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#include "opencv2/datasets/tr_chars.hpp"

#include <opencv2/core.hpp>

#include "opencv2/text.hpp"
#include "opencv2/imgproc.hpp"
#include "opencv2/imgcodecs.hpp"

#include <cstdio>
#include <cstdlib> // atoi

#include <string>
#include <vector>

using namespace std;
using namespace cv;
using namespace cv::datasets;
using namespace cv::text;

int main(int argc, char *argv[])
{
    const char *keys =
            "{ help h usage ? |    | show this message }"
            "{ path p         |true| path to dataset description file ( list_English_Img.m ) and Img folder.}";
    CommandLineParser parser(argc, argv, keys);
    string path(parser.get<string>("path"));
    if (parser.has("help") || path=="true")
    {
        parser.printMessage();
        return -1;
    }

    Ptr<TR_chars> dataset = TR_chars::create();
    dataset->load(path);

    // ***************
    // dataset. train, test contain information about each element of appropriate sets and splits.
    // For example, let output first elements of these vectors and their sizes for last split.
    // And number of splits.
    int numSplits = dataset->getNumSplits();
    printf("splits number: %u\n", numSplits);

    vector< Ptr<Object> > &currTrain = dataset->getTrain(numSplits-1);
    vector< Ptr<Object> > &currTest = dataset->getTest(numSplits-1);
    vector< Ptr<Object> > &currValidation = dataset->getValidation(numSplits-1);
    printf("train size: %u\n", (unsigned int)currTrain.size());
    printf("test size: %u\n", (unsigned int)currTest.size());
    printf("validation size: %u\n", (unsigned int)currValidation.size());


    // WARNING: The order of classes' labels is different in Chars74k and in the output of our classifier
    string src_classes = "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789"; // labels order as in the clasifier output
    string tar_classes = "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"; // labels order as in the Chars74k dataset

    Ptr<OCRHMMDecoder::ClassifierCallback> ocr = loadOCRHMMClassifierCNN("OCRBeamSearch_CNN_model_data.xml.gz");

    int numOK = 0;
    int upperNumOK = 0;

    for (unsigned int i=0; i<(unsigned int)currTest.size(); i++)
    {
        TR_charsObj *exampleTest = static_cast<TR_charsObj *>(currTest[i].get());
        printf("processed image: %u, name: %s\n", i, exampleTest->imgName.c_str());
        printf("  label: %u,", exampleTest->label);

        string imfilename = path+string("/Img/")+exampleTest->imgName.c_str()+string(".png");
        Mat image  = imread(imfilename);
        vector<int> out_classes;
        vector<double> out_confidences;
        ocr->eval(image, out_classes, out_confidences);
        int prediction = 1 + tar_classes.find_first_of(src_classes[out_classes[0]]);
        printf(" prediction: %u\n", prediction);

        if (exampleTest->label == prediction)
            numOK++;

        char l = tar_classes[exampleTest->label];
        char p = tar_classes[prediction];
        if (toupper(l) == toupper(p))
            upperNumOK++;
    }

    printf("\n---------------------------------------------\n");
    printf("Chars74k Classification Accuracy (case-sensitive): %f\n",(float)numOK/currTest.size());
    printf("Chars74k Classification Accuracy (case-insensitive): %f\n",(float)upperNumOK/currTest.size());

    return 0;
}
