1 files changed, 102 insertions, 0 deletions
diff --git a/samples/ObjectDetection/src/NetworkPipeline.cpp b/samples/ObjectDetection/src/NetworkPipeline.cpp
new file mode 100644
index 0000000000..7f05882fc4
--- /dev/null
+++ b/samples/ObjectDetection/src/NetworkPipeline.cpp
@@ -0,0 +1,102 @@
+//
+// Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "NetworkPipeline.hpp"
+#include "ImageUtils.hpp"
+
+namespace od
+{
+
+ObjDetectionPipeline::ObjDetectionPipeline(std::unique_ptr<ArmnnNetworkExecutor> executor,
+                                           std::unique_ptr<IDetectionResultDecoder> decoder) :
+        m_executor(std::move(executor)),
+        m_decoder(std::move(decoder)){}
+
+void od::ObjDetectionPipeline::Inference(const cv::Mat& processed, InferenceResults& result)
+{
+    m_executor->Run(processed.data, processed.total() * processed.elemSize(), result);
+}
+
+void ObjDetectionPipeline::PostProcessing(InferenceResults& inferenceResult,
+        const std::function<void (DetectedObjects)>& callback)
+{
+    DetectedObjects detections = m_decoder->Decode(inferenceResult, m_inputImageSize,
+                                           m_executor->GetImageAspectRatio(), {});
+    if (callback)
+    {
+        callback(detections);
+    }
+}
+
+void ObjDetectionPipeline::PreProcessing(const cv::Mat& frame, cv::Mat& processed)
+{
+    m_inputImageSize.m_Height = frame.rows;
+    m_inputImageSize.m_Width = frame.cols;
+    ResizeWithPad(frame, processed, m_processedFrame, m_executor->GetImageAspectRatio());
+}
+
+MobileNetSSDv1::MobileNetSSDv1(std::unique_ptr<ArmnnNetworkExecutor> executor,
+                               float objectThreshold) :
+        ObjDetectionPipeline(std::move(executor),
+                             std::make_unique<SSDResultDecoder>(objectThreshold))
+{}
+
+void MobileNetSSDv1::PreProcessing(const cv::Mat& frame, cv::Mat& processed)
+{
+    ObjDetectionPipeline::PreProcessing(frame, processed);
+    if (m_executor->GetInputDataType() == armnn::DataType::Float32)
+    {
+        // [0, 255] => [-1.0, 1.0]
+        processed.convertTo(processed, CV_32FC3, 1 / 127.5, -1);
+    }
+}
+
+YoloV3Tiny::YoloV3Tiny(std::unique_ptr<ArmnnNetworkExecutor> executor,
+                       float NMSThreshold, float ClsThreshold, float ObjectThreshold) :
+        ObjDetectionPipeline(std::move(executor),
+                             std::move(std::make_unique<YoloResultDecoder>(NMSThreshold,
+                                                                           ClsThreshold,
+                                                                           ObjectThreshold)))
+{}
+
+void YoloV3Tiny::PreProcessing(const cv::Mat& frame, cv::Mat& processed)
+{
+    ObjDetectionPipeline::PreProcessing(frame, processed);
+    if (m_executor->GetInputDataType() == armnn::DataType::Float32)
+    {
+        processed.convertTo(processed, CV_32FC3);
+    }
+}
+
+IPipelinePtr CreatePipeline(od::ODPipelineOptions& config)
+{
+    auto executor = std::make_unique<od::ArmnnNetworkExecutor>(config.m_ModelFilePath, config.m_backends);
+
+    if (config.m_ModelName == "SSD_MOBILE")
+    {
+        float detectionThreshold = 0.6;
+
+        return std::make_unique<od::MobileNetSSDv1>(std::move(executor),
+                                                    detectionThreshold
+        );
+    }
+    else if (config.m_ModelName == "YOLO_V3_TINY")
+    {
+        float NMSThreshold = 0.6f;
+        float ClsThreshold = 0.6f;
+        float ObjectThreshold = 0.6f;
+        return std::make_unique<od::YoloV3Tiny>(std::move(executor),
+                                                NMSThreshold,
+                                                ClsThreshold,
+                                                ObjectThreshold
+        );
+    }
+    else
+    {
+        throw std::invalid_argument("Unknown Model name: " + config.m_ModelName + " supplied by user.");
+    }
+
+}
+}// namespace od
+\ No newline at end of file