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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, memory_read)
{
    const string proto = findDataFile("dnn/bvlc_googlenet.prototxt", false);
    const string model = findDataFile("dnn/bvlc_googlenet.caffemodel", false);

    string dataProto;
    ASSERT_TRUE(readFileInMemory(proto, dataProto));
    string dataModel;
    ASSERT_TRUE(readFileInMemory(model, dataModel));

    Net net = readNetFromCaffe(dataProto.c_str(), dataProto.size());
    ASSERT_FALSE(net.empty());

    Net net2 = readNetFromCaffe(dataProto.c_str(), dataProto.size(),
                                dataModel.c_str(), dataModel.size());
    ASSERT_FALSE(net2.empty());
}

TEST(Test_Caffe, read_gtsrb)
{
    Net net = readNetFromCaffe(_tf("gtsrb.prototxt"));
    ASSERT_FALSE(net.empty());
}

TEST(Test_Caffe, read_googlenet)
{
    Net net = readNetFromCaffe(_tf("bvlc_googlenet.prototxt"));
    ASSERT_FALSE(net.empty());
}

typedef testing::TestWithParam<tuple<bool> > Reproducibility_AlexNet;
TEST_P(Reproducibility_AlexNet, Accuracy)
{
    bool readFromMemory = get<0>(GetParam());
    Net net;
    {
        const string proto = findDataFile("dnn/bvlc_alexnet.prototxt", false);
        const string model = findDataFile("dnn/bvlc_alexnet.caffemodel", false);
        if (readFromMemory)
        {
            string dataProto;
            ASSERT_TRUE(readFileInMemory(proto, dataProto));
            string dataModel;
            ASSERT_TRUE(readFileInMemory(model, dataModel));

            net = readNetFromCaffe(dataProto.c_str(), dataProto.size(),
                                   dataModel.c_str(), dataModel.size());
        }
        else
            net = readNetFromCaffe(proto, model);
        ASSERT_FALSE(net.empty());
    }

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

    net.setInput(blobFromImage(sample, 1.0f, Size(227, 227), Scalar(), false), "data");
    Mat out = net.forward("prob");
    Mat ref = blobFromNPY(_tf("caffe_alexnet_prob.npy"));
    normAssert(ref, out);
}

INSTANTIATE_TEST_CASE_P(Test_Caffe, Reproducibility_AlexNet, testing::Values(true, false));

#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);
        net = readNetFromCaffe(proto, model);
        ASSERT_FALSE(net.empty());
    }

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

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

    net.setInput(blobFromImage(sample, 1.0f, Size(500, 500), Scalar(), false), "data");
    Mat out = net.forward("score");

    Mat refData = imread(_tf("caffe_fcn8s_prob.png"), IMREAD_ANYDEPTH);
    int shape[] = {1, 21, 500, 500};
    Mat ref(4, shape, CV_32FC1, refData.data);

    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);
        net = readNetFromCaffe(proto, model);
        ASSERT_FALSE(net.empty());
    }

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

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

    Mat in_blob = blobFromImage(sample, 1.0f, Size(300, 300), Scalar(), false);
    net.setInput(in_blob, "data");
    Mat out = net.forward("detection_out");

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

TEST(Reproducibility_MobileNet_SSD, Accuracy)
{
    const string proto = findDataFile("dnn/MobileNetSSD_deploy.prototxt", false);
    const string model = findDataFile("dnn/MobileNetSSD_deploy.caffemodel", false);
    Net net = readNetFromCaffe(proto, model);

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

    Mat inp = blobFromImage(sample, 1.0f / 127.5, Size(300, 300), Scalar(127.5, 127.5, 127.5), false);
    net.setInput(inp);
    Mat out = net.forward();

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

    // Check that detections aren't preserved.
    inp.setTo(0.0f);
    net.setInput(inp);
    out = net.forward();

    const int numDetections = out.size[2];
    ASSERT_NE(numDetections, 0);
    for (int i = 0; i < numDetections; ++i)
    {
        float confidence = out.ptr<float>(0, 0, i)[2];
        ASSERT_EQ(confidence, 0);
    }
}

TEST(Reproducibility_ResNet50, Accuracy)
{
    Net net = readNetFromCaffe(findDataFile("dnn/ResNet-50-deploy.prototxt", false),
                               findDataFile("dnn/ResNet-50-model.caffemodel", false));

