Vital/TestNcnn/libYoloNcnnAndroid/YoloNcnnAndroid/ocr.cpp

// Tencent is pleased to support the open source community by making ncnn available.
//
// Copyright (C) 2022 THL A29 Limited, a Tencent company. All rights reserved.
//
// Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
// in compliance with the License. You may obtain a copy of the License at
//
// https://opensource.org/licenses/BSD-3-Clause
//
// 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 "layer.h"
#include "net.h"

#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <cfloat>
#include <vector>
#include <opencv2/imgproc.hpp>
#include <iostream>
#include <fstream>
#include "math.h"
#include <locale>
#include <codecvt>

#include "android/log.h"
#include <datareader.h>

using namespace cv;
using namespace std;

std::vector<int> prebLabel;

std::vector<string> alphabet;

struct TextBlock
{
    std::vector<Point2f> boxVertices; // 矩形框的顶点坐标

    float angle; // 矩形框的角度

    float boxScore;

    float blockTime;
};

// 该函数计算二进制图像中指定轮廓的分数
float contourScore(cv::Mat& binary, std::vector<Point>& contour)
{
    // 计算轮廓的边界矩形
    Rect rect = boundingRect(contour);
    // 计算边界框在二进制图像中的有效范围
    int xmin = max(rect.x, 0);
    int xmax = min(rect.x + rect.width, binary.cols - 1);
    int ymin = max(rect.y, 0);
    int ymax = min(rect.y + rect.height, binary.rows - 1);
    // 提取二进制图像中边界框的ROI（感兴趣区域）
    cv::Mat binROI = binary(Rect(xmin, ymin, xmax - xmin + 1, ymax - ymin + 1));
    // 创建一个掩码，用于标识ROI中的像素
    cv::Mat mask = cv::Mat::zeros(ymax - ymin + 1, xmax - xmin + 1, CV_8U);
    // 将轮廓中的点坐标调整为ROI内的坐标
    std::vector<Point> roiContour;
    for (size_t i = 0; i < contour.size(); i++) {
        Point pt = Point(contour[i].x - xmin, contour[i].y - ymin);
        roiContour.push_back(pt);
    }
    // 使用填充多边形函数将ROI内的轮廓标记为1
    std::vector<std::vector<Point>> roiContours = { roiContour };
    fillPoly(mask, roiContours, Scalar(1));
    // 计算ROI内二进制图像的均值，以掩码为权重
    float score = mean(binROI, mask).val[0];
    return score;
}

void unclip(std::vector<Point2f>& inPoly, std::vector<Point2f>& outPoly)
{
    float unclipRatio = 1.6;

    // 计算轮廓的面积
    float area = contourArea(inPoly);
    float length = arcLength(inPoly, true); // 计算轮廓的周长
    float distance = area * unclipRatio / length; // 计算解剪距离
    // 获取输入轮廓的点数
    size_t numPoints = inPoly.size();
    // 存储新的轮廓线段
    std::vector<std::vector<Point2f>> newLines;
    // 遍历原始轮廓的每个点
    for (size_t i = 0; i < numPoints; i++)
    {
        std::vector<Point2f> newLine;
        Point pt1 = inPoly[i];
        Point pt2 = inPoly[(i - 1) % numPoints];
        Point vec = pt1 - pt2;
        // 计算解剪距离
        float unclipDis = (float)(distance / norm(vec));
        // 计算旋转后的向量
        Point2f rotateVec = Point2f(vec.y * unclipDis, -vec.x * unclipDis);
        // 添加旋转后的点到新线段
        newLine.push_back(Point2f(pt1.x + rotateVec.x, pt1.y + rotateVec.y));
        newLine.push_back(Point2f(pt2.x + rotateVec.x, pt2.y + rotateVec.y));
        newLines.push_back(newLine);
    }
    // 获取新线段的数量
    size_t numLines = newLines.size();
    // 遍历新线段集合
    for (size_t i = 0; i < numLines; i++)
    {
        Point2f a = newLines[i][0];
        Point2f b = newLines[i][1];
        Point2f c = newLines[(i + 1) % numLines][0];
        Point2f d = newLines[(i + 1) % numLines][1];
        Point2f pt;
        // 计算两向量的夹角余弦值
        Point2f v1 = b - a;
        Point2f v2 = d - c;
        float cosAngle = (v1.x * v2.x + v1.y * v2.y) / (norm(v1) * norm(v2));
        // 根据夹角余弦值判断旋转后的点位置
        if (fabs(cosAngle) > 0.7)
        {
            pt.x = (b.x + c.x) * 0.5;
            pt.y = (b.y + c.y) * 0.5;
        }
        else
        {
            float denom = a.x * (float)(d.y - c.y) + b.x * (float)(c.y - d.y) +
                d.x * (float)(b.y - a.y) + c.x * (float)(a.y - b.y);
            float num = a.x * (float)(d.y - c.y) + c.x * (float)(a.y - d.y) + d.x * (float)(c.y - a.y);
            float s = num / denom;

