update code

deploy/cpp/include/jde_detector.h

+//   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_inference_api.h"  // NOLINT
+
+#include "include/config_parser.h"
+#include "include/preprocess_op.h"
+#include "include/tracker.h"
+
+using namespace paddle_infer;
+
+namespace PaddleDetection {
+// JDE Detection Result
+struct MOT_Rect {
+  float left;
+  float top;
+  float right;
+  float bottom;
+};
+
+struct MOT_Track {
+  int ids;
+  float score;
+  MOT_Rect rects;
+};
+
+typedef std::vector<MOT_Track> MOT_Result;
+
+// Generate visualization color
+cv::Scalar GetColor(int idx);
+
+// Visualiztion Detection Result
+cv::Mat VisualizeTrackResult(const cv::Mat& img,
+                             const MOT_Result& results,
+                             const float fps,
+                             const int frame_id);
+
+class JDEDetector {
+ public:
+  explicit JDEDetector(const std::string& model_dir,
+                       const std::string& device = "CPU",
+                       bool use_mkldnn = false,
+                       int cpu_threads = 1,
+                       const std::string& run_mode = "paddle",
+                       const int batch_size = 1,
+                       const int gpu_id = 0,
+                       const int trt_min_shape = 1,
+                       const int trt_max_shape = 1280,
+                       const int trt_opt_shape = 640,
+                       bool trt_calib_mode = false,
+                       const int min_box_area = 200) {
+    this->device_ = device;
+    this->gpu_id_ = gpu_id;
+    this->cpu_math_library_num_threads_ = cpu_threads;
+    this->use_mkldnn_ = use_mkldnn;
+
+    this->trt_min_shape_ = trt_min_shape;
+    this->trt_max_shape_ = trt_max_shape;
+    this->trt_opt_shape_ = trt_opt_shape;
+    this->trt_calib_mode_ = trt_calib_mode;
+    config_.load_config(model_dir);
+    this->use_dynamic_shape_ = config_.use_dynamic_shape_;
+    this->min_subgraph_size_ = config_.min_subgraph_size_;
+    threshold_ = config_.draw_threshold_;
+    preprocessor_.Init(config_.preprocess_info_);
+    LoadModel(model_dir, batch_size, run_mode);
+    this->min_box_area_ = min_box_area;
+    this->conf_thresh_ = config_.conf_thresh_;
+  }
+
+  // Load Paddle inference model
+  void LoadModel(const std::string& model_dir,
+                 const int batch_size = 1,
+                 const std::string& run_mode = "paddle");
+
+  // Run predictor
+  void Predict(const std::vector<cv::Mat> imgs,
+               const double threshold = 0.5,
+               const int warmup = 0,
+               const int repeats = 1,
+               MOT_Result* result = nullptr,
+               std::vector<double>* times = nullptr);
+
+ private:
+  std::string device_ = "CPU";
+  int gpu_id_ = 0;
+  int cpu_math_library_num_threads_ = 1;
+  bool use_mkldnn_ = false;
+  int min_subgraph_size_ = 3;
+  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 cv::Mat dets, const cv::Mat emb, MOT_Result* result);
+
+  std::shared_ptr<Predictor> predictor_;
+  Preprocessor preprocessor_;
+  ImageBlob inputs_;
+  std::vector<float> bbox_data_;
+  std::vector<float> emb_data_;
+  float threshold_;
+  ConfigPaser config_;
+  float min_box_area_;
+  float conf_thresh_;
+};
+
+}  // namespace PaddleDetection

deploy/cpp/include/keypoint_detector.h

+//   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_inference_api.h"  // NOLINT
+
+#include "include/config_parser.h"
+#include "include/keypoint_postprocess.h"
+#include "include/preprocess_op.h"
+
+using namespace paddle_infer;
+
+namespace PaddleDetection {
+
+// Visualiztion KeyPoint Result
+cv::Mat VisualizeKptsResult(const cv::Mat& img,
+                            const std::vector<KeyPointResult>& results,
+                            const std::vector<int>& colormap);
+
+class KeyPointDetector {
+ public:
+  explicit KeyPointDetector(const std::string& model_dir,
+                            const std::string& device = "CPU",
+                            bool use_mkldnn = false,
+                            int cpu_threads = 1,
+                            const std::string& run_mode = "paddle",
+                            const int batch_size = 1,
+                            const int gpu_id = 0,
+                            const int trt_min_shape = 1,
+                            const int trt_max_shape = 1280,
+                            const int trt_opt_shape = 640,
+                            bool trt_calib_mode = false,
+                            bool use_dark = true) {
+    this->device_ = device;
+    this->gpu_id_ = gpu_id;
+    this->cpu_math_library_num_threads_ = cpu_threads;
+    this->use_mkldnn_ = use_mkldnn;
+    this->use_dark = use_dark;
+
+    this->trt_min_shape_ = trt_min_shape;
+    this->trt_max_shape_ = trt_max_shape;
+    this->trt_opt_shape_ = trt_opt_shape;
+    this->trt_calib_mode_ = trt_calib_mode;
+    config_.load_config(model_dir);
+    this->use_dynamic_shape_ = config_.use_dynamic_shape_;
+    this->min_subgraph_size_ = config_.min_subgraph_size_;
+    threshold_ = config_.draw_threshold_;
+    preprocessor_.Init(config_.preprocess_info_);
+    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 = "paddle");
+
+  // Run predictor
+  void Predict(const std::vector<cv::Mat> imgs,
+               std::vector<std::vector<float>>& center,
+               std::vector<std::vector<float>>& scale,
+               const double threshold = 0.5,
+               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_;
+  }
+
+ private:
+  std::string device_ = "CPU";
+  int gpu_id_ = 0;
+  int cpu_math_library_num_threads_ = 1;
+  bool use_dark = true;
+  bool use_mkldnn_ = false;
+  int min_subgraph_size_ = 3;
+  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(std::vector<float>& output,
+                   std::vector<int> output_shape,
+                   std::vector<int64_t>& idxout,
+                   std::vector<int> idx_shape,
+                   std::vector<KeyPointResult>* result,
+                   std::vector<std::vector<float>>& center,
+                   std::vector<std::vector<float>>& scale);
+
+  std::shared_ptr<Predictor> predictor_;
+  Preprocessor preprocessor_;
+  ImageBlob inputs_;
+  std::vector<float> output_data_;
+  std::vector<int64_t> idx_data_;
+  float threshold_;
+  ConfigPaser config_;
+};
+
+}  // namespace PaddleDetection

deploy/cpp/include/keypoint_postprocess.h

+//   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 <math.h>
+#include <opencv2/core/core.hpp>
+#include <opencv2/highgui/highgui.hpp>
+#include <opencv2/imgproc/imgproc.hpp>
+#include <vector>
+
+namespace PaddleDetection {
+
+std::vector<float> get_3rd_point(std::vector<float>& a, std::vector<float>& b);
+
+std::vector<float> get_dir(float src_point_x, float src_point_y, float rot_rad);
+
+void affine_tranform(
+    float pt_x, float pt_y, cv::Mat& trans, std::vector<float>& preds, int p);
+
+cv::Mat get_affine_transform(std::vector<float>& center,
+                             std::vector<float>& scale,
+                             float rot,
+                             std::vector<int>& output_size,
+                             int inv);
+
+void transform_preds(std::vector<float>& coords,
+                     std::vector<float>& center,
+                     std::vector<float>& scale,
+                     std::vector<int>& output_size,
+                     std::vector<int>& dim,
+                     std::vector<float>& target_coords,
+                     bool affine = false);
+
+void box_to_center_scale(std::vector<int>& box,
+                         int width,
+                         int height,
+                         std::vector<float>& center,
+                         std::vector<float>& scale);
+
+void get_max_preds(float* heatmap,
+                   std::vector<int>& dim,
+                   std::vector<float>& preds,
+                   float* maxvals,
+                   int batchid,
+                   int joint_idx);
+
+void get_final_preds(std::vector<float>& heatmap,
+                     std::vector<int>& dim,
+                     std::vector<int64_t>& idxout,
+                     std::vector<int>& idxdim,
+                     std::vector<float>& center,
+                     std::vector<float> scale,
+                     std::vector<float>& preds,
+                     int batchid,
+                     bool DARK = true);
+
+// 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;
+};
+
+class PoseSmooth {
+ public:
+  explicit PoseSmooth(const int width,
+                      const int height,
+                      std::string filter_type = "OneEuro",
+                      float alpha = 0.5,
+                      float fc_d = 0.1,
+                      float fc_min = 0.1,
+                      float beta = 0.1,
+                      float thres_mult = 0.3)
+      : width(width),
+        height(height),
+        alpha(alpha),
+        fc_d(fc_d),
+        fc_min(fc_min),
+        beta(beta),
+        filter_type(filter_type),
+        thres_mult(thres_mult){};
+
+  // Run predictor
+  KeyPointResult smooth_process(KeyPointResult* result);
+  void PointSmooth(KeyPointResult* result,
+                   KeyPointResult* keypoint_smoothed,
+                   std::vector<float> thresholds,
+                   int index);
+  float OneEuroFilter(float x_cur, float x_pre, int loc);
+  float smoothing_factor(float te, float fc);
+  float ExpSmoothing(float x_cur, float x_pre, int loc = 0);
+
+ private:
+  int width = 0;
+  int height = 0;
+  float alpha = 0.;
+  float fc_d = 1.;
+  float fc_min = 0.;
+  float beta = 1.;
+  float thres_mult = 1.;
+  std::string filter_type = "OneEuro";
+  std::vector<float> thresholds = {0.005,
+                                   0.005,
+                                   0.005,
+                                   0.005,
+                                   0.005,
+                                   0.01,
+                                   0.01,
+                                   0.01,
+                                   0.01,
+                                   0.01,
+                                   0.01,
+                                   0.01,
+                                   0.01,
+                                   0.01,
+                                   0.01,
+                                   0.01,
+                                   0.01};
+  KeyPointResult x_prev_hat;
+  KeyPointResult dx_prev_hat;
+};
+}  // namespace PaddleDetection

deploy/cpp/include/lapjv.h

+//   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.
+
+// The code is based on:
+// https://github.com/gatagat/lap/blob/master/lap/lapjv.h
+// Ths copyright of gatagat/lap is as follows:
+// MIT License
+
+#ifndef LAPJV_H
+#define LAPJV_H
+
+#define LARGE 1000000
+
+#if !defined TRUE
+#define TRUE 1
+#endif
+#if !defined FALSE
+#define FALSE 0
+#endif
+
+#define NEW(x, t, n) if ((x = (t *)malloc(sizeof(t) * (n))) == 0) {return -1;}
+#define FREE(x) if (x != 0) { free(x); x = 0; }
+#define SWAP_INDICES(a, b) { int_t _temp_index = a; a = b; b = _temp_index; }
+#include <opencv2/opencv.hpp>
+
+namespace PaddleDetection {
+
+typedef signed int int_t;
+typedef unsigned int uint_t;
+typedef double cost_t;
+typedef char boolean;
+typedef enum fp_t { FP_1 = 1, FP_2 = 2, FP_DYNAMIC = 3 } fp_t;
+
+int lapjv_internal(
+    const cv::Mat &cost, const bool extend_cost, const float cost_limit,
+    int *x, int *y);
+
+} // namespace PaddleDetection
+
+#endif // LAPJV_H
+

deploy/cpp/include/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 <numeric>
+#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/config_parser.h"
+#include "include/picodet_postprocess.h"
+#include "include/preprocess_op.h"
+#include "include/utils.h"
+
+using namespace paddle_infer;
+namespace PaddleDetection {
+
+// 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<PaddleDetection::ObjectResult> &results,
+                const std::vector<std::string> &lables,
+                const std::vector<int> &colormap, const bool is_rbox);
+
+class ObjectDetector {
+public:
+  explicit ObjectDetector(const std::string &model_dir,
+                          const std::string &device = "CPU",
+                          bool use_mkldnn = false, int cpu_threads = 1,
+                          const std::string &run_mode = "paddle",
+                          const int batch_size = 1, const int gpu_id = 0,
+                          const int trt_min_shape = 1,
+                          const int trt_max_shape = 1280,
+                          const int trt_opt_shape = 640,
+                          bool trt_calib_mode = false) {
+    this->device_ = device;
+    this->gpu_id_ = gpu_id;
+    this->cpu_math_library_num_threads_ = cpu_threads;
+    this->use_mkldnn_ = use_mkldnn;
+
+    this->trt_min_shape_ = trt_min_shape;
+    this->trt_max_shape_ = trt_max_shape;
+    this->trt_opt_shape_ = trt_opt_shape;
+    this->trt_calib_mode_ = trt_calib_mode;
+    config_.load_config(model_dir);
+    this->use_dynamic_shape_ = config_.use_dynamic_shape_;
+    this->min_subgraph_size_ = config_.min_subgraph_size_;
+    threshold_ = config_.draw_threshold_;
+    preprocessor_.Init(config_.preprocess_info_);
+    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 = "paddle");
+
+  // Run predictor
+  void Predict(const std::vector<cv::Mat> imgs, const double threshold = 0.5,
+               const int warmup = 0, const int repeats = 1,
+               std::vector<PaddleDetection::ObjectResult> *result = nullptr,
+               std::vector<int> *bbox_num = nullptr,
+               std::vector<double> *times = nullptr);
+
+  // Get Model Label list
+  const std::vector<std::string> &GetLabelList() const {
+    return config_.label_list_;
+  }
+
+private:
+  std::string device_ = "CPU";
+  int gpu_id_ = 0;
+  int cpu_math_library_num_threads_ = 1;
+  bool use_mkldnn_ = false;
+  int min_subgraph_size_ = 3;
+  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<PaddleDetection::ObjectResult> *result,
+                   std::vector<int> bbox_num, std::vector<float> output_data_,
+                   std::vector<int> output_mask_data_, bool is_rbox);
+
+  void SOLOv2Postprocess(
+      const std::vector<cv::Mat> mats, std::vector<ObjectResult> *result,
+      std::vector<int> *bbox_num, std::vector<int> out_bbox_num_data_,
+      std::vector<int64_t> out_label_data_, std::vector<float> out_score_data_,
+      std::vector<uint8_t> out_global_mask_data_, float threshold = 0.5);
+
+  std::shared_ptr<Predictor> predictor_;
+  Preprocessor preprocessor_;
+  ImageBlob inputs_;
+  float threshold_;
+  ConfigPaser config_;
+};
+
+} // namespace PaddleDetection

deploy/cpp/include/picodet_postprocess.h

+//   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 <cmath>
+#include <ctime>
+#include <memory>
+#include <numeric>
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "include/utils.h"
+
+namespace PaddleDetection {
+
+void PicoDetPostProcess(std::vector<PaddleDetection::ObjectResult> *results,
+                        std::vector<const float *> outs,
+                        std::vector<int> fpn_stride,
+                        std::vector<float> im_shape,
+                        std::vector<float> scale_factor,
+                        float score_threshold = 0.3, float nms_threshold = 0.5,
+                        int num_class = 80, int reg_max = 7);
+
+} // namespace PaddleDetection

deploy/cpp/include/preprocess_op.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 <glog/logging.h>
+#include <yaml-cpp/yaml.h>
+
+#include <iostream>
+#include <memory>
+#include <string>
+#include <unordered_map>
+#include <utility>
+#include <vector>
+
+#include <opencv2/core/core.hpp>
+#include <opencv2/highgui/highgui.hpp>
+#include <opencv2/imgproc/imgproc.hpp>
+
+namespace PaddleDetection {
+
+// Object for storing all preprocessed data
+class ImageBlob {
+ public:
+  // image width and height
+  std::vector<float> im_shape_;
+  // Buffer for image data after preprocessing
+  std::vector<float> im_data_;
+  // in net data shape(after pad)
+  std::vector<float> in_net_shape_;
+  // Evaluation image width and height
+  // std::vector<float>  eval_im_size_f_;
+  // Scale factor for image size to origin image size
+  std::vector<float> scale_factor_;
+  // in net image after preprocessing
+  cv::Mat in_net_im_;
+};
+
+// Abstraction of preprocessing opration class
+class PreprocessOp {
+ public:
+  virtual void Init(const YAML::Node& item) = 0;
+  virtual void Run(cv::Mat* im, ImageBlob* data) = 0;
+};
+
+class InitInfo : public PreprocessOp {
+ public:
+  virtual void Init(const YAML::Node& item) {}
+  virtual void Run(cv::Mat* im, ImageBlob* data);
+};
+
+class NormalizeImage : public PreprocessOp {
+ public:
+  virtual void Init(const YAML::Node& item) {
+    mean_ = item["mean"].as<std::vector<float>>();
+    scale_ = item["std"].as<std::vector<float>>();
+    if (item["is_scale"]) is_scale_ = item["is_scale"].as<bool>();
+    if (item["norm_type"]) norm_type_ = item["norm_type"].as<std::string>();
+  }
+
+  virtual void Run(cv::Mat* im, ImageBlob* data);
+
+ private:
+  // CHW or HWC
+  std::vector<float> mean_;
+  std::vector<float> scale_;
+  bool is_scale_ = true;
+  std::string norm_type_ = "mean_std";
+};
+
+class Permute : public PreprocessOp {
+ public:
+  virtual void Init(const YAML::Node& item) {}
+  virtual void Run(cv::Mat* im, ImageBlob* data);
+};
+
+class Resize : public PreprocessOp {
+ public:
+  virtual void Init(const YAML::Node& item) {
+    interp_ = item["interp"].as<int>();
+    keep_ratio_ = item["keep_ratio"].as<bool>();
+    target_size_ = item["target_size"].as<std::vector<int>>();
+  }
+
+  // Compute best resize scale for x-dimension, y-dimension
+  std::pair<float, float> GenerateScale(const cv::Mat& im);
+
+  virtual void Run(cv::Mat* im, ImageBlob* data);
+
+ private:
+  int interp_;
+  bool keep_ratio_;
+  std::vector<int> target_size_;
+  std::vector<int> in_net_shape_;
+};
+
+class LetterBoxResize : public PreprocessOp {
+ public:
+  virtual void Init(const YAML::Node& item) {
+    target_size_ = item["target_size"].as<std::vector<int>>();
+  }
+
+  float GenerateScale(const cv::Mat& im);
+
+  virtual void Run(cv::Mat* im, ImageBlob* data);
+
+ private:
+  std::vector<int> target_size_;
+  std::vector<int> in_net_shape_;
+};
+// Models with FPN need input shape % stride == 0
+class PadStride : public PreprocessOp {
+ public:
+  virtual void Init(const YAML::Node& item) {
+    stride_ = item["stride"].as<int>();
+  }
+
+  virtual void Run(cv::Mat* im, ImageBlob* data);
+
+ private:
+  int stride_;
+};
+
+class TopDownEvalAffine : public PreprocessOp {
+ public:
+  virtual void Init(const YAML::Node& item) {
+    trainsize_ = item["trainsize"].as<std::vector<int>>();
+  }
+
+  virtual void Run(cv::Mat* im, ImageBlob* data);
+
+ private:
+  int interp_ = 1;
+  std::vector<int> trainsize_;
+};
+
+class WarpAffine : public PreprocessOp {
+ public:
+  virtual void Init(const YAML::Node& item) {
+    input_h_ = item["input_h"].as<int>();
+    input_w_ = item["input_w"].as<int>();
+    keep_res_ = item["keep_res"].as<bool>();
+  }
+
+  virtual void Run(cv::Mat* im, ImageBlob* data);
+
+ private:
+  int input_h_;
+  int input_w_;
+  int interp_ = 1;
+  bool keep_res_ = true;
+  int pad_ = 31;
+};
+
+class Pad : public PreprocessOp {
+ public:
+  virtual void Init(const YAML::Node& item) {
+    size_ = item["size"].as<std::vector<int>>();
+    fill_value_ = item["fill_value"].as<std::vector<float>>();
+  }
+
+  virtual void Run(cv::Mat* im, ImageBlob* data);
+
+ private:
+  std::vector<int> size_;
+  std::vector<float> fill_value_;
+};
+
+void CropImg(cv::Mat& img,
+             cv::Mat& crop_img,
+             std::vector<int>& area,
+             std::vector<float>& center,
+             std::vector<float>& scale,
+             float expandratio = 0.15);
+
+// check whether the input size is dynamic
+bool CheckDynamicInput(const std::vector<cv::Mat>& imgs);
+
+// Pad images in batch
+std::vector<cv::Mat> PadBatch(const std::vector<cv::Mat>& imgs);
+
+class Preprocessor {
+ public:
+  void Init(const YAML::Node& config_node) {
+    // initialize image info at first
+    ops_["InitInfo"] = std::make_shared<InitInfo>();
+    for (const auto& item : config_node) {
+      auto op_name = item["type"].as<std::string>();
+
+      ops_[op_name] = CreateOp(op_name);
+      ops_[op_name]->Init(item);
+    }
+  }
+
+  std::shared_ptr<PreprocessOp> CreateOp(const std::string& name) {
+    if (name == "Resize") {
+      return std::make_shared<Resize>();
+    } else if (name == "LetterBoxResize") {
+      return std::make_shared<LetterBoxResize>();
+    } else if (name == "Permute") {
+      return std::make_shared<Permute>();
+    } else if (name == "NormalizeImage") {
+      return std::make_shared<NormalizeImage>();
+    } else if (name == "PadStride") {
+      // use PadStride instead of PadBatch
+      return std::make_shared<PadStride>();
+    } else if (name == "TopDownEvalAffine") {
+      return std::make_shared<TopDownEvalAffine>();
+    } else if (name == "WarpAffine") {
+      return std::make_shared<WarpAffine>();
+    }else if (name == "Pad") {
+      return std::make_shared<Pad>();
+    }
+    std::cerr << "can not find function of OP: " << name
+              << " and return: nullptr" << std::endl;
+    return nullptr;
+  }
+
+  void Run(cv::Mat* im, ImageBlob* data);
+
+ public:
+  static const std::vector<std::string> RUN_ORDER;
+
+ private:
+  std::unordered_map<std::string, std::shared_ptr<PreprocessOp>> ops_;
+};
+
+}  // namespace PaddleDetection

