PaddleDetection部署c++生成dll并进行调用

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前言

上一篇文章我们讲到了如何用c++部署PaddleDetection，以及遇到一些常见报错的解决方法。这篇文章我们将在上一篇文章的基础上讲一下如何将C++预测代码封装成一个dll，并利用python和c++进行调用。
上一篇文章链接：PaddleDetection部署c++测试图片视频

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1、必要环境

本文使用cuda10.1+cudnn7.6.5+vs2017+cmake3.17.0

2、封装流程

2.1 修改CMakeLists.txt

1. 修改文件夹 PaddleDetection/deploy/cpp下的CMakeLists.txt如下：
   
   
   将 add_executable(main src/main.cc src/preprocess_op.cc src/object_detector.cc)
   改为ADD_library(main SHARED src/main.cc src/preprocess_op.cc src/object_detector.cc)

2.2 重新进行cmake编译

依次点击configure、Generate、Open Project

2.3 生成dll

1.包含目录中添加dirent.h的路径

2.将附加依赖项的.lib改为paddle_inference.lib


3.生成后事件->在生成中使用改为 否

4.右击main重新生成


这时我们会发现在release目录下成功生成main.dll

不急，现在还不能用，继续往下看

5.将main.cc代码修改成如下形式

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 

#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 

#ifdef _WIN32
#include 
#include 
#elif LINUX
#include 
#include 
#endif

extern "C" __declspec(dllexport) int  main_test();
extern "C" __declspec(dllexport) int add(int a, int b);

int add(int a, int b) {
	return a + b;
}

#include "include/object_detector.h"
#include 
using namespace std;
std::string model_dir ;
std::string image_file;
std::string video_file;
std::string image_dir;
int batch_size=1;
bool use_gpu=true;
int camera_id = -1;
double threshold = 0.1;
std::string output_dir = "output";
std::string run_mode="fluid";
int gpu_id = 0;
bool run_benchmark = false;
bool use_mkldnn = false;
double cpu_threads = 0.9;
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;

class MyClass
{

public:
	void PrintBenchmarkLog(std::vector<double> det_time, int img_num);
	static string DirName(const std::string &filepath);
	static bool PathExists(const std::string& path);
	static void MkDir(const std::string& path);
	static void MkDirs(const std::string& path);
	void GetAllFiles(const char *dir_name, std::vector<std::string> &all_inputs);
	void PredictVideo(const std::string& video_path, PaddleDetection::ObjectDetector* det);
	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");
};

void MyClass::PrintBenchmarkLog(std::vector<double> det_time, int img_num) {
	LOG(INFO) << "----------------------- Config info -----------------------";
	LOG(INFO) << "runtime_device: " << (use_gpu ? "gpu" : "cpu");
	LOG(INFO) << "ir_optim: " << "True";
	LOG(INFO) << "enable_memory_optim: " << "True";
	int has_trt = run_mode.find("trt");
	if (has_trt >= 0) {
		LOG(INFO) << "enable_tensorrt: " << "True";
		std::string precision = run_mode.substr(4, 8);
		LOG(INFO) << "precision: " << precision;
	}
	else {
		LOG(INFO) << "enable_tensorrt: " << "False";
		LOG(INFO) << "precision: " << "fp32";
	}
	LOG(INFO) << "enable_mkldnn: " << (use_mkldnn ? "True" : "False");
	LOG(INFO) << "cpu_math_library_num_threads: " << cpu_threads;
	LOG(INFO) << "----------------------- Data info -----------------------";
	LOG(INFO) << "batch_size: " << batch_size;
	LOG(INFO) << "input_shape: " << "dynamic shape";
	LOG(INFO) << "----------------------- Model info -----------------------";
	model_dir.erase(model_dir.find_last_not_of("/") + 1);
	LOG(INFO) << "model_name: " << model_dir.substr(model_dir.find_last_of('/') + 1);
	LOG(INFO) << "----------------------- Perf info ------------------------";
	LOG(INFO) << "Total number of predicted data: " << img_num
		<< " and total time spent(s): "
		<< 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];
}

string MyClass::DirName(const std::string &filepath) {
	auto pos = filepath.rfind(OS_PATH_SEP);
	if (pos == std::string::npos) {
		return "";
	}
	return filepath.substr(0, pos);
}


bool MyClass::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
}


void MyClass::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);
	}
}

void MyClass::MkDirs(const std::string& path) {
	if (path.empty()) return;
	if (PathExists(path)) return;

