ubuntu调用pythonhttps://blog.csdn.net/qq_39942341/article/details/129333969

我这里mingw是用msys2的
opencv也是msys2装的
安装msys2和opencv可以参考这个https://blog.csdn.net/qq_39942341/article/details/129380197?spm=1001.2014.3001.5502

环境变量里加入python路径，例如D:\Miniconda3\envs\DL

D:\Miniconda3\envs\DL\libs里，python37.lib复制一份改成python37_d.lib

有个玄学的问题，我用不了imgaug这个库

opencv+numpy+pytorch

main.cpp

load_model
加载模型

get_predict_xy
用C++的opencv读图片，转numpy传入python
python再用pytorch预测，返回一个numpy

simple_test
用C++的opencv读图片，转numpy传入python
python直接传回来给C++，转opencv

顺带提一下，import_array()一定要写

#include <Python.h>
#include <iostream>
#include <string>
#include <numpy/arrayobject.h>
#include <opencv2/opencv.hpp>

void load_model(PyObject* pModule, const std::string& model_path){
    PyObject* init_model = PyObject_GetAttrString(pModule, "init_model");
    if (NULL == init_model || 0 == PyCallable_Check(init_model)) {
        std::cout << "not found function init_model" << std::endl;
        exit(-1);
    }
    PyObject *pArgs = PyTuple_New(1);
    PyTuple_SetItem(pArgs, 0, Py_BuildValue("s", model_path.c_str()));

    PyObject* result = PyObject_CallObject(init_model, pArgs);
    if(NULL == result){
        std::cout << "init_model failed" << std::endl;
        exit(-1);
    }
    int return_value = -1;
    PyArg_Parse(result, "i", &return_value);
    std::cout<<"returned "<<return_value<<std::endl;
}

void get_predict_xy(PyObject* pModule, const std::string& img_path){
    cv::Mat img = cv::imread(img_path, 0);
    PyObject* predict = PyObject_GetAttrString(pModule, "get_predict_xy");
    if (NULL == predict || 0 == PyCallable_Check(predict)) {
        std::cout << "not found function get_predict_xy" << std::endl;
        exit(-1);
    }
    npy_intp dims[] = {img.rows, img.cols};
    PyObject* pValue = PyArray_SimpleNewFromData(2, dims, NPY_UINT8, img.data);
    PyObject *pArgs = PyTuple_New(1);
    // PyTuple_SetItem(pArgs, 0, Py_BuildValue("s", img_path.c_str()));
    PyTuple_SetItem(pArgs, 0, pValue);

    PyObject* result = PyEval_CallObject(predict, pArgs);
    if(NULL == result){
        std::cout << "get_predict_xy failed" << std::endl;
        exit(-1);
    }
    if(!PyArray_Check(result)){//None
        std::cout << "didn't return numpy" << std::endl;
        exit(-1);
    }
    PyArrayObject* ret_array;
    PyArray_OutputConverter(result, &ret_array);
    if(2 != PyArray_NDIM(ret_array)){
        exit(-1);
    }
    npy_intp* shape = PyArray_SHAPE(ret_array);
    int n = shape[0];
    int m = shape[1];
    cv::Mat return_key_points(n,m,CV_32F,PyArray_DATA(ret_array));
    for(int i = 0; i < n; ++i){
        for(int j = 0; j < m; ++j){
            int* cur = reinterpret_cast<int*>(PyArray_GETPTR2(ret_array, i, j));
            std::cout<<*cur<<' ';

        }
        std::cout<<std::endl;
    }

    //PyArray_GETPTR2
}

void simple_test(PyObject* pModule, const std::string& img_path){
    cv::Mat img = cv::imread(img_path, 0);
    PyObject* predict = PyObject_GetAttrString(pModule, "simple_test");
    if (NULL == predict || 0 == PyCallable_Check(predict)) {
        std::cout << "not found function simple_test" << std::endl;
        exit(-1);
    }
    npy_intp dims[] = {img.rows, img.cols};
    PyObject* pValue = PyArray_SimpleNewFromData(2, dims, NPY_UINT8, img.data);
    PyObject *pArgs = PyTuple_New(1);
    // PyTuple_SetItem(pArgs, 0, Py_BuildValue("s", img_path.c_str()));
    PyTuple_SetItem(pArgs, 0, pValue);

    PyObject* result = PyEval_CallObject(predict, pArgs);
    if(NULL == result){
        std::cout << "simple_test failed" << std::endl;
        exit(-1);
    }
    if(!PyArray_Check(result)){//None
        std::cout << "didn't return numpy" << std::endl;
        exit(-1);
    }
    PyArrayObject* ret_array;
    PyArray_OutputConverter(result, &ret_array);
    if(2 != PyArray_NDIM(ret_array)){
        exit(-1);
    }
    npy_intp* shape = PyArray_SHAPE(ret_array);
    int n = shape[0];
    int m = shape[1];
    cv::Mat return_img(n,m,CV_8UC1,PyArray_DATA(ret_array));
    // cv::imshow("test", return_img);
    // cv::waitKey(0);
    // cv::destroyAllWindows();
    for(int i = 0; i < n; ++i){
        uchar* data1 = img.ptr<uchar>(i);
        uchar* data2 = return_img.ptr<uchar>(i);
        for(int j = 0; j < m; ++j){
            if(data1[j] != data2[j]){
                std::cout<<"not equal"<<std::endl;
                return;
            }
        }
    }
    std::cout<<"equal"<<std::endl;
}

int main() {
    Py_SetPythonHome(L"D:\\Miniconda3\\envs\\DL");
    Py_Initialize();
    if (0 == Py_IsInitialized()) {
        std::cout << "python init fail" << std::endl;
        return -1;
    }
    import_array();
    PyRun_SimpleString("import sys");
    PyRun_SimpleString("import os");
    PyRun_SimpleString("print(os.path.abspath('.'))");
    PyRun_SimpleString("sys.path.append('../python_script')");

