Python调用C++动态库，实现图像拼接（调用输出结果有问题）

#include <iostream>
#include <fstream>
#include <string>
#include "opencv2/opencv_modules.hpp"
#include <opencv2/core/utility.hpp>
#include "opencv2/imgcodecs.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/stitching/detail/autocalib.hpp"
#include "opencv2/stitching/detail/blenders.hpp"
#include "opencv2/stitching/detail/timelapsers.hpp"
#include "opencv2/stitching/detail/camera.hpp"
#include "opencv2/stitching/detail/exposure_compensate.hpp"
#include "opencv2/stitching/detail/matchers.hpp"
#include "opencv2/stitching/detail/motion_estimators.hpp"
#include "opencv2/stitching/detail/seam_finders.hpp"
#include "opencv2/stitching/detail/warpers.hpp"
#include "opencv2/stitching/warpers.hpp"
#ifdef HAVE_OPENCV_XFEATURES2D
#include "opencv2/xfeatures2d/nonfree.hpp"
#endif
#define ENABLE_LOG 1
#define LOG(msg) std::cout << msg
#define LOGLN(msg) std::cout << msg << std::endl
using namespace std;
using namespace cv;
using namespace cv::detail;
// Default command line args

#if 1
#define DLL_API __declspec(dllexport)
#else
#define DLL_API __declspec(dllimport)
#endif


extern "C" { //由于编译过程的原因，python一般只支持c的接口
    typedef struct ImageBase {
        int w;                   //图像的宽
        int h;                    //图像的高
        int c;                    //通道数
        unsigned char *data;    //我们要写python和c++交互的数据结构，0-255的单字符指针
    }ImageMeta;
    //typedef ImageBase ImageMeta;

    DLL_API int Stitch(ImageMeta *im1, ImageMeta *im2);//函数导出，要改

};

//vector<String> img_names;
int num_images;
bool preview = false;
bool try_cuda = false;
double work_megapix = 0.6;
double seam_megapix = 0.1;
double compose_megapix = -1;
float conf_thresh = 1.f;
#ifdef HAVE_OPENCV_XFEATURES2D
string features_type = "surf";
#else
string features_type = "orb";
#endif
string matcher_type = "homography";
string estimator_type = "homography";
string ba_cost_func = "ray";
string ba_refine_mask = "xxxxx";
bool do_wave_correct = true;
WaveCorrectKind wave_correct = detail::WAVE_CORRECT_HORIZ;
bool save_graph = false;
std::string save_graph_to;
string warp_type = "spherical";
int expos_comp_type = ExposureCompensator::GAIN_BLOCKS;
int expos_comp_nr_feeds = 1;
int expos_comp_nr_filtering = 2;
int expos_comp_block_size = 32;
float match_conf = 0.3f;
string seam_find_type = "gc_color";
int blend_type = Blender::MULTI_BAND;
int timelapse_type = Timelapser::AS_IS;//延时摄影
float blend_strength = 5;
string result_name = "D:/result.jpg";//
bool timelapse = false;//首先定义timelapse的默认布尔类型为False
int range_width = -1;

DLL_API int Stitch(ImageMeta *im1, ImageMeta *im2)//入参两个数组指针，出参一个数组指针
//vector<Mat> img_list
{//一个int数；一个图片类型的列表
//predict先判断长度 然后长度作为一个参数传给
    //preview = true;
    //try_cuda = true;
    //preview = true;
    //result = 'D:/result.jpg';
    //work_megapix = -1;
    //features_type = "orb";

    Mat img1 = Mat::zeros(Size(im1->w, im1->h), CV_8UC3);
    //先从输入的指针对象提取w,h，data；将python传来的参数转变成C处理的格式。用的是相同的结构：结构体。
    img1.data = im1->data;

    Mat img2 = Mat::zeros(Size(im2->w, im2->h), CV_8UC3);
    //先从输入的指针对象提取w,h，data；将python传来的参数转变成C处理的格式。用的是相同的结构：结构体。
    img2.data = im2->data;


