forked from PulseFocusPlatform/PulseFocusPlatform
139 lines
4.2 KiB
C++
139 lines
4.2 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 <string>
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#include <vector>
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#include <memory>
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#include <utility>
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#include <ctime>
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#include <opencv2/core/core.hpp>
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#include <opencv2/imgproc/imgproc.hpp>
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#include <opencv2/highgui/highgui.hpp>
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#include "paddle_inference_api.h" // NOLINT
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#include "include/preprocess_op.h"
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#include "include/config_parser.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,
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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 std::string& model_dir,
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bool use_gpu=false,
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bool use_mkldnn=false,
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int cpu_threads=1,
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const std::string& run_mode="fluid",
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const int batch_size=1,
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const int gpu_id=0,
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bool use_dynamic_shape=false,
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const int trt_min_shape=1,
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const int trt_max_shape=1280,
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const int trt_opt_shape=640,
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bool trt_calib_mode=false) {
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this->use_gpu_ = use_gpu;
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this->gpu_id_ = gpu_id;
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this->cpu_math_library_num_threads_ = cpu_threads;
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this->use_mkldnn_ = use_mkldnn;
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this->use_dynamic_shape_ = use_dynamic_shape;
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this->trt_min_shape_ = trt_min_shape;
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this->trt_max_shape_ = trt_max_shape;
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this->trt_opt_shape_ = trt_opt_shape;
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this->trt_calib_mode_ = trt_calib_mode;
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config_.load_config(model_dir);
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this->min_subgraph_size_ = config_.min_subgraph_size_;
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threshold_ = config_.draw_threshold_;
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preprocessor_.Init(config_.preprocess_info_);
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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(
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const std::string& model_dir,
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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,
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const double threshold = 0.5,
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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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// 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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bool use_gpu_ = false;
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int gpu_id_ = 0;
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int cpu_math_library_num_threads_ = 1;
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bool use_mkldnn_ = false;
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int min_subgraph_size_ = 3;
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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(
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const std::vector<cv::Mat> mats,
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std::vector<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<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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float threshold_;
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ConfigPaser config_;
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};
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} // namespace PaddleDetection
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