deploy/cpp/include/tracker.h

+//   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.
+
+// The code is based on:
+// https://github.com/CnybTseng/JDE/blob/master/platforms/common/jdetracker.h
+// Ths copyright of CnybTseng/JDE is as follows:
+// MIT License 
+
+#pragma once
+
+#include <map>
+#include <vector>
+#include <opencv2/opencv.hpp>
+
+#include "trajectory.h"
+
+namespace PaddleDetection {
+
+typedef std::map<int, int> Match;
+typedef std::map<int, int>::iterator MatchIterator;
+
+struct Track
+{
+    int id;
+    float score;
+    cv::Vec4f ltrb;
+};
+
+class JDETracker
+{
+public:
+    static JDETracker *instance(void);
+    virtual bool update(const cv::Mat &dets, const cv::Mat &emb, std::vector<Track> &tracks);
+private:
+    JDETracker(void);
+    virtual ~JDETracker(void) {}
+    cv::Mat motion_distance(const TrajectoryPtrPool &a, const TrajectoryPool &b);
+    void linear_assignment(const cv::Mat &cost, float cost_limit, Match &matches,
+        std::vector<int> &mismatch_row, std::vector<int> &mismatch_col);
+    void remove_duplicate_trajectory(TrajectoryPool &a, TrajectoryPool &b, float iou_thresh=0.15f);
+private:
+    static JDETracker *me;
+    int timestamp;
+    TrajectoryPool tracked_trajectories;
+    TrajectoryPool lost_trajectories;
+    TrajectoryPool removed_trajectories;
+    int max_lost_time;
+    float lambda;
+    float det_thresh;
+};
+
+} // namespace PaddleDetection

deploy/cpp/include/trajectory.h

+//   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.
+
+// The code is based on:
+// https://github.com/CnybTseng/JDE/blob/master/platforms/common/trajectory.h
+// Ths copyright of CnybTseng/JDE is as follows:
+// MIT License
+
+#pragma once
+
+#include <vector>
+#include <opencv2/opencv.hpp>
+
+namespace PaddleDetection {
+
+typedef enum
+{
+    New = 0,
+    Tracked = 1,
+    Lost = 2,
+    Removed = 3
+} TrajectoryState;
+
+class Trajectory;
+typedef std::vector<Trajectory> TrajectoryPool;
+typedef std::vector<Trajectory>::iterator TrajectoryPoolIterator;
+typedef std::vector<Trajectory *>TrajectoryPtrPool;
+typedef std::vector<Trajectory *>::iterator TrajectoryPtrPoolIterator;
+
+class TKalmanFilter : public cv::KalmanFilter
+{
+public:
+    TKalmanFilter(void);
+    virtual ~TKalmanFilter(void) {}
+    virtual void init(const cv::Mat &measurement);
+    virtual const cv::Mat &predict();
+    virtual const cv::Mat &correct(const cv::Mat &measurement);
+    virtual void project(cv::Mat &mean, cv::Mat &covariance) const;
+private:
+    float std_weight_position;
+    float std_weight_velocity;
+};
+
+inline TKalmanFilter::TKalmanFilter(void) : cv::KalmanFilter(8, 4)
+{
+    cv::KalmanFilter::transitionMatrix = cv::Mat::eye(8, 8, CV_32F);
+    for (int i = 0; i < 4; ++i)
+        cv::KalmanFilter::transitionMatrix.at<float>(i, i + 4) = 1;
+    cv::KalmanFilter::measurementMatrix = cv::Mat::eye(4, 8, CV_32F);
+    std_weight_position = 1/20.f;
+    std_weight_velocity = 1/160.f;
+}
+
+class Trajectory : public TKalmanFilter
+{
+public:
+    Trajectory();
+    Trajectory(cv::Vec4f &ltrb, float score, const cv::Mat &embedding);
+    Trajectory(const Trajectory &other);
+    Trajectory &operator=(const Trajectory &rhs);
+    virtual ~Trajectory(void) {};
+
+    static int next_id();
+    virtual const cv::Mat &predict(void);
+    virtual void update(Trajectory &traj, int timestamp, bool update_embedding=true);
+    virtual void activate(int timestamp);
+    virtual void reactivate(Trajectory &traj, int timestamp, bool newid=false);
+    virtual void mark_lost(void);
+    virtual void mark_removed(void);
+
+    friend TrajectoryPool operator+(const TrajectoryPool &a, const TrajectoryPool &b);
+    friend TrajectoryPool operator+(const TrajectoryPool &a, const TrajectoryPtrPool &b);
+    friend TrajectoryPool &operator+=(TrajectoryPool &a, const TrajectoryPtrPool &b);
+    friend TrajectoryPool operator-(const TrajectoryPool &a, const TrajectoryPool &b);
+    friend TrajectoryPool &operator-=(TrajectoryPool &a, const TrajectoryPool &b);
+    friend TrajectoryPtrPool operator+(const TrajectoryPtrPool &a, const TrajectoryPtrPool &b);
+    friend TrajectoryPtrPool operator+(const TrajectoryPtrPool &a, TrajectoryPool &b);
+    friend TrajectoryPtrPool operator-(const TrajectoryPtrPool &a, const TrajectoryPtrPool &b);
+
+    friend cv::Mat embedding_distance(const TrajectoryPool &a, const TrajectoryPool &b);
+    friend cv::Mat embedding_distance(const TrajectoryPtrPool &a, const TrajectoryPtrPool &b);
+    friend cv::Mat embedding_distance(const TrajectoryPtrPool &a, const TrajectoryPool &b);
+
+    friend cv::Mat mahalanobis_distance(const TrajectoryPool &a, const TrajectoryPool &b);
+    friend cv::Mat mahalanobis_distance(const TrajectoryPtrPool &a, const TrajectoryPtrPool &b);
+    friend cv::Mat mahalanobis_distance(const TrajectoryPtrPool &a, const TrajectoryPool &b);
+
+    friend cv::Mat iou_distance(const TrajectoryPool &a, const TrajectoryPool &b);
+    friend cv::Mat iou_distance(const TrajectoryPtrPool &a, const TrajectoryPtrPool &b);
+    friend cv::Mat iou_distance(const TrajectoryPtrPool &a, const TrajectoryPool &b);
+private:   
+    void update_embedding(const cv::Mat &embedding);
+public:
+    TrajectoryState state;
+    cv::Vec4f ltrb;
+    cv::Mat smooth_embedding;
+    int id;
+    bool is_activated;
+    int timestamp;
+    int starttime;
+    float score;
+private:
+    static int count;
+    cv::Vec4f xyah;
+    cv::Mat current_embedding;
+    float eta;
+    int length;
+};
+
+inline cv::Vec4f ltrb2xyah(cv::Vec4f &ltrb)
+{
+    cv::Vec4f xyah;
+    xyah[0] = (ltrb[0] + ltrb[2]) * 0.5f;
+    xyah[1] = (ltrb[1] + ltrb[3]) * 0.5f;
+    xyah[3] =  ltrb[3] - ltrb[1];
+    xyah[2] = (ltrb[2] - ltrb[0]) / xyah[3];
+    return xyah;
+}
+
+inline Trajectory::Trajectory() :
+    state(New), ltrb(cv::Vec4f()), smooth_embedding(cv::Mat()), id(0),
+    is_activated(false), timestamp(0), starttime(0), score(0), eta(0.9), length(0)
+{
+}
+
+inline Trajectory::Trajectory(cv::Vec4f &ltrb_, float score_, const cv::Mat &embedding) :
+    state(New), ltrb(ltrb_), smooth_embedding(cv::Mat()), id(0),
+    is_activated(false), timestamp(0), starttime(0), score(score_), eta(0.9), length(0)
+{
+    xyah = ltrb2xyah(ltrb);
+    update_embedding(embedding);
+}
+
+inline Trajectory::Trajectory(const Trajectory &other):
+    state(other.state), ltrb(other.ltrb), id(other.id), is_activated(other.is_activated),
+    timestamp(other.timestamp), starttime(other.starttime), xyah(other.xyah),
+    score(other.score), eta(other.eta), length(other.length)
+{    
+    other.smooth_embedding.copyTo(smooth_embedding);
+    other.current_embedding.copyTo(current_embedding);
+    // copy state in KalmanFilter
+    
+    other.statePre.copyTo(cv::KalmanFilter::statePre);
+    other.statePost.copyTo(cv::KalmanFilter::statePost);
+    other.errorCovPre.copyTo(cv::KalmanFilter::errorCovPre);
+    other.errorCovPost.copyTo(cv::KalmanFilter::errorCovPost);
+    
+}
+
+inline Trajectory &Trajectory::operator=(const Trajectory &rhs)
+{
+    this->state = rhs.state;
+    this->ltrb = rhs.ltrb;
+    rhs.smooth_embedding.copyTo(this->smooth_embedding);
+    this->id = rhs.id;
+    this->is_activated = rhs.is_activated;
+    this->timestamp = rhs.timestamp;
+    this->starttime = rhs.starttime; 
+    this->xyah = rhs.xyah;
+    this->score = rhs.score;
+    rhs.current_embedding.copyTo(this->current_embedding);            
+    this->eta = rhs.eta;    
+    this->length = rhs.length;
+
+    // copy state in KalmanFilter
+    
+    rhs.statePre.copyTo(cv::KalmanFilter::statePre);
+    rhs.statePost.copyTo(cv::KalmanFilter::statePost);
+    rhs.errorCovPre.copyTo(cv::KalmanFilter::errorCovPre);
+    rhs.errorCovPost.copyTo(cv::KalmanFilter::errorCovPost);
+    
+    return *this;
+}
+
+inline int Trajectory::next_id()
+{
+    ++count;
+    return count;
+}
+
+inline void Trajectory::mark_lost(void)
+{
+    state = Lost;
+}
+
+inline void Trajectory::mark_removed(void)
+{
+    state = Removed;
+}
+
+}   // namespace PaddleDetection 

deploy/cpp/include/utils.h

+//   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 <algorithm>
+#include <ctime>
+#include <memory>
+#include <numeric>
+#include <string>
+#include <utility>
+#include <vector>
+
+namespace PaddleDetection {
+
+// 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;
+  // Mask of detected object
+  std::vector<int> mask;
+};
+
+void nms(std::vector<ObjectResult> &input_boxes, float nms_threshold);
+
+}  // namespace PaddleDetection

deploy/cpp/scripts/build.sh

+# 是否使用GPU(即是否使用 CUDA)
+WITH_GPU=OFF
+
+# 是否使用MKL or openblas，TX2需要设置为OFF
+WITH_MKL=ON
+
+# 是否集成 TensorRT(仅WITH_GPU=ON 有效)
+WITH_TENSORRT=OFF
+
+# paddle 预测库lib名称，由于不同平台不同版本预测库lib名称不同，请查看所下载的预测库中`paddle_inference/lib/`文件夹下`lib`的名称
+PADDLE_LIB_NAME=libpaddle_inference
+
+# TensorRT 的include路径
+TENSORRT_INC_DIR=/path/to/tensorrt/include
+
+# TensorRT 的lib路径
+TENSORRT_LIB_DIR=/path/to/tensorrt/lib
+
+# Paddle 预测库路径
+PADDLE_DIR=/path/to/paddle_inference
+
+# CUDA 的 lib 路径
+CUDA_LIB=/path/to/cuda/lib
+
+# CUDNN 的 lib 路径
+CUDNN_LIB=/path/to/cudnn/lib
+
+# 是否开启关键点模型预测功能
+WITH_KEYPOINT=OFF
+
+# 是否开启跟踪模型预测功能
+WITH_MOT=OFF
+
+MACHINE_TYPE=`uname -m`
+echo "MACHINE_TYPE: "${MACHINE_TYPE}
+
+
+if [ "$MACHINE_TYPE" = "x86_64" ]
+then
+  echo "set OPENCV_DIR for x86_64"
+  # linux系统通过以下命令下载预编译的opencv
+  mkdir -p $(pwd)/deps && cd $(pwd)/deps
+  wget -c https://paddledet.bj.bcebos.com/data/opencv-3.4.16_gcc8.2_ffmpeg.tar.gz
+  tar -xvf opencv-3.4.16_gcc8.2_ffmpeg.tar.gz && cd ..
+
+  # set OPENCV_DIR
+  OPENCV_DIR=$(pwd)/deps/opencv-3.4.16_gcc8.2_ffmpeg
+
+elif [ "$MACHINE_TYPE" = "aarch64" ]
+then
+  echo "set OPENCV_DIR for aarch64"
+  # TX2平台通过以下命令下载预编译的opencv
+  mkdir -p $(pwd)/deps && cd $(pwd)/deps
+  wget -c https://bj.bcebos.com/v1/paddledet/data/TX2_JetPack4.3_opencv_3.4.6_gcc7.5.0.tar.gz
+  tar -xvf TX2_JetPack4.3_opencv_3.4.6_gcc7.5.0.tar.gz && cd ..
+
+  # set OPENCV_DIR
+  OPENCV_DIR=$(pwd)/deps/TX2_JetPack4.3_opencv_3.4.6_gcc7.5.0/
+
+else
+  echo "Please set OPENCV_DIR manually"
+fi
+
+echo "OPENCV_DIR: "$OPENCV_DIR
+
+# 以下无需改动
+rm -rf build
+mkdir -p build
+cd build
+cmake .. \
+    -DWITH_GPU=${WITH_GPU} \
+    -DWITH_MKL=${WITH_MKL} \
+    -DWITH_TENSORRT=${WITH_TENSORRT} \
+    -DTENSORRT_LIB_DIR=${TENSORRT_LIB_DIR} \
+    -DTENSORRT_INC_DIR=${TENSORRT_INC_DIR} \
+    -DPADDLE_DIR=${PADDLE_DIR} \
+    -DWITH_STATIC_LIB=${WITH_STATIC_LIB} \
+    -DCUDA_LIB=${CUDA_LIB} \
+    -DCUDNN_LIB=${CUDNN_LIB} \
+    -DOPENCV_DIR=${OPENCV_DIR} \
+    -DPADDLE_LIB_NAME=${PADDLE_LIB_NAME} \
+    -DWITH_KEYPOINT=${WITH_KEYPOINT} \
+    -DWITH_MOT=${WITH_MOT}
+
+make
+echo "make finished!"