    //Mat img1, img2;
    //img1 = imread("D:/1Hill.jpg");
    //img2 = imread("D:/2Hill.jpg");
    vector<Mat> ALLimages(2);
    ALLimages[0] = img1.clone();
    ALLimages[1] = img2.clone();
    //img_names.push_back("D:/1Hill.jpg");
    //img_names.push_back("D:/2Hill.jpg");//??
    //img_names.push_back("D:/3Hill.jpg");//??
    num_images = 2;
#if ENABLE_LOG
    int64 app_start_time = getTickCount();
#endif
#if 0
    cv::setBreakOnError(true);
#endif
    //int retval = parseCmdArgs(argc, argv);
    //if (retval)
        //return retval;
    // Check if have enough images
    //int num_images = static_cast<int>(img_names.size());
    if (num_images < 2)
    {
        LOGLN("Need more images");
        return -1;
    }
    double work_scale = 1, seam_scale = 1, compose_scale = 1;
    bool is_work_scale_set = false, is_seam_scale_set = false, is_compose_scale_set = false;
    LOGLN("Finding features...");
#if ENABLE_LOG
    int64 t = getTickCount();
#endif
    Ptr<Feature2D> finder;
    if (features_type == "orb")
    {
        finder = ORB::create();
    }
    else if (features_type == "akaze")
    {
        finder = AKAZE::create();
    }
#ifdef HAVE_OPENCV_XFEATURES2D
    else if (features_type == "surf")
    {
        finder = xfeatures2d::SURF::create();
    }
    else if (features_type == "sift") {
        finder = xfeatures2d::SIFT::create();
    }
#endif
    else
    {
        cout << "Unknown 2D features type: '" << features_type << "'.
";
        return -1;
    }
    Mat full_img, img;
    vector<ImageFeatures> features(num_images);
    vector<Mat> images(num_images);
    vector<Size> full_img_sizes(num_images);
    double seam_work_aspect = 1;
    for (int i = 0; i < num_images; ++i)
    {
        full_img = ALLimages[i];

        //获取一张图。imread_img -------->Mat
        //先把一边调通了再去组合调试，分治
        //full_img = img_list;
        //python传进来n张图片的base64,可以转成读取后的图片。

        //先在c中定义图像HWC结构数组数组转一次 Mat, dll返回Mat结果，Mat转一次结构体
        //main输入 Mat1,Mat2
        //dll返回数组，python转化成cv2image,然后输出image2base64

        //full_image里面是读取的imread_img类型
        //base64的size容易确定
        //先在predict前提取到图片的整个Mat传给DLL
        full_img_sizes[i] = full_img.size();//结果：full_img_sizes = [(500,300),(200,100)]
        if (full_img.empty())
        {
            //LOGLN("Can't open image " << img_names[i]);//访问了空指针，和img_names有关
            return -2;
        }
        if (work_megapix < 0)
        {
            img = full_img;
            work_scale = 1;
            is_work_scale_set = true;
        }
        else
        {
            if (!is_work_scale_set)
            {
                work_scale = min(1.0, sqrt(work_megapix * 1e6 / full_img.size().area()));
                is_work_scale_set = true;
            }
            resize(full_img, img, Size(), work_scale, work_scale, INTER_LINEAR_EXACT);
        }
        if (!is_seam_scale_set)
        {
            seam_scale = min(1.0, sqrt(seam_megapix * 1e6 / full_img.size().area()));
            seam_work_aspect = seam_scale / work_scale;
            is_seam_scale_set = true;
        }
        computeImageFeatures(finder, img, features[i]);
        features[i].img_idx = i;
        LOGLN("Features in image #" << i + 1 << ": " << features[i].keypoints.size());
        resize(full_img, img, Size(), seam_scale, seam_scale, INTER_LINEAR_EXACT);
        images[i] = img.clone();
        //循环是为了找到每张图的特征，然后把图片copy到images里
    }
    full_img.release();
    img.release();
    LOGLN("Finding features, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");
    LOG("Pairwise matching");
#if ENABLE_LOG
    t = getTickCount();
#endif
    vector<MatchesInfo> pairwise_matches;
    Ptr<FeaturesMatcher> matcher;
    if (matcher_type == "affine")
        matcher = makePtr<AffineBestOf2NearestMatcher>(false, try_cuda, match_conf);
    else if (range_width == -1)
        matcher = makePtr<BestOf2NearestMatcher>(try_cuda, match_conf);
    else
        matcher = makePtr<BestOf2NearestRangeMatcher>(range_width, try_cuda, match_conf);
    (*matcher)(features, pairwise_matches);
    matcher->collectGarbage();
    LOGLN("Pairwise matching, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");
    // Check if we should save matches graph
    //if (save_graph)
    //{
    //    LOGLN("Saving matches graph...");
    //    ofstream f(save_graph_to.c_str());
    //    f << matchesGraphAsString(img_names, pairwise_matches, conf_thresh);
    //}
    // Leave only images we are sure are from the same panorama
    vector<int> indices = leaveBiggestComponent(features, pairwise_matches, conf_thresh);
    if (indices.size() != 2)//判断两个图片的相关性
        return -1;

