#include "AI_Edge_Algin.h"
#include "CheckErrorCodeDefine.hpp"

#define EDGE_GPU 0

AI_Edge_Algin::AI_Edge_Algin()
{
    m_bInitialized = false;
    m_bModelSucc = false;
    m_str_curCamName = "";
    AI_Factory = AIFactory::GetInstance();
    CheckUtil::CreateDir("/home/aidlux/BOE/FOG/Edge/Error/");
    CheckUtil::CreateDir("/home/aidlux/BOE/FOG/Edge/Result/");
    CheckUtil::CreateDir("/home/aidlux/BOE/FOG/Edge/Smasll/");
    CheckUtil::CreateDir("/home/aidlux/BOE/FOG/Edge/Big/");
    runner = std::make_shared<AIMulThreadRunBase>();
    runner->Start();
}

AI_Edge_Algin::~AI_Edge_Algin()
{
}

int AI_Edge_Algin::Detect(const cv::Mat &img, DetConfig *pDetConfig, std::shared_ptr<Edge_AI_Result> &pCheckResult_Aling)
{
    m_pDetConfig = pDetConfig;
    m_str_curCamName = pDetConfig->strCamName;
    // printf("save type saveProcessImg %d bSaveResultImg %d cam %s\n",
    //        pDetConfig->saveProcessImg, m_pDetConfig->bDebugSaveImg, m_str_curCamName.c_str());

    static int erridx = 0;
    std::string str_error = "";

    // 保存过程图片
    if (m_pDetConfig->IsSaveProcessImg())
    {
        erridx++;
        if (erridx > 9999999)
        {
            erridx = 0;
        }
        str_error = "/home/aidlux/BOE/FOG/Edge/Error/" + std::to_string(erridx) + "_src.png";
    }
    cv::Mat showimg;
    // 保存结果图片
    if (m_pDetConfig->bDebugSaveImg)
    {
        cv::cvtColor(img, showimg, cv::COLOR_GRAY2BGR);
    }
    pCheckResult_Aling = std::make_shared<Edge_AI_Result>();
    if (img.empty())
    {
        return 1;
    }
    long t1 = CheckUtil::getcurTime();
    // 1、初步定位 找到产品大致区域
    int re = 0;
    vector<Rect> rois;
    cv::Rect Big_roi;
    cv::Mat mask;
    cv::Mat big_mask;
    re = Det_big(img, rois, pCheckResult_Aling->edge_RoiList, Big_roi, big_mask);
    if (re != 0)
    {
      //  printf("AICheck_Edge_Big----error  %d \n", re);
        if (m_pDetConfig->IsSaveProcessImg())
        {
            cv::imwrite(str_error, img);
        }
        return re;
    }
    long t2 = CheckUtil::getcurTime();
    if (m_pDetConfig->bDebugSaveImg)
    {
        for (const auto &roi : rois)
        {
            cv::rectangle(showimg, roi, cv::Scalar(0, 0, 255));
        }
        cv::rectangle(showimg, Big_roi, cv::Scalar(255, 255, 255), 2);
        cv::imwrite(pDetConfig->strCamName + "_0_edge_Small_Det_ROI.png", showimg);
    }
    cv::Mat Src_Mask;
    re = Dtet_small(img, rois, Src_Mask);
    if (re != 0)
    {
       // printf("AICheck_Edge_Small----error  %d \n", re);
        if (m_pDetConfig->IsSaveProcessImg())
        {
            cv::imwrite(str_error, img);
        }
        return re;
    }
    long t3 = CheckUtil::getcurTime();
    cv::Mat Src_Masksss;

    if (m_pDetConfig->bDebugSaveImg)
    {
        cv::imwrite(pDetConfig->strCamName + "_1_edge_Small_Out_Mask.png", Src_Mask);
    }
    cv::Rect result_roi;
    Mat resultMask = Mat::zeros(img.size(), CV_8UC1);

    {
        vector<vector<Point>> contours;
        findContours(Src_Mask, contours, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE);
        // 找到最大轮廓
        double max_area = 0;
        int max_contour_index = 0;

        for (int i = 0; i < contours.size(); i++)
        {
            double area = contourArea(contours[i]);
            if (area > max_area)
            {
                max_area = area;
                max_contour_index = i;
            }
        }
        if (max_contour_index < 0)
        {
            cerr << "No contours found!" << endl;
            return 7;
        }

        if (max_contour_index >= 0)
        {
            result_roi = boundingRect(contours[max_contour_index]);
        }
        drawContours(resultMask, contours, max_contour_index, Scalar(255), FILLED); // -1表示填充所有轮廓
    }