deploy/cpp/src/jde_detector.cc

+//   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.
+#include <sstream>
+// for setprecision
+#include <chrono>
+#include <iomanip>
+#include "include/jde_detector.h"
+
+using namespace paddle_infer;
+
+namespace PaddleDetection {
+
+// Load Model and create model predictor
+void JDEDetector::LoadModel(const std::string& model_dir,
+                            const int batch_size,
+                            const std::string& run_mode) {
+  paddle_infer::Config config;
+  std::string prog_file = model_dir + OS_PATH_SEP + "model.pdmodel";
+  std::string params_file = model_dir + OS_PATH_SEP + "model.pdiparams";
+  config.SetModel(prog_file, params_file);
+  if (this->device_ == "GPU") {
+    config.EnableUseGpu(200, this->gpu_id_);
+    config.SwitchIrOptim(true);
+    // use tensorrt
+    if (run_mode != "paddle") {
+      auto precision = paddle_infer::Config::Precision::kFloat32;
+      if (run_mode == "trt_fp32") {
+        precision = paddle_infer::Config::Precision::kFloat32;
+      } else if (run_mode == "trt_fp16") {
+        precision = paddle_infer::Config::Precision::kHalf;
+      } else if (run_mode == "trt_int8") {
+        precision = paddle_infer::Config::Precision::kInt8;
+      } else {
+        printf(
+            "run_mode should be 'paddle', 'trt_fp32', 'trt_fp16' or "
+            "'trt_int8'");
+      }
+      // set tensorrt
+      config.EnableTensorRtEngine(1 << 30,
+                                  batch_size,
+                                  this->min_subgraph_size_,
+                                  precision,
+                                  false,
+                                  this->trt_calib_mode_);
+
+      // set use dynamic shape
+      if (this->use_dynamic_shape_) {
+        // set DynamicShsape for image tensor
+        const std::vector<int> min_input_shape = {
+            1, 3, this->trt_min_shape_, this->trt_min_shape_};
+        const std::vector<int> max_input_shape = {
+            1, 3, this->trt_max_shape_, this->trt_max_shape_};
+        const std::vector<int> opt_input_shape = {
+            1, 3, this->trt_opt_shape_, this->trt_opt_shape_};
+        const std::map<std::string, std::vector<int>> map_min_input_shape = {
+            {"image", min_input_shape}};
+        const std::map<std::string, std::vector<int>> map_max_input_shape = {
+            {"image", max_input_shape}};
+        const std::map<std::string, std::vector<int>> map_opt_input_shape = {
+            {"image", opt_input_shape}};
+
+        config.SetTRTDynamicShapeInfo(
+            map_min_input_shape, map_max_input_shape, map_opt_input_shape);
+        std::cout << "TensorRT dynamic shape enabled" << std::endl;
+      }
+    }
+
+  } else if (this->device_ == "XPU") {
+    config.EnableXpu(10 * 1024 * 1024);
+  } else {
+    config.DisableGpu();
+    if (this->use_mkldnn_) {
+      config.EnableMKLDNN();
+      // cache 10 different shapes for mkldnn to avoid memory leak
+      config.SetMkldnnCacheCapacity(10);
+    }
+    config.SetCpuMathLibraryNumThreads(this->cpu_math_library_num_threads_);
+  }
+  config.SwitchUseFeedFetchOps(false);
+  config.SwitchIrOptim(true);
+  config.DisableGlogInfo();
+  // Memory optimization
+  config.EnableMemoryOptim();
+  predictor_ = std::move(CreatePredictor(config));
+}
+
+// Visualiztion results
+cv::Mat VisualizeTrackResult(const cv::Mat& img,
+                             const MOT_Result& results,
+                             const float fps,
+                             const int frame_id) {
+  cv::Mat vis_img = img.clone();
+  int im_h = img.rows;
+  int im_w = img.cols;
+  float text_scale = std::max(1, int(im_w / 1600.));
+  float text_thickness = 2.;
+  float line_thickness = std::max(1, int(im_w / 500.));
+
+  std::ostringstream oss;
+  oss << std::setiosflags(std::ios::fixed) << std::setprecision(4);
+  oss << "frame: " << frame_id << " ";
+  oss << "fps: " << fps << " ";
+  oss << "num: " << results.size();
+  std::string text = oss.str();
+
+  cv::Point origin;
+  origin.x = 0;
+  origin.y = int(15 * text_scale);
+  cv::putText(vis_img,
+              text,
+              origin,
+              cv::FONT_HERSHEY_PLAIN,
+              text_scale,
+              (0, 0, 255),
+              2);
+
+  for (int i = 0; i < results.size(); ++i) {
+    const int obj_id = results[i].ids;
+    const float score = results[i].score;
+
+    cv::Scalar color = GetColor(obj_id);
+
+    cv::Point pt1 = cv::Point(results[i].rects.left, results[i].rects.top);
+    cv::Point pt2 = cv::Point(results[i].rects.right, results[i].rects.bottom);
+    cv::Point id_pt =
+        cv::Point(results[i].rects.left, results[i].rects.top + 10);
+    cv::Point score_pt =
+        cv::Point(results[i].rects.left, results[i].rects.top - 10);
+    cv::rectangle(vis_img, pt1, pt2, color, line_thickness);
+
+    std::ostringstream idoss;
+    idoss << std::setiosflags(std::ios::fixed) << std::setprecision(4);
+    idoss << obj_id;
+    std::string id_text = idoss.str();
+
+    cv::putText(vis_img,
+                id_text,
+                id_pt,
+                cv::FONT_HERSHEY_PLAIN,
+                text_scale,
+                cv::Scalar(0, 255, 255),
+                text_thickness);
+
+    std::ostringstream soss;
+    soss << std::setiosflags(std::ios::fixed) << std::setprecision(2);
+    soss << score;
+    std::string score_text = soss.str();
+
+    cv::putText(vis_img,
+                score_text,
+                score_pt,
+                cv::FONT_HERSHEY_PLAIN,
+                text_scale,
+                cv::Scalar(0, 255, 255),
+                text_thickness);
+  }
+  return vis_img;
+}
+
+void FilterDets(const float conf_thresh,
+                const cv::Mat dets,
+                std::vector<int>* index) {
+  for (int i = 0; i < dets.rows; ++i) {
+    float score = *dets.ptr<float>(i, 4);
+    if (score > conf_thresh) {
+      index->push_back(i);
+    }
+  }
+}
+
+void JDEDetector::Preprocess(const cv::Mat& ori_im) {
+  // Clone the image : keep the original mat for postprocess
+  cv::Mat im = ori_im.clone();
+  preprocessor_.Run(&im, &inputs_);
+}
+
+void JDEDetector::Postprocess(const cv::Mat dets,
+                              const cv::Mat emb,
+                              MOT_Result* result) {
+  result->clear();
+  std::vector<Track> tracks;
+  std::vector<int> valid;
+  FilterDets(conf_thresh_, dets, &valid);
+  cv::Mat new_dets, new_emb;
+  for (int i = 0; i < valid.size(); ++i) {
+    new_dets.push_back(dets.row(valid[i]));
+    new_emb.push_back(emb.row(valid[i]));
+  }
+  JDETracker::instance()->update(new_dets, new_emb, tracks);
+  if (tracks.size() == 0) {
+    MOT_Track mot_track;
+    MOT_Rect ret = {*dets.ptr<float>(0, 0),
+                    *dets.ptr<float>(0, 1),
+                    *dets.ptr<float>(0, 2),
+                    *dets.ptr<float>(0, 3)};
+    mot_track.ids = 1;
+    mot_track.score = *dets.ptr<float>(0, 4);
+    mot_track.rects = ret;
+    result->push_back(mot_track);
+  } else {
+    std::vector<Track>::iterator titer;
+    for (titer = tracks.begin(); titer != tracks.end(); ++titer) {
+      if (titer->score < threshold_) {
+        continue;
+      } else {
+        float w = titer->ltrb[2] - titer->ltrb[0];
+        float h = titer->ltrb[3] - titer->ltrb[1];
+        bool vertical = w / h > 1.6;
+        float area = w * h;
+        if (area > min_box_area_ && !vertical) {
+          MOT_Track mot_track;
+          MOT_Rect ret = {
+              titer->ltrb[0], titer->ltrb[1], titer->ltrb[2], titer->ltrb[3]};
+          mot_track.rects = ret;
+          mot_track.score = titer->score;
+          mot_track.ids = titer->id;
+          result->push_back(mot_track);
+        }
+      }
+    }
+  }
+}
+
+void JDEDetector::Predict(const std::vector<cv::Mat> imgs,
+                          const double threshold,
+                          const int warmup,
+                          const int repeats,
+                          MOT_Result* result,
+                          std::vector<double>* times) {
+  auto preprocess_start = std::chrono::steady_clock::now();
+  int batch_size = imgs.size();
+
+  // in_data_batch
+  std::vector<float> in_data_all;
+  std::vector<float> im_shape_all(batch_size * 2);
+  std::vector<float> scale_factor_all(batch_size * 2);
+
+  // Preprocess image
+  for (int bs_idx = 0; bs_idx < batch_size; bs_idx++) {
+    cv::Mat im = imgs.at(bs_idx);
+    Preprocess(im);
+    im_shape_all[bs_idx * 2] = inputs_.im_shape_[0];
+    im_shape_all[bs_idx * 2 + 1] = inputs_.im_shape_[1];
+
+    scale_factor_all[bs_idx * 2] = inputs_.scale_factor_[0];
+    scale_factor_all[bs_idx * 2 + 1] = inputs_.scale_factor_[1];
+
+    // TODO: reduce cost time
+    in_data_all.insert(
+        in_data_all.end(), inputs_.im_data_.begin(), inputs_.im_data_.end());
+  }
+
+  // Prepare input tensor
+  auto input_names = predictor_->GetInputNames();
+  for (const auto& tensor_name : input_names) {
+    auto in_tensor = predictor_->GetInputHandle(tensor_name);
+    if (tensor_name == "image") {
+      int rh = inputs_.in_net_shape_[0];
+      int rw = inputs_.in_net_shape_[1];
+      in_tensor->Reshape({batch_size, 3, rh, rw});
+      in_tensor->CopyFromCpu(in_data_all.data());
+    } else if (tensor_name == "im_shape") {
+      in_tensor->Reshape({batch_size, 2});
+      in_tensor->CopyFromCpu(im_shape_all.data());
+    } else if (tensor_name == "scale_factor") {
+      in_tensor->Reshape({batch_size, 2});
+      in_tensor->CopyFromCpu(scale_factor_all.data());
+    }
+  }
+
+  auto preprocess_end = std::chrono::steady_clock::now();
+  std::vector<int> bbox_shape;
+  std::vector<int> emb_shape;
+  // Run predictor
+  // warmup
+  for (int i = 0; i < warmup; i++) {
+    predictor_->Run();
+    // Get output tensor
+    auto output_names = predictor_->GetOutputNames();
+    auto bbox_tensor = predictor_->GetOutputHandle(output_names[0]);
+    bbox_shape = bbox_tensor->shape();
+    auto emb_tensor = predictor_->GetOutputHandle(output_names[1]);
+    emb_shape = emb_tensor->shape();
+    // Calculate bbox length
+    int bbox_size = 1;
+    for (int j = 0; j < bbox_shape.size(); ++j) {
+      bbox_size *= bbox_shape[j];
+    }
+    // Calculate emb length
+    int emb_size = 1;
+    for (int j = 0; j < emb_shape.size(); ++j) {
+      emb_size *= emb_shape[j];
+    }
+
+    bbox_data_.resize(bbox_size);
+    bbox_tensor->CopyToCpu(bbox_data_.data());
+
+    emb_data_.resize(emb_size);
+    emb_tensor->CopyToCpu(emb_data_.data());
+  }
+
+  auto inference_start = std::chrono::steady_clock::now();
+  for (int i = 0; i < repeats; i++) {
+    predictor_->Run();
+    // Get output tensor
+    auto output_names = predictor_->GetOutputNames();
+    auto bbox_tensor = predictor_->GetOutputHandle(output_names[0]);
+    bbox_shape = bbox_tensor->shape();
+    auto emb_tensor = predictor_->GetOutputHandle(output_names[1]);
+    emb_shape = emb_tensor->shape();
+    // Calculate bbox length
+    int bbox_size = 1;
+    for (int j = 0; j < bbox_shape.size(); ++j) {
+      bbox_size *= bbox_shape[j];
+    }
+    // Calculate emb length
+    int emb_size = 1;
+    for (int j = 0; j < emb_shape.size(); ++j) {
+      emb_size *= emb_shape[j];
+    }
+
+    bbox_data_.resize(bbox_size);
+    bbox_tensor->CopyToCpu(bbox_data_.data());
+
+    emb_data_.resize(emb_size);
+    emb_tensor->CopyToCpu(emb_data_.data());
+  }
+  auto inference_end = std::chrono::steady_clock::now();
+  auto postprocess_start = std::chrono::steady_clock::now();
+  // Postprocessing result
+  result->clear();
+
+  cv::Mat dets(bbox_shape[0], 6, CV_32FC1, bbox_data_.data());
+  cv::Mat emb(bbox_shape[0], emb_shape[1], CV_32FC1, emb_data_.data());
+
+  Postprocess(dets, emb, result);
+
+  auto postprocess_end = std::chrono::steady_clock::now();
+
+  std::chrono::duration<float> preprocess_diff =
+      preprocess_end - preprocess_start;
+  (*times)[0] += double(preprocess_diff.count() * 1000);
+  std::chrono::duration<float> inference_diff = inference_end - inference_start;
+  (*times)[1] += double(inference_diff.count() * 1000);
+  std::chrono::duration<float> postprocess_diff =
+      postprocess_end - postprocess_start;
+  (*times)[2] += double(postprocess_diff.count() * 1000);
+}
+
+cv::Scalar GetColor(int idx) {
+  idx = idx * 3;
+  cv::Scalar color =
+      cv::Scalar((37 * idx) % 255, (17 * idx) % 255, (29 * idx) % 255);
+  return color;
+}
+
+}  // namespace PaddleDetection

deploy/cpp/src/keypoint_detector.cc

+//   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.
+#include <sstream>
+// for setprecision
+#include <chrono>
+#include <iomanip>
+#include "include/keypoint_detector.h"
+
+using namespace paddle_infer;
+
+namespace PaddleDetection {
+
+// Load Model and create model predictor
+void KeyPointDetector::LoadModel(const std::string& model_dir,
+                                 const int batch_size,
+                                 const std::string& run_mode) {
+  paddle_infer::Config config;
+  std::string prog_file = model_dir + OS_PATH_SEP + "model.pdmodel";
+  std::string params_file = model_dir + OS_PATH_SEP + "model.pdiparams";
+  config.SetModel(prog_file, params_file);
+  if (this->device_ == "GPU") {
+    config.EnableUseGpu(200, this->gpu_id_);
+    config.SwitchIrOptim(true);
+    // use tensorrt
+    if (run_mode != "paddle") {
+      auto precision = paddle_infer::Config::Precision::kFloat32;
+      if (run_mode == "trt_fp32") {
+        precision = paddle_infer::Config::Precision::kFloat32;
+      } else if (run_mode == "trt_fp16") {
+        precision = paddle_infer::Config::Precision::kHalf;
+      } else if (run_mode == "trt_int8") {
+        precision = paddle_infer::Config::Precision::kInt8;
+      } else {
+        printf(
+            "run_mode should be 'paddle', 'trt_fp32', 'trt_fp16' or "
+            "'trt_int8'");
+      }
+      // set tensorrt
+      config.EnableTensorRtEngine(1 << 30,
+                                  batch_size,
+                                  this->min_subgraph_size_,
+                                  precision,
+                                  false,
+                                  this->trt_calib_mode_);
+
+      // set use dynamic shape
+      if (this->use_dynamic_shape_) {
+        // set DynamicShsape for image tensor
+        const std::vector<int> min_input_shape = {
+            1, 3, this->trt_min_shape_, this->trt_min_shape_};
+        const std::vector<int> max_input_shape = {
+            1, 3, this->trt_max_shape_, this->trt_max_shape_};
+        const std::vector<int> opt_input_shape = {
+            1, 3, this->trt_opt_shape_, this->trt_opt_shape_};
+        const std::map<std::string, std::vector<int>> map_min_input_shape = {
+            {"image", min_input_shape}};
+        const std::map<std::string, std::vector<int>> map_max_input_shape = {
+            {"image", max_input_shape}};
+        const std::map<std::string, std::vector<int>> map_opt_input_shape = {
+            {"image", opt_input_shape}};
+
+        config.SetTRTDynamicShapeInfo(
+            map_min_input_shape, map_max_input_shape, map_opt_input_shape);
+        std::cout << "TensorRT dynamic shape enabled" << std::endl;
+      }
+    }
+
+  } else if (this->device_ == "XPU") {
+    config.EnableXpu(10 * 1024 * 1024);
+  } else {
+    config.DisableGpu();
+    if (this->use_mkldnn_) {
+      config.EnableMKLDNN();
+      // cache 10 different shapes for mkldnn to avoid memory leak
+      config.SetMkldnnCacheCapacity(10);
+    }
+    config.SetCpuMathLibraryNumThreads(this->cpu_math_library_num_threads_);
+  }
+  config.SwitchUseFeedFetchOps(false);
+  config.SwitchIrOptim(true);
+  config.DisableGlogInfo();
+  // Memory optimization
+  config.EnableMemoryOptim();
+  predictor_ = std::move(CreatePredictor(config));
+}
+
+// Visualization MaskDetector results
+cv::Mat VisualizeKptsResult(const cv::Mat& img,
+                            const std::vector<KeyPointResult>& results,
+                            const std::vector<int>& colormap) {
+  const int edge[][2] = {{0, 1},
+                         {0, 2},
+                         {1, 3},
+                         {2, 4},
+                         {3, 5},
+                         {4, 6},
+                         {5, 7},
+                         {6, 8},
+                         {7, 9},
+                         {8, 10},
+                         {5, 11},
+                         {6, 12},
+                         {11, 13},
+                         {12, 14},
+                         {13, 15},
+                         {14, 16},
+                         {11, 12}};
+  cv::Mat vis_img = img.clone();
+  for (int batchid = 0; batchid < results.size(); batchid++) {
+    for (int i = 0; i < results[batchid].num_joints; i++) {
+      if (results[batchid].keypoints[i * 3] > 0.5) {
+        int x_coord = int(results[batchid].keypoints[i * 3 + 1]);
+        int y_coord = int(results[batchid].keypoints[i * 3 + 2]);
+        cv::circle(vis_img,
+                   cv::Point2d(x_coord, y_coord),
+                   1,
+                   cv::Scalar(0, 0, 255),
+                   2);
+      }
+    }
+    for (int i = 0; i < results[batchid].num_joints; i++) {
+      int x_start = int(results[batchid].keypoints[edge[i][0] * 3 + 1]);
+      int y_start = int(results[batchid].keypoints[edge[i][0] * 3 + 2]);
+      int x_end = int(results[batchid].keypoints[edge[i][1] * 3 + 1]);
+      int y_end = int(results[batchid].keypoints[edge[i][1] * 3 + 2]);
+      cv::line(vis_img,
+               cv::Point2d(x_start, y_start),
+               cv::Point2d(x_end, y_end),
+               colormap[i],
+               1);
+    }
+  }
+  return vis_img;
+}
+
+void KeyPointDetector::Preprocess(const cv::Mat& ori_im) {
+  // Clone the image : keep the original mat for postprocess
+  cv::Mat im = ori_im.clone();
+  cv::cvtColor(im, im, cv::COLOR_BGR2RGB);
+  preprocessor_.Run(&im, &inputs_);
+}
+
+void KeyPointDetector::Postprocess(std::vector<float>& output,
+                                   std::vector<int> output_shape,
+                                   std::vector<int64_t>& idxout,
+                                   std::vector<int> idx_shape,
+                                   std::vector<KeyPointResult>* result,
+                                   std::vector<std::vector<float>>& center_bs,
+                                   std::vector<std::vector<float>>& scale_bs) {
+  std::vector<float> preds(output_shape[1] * 3, 0);
+
+  for (int batchid = 0; batchid < output_shape[0]; batchid++) {
+    get_final_preds(output,
+                    output_shape,
+                    idxout,
+                    idx_shape,
+                    center_bs[batchid],
+                    scale_bs[batchid],
+                    preds,
+                    batchid,
+                    this->use_dark);
+    KeyPointResult result_item;
+    result_item.num_joints = output_shape[1];
+    result_item.keypoints.clear();
+    for (int i = 0; i < output_shape[1]; i++) {
+      result_item.keypoints.emplace_back(preds[i * 3]);
+      result_item.keypoints.emplace_back(preds[i * 3 + 1]);
+      result_item.keypoints.emplace_back(preds[i * 3 + 2]);
+    }
+    result->push_back(result_item);
+  }
+}
+
+void KeyPointDetector::Predict(const std::vector<cv::Mat> imgs,
+                               std::vector<std::vector<float>>& center_bs,
+                               std::vector<std::vector<float>>& scale_bs,
+                               const double threshold,
+                               const int warmup,
+                               const int repeats,
+                               std::vector<KeyPointResult>* result,
+                               std::vector<double>* times) {
+  auto preprocess_start = std::chrono::steady_clock::now();
+  int batch_size = imgs.size();
+
+  // in_data_batch
+  std::vector<float> in_data_all;
+  std::vector<float> im_shape_all(batch_size * 2);
+  std::vector<float> scale_factor_all(batch_size * 2);
+
+  // Preprocess image
+  for (int bs_idx = 0; bs_idx < batch_size; bs_idx++) {
+    cv::Mat im = imgs.at(bs_idx);
+    Preprocess(im);
+    im_shape_all[bs_idx * 2] = inputs_.im_shape_[0];
+    im_shape_all[bs_idx * 2 + 1] = inputs_.im_shape_[1];
+
+    scale_factor_all[bs_idx * 2] = inputs_.scale_factor_[0];
+    scale_factor_all[bs_idx * 2 + 1] = inputs_.scale_factor_[1];
+
+    // TODO: reduce cost time
+    in_data_all.insert(
+        in_data_all.end(), inputs_.im_data_.begin(), inputs_.im_data_.end());
+  }
+
+  // Prepare input tensor
+
+  auto input_names = predictor_->GetInputNames();
+  for (const auto& tensor_name : input_names) {
+    auto in_tensor = predictor_->GetInputHandle(tensor_name);
+    if (tensor_name == "image") {
+      int rh = inputs_.in_net_shape_[0];
+      int rw = inputs_.in_net_shape_[1];
+      in_tensor->Reshape({batch_size, 3, rh, rw});
+      in_tensor->CopyFromCpu(in_data_all.data());
+    } else if (tensor_name == "im_shape") {
+      in_tensor->Reshape({batch_size, 2});
+      in_tensor->CopyFromCpu(im_shape_all.data());
+    } else if (tensor_name == "scale_factor") {
+      in_tensor->Reshape({batch_size, 2});
+      in_tensor->CopyFromCpu(scale_factor_all.data());
+    }
+  }
+
+  auto preprocess_end = std::chrono::steady_clock::now();
+  std::vector<int> output_shape, idx_shape;
+  // Run predictor
+  // warmup
+  for (int i = 0; i < warmup; i++) {
+    predictor_->Run();
+    // Get output tensor
+    auto output_names = predictor_->GetOutputNames();
+    auto out_tensor = predictor_->GetOutputHandle(output_names[0]);
+    output_shape = out_tensor->shape();
+    // Calculate output length
+    int output_size = 1;
+    for (int j = 0; j < output_shape.size(); ++j) {
+      output_size *= output_shape[j];
+    }
+    output_data_.resize(output_size);
+    out_tensor->CopyToCpu(output_data_.data());
+
+    auto idx_tensor = predictor_->GetOutputHandle(output_names[1]);
+    idx_shape = idx_tensor->shape();
+    // Calculate output length
+    output_size = 1;
+    for (int j = 0; j < idx_shape.size(); ++j) {
+      output_size *= idx_shape[j];
+    }
+    idx_data_.resize(output_size);
+    idx_tensor->CopyToCpu(idx_data_.data());
+  }
+
+  auto inference_start = std::chrono::steady_clock::now();
+  for (int i = 0; i < repeats; i++) {
+    predictor_->Run();
+    // Get output tensor
+    auto output_names = predictor_->GetOutputNames();
+    auto out_tensor = predictor_->GetOutputHandle(output_names[0]);
+    output_shape = out_tensor->shape();
+    // Calculate output length
+    int output_size = 1;
+    for (int j = 0; j < output_shape.size(); ++j) {
+      output_size *= output_shape[j];
+    }
+    if (output_size < 6) {
+      std::cerr << "[WARNING] No object detected." << std::endl;
+    }
+    output_data_.resize(output_size);
+    out_tensor->CopyToCpu(output_data_.data());
+
+    auto idx_tensor = predictor_->GetOutputHandle(output_names[1]);
+    idx_shape = idx_tensor->shape();
+    // Calculate output length
+    output_size = 1;
+    for (int j = 0; j < idx_shape.size(); ++j) {
+      output_size *= idx_shape[j];
+    }
+    idx_data_.resize(output_size);
+    idx_tensor->CopyToCpu(idx_data_.data());
+  }
+  auto inference_end = std::chrono::steady_clock::now();
+  auto postprocess_start = std::chrono::steady_clock::now();
+  // Postprocessing result
+  Postprocess(output_data_,
+              output_shape,
+              idx_data_,
+              idx_shape,
+              result,
+              center_bs,
+              scale_bs);
+  auto postprocess_end = std::chrono::steady_clock::now();
+
+  std::chrono::duration<float> preprocess_diff =
+      preprocess_end - preprocess_start;
+  times->push_back(double(preprocess_diff.count() * 1000));
+  std::chrono::duration<float> inference_diff = inference_end - inference_start;
+  times->push_back(double(inference_diff.count() / repeats * 1000));
+  std::chrono::duration<float> postprocess_diff =
+      postprocess_end - postprocess_start;
+  times->push_back(double(postprocess_diff.count() * 1000));
+}
+
+}  // namespace PaddleDetection