    if (num_images < 2)
    {
        LOGLN("Need more images");
        return -1;
    }
    Ptr<Estimator> estimator;
    if (estimator_type == "affine")
        estimator = makePtr<AffineBasedEstimator>();
    else
        estimator = makePtr<HomographyBasedEstimator>();
    vector<CameraParams> cameras;
    if (!(*estimator)(features, pairwise_matches, cameras))
    {
        cout << "Homography estimation failed.
";
        return -1;
    }
    for (size_t i = 0; i < cameras.size(); ++i)
    {
        Mat R;
        cameras[i].R.convertTo(R, CV_32F);
        cameras[i].R = R;
        //LOGLN("Initial camera intrinsics #" << indices[i] + 1 << ":
K:
" << cameras[i].K() << "
R:
" << cameras[i].R);
    }
    Ptr<detail::BundleAdjusterBase> adjuster;
    if (ba_cost_func == "reproj") adjuster = makePtr<detail::BundleAdjusterReproj>();
    else if (ba_cost_func == "ray") adjuster = makePtr<detail::BundleAdjusterRay>();
    else if (ba_cost_func == "affine") adjuster = makePtr<detail::BundleAdjusterAffinePartial>();
    else if (ba_cost_func == "no") adjuster = makePtr<NoBundleAdjuster>();
    else
    {
        cout << "Unknown bundle adjustment cost function: '" << ba_cost_func << "'.
";
        return -1;
    }
    adjuster->setConfThresh(conf_thresh);
    Mat_<uchar> refine_mask = Mat::zeros(3, 3, CV_8U);
    if (ba_refine_mask[0] == 'x') refine_mask(0, 0) = 1;
    if (ba_refine_mask[1] == 'x') refine_mask(0, 1) = 1;
    if (ba_refine_mask[2] == 'x') refine_mask(0, 2) = 1;
    if (ba_refine_mask[3] == 'x') refine_mask(1, 1) = 1;
    if (ba_refine_mask[4] == 'x') refine_mask(1, 2) = 1;
    adjuster->setRefinementMask(refine_mask);
    if (!(*adjuster)(features, pairwise_matches, cameras))
    {
        cout << "Camera parameters adjusting failed.
";
        return -1;
    }
    // Find median focal length
    vector<double> focals;
    for (size_t i = 0; i < cameras.size(); ++i)
    {
        //LOGLN("Camera #" << indices[i] + 1 << ":
K:
" << cameras[i].K() << "
R:
" << cameras[i].R);
        focals.push_back(cameras[i].focal);
    }
    sort(focals.begin(), focals.end());
    float warped_image_scale;
    if (focals.size() % 2 == 1)
        warped_image_scale = static_cast<float>(focals[focals.size() / 2]);
    else
        warped_image_scale = static_cast<float>(focals[focals.size() / 2 - 1] + focals[focals.size() / 2]) * 0.5f;
    if (do_wave_correct)
    {
        vector<Mat> rmats;
        for (size_t i = 0; i < cameras.size(); ++i)
            rmats.push_back(cameras[i].R.clone());
        waveCorrect(rmats, wave_correct);
        for (size_t i = 0; i < cameras.size(); ++i)
            cameras[i].R = rmats[i];
    }
    LOGLN("Warping images (auxiliary)... ");
#if ENABLE_LOG
    t = getTickCount();
#endif
    vector<Point> corners(num_images);
    vector<UMat> masks_warped(num_images);
    vector<UMat> images_warped(num_images);
    vector<Size> sizes(num_images);
    vector<UMat> masks(num_images);
    // Prepare images masks
    for (int i = 0; i < num_images; ++i)
    {
        masks[i].create(images[i].size(), CV_8U);
        masks[i].setTo(Scalar::all(255));
    }
    // Warp images and their masks
    Ptr<WarperCreator> warper_creator;
#ifdef HAVE_OPENCV_CUDAWARPING
    if (try_cuda && cuda::getCudaEnabledDeviceCount() > 0)
    {
        if (warp_type == "plane")
            warper_creator = makePtr<cv::PlaneWarperGpu>();
        else if (warp_type == "cylindrical")
            warper_creator = makePtr<cv::CylindricalWarperGpu>();
        else if (warp_type == "spherical")
            warper_creator = makePtr<cv::SphericalWarperGpu>();
    }
    else
#endif
    {