    long t4 = CheckUtil::getcurTime();
    // 和原始big模型做比对
    {

        double widthDiff = std::abs(result_roi.width - Big_roi.width) * 1.0f / std::min(result_roi.width, Big_roi.width);
        double heightDiff = std::abs(result_roi.height - Big_roi.height) * 1.0f / std::min(result_roi.height, Big_roi.height);
        // printf("big roi %s  det %swidthDiff %f heightDiff %f\n", CheckUtil::GetRectString(Big_roi).c_str(), CheckUtil::GetRectString(result_roi).c_str(), widthDiff, heightDiff);
        // 如果差异超过10%，抛出异常
        if (widthDiff > 0.10)
        {
            return 51;
        }
        if (heightDiff > 0.10)
        {
            return 52;
        }
    }
    if (m_pDetConfig->bUseDrawRoi_Check)
    {
        double widthDiff = std::abs(result_roi.width - m_pDetConfig->drawRoi.width) * 1.0f / std::min(result_roi.width, m_pDetConfig->drawRoi.width);
        double heightDiff = std::abs(result_roi.height - m_pDetConfig->drawRoi.height) * 1.0f / std::min(result_roi.height, m_pDetConfig->drawRoi.height);
        //printf("draw roi %s  det %s  widthDiff %f heightDiff %f\n", CheckUtil::GetRectString(m_pDetConfig->drawRoi).c_str(), CheckUtil::GetRectString(result_roi).c_str(), widthDiff, heightDiff);
        // 如果差异超过10%，抛出异常
        if (widthDiff > 0.10)
        {
            return 53;
        }
        if (heightDiff > 0.10)
        {
            return 54;
        }
    }
    long t5 = CheckUtil::getcurTime();
    // 用big 的来测试
    if (false)
    {
        cv::Size sz_big;
        sz_big.width = big_mask.cols;
        sz_big.height = big_mask.rows;
        cv::Mat size_prodct;
        cv::resize(resultMask, size_prodct, sz_big, 0, 0, cv::INTER_AREA);

        int va_big = countNonZero(big_mask);
        int va_small = countNonZero(size_prodct);
        if (va_big > 0)
        {
            int diff = std::abs(va_big - va_small); // 面积的差异值。
            float fs = diff * 1.0f / va_big;
           // printf("va_big %d va_small %d dff %d  diffscale %f  \n", va_big, va_small, diff, fs);
            if (fs > 0.1)
            {
                // if (true)
                // {
                //     cv::imwrite("big_mask.png",big_mask);
                //       cv::imwrite("size_prodct.png",size_prodct);
                //       getchar();
                // }

                return 55;
            }
        }

        /* code */
    }
    if (m_pDetConfig->bUseDrawRoi_Check && !m_pDetConfig->drawMask.empty())
    {
        cv::Size sz_big;
        sz_big.width = big_mask.cols;
        sz_big.height = big_mask.rows;
        cv::Mat size_prodct;
        cv::resize(resultMask, size_prodct, sz_big, 0, 0, cv::INTER_AREA);

        cv::Mat size_drawmask;
        cv::resize(m_pDetConfig->drawMask, size_drawmask, sz_big, 0, 0, cv::INTER_AREA);

        int va_draw = countNonZero(size_drawmask);
        int va_small = countNonZero(size_prodct);
        if (va_draw > 0)
        {
            int diff = std::abs(va_draw - va_small); // 面积的差异值。
            float fs = diff * 1.0f / va_draw;
          //  printf("va_draw %d va_small %d dff %d  diffscale %f  \n", va_draw, va_small, diff, fs);
            if (fs > 0.1)
            {
                return 56;
            }
        }
    }
    long t6 = CheckUtil::getcurTime();
    if (m_pDetConfig->bDebugSaveImg)
    {
        cv::imwrite(pDetConfig->strCamName + "_3_edge_product_det_mask.png", resultMask);
        if (!m_pDetConfig->drawMask.empty())
        {
            cv::imwrite(pDetConfig->strCamName + "_4_edge_product_draw_mask.png", m_pDetConfig->drawMask);
        }
    }

    pCheckResult_Aling->DetMask_src = resultMask;

    cv::Mat resultMask_erode_small;
    // 膨胀腐蚀处理
    {
        // printf("m_pDetConfig->nAIErodesize %d\n", m_pDetConfig->nAIErodesize);
        if (m_pDetConfig->nAIErodesize <= 0)
        {
            resultMask_erode_small = resultMask(result_roi).clone();
        }
        else
        {
            // 定义膨胀核
            int dilation_size = 7; // 膨胀核的大小
            if (m_pDetConfig->nAIErodesize > 0 && m_pDetConfig->nAIErodesize < 101)
            {
                dilation_size = 2 * m_pDetConfig->nAIErodesize;
            }
            cv::Mat kernel = cv::getStructuringElement(cv::MORPH_RECT, cv::Size(dilation_size, dilation_size));

