Crnn.cpp

#include <Crnn.h>
#include <migraphx/onnx.hpp>
#include <migraphx/gpu/target.hpp>
#include <Filesystem.h>
#include <SimpleLog.h>


namespace migraphxSamples
{

Crnn::Crnn()
{

}

Crnn::~Crnn()
{
    configurationFile.release();
}

ErrorCode Crnn::Initialize(InitializationParameterOfOcr initializationParameterOfOcr, bool dynamic)
{
    // 读取配置文件
    std::string configFilePath=initializationParameterOfOcr.configFilePath;
    if(!Exists(configFilePath))
    {
        LOG_ERROR(stdout, "no configuration file!\n");
        return CONFIG_FILE_NOT_EXIST;
    }
    if(!configurationFile.open(configFilePath, cv::FileStorage::READ))
    {
       LOG_ERROR(stdout, "fail to open configuration file\n");
       return FAIL_TO_OPEN_CONFIG_FILE;
    }
    LOG_INFO(stdout, "succeed to open configuration file\n");

    // 获取配置文件参数
    cv::FileNode netNode = configurationFile["CrnnDynamic"];
    std::string modelPath=(std::string)netNode["ModelPath"];

    // 加载模型
    if(!Exists(modelPath))
    {
        LOG_ERROR(stdout,"%s not exist!\n",modelPath.c_str());
        return MODEL_NOT_EXIST;
    }

    if(dynamic)
    {
        migraphx::onnx_options onnx_options;
        onnx_options.map_input_dims["input"]={1,1,32,512};

        net = migraphx::parse_onnx(modelPath, onnx_options);
        LOG_INFO(stdout,"succeed to load model: %s\n",GetFileName(modelPath).c_str());

        // 获取模型输入/输出节点信息
        std::unordered_map<std::string, migraphx::shape> inputs=net.get_inputs();
        std::unordered_map<std::string, migraphx::shape> outputs=net.get_outputs();
        inputName=inputs.begin()->first;
        inputShape=inputs.begin()->second;
        int N=inputShape.lens()[0];
        int C=inputShape.lens()[1];
        int H=inputShape.lens()[2];
        int W=inputShape.lens()[3];
        inputSize=cv::Size(W,H);

        // log输出日志信息
        LOG_INFO(stdout,"InputMaxSize:%dx%d\n",inputSize.width,inputSize.height);
        LOG_INFO(stdout,"InputName:%s\n",inputName.c_str());
    }
    else
    {
        migraphx::onnx_options onnx_options;
        onnx_options.map_input_dims["input"]={1,1,32,100};

        net = migraphx::parse_onnx(modelPath, onnx_options);
        LOG_INFO(stdout,"succeed to load model: %s\n",GetFileName(modelPath).c_str());

        // 获取模型输入/输出节点信息
        std::unordered_map<std::string, migraphx::shape> inputs=net.get_inputs();
        std::unordered_map<std::string, migraphx::shape> outputs=net.get_outputs();
        inputName=inputs.begin()->first;
        inputShape=inputs.begin()->second;
        int N=inputShape.lens()[0];
        int C=inputShape.lens()[1];
        int H=inputShape.lens()[2];
        int W=inputShape.lens()[3];
        inputSize=cv::Size(W,H);

        // log输出日志信息
        LOG_INFO(stdout,"InputSize:%dx%d\n",inputSize.width,inputSize.height);
        LOG_INFO(stdout,"InputName:%s\n",inputName.c_str());
    }

    // 设置模型为GPU模式
    migraphx::target gpuTarget = migraphx::gpu::target{};

    // 编译模型
    migraphx::compile_options options;
    options.device_id=0;                          // 设置GPU设备，默认为0号设备
    options.offload_copy=true;
    net.compile(gpuTarget,options);
    LOG_INFO(stdout,"succeed to compile model: %s\n",GetFileName(modelPath).c_str());

    // warm up
    std::unordered_map<std::string, migraphx::argument> inputData;
    inputData[inputName]=migraphx::argument{inputShape};
    net.eval(inputData);

    return SUCCESS;
}

ErrorCode Crnn::Infer(const cv::Mat &srcImage, std::vector<char> &resultsChar, bool raw, bool dynamic)
{
    if(srcImage.empty() || srcImage.type()!=CV_8UC3)
    {
        LOG_ERROR(stdout, "image error!\n");
        return IMAGE_ERROR;
    }

    cv::Mat inputImage, inputBlob;
    cv::cvtColor(srcImage, inputImage, CV_BGR2GRAY);

    int height, width, widthRaw;
    widthRaw = inputImage.cols;
    if(dynamic)
    {
        cv::resize(inputImage, inputImage, cv::Size(widthRaw, 32));
        height = inputImage.rows, width = inputImage.cols;
    }
    else
    {
        cv::resize(inputImage, inputImage, cv::Size(100, 32));
        height = inputImage.rows, width = inputImage.cols;
    }
    inputBlob = cv::dnn::blobFromImage(inputImage);

    for(int i=0; i<width * height; i++)
    {
        *((float*)inputBlob.data+i) = ((*((float*)inputBlob.data+i))/255.f - 0.5)/0.5;
    }

    // 创建输入数据
    std::unordered_map<std::string, migraphx::argument> inputData;
    if(dynamic)
    {
        std::vector<std::size_t> dynamicShape = {1, 1, 32, width};
        inputData[inputName] = migraphx::argument{migraphx::shape(inputShape.type(),dynamicShape), (float*)inputBlob.data};
    }
    else
    {
        inputData[inputName] = migraphx::argument{inputShape, (float*)inputBlob.data};
    }

    // 推理
    std::vector<migraphx::argument> inferenceResults = net.eval(inputData);

    // 获取推理结果
    std::vector<cv::Mat> outs;
    migraphx::argument result = inferenceResults[0];

    // 转换为cv::Mat
    migraphx::shape outputShape = result.get_shape();
    int shape[]={outputShape.lens()[0],outputShape.lens()[1],outputShape.lens()[2]};
    cv::Mat out(3,shape,CV_32F);
    memcpy(out.data,result.data(),sizeof(float)*outputShape.elements());
    outs.push_back(out);

    std::vector<int> predChars;
    const std::string alphabet = "-0123456789abcdefghijklmnopqrstuvwxyz";

    //获取字符索引序列
    for(uint i = 0; i < outs[0].size[0]; i++)
    {
        cv::Mat scores = cv::Mat(1,outs[0].size[2],CV_32F,outs[0].ptr<float>(i));
        cv::Point charIdPoint;
        double maxCharScore;
        cv::minMaxLoc(scores, 0, &maxCharScore, 0, &charIdPoint);
        int maxIdx = charIdPoint.x;
        predChars.push_back(maxIdx);
    }

    //字符转录处理
    for(uint i=0; i < predChars.size(); i++)
    {
        if(raw)
        {
            resultsChar.push_back(alphabet[predChars[i]]);
        }
        else
        {
            if(predChars[i] != 0)
            {
                if(!(i > 0 && predChars[i-1] == predChars[i]))
                {
                    resultsChar.push_back(alphabet[predChars[i]]);
                }
            }
        }
    }

    return SUCCESS;
}
}