            pt.x = a.x + s * (b.x - a.x);
            pt.y = a.y + s * (b.y - a.y);
        }
        // 将计算得到的点添加到输出轮廓
        outPoly.push_back(pt);
    }
}

void GetTextBoxes(cv::Mat& binaryIN, cv::Mat& srcimgIN, std::vector<TextBlock> &rsBoxes)
{
    __android_log_print(ANDROID_LOG_INFO, "EvaluateOneImage", "begin get boxes");
    float binaryThreshold = 0.3f;
    int maxCandidates = 1000;
    float polygonThreshold = 0.5;
    int longSideThresh = 3;//minBox 长边门限

    // 获取图像的高度和宽度
    int h = srcimgIN.rows;
    int w = srcimgIN.cols;
    // 二值化处理
    Mat bitmap;
    threshold(binaryIN, bitmap, binaryThreshold, 255, THRESH_BINARY);
    //// 计算图像缩放比例
    float scaleHeight = (float)(h) / (float)(binaryIN.size[0]);
    float scaleWidth = (float)(w) / (float)(binaryIN.size[1]);
    // 寻找轮廓
    vector< vector<Point> > contours;
    bitmap.convertTo(bitmap, CV_8UC1);
    __android_log_print(ANDROID_LOG_INFO, "EvaluateOneImage", "3 %d %d %d", bitmap.rows, bitmap.cols, bitmap.channels());
    findContours(bitmap, contours, RETR_LIST, CHAIN_APPROX_SIMPLE);

    // 限制候选框的数量
    size_t numCandidate = min(contours.size(), (size_t)(maxCandidates > 0 ? maxCandidates : INT_MAX));
    vector<float> confidences;

    // 遍历每个候选框
    for (size_t i = 0; i < numCandidate; i++)
    {
        vector<Point>& contour = contours[i];

        // 计算文本轮廓分数
        float score = contourScore(binaryIN, contour);
        float boxScore = 0.0f;

        if (score < polygonThreshold) {
            boxScore = score;
            continue;
        }
        // 对轮廓进行缩放
        vector<Point> contourScaled; contourScaled.reserve(contour.size());
        for (size_t j = 0; j < contour.size(); j++)
        {
            contourScaled.push_back(Point(int(contour[j].x * scaleWidth),
                int(contour[j].y * scaleHeight)));
        }
        // 检查坐标是否有效
        bool coordinatesValid = true;
        for (size_t j = 0; j < contourScaled.size(); j++) {
            if (contourScaled[j].x < 0 || contourScaled[j].y < 0 ||
                contourScaled[j].x >= w || contourScaled[j].y >= h) {
                coordinatesValid = false;
                break;
            }
        }

        // 如果坐标有效，则处理该结果
        if (coordinatesValid)
        {
            TextBlock detectedBox;
            // 解除裁剪
            RotatedRect box = minAreaRect(contourScaled);
            float longSide = std::max(box.size.width, box.size.height);
            if (longSide < longSideThresh)
            {
                continue;
            }

            // minArea() rect is not normalized, it may return rectangles with angle=-90 or height < width
            const float angle_threshold = 60;  // do not expect vertical text, TODO detection algo property
            bool swap_size = false;
            if (box.size.width < box.size.height)  // horizontal-wide text area is expected
                swap_size = true;
            else if (fabs(box.angle) >= angle_threshold)  // don't work with vertical rectangles
                swap_size = true;
            if (swap_size)
            {
                swap(box.size.width, box.size.height);
                if (box.angle < 0)
                    box.angle += 90;
                else if (box.angle > 0)
                    box.angle -= 90;
            }