            // 进行膨胀操作

            cv::erode(resultMask(result_roi), resultMask_erode_small, kernel);
        }

        pCheckResult_Aling->mask = ~resultMask_erode_small;
    }

    if (m_pDetConfig->bDebugSaveImg)
    {
        // cv::imwrite("edge_result_src_roi_thresholdvalue.png", temimg);
        cv::imwrite(pDetConfig->strCamName + "_5_edge_result_src.png", img(result_roi));
        cv::imwrite(pDetConfig->strCamName + "_6_edge_result_mask.png", pCheckResult_Aling->mask);
        cv::Mat temAdd = img(result_roi).clone();
        temAdd += pCheckResult_Aling->mask * 0.25;

        {
            // 查找轮廓
            std::vector<std::vector<cv::Point>> contours;
            std::vector<cv::Vec4i> hierarchy;
            cv::findContours(resultMask_erode_small.clone(), contours, hierarchy, cv::RETR_EXTERNAL, cv::CHAIN_APPROX_SIMPLE);

            // 创建一个空白图像用于绘制轮廓

            // 绘制轮廓
            cv::drawContours(temAdd, contours, -1, cv::Scalar(128, 255, 0), 2); // 绿色，线宽2
        }
        cv::imwrite(pDetConfig->strCamName + "_7_edge_result_Merge.png", temAdd);
    }
    long t7 = CheckUtil::getcurTime();
    // 存在中间结果
    if (m_pDetConfig->saveProcessImg != Save_Close)
    {
        static int svidx = 0;
        svidx++;
        if (svidx > 9999999)
        {
            svidx = 0;
            /* code */
        }
        std::string str1 = "/home/aidlux/BOE/FOG/Edge/Result/" + std::to_string(svidx) + "_mask.png";
        std::string str2 = "/home/aidlux/BOE/FOG/Edge/Result/" + std::to_string(svidx) + "_show.png";

        int newWidth = 1280;
        float aspectRatio = static_cast<float>(resultMask_erode_small.rows) / resultMask_erode_small.cols;
        int newHeight = static_cast<int>(newWidth * aspectRatio);

        // 缩放图像
        cv::Mat resizedImage;
        cv::resize(resultMask_erode_small, resizedImage, cv::Size(newWidth, newHeight));

        cv::Mat temAdd = img(result_roi).clone();
        cv::resize(temAdd, temAdd, cv::Size(newWidth, newHeight));
        {
            // 查找轮廓
            std::vector<std::vector<cv::Point>> contours;
            std::vector<cv::Vec4i> hierarchy;
            cv::findContours(resizedImage.clone(), contours, hierarchy, cv::RETR_EXTERNAL, cv::CHAIN_APPROX_SIMPLE);

            // 创建一个空白图像用于绘制轮廓

            // 绘制轮廓
            cv::drawContours(temAdd, contours, -1, cv::Scalar(255, 255, 0), 1); // 绿色，线宽2
        }
        cv::imwrite(str1, resizedImage);

        cv::imwrite(str2, temAdd);
    }
    long t8 = CheckUtil::getcurTime();

    // printf("use time %ld  %ld  %ld  %ld  %ld  %ld  %ld  %ld \n", t8 - t1, t2 - t1, t3 - t2, t4 - t3, t5 - t4, t6 - t5, t7 - t6, t8 - t7);

    pCheckResult_Aling->roi = result_roi;

    return 0;
}

int AI_Edge_Algin::SaveSmallImg(const cv::Mat &img, const cv::Mat &mask, cv::Rect roi)
{
    // 是否要保存中间过程的小图
    if (m_pDetConfig->IsSaveProcessImg())
    {
        static int svsmallidx = 0;
        svsmallidx++;
        if (svsmallidx > 9999999)
        {
            svsmallidx = 0;
            /* code */
        }
        bool bssss = false;

        if (m_pDetConfig->saveProcessImg == Save_Filter)
        {
            // 4. 查找轮廓
            vector<vector<Point>> contours;
            vector<Vec4i> hierarchy;
            findContours(mask.clone(), contours, hierarchy, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE);
            if (contours.size() > 1)
            {
                bssss = true;
            }
            else if (contours.size() == 1)
            {
                int pointNum = 0;
                for (int i = 0; i < contours.size(); i++)
                {
                    pointNum += contours[i].size();
                }
                // printf("pointNum============== %d\n", pointNum);
                if (pointNum > 30)
                {
                    bssss = true;
                }
            }
        }
        else if (m_pDetConfig->saveProcessImg == Save_ALL)
        {
            bssss = true;
        }