    Mat input = blobFromImage(imread(_tf("googlenet_0.png")), 1.0f, Size(224,224), Scalar(), false);
    ASSERT_TRUE(!input.empty());

    net.setInput(input);
    Mat out = net.forward();

    Mat ref = blobFromNPY(_tf("resnet50_prob.npy"));
    normAssert(ref, out);
}

TEST(Reproducibility_SqueezeNet_v1_1, Accuracy)
{
    Net net = readNetFromCaffe(findDataFile("dnn/squeezenet_v1.1.prototxt", false),
                               findDataFile("dnn/squeezenet_v1.1.caffemodel", false));

    Mat input = blobFromImage(imread(_tf("googlenet_0.png")), 1.0f, Size(227,227), Scalar(), false);
    ASSERT_TRUE(!input.empty());

    net.setInput(input);
    Mat out = net.forward();

    Mat ref = blobFromNPY(_tf("squeezenet_v1.1_prob.npy"));
    normAssert(ref, out);
}

TEST(Reproducibility_AlexNet_fp16, Accuracy)
{
    const float l1 = 1e-5;
    const float lInf = 3e-3;

    const string proto = findDataFile("dnn/bvlc_alexnet.prototxt", false);
    const string model = findDataFile("dnn/bvlc_alexnet.caffemodel", false);

    shrinkCaffeModel(model, "bvlc_alexnet.caffemodel_fp16");
    Net net = readNetFromCaffe(proto, "bvlc_alexnet.caffemodel_fp16");

    Mat sample = imread(findDataFile("dnn/grace_hopper_227.png", false));

    net.setInput(blobFromImage(sample, 1.0f, Size(227, 227), Scalar(), false));
    Mat out = net.forward();
    Mat ref = blobFromNPY(findDataFile("dnn/caffe_alexnet_prob.npy", false));
    normAssert(ref, out, "", l1, lInf);
}

TEST(Reproducibility_GoogLeNet_fp16, Accuracy)
{
    const float l1 = 1e-5;
    const float lInf = 3e-3;

    const string proto = findDataFile("dnn/bvlc_googlenet.prototxt", false);
    const string model = findDataFile("dnn/bvlc_googlenet.caffemodel", false);

    shrinkCaffeModel(model, "bvlc_googlenet.caffemodel_fp16");
    Net net = readNetFromCaffe(proto, "bvlc_googlenet.caffemodel_fp16");

    std::vector<Mat> inpMats;
    inpMats.push_back( imread(_tf("googlenet_0.png")) );
    inpMats.push_back( imread(_tf("googlenet_1.png")) );
    ASSERT_TRUE(!inpMats[0].empty() && !inpMats[1].empty());

    net.setInput(blobFromImages(inpMats, 1.0f, Size(), Scalar(), false), "data");
    Mat out = net.forward("prob");

    Mat ref = blobFromNPY(_tf("googlenet_prob.npy"));
    normAssert(out, ref, "", l1, lInf);
}

// https://github.com/richzhang/colorization
TEST(Reproducibility_Colorization, Accuracy)
{
    const float l1 = 1e-5;
    const float lInf = 3e-3;

    Mat inp = blobFromNPY(_tf("colorization_inp.npy"));
    Mat ref = blobFromNPY(_tf("colorization_out.npy"));
    Mat kernel = blobFromNPY(_tf("colorization_pts_in_hull.npy"));

    const string proto = findDataFile("dnn/colorization_deploy_v2.prototxt", false);
    const string model = findDataFile("dnn/colorization_release_v2.caffemodel", false);
    Net net = readNetFromCaffe(proto, model);

    net.getLayer(net.getLayerId("class8_ab"))->blobs.push_back(kernel);
    net.getLayer(net.getLayerId("conv8_313_rh"))->blobs.push_back(Mat(1, 313, CV_32F, 2.606));

    net.setInput(inp);
    Mat out = net.forward();

    normAssert(out, ref, "", l1, lInf);
}

TEST(Reproducibility_DenseNet_121, Accuracy)
{
    const string proto = findDataFile("dnn/DenseNet_121.prototxt", false);
    const string model = findDataFile("dnn/DenseNet_121.caffemodel", false);

    Mat inp = imread(_tf("dog416.png"));
    inp = blobFromImage(inp, 1.0 / 255, Size(224, 224));
    Mat ref = blobFromNPY(_tf("densenet_121_output.npy"));

    Net net = readNetFromCaffe(proto, model);

    net.setInput(inp);
    Mat out = net.forward();

    normAssert(out, ref);
}

}