            Point2f vertex[4];
            box.points(vertex);  // order: bl, tl, tr, br
            vector<Point2f> approx;
            for (int j = 0; j < 4; j++)
                approx.emplace_back(vertex[j]);
            vector<Point2f> polygon;
            unclip(approx, polygon);

            box = minAreaRect(polygon);
            longSide = std::max(box.size.width, box.size.height);
            if (longSide < longSideThresh + 2)
            {
                continue;
            }
            detectedBox.boxVertices = polygon;
            detectedBox.angle = box.angle;
            detectedBox.boxScore = boxScore;
            rsBoxes.push_back(detectedBox);

            __android_log_print(ANDROID_LOG_INFO, "EvaluateOneImage", "box is (%f %f) (%f %f) (%f %f) (%f %f)", detectedBox.boxVertices[0].x, detectedBox.boxVertices[0].y,
                detectedBox.boxVertices[1].x, detectedBox.boxVertices[1].y,
                detectedBox.boxVertices[2].x, detectedBox.boxVertices[2].y,
                detectedBox.boxVertices[3].x, detectedBox.boxVertices[3].y);
        }
    }
    confidences = vector<float>(contours.size(), 1.0f);

    __android_log_print(ANDROID_LOG_INFO, "EvaluateOneImage", "finish run detect %d", rsBoxes.size());

    // 对 results 进行倒序处理
    std::reverse(rsBoxes.begin(), rsBoxes.end());
}

// 该函数用于对输入图像进行预处理，包括颜色空间转换和图像缩放
cv::Mat preprocess(cv::Mat srcimg)
{
    int shortSize = 736;
    cv::Mat dstimg;
    // 将输入图像从BGR颜色空间转换为RGB颜色空间
    cvtColor(srcimg, dstimg, COLOR_BGR2RGB);
    int h = srcimg.rows;
    int w = srcimg.cols;
    // 初始化高度和宽度的缩放比例
    float scale_h = 1;
    float scale_w = 1;
    // 根据图像的高度和宽度选择缩放比例
    if (h < w)
    {
        // 如果图像高度小于宽度 计算高度缩放比例
        scale_h = (float)shortSize / (float)h;
        float tar_w = (float)w * scale_h;
        tar_w = tar_w - (int)tar_w % 32;
        tar_w = max((float)32, tar_w);
        scale_w = tar_w / (float)w;
    }
    else
    {
        // 如果图像宽度小于等于高度 计算宽度缩放比例
        scale_w = (float)shortSize / (float)w;
        float tar_h = (float)h * scale_w;
        tar_h = tar_h - (int)tar_h % 32;
        tar_h = max((float)32, tar_h);
        scale_h = tar_h / (float)h;
    }
    // 使用线性插值对图像进行缩放，以调整到目标尺寸
    resize(dstimg, dstimg, Size(int(scale_w * dstimg.cols), int(scale_h * dstimg.rows)), INTER_LINEAR);
    return dstimg;
}

std::vector<float> normalize_(cv::Mat img)
{
    float meanValues[3] = { 0.485, 0.456, 0.406 };
    float normValues[3] = { 0.229, 0.224, 0.225 };

    std::vector<float> input_image;
    //    img.convertTo(img, CV_32F);
    int row = img.rows;
    int col = img.cols;
    input_image.resize(row * col * img.channels());
    for (int c = 0; c < 3; c++)
    {
        for (int i = 0; i < row; i++)
        {
            for (int j = 0; j < col; j++)
            {
                float pix = img.ptr<uchar>(i)[j * 3 + c];
                input_image[c * row * col + i * col + j] = (pix / 255.0 - meanValues[c]) / normValues[c];
            }
        }
    }

    return input_image;
}

cv::Mat getRotateCropImage(cv::Mat& frame, std::vector<Point2f> vertices)
{
    // 计算包围轮廓的最小矩形
    Rect rect = boundingRect(cv::Mat(vertices));
    // 从原始图像中提取感兴趣区域（ROI）
    cv::Mat crop_img = frame(rect);

    // 设置输出图像的大小为矩形的宽度和高度
    const Size outputSize = Size(rect.width, rect.height);
    // 定义目标矩形的四个顶点坐标
    std::vector<Point2f> targetVertices{ Point2f(0, outputSize.height), Point2f(0, 0), Point2f(outputSize.width, 0), Point2f(outputSize.width, outputSize.height) };


