/// <returns></returns>
public HighlightInfo GetHighlightPeriod(MatchMetricGroup matchMetricGroup, bool testConfiguration = false)
{
if (testConfiguration)
{
_deepLearnerScriptPath = ConfigurationManager.AppSettings["AltScriptsPath"] + "DeepLearningModel.py";
}
// Packages matchMetricGroup info into a chunked up form that Tensorflow can understand and creates a csv file for the Tensorflow python script to reference.
var(matchPath, predictedDataPath) = PrepareMatchForTensorFlow(matchMetricGroup, false);
// Runs the python script that outputs a csv file for the predictions the Tensorflow model made about the excitement level at a particular time in the match.
GetHighlightInfo(matchPath, predictedDataPath);
// Load in the predictions made by the model.
List <string> predictedDataRaw;
try
{
predictedDataRaw = File.ReadAllLines(_tensorflowPath + "Predictions\\" + predictedDataPath).ToList();
}
catch (Exception)
{
// Very rare edge case when multiple parallel uses of the model can cause a failure to predict.
GetHighlightInfo(matchPath, predictedDataPath);
predictedDataRaw = File.ReadAllLines(_tensorflowPath + "Predictions\\" + predictedDataPath).ToList();
}
Console.WriteLine(predictedDataPath + " Complete.");
List <double> predictedData = new List <double>();
List <double> matchOffset = new List <double>();
var matchAnalyzer = new MatchAnalyzer();
// Parse predicted data into relevant objects. reference is via an offset from the original raw Broadcast video start time.
var counter = 0.0;
foreach (var line in predictedDataRaw)
{
predictedData.Add(double.Parse(line));
matchOffset.Add(matchAnalyzer.ConvertVideoTimeToMatchOffset(counter * 15, matchMetricGroup.Match));
counter += 1;
}
// Find the most exciting period in the match and its score.
var highestScore = 0.0;
var index = 0;
var highestScoreIndex = 0;
foreach (var score in predictedData)
{
if (score > highestScore)
{
highestScore = score;
highestScoreIndex = index;
}
index += 1;
}
// We offset the start by an additional 90 seconds to account for time slippage.
return(new HighlightInfo(matchOffset[highestScoreIndex] + 90, 40, highestScore));
}
