object_detector.h

//   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
//
// 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.

#pragma once

#include <ctime>
#include <memory>
#include <string>
#include <utility>
#include <vector>

#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>

#include "paddle_inference_api.h" // NOLINT

#include "include/preprocess_op_det.h"
#include "include/yaml_config.h"

using namespace paddle_infer;

namespace Detection {
// Object Detection Result
    struct ObjectResult {
        // Rectangle coordinates of detected object: left, right, top, down
        std::vector<int> rect;
        // Class id of detected object
        int class_id;
        // Confidence of detected object
        float confidence;
    };

// Generate visualization colormap for each class
    std::vector<int> GenerateColorMap(int num_class);

// Visualiztion Detection Result
    cv::Mat VisualizeResult(const cv::Mat &img,
                            const std::vector <ObjectResult> &results,
                            const std::vector <std::string> &lables,
                            const std::vector<int> &colormap, const bool is_rbox);

    class ObjectDetector {
    public:
        explicit ObjectDetector(const YAML::Node &config_file) {
            this->use_gpu_ = config_file["Global"]["use_gpu"].as<bool>();
            if (config_file["Global"]["gpu_id"].IsDefined())
                this->gpu_id_ = config_file["Global"]["gpu_id"].as<int>();
            this->gpu_mem_ = config_file["Global"]["gpu_mem"].as<int>();
            this->cpu_math_library_num_threads_ =
                    config_file["Global"]["cpu_num_threads"].as<int>();
            this->use_mkldnn_ = config_file["Global"]["enable_mkldnn"].as<bool>();
            this->use_tensorrt_ = config_file["Global"]["use_tensorrt"].as<bool>();
            this->use_fp16_ = config_file["Global"]["use_fp16"].as<bool>();
            this->model_dir_ =
                    config_file["Global"]["det_inference_model_dir"].as<std::string>();
            this->threshold_ = config_file["Global"]["threshold"].as<float>();
            this->max_det_results_ = config_file["Global"]["max_det_results"].as<int>();
            this->image_shape_ =
                    config_file["Global"]["image_shape"].as < std::vector < int >> ();
            this->label_list_ =
                    config_file["Global"]["labe_list"].as < std::vector < std::string >> ();
            this->ir_optim_ = config_file["Global"]["ir_optim"].as<bool>();
            this->batch_size_ = config_file["Global"]["batch_size"].as<int>();

            preprocessor_.Init(config_file["DetPreProcess"]["transform_ops"]);
            LoadModel(model_dir_, batch_size_, run_mode);
        }

        // Load Paddle inference model
        void LoadModel(const std::string &model_dir, const int batch_size = 1,
                       const std::string &run_mode = "fluid");

        // Run predictor
        void Predict(const std::vector <cv::Mat> imgs, const int warmup = 0,
                     const int repeats = 1,
                     std::vector <ObjectResult> *result = nullptr,
                     std::vector<int> *bbox_num = nullptr,
                     std::vector<double> *times = nullptr);

        const std::vector <std::string> &GetLabelList() const {
            return this->label_list_;
        }

        const float &GetThreshold() const { return this->threshold_; }

    private:
        bool use_gpu_ = true;
        int gpu_id_ = 0;
        int gpu_mem_ = 800;
        int cpu_math_library_num_threads_ = 6;
        std::string run_mode = "fluid";
        bool use_mkldnn_ = false;
        bool use_tensorrt_ = false;
        bool batch_size_ = 1;
        bool use_fp16_ = false;
        std::string model_dir_;
        float threshold_ = 0.5;
        float max_det_results_ = 5;
        std::vector<int> image_shape_ = {3, 640, 640};
        std::vector <std::string> label_list_;
        bool ir_optim_ = true;
        bool det_permute_ = true;
        bool det_postprocess_ = true;
        int min_subgraph_size_ = 30;
        bool use_dynamic_shape_ = false;
        int trt_min_shape_ = 1;
        int trt_max_shape_ = 1280;
        int trt_opt_shape_ = 640;
        bool trt_calib_mode_ = false;

        // Preprocess image and copy data to input buffer
        void Preprocess(const cv::Mat &image_mat);

        // Postprocess result
        void Postprocess(const std::vector <cv::Mat> mats,
                         std::vector <ObjectResult> *result, std::vector<int> bbox_num,
                         bool is_rbox);

        std::shared_ptr <Predictor> predictor_;
        Preprocessor preprocessor_;
        ImageBlob inputs_;
        std::vector<float> output_data_;
        std::vector<int> out_bbox_num_data_;
    };

} // namespace Detection