    // 将原始轮廓的顶点坐标调整为在裁剪后的图像中的坐标
    for (int i = 0; i < 4; i++)
    {
        vertices[i].x -= rect.x;
        vertices[i].y -= rect.y;
    }
    // 计算透视变换矩阵，将原始轮廓映射到目标矩形
    //Mat rotationcv::Matrix = getPerspectiveTransform(vertices, targetVertices);
    cv::Mat rotationMatrix = getPerspectiveTransform(vertices, targetVertices);

    // 应用透视变换，旋转和裁剪原始图像的感兴趣区域
    Mat result;
    //warpPerspective(crop_img, result, rotationcv::Matrix, outputSize, cv::BORDER_REPLICATE);
    warpPerspective(crop_img, result, rotationMatrix, outputSize, cv::BORDER_REPLICATE);

    return result;
}

string PostProcess(int wIn, int hIn, float* pdataIn)
{
    int i = 0, j = 0;
    // 遍历输出，获取每列的最大值的索引作为标签
    for (i = 0; i < wIn; i++)
    {
        int one_label_idx = 0;
        float max_data = -10000;
        for (j = 0; j < hIn; j++)
        {
            float data_ = pdataIn[i * hIn + j];
            if (data_ > max_data)
            {
                max_data = data_;
                one_label_idx = j;
            }
        }
        prebLabel[i] = one_label_idx;
    }
    // 存储去重后的非空白标签
    std::vector<int> no_repeat_blank_label;
    for (size_t elementIndex = 0; elementIndex < wIn; ++elementIndex)
    {
        if (prebLabel[elementIndex] != 0 && !(elementIndex > 0 && prebLabel[elementIndex - 1] == prebLabel[elementIndex]))
        {
            no_repeat_blank_label.push_back(prebLabel[elementIndex] - 1);
        }
    }
    // 构建最终的预测文本
    int len_s = no_repeat_blank_label.size();
    std::string plate_text;

    // todo

    for (i = 0; i < len_s; i++)
    {
        plate_text += alphabet[no_repeat_blank_label[i]];
    }
    return plate_text;
}

int inpWidth = 320;
int inpHeight = 48;