deploy/cpp/src/keypoint_postprocess.cc

+//   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.
+#include "include/keypoint_postprocess.h"
+#include <math.h>
+#define PI 3.1415926535
+#define HALF_CIRCLE_DEGREE 180
+
+namespace PaddleDetection {
+
+cv::Point2f get_3rd_point(cv::Point2f& a, cv::Point2f& b) {
+  cv::Point2f direct{a.x - b.x, a.y - b.y};
+  return cv::Point2f(a.x - direct.y, a.y + direct.x);
+}
+
+std::vector<float> get_dir(float src_point_x,
+                           float src_point_y,
+                           float rot_rad) {
+  float sn = sin(rot_rad);
+  float cs = cos(rot_rad);
+  std::vector<float> src_result{0.0, 0.0};
+  src_result[0] = src_point_x * cs - src_point_y * sn;
+  src_result[1] = src_point_x * sn + src_point_y * cs;
+  return src_result;
+}
+
+void affine_tranform(
+    float pt_x, float pt_y, cv::Mat& trans, std::vector<float>& preds, int p) {
+  double new1[3] = {pt_x, pt_y, 1.0};
+  cv::Mat new_pt(3, 1, trans.type(), new1);
+  cv::Mat w = trans * new_pt;
+  preds[p * 3 + 1] = static_cast<float>(w.at<double>(0, 0));
+  preds[p * 3 + 2] = static_cast<float>(w.at<double>(1, 0));
+}
+
+void get_affine_transform(std::vector<float>& center,
+                          std::vector<float>& scale,
+                          float rot,
+                          std::vector<int>& output_size,
+                          cv::Mat& trans,
+                          int inv) {
+  float src_w = scale[0];
+  float dst_w = static_cast<float>(output_size[0]);
+  float dst_h = static_cast<float>(output_size[1]);
+  float rot_rad = rot * PI / HALF_CIRCLE_DEGREE;
+  std::vector<float> src_dir = get_dir(-0.5 * src_w, 0, rot_rad);
+  std::vector<float> dst_dir{-0.5f * dst_w, 0.0};
+  cv::Point2f srcPoint2f[3], dstPoint2f[3];
+  srcPoint2f[0] = cv::Point2f(center[0], center[1]);
+  srcPoint2f[1] = cv::Point2f(center[0] + src_dir[0], center[1] + src_dir[1]);
+  srcPoint2f[2] = get_3rd_point(srcPoint2f[0], srcPoint2f[1]);
+
+  dstPoint2f[0] = cv::Point2f(dst_w * 0.5, dst_h * 0.5);
+  dstPoint2f[1] =
+      cv::Point2f(dst_w * 0.5 + dst_dir[0], dst_h * 0.5 + dst_dir[1]);
+  dstPoint2f[2] = get_3rd_point(dstPoint2f[0], dstPoint2f[1]);
+  if (inv == 0) {
+    trans = cv::getAffineTransform(srcPoint2f, dstPoint2f);
+  } else {
+    trans = cv::getAffineTransform(dstPoint2f, srcPoint2f);
+  }
+}
+
+void transform_preds(std::vector<float>& coords,
+                     std::vector<float>& center,
+                     std::vector<float>& scale,
+                     std::vector<int>& output_size,
+                     std::vector<int>& dim,
+                     std::vector<float>& target_coords,
+                     bool affine) {
+  if (affine) {
+    cv::Mat trans(2, 3, CV_64FC1);
+    get_affine_transform(center, scale, 0, output_size, trans, 1);
+    for (int p = 0; p < dim[1]; ++p) {
+      affine_tranform(
+          coords[p * 2], coords[p * 2 + 1], trans, target_coords, p);
+    }
+  } else {
+    float heat_w = static_cast<float>(output_size[0]);
+    float heat_h = static_cast<float>(output_size[1]);
+    float x_scale = scale[0] / heat_w;
+    float y_scale = scale[1] / heat_h;
+    float offset_x = center[0] - scale[0] / 2.;
+    float offset_y = center[1] - scale[1] / 2.;
+    for (int i = 0; i < dim[1]; i++) {
+      target_coords[i * 3 + 1] = x_scale * coords[i * 2] + offset_x;
+      target_coords[i * 3 + 2] = y_scale * coords[i * 2 + 1] + offset_y;
+    }
+  }
+}
+
+// only for batchsize == 1
+void get_max_preds(float* heatmap,
+                   std::vector<int>& dim,
+                   std::vector<float>& preds,
+                   float* maxvals,
+                   int batchid,
+                   int joint_idx) {
+  int num_joints = dim[1];
+  int width = dim[3];
+  std::vector<int> idx;
+  idx.resize(num_joints * 2);
+
+  for (int j = 0; j < dim[1]; j++) {
+    float* index = &(
+        heatmap[batchid * num_joints * dim[2] * dim[3] + j * dim[2] * dim[3]]);
+    float* end = index + dim[2] * dim[3];
+    float* max_dis = std::max_element(index, end);
+    auto max_id = std::distance(index, max_dis);
+    maxvals[j] = *max_dis;
+    if (*max_dis > 0) {
+      preds[j * 2] = static_cast<float>(max_id % width);
+      preds[j * 2 + 1] = static_cast<float>(max_id / width);
+    }
+  }
+}
+
+void dark_parse(std::vector<float>& heatmap,
+                std::vector<int>& dim,
+                std::vector<float>& coords,
+                int px,
+                int py,
+                int index,
+                int ch) {
+  /*DARK postpocessing, Zhang et al. Distribution-Aware Coordinate
+  Representation for Human Pose Estimation (CVPR 2020).
+  1) offset = - hassian.inv() * derivative
+  2) dx = (heatmap[x+1] - heatmap[x-1])/2.
+  3) dxx = (dx[x+1] - dx[x-1])/2.
+  4) derivative = Mat([dx, dy])
+  5) hassian = Mat([[dxx, dxy], [dxy, dyy]])
+  */
+  std::vector<float>::const_iterator first1 = heatmap.begin() + index;
+  std::vector<float>::const_iterator last1 =
+      heatmap.begin() + index + dim[2] * dim[3];
+  std::vector<float> heatmap_ch(first1, last1);
+  cv::Mat heatmap_mat = cv::Mat(heatmap_ch).reshape(0, dim[2]);
+  heatmap_mat.convertTo(heatmap_mat, CV_32FC1);
+  cv::GaussianBlur(heatmap_mat, heatmap_mat, cv::Size(3, 3), 0, 0);
+  heatmap_mat = heatmap_mat.reshape(1, 1);
+  heatmap_ch = std::vector<float>(heatmap_mat.reshape(1, 1));
+
+  float epsilon = 1e-10;
+  // sample heatmap to get values in around target location
+  float xy = log(fmax(heatmap_ch[py * dim[3] + px], epsilon));
+  float xr = log(fmax(heatmap_ch[py * dim[3] + px + 1], epsilon));
+  float xl = log(fmax(heatmap_ch[py * dim[3] + px - 1], epsilon));
+
+  float xr2 = log(fmax(heatmap_ch[py * dim[3] + px + 2], epsilon));
+  float xl2 = log(fmax(heatmap_ch[py * dim[3] + px - 2], epsilon));
+  float yu = log(fmax(heatmap_ch[(py + 1) * dim[3] + px], epsilon));
+  float yd = log(fmax(heatmap_ch[(py - 1) * dim[3] + px], epsilon));
+  float yu2 = log(fmax(heatmap_ch[(py + 2) * dim[3] + px], epsilon));
+  float yd2 = log(fmax(heatmap_ch[(py - 2) * dim[3] + px], epsilon));
+  float xryu = log(fmax(heatmap_ch[(py + 1) * dim[3] + px + 1], epsilon));
+  float xryd = log(fmax(heatmap_ch[(py - 1) * dim[3] + px + 1], epsilon));
+  float xlyu = log(fmax(heatmap_ch[(py + 1) * dim[3] + px - 1], epsilon));
+  float xlyd = log(fmax(heatmap_ch[(py - 1) * dim[3] + px - 1], epsilon));
+
+  // compute dx/dy and dxx/dyy with sampled values
+  float dx = 0.5 * (xr - xl);
+  float dy = 0.5 * (yu - yd);
+  float dxx = 0.25 * (xr2 - 2 * xy + xl2);
+  float dxy = 0.25 * (xryu - xryd - xlyu + xlyd);
+  float dyy = 0.25 * (yu2 - 2 * xy + yd2);
+
+  // finally get offset by derivative and hassian, which combined by dx/dy and
+  // dxx/dyy
+  if (dxx * dyy - dxy * dxy != 0) {
+    float M[2][2] = {dxx, dxy, dxy, dyy};
+    float D[2] = {dx, dy};
+    cv::Mat hassian(2, 2, CV_32F, M);
+    cv::Mat derivative(2, 1, CV_32F, D);
+    cv::Mat offset = -hassian.inv() * derivative;
+    coords[ch * 2] += offset.at<float>(0, 0);
+    coords[ch * 2 + 1] += offset.at<float>(1, 0);
+  }
+}
+
+void get_final_preds(std::vector<float>& heatmap,
+                     std::vector<int>& dim,
+                     std::vector<int64_t>& idxout,
+                     std::vector<int>& idxdim,
+                     std::vector<float>& center,
+                     std::vector<float> scale,
+                     std::vector<float>& preds,
+                     int batchid,
+                     bool DARK) {
+  std::vector<float> coords;
+  coords.resize(dim[1] * 2);
+  int heatmap_height = dim[2];
+  int heatmap_width = dim[3];
+
+  for (int j = 0; j < dim[1]; ++j) {
+    int index = (batchid * dim[1] + j) * dim[2] * dim[3];
+
+    int idx = idxout[batchid * dim[1] + j];
+    preds[j * 3] = heatmap[index + idx];
+    coords[j * 2] = idx % heatmap_width;
+    coords[j * 2 + 1] = idx / heatmap_width;
+
+    int px = int(coords[j * 2] + 0.5);
+    int py = int(coords[j * 2 + 1] + 0.5);
+
+    if (DARK && px > 1 && px < heatmap_width - 2 && py > 1 &&
+        py < heatmap_height - 2) {
+      dark_parse(heatmap, dim, coords, px, py, index, j);
+    } else {
+      if (px > 0 && px < heatmap_width - 1) {
+        float diff_x = heatmap[index + py * dim[3] + px + 1] -
+                       heatmap[index + py * dim[3] + px - 1];
+        coords[j * 2] += diff_x > 0 ? 1 : -1 * 0.25;
+      }
+      if (py > 0 && py < heatmap_height - 1) {
+        float diff_y = heatmap[index + (py + 1) * dim[3] + px] -
+                       heatmap[index + (py - 1) * dim[3] + px];
+        coords[j * 2 + 1] += diff_y > 0 ? 1 : -1 * 0.25;
+      }
+    }
+  }
+
+  std::vector<int> img_size{heatmap_width, heatmap_height};
+  transform_preds(coords, center, scale, img_size, dim, preds);
+}
+
+// Run predictor
+KeyPointResult PoseSmooth::smooth_process(KeyPointResult* result) {
+  KeyPointResult keypoint_smoothed = *result;
+  if (this->x_prev_hat.num_joints == -1) {
+    this->x_prev_hat = *result;
+    this->dx_prev_hat = *result;
+    std::fill(dx_prev_hat.keypoints.begin(), dx_prev_hat.keypoints.end(), 0.);
+    return keypoint_smoothed;
+  } else {
+    for (int i = 0; i < result->num_joints; i++) {
+      this->PointSmooth(result, &keypoint_smoothed, this->thresholds, i);
+    }
+    return keypoint_smoothed;
+  }
+}
+
+void PoseSmooth::PointSmooth(KeyPointResult* result,
+                             KeyPointResult* keypoint_smoothed,
+                             std::vector<float> thresholds,
+                             int index) {
+  float distance = sqrt(pow((result->keypoints[index * 3 + 1] -
+                             this->x_prev_hat.keypoints[index * 3 + 1]) /
+                                this->width,
+                            2) +
+                        pow((result->keypoints[index * 3 + 2] -
+                             this->x_prev_hat.keypoints[index * 3 + 2]) /
+                                this->height,
+                            2));
+  if (distance < thresholds[index] * this->thres_mult) {
+    keypoint_smoothed->keypoints[index * 3 + 1] =
+        this->x_prev_hat.keypoints[index * 3 + 1];
+    keypoint_smoothed->keypoints[index * 3 + 2] =
+        this->x_prev_hat.keypoints[index * 3 + 2];
+  } else {
+    if (this->filter_type == "OneEuro") {
+      keypoint_smoothed->keypoints[index * 3 + 1] =
+          this->OneEuroFilter(result->keypoints[index * 3 + 1],
+                              this->x_prev_hat.keypoints[index * 3 + 1],
+                              index * 3 + 1);
+      keypoint_smoothed->keypoints[index * 3 + 2] =
+          this->OneEuroFilter(result->keypoints[index * 3 + 2],
+                              this->x_prev_hat.keypoints[index * 3 + 2],
+                              index * 3 + 2);
+    } else {
+      keypoint_smoothed->keypoints[index * 3 + 1] =
+          this->ExpSmoothing(result->keypoints[index * 3 + 1],
+                             this->x_prev_hat.keypoints[index * 3 + 1],
+                             index * 3 + 1);
+      keypoint_smoothed->keypoints[index * 3 + 2] =
+          this->ExpSmoothing(result->keypoints[index * 3 + 2],
+                             this->x_prev_hat.keypoints[index * 3 + 2],
+                             index * 3 + 2);
+    }
+  }
+  return;
+}
+
+float PoseSmooth::OneEuroFilter(float x_cur, float x_pre, int loc) {
+  float te = 1.0;
+  this->alpha = this->smoothing_factor(te, this->fc_d);
+  float dx_cur = (x_cur - x_pre) / te;
+  float dx_cur_hat =
+      this->ExpSmoothing(dx_cur, this->dx_prev_hat.keypoints[loc]);
+
+  float fc = this->fc_min + this->beta * abs(dx_cur_hat);
+  this->alpha = this->smoothing_factor(te, fc);
+  float x_cur_hat = this->ExpSmoothing(x_cur, x_pre);
+  this->x_prev_hat.keypoints[loc] = x_cur_hat;
+  this->dx_prev_hat.keypoints[loc] = dx_cur_hat;
+  return x_cur_hat;
+}
+
+float PoseSmooth::smoothing_factor(float te, float fc) {
+  float r = 2 * PI * fc * te;
+  return r / (r + 1);
+}
+
+float PoseSmooth::ExpSmoothing(float x_cur, float x_pre, int loc) {
+  return this->alpha * x_cur + (1 - this->alpha) * x_pre;
+}
+}  // namespace PaddleDetection

deploy/cpp/src/lapjv.cpp

+//   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.
+
+// The code is based on:
+// https://github.com/gatagat/lap/blob/master/lap/lapjv.cpp
+// Ths copyright of gatagat/lap is as follows:
+// MIT License
+
+#include <stdio.h>
+#include <stdlib.h>
+#include <string.h>
+
+#include "include/lapjv.h"
+
+namespace PaddleDetection {
+
+/** Column-reduction and reduction transfer for a dense cost matrix.
+ */
+int _ccrrt_dense(const int n, float *cost[],
+                     int *free_rows, int *x, int *y, float *v)
+{
+    int n_free_rows;
+    bool *unique;
+
+    for (int i = 0; i < n; i++) {
+        x[i] = -1;
+        v[i] = LARGE;
+        y[i] = 0;
+    }
+    for (int i = 0; i < n; i++) {
+        for (int j = 0; j < n; j++) {
+            const float c = cost[i][j];
+            if (c < v[j]) {
+                v[j] = c;
+                y[j] = i;
+            }
+        }
+    }
+    NEW(unique, bool, n);
+    memset(unique, TRUE, n);
+    {
+        int j = n;
+        do {
+            j--;
+            const int i = y[j];
+            if (x[i] < 0) {
+                x[i] = j;
+            } else {
+                unique[i] = FALSE;
+                y[j] = -1;
+            }
+        } while (j > 0);
+    }
+    n_free_rows = 0;
+    for (int i = 0; i < n; i++) {
+        if (x[i] < 0) {
+            free_rows[n_free_rows++] = i;
+        } else if (unique[i]) {
+            const int j = x[i];
+            float min = LARGE;
+            for (int j2 = 0; j2 < n; j2++) {
+                if (j2 == (int)j) {
+                    continue;
+                }
+                const float c = cost[i][j2] - v[j2];
+                if (c < min) {
+                    min = c;
+                }
+            }
+            v[j] -= min;
+        }
+    }
+    FREE(unique);
+    return n_free_rows;
+}
+
+
+/** Augmenting row reduction for a dense cost matrix.
+ */
+int _carr_dense(
+    const int n, float *cost[],
+    const int n_free_rows,
+    int *free_rows, int *x, int *y, float *v)
+{
+    int current = 0;
+    int new_free_rows = 0;
+    int rr_cnt = 0;
+    while (current < n_free_rows) {
+        int i0;
+        int j1, j2;
+        float v1, v2, v1_new;
+        bool v1_lowers;
+
+        rr_cnt++;
+        const int free_i = free_rows[current++];
+        j1 = 0;
+        v1 = cost[free_i][0] - v[0];
+        j2 = -1;
+        v2 = LARGE;
+        for (int j = 1; j < n; j++) {
+            const float c = cost[free_i][j] - v[j];
+            if (c < v2) {
+                if (c >= v1) {
+                    v2 = c;
+                    j2 = j;
+                } else {
+                    v2 = v1;
+                    v1 = c;
+                    j2 = j1;
+                    j1 = j;
+                }
+            }
+        }
+        i0 = y[j1];
+        v1_new = v[j1] - (v2 - v1);
+        v1_lowers = v1_new < v[j1];
+        if (rr_cnt < current * n) {
+            if (v1_lowers) {
+                v[j1] = v1_new;
+            } else if (i0 >= 0 && j2 >= 0) {
+                j1 = j2;
+                i0 = y[j2];
+            }
+            if (i0 >= 0) {
+                if (v1_lowers) {
+                    free_rows[--current] = i0;
+                } else {
+                    free_rows[new_free_rows++] = i0;
+                }
+            }
+        } else {
+            if (i0 >= 0) {
+                free_rows[new_free_rows++] = i0;
+            }
+        }
+        x[free_i] = j1;
+        y[j1] = free_i;
+    }
+    return new_free_rows;
+}
+
+
+/** Find columns with minimum d[j] and put them on the SCAN list.
+ */
+int _find_dense(const int n, int lo, float *d, int *cols, int *y)
+{
+    int hi = lo + 1;
+    float mind = d[cols[lo]];
+    for (int k = hi; k < n; k++) {
+        int j = cols[k];
+        if (d[j] <= mind) {
+            if (d[j] < mind) {
+                hi = lo;
+                mind = d[j];
+            }
+            cols[k] = cols[hi];
+            cols[hi++] = j;
+        }
+    }
+    return hi;
+}
+
+
+// Scan all columns in TODO starting from arbitrary column in SCAN
+// and try to decrease d of the TODO columns using the SCAN column.
+int _scan_dense(const int n, float *cost[],
+                    int *plo, int*phi,
+                    float *d, int *cols, int *pred,
+                    int *y, float *v)
+{
+    int lo = *plo;
+    int hi = *phi;
+    float h, cred_ij;
+
+    while (lo != hi) {
+        int j = cols[lo++];
+        const int i = y[j];
+        const float mind = d[j];
+        h = cost[i][j] - v[j] - mind;
+        // For all columns in TODO
+        for (int k = hi; k < n; k++) {
+            j = cols[k];
+            cred_ij = cost[i][j] - v[j] - h;
+            if (cred_ij < d[j]) {
+                d[j] = cred_ij;
+                pred[j] = i;
+                if (cred_ij == mind) {
+                    if (y[j] < 0) {
+                        return j;
+                    }
+                    cols[k] = cols[hi];
+                    cols[hi++] = j;
+                }
+            }
+        }
+    }
+    *plo = lo;
+    *phi = hi;
+    return -1;
+}
+
+
+/** Single iteration of modified Dijkstra shortest path algorithm as explained in the JV paper.
+ *
+ * This is a dense matrix version.
+ *
+ * \return The closest free column index.
+ */
+int find_path_dense(
+    const int n, float *cost[],
+    const int start_i,
+    int *y, float *v,
+    int *pred)
+{
+    int lo = 0, hi = 0;
+    int final_j = -1;
+    int n_ready = 0;
+    int *cols;
+    float *d;
+
+    NEW(cols, int, n);
+    NEW(d, float, n);
+
+    for (int i = 0; i < n; i++) {
+        cols[i] = i;
+        pred[i] = start_i;
+        d[i] = cost[start_i][i] - v[i];
+    }
+    while (final_j == -1) {
+        // No columns left on the SCAN list.
+        if (lo == hi) {
+            n_ready = lo;
+            hi = _find_dense(n, lo, d, cols, y);
+            for (int k = lo; k < hi; k++) {
+                const int j = cols[k];
+                if (y[j] < 0) {
+                    final_j = j;
+                }
+            }
+        }
+        if (final_j == -1) {
+            final_j = _scan_dense(
+                    n, cost, &lo, &hi, d, cols, pred, y, v);
+        }
+    }
+
+    {
+        const float mind = d[cols[lo]];
+        for (int k = 0; k < n_ready; k++) {
+            const int j = cols[k];
+            v[j] += d[j] - mind;
+        }
+    }
+
+    FREE(cols);
+    FREE(d);
+
+    return final_j;
+}
+
+
+/** Augment for a dense cost matrix.
+ */
+int _ca_dense(
+    const int n, float *cost[],
+    const int n_free_rows,
+    int *free_rows, int *x, int *y, float *v)
+{
+    int *pred;
+
+    NEW(pred, int, n);
+
+    for (int *pfree_i = free_rows; pfree_i < free_rows + n_free_rows; pfree_i++) {
+        int i = -1, j;
+        int k = 0;
+
+        j = find_path_dense(n, cost, *pfree_i, y, v, pred);
+        while (i != *pfree_i) {
+            i = pred[j];
+            y[j] = i;
+            SWAP_INDICES(j, x[i]);
+            k++;
+        }
+    }
+    FREE(pred);
+    return 0;
+}
+
+
+/** Solve dense sparse LAP.
+ */
+int lapjv_internal(
+    const cv::Mat &cost, const bool extend_cost, const float cost_limit,
+    int *x, int *y ) {
+    int n_rows = cost.rows;
+    int n_cols = cost.cols;
+    int n;
+    if (n_rows == n_cols) {
+      n = n_rows;
+    } else if (!extend_cost) {
+      throw std::invalid_argument("Square cost array expected. If cost is intentionally non-square, pass extend_cost=True.");
+    }
+
+    // Get extend cost
+    if (extend_cost || cost_limit < LARGE) {
+      n = n_rows + n_cols;
+    }
+    cv::Mat cost_expand(n, n, CV_32F);
+    float expand_value;
+    if (cost_limit < LARGE) {
+      expand_value = cost_limit / 2;
+    } else {
+      double max_v;
+      minMaxLoc(cost, nullptr, &max_v);
+      expand_value = (float)max_v + 1;
+    }
+
+    for (int i = 0; i < n; ++i) {
+      for (int j = 0; j < n; ++j) {
+        cost_expand.at<float>(i, j) = expand_value;
+        if (i >= n_rows && j >= n_cols) {
+          cost_expand.at<float>(i, j) = 0;
+        } else if (i < n_rows && j < n_cols) {
+          cost_expand.at<float>(i, j) = cost.at<float>(i, j);
+        }
+      }
+    } 
+
+    // Convert Mat to pointer array
+    float **cost_ptr;
+    NEW(cost_ptr, float *, n);
+    for (int i = 0; i < n; ++i) {
+      NEW(cost_ptr[i], float, n);
+    }
+    for (int i = 0; i < n; ++i) {
+      for (int j = 0; j < n; ++j) {
+        cost_ptr[i][j] = cost_expand.at<float>(i, j);
+      }
+    }
+
+    int ret;
+    int *free_rows;
+    float *v;
+    int *x_c;
+    int *y_c;
+
+    NEW(free_rows, int, n);
+    NEW(v, float, n);
+    NEW(x_c, int, n);
+    NEW(y_c, int, n);
+
+    ret = _ccrrt_dense(n, cost_ptr, free_rows, x_c, y_c, v);
+    int i = 0;
+    while (ret > 0 && i < 2) {
+      ret = _carr_dense(n, cost_ptr, ret, free_rows, x_c, y_c, v);
+      i++;
+    }
+    if (ret > 0) {
+      ret = _ca_dense(n, cost_ptr, ret, free_rows, x_c, y_c, v);
+    }
+    FREE(v);
+    FREE(free_rows);
+    for (int i = 0; i < n; ++i) {
+      FREE(cost_ptr[i]);
+    }
+    FREE(cost_ptr);
+    if (ret != 0) {
+      if (ret == -1){
+        throw "Out of memory.";
+      }
+      throw "Unknown error (lapjv_internal)";
+    }
+    // Get output of x, y, opt
+    for (int i = 0; i < n; ++i) {
+      if (i < n_rows) {
+        x[i] = x_c[i];
+        if (x[i] >= n_cols) {
+          x[i] = -1;
+        }
+      }      
+      if (i < n_cols) {
+        y[i] = y_c[i];
+        if (y[i] >= n_rows) {
+          y[i] = -1;
+        }
+      } 
+    }
+    
+    FREE(x_c);
+    FREE(y_c);
+    return ret;
+}
+
+} // namespace PaddleDetection

