/* * 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_POST_PROCESSING_HPP #define DETECTOR_POST_PROCESSING_HPP #include "UseCaseCommonUtils.hpp" #include "ImageUtils.hpp" #include "DetectionResult.hpp" #include "YoloFastestModel.hpp" #include namespace arm { namespace app { namespace object_detection { struct Branch { int resolution; int numBox; const float* anchor; int8_t* modelOutput; float scale; int zeroPoint; size_t size; }; struct Network { int inputWidth; int inputHeight; int numClasses; std::vector branches; int topN; }; /** * @brief Helper class to manage tensor post-processing for "object_detection" * output. */ class DetectorPostprocessing { 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. **/ DetectorPostprocessing(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] imgIn Pointer to the input image,detection bounding boxes drown on it. * @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. **/ void RunPostProcessing(uint8_t* imgIn, uint32_t imgRows, uint32_t imgCols, TfLiteTensor* modelOutput0, TfLiteTensor* modelOutput1, std::vector& resultsOut); private: float m_threshold; /* Post-processing threshold */ float m_nms; /* NMS threshold */ int m_numClasses; /* Number of classes */ int m_topN; /* TopN */ /** * @brief Insert the given Detection in the list. * @param[in] detections List of detections. * @param[in] det Detection to be inserted. **/ void InsertTopNDetections(std::forward_list& detections, image::Detection& det); /** * @brief Given a Network calculate the detection boxes. * @param[in] net Network. * @param[in] imageWidth Original image width. * @param[in] imageHeight Original image height. * @param[in] threshold Detections threshold. * @param[out] detections Detection boxes. **/ void GetNetworkBoxes(Network& net, int imageWidth, int imageHeight, float threshold, std::forward_list& 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 */ #endif /* DETECTOR_POST_PROCESSING_HPP */