        if (bssss)
        {
            std::string str1 = "/home/aidlux/BOE/FOG/Edge/Smasll/" + std::to_string(svsmallidx) + "_in.png";
            std::string str2 = "/home/aidlux/BOE/FOG/Edge/Smasll/" + std::to_string(svsmallidx) + "_in_mask.png";
            // std::string st3 = "/home/aidlux/BOE/Edge/Smasll/" + std::to_string(svsmallidx) + "_in_show.png";
            cv::imwrite(str1, img);
            cv::imwrite(str2, mask);
            // cv::Mat showsss = temDet + smask * 0.4;
            // cv::imwrite(st3, showsss);
        }
    }
    return 0;
}

int AI_Edge_Algin::InitModel_ALL()
{
    m_bModelSucc = false;
    int re = InitModel_Big();
    if (re != 0)
    {
       // printf("AI_Edge_Algin InitModel_Big Error \n");
        return re;
    }
    re = InitModel_Small();
    if (re != 0)
    {
       // printf("AI_Edge_Algin InitModel_Small Error \n");

        return re;
    }
    m_bModelSucc = true;
    return 0;
}

int AI_Edge_Algin::InitModel_Big()
{

    return 0;
}

int AI_Edge_Algin::InitModel_Small()
{

    return 0;
}

int AI_Edge_Algin::Det_big(const cv::Mat &img, vector<Rect> &smallRoiList, std::shared_ptr<std::vector<cv::Rect>> edgeRoiList, cv::Rect &bigRoi, cv::Mat &big_mask)
{
    std::shared_ptr<AIModel_Base> pAI_Model = AI_Factory->AI_defect_Edge_Big;

    cv::Size sz;
    sz.width = pAI_Model->input_0.width;
    sz.height = pAI_Model->input_0.height;
    cv::Mat detImg;
    cv::resize(img, detImg, sz);
    int re = 0;
    cv::Mat mask;
    // printf("sz %d %d  \n", sz.width, sz.height);

    re = pAI_Model->AIDet(detImg, mask);
    big_mask = mask;
    if (re != 0)
    {
       // printf("AICheck_Edge_Big----error \n");
        int re123 = 100 + re;
        return re123;
    }
    if (m_pDetConfig->bDebugSaveImg)
    {
        std::string str1 = m_str_curCamName + "_edge_big_in.png";
        cv::imwrite(str1, detImg);
        std::string str2 = m_str_curCamName + "_edge_big_out_mask.png";
        cv::imwrite(str2, mask);
    }
    if (m_pDetConfig->IsSaveProcessImg())
    {
        static int bigidx = 0;
        bigidx++;
        if (bigidx > 9999999)
        {
            bigidx = 0;
            /* code */
        }
        std::string str1 = "/home/aidlux/BOE/FOG/Edge/Big/" + std::to_string(bigidx) + "_in.png";
        std::string str2 = "/home/aidlux/BOE/FOG/Edge/Big/" + std::to_string(bigidx) + "_in_mask.png";
        cv::imwrite(str1, detImg);
        cv::imwrite(str2, mask);
    }

    // 找到最大轮廓

    bool found;
    cv::Rect boundingBox = CheckUtil::getLargestContourROI(mask, found);
    if (!found)
    {
        cerr << "No contours found!" << endl;
        return 4;
    }

    // 形成 小图分割的检测区域
    float fresize_x = img.cols * 1.0f / sz.width;
    float fresize_y = img.rows * 1.0f / sz.height;

    bigRoi.x = boundingBox.x * fresize_x;
    bigRoi.y = boundingBox.y * fresize_y;
    bigRoi.width = boundingBox.width * fresize_x;
    bigRoi.height = boundingBox.height * fresize_y;

    // 使用手动绘制的roi进行校验
    if (m_pDetConfig->bUseDrawRoi_Check)
    {
        double widthDiff = std::abs(bigRoi.width - m_pDetConfig->drawRoi.width) * 1.0f / std::min(bigRoi.width, m_pDetConfig->drawRoi.width);
        double heightDiff = std::abs(bigRoi.height - m_pDetConfig->drawRoi.height) * 1.0f / std::min(bigRoi.height, m_pDetConfig->drawRoi.height);
        // printf("draw roi %s  bigRoi %s  widthDiff %f heightDiff %f\n", CheckUtil::GetRectString(m_pDetConfig->drawRoi).c_str(), CheckUtil::GetRectString(bigRoi).c_str(), widthDiff, heightDiff);
        // 如果差异超过10%，抛出异常
        if (widthDiff > 0.10)
        {
            return 5;
        }
        if (heightDiff > 0.10)
        {
            return 5;
        }
    }