    //相当于import
    PyObject* pModule = PyImport_ImportModule("predict");
    if (NULL == pModule) {
        std::cout << "module not found" << std::endl;
        return -1;
    }
    simple_test(pModule, "../python_script/001.bmp");
    load_model(pModule, "../python_script/best.pth");
    get_predict_xy(pModule, "../python_script/001.bmp");
    get_predict_xy(pModule, "../python_script/001.bmp");

    Py_Finalize();
    return 0;
}

predict.py
UNet我没放出来

#!/usr/bin/env python
# _*_ coding:utf-8 _*_
import os
import numpy as np

from model.u2net import UNet
import torch
from cv2 import cv2

#import imgaug.augmenters as iaa

model = UNet(in_channels=1, out_channels=19)
device = torch.device('cuda:0')


# augmentation = iaa.Sequential([
#     iaa.Resize({"width": 416, "height": 512})
# ])


def init_model(path):
    global model, device
    if not os.path.exists(path):
        print(f'not found {os.path.abspath(path)}')
        return -1
    model_state_dict = torch.load(path)
    model.load_state_dict(model_state_dict)
    model = model.to(device)
    return 0


def get_img_aug(img):
    # global augmentation
    print('----get_img_aug------')
    print(img.shape)
    print('------------------')
    # img = cv2.imread(path, 0)  # 2490*1935
    # img_aug = augmentation(image=img)
    img_aug = cv2.resize(img, (416, 512), interpolation=cv2.INTER_LINEAR)
    img_aug = (img_aug - img_aug.min()) / (img_aug.max() - img_aug.min())
    img_aug = torch.FloatTensor(img_aug).unsqueeze(0).unsqueeze(0)  # torch.Size([1, 1, 512, 416])
    return img_aug


def get_heatmap_coordination_batch_numpy(heatmap):
    """
    get heatmap coordination by batch

    :param heatmap: (B,C,H,W) or (B,C,H,W,D) (C is the num of landmark)
    :return: coordination (B,C,2) or (B,C,3)
    """
    origin_shape = heatmap.shape
    heatmap = heatmap.reshape(*origin_shape[:2], -1)
    temp = np.argmax(heatmap, axis=-1)[..., np.newaxis]

    # unravel_index
    out = []
    for dim in reversed(origin_shape[2:]):
        out.append(temp % dim)
        temp = np.floor_divide(temp, dim)
    out = np.concatenate(out[::-1], axis=-1)
    return out


def get_predict_xy(img):
    global model
    # if not os.path.exists(path):
    #     return None
    img = get_img_aug(img).to(device)  # 1 * 1 * 512 * 416
    output = model(img)['output'].to('cpu').detach().numpy()  # 1 * 1 * 19 * 2
    predict_xy = get_heatmap_coordination_batch_numpy(output).squeeze(0)  # 19 * 2
    print(predict_xy)
    return predict_xy


def simple_test(img):
    return img


if __name__ == '__main__':
    path = r'E:\PyCharmProject\pythonProject3\001.bmp'
    img = cv2.imread(path, 0)
    init_model('best.pth')
    print('finish_init')
    print(get_predict_xy(img).shape)
    print(get_predict_xy(img).dtype)

CMakeLists.txt

cmake_minimum_required(VERSION 3.24)
project(C_PLUS_PLUS VERSION 0.1.0)

IF(NOT CMAKE_BUILD_TYPE)
  SET(CMAKE_BUILD_TYPE Release)
ENDIF()

set(CMAKE_CXX_STANDARD 17)
set(PYTHON_INCLUDE_DIRS "D:/Miniconda3/envs/DL/include/")
set(NUMPY_INCLUDE_DIR "D:/Miniconda3/envs/DL/Lib/site-packages/numpy/core/include")
INCLUDE_DIRECTORIES(${PYTHON_INCLUDE_DIRS} ${NUMPY_INCLUDE_DIR})
link_directories("D:/Miniconda3/envs/DL/libs")
set(PYTHON_LIBRARIES "D:/Miniconda3/envs/DL/libs/python37.lib")
add_executable(${PROJECT_NAME} main.cpp)
target_link_libraries(${PROJECT_NAME} ${PYTHON_LIBRARIES})

# Where to find CMake modules and OpenCV
# set(OpenCV_DIR "D:\\opencv-4.5.5\\opencv-4.5.5\\build")
find_package(OpenCV REQUIRED)
message(STATUS "OpenCV Include: ${OpenCV_INCLUDE_DIRS}")
message(STATUS "OpenCV LIBRARIES: ${OpenCV_LIBRARIES}")
message(STATUS "OpenCV Libs: ${OpenCV_LIBS}")

INCLUDE_DIRECTORIES(${OpenCV_INCLUDE_DIRS})
target_link_libraries(${PROJECT_NAME}  ${OpenCV_LIBS})