//cv::Mat preprocessRec(cv::Mat srcimg)
//{
//    cv::Mat dstimg;
//    int h = srcimg.rows;
//    int w = srcimg.cols;
//    const float ratio = w / float(h);
//    int resized_w = int(ceil((float)inpHeight * ratio));
//    if (ceil(inpHeight * ratio) > inpWidth)
//    {
//        resized_w = inpWidth;
//    }
//
//    resize(srcimg, dstimg, Size(resized_w, inpHeight), INTER_LINEAR);
//    return dstimg;
//}
////std::vector<float> normalizeRec(cv::Mat img)
////{
////
////    std::vector<float> input_image;
////    //    img.convertTo(img, CV_32F);
////    int row = img.rows;
////    int col = img.cols;
////    input_image.resize(inpHeight * inpWidth * img.channels());
////    for (int c = 0; c < 3; c++)
////    {
////        for (int i = 0; i < row; i++)
////        {
////            for (int j = 0; j < inpWidth; j++)
////            {
////                if (j < col)
////                {
////                    float pix = img.ptr<uchar>(i)[j * 3 + c];
////                    //input_image[c * row * inpWidth + i * inpWidth + j] = (pix / 255.0 - 0.5) / 0.5;
////                    input_image[c * row * inpWidth + i * inpWidth + j] = (pix);
////                }
////                else
////                {
////                    input_image[c * row * inpWidth + i * inpWidth + j] = 0;
////                }
////            }
////        }
////    }
////    return input_image;
////}
//struct TextBox {
//    std::vector<cv::Point> boxPoint;
//    float score;
//    std::string text;
//};
//bool compareBoxWidth(const TextBox& a, const TextBox& b)
//{
//    return abs(a.boxPoint[0].x - a.boxPoint[1].x) > abs(b.boxPoint[0].x - b.boxPoint[1].x);
//}
//cv::Mat getRotateCropImage2(const cv::Mat& src, std::vector<cv::Point> box) {
//    cv::Mat image;
//    src.copyTo(image);
//    std::vector<cv::Point> points = box;
//
//    int collectX[4] = { box[0].x, box[1].x, box[2].x, box[3].x };
//    int collectY[4] = { box[0].y, box[1].y, box[2].y, box[3].y };
//    int left = int(*std::min_element(collectX, collectX + 4));
//    int right = int(*std::max_element(collectX, collectX + 4));
//    int top = int(*std::min_element(collectY, collectY + 4));
//    int bottom = int(*std::max_element(collectY, collectY + 4));
//
//    cv::Mat imgCrop;
//    image(cv::Rect(left, top, right - left, bottom - top)).copyTo(imgCrop);
//
//    for (int i = 0; i < points.size(); i++) {
//        points[i].x -= left;
//        points[i].y -= top;
//    }
//
//
//    int imgCropWidth = int(sqrt(pow(points[0].x - points[1].x, 2) +
//        pow(points[0].y - points[1].y, 2)));
//    int imgCropHeight = int(sqrt(pow(points[0].x - points[3].x, 2) +
//        pow(points[0].y - points[3].y, 2)));
//
//    cv::Point2f ptsDst[4];
//    ptsDst[0] = cv::Point2f(0., 0.);
//    ptsDst[1] = cv::Point2f(imgCropWidth, 0.);
//    ptsDst[2] = cv::Point2f(imgCropWidth, imgCropHeight);
//    ptsDst[3] = cv::Point2f(0.f, imgCropHeight);
//
//    cv::Point2f ptsSrc[4];
//    ptsSrc[0] = cv::Point2f(points[0].x, points[0].y);
//    ptsSrc[1] = cv::Point2f(points[1].x, points[1].y);
//    ptsSrc[2] = cv::Point2f(points[2].x, points[2].y);
//    ptsSrc[3] = cv::Point2f(points[3].x, points[3].y);
//
//    cv::Mat M = cv::getPerspectiveTransform(ptsSrc, ptsDst);
//
//    cv::Mat partImg;
//    cv::warpPerspective(imgCrop, partImg, M,
//        cv::Size(imgCropWidth, imgCropHeight),
//        cv::BORDER_REPLICATE);
//
//    if (float(partImg.rows) >= float(partImg.cols) * 1.5) {
//        cv::Mat srcCopy = cv::Mat(partImg.rows, partImg.cols, partImg.depth());
//        cv::transpose(partImg, srcCopy);
//        cv::flip(srcCopy, srcCopy, 0);
//        return srcCopy;
//    }
//    else {
//        return partImg;
//    }
//}
//std::vector<cv::Mat> getPartImages(const cv::Mat& src, std::vector<TextBox>& textBoxes)
//{
//    std::sort(textBoxes.begin(), textBoxes.end(), compareBoxWidth);
//    std::vector<cv::Mat> partImages;
//    if (textBoxes.size() > 0)
//    {
//        for (int i = 0; i < textBoxes.size(); ++i)
//        {
//            cv::Mat partImg = getRotateCropImage2(src, textBoxes[i].boxPoint);
//            partImages.emplace_back(partImg);
//        }
//    }
//
//    return partImages;
//}
//struct TextLine {
//    std::string text;
//    std::vector<float> charScores;
//};
//
//template<class ForwardIterator>
//inline static size_t argmax(ForwardIterator first, ForwardIterator last) {
//    return std::distance(first, std::max_element(first, last));
//}
//
//TextLine scoreToTextLine(const std::vector<float>& outputData, int h, int w)
//{
//    int keySize = alphabet.size();
//    std::string strRes;
//    std::vector<float> scores;
//    int lastIndex = 0;
//    int maxIndex;
//    float maxValue;
//
//    for (int i = 0; i < h; i++)
//    {
//        maxIndex = 0;
//        maxValue = -1000.f;
//
//        maxIndex = int(argmax(outputData.begin() + i * w, outputData.begin() + i * w + w));
//        maxValue = float(*std::max_element(outputData.begin() + i * w, outputData.begin() + i * w + w));// / partition;
//        if (maxIndex > 0 && maxIndex < keySize && (!(i > 0 && maxIndex == lastIndex))) {
//            scores.emplace_back(maxValue);
//            strRes.append(alphabet[maxIndex - 1]);
//        }
//        lastIndex = maxIndex;
//    }
//    return { strRes, scores };
//}

static int ocrProcess(ncnn::Net* dectModel, ncnn::Net* recModel, const cv::Mat& bgr, std::vector<string>& textList)
{
    __android_log_print(ANDROID_LOG_INFO, "EvaluateOneImage", "start");

    prebLabel.clear();

    const float prob_threshold = 0.25f;
    const float nms_threshold = 0.45f;

    int img_w = bgr.cols;
    int img_h = bgr.rows;

    cv::Mat dstimg = preprocess(bgr);
    float pix = dstimg.ptr<uchar>(200)[200 * 3 + 1];
    std::vector<float>result = normalize_(dstimg);

