136 lines
4.7 KiB
C++
136 lines
4.7 KiB
C++
// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#pragma once
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#include <ctime>
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#include <memory>
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#include <string>
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#include <utility>
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#include <vector>
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#include <opencv2/core/core.hpp>
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#include <opencv2/highgui/highgui.hpp>
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#include <opencv2/imgproc/imgproc.hpp>
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#include "paddle_inference_api.h" // NOLINT
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#include "include/preprocess_op_det.h"
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#include "include/yaml_config.h"
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using namespace paddle_infer;
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namespace PaddleDetection {
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// Object Detection Result
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struct ObjectResult {
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// Rectangle coordinates of detected object: left, right, top, down
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std::vector<int> rect;
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// Class id of detected object
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int class_id;
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// Confidence of detected object
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float confidence;
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};
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// Generate visualization colormap for each class
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std::vector<int> GenerateColorMap(int num_class);
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// Visualiztion Detection Result
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cv::Mat VisualizeResult(const cv::Mat &img,
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const std::vector<ObjectResult> &results,
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const std::vector<std::string> &lables,
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const std::vector<int> &colormap, const bool is_rbox);
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class ObjectDetector {
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public:
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explicit ObjectDetector(const YAML::Node &config_file) {
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this->use_gpu_ = config_file["Global"]["use_gpu"].as<bool>();
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if (config_file["Global"]["gpu_id"].IsDefined())
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this->gpu_id_ = config_file["Global"]["gpu_id"].as<int>();
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this->gpu_mem_ = config_file["Global"]["gpu_mem"].as<int>();
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this->cpu_math_library_num_threads_ =
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config_file["Global"]["cpu_num_threads"].as<int>();
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this->use_mkldnn_ = config_file["Global"]["enable_mkldnn"].as<bool>();
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this->use_tensorrt_ = config_file["Global"]["use_tensorrt"].as<bool>();
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this->use_fp16_ = config_file["Global"]["use_fp16"].as<bool>();
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this->model_dir_ =
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config_file["Global"]["det_inference_model_dir"].as<std::string>();
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this->threshold_ = config_file["Global"]["threshold"].as<float>();
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this->max_det_results_ = config_file["Global"]["max_det_results"].as<int>();
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this->image_shape_ =
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config_file["Global"]["image_shape"].as<std::vector<int>>();
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this->label_list_ =
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config_file["Global"]["labe_list"].as<std::vector<std::string>>();
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this->ir_optim_ = config_file["Global"]["ir_optim"].as<bool>();
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this->batch_size_ = config_file["Global"]["batch_size"].as<int>();
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preprocessor_.Init(config_file["DetPreProcess"]["transform_ops"]);
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LoadModel(model_dir_, batch_size_, run_mode);
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}
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// Load Paddle inference model
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void LoadModel(const std::string &model_dir, const int batch_size = 1,
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const std::string &run_mode = "fluid");
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// Run predictor
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void Predict(const std::vector<cv::Mat> imgs, const int warmup = 0,
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const int repeats = 1,
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std::vector<ObjectResult> *result = nullptr,
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std::vector<int> *bbox_num = nullptr,
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std::vector<double> *times = nullptr);
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const std::vector<std::string> &GetLabelList() const {
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return this->label_list_;
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}
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const float &GetThreshold() const { return this->threshold_; }
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private:
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bool use_gpu_ = true;
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int gpu_id_ = 0;
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int gpu_mem_ = 800;
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int cpu_math_library_num_threads_ = 6;
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std::string run_mode = "fluid";
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bool use_mkldnn_ = false;
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bool use_tensorrt_ = false;
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bool batch_size_ = 1;
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bool use_fp16_ = false;
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std::string model_dir_;
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float threshold_ = 0.5;
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float max_det_results_ = 5;
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std::vector<int> image_shape_ = {3, 640, 640};
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std::vector<std::string> label_list_;
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bool ir_optim_ = true;
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bool det_permute_ = true;
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bool det_postprocess_ = true;
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int min_subgraph_size_ = 30;
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bool use_dynamic_shape_ = false;
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int trt_min_shape_ = 1;
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int trt_max_shape_ = 1280;
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int trt_opt_shape_ = 640;
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bool trt_calib_mode_ = false;
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// Preprocess image and copy data to input buffer
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void Preprocess(const cv::Mat &image_mat);
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// Postprocess result
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void Postprocess(const std::vector<cv::Mat> mats,
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std::vector<ObjectResult> *result, std::vector<int> bbox_num,
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bool is_rbox);
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std::shared_ptr<Predictor> predictor_;
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Preprocessor preprocessor_;
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ImageBlob inputs_;
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std::vector<float> output_data_;
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std::vector<int> out_bbox_num_data_;
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};
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} // namespace PaddleDetection
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