deploy/cpp/src/main.cc

+//   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.
+
+#include <glog/logging.h>
+
+#include <math.h>
+#include <sys/stat.h>
+#include <sys/types.h>
+#include <algorithm>
+#include <iostream>
+#include <numeric>
+#include <string>
+#include <vector>
+
+#ifdef _WIN32
+#include <direct.h>
+#include <io.h>
+#elif LINUX
+#include <stdarg.h>
+#include <sys/stat.h>
+#endif
+
+#include <gflags/gflags.h>
+#include "include/object_detector.h"
+
+DEFINE_string(model_dir, "", "Path of inference model");
+DEFINE_string(image_file, "", "Path of input image");
+DEFINE_string(image_dir,
+              "",
+              "Dir of input image, `image_file` has a higher priority.");
+DEFINE_int32(batch_size, 1, "batch_size");
+DEFINE_string(
+    video_file,
+    "",
+    "Path of input video, `video_file` or `camera_id` has a highest priority.");
+DEFINE_int32(camera_id, -1, "Device id of camera to predict");
+DEFINE_bool(
+    use_gpu,
+    false,
+    "Deprecated, please use `--device` to set the device you want to run.");
+DEFINE_string(device,
+              "CPU",
+              "Choose the device you want to run, it can be: CPU/GPU/XPU, "
+              "default is CPU.");
+DEFINE_double(threshold, 0.5, "Threshold of score.");
+DEFINE_string(output_dir, "output", "Directory of output visualization files.");
+DEFINE_string(run_mode,
+              "paddle",
+              "Mode of running(paddle/trt_fp32/trt_fp16/trt_int8)");
+DEFINE_int32(gpu_id, 0, "Device id of GPU to execute");
+DEFINE_bool(run_benchmark,
+            false,
+            "Whether to predict a image_file repeatedly for benchmark");
+DEFINE_bool(use_mkldnn, false, "Whether use mkldnn with CPU");
+DEFINE_int32(cpu_threads, 1, "Num of threads with CPU");
+DEFINE_int32(trt_min_shape, 1, "Min shape of TRT DynamicShapeI");
+DEFINE_int32(trt_max_shape, 1280, "Max shape of TRT DynamicShapeI");
+DEFINE_int32(trt_opt_shape, 640, "Opt shape of TRT DynamicShapeI");
+DEFINE_bool(trt_calib_mode,
+            false,
+            "If the model is produced by TRT offline quantitative calibration, "
+            "trt_calib_mode need to set True");
+
+void PrintBenchmarkLog(std::vector<double> det_time, int img_num) {
+  LOG(INFO) << "----------------------- Config info -----------------------";
+  LOG(INFO) << "runtime_device: " << FLAGS_device;
+  LOG(INFO) << "ir_optim: "
+            << "True";
+  LOG(INFO) << "enable_memory_optim: "
+            << "True";
+  int has_trt = FLAGS_run_mode.find("trt");
+  if (has_trt >= 0) {
+    LOG(INFO) << "enable_tensorrt: "
+              << "True";
+    std::string precision = FLAGS_run_mode.substr(4, 8);
+    LOG(INFO) << "precision: " << precision;
+  } else {
+    LOG(INFO) << "enable_tensorrt: "
+              << "False";
+    LOG(INFO) << "precision: "
+              << "fp32";
+  }
+  LOG(INFO) << "enable_mkldnn: " << (FLAGS_use_mkldnn ? "True" : "False");
+  LOG(INFO) << "cpu_math_library_num_threads: " << FLAGS_cpu_threads;
+  LOG(INFO) << "----------------------- Data info -----------------------";
+  LOG(INFO) << "batch_size: " << FLAGS_batch_size;
+  LOG(INFO) << "input_shape: "
+            << "dynamic shape";
+  LOG(INFO) << "----------------------- Model info -----------------------";
+  FLAGS_model_dir.erase(FLAGS_model_dir.find_last_not_of("/") + 1);
+  LOG(INFO) << "model_name: "
+            << FLAGS_model_dir.substr(FLAGS_model_dir.find_last_of('/') + 1);
+  LOG(INFO) << "----------------------- Perf info ------------------------";
+  LOG(INFO) << "Total number of predicted data: " << img_num
+            << " and total time spent(ms): "
+            << std::accumulate(det_time.begin(), det_time.end(), 0);
+  LOG(INFO) << "preproce_time(ms): " << det_time[0] / img_num
+            << ", inference_time(ms): " << det_time[1] / img_num
+            << ", postprocess_time(ms): " << det_time[2] / img_num;
+}
+
+static std::string DirName(const std::string& filepath) {
+  auto pos = filepath.rfind(OS_PATH_SEP);
+  if (pos == std::string::npos) {
+    return "";
+  }
+  return filepath.substr(0, pos);
+}
+
+static bool PathExists(const std::string& path) {
+#ifdef _WIN32
+  struct _stat buffer;
+  return (_stat(path.c_str(), &buffer) == 0);
+#else
+  struct stat buffer;
+  return (stat(path.c_str(), &buffer) == 0);
+#endif  // !_WIN32
+}
+
+static void MkDir(const std::string& path) {
+  if (PathExists(path)) return;
+  int ret = 0;
+#ifdef _WIN32
+  ret = _mkdir(path.c_str());
+#else
+  ret = mkdir(path.c_str(), 0755);
+#endif  // !_WIN32
+  if (ret != 0) {
+    std::string path_error(path);
+    path_error += " mkdir failed!";
+    throw std::runtime_error(path_error);
+  }
+}
+
+static void MkDirs(const std::string& path) {
+  if (path.empty()) return;
+  if (PathExists(path)) return;
+
+  MkDirs(DirName(path));
+  MkDir(path);
+}
+
+void PredictVideo(const std::string& video_path,
+                  PaddleDetection::ObjectDetector* det,
+                  const std::string& output_dir = "output") {
+  // Open video
+  cv::VideoCapture capture;
+  std::string video_out_name = "output.mp4";
+  if (FLAGS_camera_id != -1) {
+    capture.open(FLAGS_camera_id);
+  } else {
+    capture.open(video_path.c_str());
+    video_out_name =
+        video_path.substr(video_path.find_last_of(OS_PATH_SEP) + 1);
+  }
+  if (!capture.isOpened()) {
+    printf("can not open video : %s\n", video_path.c_str());
+    return;
+  }
+
+  // Get Video info : resolution, fps, frame count
+  int video_width = static_cast<int>(capture.get(CV_CAP_PROP_FRAME_WIDTH));
+  int video_height = static_cast<int>(capture.get(CV_CAP_PROP_FRAME_HEIGHT));
+  int video_fps = static_cast<int>(capture.get(CV_CAP_PROP_FPS));
+  int video_frame_count =
+      static_cast<int>(capture.get(CV_CAP_PROP_FRAME_COUNT));
+  printf("fps: %d, frame_count: %d\n", video_fps, video_frame_count);
+
+  // Create VideoWriter for output
+  cv::VideoWriter video_out;
+  std::string video_out_path(output_dir);
+  if (output_dir.rfind(OS_PATH_SEP) != output_dir.size() - 1) {
+    video_out_path += OS_PATH_SEP;
+  }
+  video_out_path += video_out_name;
+  video_out.open(video_out_path.c_str(),
+                 0x00000021,
+                 video_fps,
+                 cv::Size(video_width, video_height),
+                 true);
+  if (!video_out.isOpened()) {
+    printf("create video writer failed!\n");
+    return;
+  }
+
+  std::vector<PaddleDetection::ObjectResult> result;
+  std::vector<int> bbox_num;
+  std::vector<double> det_times;
+  auto labels = det->GetLabelList();
+  auto colormap = PaddleDetection::GenerateColorMap(labels.size());
+  // Capture all frames and do inference
+  cv::Mat frame;
+  int frame_id = 1;
+  bool is_rbox = false;
+  while (capture.read(frame)) {
+    if (frame.empty()) {
+      break;
+    }
+    std::vector<cv::Mat> imgs;
+    imgs.push_back(frame);
+    printf("detect frame: %d\n", frame_id);
+    det->Predict(imgs, FLAGS_threshold, 0, 1, &result, &bbox_num, &det_times);
+    std::vector<PaddleDetection::ObjectResult> out_result;
+    for (const auto& item : result) {
+      if (item.confidence < FLAGS_threshold || item.class_id == -1) {
+        continue;
+      }
+      out_result.push_back(item);
+      if (item.rect.size() > 6) {
+        is_rbox = true;
+        printf("class=%d confidence=%.4f rect=[%d %d %d %d %d %d %d %d]\n",
+               item.class_id,
+               item.confidence,
+               item.rect[0],
+               item.rect[1],
+               item.rect[2],
+               item.rect[3],
+               item.rect[4],
+               item.rect[5],
+               item.rect[6],
+               item.rect[7]);
+      } else {
+        printf("class=%d confidence=%.4f rect=[%d %d %d %d]\n",
+               item.class_id,
+               item.confidence,
+               item.rect[0],
+               item.rect[1],
+               item.rect[2],
+               item.rect[3]);
+      }
+    }
+
+    cv::Mat out_im = PaddleDetection::VisualizeResult(
+        frame, out_result, labels, colormap, is_rbox);
+
+    video_out.write(out_im);
+    frame_id += 1;
+  }
+  capture.release();
+  video_out.release();
+}
+
+void PredictImage(const std::vector<std::string> all_img_paths,
+                  const int batch_size,
+                  const double threshold,
+                  const bool run_benchmark,
+                  PaddleDetection::ObjectDetector* det,
+                  const std::string& output_dir = "output") {
+  std::vector<double> det_t = {0, 0, 0};
+  int steps = ceil(float(all_img_paths.size()) / batch_size);
+  printf("total images = %d, batch_size = %d, total steps = %d\n",
+         all_img_paths.size(),
+         batch_size,
+         steps);
+  for (int idx = 0; idx < steps; idx++) {
+    std::vector<cv::Mat> batch_imgs;
+    int left_image_cnt = all_img_paths.size() - idx * batch_size;
+    if (left_image_cnt > batch_size) {
+      left_image_cnt = batch_size;
+    }
+    for (int bs = 0; bs < left_image_cnt; bs++) {
+      std::string image_file_path = all_img_paths.at(idx * batch_size + bs);
+      cv::Mat im = cv::imread(image_file_path, 1);
+      batch_imgs.insert(batch_imgs.end(), im);
+    }
+
+    // Store all detected result
+    std::vector<PaddleDetection::ObjectResult> result;
+    std::vector<int> bbox_num;
+    std::vector<double> det_times;
+    bool is_rbox = false;
+    if (run_benchmark) {
+      det->Predict(
+          batch_imgs, threshold, 10, 10, &result, &bbox_num, &det_times);
+    } else {
+      det->Predict(batch_imgs, threshold, 0, 1, &result, &bbox_num, &det_times);
+      // get labels and colormap
+      auto labels = det->GetLabelList();
+      auto colormap = PaddleDetection::GenerateColorMap(labels.size());
+
+      int item_start_idx = 0;
+      for (int i = 0; i < left_image_cnt; i++) {
+        cv::Mat im = batch_imgs[i];
+        std::vector<PaddleDetection::ObjectResult> im_result;
+        int detect_num = 0;
+
+        for (int j = 0; j < bbox_num[i]; j++) {
+          PaddleDetection::ObjectResult item = result[item_start_idx + j];
+          if (item.confidence < threshold || item.class_id == -1) {
+            continue;
+          }
+          detect_num += 1;
+          im_result.push_back(item);
+          if (item.rect.size() > 6) {
+            is_rbox = true;
+            printf("class=%d confidence=%.4f rect=[%d %d %d %d %d %d %d %d]\n",
+                   item.class_id,
+                   item.confidence,
+                   item.rect[0],
+                   item.rect[1],
+                   item.rect[2],
+                   item.rect[3],
+                   item.rect[4],
+                   item.rect[5],
+                   item.rect[6],
+                   item.rect[7]);
+          } else {
+            printf("class=%d confidence=%.4f rect=[%d %d %d %d]\n",
+                   item.class_id,
+                   item.confidence,
+                   item.rect[0],
+                   item.rect[1],
+                   item.rect[2],
+                   item.rect[3]);
+          }
+        }
+        std::cout << all_img_paths.at(idx * batch_size + i)
+                  << " The number of detected box: " << detect_num << std::endl;
+        item_start_idx = item_start_idx + bbox_num[i];
+        // Visualization result
+        cv::Mat vis_img = PaddleDetection::VisualizeResult(
+            im, im_result, labels, colormap, is_rbox);
+        std::vector<int> compression_params;
+        compression_params.push_back(CV_IMWRITE_JPEG_QUALITY);
+        compression_params.push_back(95);
+        std::string output_path(output_dir);
+        if (output_dir.rfind(OS_PATH_SEP) != output_dir.size() - 1) {
+          output_path += OS_PATH_SEP;
+        }
+        std::string image_file_path = all_img_paths.at(idx * batch_size + i);
+        output_path +=
+            image_file_path.substr(image_file_path.find_last_of('/') + 1);
+        cv::imwrite(output_path, vis_img, compression_params);
+        printf("Visualized output saved as %s\n", output_path.c_str());
+      }
+    }
+    det_t[0] += det_times[0];
+    det_t[1] += det_times[1];
+    det_t[2] += det_times[2];
+    det_times.clear();
+  }
+  PrintBenchmarkLog(det_t, all_img_paths.size());
+}
+
+int main(int argc, char** argv) {
+  // Parsing command-line
+  google::ParseCommandLineFlags(&argc, &argv, true);
+  if (FLAGS_model_dir.empty() ||
+      (FLAGS_image_file.empty() && FLAGS_image_dir.empty() &&
+       FLAGS_video_file.empty())) {
+    std::cout << "Usage: ./main --model_dir=/PATH/TO/INFERENCE_MODEL/ "
+              << "--image_file=/PATH/TO/INPUT/IMAGE/" << std::endl;
+    return -1;
+  }
+  if (!(FLAGS_run_mode == "paddle" || FLAGS_run_mode == "trt_fp32" ||
+        FLAGS_run_mode == "trt_fp16" || FLAGS_run_mode == "trt_int8")) {
+    std::cout
+        << "run_mode should be 'paddle', 'trt_fp32', 'trt_fp16' or 'trt_int8'.";
+    return -1;
+  }
+  transform(FLAGS_device.begin(),
+            FLAGS_device.end(),
+            FLAGS_device.begin(),
+            ::toupper);
+  if (!(FLAGS_device == "CPU" || FLAGS_device == "GPU" ||
+        FLAGS_device == "XPU")) {
+    std::cout << "device should be 'CPU', 'GPU' or 'XPU'.";
+    return -1;
+  }
+  if (FLAGS_use_gpu) {
+    std::cout << "Deprecated, please use `--device` to set the device you want "
+                 "to run.";
+    return -1;
+  }
+  // Load model and create a object detector
+  PaddleDetection::ObjectDetector det(FLAGS_model_dir,
+                                      FLAGS_device,
+                                      FLAGS_use_mkldnn,
+                                      FLAGS_cpu_threads,
+                                      FLAGS_run_mode,
+                                      FLAGS_batch_size,
+                                      FLAGS_gpu_id,
+                                      FLAGS_trt_min_shape,
+                                      FLAGS_trt_max_shape,
+                                      FLAGS_trt_opt_shape,
+                                      FLAGS_trt_calib_mode);
+  // Do inference on input video or image
+  if (!PathExists(FLAGS_output_dir)) {
+    MkDirs(FLAGS_output_dir);
+  }
+  if (!FLAGS_video_file.empty() || FLAGS_camera_id != -1) {
+    PredictVideo(FLAGS_video_file, &det, FLAGS_output_dir);
+  } else if (!FLAGS_image_file.empty() || !FLAGS_image_dir.empty()) {
+    std::vector<std::string> all_img_paths;
+    std::vector<cv::String> cv_all_img_paths;
+    if (!FLAGS_image_file.empty()) {
+      all_img_paths.push_back(FLAGS_image_file);
+      if (FLAGS_batch_size > 1) {
+        std::cout << "batch_size should be 1, when set `image_file`."
+                  << std::endl;
+        return -1;
+      }
+    } else {
+      cv::glob(FLAGS_image_dir, cv_all_img_paths);
+      for (const auto& img_path : cv_all_img_paths) {
+        all_img_paths.push_back(img_path);
+      }
+    }
+    PredictImage(all_img_paths,
+                 FLAGS_batch_size,
+                 FLAGS_threshold,
+                 FLAGS_run_benchmark,
+                 &det,
+                 FLAGS_output_dir);
+  }
+  return 0;
+}