    __android_log_print(ANDROID_LOG_INFO, "EvaluateOneImage", "(200, 200, 1))%f ", pix);

    ncnn::Mat in_pad(dstimg.cols, dstimg.rows, 3, (void*)result.data());

    ncnn::Extractor dect = dectModel->create_extractor();
    dect.input("x", in_pad);

    ncnn::Mat out;
    dect.extract("sigmoid_0.tmp_0", out);

    Mat binary(out.h, out.w, CV_32FC1);
    memcpy(binary.data, out.data, out.w* out.h * sizeof(float));

    __android_log_print(ANDROID_LOG_INFO, "EvaluateOneImage", "dect out %d %d ", out.w , out.h);

    std::vector<TextBlock> textBlocks;
    GetTextBoxes(binary, const_cast<cv::Mat&>(bgr), textBlocks);

    __android_log_print(ANDROID_LOG_INFO, "EvaluateOneImage", "finish get boxes");

    for (size_t i = 0; i < textBlocks.size(); i++)
    {
        cv::Mat textimg = getRotateCropImage(const_cast<cv::Mat&>(bgr), textBlocks[i].boxVertices);

        const float mean_vals[3] = { 127.5, 127.5, 127.5 };
        const float norm_vals[3] = { 1.0 / 127.5, 1.0 / 127.5, 1.0 / 127.5 };

        float scale = (float)inpHeight / (float)textimg.rows;
        int dstWidth = int((float)textimg.cols * scale);

        cv::Mat srcResize;
        cv::resize(textimg, srcResize, cv::Size(dstWidth, inpHeight));

        //if you use PP-OCRv3 you should change PIXEL_RGB to PIXEL_RGB2BGR
        ncnn::Mat input = ncnn::Mat::from_pixels(srcResize.data, ncnn::Mat::PIXEL_RGB2BGR, srcResize.cols, srcResize.rows);
        input.substract_mean_normalize(mean_vals, norm_vals);

        ncnn::Mat in_pack3;
        ncnn::convert_packing(input, in_pack3, 3);
        cv::Mat aa(input.h, input.w, CV_32FC3);
        memcpy((uchar*)aa.data, in_pack3.data, input.w * input.h * 3 * sizeof(float));

        ncnn::Extractor rec = recModel->create_extractor();
        rec.input("input", input);

        ncnn::Mat out;
        rec.extract("out", out);

        __android_log_print(ANDROID_LOG_INFO, "EvaluateOneImage", "finish run rec, out %d %d", out.w, out.h);

        float* floatArray = (float*)out.data;
        std::vector<float> outputData(floatArray, floatArray + out.h * out.w);

        string textResult = "";
        // 存储预测的标签
        prebLabel.resize(out.h);
        string text = PostProcess(out.h, out.w, floatArray);

        // 删除标点符号和空格
        text.erase(std::remove_if(text.begin(), text.end(), [](unsigned char c) {
            return std::ispunct(c) || std::isspace(c);
            }), text.end());

        // 如果文本内容不为空，则处理结果
        if (!text.empty())
        {
            textResult = text;
        }

        textList.push_back(textResult);
    }
    for (int i=0; i< textBlocks.size(); ++i)
    {
        textBlocks[i].boxVertices.clear();
    }
    textBlocks.clear();
    textBlocks.shrink_to_fit();