        if (warp_type == "plane")
            warper_creator = makePtr<cv::PlaneWarper>();
        else if (warp_type == "affine")
            warper_creator = makePtr<cv::AffineWarper>();
        else if (warp_type == "cylindrical")
            warper_creator = makePtr<cv::CylindricalWarper>();
        else if (warp_type == "spherical")
            warper_creator = makePtr<cv::SphericalWarper>();
        else if (warp_type == "fisheye")
            warper_creator = makePtr<cv::FisheyeWarper>();
        else if (warp_type == "stereographic")
            warper_creator = makePtr<cv::StereographicWarper>();
        else if (warp_type == "compressedPlaneA2B1")
            warper_creator = makePtr<cv::CompressedRectilinearWarper>(2.0f, 1.0f);
        else if (warp_type == "compressedPlaneA1.5B1")
            warper_creator = makePtr<cv::CompressedRectilinearWarper>(1.5f, 1.0f);
        else if (warp_type == "compressedPlanePortraitA2B1")
            warper_creator = makePtr<cv::CompressedRectilinearPortraitWarper>(2.0f, 1.0f);
        else if (warp_type == "compressedPlanePortraitA1.5B1")
            warper_creator = makePtr<cv::CompressedRectilinearPortraitWarper>(1.5f, 1.0f);
        else if (warp_type == "paniniA2B1")
            warper_creator = makePtr<cv::PaniniWarper>(2.0f, 1.0f);
        else if (warp_type == "paniniA1.5B1")
            warper_creator = makePtr<cv::PaniniWarper>(1.5f, 1.0f);
        else if (warp_type == "paniniPortraitA2B1")
            warper_creator = makePtr<cv::PaniniPortraitWarper>(2.0f, 1.0f);
        else if (warp_type == "paniniPortraitA1.5B1")
            warper_creator = makePtr<cv::PaniniPortraitWarper>(1.5f, 1.0f);
        else if (warp_type == "mercator")
            warper_creator = makePtr<cv::MercatorWarper>();
        else if (warp_type == "transverseMercator")
            warper_creator = makePtr<cv::TransverseMercatorWarper>();
    }
    if (!warper_creator)
    {
        cout << "Can't create the following warper '" << warp_type << "'
";
        return 1;
    }
    Ptr<RotationWarper> warper = warper_creator->create(static_cast<float>(warped_image_scale * seam_work_aspect));
    for (int i = 0; i < num_images; ++i)
    {
        Mat_<float> K;
        cameras[i].K().convertTo(K, CV_32F);
        float swa = (float)seam_work_aspect;
        K(0, 0) *= swa; K(0, 2) *= swa;
        K(1, 1) *= swa; K(1, 2) *= swa;
        corners[i] = warper->warp(images[i], K, cameras[i].R, INTER_LINEAR, BORDER_REFLECT, images_warped[i]);
        sizes[i] = images_warped[i].size();
        warper->warp(masks[i], K, cameras[i].R, INTER_NEAREST, BORDER_CONSTANT, masks_warped[i]);
    }
    vector<UMat> images_warped_f(num_images);
    for (int i = 0; i < num_images; ++i)
        images_warped[i].convertTo(images_warped_f[i], CV_32F);
    LOGLN("Warping images, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");
    LOGLN("Compensating exposure...");
#if ENABLE_LOG
    t = getTickCount();
#endif
    Ptr<ExposureCompensator> compensator = ExposureCompensator::createDefault(expos_comp_type);
    if (dynamic_cast<GainCompensator*>(compensator.get()))
    {
        GainCompensator* gcompensator = dynamic_cast<GainCompensator*>(compensator.get());
        gcompensator->setNrFeeds(expos_comp_nr_feeds);
    }
    if (dynamic_cast<ChannelsCompensator*>(compensator.get()))
    {
        ChannelsCompensator* ccompensator = dynamic_cast<ChannelsCompensator*>(compensator.get());
        ccompensator->setNrFeeds(expos_comp_nr_feeds);
    }
    if (dynamic_cast<BlocksCompensator*>(compensator.get()))
    {
        BlocksCompensator* bcompensator = dynamic_cast<BlocksCompensator*>(compensator.get());
