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#include "test_precomp.hpp"
#include "npy_blob.hpp"
#include <opencv2/dnn/shape_utils.hpp>

namespace cvtest
{

using namespace cv;
using namespace cv::dnn;

template<typename TString>
static std::string _tf(TString filename)
{
    return (getOpenCVExtraDir() + "/dnn/") + filename;
}

TEST(Test_Caffe, read_gtsrb)
{
    Net net;
    {
        Ptr<Importer> importer = createCaffeImporter(_tf("gtsrb.prototxt"), "");
        ASSERT_TRUE(importer != NULL);
        importer->populateNet(net);
    }
}

TEST(Test_Caffe, read_googlenet)
{
    Net net;
    {
        Ptr<Importer> importer = createCaffeImporter(_tf("bvlc_googlenet.prototxt"), "");
        ASSERT_TRUE(importer != NULL);
        importer->populateNet(net);
    }
}

TEST(Reproducibility_AlexNet, Accuracy)
{
    Net net;
    {
        const string proto = findDataFile("dnn/bvlc_alexnet.prototxt", false);
        const string model = findDataFile("dnn/bvlc_alexnet.caffemodel", false);
        Ptr<Importer> importer = createCaffeImporter(proto, model);
        ASSERT_TRUE(importer != NULL);
        importer->populateNet(net);
    }

    Mat sample = imread(_tf("grace_hopper_227.png"));
    ASSERT_TRUE(!sample.empty());

    Size inputSize(227, 227);

    if (sample.size() != inputSize)
        resize(sample, sample, inputSize);

    net.setInput(blobFromImage(sample), "data");
    Mat out = net.forward("prob");
    Mat ref = blobFromNPY(_tf("caffe_alexnet_prob.npy"));
    normAssert(ref, out);
}

#if !defined(_WIN32) || defined(_WIN64)
TEST(Reproducibility_FCN, Accuracy)
{
    Net net;
    {
        const string proto = findDataFile("dnn/fcn8s-heavy-pascal.prototxt", false);
        const string model = findDataFile("dnn/fcn8s-heavy-pascal.caffemodel", false);
        Ptr<Importer> importer = createCaffeImporter(proto, model);
        ASSERT_TRUE(importer != NULL);
        importer->populateNet(net);
    }

    Mat sample = imread(_tf("street.png"));
    ASSERT_TRUE(!sample.empty());

    Size inputSize(500, 500);
    if (sample.size() != inputSize)
        resize(sample, sample, inputSize);

    std::vector<int> layerIds;
    std::vector<size_t> weights, blobs;
    net.getMemoryConsumption(shape(1,3,227,227), layerIds, weights, blobs);

    net.setInput(blobFromImage(sample), "data");
    Mat out = net.forward("score");
    Mat ref = blobFromNPY(_tf("caffe_fcn8s_prob.npy"));
    normAssert(ref, out);
}
#endif

TEST(Reproducibility_SSD, Accuracy)
{
    Net net;
    {
        const string proto = findDataFile("dnn/ssd_vgg16.prototxt", false);
        const string model = findDataFile("dnn/VGG_ILSVRC2016_SSD_300x300_iter_440000.caffemodel", false);
        Ptr<Importer> importer = createCaffeImporter(proto, model);
        ASSERT_TRUE(importer != NULL);
        importer->populateNet(net);
    }

    Mat sample = imread(_tf("street.png"));
    ASSERT_TRUE(!sample.empty());

    if (sample.channels() == 4)
        cvtColor(sample, sample, COLOR_BGRA2BGR);

    sample.convertTo(sample, CV_32F);
    resize(sample, sample, Size(300, 300));

    Mat in_blob = blobFromImage(sample);
    net.setInput(in_blob, "data");
    Mat out = net.forward("detection_out");

    Mat ref = blobFromNPY(_tf("ssd_out.npy"));
    normAssert(ref, out);
}
}