| /* |
| * 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 <forward_list> |
| |
| 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<Branch> 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. |
| **/ |
| explicit 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] 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(uint32_t imgRows, |
| uint32_t imgCols, |
| TfLiteTensor* modelOutput0, |
| TfLiteTensor* modelOutput1, |
| std::vector<DetectionResult>& 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<image::Detection>& 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<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 */ |
| |
| #endif /* DETECTOR_POST_PROCESSING_HPP */ |