    float fresize_x_1 = sz.width * 1.0f / img.cols;
    float fresize_y_1 = sz.height * 1.0f / img.rows;

    int resize_Small_Roi_width = 320 * fresize_x_1;
    int resize_Small_Roi_height = 320 * fresize_y_1;
    int haf_w = resize_Small_Roi_width / 2;
    int haf_h = resize_Small_Roi_height / 2;
    int step_w = resize_Small_Roi_width * 0.85;
    int step_h = resize_Small_Roi_height * 0.85;
    smallRoiList.clear();
    int sizeare = resize_Small_Roi_width * resize_Small_Roi_height;
    for (int y = boundingBox.y; y < boundingBox.y + boundingBox.height + haf_h; y += step_h)
    {
        int roiY = y - haf_h;
        for (int x = boundingBox.x; x < boundingBox.x + boundingBox.width + haf_w; x += step_w)
        {
            int roiX = x - haf_w;
            if (roiX < 0)
                roiX = 0;
            if (roiY < 0)
                roiY = 0;
            if (roiX + resize_Small_Roi_width >= mask.cols)
                roiX = mask.cols - resize_Small_Roi_width;
            if (roiY + resize_Small_Roi_height > mask.rows)
                roiY = mask.rows - resize_Small_Roi_height;
            Rect rect(roiX, roiY, resize_Small_Roi_width, resize_Small_Roi_height);
            if (!CheckUtil::RoiInImg(rect, mask))
            {
                continue;
            }

            if (roiX >= 0 && roiY >= 0)
            {

                if (roiX == 0 || roiX + resize_Small_Roi_width == mask.cols)
                {
                    cv::Rect src_Roi;
                    src_Roi.x = rect.x * fresize_x;
                    src_Roi.y = rect.y * fresize_y;
                    src_Roi.width = 320;
                    src_Roi.height = 320;
                    smallRoiList.push_back(src_Roi); // 存储ROI的矩形框

                    // printf("\n\n\n\n\n\n\n=================%d===============\n\n\n\n\n\n\n", smallRoiList.size());
                }
                else
                {
                    int va = countNonZero(mask(rect));
                    if (va > 0 && va < sizeare)
                    {
                        cv::Rect src_Roi;
                        src_Roi.x = rect.x * fresize_x;
                        src_Roi.y = rect.y * fresize_y;
                        src_Roi.width = 320;
                        src_Roi.height = 320;
                        smallRoiList.push_back(src_Roi); // 存储ROI的矩形框
                    }
                }
            }
        }
    }
    for (int i = 0; i < smallRoiList.size(); i++)
    {
        cv::Rect src_Roi = smallRoiList.at(i);
        if (src_Roi.x < 0)
        {
            src_Roi.x = 0;
        }
        if (src_Roi.y < 0)
        {
            src_Roi.y = 0;
        }
        if (src_Roi.x + src_Roi.width > img.cols)
        {
            src_Roi.x = img.cols - src_Roi.width;
        }
        if (src_Roi.y + src_Roi.height > img.rows)
        {
            src_Roi.y = img.rows - src_Roi.height;
        }
        smallRoiList.at(i) = src_Roi;
    }

    {
        // -------------------------
        // 2. 查找轮廓
        // -------------------------
        std::vector<std::vector<cv::Point>> contours;
        cv::findContours(mask, contours, cv::RETR_EXTERNAL, cv::CHAIN_APPROX_SIMPLE);

        if (contours.empty())
        {
            std::cout << "No contours found" << std::endl;
            return -1;
        }

        // -------------------------
        // 3. 找最大面积轮廓
        // -------------------------
        int max_idx = 0;
        double max_area = 0;
        for (size_t i = 0; i < contours.size(); i++)
        {
            double area = cv::contourArea(contours[i]);
            if (area > max_area)
            {
                max_area = area;
                max_idx = i;
            }
        }
        std::vector<cv::Point> max_contour = contours[max_idx];
        float fresize_x_1 = mask.cols * 1.0f / img.cols;
        float fresize_y_1 = mask.rows * 1.0f / img.rows;

        int resize_Small_Roi_width = 320 * fresize_x_1;
        int resize_Small_Roi_height = 320 * fresize_y_1;

        int roi_size_X = resize_Small_Roi_width;
        int roi_size_Y = resize_Small_Roi_height;
        double overlap_ratio = 0.2; // ROI 重叠比例，可调
        double step_len_X = roi_size_X * (1.0 - overlap_ratio);
        double step_len_Y = roi_size_Y * (1.0 - overlap_ratio);

        std::vector<cv::Rect> rois;
        std::set<std::pair<int, int>> roi_set;

        double accumulated_len = 0.0;
        for (size_t i = 0; i < max_contour.size(); i++)
        {
            cv::Point p1 = max_contour[i];
            cv::Point p2 = max_contour[(i + 1) % max_contour.size()]; // 下一个点，闭合轮廓
            double segment_len = cv::norm(p2 - p1);

            if (segment_len <= 0)
            {
                continue;
            }

            double dx = p2.x - p1.x;
            double dy = p2.y - p1.y;

            while (accumulated_len + 1e-6 < segment_len)
            { // 沿线段放置 ROI
                double t = accumulated_len / segment_len;
                cv::Point center = p1 + t * (p2 - p1);

                int x = center.x - roi_size_X / 2;
                int y = center.y - roi_size_Y / 2;

