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// Copyright (c) 2021 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_api.h" // NOLINT
#include "include/config_parser.h"
#include "include/keypoint_postprocess.h"
#include "include/preprocess_op.h"
using namespace paddle::lite_api; // NOLINT
namespace PaddleDetection {
// Object KeyPoint Result
struct KeyPointResult {
// Keypoints: shape(N x 3); N: number of Joints; 3: x,y,conf
std::vector<float> keypoints;
int num_joints = -1;
};
// Visualiztion KeyPoint Result
cv::Mat VisualizeKptsResult(const cv::Mat& img,
const std::vector<KeyPointResult>& results,
const std::vector<int>& colormap,
float threshold = 0.2);
class KeyPointDetector {
public:
explicit KeyPointDetector(const std::string& model_dir,
int cpu_threads = 1,
const int batch_size = 1,
bool use_dark = true) {
config_.load_config(model_dir);
threshold_ = config_.draw_threshold_;
use_dark_ = use_dark;
preprocessor_.Init(config_.preprocess_info_);
printf("before keypoint detector\n");
LoadModel(model_dir, cpu_threads);
printf("create keypoint detector\n");
}
// Load Paddle inference model
void LoadModel(std::string model_file, int num_theads);
// Run predictor
void Predict(const std::vector<cv::Mat> imgs,
std::vector<std::vector<float>>& center,
std::vector<std::vector<float>>& scale,
const int warmup = 0,
const int repeats = 1,
std::vector<KeyPointResult>* result = nullptr,
std::vector<double>* times = nullptr);
// Get Model Label list
const std::vector<std::string>& GetLabelList() const {
return config_.label_list_;
}
bool use_dark(){return this->use_dark_;}
inline float get_threshold() {return threshold_;};
private:
// Preprocess image and copy data to input buffer
void Preprocess(const cv::Mat& image_mat);
// Postprocess result
void Postprocess(std::vector<float>& output,
std::vector<int64_t>& output_shape,
std::vector<int64_t>& idxout,
std::vector<int64_t>& idx_shape,
std::vector<KeyPointResult>* result,
std::vector<std::vector<float>>& center,
std::vector<std::vector<float>>& scale);
std::shared_ptr<PaddlePredictor> predictor_;
Preprocessor preprocessor_;
ImageBlob inputs_;
std::vector<float> output_data_;
std::vector<int64_t> idx_data_;
float threshold_;
ConfigPaser config_;
bool use_dark_;
};
} // namespace PaddleDetection
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