diff options
Diffstat (limited to 'source/use_case/object_detection/include')
| -rw-r--r-- | source/use_case/object_detection/include/DetectorPostProcessing.hpp | 88 | ||||
| -rw-r--r-- | source/use_case/object_detection/include/DetectorPreProcessing.hpp | 60 |
2 files changed, 102 insertions, 46 deletions
diff --git a/source/use_case/object_detection/include/DetectorPostProcessing.hpp b/source/use_case/object_detection/include/DetectorPostProcessing.hpp index cdb14f5..b3ddb2c 100644 --- a/source/use_case/object_detection/include/DetectorPostProcessing.hpp +++ b/source/use_case/object_detection/include/DetectorPostProcessing.hpp @@ -21,11 +21,13 @@ #include "ImageUtils.hpp" #include "DetectionResult.hpp" #include "YoloFastestModel.hpp" +#include "BaseProcessing.hpp" #include <forward_list> namespace arm { namespace app { + namespace object_detection { struct Branch { @@ -46,42 +48,55 @@ namespace object_detection { int topN; }; +} /* namespace object_detection */ + /** - * @brief Helper class to manage tensor post-processing for "object_detection" - * output. + * @brief Post-processing class for Object Detection use case. + * Implements methods declared by BasePostProcess and anything else needed + * to populate result vector. */ - class DetectorPostprocessing { + class DetectorPostProcess : public BasePostProcess { public: /** - * @brief Constructor. - * @param[in] threshold Post-processing threshold. - * @param[in] nms Non-maximum Suppression threshold. - * @param[in] numClasses Number of classes. - * @param[in] topN Top N for each class. + * @brief Constructor. + * @param[in] outputTensor0 Pointer to the TFLite Micro output Tensor at index 0. + * @param[in] outputTensor1 Pointer to the TFLite Micro output Tensor at index 1. + * @param[out] results Vector of detected results. + * @param[in] inputImgRows Number of rows in the input image. + * @param[in] inputImgCols Number of columns in the input image. + * @param[in] threshold Post-processing threshold. + * @param[in] nms Non-maximum Suppression threshold. + * @param[in] numClasses Number of classes. + * @param[in] topN Top N for each class. **/ - explicit DetectorPostprocessing(float threshold = 0.5f, - float nms = 0.45f, - int numClasses = 1, - int topN = 0); + explicit DetectorPostProcess(TfLiteTensor* outputTensor0, + TfLiteTensor* outputTensor1, + std::vector<object_detection::DetectionResult>& results, + int inputImgRows, + int inputImgCols, + float threshold = 0.5f, + float nms = 0.45f, + int numClasses = 1, + int topN = 0); /** - * @brief Post processing part of YOLO object detection CNN. - * @param[in] imgRows Number of rows in the input image. - * @param[in] imgCols Number of columns in the input image. - * @param[in] modelOutput Output tensors after CNN invoked. - * @param[out] resultsOut Vector of detected results. + * @brief Should perform YOLO post-processing of the result of inference then + * populate Detection result data for any later use. + * @return true if successful, false otherwise. **/ - void RunPostProcessing(uint32_t imgRows, - uint32_t imgCols, - TfLiteTensor* modelOutput0, - TfLiteTensor* modelOutput1, - std::vector<DetectionResult>& resultsOut); + bool DoPostProcess() override; private: - float m_threshold; /* Post-processing threshold */ - float m_nms; /* NMS threshold */ - int m_numClasses; /* Number of classes */ - int m_topN; /* TopN */ + TfLiteTensor* m_outputTensor0; /* Output tensor index 0 */ + TfLiteTensor* m_outputTensor1; /* Output tensor index 1 */ + std::vector<object_detection::DetectionResult>& m_results; /* Single inference results. */ + int m_inputImgRows; /* Number of rows for model input. */ + int m_inputImgCols; /* Number of cols for model input. */ + float m_threshold; /* Post-processing threshold. */ + float m_nms; /* NMS threshold. */ + int m_numClasses; /* Number of classes. */ + int m_topN; /* TopN. */ + object_detection::Network m_net; /* YOLO network object. */ /** * @brief Insert the given Detection in the list. @@ -98,32 +113,13 @@ namespace object_detection { * @param[in] threshold Detections threshold. * @param[out] detections Detection boxes. **/ - void GetNetworkBoxes(Network& net, + void GetNetworkBoxes(object_detection::Network& net, int imageWidth, int imageHeight, float threshold, std::forward_list<image::Detection>& detections); - - /** - * @brief Draw on the given image a bounding box starting at (boxX, boxY). - * @param[in/out] imgIn Image. - * @param[in] imWidth Image width. - * @param[in] imHeight Image height. - * @param[in] boxX Axis X starting point. - * @param[in] boxY Axis Y starting point. - * @param[in] boxWidth Box width. - * @param[in] boxHeight Box height. - **/ - void DrawBoxOnImage(uint8_t* imgIn, - int imWidth, - int imHeight, - int boxX, - int boxY, - int boxWidth, - int boxHeight); }; -} /* namespace object_detection */ } /* namespace app */ } /* namespace arm */ diff --git a/source/use_case/object_detection/include/DetectorPreProcessing.hpp b/source/use_case/object_detection/include/DetectorPreProcessing.hpp new file mode 100644 index 0000000..4936048 --- /dev/null +++ b/source/use_case/object_detection/include/DetectorPreProcessing.hpp @@ -0,0 +1,60 @@ +/* + * Copyright (c) 2022 Arm Limited. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef DETECTOR_PRE_PROCESSING_HPP +#define DETECTOR_PRE_PROCESSING_HPP + +#include "BaseProcessing.hpp" +#include "Classifier.hpp" + +namespace arm { +namespace app { + + /** + * @brief Pre-processing class for Object detection use case. + * Implements methods declared by BasePreProcess and anything else needed + * to populate input tensors ready for inference. + */ + class DetectorPreProcess : public BasePreProcess { + + public: + /** + * @brief Constructor + * @param[in] inputTensor Pointer to the TFLite Micro input Tensor. + * @param[in] rgb2Gray Convert image from 3 channel RGB to 1 channel grayscale. + * @param[in] convertToInt8 Convert the image from uint8 to int8 range. + **/ + explicit DetectorPreProcess(TfLiteTensor* inputTensor, bool rgb2Gray, bool convertToInt8); + + /** + * @brief Should perform pre-processing of 'raw' input image data and load it into + * TFLite Micro input tensor ready for inference + * @param[in] input Pointer to the data that pre-processing will work on. + * @param[in] inputSize Size of the input data. + * @return true if successful, false otherwise. + **/ + bool DoPreProcess(const void* input, size_t inputSize) override; + + private: + TfLiteTensor* m_inputTensor; + bool m_rgb2Gray; + bool m_convertToInt8; + }; + +} /* namespace app */ +} /* namespace arm */ + +#endif /* DETECTOR_PRE_PROCESSING_HPP */
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