public Person? Clasify(byte [] image)
        {
            var featureVector = ExractFeatureVector(_waveletTransformer.Transform(image, _settings.Classficator.Ocataves, TransformFunction.Haar));

            var existingVectors = _personService
                .ReadAll(p => p.Include(o => o.Fingerprints).ThenInclude(o => o.Vector))
                .SelectMany(p => p.Fingerprints)
                .Select(f =>
                            new {
                                Person =  f.Owner,
                                Vector = TransformFunction.Haar.GetFeatureVectorFiled(f.Vector),
                                Distance = CalculateDistance(TransformFunction.Haar.GetFeatureVectorFiled(f.Vector), featureVector)
                            });

            return existingVectors?.OrderBy(v => v.Distance).Take(_settings.Classficator.K).GroupBy(v => v.Person).Max().Key;
        }

        public Fingerprint Train(Fingerprint fingerprint)
        {
            var result = new FeatureVector();
            foreach (var function in new TransformFunction[] { TransformFunction.Haar, TransformFunction.D4 })
            {
                var value = _waveletTransformer.Transform(fingerprint.Image, _settings.Classficator.Ocataves, function);
                function.SetFeatureVectorField(result, ExractFeatureVector(value));
                _logger.LogInformation($"Vector generated for {fingerprint.Hand} - {fingerprint.Finger} for function {function.ToString()}.");
            }
            fingerprint.Vector = result;
            return fingerprint;
        }