	MkDirs(DirName(path));
	MkDir(path);
}


void MyClass::GetAllFiles(const char *dir_name,
	std::vector<std::string> &all_inputs) {
	if (NULL == dir_name) {
		std::cout << " dir_name is null ! " << std::endl;
		return;
	}
	struct stat s;
	stat(dir_name, &s);
	if (!S_ISDIR(s.st_mode)) {
		std::cout << "dir_name is not a valid directory !" << std::endl;
		all_inputs.push_back(dir_name);
		return;
	}
	else {
		struct dirent *filename; // return value for readdir()
		DIR *dir;                // return value for opendir()
		dir = opendir(dir_name);
		if (NULL == dir) {
			std::cout << "Can not open dir " << dir_name << std::endl;
			return;
		}
		std::cout << "Successfully opened the dir !" << std::endl;
		while ((filename = readdir(dir)) != NULL) {
			if (strcmp(filename->d_name, ".") == 0 ||
				strcmp(filename->d_name, "..") == 0)
				continue;
			all_inputs.push_back(dir_name + std::string("/") +
				std::string(filename->d_name));
		}
	}
}


void MyClass::PredictVideo(const std::string& video_path,
                  PaddleDetection::ObjectDetector* det) {
  // Open video
  cv::VideoCapture capture;
  if (camera_id != -1){
    capture.open(camera_id);
  }else{
    capture.open(video_path.c_str());
  }
  if (!capture.isOpened()) {
    printf("can not open video : %s\n", video_path.c_str());
    return;
  }

  // Get Video info : resolution, fps
  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));

  // Create VideoWriter for output
  cv::VideoWriter video_out;
  std::string video_out_path = "output.avi";
  video_out.open(video_out_path.c_str(),
				CV_FOURCC('D', 'I', 'V', 'X'),
                 video_fps,
                 cv::Size(video_width, video_height),
                 true);
  std::cout << video_out.isOpened();
  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 = 0;
  bool is_rbox = false;
  int icu = 0;
  while (capture.read(frame)) {
	  icu += 1;
	  std::cout << icu<<"frame"<<std::endl;
    if (frame.empty()) {
      break;
    }
    std::vector<cv::Mat> imgs;
    imgs.push_back(frame);
    det->Predict(imgs, 0.5, 0, 1, &result, &bbox_num, &det_times);
    for (const auto& item : result) {
      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, result, labels, colormap, is_rbox);

    video_out.write(out_im);
    frame_id += 1;
  }
  capture.release();
  video_out.release();
}

void MyClass::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) {
  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++) {
        std::cout << all_img_paths.at(idx * batch_size + i) << "result" << std::endl;
        if (bbox_num[i] <= 1) {
            continue;
        }
        for (int j = 0; j < bbox_num[i]; j++) {
          PaddleDetection::ObjectResult item = result[item_start_idx + j];
          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]);
          }
        }
        item_start_idx = item_start_idx + bbox_num[i];
      }
      // Visualization result
      int bbox_idx = 0;
      for (int bs = 0; bs < batch_imgs.size(); bs++) {
        if (bbox_num[bs] <= 1) {
            continue;
        }
        cv::Mat im = batch_imgs[bs];
        std::vector<PaddleDetection::ObjectResult> im_result;
        for (int k = 0; k < bbox_num[bs]; k++) {
          im_result.push_back(result[bbox_idx+k]);
        }
        bbox_idx += bbox_num[bs];
        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 + bs);
        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];
  }
  PrintBenchmarkLog(det_t, all_img_paths.size());
}

int main_test() {
	model_dir = "D:/projects/PaddleDetection/deploy/cpp/out/Release/models";
	image_file = "D:/projects/PaddleDetection/deploy/cpp/out/Release/images/1.jpg";
	//video_file = "bb.mp4";
	//image_dir = "";
  // Parsing command-line
  //google::ParseCommandLineFlags(&argc, &argv, true);