                // 边界裁剪
                if (x < 0)
                    x = 0;
                if (y < 0)
                    y = 0;
                if (x + roi_size_X > mask.cols)
                    x = mask.cols - roi_size_X;
                if (y + roi_size_Y > mask.rows)
                    y = mask.rows - roi_size_Y;

                auto key = std::make_pair(x, y);
                if (roi_set.find(key) == roi_set.end())
                {
                    roi_set.insert(key);
                    rois.push_back(cv::Rect(x, y, roi_size_X, roi_size_Y));
                }

                double step = (std::abs(dx) > std::abs(dy)) ? step_len_X : step_len_Y;
                accumulated_len += step;
            }

            accumulated_len -= segment_len; // 移动到下一个线段
            if (accumulated_len < 0)
                accumulated_len = 0;
        }
        edgeRoiList->clear();
        for (const auto &r : rois)
        {
            cv::Rect roi;
            roi.x = r.x / fresize_x_1;
            roi.y = r.y / fresize_y_1;
            roi.width = 320;
            roi.height = 320;

            if (roi.x < 0)
            {
                roi.x = 0;
            }
            if (roi.y < 0)
            {
                roi.y = 0;
            }
            if (roi.x + roi.width > img.cols)
            {
                roi.x = img.cols - roi.width;
            }
            if (roi.y + roi.height > img.rows)
            {
                roi.y = img.rows - roi.height;
            }

            edgeRoiList->push_back(roi);
        }

        if (m_pDetConfig->bDebugSaveImg)
        {
            // -------------------------
            // 5. 可视化
            // -------------------------
            cv::Mat vis;
            cv::cvtColor(mask, vis, cv::COLOR_GRAY2BGR);
            cv::Mat vis22;
            cv::cvtColor(img, vis22, cv::COLOR_GRAY2BGR);

            for (auto &r : rois)
            {
                cv::rectangle(vis, r, cv::Scalar(0, 0, 255), 2);
            }
            for (auto &r : rois)
            {
                cv::Rect roi;
                roi.x = r.x / fresize_x_1;
                roi.y = r.y / fresize_y_1;
                roi.width = 320;
                roi.height = 320;
                cv::rectangle(vis22, roi, cv::Scalar(0, 0, 255), 2);
            }

            cv::drawContours(vis, std::vector<std::vector<cv::Point>>{max_contour}, -1, cv::Scalar(0, 255, 0), 1);

            cv::imwrite("edge_det_roi_small.png", vis);
            cv::imwrite("edge_det_roi_big.png", vis22);
            // getchar();
        }
    }

    return 0;
}

int AI_Edge_Algin::Dtet_small(const cv::Mat &img, vector<Rect> &smallRoiList, cv::Mat &Src_Mask)
{
    std::shared_ptr<AIModel_Base> pAIDet = AI_Factory->AI_defect_Edge_Samll;
    // 2、对每个小区域进行处理
    Src_Mask = cv::Mat(img.rows, img.cols, CV_8U, cv::Scalar(0));

    const int totalTasks = smallRoiList.size();
    int submitted = 0;
    int completed = 0;
    while (completed < totalTasks)
    {
        // 如果任务还没提交完，且当前处理任务数 < 2，提交新任务
        if (submitted < totalTasks && runner->GetProcessingCount() < 10)
        {

            std::shared_ptr<AIMulThreadRunBase::AITask> task = std::make_shared<AIMulThreadRunBase::AITask>();
            task->id = completed;
            task->roi = smallRoiList.at(submitted);

            if (!CheckUtil::RoiInImg(task->roi, img))
            {
                CheckUtil::printROI(task->roi, "small roi 1 ");
               // printf("img  %d %d \n", img.cols, img.rows);
            }

            task->input = img(smallRoiList.at(submitted)).clone();
            task->output = std::make_shared<cv::Mat>();
            task->engine = pAIDet;

            runner->SubmitTask(task);
            submitted++;
        }

        // 尝试取结果
        std::shared_ptr<AIMulThreadRunBase::AITask> result;
        if (runner->PopResult(result))
        {

            cv::Mat &outimg = *(result->output);
            if (!outimg.empty())
            {
                outimg.copyTo(Src_Mask(result->roi), outimg);
            }
            // 保存过程小图
            SaveSmallImg(result->input, outimg, result->roi);

            completed++;
        }
        else
        {
            std::this_thread::sleep_for(std::chrono::milliseconds(0));
        }
    }
    return 0;
}