        bcompensator->setNrFeeds(expos_comp_nr_feeds);
        bcompensator->setNrGainsFilteringIterations(expos_comp_nr_filtering);
        bcompensator->setBlockSize(expos_comp_block_size, expos_comp_block_size);
    }
    compensator->feed(corners, images_warped, masks_warped);
    LOGLN("Compensating exposure, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");
    LOGLN("Finding seams...");
#if ENABLE_LOG
    t = getTickCount();
#endif
    Ptr<SeamFinder> seam_finder;
    if (seam_find_type == "no")
        seam_finder = makePtr<detail::NoSeamFinder>();
    else if (seam_find_type == "voronoi")
        seam_finder = makePtr<detail::VoronoiSeamFinder>();
    else if (seam_find_type == "gc_color")
    {
#ifdef HAVE_OPENCV_CUDALEGACY
        if (try_cuda && cuda::getCudaEnabledDeviceCount() > 0)
            seam_finder = makePtr<detail::GraphCutSeamFinderGpu>(GraphCutSeamFinderBase::COST_COLOR);
        else
#endif
            seam_finder = makePtr<detail::GraphCutSeamFinder>(GraphCutSeamFinderBase::COST_COLOR);
    }
    else if (seam_find_type == "gc_colorgrad")
    {
#ifdef HAVE_OPENCV_CUDALEGACY
        if (try_cuda && cuda::getCudaEnabledDeviceCount() > 0)
            seam_finder = makePtr<detail::GraphCutSeamFinderGpu>(GraphCutSeamFinderBase::COST_COLOR_GRAD);
        else
#endif
            seam_finder = makePtr<detail::GraphCutSeamFinder>(GraphCutSeamFinderBase::COST_COLOR_GRAD);
    }
    else if (seam_find_type == "dp_color")
        seam_finder = makePtr<detail::DpSeamFinder>(DpSeamFinder::COLOR);
    else if (seam_find_type == "dp_colorgrad")
        seam_finder = makePtr<detail::DpSeamFinder>(DpSeamFinder::COLOR_GRAD);
    if (!seam_finder)
    {
        cout << "Can't create the following seam finder '" << seam_find_type << "'
";
        return 1;
    }
    seam_finder->find(images_warped_f, corners, masks_warped);
    LOGLN("Finding seams, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");
    // Release unused memory
    images.clear();
    images_warped.clear();
    images_warped_f.clear();
    masks.clear();
    LOGLN("Compositing...");
#if ENABLE_LOG
    t = getTickCount();
#endif
    Mat img_warped, img_warped_s;
    Mat dilated_mask, seam_mask, mask, mask_warped;
    Ptr<Blender> blender;
    Ptr<Timelapser> timelapser;
    //double compose_seam_aspect = 1;
    double compose_work_aspect = 1;
    for (int img_idx = 0; img_idx < num_images; ++img_idx)
    {
        //LOGLN("Compositing image #" << indices[img_idx] + 1);
        // Read image and resize it if necessary
        full_img = ALLimages[img_idx];
        if (!is_compose_scale_set)
        {
            if (compose_megapix > 0)
                compose_scale = min(1.0, sqrt(compose_megapix * 1e6 / full_img.size().area()));
            is_compose_scale_set = true;
            // Compute relative scales
            //compose_seam_aspect = compose_scale / seam_scale;
            compose_work_aspect = compose_scale / work_scale;
            // Update warped image scale
            warped_image_scale *= static_cast<float>(compose_work_aspect);
            warper = warper_creator->create(warped_image_scale);
            // Update corners and sizes
            for (int i = 0; i < num_images; ++i)
            {
                // Update intrinsics
                cameras[i].focal *= compose_work_aspect;
                cameras[i].ppx *= compose_work_aspect;
                cameras[i].ppy *= compose_work_aspect;
                // Update corner and size
                Size sz = full_img_sizes[i];
                Mat K;
                cameras[i].K().convertTo(K, CV_32F);
