103 lines
3.2 KiB
C++
103 lines
3.2 KiB
C++
// Copyright (c) 2021 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 <stdlib.h>
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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 "json/json.h"
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#include "paddle_api.h" // NOLINT
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#include "include/config_parser.h"
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#include "include/preprocess_op.h"
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#include "include/utils.h"
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#include "include/picodet_postprocess.h"
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using namespace paddle::lite_api; // NOLINT
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namespace PPShiTu {
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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<PPShiTu::ObjectResult>& results,
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const std::vector<std::string>& lables,
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const std::vector<int>& colormap,
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const bool is_rbox);
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class ObjectDetector {
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public:
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explicit ObjectDetector(const Json::Value& config,
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const std::string& model_dir,
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int cpu_threads = 1,
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const int batch_size = 1) {
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config_.load_config(config);
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printf("config created\n");
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preprocessor_.Init(config_.preprocess_info_);
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printf("before object detector\n");
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if(config["Global"]["det_model_path"].as<std::string>().empty()){
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std::cout << "Please set [det_model_path] in config file" << std::endl;
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exit(-1);
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}
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LoadModel(config["Global"]["det_model_path"].as<std::string>(), cpu_threads);
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printf("create object detector\n"); }
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// Load Paddle inference model
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void LoadModel(std::string model_file, int num_theads);
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// Run predictor
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void Predict(const std::vector<cv::Mat>& imgs,
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const int warmup = 0,
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const int repeats = 1,
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std::vector<PPShiTu::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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// Get Model Label list
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const std::vector<std::string>& GetLabelList() const {
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return config_.label_list_;
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}
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private:
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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<PPShiTu::ObjectResult>* result,
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std::vector<int> bbox_num,
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bool is_rbox);
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std::shared_ptr<PaddlePredictor> 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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float threshold_;
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ConfigPaser config_;
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};
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} // namespace PPShiTu
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