deploy/cpp/src/main_jde.cc

+//   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.
+
+#include <glog/logging.h>
+
+#include <math.h>
+#include <sys/stat.h>
+#include <sys/types.h>
+#include <algorithm>
+#include <iostream>
+#include <numeric>
+#include <string>
+#include <vector>
+
+#ifdef _WIN32
+#include <direct.h>
+#include <io.h>
+#elif LINUX
+#include <stdarg.h>
+#include <sys/stat.h>
+#endif
+
+#include <gflags/gflags.h>
+#include <opencv2/opencv.hpp>
+#include "include/jde_detector.h"
+#include "include/object_detector.h"
+
+DEFINE_string(model_dir, "", "Path of inference model");
+DEFINE_int32(batch_size, 1, "batch_size");
+DEFINE_string(
+    video_file,
+    "",
+    "Path of input video, `video_file` or `camera_id` has a highest priority.");
+DEFINE_int32(camera_id, -1, "Device id of camera to predict");
+DEFINE_bool(
+    use_gpu,
+    false,
+    "Deprecated, please use `--device` to set the device you want to run.");
+DEFINE_string(device,
+              "CPU",
+              "Choose the device you want to run, it can be: CPU/GPU/XPU, "
+              "default is CPU.");
+DEFINE_double(threshold, 0.5, "Threshold of score.");
+DEFINE_string(output_dir, "output", "Directory of output visualization files.");
+DEFINE_string(run_mode,
+              "paddle",
+              "Mode of running(paddle/trt_fp32/trt_fp16/trt_int8)");
+DEFINE_int32(gpu_id, 0, "Device id of GPU to execute");
+DEFINE_bool(run_benchmark,
+            false,
+            "Whether to predict a image_file repeatedly for benchmark");
+DEFINE_bool(use_mkldnn, false, "Whether use mkldnn with CPU");
+DEFINE_int32(cpu_threads, 1, "Num of threads with CPU");
+DEFINE_int32(trt_min_shape, 1, "Min shape of TRT DynamicShapeI");
+DEFINE_int32(trt_max_shape, 1280, "Max shape of TRT DynamicShapeI");
+DEFINE_int32(trt_opt_shape, 640, "Opt shape of TRT DynamicShapeI");
+DEFINE_bool(trt_calib_mode,
+            false,
+            "If the model is produced by TRT offline quantitative calibration, "
+            "trt_calib_mode need to set True");
+
+void PrintBenchmarkLog(std::vector<double> det_time, int img_num) {
+  LOG(INFO) << "----------------------- Config info -----------------------";
+  LOG(INFO) << "runtime_device: " << FLAGS_device;
+  LOG(INFO) << "ir_optim: "
+            << "True";
+  LOG(INFO) << "enable_memory_optim: "
+            << "True";
+  int has_trt = FLAGS_run_mode.find("trt");
+  if (has_trt >= 0) {
+    LOG(INFO) << "enable_tensorrt: "
+              << "True";
+    std::string precision = FLAGS_run_mode.substr(4, 8);
+    LOG(INFO) << "precision: " << precision;
+  } else {
+    LOG(INFO) << "enable_tensorrt: "
+              << "False";
+    LOG(INFO) << "precision: "
+              << "fp32";
+  }
+  LOG(INFO) << "enable_mkldnn: " << (FLAGS_use_mkldnn ? "True" : "False");
+  LOG(INFO) << "cpu_math_library_num_threads: " << FLAGS_cpu_threads;
+  LOG(INFO) << "----------------------- Data info -----------------------";
+  LOG(INFO) << "batch_size: " << FLAGS_batch_size;
+  LOG(INFO) << "input_shape: "
+            << "dynamic shape";
+  LOG(INFO) << "----------------------- Model info -----------------------";
+  FLAGS_model_dir.erase(FLAGS_model_dir.find_last_not_of("/") + 1);
+  LOG(INFO) << "model_name: "
+            << FLAGS_model_dir.substr(FLAGS_model_dir.find_last_of('/') + 1);
+  LOG(INFO) << "----------------------- Perf info ------------------------";
+  LOG(INFO) << "Total number of predicted data: " << img_num
+            << " and total time spent(ms): "
+            << std::accumulate(det_time.begin(), det_time.end(), 0);
+  LOG(INFO) << "preproce_time(ms): " << det_time[0] / img_num
+            << ", inference_time(ms): " << det_time[1] / img_num
+            << ", postprocess_time(ms): " << det_time[2] / img_num;
+}
+
+static std::string DirName(const std::string& filepath) {
+  auto pos = filepath.rfind(OS_PATH_SEP);
+  if (pos == std::string::npos) {
+    return "";
+  }
+  return filepath.substr(0, pos);
+}
+
+static bool PathExists(const std::string& path) {
+#ifdef _WIN32
+  struct _stat buffer;
+  return (_stat(path.c_str(), &buffer) == 0);
+#else
+  struct stat buffer;
+  return (stat(path.c_str(), &buffer) == 0);
+#endif  // !_WIN32
+}
+
+static void MkDir(const std::string& path) {
+  if (PathExists(path)) return;
+  int ret = 0;
+#ifdef _WIN32
+  ret = _mkdir(path.c_str());
+#else
+  ret = mkdir(path.c_str(), 0755);
+#endif  // !_WIN32
+  if (ret != 0) {
+    std::string path_error(path);
+    path_error += " mkdir failed!";
+    throw std::runtime_error(path_error);
+  }
+}
+
+static void MkDirs(const std::string& path) {
+  if (path.empty()) return;
+  if (PathExists(path)) return;
+
+  MkDirs(DirName(path));
+  MkDir(path);
+}
+
+void PredictVideo(const std::string& video_path,
+                  PaddleDetection::JDEDetector* mot,
+                  const std::string& output_dir = "output") {
+  // Open video
+  cv::VideoCapture capture;
+  std::string video_out_name = "output.mp4";
+  if (FLAGS_camera_id != -1) {
+    capture.open(FLAGS_camera_id);
+  } else {
+    capture.open(video_path.c_str());
+    video_out_name =
+        video_path.substr(video_path.find_last_of(OS_PATH_SEP) + 1);
+  }
+  if (!capture.isOpened()) {
+    printf("can not open video : %s\n", video_path.c_str());
+    return;
+  }
+
+  // Get Video info : resolution, fps, frame count
+  int video_width = static_cast<int>(capture.get(CV_CAP_PROP_FRAME_WIDTH));
+  int video_height = static_cast<int>(capture.get(CV_CAP_PROP_FRAME_HEIGHT));
+  int video_fps = static_cast<int>(capture.get(CV_CAP_PROP_FPS));
+  int video_frame_count =
+      static_cast<int>(capture.get(CV_CAP_PROP_FRAME_COUNT));
+  printf("fps: %d, frame_count: %d\n", video_fps, video_frame_count);
+
+  // Create VideoWriter for output
+  cv::VideoWriter video_out;
+  std::string video_out_path(output_dir);
+  if (output_dir.rfind(OS_PATH_SEP) != output_dir.size() - 1) {
+    video_out_path += OS_PATH_SEP;
+  }
+  video_out_path += video_out_name;
+  video_out.open(video_out_path.c_str(),
+                 0x00000021,
+                 video_fps,
+                 cv::Size(video_width, video_height),
+                 true);
+  if (!video_out.isOpened()) {
+    printf("create video writer failed!\n");
+    return;
+  }
+
+  PaddleDetection::MOT_Result result;
+  std::vector<double> det_times(3);
+  double times;
+  // Capture all frames and do inference
+  cv::Mat frame;
+  int frame_id = 1;
+  while (capture.read(frame)) {
+    if (frame.empty()) {
+      break;
+    }
+    std::vector<cv::Mat> imgs;
+    imgs.push_back(frame);
+    printf("detect frame: %d\n", frame_id);
+    mot->Predict(imgs, FLAGS_threshold, 0, 1, &result, &det_times);
+    frame_id += 1;
+    times = std::accumulate(det_times.begin(), det_times.end(), 0) / frame_id;
+
+    cv::Mat out_im = PaddleDetection::VisualizeTrackResult(
+        frame, result, 1000. / times, frame_id);
+
+    video_out.write(out_im);
+  }
+  capture.release();
+  video_out.release();
+  PrintBenchmarkLog(det_times, frame_id);
+  printf("Visualized output saved as %s\n", video_out_path.c_str());
+}
+
+int main(int argc, char** argv) {
+  // Parsing command-line
+  google::ParseCommandLineFlags(&argc, &argv, true);
+  if (FLAGS_model_dir.empty() || FLAGS_video_file.empty()) {
+    std::cout << "Usage: ./main --model_dir=/PATH/TO/INFERENCE_MODEL/ "
+              << "--video_file=/PATH/TO/INPUT/VIDEO/" << std::endl;
+    return -1;
+  }
+  if (!(FLAGS_run_mode == "paddle" || FLAGS_run_mode == "trt_fp32" ||
+        FLAGS_run_mode == "trt_fp16" || FLAGS_run_mode == "trt_int8")) {
+    std::cout
+        << "run_mode should be 'paddle', 'trt_fp32', 'trt_fp16' or 'trt_int8'.";
+    return -1;
+  }
+  transform(FLAGS_device.begin(),
+            FLAGS_device.end(),
+            FLAGS_device.begin(),
+            ::toupper);
+  if (!(FLAGS_device == "CPU" || FLAGS_device == "GPU" ||
+        FLAGS_device == "XPU")) {
+    std::cout << "device should be 'CPU', 'GPU' or 'XPU'.";
+    return -1;
+  }
+  if (FLAGS_use_gpu) {
+    std::cout << "Deprecated, please use `--device` to set the device you want "
+                 "to run.";
+    return -1;
+  }
+
+  // Do inference on input video or image
+  PaddleDetection::JDEDetector mot(FLAGS_model_dir,
+                                   FLAGS_device,
+                                   FLAGS_use_mkldnn,
+                                   FLAGS_cpu_threads,
+                                   FLAGS_run_mode,
+                                   FLAGS_batch_size,
+                                   FLAGS_gpu_id,
+                                   FLAGS_trt_min_shape,
+                                   FLAGS_trt_max_shape,
+                                   FLAGS_trt_opt_shape,
+                                   FLAGS_trt_calib_mode);
+  if (!PathExists(FLAGS_output_dir)) {
+    MkDirs(FLAGS_output_dir);
+  }
+  PredictVideo(FLAGS_video_file, &mot, FLAGS_output_dir);
+  return 0;
+}