Image_Feature_Algin::Image_Feature_Algin()
{
}

Image_Feature_Algin::~Image_Feature_Algin()
{
}

int Image_Feature_Algin::Detect(DetConfig *pDetConfig, Align_Result *pResult, std::vector<std::string> &LogList)
{
    // 检测目标：找到 参数模版图到 检测图的 映射关系。包括 缩放和移动。

    // 先缩放 在 移动
    std::string strlog = "";
    if (!pDetConfig)
    {
        return 1;
    }
    if (pDetConfig->TemplateImg.empty())
    {
        strlog = m_PrintLog.printstr(Print_Level_Error, "Image_Align", "TemplateImg is empty ");
        LogList.push_back(strlog);
        return 1;
    }
    // 裁切位置；
    pResult->Crop_Roi_DetImg = pDetConfig->DetImg_CropROi;
    // 1、缩放尺度
    float fx = 1;
    float fy = 1;
    // 裁切尺寸 存在 并合理
    if (pDetConfig->param_CropRoi.width > 0 && pDetConfig->param_CropRoi.height > 0 &&
        pDetConfig->DetImg_CropROi.width > 0 && pDetConfig->DetImg_CropROi.height > 0)
    {
        fx = pDetConfig->DetImg_CropROi.width * 1.0f / pDetConfig->param_CropRoi.width;
        fy = pDetConfig->DetImg_CropROi.height * 1.0f / pDetConfig->param_CropRoi.height;
        if (fx > 0.5 && fx < 2 && fy > 0.5 && fy < 2)
        {
            pResult->fCropROI_Scale_ParmToDet_X = fx;
            pResult->fCropROI_Scale_ParmToDet_Y = fy;
        }
        else
        {
            strlog = m_PrintLog.printstr(Print_Level_Error, "Image_Align", "Scale out  0.5--2");
            LogList.push_back(strlog);
            return 1;
        }
    }
    else
    {
        strlog = m_PrintLog.printstr(Print_Level_Error, "Image_Align", "crop ROI Error");
        LogList.push_back(strlog);
        return 1;
    }
    // strlog = m_PrintLog.printstr(Print_Level_Info, "Image_Align", "Scale x %f y %f\n", fx, fy);
    // LogList.push_back(strlog);

    // 2、定位
    // 1)、模版特征图片的 缩放。
    cv::Mat TemplateFeature;
    cv::Size sz;
    // fx = 1;
    // fy = 1;
    sz.width = int(pDetConfig->TemplateImg.cols * fx);
    sz.height = int(pDetConfig->TemplateImg.rows * fy);
    cv::resize(pDetConfig->TemplateImg, TemplateFeature, sz);

    if (!CheckUtil::RoiInImg(pDetConfig->Search_Roi, pDetConfig->DetImg))
    {
        strlog = m_PrintLog.printstr(Print_Level_Error, "Image_Align", "Search_Roi ROI Error Not In img");
        LogList.push_back(strlog);
        return 1;
    }

    cv::Mat DetFeature = pDetConfig->DetImg(pDetConfig->Search_Roi).clone();

    double confidence = 0;

    int kernel_size = 128;
    int search_size = 1024;
    int det_search_min_size = DetFeature.cols;
    if (DetFeature.rows < det_search_min_size)
    {
        det_search_min_size = DetFeature.rows;
    }
    int template_kernel_min_size = TemplateFeature.cols;
    if (TemplateFeature.rows < template_kernel_min_size)
    {
        template_kernel_min_size = TemplateFeature.rows;
    }
    float f_search = search_size * 1.0f / det_search_min_size;
    float f_Kernel = kernel_size * 1.0f / template_kernel_min_size;
    float falign = f_search;
    if (f_Kernel > falign)
    {
        falign = f_Kernel;
    }
    cv::Size Search_sz;
    Search_sz.width = int(DetFeature.cols * falign);
    Search_sz.height = int(DetFeature.rows * falign);
    cv::Mat Search_img;
    cv::resize(DetFeature, Search_img, Search_sz);
    cv::Size Kernel_sz;
    Kernel_sz.width = int(TemplateFeature.cols * falign);
    Kernel_sz.height = int(TemplateFeature.rows * falign);
    cv::Mat Kernel_img;
    cv::resize(TemplateFeature, Kernel_img, Kernel_sz);

    auto bestMatch = findBestTemplateMatch(Search_img, Kernel_img, confidence);

    bestMatch.x /= falign;
    bestMatch.y /= falign;

    pResult->bestMatch = bestMatch;
    if (pDetConfig->bSaveImg)
    {
        cv::imwrite("Align_template.png", TemplateFeature);
        cv::imwrite("Align_Det.png", DetFeature);
    }

    if (confidence != -1)
    {
        std::cout << "最佳匹配位置: (" << bestMatch.x << ", " << bestMatch.y
                  << "), 得分: " << confidence << std::endl;
        if (confidence > pDetConfig->fscore)
        {

