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Diffstat (limited to 'samples/ObjectDetection/include/ObjectDetectionPipeline.hpp')
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diff --git a/samples/ObjectDetection/include/ObjectDetectionPipeline.hpp b/samples/ObjectDetection/include/ObjectDetectionPipeline.hpp new file mode 100644 index 0000000000..38de65b007 --- /dev/null +++ b/samples/ObjectDetection/include/ObjectDetectionPipeline.hpp @@ -0,0 +1,148 @@ +// +// 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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