deploy/cpp/src/main_keypoint.cc

+//   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.
+
+#include <glog/logging.h>
+
+#include <math.h>
+#include <sys/stat.h>
+#include <sys/types.h>
+#include <algorithm>
+#include <iostream>
+#include <numeric>
+#include <string>
+#include <vector>
+
+#ifdef _WIN32
+#include <direct.h>
+#include <io.h>
+#elif LINUX
+#include <stdarg.h>
+#endif
+
+#include <gflags/gflags.h>
+#include "include/keypoint_detector.h"
+#include "include/object_detector.h"
+#include "include/preprocess_op.h"
+
+DEFINE_string(model_dir, "", "Path of object detector inference model");
+DEFINE_string(model_dir_keypoint,
+              "",
+              "Path of keypoint detector inference model");
+DEFINE_string(image_file, "", "Path of input image");
+DEFINE_string(image_dir,
+              "",
+              "Dir of input image, `image_file` has a higher priority.");
+DEFINE_int32(batch_size, 1, "batch_size of object detector");
+DEFINE_int32(batch_size_keypoint, 8, "batch_size of keypoint detector");
+DEFINE_string(
+    video_file,
+    "",
+    "Path of input video, `video_file` or `camera_id` has a highest priority.");
+DEFINE_int32(camera_id, -1, "Device id of camera to predict");
+DEFINE_bool(
+    use_gpu,
+    false,
+    "Deprecated, please use `--device` to set the device you want to run.");
+DEFINE_string(device,
+              "CPU",
+              "Choose the device you want to run, it can be: CPU/GPU/XPU, "
+              "default is CPU.");
+DEFINE_double(threshold, 0.5, "Threshold of score.");
+DEFINE_double(threshold_keypoint, 0.5, "Threshold of score.");
+DEFINE_string(output_dir, "output", "Directory of output visualization files.");
+DEFINE_string(run_mode,
+              "paddle",
+              "Mode of running(paddle/trt_fp32/trt_fp16/trt_int8)");
+DEFINE_int32(gpu_id, 0, "Device id of GPU to execute");
+DEFINE_bool(run_benchmark,
+            false,
+            "Whether to predict a image_file repeatedly for benchmark");
+DEFINE_bool(use_mkldnn, false, "Whether use mkldnn with CPU");
+DEFINE_int32(cpu_threads, 1, "Num of threads with CPU");
+DEFINE_int32(trt_min_shape, 1, "Min shape of TRT DynamicShapeI");
+DEFINE_int32(trt_max_shape, 1280, "Max shape of TRT DynamicShapeI");
+DEFINE_int32(trt_opt_shape, 640, "Opt shape of TRT DynamicShapeI");
+DEFINE_bool(trt_calib_mode,
+            false,
+            "If the model is produced by TRT offline quantitative calibration, "
+            "trt_calib_mode need to set True");
+DEFINE_bool(use_dark, true, "Whether use dark decode in keypoint postprocess");
+
+void PrintBenchmarkLog(std::vector<double> det_time, int img_num) {
+  LOG(INFO) << "----------------------- Config info -----------------------";
+  LOG(INFO) << "runtime_device: " << FLAGS_device;
+  LOG(INFO) << "ir_optim: "
+            << "True";
+  LOG(INFO) << "enable_memory_optim: "
+            << "True";
+  int has_trt = FLAGS_run_mode.find("trt");
+  if (has_trt >= 0) {
+    LOG(INFO) << "enable_tensorrt: "
+              << "True";
+    std::string precision = FLAGS_run_mode.substr(4, 8);
+    LOG(INFO) << "precision: " << precision;
+  } else {
+    LOG(INFO) << "enable_tensorrt: "
+              << "False";
+    LOG(INFO) << "precision: "
+              << "fp32";
+  }
+  LOG(INFO) << "enable_mkldnn: " << (FLAGS_use_mkldnn ? "True" : "False");
+  LOG(INFO) << "cpu_math_library_num_threads: " << FLAGS_cpu_threads;
+  LOG(INFO) << "----------------------- Data info -----------------------";
+  LOG(INFO) << "batch_size: " << FLAGS_batch_size;
+  LOG(INFO) << "input_shape: "
+            << "dynamic shape";
+  LOG(INFO) << "----------------------- Model info -----------------------";
+  FLAGS_model_dir.erase(FLAGS_model_dir.find_last_not_of(OS_PATH_SEP) + 1);
+  LOG(INFO) << "model_name: " << FLAGS_model_dir;
+  LOG(INFO) << "----------------------- Perf info ------------------------";
+  LOG(INFO) << "Total number of predicted data: " << img_num
+            << " and total time spent(ms): "
+            << std::accumulate(det_time.begin(), det_time.end(), 0.);
+  img_num = std::max(1, img_num);
+  LOG(INFO) << "preproce_time(ms): " << det_time[0] / img_num
+            << ", inference_time(ms): " << det_time[1] / img_num
+            << ", postprocess_time(ms): " << det_time[2] / img_num;
+}
+
+void PrintKptsBenchmarkLog(std::vector<double> det_time, int img_num) {
+  LOG(INFO) << "----------------------- Data info -----------------------";
+  LOG(INFO) << "batch_size_keypoint: " << FLAGS_batch_size_keypoint;
+  LOG(INFO) << "----------------------- Model info -----------------------";
+  FLAGS_model_dir_keypoint.erase(
+      FLAGS_model_dir_keypoint.find_last_not_of(OS_PATH_SEP) + 1);
+  LOG(INFO) << "keypoint_model_name: " << FLAGS_model_dir_keypoint;
+  LOG(INFO) << "----------------------- Perf info ------------------------";
+  LOG(INFO) << "Total number of predicted data: " << img_num
+            << " and total time spent(ms): "
+            << std::accumulate(det_time.begin(), det_time.end(), 0.);
+  img_num = std::max(1, img_num);
+  LOG(INFO) << "Average time cost per person:";
+  LOG(INFO) << "preproce_time(ms): " << det_time[0] / img_num
+            << ", inference_time(ms): " << det_time[1] / img_num
+            << ", postprocess_time(ms): " << det_time[2] / img_num;
+}
+
+static std::string DirName(const std::string& filepath) {
+  auto pos = filepath.rfind(OS_PATH_SEP);
+  if (pos == std::string::npos) {
+    return "";
+  }
+  return filepath.substr(0, pos);
+}
+
+static bool PathExists(const std::string& path) {
+#ifdef _WIN32
+  struct _stat buffer;
+  return (_stat(path.c_str(), &buffer) == 0);
+#else
+  struct stat buffer;
+  return (stat(path.c_str(), &buffer) == 0);
+#endif  // !_WIN32
+}
+
+static void MkDir(const std::string& path) {
+  if (PathExists(path)) return;
+  int ret = 0;
+#ifdef _WIN32
+  ret = _mkdir(path.c_str());
+#else
+  ret = mkdir(path.c_str(), 0755);
+#endif  // !_WIN32
+  if (ret != 0) {
+    std::string path_error(path);
+    path_error += " mkdir failed!";
+    throw std::runtime_error(path_error);
+  }
+}
+
+static void MkDirs(const std::string& path) {
+  if (path.empty()) return;
+  if (PathExists(path)) return;
+
+  MkDirs(DirName(path));
+  MkDir(path);
+}
+
+void PredictVideo(const std::string& video_path,
+                  PaddleDetection::ObjectDetector* det,
+                  PaddleDetection::KeyPointDetector* keypoint,
+                  const std::string& output_dir = "output") {
+  // Open video
+  cv::VideoCapture capture;
+  std::string video_out_name = "output.mp4";
+  if (FLAGS_camera_id != -1) {
+    capture.open(FLAGS_camera_id);
+  } else {
+    capture.open(video_path.c_str());
+    video_out_name =
+        video_path.substr(video_path.find_last_of(OS_PATH_SEP) + 1);
+  }
+  if (!capture.isOpened()) {
+    printf("can not open video : %s\n", video_path.c_str());
+    return;
+  }
+
+  // Get Video info : resolution, fps, frame count
+  int video_width = static_cast<int>(capture.get(CV_CAP_PROP_FRAME_WIDTH));
+  int video_height = static_cast<int>(capture.get(CV_CAP_PROP_FRAME_HEIGHT));
+  int video_fps = static_cast<int>(capture.get(CV_CAP_PROP_FPS));
+  int video_frame_count =
+      static_cast<int>(capture.get(CV_CAP_PROP_FRAME_COUNT));
+  printf("fps: %d, frame_count: %d\n", video_fps, video_frame_count);
+
+  // Create VideoWriter for output
+  cv::VideoWriter video_out;
+  std::string video_out_path(output_dir);
+  if (output_dir.rfind(OS_PATH_SEP) != output_dir.size() - 1) {
+    video_out_path += OS_PATH_SEP;
+  }
+  video_out_path += video_out_name;
+  video_out.open(video_out_path.c_str(),
+                 0x00000021,
+                 video_fps,
+                 cv::Size(video_width, video_height),
+                 true);
+  if (!video_out.isOpened()) {
+    printf("create video writer failed!\n");
+    return;
+  }
+  PaddleDetection::PoseSmooth smoother =
+      PaddleDetection::PoseSmooth(video_width, video_height);
+
+  std::vector<PaddleDetection::ObjectResult> result;
+  std::vector<int> bbox_num;
+  std::vector<double> det_times;
+  auto labels = det->GetLabelList();
+  auto colormap = PaddleDetection::GenerateColorMap(labels.size());
+
+  // Store keypoint results
+  std::vector<PaddleDetection::KeyPointResult> result_kpts;
+  std::vector<cv::Mat> imgs_kpts;
+  std::vector<std::vector<float>> center_bs;
+  std::vector<std::vector<float>> scale_bs;
+  std::vector<int> colormap_kpts = PaddleDetection::GenerateColorMap(20);
+  // Capture all frames and do inference
+  cv::Mat frame;
+  int frame_id = 1;
+  bool is_rbox = false;
+  while (capture.read(frame)) {
+    if (frame.empty()) {
+      break;
+    }
+    std::vector<cv::Mat> imgs;
+    imgs.push_back(frame);
+    printf("detect frame: %d\n", frame_id);
+    det->Predict(imgs, FLAGS_threshold, 0, 1, &result, &bbox_num, &det_times);
+    std::vector<PaddleDetection::ObjectResult> out_result;
+    for (const auto& item : result) {
+      if (item.confidence < FLAGS_threshold || item.class_id == -1) {
+        continue;
+      }
+      out_result.push_back(item);
+      if (item.rect.size() > 6) {
+        is_rbox = true;
+        printf("class=%d confidence=%.4f rect=[%d %d %d %d %d %d %d %d]\n",
+               item.class_id,
+               item.confidence,
+               item.rect[0],
+               item.rect[1],
+               item.rect[2],
+               item.rect[3],
+               item.rect[4],
+               item.rect[5],
+               item.rect[6],
+               item.rect[7]);
+      } else {
+        printf("class=%d confidence=%.4f rect=[%d %d %d %d]\n",
+               item.class_id,
+               item.confidence,
+               item.rect[0],
+               item.rect[1],
+               item.rect[2],
+               item.rect[3]);
+      }
+    }
+
+    if (keypoint) {
+      result_kpts.clear();
+      int imsize = out_result.size();
+      for (int i = 0; i < imsize; i++) {
+        auto item = out_result[i];
+        cv::Mat crop_img;
+        std::vector<double> keypoint_times;
+        std::vector<int> rect = {
+            item.rect[0], item.rect[1], item.rect[2], item.rect[3]};
+        std::vector<float> center;
+        std::vector<float> scale;
+        if (item.class_id == 0) {
+          PaddleDetection::CropImg(frame, crop_img, rect, center, scale);
+          center_bs.emplace_back(center);
+          scale_bs.emplace_back(scale);
+          imgs_kpts.emplace_back(crop_img);
+        }
+
+        if (imgs_kpts.size() == FLAGS_batch_size_keypoint ||
+            ((i == imsize - 1) && !imgs_kpts.empty())) {
+          keypoint->Predict(imgs_kpts,
+                            center_bs,
+                            scale_bs,
+                            FLAGS_threshold,
+                            0,
+                            1,
+                            &result_kpts,
+                            &keypoint_times);
+          imgs_kpts.clear();
+          center_bs.clear();
+          scale_bs.clear();
+        }
+      }
+
+      if (result_kpts.size() == 1) {
+        for (int i = 0; i < result_kpts.size(); i++) {
+          result_kpts[i] = smoother.smooth_process(&(result_kpts[i]));
+        }
+      }
+
+      cv::Mat out_im = VisualizeKptsResult(frame, result_kpts, colormap_kpts);
+      video_out.write(out_im);
+    } else {
+      // Visualization result
+      cv::Mat out_im = PaddleDetection::VisualizeResult(
+          frame, out_result, labels, colormap, is_rbox);
+      video_out.write(out_im);
+    }
+
+    frame_id += 1;
+  }
+  capture.release();
+  video_out.release();
+}
+
+void PredictImage(const std::vector<std::string> all_img_paths,
+                  const int batch_size,
+                  const double threshold,
+                  const bool run_benchmark,
+                  PaddleDetection::ObjectDetector* det,
+                  PaddleDetection::KeyPointDetector* keypoint,
+                  const std::string& output_dir = "output") {
+  std::vector<double> det_t = {0, 0, 0};
+  int steps = ceil(static_cast<float>(all_img_paths.size()) / batch_size);
+  int kpts_imgs = 0;
+  std::vector<double> keypoint_t = {0, 0, 0};
+  printf("total images = %d, batch_size = %d, total steps = %d\n",
+         all_img_paths.size(),
+         batch_size,
+         steps);
+  for (int idx = 0; idx < steps; idx++) {
+    std::vector<cv::Mat> batch_imgs;
+    int left_image_cnt = all_img_paths.size() - idx * batch_size;
+    if (left_image_cnt > batch_size) {
+      left_image_cnt = batch_size;
+    }
+    for (int bs = 0; bs < left_image_cnt; bs++) {
+      std::string image_file_path = all_img_paths.at(idx * batch_size + bs);
+      cv::Mat im = cv::imread(image_file_path, 1);
+      batch_imgs.insert(batch_imgs.end(), im);
+    }
+
+    // Store all detected result
+    std::vector<PaddleDetection::ObjectResult> result;
+    std::vector<int> bbox_num;
+    std::vector<double> det_times;
+
+    // Store keypoint results
+    std::vector<PaddleDetection::KeyPointResult> result_kpts;
+    std::vector<cv::Mat> imgs_kpts;
+    std::vector<std::vector<float>> center_bs;
+    std::vector<std::vector<float>> scale_bs;
+    std::vector<int> colormap_kpts = PaddleDetection::GenerateColorMap(20);
+
+    bool is_rbox = false;
+    if (run_benchmark) {
+      det->Predict(
+          batch_imgs, threshold, 10, 10, &result, &bbox_num, &det_times);
+    } else {
+      det->Predict(batch_imgs, threshold, 0, 1, &result, &bbox_num, &det_times);
+    }
+    // get labels and colormap
+    auto labels = det->GetLabelList();
+    auto colormap = PaddleDetection::GenerateColorMap(labels.size());
+    int item_start_idx = 0;
+    for (int i = 0; i < left_image_cnt; i++) {
+      cv::Mat im = batch_imgs[i];
+      std::vector<PaddleDetection::ObjectResult> im_result;
+      int detect_num = 0;
+      for (int j = 0; j < bbox_num[i]; j++) {
+        PaddleDetection::ObjectResult item = result[item_start_idx + j];
+        if (item.confidence < threshold || item.class_id == -1) {
+          continue;
+        }
+        detect_num += 1;
+        im_result.push_back(item);
+        if (item.rect.size() > 6) {
+          is_rbox = true;
+          printf("class=%d confidence=%.4f rect=[%d %d %d %d %d %d %d %d]\n",
+                 item.class_id,
+                 item.confidence,
+                 item.rect[0],
+                 item.rect[1],
+                 item.rect[2],
+                 item.rect[3],
+                 item.rect[4],
+                 item.rect[5],
+                 item.rect[6],
+                 item.rect[7]);
+        } else {
+          printf("class=%d confidence=%.4f rect=[%d %d %d %d]\n",
+                 item.class_id,
+                 item.confidence,
+                 item.rect[0],
+                 item.rect[1],
+                 item.rect[2],
+                 item.rect[3]);
+        }
+      }
+      std::cout << all_img_paths.at(idx * batch_size + i)
+                << " The number of detected box: " << detect_num << std::endl;
+      item_start_idx = item_start_idx + bbox_num[i];
+
+      std::vector<int> compression_params;
+      compression_params.push_back(CV_IMWRITE_JPEG_QUALITY);
+      compression_params.push_back(95);
+      std::string output_path(output_dir);
+      if (output_dir.rfind(OS_PATH_SEP) != output_dir.size() - 1) {
+        output_path += OS_PATH_SEP;
+      }
+      std::string image_file_path = all_img_paths.at(idx * batch_size + i);
+      if (keypoint) {
+        int imsize = im_result.size();
+        for (int i = 0; i < imsize; i++) {
+          auto item = im_result[i];
+          cv::Mat crop_img;
+          std::vector<double> keypoint_times;
+          std::vector<int> rect = {
+              item.rect[0], item.rect[1], item.rect[2], item.rect[3]};
+          std::vector<float> center;
+          std::vector<float> scale;
+          if (item.class_id == 0) {
+            PaddleDetection::CropImg(im, crop_img, rect, center, scale);
+            center_bs.emplace_back(center);
+            scale_bs.emplace_back(scale);
+            imgs_kpts.emplace_back(crop_img);
+            kpts_imgs += 1;
+          }
+
+          if (imgs_kpts.size() == FLAGS_batch_size_keypoint ||
+              ((i == imsize - 1) && !imgs_kpts.empty())) {
+            if (run_benchmark) {
+              keypoint->Predict(imgs_kpts,
+                                center_bs,
+                                scale_bs,
+                                0.5,
+                                10,
+                                10,
+                                &result_kpts,
+                                &keypoint_times);
+            } else {
+              keypoint->Predict(imgs_kpts,
+                                center_bs,
+                                scale_bs,
+                                0.5,
+                                0,
+                                1,
+                                &result_kpts,
+                                &keypoint_times);
+            }
+            imgs_kpts.clear();
+            center_bs.clear();
+            scale_bs.clear();
+            keypoint_t[0] += keypoint_times[0];
+            keypoint_t[1] += keypoint_times[1];
+            keypoint_t[2] += keypoint_times[2];
+          }
+        }
+        std::string kpts_savepath =
+            output_path + "keypoint_" +
+            image_file_path.substr(image_file_path.find_last_of(OS_PATH_SEP) + 1);
+        cv::Mat kpts_vis_img =
+            VisualizeKptsResult(im, result_kpts, colormap_kpts);
+        cv::imwrite(kpts_savepath, kpts_vis_img, compression_params);
+        printf("Visualized output saved as %s\n", kpts_savepath.c_str());
+      } else {
+        // Visualization result
+        cv::Mat vis_img = PaddleDetection::VisualizeResult(
+            im, im_result, labels, colormap, is_rbox);
+        std::string det_savepath =
+            output_path +
+            image_file_path.substr(image_file_path.find_last_of(OS_PATH_SEP) + 1);
+        cv::imwrite(det_savepath, vis_img, compression_params);
+        printf("Visualized output saved as %s\n", det_savepath.c_str());
+      }
+    }
+
+    det_t[0] += det_times[0];
+    det_t[1] += det_times[1];
+    det_t[2] += det_times[2];
+  }
+  PrintBenchmarkLog(det_t, all_img_paths.size());
+  if (keypoint) {
+    PrintKptsBenchmarkLog(keypoint_t, kpts_imgs);
+  }
+}
+
+int main(int argc, char** argv) {
+  // Parsing command-line
+  google::ParseCommandLineFlags(&argc, &argv, true);
+  if (FLAGS_model_dir.empty() ||
+      (FLAGS_image_file.empty() && FLAGS_image_dir.empty() &&
+       FLAGS_video_file.empty())) {
+    std::cout << "Usage: ./main --model_dir=/PATH/TO/INFERENCE_MODEL/ "
+                 "(--model_dir_keypoint=/PATH/TO/INFERENCE_MODEL/)"
+              << "--image_file=/PATH/TO/INPUT/IMAGE/" << std::endl;
+    return -1;
+  }
+  if (!(FLAGS_run_mode == "paddle" || FLAGS_run_mode == "trt_fp32" ||
+        FLAGS_run_mode == "trt_fp16" || FLAGS_run_mode == "trt_int8")) {
+    std::cout
+        << "run_mode should be 'paddle', 'trt_fp32', 'trt_fp16' or 'trt_int8'.";
+    return -1;
+  }
+  transform(FLAGS_device.begin(),
+            FLAGS_device.end(),
+            FLAGS_device.begin(),
+            ::toupper);
+  if (!(FLAGS_device == "CPU" || FLAGS_device == "GPU" ||
+        FLAGS_device == "XPU")) {
+    std::cout << "device should be 'CPU', 'GPU' or 'XPU'.";
+    return -1;
+  }
+  if (FLAGS_use_gpu) {
+    std::cout << "Deprecated, please use `--device` to set the device you want "
+                 "to run.";
+    return -1;
+  }
+  // Load model and create a object detector
+  PaddleDetection::ObjectDetector det(FLAGS_model_dir,
+                                      FLAGS_device,
+                                      FLAGS_use_mkldnn,
+                                      FLAGS_cpu_threads,
+                                      FLAGS_run_mode,
+                                      FLAGS_batch_size,
+                                      FLAGS_gpu_id,
+                                      FLAGS_trt_min_shape,
+                                      FLAGS_trt_max_shape,
+                                      FLAGS_trt_opt_shape,
+                                      FLAGS_trt_calib_mode);
+
+  PaddleDetection::KeyPointDetector* keypoint = nullptr;
+  if (!FLAGS_model_dir_keypoint.empty()) {
+    keypoint = new PaddleDetection::KeyPointDetector(FLAGS_model_dir_keypoint,
+                                                     FLAGS_device,
+                                                     FLAGS_use_mkldnn,
+                                                     FLAGS_cpu_threads,
+                                                     FLAGS_run_mode,
+                                                     FLAGS_batch_size_keypoint,
+                                                     FLAGS_gpu_id,
+                                                     FLAGS_trt_min_shape,
+                                                     FLAGS_trt_max_shape,
+                                                     FLAGS_trt_opt_shape,
+                                                     FLAGS_trt_calib_mode,
+                                                     FLAGS_use_dark);
+  }
+  // Do inference on input video or image
+  if (!PathExists(FLAGS_output_dir)) {
+    MkDirs(FLAGS_output_dir);
+  }
+  if (!FLAGS_video_file.empty() || FLAGS_camera_id != -1) {
+    PredictVideo(FLAGS_video_file, &det, keypoint, FLAGS_output_dir);
+  } else if (!FLAGS_image_file.empty() || !FLAGS_image_dir.empty()) {
+    std::vector<std::string> all_img_paths;
+    std::vector<cv::String> cv_all_img_paths;
+    if (!FLAGS_image_file.empty()) {
+      all_img_paths.push_back(FLAGS_image_file);
+      if (FLAGS_batch_size > 1) {
+        std::cout << "batch_size should be 1, when set `image_file`."
+                  << std::endl;
+        return -1;
+      }
+    } else {
+      cv::glob(FLAGS_image_dir, cv_all_img_paths);
+      for (const auto& img_path : cv_all_img_paths) {
+        all_img_paths.push_back(img_path);
+      }
+    }
+    PredictImage(all_img_paths,
+                 FLAGS_batch_size,
+                 FLAGS_threshold,
+                 FLAGS_run_benchmark,
+                 &det,
+                 keypoint,
+                 FLAGS_output_dir);
+  }
+  delete keypoint;
+  keypoint = nullptr;
+  return 0;
+}

