如何在python调用cpp的dll或者so库

本文使用的是pybind11来调用so和dll（pyd），实现0时间损失调用，以下会用yolov11 pose的tensorrt部署来举例

1.第一步下载pybind11

2.改cmakelists

（1）添加拉下来的pybind11-2.13的include

include_directories(
    ${TensorRT_INCLUDES_DIRS}
    ${CUDNN_INCLUDES}
    ${CUDA_INCLUDE_DIRS}
    ${PROJECT_SOURCE_DIR}/include
    ${OpenCV_INCLUDE_DIRS}
    ${PROJECT_SOURCE_DIR}/third/pybind11-2.13/include  # Include pybind11 headers directly
)

（2）添加python的编译头文件

# Specify Anaconda environment's Python interpreter path
set(Python3_EXECUTABLE "D:/anaconda3/envs/pytorch/python.exe")  # Replace with your actual path

# Find Python3 components
find_package(Python3 COMPONENTS Interpreter Development REQUIRED)

# Include Python headers
include_directories(${Python3_INCLUDE_DIRS})

（3）链接头文件和源文件，并设置导出

# Collect source files
file(GLOB_RECURSE SOURCE_FILES src/*.cpp src/*.cu)
file(GLOB_RECURSE HEADER_FILES include/*.h include/*.cuh)

# Add pybind11 wrapper file
# Ensure that pybind_wrapper.cpp is included
list(APPEND SOURCE_FILES src/pybind_wrapper.cpp)


# Create Python module as shared library
add_library(CPDAI_module SHARED ${SOURCE_FILES} ${HEADER_FILES})

set_target_properties(CPDAI_module PROPERTIES
    PREFIX ""  # Remove default 'lib' prefix
    SUFFIX ".pyd"  # On Windows, Python modules have extension .pyd
    RUNTIME_OUTPUT_DIRECTORY "${PROJECT_SOURCE_DIR}/output"  # Specify your desired output directory
    RUNTIME_OUTPUT_NAME "CPDAI_module"  # Name of the output file without extension
    POSITION_INDEPENDENT_CODE ON  # Ensure that the module is built with position-independent code
)



target_link_libraries(CPDAI_module PRIVATE
    nvinfer
    nvinfer_plugin
    nvonnxparser
    cudart
    ${OpenCV_LIBS}
    nppc nppial nppicc nppidei nppif nppig nppim nppist nppisu nppitc
    ${Python3_LIBRARIES}  # Link Python libraries
)


target_compile_definitions(CPDAI_module PRIVATE
    DLL_API
)

3.输出文件的改写

这边我们的输出文件是这个pybind_wrapper.cpp,他调取yolov11这个类，得到一个结构体

#include <pybind11/pybind11.h>
#include <pybind11/stl.h>
#include <pybind11/numpy.h>
#include <opencv2/opencv.hpp>
#include "YoloPointDetection.h"

namespace py = pybind11;

PYBIND11_MODULE(CPDAI_module, m) {
	py::class_<Box>(m, "Box")
		.def(py::init<>())
		.def_readwrite("x1", &Box::x1)
		.def_readwrite("y1", &Box::y1)
		.def_readwrite("x2", &Box::x2)
		.def_readwrite("y2", &Box::y2)
		.def_readwrite("cls_conf", &Box::cls_conf)
		.def_readwrite("cls_id", &Box::cls_id)
		.def_readwrite("point1_x", &Box::point1_x)
		.def_readwrite("point1_y", &Box::point1_y)
		.def_readwrite("point1_conf", &Box::point1_conf)
		.def_readwrite("point2_x", &Box::point2_x)
		.def_readwrite("point2_y", &Box::point2_y)
		.def_readwrite("point2_conf", &Box::point2_conf);

	py::class_<Yolov11_detection>(m, "Yolov11_detection")
		.def(py::init<>())
		.def("init_model", &Yolov11_detection::init_model)
		.def("yolov11_Predict", [](Yolov11_detection &self, py::array_t<uint8_t> img_array, float conf_thresh, float nms_thresh) {
		// 将 NumPy 数组转换为 cv::Mat
		py::buffer_info buf = img_array.request();
		if (buf.ndim != 3 || buf.shape[2] != 3) {
			throw std::runtime_error("输入图像必须是 3 通道彩色图像");
		}
		cv::Mat img(buf.shape[0], buf.shape[1], CV_8UC3, buf.ptr);
		cv::Mat img_copy = img.clone(); // 深拷贝，防止数据不一致
		std::vector<Box> boxes(25200);  // 假设最多 25200 个框
		int final_box;
		self.yolov11_Predict(img_copy, boxes, final_box, conf_thresh, nms_thresh);
		boxes.resize(final_box); // 只保留有效的框
		return boxes;
			}, py::arg("img"), py::arg("conf_thresh") = 0.5f, py::arg("nms_thresh") = 0.4f);
}


struct Box {
	float x1, y1, x2, y2;
	float cls_conf;
	int cls_id;
	float point1_x;
	float point1_y;
	float point1_conf;
	float point2_x;
	float point2_y;
	float point2_conf;
};

class DLL Yolov11_detection
{
public:
	Yolov11_detection();
	~Yolov11_detection();

	void init_model(std::string model_path);
	void yolov11_Predict(cv::Mat& img, std::vector<Box>& box, int& final_box_num, float conf_thresh, float nms_thresh);
	void trt2onnx(std::string onnx_path, std::string trt_path);

4.如何调用

方法1：直接把输出的pyd和需要调用的dll（或者加环境变量）库放在python main函数同一文件夹下（linux直接放so文件就行），然后python直接import就行，如果编译器爆红也请大胆尝试跑，因为是正常的现象。

方法2：将pyd和dll文件放入anaconda3的当前环境的libs文件下，直接能被anconda3读到

import CPDAI_module  

model = CPDAI_module.Yolov11_detection()
model.init_model(model_path)
 for img_way in data_path:
     if not os.path.exists(img_way):
        continue
     slice = img_way.split('_')[-1].split('.')[0]
     img = cv2.imread(img_way)
     results = self.model.yolov11_Predict(img, conf_thresh=0.65, nms_thresh=0.7)

results就是一个指针，直接可以.xx来获取结构体里面的东西

cls = result.cls_id  # 获取类别
conf = result.cls_conf

总结：

因为为了满足一些框架无法调用cpp的目的，折腾了几天还是成功搞出来了，而且这个方法目前测试基本和cpp运行时间，有什么问题欢迎评论区

如何在python调用cpp的dll或者so库

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