            /* code */

            int m_x = pDetConfig->feature_Roi.x * fx - pDetConfig->Search_Roi.x;
            int m_y = pDetConfig->feature_Roi.y * fy - pDetConfig->Search_Roi.y;
            pResult->offt_x = bestMatch.x - m_x;
            pResult->offt_y = bestMatch.y - m_y;
            // printf("m_x %d bestMatch.x %d offt_x %d\n", m_x, bestMatch.x, pResult->offt_x);
            // printf("m_y %d bestMatch.y %d offt_y %d\n", m_y, bestMatch.y, pResult->offt_y);

            pResult->bDet = true;

            pResult->Crop_Roi_ParmImg = pResult->Det_srcToParm_src_Rect(pResult->Crop_Roi_DetImg);

            strlog = m_PrintLog.printstr(Print_Level_Info, "Image_Align", "  -- Succ :Align score  %f > %f offt x %d y %d Scale x %f y %f",
                                         confidence, pDetConfig->fscore, pResult->offt_x, pResult->offt_y, pResult->fCropROI_Scale_ParmToDet_X, pResult->fCropROI_Scale_ParmToDet_Y);

            LogList.push_back(strlog);
        }
        else
        {

            strlog = m_PrintLog.printstr(Print_Level_Error, "Image_Align", "  error :Align score  %f< 0.9", confidence);
            pResult->fCropROI_Scale_ParmToDet_X = 1;
            pResult->fCropROI_Scale_ParmToDet_Y = 1;
            LogList.push_back(strlog);
        }
    }
    else
    {
        pResult->fCropROI_Scale_ParmToDet_X = 1;
        pResult->fCropROI_Scale_ParmToDet_Y = 1;
        std::cout << "未找到有效匹配" << std::endl;
    }

    return 0;
}

cv::Point Image_Feature_Algin::findBestTemplateMatch(
    const cv::Mat &detectionImage,
    const cv::Mat &templateImage,
    double &bestScore,
    int method)
{
    // 输入验证
    if (detectionImage.empty() || templateImage.empty())
    {
        throw std::invalid_argument("输入图像不能为空");
    }
    if (detectionImage.channels() != 1 || templateImage.channels() != 1)
    {
        throw std::invalid_argument("必须输入灰度图像");
    }
    if (templateImage.rows > detectionImage.rows ||
        templateImage.cols > detectionImage.cols)
    {
        throw std::invalid_argument("模板尺寸不能大于被检测图像");
    }

    // cv::imwrite("detectionImage.png", detectionImage);
    // cv::imwrite("templateImage.png", templateImage);
    // 执行模板匹配
    cv::Mat resultMatrix;
    cv::matchTemplate(detectionImage, templateImage, resultMatrix, method);

    // 确定极值搜索方式
    const bool findMinima = (method == cv::TM_SQDIFF || method == cv::TM_SQDIFF_NORMED);

    // 查找极值位置
    cv::Point extremaLoc = cv::Point(0, 0);
    double extremaVal;
    cv::minMaxLoc(resultMatrix,
                  findMinima ? &extremaVal : nullptr,
                  findMinima ? nullptr : &extremaVal,
                  findMinima ? &extremaLoc : nullptr,
                  findMinima ? nullptr : &extremaLoc);

    // 设置有效性检查阈值（可根据方法动态调整）
    double threshold = 0.0;
    switch (method)
    {
    case cv::TM_CCOEFF_NORMED:
        threshold = 0.6;
        break; // [-1, 1]
    case cv::TM_CCORR_NORMED:
        threshold = 0.7;
        break; // [0, 1]
    case cv::TM_SQDIFF_NORMED:
        threshold = 0.2;
        break; // [0, 1]
    default:
        threshold = 0.0;
    }

    // 验证匹配有效性
    const bool isValid = findMinima ? (extremaVal <= threshold) : (extremaVal >= threshold);

    if (isValid)
    {
        bestScore = extremaVal;
        return extremaLoc;
    }
    bestScore = -1; // 无效时的默认值
    return extremaLoc;
}