MLECO-1252 ASR sample application using the public ArmNN C++ API.
Change-Id: I98cd505b8772a8c8fa88308121bc94135bb45068
Signed-off-by: Éanna Ó Catháin <eanna.ocathain@arm.com>
diff --git a/samples/ObjectDetection/include/ObjectDetectionPipeline.hpp b/samples/ObjectDetection/include/ObjectDetectionPipeline.hpp
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+//
+// Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#pragma once
+
+#include "ArmnnNetworkExecutor.hpp"
+#include "YoloResultDecoder.hpp"
+#include "SSDResultDecoder.hpp"
+# include "ImageUtils.hpp"
+
+#include <opencv2/opencv.hpp>
+
+namespace od
+{
+/**
+ * Generic object detection pipeline with 3 steps: data pre-processing, inference execution and inference
+ * result post-processing.
+ *
+ */
+class ObjDetectionPipeline {
+public:
+
+ /**
+ * Creates object detection pipeline with given network executor and decoder.
+ * @param executor - unique pointer to inference runner
+ * @param decoder - unique pointer to inference results decoder
+ */
+ ObjDetectionPipeline(std::unique_ptr<common::ArmnnNetworkExecutor<float>> executor,
+ std::unique_ptr<IDetectionResultDecoder> decoder);
+
+ /**
+ * @brief Standard image pre-processing implementation.
+ *
+ * Re-sizes an image keeping aspect ratio, pads if necessary to fit the network input layer dimensions.
+
+ * @param[in] frame - input image, expected data type is uint8.
+ * @param[out] processed - output image, data type is preserved.
+ */
+ virtual void PreProcessing(const cv::Mat& frame, cv::Mat& processed);
+
+ /**
+ * @brief Executes inference
+ *
+ * Calls inference runner provided during instance construction.
+ *
+ * @param[in] processed - input inference data. Data type should be aligned with input tensor.
+ * @param[out] result - raw floating point inference results.
+ */
+ virtual void Inference(const cv::Mat& processed, common::InferenceResults<float>& result);
+
+ /**
+ * @brief Standard inference results post-processing implementation.
+ *
+ * Decodes inference results using decoder provided during construction.
+ *
+ * @param[in] inferenceResult - inference results to be decoded.
+ * @param[in] callback - a function to be called after successful inference results decoding.
+ */
+ virtual void PostProcessing(common::InferenceResults<float>& inferenceResult,
+ const std::function<void (DetectedObjects)>& callback);
+
+protected:
+ std::unique_ptr<common::ArmnnNetworkExecutor<float>> m_executor;
+ std::unique_ptr<IDetectionResultDecoder> m_decoder;
+ common::Size m_inputImageSize{};
+ cv::Mat m_processedFrame;
+};
+
+/**
+ * Specific to Yolo v3 tiny object detection pipeline implementation.
+ */
+class YoloV3Tiny: public ObjDetectionPipeline{
+public:
+
+ /**
+ * Constructs object detection pipeline for Yolo v3 tiny network.
+ *
+ * Network input is expected to be uint8 or fp32. Data range [0, 255].
+ * Network output is FP32.
+ *
+ * @param executor[in] - unique pointer to inference runner
+ * @param NMSThreshold[in] - non max suppression threshold for decoding step
+ * @param ClsThreshold[in] - class probability threshold for decoding step
+ * @param ObjectThreshold[in] - detected object score threshold for decoding step
+ */
+ YoloV3Tiny(std::unique_ptr<common::ArmnnNetworkExecutor<float>> executor,
+ float NMSThreshold, float ClsThreshold, float ObjectThreshold);
+
+ /**
+ * @brief Yolo v3 tiny image pre-processing implementation.
+ *
+ * On top of the standard pre-processing, converts input data type according to the network input tensor data type.
+ * Supported data types: uint8 and float32.
+ *
+ * @param[in] original - input image data
+ * @param[out] processed - image data ready to be used for inference.
+ */
+ void PreProcessing(const cv::Mat& original, cv::Mat& processed);
+
+};
+
+/**
+ * Specific to MobileNet SSD v1 object detection pipeline implementation.
+ */
+class MobileNetSSDv1: public ObjDetectionPipeline {
+
+public:
+ /**
+ * Constructs object detection pipeline for MobileNet SSD network.
+ *
+ * Network input is expected to be uint8 or fp32. Data range [-1, 1].
+ * Network output is FP32.
+ *
+ * @param[in] - unique pointer to inference runner
+ * @paramp[in] objectThreshold - detected object score threshold for decoding step
+ */
+ MobileNetSSDv1(std::unique_ptr<common::ArmnnNetworkExecutor<float>> executor,
+ float objectThreshold);
+
+ /**
+ * @brief MobileNet SSD image pre-processing implementation.
+ *
+ * On top of the standard pre-processing, converts input data type according to the network input tensor data type
+ * and scales input data from [0, 255] to [-1, 1] for FP32 input.
+ *
+ * Supported input data types: uint8 and float32.
+ *
+ * @param[in] original - input image data
+ * @param processed[out] - image data ready to be used for inference.
+ */
+ void PreProcessing(const cv::Mat& original, cv::Mat& processed);
+
+};
+
+using IPipelinePtr = std::unique_ptr<od::ObjDetectionPipeline>;
+
+/**
+ * Constructs object detection pipeline based on configuration provided.
+ *
+ * @param[in] config - object detection pipeline configuration.
+ *
+ * @return unique pointer to object detection pipeline.
+ */
+IPipelinePtr CreatePipeline(common::PipelineOptions& config);
+
+}// namespace od
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