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/*
* 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 "ImageUtils.hpp"
#include "DetectionResult.hpp"
#include "YoloFastestModel.hpp"
#include "BaseProcessing.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;
};
} /* namespace object_detection */
/**
* @brief Post-processing class for Object Detection use case.
* Implements methods declared by BasePostProcess and anything else needed
* to populate result vector.
*/
class DetectorPostProcess : public BasePostProcess {
public:
/**
* @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 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 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.
**/
bool DoPostProcess() override;
private:
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.
* @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(object_detection::Network& net,
int imageWidth,
int imageHeight,
float threshold,
std::forward_list<image::Detection>& detections);
};
} /* namespace app */
} /* namespace arm */
#endif /* DETECTOR_POST_PROCESSING_HPP */
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