                Rect roi = warper->warpRoi(sz, K, cameras[i].R);
                corners[i] = roi.tl();
                sizes[i] = roi.size();
            }
        }
        if (abs(compose_scale - 1) > 1e-1)//没用
            resize(full_img, img, Size(), compose_scale, compose_scale, INTER_LINEAR_EXACT);
        else
            img = full_img;
        full_img.release();
        Size img_size = img.size();
        Mat K;
        cameras[img_idx].K().convertTo(K, CV_32F);
        // Warp the current image
        warper->warp(img, K, cameras[img_idx].R, INTER_LINEAR, BORDER_REFLECT, img_warped);
        // Warp the current image mask
        mask.create(img_size, CV_8U);
        mask.setTo(Scalar::all(255));
        warper->warp(mask, K, cameras[img_idx].R, INTER_NEAREST, BORDER_CONSTANT, mask_warped);
        // Compensate exposure
        compensator->apply(img_idx, corners[img_idx], img_warped, mask_warped);
        img_warped.convertTo(img_warped_s, CV_16S);
        img_warped.release();
        img.release();
        mask.release();
        dilate(masks_warped[img_idx], dilated_mask, Mat());
        resize(dilated_mask, seam_mask, mask_warped.size(), 0, 0, INTER_LINEAR_EXACT);
        mask_warped = seam_mask & mask_warped;
        if (!blender && !timelapse)//blender是False,timelapse也是False,这里运行了！
        {//做multiband
            blender = Blender::createDefault(blend_type, try_cuda);
            Size dst_sz = resultRoi(corners, sizes).size();
            float blend_width = sqrt(static_cast<float>(dst_sz.area())) * blend_strength / 100.f;
            if (blend_width < 1.f)
                blender = Blender::createDefault(Blender::NO, try_cuda);
            else if (blend_type == Blender::MULTI_BAND)
            {
                MultiBandBlender* mb = dynamic_cast<MultiBandBlender*>(blender.get());
                mb->setNumBands(static_cast<int>(ceil(log(blend_width) / log(2.)) - 1.));
                LOGLN("Multi-band blender, number of bands: " << mb->numBands());
            }
            else if (blend_type == Blender::FEATHER)//未运行
            {
                FeatherBlender* fb = dynamic_cast<FeatherBlender*>(blender.get());
                fb->setSharpness(1.f / blend_width);
                LOGLN("Feather blender, sharpness: " << fb->sharpness());
            }
            blender->prepare(corners, sizes);
        }
        else if (!timelapser && timelapse)//timelapse是假，timelapser是什么？？没运行
        {
            timelapser = Timelapser::createDefault(timelapse_type);
            timelapser->initialize(corners, sizes);
            cout << "----------------------------运行---------------------------------" << endl;
        }
        // Blend the current image
        if (timelapse)//默认是假
        {
            cout << "----------------------------运行2---------------------------------" << endl;
        }
        else
        {//这里运行了两次，因为在循环体中，图片有两张
            blender->feed(img_warped_s, mask_warped, corners[img_idx]);
            cout << "----------------------------运行3---------------------------------" << endl;
        }
    }
    if (!timelapse)//运行了
    {
        Mat result, result_mask;
        blender->blend(result, result_mask);
        LOGLN("Compositing, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");
        imwrite(result_name, result);
        //(result.cols)*(result.rows)
        memcpy(im2->data, result.clone().data, (result.cols)*(result.rows));//从哪里拷贝多少个字节。。
        im2->w = result.cols;
        im2->h = result.rows;
        im2->c = 3;
    }
    LOGLN("Finished, total time: " << ((getTickCount() - app_start_time) / getTickFrequency()) << " sec");
    return 0;
}