deploy/cpp/src/object_detector.cc

+//   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.
+#include <sstream>
+// for setprecision
+#include <chrono>
+#include <iomanip>
+
+#include "include/object_detector.h"
+
+namespace PaddleDetection {
+
+// Load Model and create model predictor
+void ObjectDetector::LoadModel(const std::string &model_dir,
+                               const int batch_size,
+                               const std::string &run_mode) {
+  paddle_infer::Config config;
+  std::string prog_file = model_dir + OS_PATH_SEP + "model.pdmodel";
+  std::string params_file = model_dir + OS_PATH_SEP + "model.pdiparams";
+  config.SetModel(prog_file, params_file);
+  if (this->device_ == "GPU") {
+    config.EnableUseGpu(200, this->gpu_id_);
+    config.SwitchIrOptim(true);
+    // use tensorrt
+    if (run_mode != "paddle") {
+      auto precision = paddle_infer::Config::Precision::kFloat32;
+      if (run_mode == "trt_fp32") {
+        precision = paddle_infer::Config::Precision::kFloat32;
+      } else if (run_mode == "trt_fp16") {
+        precision = paddle_infer::Config::Precision::kHalf;
+      } else if (run_mode == "trt_int8") {
+        precision = paddle_infer::Config::Precision::kInt8;
+      } else {
+        printf("run_mode should be 'paddle', 'trt_fp32', 'trt_fp16' or "
+               "'trt_int8'");
+      }
+      // set tensorrt
+      config.EnableTensorRtEngine(1 << 30, batch_size, this->min_subgraph_size_,
+                                  precision, false, this->trt_calib_mode_);
+
+      // set use dynamic shape
+      if (this->use_dynamic_shape_) {
+        // set DynamicShape for image tensor
+        const std::vector<int> min_input_shape = {
+            batch_size, 3, this->trt_min_shape_, this->trt_min_shape_};
+        const std::vector<int> max_input_shape = {
+            batch_size, 3, this->trt_max_shape_, this->trt_max_shape_};
+        const std::vector<int> opt_input_shape = {
+            batch_size, 3, this->trt_opt_shape_, this->trt_opt_shape_};
+        const std::map<std::string, std::vector<int>> map_min_input_shape = {
+            {"image", min_input_shape}};
+        const std::map<std::string, std::vector<int>> map_max_input_shape = {
+            {"image", max_input_shape}};
+        const std::map<std::string, std::vector<int>> map_opt_input_shape = {
+            {"image", opt_input_shape}};
+
+        config.SetTRTDynamicShapeInfo(map_min_input_shape, map_max_input_shape,
+                                      map_opt_input_shape);
+        std::cout << "TensorRT dynamic shape enabled" << std::endl;
+      }
+    }
+
+  } else if (this->device_ == "XPU") {
+    config.EnableXpu(10 * 1024 * 1024);
+  } else {
+    config.DisableGpu();
+    if (this->use_mkldnn_) {
+      config.EnableMKLDNN();
+      // cache 10 different shapes for mkldnn to avoid memory leak
+      config.SetMkldnnCacheCapacity(10);
+    }
+    config.SetCpuMathLibraryNumThreads(this->cpu_math_library_num_threads_);
+  }
+  config.SwitchUseFeedFetchOps(false);
+  config.SwitchIrOptim(true);
+  config.DisableGlogInfo();
+  // Memory optimization
+  config.EnableMemoryOptim();
+  predictor_ = std::move(CreatePredictor(config));
+}
+
+// Visualiztion MaskDetector results
+cv::Mat
+VisualizeResult(const cv::Mat &img,
+                const std::vector<PaddleDetection::ObjectResult> &results,
+                const std::vector<std::string> &lables,
+                const std::vector<int> &colormap, const bool is_rbox = false) {
+  cv::Mat vis_img = img.clone();
+  int img_h = vis_img.rows;
+  int img_w = vis_img.cols;
+  for (int i = 0; i < results.size(); ++i) {
+    // Configure color and text size
+    std::ostringstream oss;
+    oss << std::setiosflags(std::ios::fixed) << std::setprecision(4);
+    oss << lables[results[i].class_id] << " ";
+    oss << results[i].confidence;
+    std::string text = oss.str();
+    int c1 = colormap[3 * results[i].class_id + 0];
+    int c2 = colormap[3 * results[i].class_id + 1];
+    int c3 = colormap[3 * results[i].class_id + 2];
+    cv::Scalar roi_color = cv::Scalar(c1, c2, c3);
+    int font_face = cv::FONT_HERSHEY_COMPLEX_SMALL;
+    double font_scale = 0.5f;
+    float thickness = 0.5;
+    cv::Size text_size =
+        cv::getTextSize(text, font_face, font_scale, thickness, nullptr);
+    cv::Point origin;
+
+    if (is_rbox) {
+      // Draw object, text, and background
+      for (int k = 0; k < 4; k++) {
+        cv::Point pt1 = cv::Point(results[i].rect[(k * 2) % 8],
+                                  results[i].rect[(k * 2 + 1) % 8]);
+        cv::Point pt2 = cv::Point(results[i].rect[(k * 2 + 2) % 8],
+                                  results[i].rect[(k * 2 + 3) % 8]);
+        cv::line(vis_img, pt1, pt2, roi_color, 2);
+      }
+    } else {
+      int w = results[i].rect[2] - results[i].rect[0];
+      int h = results[i].rect[3] - results[i].rect[1];
+      cv::Rect roi = cv::Rect(results[i].rect[0], results[i].rect[1], w, h);
+      // Draw roi object, text, and background
+      cv::rectangle(vis_img, roi, roi_color, 2);
+
+      // Draw mask
+      std::vector<int> mask_v = results[i].mask;
+      if (mask_v.size() > 0) {
+        cv::Mat mask = cv::Mat(img_h, img_w, CV_32S);
+        std::memcpy(mask.data, mask_v.data(), mask_v.size() * sizeof(int));
+
+        cv::Mat colored_img = vis_img.clone();
+
+        std::vector<cv::Mat> contours;
+        cv::Mat hierarchy;
+        mask.convertTo(mask, CV_8U);
+        cv::findContours(mask, contours, hierarchy, cv::RETR_CCOMP,
+                         cv::CHAIN_APPROX_SIMPLE);
+        cv::drawContours(colored_img, contours, -1, roi_color, -1, cv::LINE_8,
+                         hierarchy, 100);
+
+        cv::Mat debug_roi = vis_img;
+        colored_img = 0.4 * colored_img + 0.6 * vis_img;
+        colored_img.copyTo(vis_img, mask);
+      }
+    }
+
+    origin.x = results[i].rect[0];
+    origin.y = results[i].rect[1];
+
+    // Configure text background
+    cv::Rect text_back =
+        cv::Rect(results[i].rect[0], results[i].rect[1] - text_size.height,
+                 text_size.width, text_size.height);
+    // Draw text, and background
+    cv::rectangle(vis_img, text_back, roi_color, -1);
+    cv::putText(vis_img, text, origin, font_face, font_scale,
+                cv::Scalar(255, 255, 255), thickness);
+  }
+  return vis_img;
+}
+
+void ObjectDetector::Preprocess(const cv::Mat &ori_im) {
+  // Clone the image : keep the original mat for postprocess
+  cv::Mat im = ori_im.clone();
+  cv::cvtColor(im, im, cv::COLOR_BGR2RGB);
+  preprocessor_.Run(&im, &inputs_);
+}
+
+void ObjectDetector::Postprocess(
+    const std::vector<cv::Mat> mats,
+    std::vector<PaddleDetection::ObjectResult> *result,
+    std::vector<int> bbox_num, std::vector<float> output_data_,
+    std::vector<int> output_mask_data_, bool is_rbox = false) {
+  result->clear();
+  int start_idx = 0;
+  int total_num = std::accumulate(bbox_num.begin(), bbox_num.end(), 0);
+  int out_mask_dim = -1;
+  if (config_.mask_) {
+    out_mask_dim = output_mask_data_.size() / total_num;
+  }
+
+  for (int im_id = 0; im_id < mats.size(); im_id++) {
+    cv::Mat raw_mat = mats[im_id];
+    int rh = 1;
+    int rw = 1;
+    for (int j = start_idx; j < start_idx + bbox_num[im_id]; j++) {
+      if (is_rbox) {
+        // Class id
+        int class_id = static_cast<int>(round(output_data_[0 + j * 10]));
+        // Confidence score
+        float score = output_data_[1 + j * 10];
+        int x1 = (output_data_[2 + j * 10] * rw);
+        int y1 = (output_data_[3 + j * 10] * rh);
+        int x2 = (output_data_[4 + j * 10] * rw);
+        int y2 = (output_data_[5 + j * 10] * rh);
+        int x3 = (output_data_[6 + j * 10] * rw);
+        int y3 = (output_data_[7 + j * 10] * rh);
+        int x4 = (output_data_[8 + j * 10] * rw);
+        int y4 = (output_data_[9 + j * 10] * rh);
+
+        PaddleDetection::ObjectResult result_item;
+        result_item.rect = {x1, y1, x2, y2, x3, y3, x4, y4};
+        result_item.class_id = class_id;
+        result_item.confidence = score;
+        result->push_back(result_item);
+      } else {
+        // Class id
+        int class_id = static_cast<int>(round(output_data_[0 + j * 6]));
+        // Confidence score
+        float score = output_data_[1 + j * 6];
+        int xmin = (output_data_[2 + j * 6] * rw);
+        int ymin = (output_data_[3 + j * 6] * rh);
+        int xmax = (output_data_[4 + j * 6] * rw);
+        int ymax = (output_data_[5 + j * 6] * rh);
+        int wd = xmax - xmin;
+        int hd = ymax - ymin;
+
+        PaddleDetection::ObjectResult result_item;
+        result_item.rect = {xmin, ymin, xmax, ymax};
+        result_item.class_id = class_id;
+        result_item.confidence = score;
+
+        if (config_.mask_) {
+          std::vector<int> mask;
+          for (int k = 0; k < out_mask_dim; ++k) {
+            if (output_mask_data_[k + j * out_mask_dim] > -1) {
+              mask.push_back(output_mask_data_[k + j * out_mask_dim]);
+            }
+          }
+          result_item.mask = mask;
+        }
+
+        result->push_back(result_item);
+      }
+    }
+    start_idx += bbox_num[im_id];
+  }
+}
+
+// This function is to convert output result from SOLOv2 to class ObjectResult
+void ObjectDetector::SOLOv2Postprocess(
+    const std::vector<cv::Mat> mats, std::vector<ObjectResult> *result,
+    std::vector<int> *bbox_num, std::vector<int> out_bbox_num_data_,
+    std::vector<int64_t> out_label_data_, std::vector<float> out_score_data_,
+    std::vector<uint8_t> out_global_mask_data_, float threshold) {
+
+  for (int im_id = 0; im_id < mats.size(); im_id++) {
+    cv::Mat mat = mats[im_id];
+
+    int valid_bbox_count = 0;
+    for (int bbox_id = 0; bbox_id < out_bbox_num_data_[im_id]; ++bbox_id) {
+      if (out_score_data_[bbox_id] >= threshold) {
+        ObjectResult result_item;
+        result_item.class_id = out_label_data_[bbox_id];
+        result_item.confidence = out_score_data_[bbox_id];
+        std::vector<int> global_mask;
+
+        for (int k = 0; k < mat.rows * mat.cols; ++k) {
+          global_mask.push_back(static_cast<int>(
+              out_global_mask_data_[k + bbox_id * mat.rows * mat.cols]));
+        }
+
+        // find minimize bounding box from mask
+        cv::Mat mask(mat.rows, mat.cols, CV_32SC1);
+        std::memcpy(mask.data, global_mask.data(),
+                    global_mask.size() * sizeof(int));
+
+        cv::Mat mask_fp;
+        cv::Mat rowSum;
+        cv::Mat colSum;
+        std::vector<float> sum_of_row(mat.rows);
+        std::vector<float> sum_of_col(mat.cols);
+
+        mask.convertTo(mask_fp, CV_32FC1);
+        cv::reduce(mask_fp, colSum, 0, CV_REDUCE_SUM, CV_32FC1);
+        cv::reduce(mask_fp, rowSum, 1, CV_REDUCE_SUM, CV_32FC1);
+
+        for (int row_id = 0; row_id < mat.rows; ++row_id) {
+          sum_of_row[row_id] = rowSum.at<float>(row_id, 0);
+        }
+
+        for (int col_id = 0; col_id < mat.cols; ++col_id) {
+          sum_of_col[col_id] = colSum.at<float>(0, col_id);
+        }
+
+        auto it = std::find_if(sum_of_row.begin(), sum_of_row.end(),
+                               [](int x) { return x > 0.5; });
+        int y1 = std::distance(sum_of_row.begin(), it);
+
+        auto it2 = std::find_if(sum_of_col.begin(), sum_of_col.end(),
+                                [](int x) { return x > 0.5; });
+        int x1 = std::distance(sum_of_col.begin(), it2);
+
+        auto rit = std::find_if(sum_of_row.rbegin(), sum_of_row.rend(),
+                                [](int x) { return x > 0.5; });
+        int y2 = std::distance(rit, sum_of_row.rend());
+
+        auto rit2 = std::find_if(sum_of_col.rbegin(), sum_of_col.rend(),
+                                 [](int x) { return x > 0.5; });
+        int x2 = std::distance(rit2, sum_of_col.rend());
+
+        result_item.rect = {x1, y1, x2, y2};
+        result_item.mask = global_mask;
+
+        result->push_back(result_item);
+        valid_bbox_count++;
+      }
+    }
+    bbox_num->push_back(valid_bbox_count);
+  }
+}
+
+void ObjectDetector::Predict(const std::vector<cv::Mat> imgs,
+                             const double threshold, const int warmup,
+                             const int repeats,
+                             std::vector<PaddleDetection::ObjectResult> *result,
+                             std::vector<int> *bbox_num,
+                             std::vector<double> *times) {
+  auto preprocess_start = std::chrono::steady_clock::now();
+  int batch_size = imgs.size();
+
+  // in_data_batch
+  std::vector<float> in_data_all;
+  std::vector<float> im_shape_all(batch_size * 2);
+  std::vector<float> scale_factor_all(batch_size * 2);
+  std::vector<const float *> output_data_list_;
+  std::vector<int> out_bbox_num_data_;
+  std::vector<int> out_mask_data_;
+
+  // these parameters are for SOLOv2 output
+  std::vector<float> out_score_data_;
+  std::vector<uint8_t> out_global_mask_data_;
+  std::vector<int64_t> out_label_data_;
+
+  // in_net img for each batch
+  std::vector<cv::Mat> in_net_img_all(batch_size);
+
+  // Preprocess image
+  for (int bs_idx = 0; bs_idx < batch_size; bs_idx++) {
+    cv::Mat im = imgs.at(bs_idx);
+    Preprocess(im);
+    im_shape_all[bs_idx * 2] = inputs_.im_shape_[0];
+    im_shape_all[bs_idx * 2 + 1] = inputs_.im_shape_[1];
+
+    scale_factor_all[bs_idx * 2] = inputs_.scale_factor_[0];
+    scale_factor_all[bs_idx * 2 + 1] = inputs_.scale_factor_[1];
+
+    in_data_all.insert(in_data_all.end(), inputs_.im_data_.begin(),
+                       inputs_.im_data_.end());
+
+    // collect in_net img
+    in_net_img_all[bs_idx] = inputs_.in_net_im_;
+  }
+
+  // Pad Batch if batch size > 1
+  if (batch_size > 1 && CheckDynamicInput(in_net_img_all)) {
+    in_data_all.clear();
+    std::vector<cv::Mat> pad_img_all = PadBatch(in_net_img_all);
+    int rh = pad_img_all[0].rows;
+    int rw = pad_img_all[0].cols;
+    int rc = pad_img_all[0].channels();
+
+    for (int bs_idx = 0; bs_idx < batch_size; bs_idx++) {
+      cv::Mat pad_img = pad_img_all[bs_idx];
+      pad_img.convertTo(pad_img, CV_32FC3);
+      std::vector<float> pad_data;
+      pad_data.resize(rc * rh * rw);
+      float *base = pad_data.data();
+      for (int i = 0; i < rc; ++i) {
+        cv::extractChannel(pad_img,
+                           cv::Mat(rh, rw, CV_32FC1, base + i * rh * rw), i);
+      }
+      in_data_all.insert(in_data_all.end(), pad_data.begin(), pad_data.end());
+    }
+    // update in_net_shape
+    inputs_.in_net_shape_ = {static_cast<float>(rh), static_cast<float>(rw)};
+  }
+
+  auto preprocess_end = std::chrono::steady_clock::now();
+  // Prepare input tensor
+  auto input_names = predictor_->GetInputNames();
+  for (const auto &tensor_name : input_names) {
+    auto in_tensor = predictor_->GetInputHandle(tensor_name);
+    if (tensor_name == "image") {
+      int rh = inputs_.in_net_shape_[0];
+      int rw = inputs_.in_net_shape_[1];
+      in_tensor->Reshape({batch_size, 3, rh, rw});
+      in_tensor->CopyFromCpu(in_data_all.data());
+    } else if (tensor_name == "im_shape") {
+      in_tensor->Reshape({batch_size, 2});
+      in_tensor->CopyFromCpu(im_shape_all.data());
+    } else if (tensor_name == "scale_factor") {
+      in_tensor->Reshape({batch_size, 2});
+      in_tensor->CopyFromCpu(scale_factor_all.data());
+    }
+  }
+
+  // Run predictor
+  std::vector<std::vector<float>> out_tensor_list;
+  std::vector<std::vector<int>> output_shape_list;
+  bool is_rbox = false;
+  int reg_max = 7;
+  int num_class = 80;
+
+  auto inference_start = std::chrono::steady_clock::now();
+  if (config_.arch_ == "SOLOv2") {
+    // warmup
+    for (int i = 0; i < warmup; i++) {
+      predictor_->Run();
+      // Get output tensor
+      auto output_names = predictor_->GetOutputNames();
+      for (int j = 0; j < output_names.size(); j++) {
+        auto output_tensor = predictor_->GetOutputHandle(output_names[j]);
+        std::vector<int> output_shape = output_tensor->shape();
+        int out_num = std::accumulate(output_shape.begin(), output_shape.end(),
+                                      1, std::multiplies<int>());
+        if (j == 0) {
+          out_bbox_num_data_.resize(out_num);
+          output_tensor->CopyToCpu(out_bbox_num_data_.data());
+        } else if (j == 1) {
+          out_label_data_.resize(out_num);
+          output_tensor->CopyToCpu(out_label_data_.data());
+        } else if (j == 2) {
+          out_score_data_.resize(out_num);
+          output_tensor->CopyToCpu(out_score_data_.data());
+        } else if (config_.mask_ && (j == 3)) {
+          out_global_mask_data_.resize(out_num);
+          output_tensor->CopyToCpu(out_global_mask_data_.data());
+        }
+      }
+    }
+
+    inference_start = std::chrono::steady_clock::now();
+    for (int i = 0; i < repeats; i++) {
+      predictor_->Run();
+      // Get output tensor
+      out_tensor_list.clear();
+      output_shape_list.clear();
+      auto output_names = predictor_->GetOutputNames();
+      for (int j = 0; j < output_names.size(); j++) {
+        auto output_tensor = predictor_->GetOutputHandle(output_names[j]);
+        std::vector<int> output_shape = output_tensor->shape();
+        int out_num = std::accumulate(output_shape.begin(), output_shape.end(),
+                                      1, std::multiplies<int>());
+        output_shape_list.push_back(output_shape);
+        if (j == 0) {
+          out_bbox_num_data_.resize(out_num);
+          output_tensor->CopyToCpu(out_bbox_num_data_.data());
+        } else if (j == 1) {
+          out_label_data_.resize(out_num);
+          output_tensor->CopyToCpu(out_label_data_.data());
+        } else if (j == 2) {
+          out_score_data_.resize(out_num);
+          output_tensor->CopyToCpu(out_score_data_.data());
+        } else if (config_.mask_ && (j == 3)) {
+          out_global_mask_data_.resize(out_num);
+          output_tensor->CopyToCpu(out_global_mask_data_.data());
+        }
+      }
+    }
+  } else {
+    // warmup
+    for (int i = 0; i < warmup; i++) {
+      predictor_->Run();
+      // Get output tensor
+      auto output_names = predictor_->GetOutputNames();
+      for (int j = 0; j < output_names.size(); j++) {
+        auto output_tensor = predictor_->GetOutputHandle(output_names[j]);
+        std::vector<int> output_shape = output_tensor->shape();
+        int out_num = std::accumulate(output_shape.begin(), output_shape.end(),
+                                      1, std::multiplies<int>());
+        if (config_.mask_ && (j == 2)) {
+          out_mask_data_.resize(out_num);
+          output_tensor->CopyToCpu(out_mask_data_.data());
+        } else if (output_tensor->type() == paddle_infer::DataType::INT32) {
+          out_bbox_num_data_.resize(out_num);
+          output_tensor->CopyToCpu(out_bbox_num_data_.data());
+        } else {
+          std::vector<float> out_data;
+          out_data.resize(out_num);
+          output_tensor->CopyToCpu(out_data.data());
+          out_tensor_list.push_back(out_data);
+        }
+      }
+    }
+
+    inference_start = std::chrono::steady_clock::now();
+    for (int i = 0; i < repeats; i++) {
+      predictor_->Run();
+      // Get output tensor
+      out_tensor_list.clear();
+      output_shape_list.clear();
+      auto output_names = predictor_->GetOutputNames();
+      for (int j = 0; j < output_names.size(); j++) {
+        auto output_tensor = predictor_->GetOutputHandle(output_names[j]);
+        std::vector<int> output_shape = output_tensor->shape();
+        int out_num = std::accumulate(output_shape.begin(), output_shape.end(),
+                                      1, std::multiplies<int>());
+        output_shape_list.push_back(output_shape);
+        if (config_.mask_ && (j == 2)) {
+          out_mask_data_.resize(out_num);
+          output_tensor->CopyToCpu(out_mask_data_.data());
+        } else if (output_tensor->type() == paddle_infer::DataType::INT32) {
+          out_bbox_num_data_.resize(out_num);
+          output_tensor->CopyToCpu(out_bbox_num_data_.data());
+        } else {
+          std::vector<float> out_data;
+          out_data.resize(out_num);
+          output_tensor->CopyToCpu(out_data.data());
+          out_tensor_list.push_back(out_data);
+        }
+      }
+    }
+  }
+
+  auto inference_end = std::chrono::steady_clock::now();
+  auto postprocess_start = std::chrono::steady_clock::now();
+  // Postprocessing result
+  result->clear();
+  bbox_num->clear();
+  if (config_.arch_ == "PicoDet") {
+    for (int i = 0; i < out_tensor_list.size(); i++) {
+      if (i == 0) {
+        num_class = output_shape_list[i][2];
+      }
+      if (i == config_.fpn_stride_.size()) {
+        reg_max = output_shape_list[i][2] / 4 - 1;
+      }
+      float *buffer = new float[out_tensor_list[i].size()];
+      memcpy(buffer, &out_tensor_list[i][0],
+             out_tensor_list[i].size() * sizeof(float));
+      output_data_list_.push_back(buffer);
+    }
+    PaddleDetection::PicoDetPostProcess(
+        result, output_data_list_, config_.fpn_stride_, inputs_.im_shape_,
+        inputs_.scale_factor_, config_.nms_info_["score_threshold"].as<float>(),
+        config_.nms_info_["nms_threshold"].as<float>(), num_class, reg_max);
+    bbox_num->push_back(result->size());
+  } else if (config_.arch_ == "SOLOv2") {
+    SOLOv2Postprocess(imgs, result, bbox_num, out_bbox_num_data_,
+                      out_label_data_, out_score_data_, out_global_mask_data_,
+                      threshold);
+  } else {
+    is_rbox = output_shape_list[0][output_shape_list[0].size() - 1] % 10 == 0;
+    Postprocess(imgs, result, out_bbox_num_data_, out_tensor_list[0],
+                out_mask_data_, is_rbox);
+    for (int k = 0; k < out_bbox_num_data_.size(); k++) {
+      int tmp = out_bbox_num_data_[k];
+      bbox_num->push_back(tmp);
+    }
+  }
+
+  auto postprocess_end = std::chrono::steady_clock::now();
+
+  std::chrono::duration<float> preprocess_diff =
+      preprocess_end - preprocess_start;
+  times->push_back(static_cast<double>(preprocess_diff.count() * 1000));
+  std::chrono::duration<float> inference_diff = inference_end - inference_start;
+  times->push_back(
+      static_cast<double>(inference_diff.count() / repeats * 1000));
+  std::chrono::duration<float> postprocess_diff =
+      postprocess_end - postprocess_start;
+  times->push_back(static_cast<double>(postprocess_diff.count() * 1000));
+}
+
+std::vector<int> GenerateColorMap(int num_class) {
+  auto colormap = std::vector<int>(3 * num_class, 0);
+  for (int i = 0; i < num_class; ++i) {
+    int j = 0;
+    int lab = i;
+    while (lab) {
+      colormap[i * 3] |= (((lab >> 0) & 1) << (7 - j));
+      colormap[i * 3 + 1] |= (((lab >> 1) & 1) << (7 - j));
+      colormap[i * 3 + 2] |= (((lab >> 2) & 1) << (7 - j));
+      ++j;
+      lab >>= 3;
+    }
+  }
+  return colormap;
+}
+
+} // namespace PaddleDetection

deploy/cpp/src/picodet_postprocess.cc

+//   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.
+//
+// The code is based on:
+// https://github.com/RangiLyu/nanodet/blob/main/demo_mnn/nanodet_mnn.cpp
+
+#include "include/picodet_postprocess.h"
+
+namespace PaddleDetection {
+
+float fast_exp(float x) {
+  union {
+    uint32_t i;
+    float f;
+  } v{};
+  v.i = (1 << 23) * (1.4426950409 * x + 126.93490512f);
+  return v.f;
+}
+
+template <typename _Tp>
+int activation_function_softmax(const _Tp *src, _Tp *dst, int length) {
+  const _Tp alpha = *std::max_element(src, src + length);
+  _Tp denominator{0};
+
+  for (int i = 0; i < length; ++i) {
+    dst[i] = fast_exp(src[i] - alpha);
+    denominator += dst[i];
+  }
+
+  for (int i = 0; i < length; ++i) {
+    dst[i] /= denominator;
+  }
+
+  return 0;
+}
+
+// PicoDet decode
+PaddleDetection::ObjectResult
+disPred2Bbox(const float *&dfl_det, int label, float score, int x, int y,
+             int stride, std::vector<float> im_shape, int reg_max) {
+  float ct_x = (x + 0.5) * stride;
+  float ct_y = (y + 0.5) * stride;
+  std::vector<float> dis_pred;
+  dis_pred.resize(4);
+  for (int i = 0; i < 4; i++) {
+    float dis = 0;
+    float *dis_after_sm = new float[reg_max + 1];
+    activation_function_softmax(dfl_det + i * (reg_max + 1), dis_after_sm,
+                                reg_max + 1);
+    for (int j = 0; j < reg_max + 1; j++) {
+      dis += j * dis_after_sm[j];
+    }
+    dis *= stride;
+    dis_pred[i] = dis;
+    delete[] dis_after_sm;
+  }
+  int xmin = (int)(std::max)(ct_x - dis_pred[0], .0f);
+  int ymin = (int)(std::max)(ct_y - dis_pred[1], .0f);
+  int xmax = (int)(std::min)(ct_x + dis_pred[2], (float)im_shape[0]);
+  int ymax = (int)(std::min)(ct_y + dis_pred[3], (float)im_shape[1]);
+
+  PaddleDetection::ObjectResult result_item;
+  result_item.rect = {xmin, ymin, xmax, ymax};
+  result_item.class_id = label;
+  result_item.confidence = score;
+
+  return result_item;
+}
+
+void PicoDetPostProcess(std::vector<PaddleDetection::ObjectResult> *results,
+                        std::vector<const float *> outs,
+                        std::vector<int> fpn_stride,
+                        std::vector<float> im_shape,
+                        std::vector<float> scale_factor, float score_threshold,
+                        float nms_threshold, int num_class, int reg_max) {
+  std::vector<std::vector<PaddleDetection::ObjectResult>> bbox_results;
+  bbox_results.resize(num_class);
+  int in_h = im_shape[0], in_w = im_shape[1];
+  for (int i = 0; i < fpn_stride.size(); ++i) {
+    int feature_h = std::ceil((float)in_h / fpn_stride[i]);
+    int feature_w = std::ceil((float)in_w / fpn_stride[i]);
+    for (int idx = 0; idx < feature_h * feature_w; idx++) {
+      const float *scores = outs[i] + (idx * num_class);
+
+      int row = idx / feature_w;
+      int col = idx % feature_w;
+      float score = 0;
+      int cur_label = 0;
+      for (int label = 0; label < num_class; label++) {
+        if (scores[label] > score) {
+          score = scores[label];
+          cur_label = label;
+        }
+      }
+      if (score > score_threshold) {
+        const float *bbox_pred =
+            outs[i + fpn_stride.size()] + (idx * 4 * (reg_max + 1));
+        bbox_results[cur_label].push_back(
+            disPred2Bbox(bbox_pred, cur_label, score, col, row, fpn_stride[i],
+                         im_shape, reg_max));
+      }
+    }
+  }
+  for (int i = 0; i < (int)bbox_results.size(); i++) {
+    PaddleDetection::nms(bbox_results[i], nms_threshold);
+
+    for (auto box : bbox_results[i]) {
+      box.rect[0] = box.rect[0] / scale_factor[1];
+      box.rect[2] = box.rect[2] / scale_factor[1];
+      box.rect[1] = box.rect[1] / scale_factor[0];
+      box.rect[3] = box.rect[3] / scale_factor[0];
+      results->push_back(box);
+    }
+  }
+}
+
+} // namespace PaddleDetection