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CNNThreshold removed for Analog100 and Digital100
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@@ -811,34 +811,21 @@ bool ClassFlowCNNGeneral::doNeuralNetwork(string time)
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case Analogue100:
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{
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int _num;
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float _fit;
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float _result_save_file;
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tflite->LoadInputImageBasis(GENERAL[_ana]->ROI[i]->image);
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tflite->Invoke();
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_num = tflite->GetOutClassification();
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_fit = tflite->GetOutputValue(_num);
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GENERAL[_ana]->ROI[i]->result_float = (float)_num / 10.0;
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_result_save_file = GENERAL[_ana]->ROI[i]->result_float;
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if (_fit < CNNGoodThreshold)
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{
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GENERAL[_ana]->ROI[i]->isReject = true;
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GENERAL[_ana]->ROI[i]->result_float = -1;
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_result_save_file+= 100; // Für den Fall, dass fit nicht ausreichend, soll trotzdem das Ergebnis mit "-10x.y" abgespeichert werden.
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string zw = "Value Rejected due to Threshold (Fit: " + to_string(_fit) + "Threshold: " + to_string(CNNGoodThreshold);
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printf("Value Rejected due to Threshold (Fit: %f, Threshold: %f\n", _fit, CNNGoodThreshold);
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LogFile.WriteToFile(zw);
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}
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else
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{
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GENERAL[_ana]->ROI[i]->isReject = false;
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}
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GENERAL[_ana]->ROI[i]->isReject = false;
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printf("Result General(Analog)%i: %f\n", i, GENERAL[_ana]->ROI[i]->result_float);
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if (isLogImage)
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@@ -88,5 +88,14 @@ void test_ZeigerEvalHybrid() {
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// the 4.4 (digital100) is not above 5 and the previous digit (analog) not over Zero (9.5)
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TEST_ASSERT_EQUAL(4, undertest.ZeigerEvalHybrid(4.5, 9.5, 9));
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// 59.96889 - Pre: 58.94888
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// 8.6 : 9.8 : 6.7
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// the 4.4 (digital100) is not above 5 and the previous digit (analog) not over Zero (9.5)
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TEST_ASSERT_EQUAL(8, undertest.ZeigerEvalHybrid(8.6, 9.8, 9));
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// issue #879 vorgaenger is -1, zahl = 6.7
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//TEST_ASSERT_EQUAL(7, undertest.ZeigerEvalHybrid(6.7, -1.0, -1));
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}
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