from ctypes import *
from io import BytesIO
import numpy as np
import cv2


# 写法是yolo的darknet.py里的

def c_array(ctype, values):  # 把图像的数据转化为内存连续的 列表 ， 使c++能使用这块内存
    arr = (ctype * len(values))()
    arr[:] = values
    return arr


def array_to_image(arr):
    c = arr.shape[2]
    h = arr.shape[0]
    w = arr.shape[1]
    arr = arr.flatten()#转化成图片后成了一维的
    data = c_array(c_uint8, arr)
    im = IMAGE(w, h, c, data)#将读进来数组转化成c接受的形式，调用class IMAGE
    return im


class IMAGE(Structure):  # 这里和ImgSegmentation.hpp里面的结构体保持一致。
    _fields_ = [("w", c_int),
                ("h", c_int),
                ("c", c_int),
                ("data", POINTER(c_uint8))]


img1 = cv2.imread('D:/1Hill.jpg')
img2 = cv2.imread('D:/2Hill.jpg')
#h, w, c = img.shape[0], img.shape[1], img.shape[2]
#h, w, c = img.shape[0], img.shape[1], img.shape[2]

#gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) * 0
#gray = np.reshape(gray, (h, w, 1))  # 一定要使用(h, w, 1)，最后的1别忘。
im1 = array_to_image(img1)#这里是将读进来的cv_imread格式图片转化成结构体，一维的
im2 = array_to_image(img2)
#gray_img = array_to_image(gray)

lib = cdll.LoadLibrary('./image_stiching.dll')  # 读取动态库文件
lib.Stitch.argtypes = [POINTER(IMAGE), POINTER(IMAGE)]  # 设置函数入参格式,声明采用指针传递。指定入参为2个数组指针，python里定义类型指针和C相反，类型在后。
#lib.Stitch.restype = c_int64
lib.Stitch(im1, im2)  # 执行函数，这里直接修改gray_img的内存数据。入参是非指针，python提取地址作为输入。因为函数原型是传递的指针，这里相当于POINTER会自动取输入im1,im2的指针作为入参。
## 因此输入的内存数据会直接被改变。
y = im2.data  # 获取data，被改变传递改变的对象名
array_length = im2.h * im2.w
#转化为numpy的ndarray
buffer_from_memory = pythonapi.PyMemoryView_FromMemory  # 这个是python 3的使用方法，提取运算缓存
buffer_from_memory.restype = py_object                  #提取缓存返回的数据格式，以上两步是下一步从缓存中提取某个变量的结果必须的。
buffer = buffer_from_memory(y, array_length)                    #提取底层的缓存指针，指定提取缓存大小
img = np.frombuffer(buffer, dtype=np.uint8)                         #提取到缓存中的数组
print("----------------------")
print(img.shape)
img = np.reshape(img, (im2.h, im2.w,1))                   #改变缓存数组的格式，用于显示
print("-------2---------")
print(img.shape)
print(img)
cv2.imshow('test', img)
cv2.imwrite("D:/RESULT_PY.JPG",img)
cv2.waitKey(0)