  if (model_dir.empty()
      || (image_file.empty() && image_dir.empty() && video_file.empty())) {
    std::cout << "Usage: ./main --model_dir=/PATH/TO/INFERENCE_MODEL/ "
                << "--image_file=/PATH/TO/INPUT/IMAGE/" << std::endl;
  }
  if (!(run_mode == "fluid" || run_mode == "trt_fp32"
      || run_mode == "trt_fp16" || run_mode == "trt_int8")) {
    std::cout << "run_mode should be 'fluid', 'trt_fp32', 'trt_fp16' or 'trt_int8'.";
    return -1;
  }
  // Load model and create a object detector
  PaddleDetection::ObjectDetector det(model_dir, use_gpu, use_mkldnn,
                        threshold, run_mode, batch_size,gpu_id, use_dynamic_shape,
                        trt_min_shape, trt_max_shape, trt_opt_shape, trt_calib_mode);
  // Do inference on input video or image
  MyClass predictvideo;
  if (!video_file.empty() || camera_id != -1) {
	  predictvideo.PredictVideo(video_file, &det);
  } else if (!image_file.empty() || !image_dir.empty()) {
    if (!predictvideo.PathExists(output_dir)) {
		predictvideo.MkDirs(output_dir);
    }
    std::vector<std::string> all_imgs;
    if (!image_file.empty()) {
      all_imgs.push_back(image_file);
      if (batch_size > 1) {
        std::cout << "batch_size should be 1, when image_file is not None" << std::endl;
        batch_size = 1;
      }
    } else {
		predictvideo.GetAllFiles((char *)image_dir.c_str(), all_imgs);
    }
	predictvideo.PredictImage(all_imgs, batch_size, threshold, run_benchmark, &det, output_dir);
  }
  return 0;
}

int main(int argc, char** argv) {
	main_test();
}

这里记得修改为自己的路径 model_dir是存放模型的文件夹路径，image_file是图片文件路径

6.右键main，再次重新生成

这时我们发现在out/Release目录下生成了.dll和.lib文件

3、在C++中进行调用

1.新建一个项目

2.将out/Release目录下的如下六个文件 复制到新建的项目目录下


3.输入如下代码进行测试

#include 
#include 
/*
Windows头文件中包含可以加载DLL的函数。
*/
typedef int(*ptrSub)(int, int);
typedef int(*ptrSub2)();
/*
在调用DLL函数之前，要定义函数指针，用来调用函数。
可以看出，函数指针的类型与DLL中的要一致。
*/
HMODULE hMod = LoadLibrary(L"main.dll");
/*
	调用LoadLibrary函数加载DLL文件。加载成功，hMod指针不为空。
	这里也可以是一个地址加文件名
*/
int main()
{
	if (hMod != NULL)
	{
		/*
		如果加载成功，则可通过GetProcAddress函数获取DLL中需要调用的函数的地址。
		获取成功，test指针不为空。
		*/
		ptrSub2 test= (ptrSub2)GetProcAddress(hMod, "main_test");
		if (test != NULL)
		{
			test();
			/*获取地址成功后，通过test调用函数功能。*/
		}
		FreeLibrary(hMod);
		/*在完成调用功能后，不在需要DLL支持，则可以通过FreeLibrary函数释放DLL。*/
	}
}

如出现如下界面，则证明测试成功！

效果图

因为这里是自己随便训练的模型，所以准确率有些低 ~

4、在python中进行调用

1.新建一个python文件，输入如下代码

from ctypes import *
dll = CDLL(r"main.dll")
print(dll.main_test())

2.同样将上述六个文件复制到python文件目录下

3.运行.py文件

如果出现下图界面，则证明调用成功！

效果如下 ~

5、可能出现的问题

修改threshold阈值对结果没有影响

本人的解决方案：

1.找到object_detector.cc文件的201行，按住ctrl键点击threshold_ 进入object_detector.h

2.将object_detector.h文件的63行和84行修改成如下

3.将main.cc修改成如下，或者直接使用我上面提供的代码。

修改之后就可以随意调整threshold设置阈值啦

如有问题，欢迎评论区交流！