    __android_log_print(ANDROID_LOG_INFO, "EvaluateOneImage", "End");
    return 0;
}

extern "C" void CreateNet(void** detectModel, void** recModel, char* decBinMem, char* decParamMem, char* recBinMem, char* recParamMem, const void* modelDataKeys, size_t modelDataLengthKeys)
{
    const auto resultDetect = new ncnn::Net;
    *detectModel = resultDetect;

    const auto resultDetectRec = new ncnn::Net;
    *recModel = resultDetectRec;

    int ret[4] = {-2};

    const unsigned char* dectBinMemP = reinterpret_cast<const unsigned char*>(decBinMem);
    ncnn::DataReaderFromMemory drDect(dectBinMemP);

    const unsigned char* recBinMemP = reinterpret_cast<const unsigned char*>(recBinMem);
    ncnn::DataReaderFromMemory drRec(recBinMemP);

    resultDetect->opt.use_vulkan_compute = false;

    ret[0] = resultDetect->load_param_mem(decParamMem);
    ret[1] = resultDetect->load_model(drDect);

    ret[2] = resultDetectRec->load_param_mem(recParamMem);
    ret[3] = resultDetectRec->load_model(drRec);

    __android_log_print(ANDROID_LOG_INFO, "EvaluateOneImage", "LoadDectBin %d %d %d %d", ret[0], ret[1], ret[2], ret[3]);

    //string keys_path = keysName;
    //ifstream ifs(keys_path);
    //std::string line;
    //while (getline(ifs, line))
    //{
    //    alphabet.push_back(line);
    //}
    //alphabet.push_back(" ");



    // 将字节数据转换为字符串
    std::string text(reinterpret_cast<const char*>(modelDataKeys), modelDataLengthKeys);

    // 使用字符串流处理字符串
    std::istringstream iss(text);
    std::string line;

    // 逐行读取并添加到 alphabet 中
    while (std::getline(iss, line))
    {
        alphabet.push_back(line);
    }
    alphabet.push_back(" ");


}


extern "C" void FreeNet(void* detectModel, void* recModel)
{
    const auto yolo = static_cast<ncnn::Net*>(detectModel);
    yolo->clear();
    const auto rec = static_cast<ncnn::Net*>(recModel);
    rec->clear();
    delete yolo;
    delete rec;
}

typedef struct _StructStringInfo
{
    char* result;
    int bufferSize;
}StructStringInfo;

extern "C" void Test(void* detectModel, void* recModel, unsigned char* input, int iw, int ih, StructStringInfo*& ob, int* cnt)
{
    ncnn::Net* dect = static_cast<ncnn::Net*>(detectModel);
    ncnn::Net* rec = static_cast<ncnn::Net*>(recModel);
    __android_log_print(ANDROID_LOG_INFO, "EvaluateOneImage", "image in");
	cv::Mat m = cv::Mat(ih, iw, CV_8UC4, (void*)input);
    cvtColor(m, m, cv::COLOR_RGBA2BGR);
    __android_log_print(ANDROID_LOG_INFO, "EvaluateOneImage", "image cvt out");


    // 获取图像在指定坐标（x, y）处的像素值
    cv::Vec3b pixel = m.at<cv::Vec3b>(100, 100);

    // 输出像素值到Android Logcat
    __android_log_print(ANDROID_LOG_INFO, "EvaluateOneImage", "Pixel value at (%d, %d): B=%d, G=%d, R=%d",
        100, 100, pixel[0], pixel[1], pixel[2]);

    // 获取图像在指定坐标（x, y）处的像素值
    pixel = m.at<cv::Vec3b>(200, 200);

    // 输出像素值到Android Logcat
    __android_log_print(ANDROID_LOG_INFO, "EvaluateOneImage", "Pixel value at (%d, %d): B=%d, G=%d, R=%d",
        200, 200, pixel[0], pixel[1], pixel[2]);

    // 获取图像在指定坐标（x, y）处的像素值
    pixel = m.at<cv::Vec3b>(300, 300);

    // 输出像素值到Android Logcat
    __android_log_print(ANDROID_LOG_INFO, "EvaluateOneImage", "Pixel value at (%d, %d): B=%d, G=%d, R=%d",
        300, 300, pixel[0], pixel[1], pixel[2]);



    std::vector<string> textLists;
    ocrProcess(dect, rec, m, textLists);
    int count = static_cast<int>(textLists.size());

    if(count > 100)
    {
        count = 100;
    }

    *cnt = count;

    ob = new StructStringInfo[count];
    for (int i=0; i< count; ++i)
    {
        ob[i].result = new char[textLists[i].length()+1];
        std::copy(textLists[i].begin(), textLists[i].end(), ob[i].result);
        ob[i].result[textLists[i].length()] = '\0'; // 添加null终止符
    }

    textLists.clear();
}