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#include "test_precomp.hpp"
#include "npy_blob.hpp"
#include <opencv2/dnn/shape_utils.hpp>
#include <opencv2/core/ocl.hpp>
#include <opencv2/ts/ocl_test.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<bool> Reproducibility_AlexNet;
TEST_P(Reproducibility_AlexNet, Accuracy)
{
    bool readFromMemory = 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::Bool());

typedef testing::TestWithParam<bool> Reproducibility_OCL_AlexNet;
OCL_TEST_P(Reproducibility_OCL_AlexNet, Accuracy)
{
    bool readFromMemory = 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());
    }

    net.setPreferableBackend(DNN_BACKEND_DEFAULT);
    net.setPreferableTarget(DNN_TARGET_OPENCL);

    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);
}

OCL_INSTANTIATE_TEST_CASE_P(Test_Caffe, Reproducibility_OCL_AlexNet, testing::Bool());

#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);
    }
}

OCL_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);

    net.setPreferableBackend(DNN_BACKEND_DEFAULT);
    net.setPreferableTarget(DNN_TARGET_OPENCL);

    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);
}

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

    net.setPreferableBackend(DNN_BACKEND_DEFAULT);
    net.setPreferableTarget(DNN_TARGET_OPENCL);

    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);

    UMat out_umat;
    net.forward(out_umat);
    normAssert(ref, out_umat, "out_umat");

    std::vector<UMat> out_umats;
    net.forward(out_umats);
    normAssert(ref, out_umats[0], "out_umat_vector");
}

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);
}

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

    net.setPreferableBackend(DNN_BACKEND_DEFAULT);
    net.setPreferableTarget(DNN_TARGET_OPENCL);

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

    // Firstly set a wrong input blob and run the model to receive a wrong output.
    net.setInput(input * 2.0f);
    Mat out = net.forward();

    // Then set a correct input blob to check CPU->GPU synchronization is working well.
    net.setInput(input);
    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 = 3e-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);
}

TEST(Test_Caffe, multiple_inputs)
{
    const string proto = findDataFile("dnn/layers/net_input.prototxt", false);
    Net net = readNetFromCaffe(proto);

    Mat first_image(10, 11, CV_32FC3);
    Mat second_image(10, 11, CV_32FC3);
    randu(first_image, -1, 1);
    randu(second_image, -1, 1);

    first_image = blobFromImage(first_image);
    second_image = blobFromImage(second_image);

    Mat first_image_blue_green = slice(first_image, Range::all(), Range(0, 2), Range::all(), Range::all());
    Mat first_image_red = slice(first_image, Range::all(), Range(2, 3), Range::all(), Range::all());
    Mat second_image_blue_green = slice(second_image, Range::all(), Range(0, 2), Range::all(), Range::all());
    Mat second_image_red = slice(second_image, Range::all(), Range(2, 3), Range::all(), Range::all());

    net.setInput(first_image_blue_green, "old_style_input_blue_green");
    net.setInput(first_image_red, "different_name_for_red");
    net.setInput(second_image_blue_green, "input_layer_blue_green");
    net.setInput(second_image_red, "old_style_input_red");
    Mat out = net.forward();

    normAssert(out, first_image + second_image);
}

TEST(Test_Caffe, opencv_face_detector)
{
    std::string proto = findDataFile("dnn/opencv_face_detector.prototxt", false);
    std::string model = findDataFile("dnn/opencv_face_detector.caffemodel", false);

    Net net = readNetFromCaffe(proto, model);
    Mat img = imread(findDataFile("gpu/lbpcascade/er.png", false));
    Mat blob = blobFromImage(img, 1.0, Size(), Scalar(104.0, 177.0, 123.0), false, false);

    net.setInput(blob);
    // Output has shape 1x1xNx7 where N - number of detections.
    // An every detection is a vector of values [id, classId, confidence, left, top, right, bottom]
    Mat out = net.forward();

    Mat ref = (Mat_<float>(6, 5) << 0.99520785, 0.80997437, 0.16379407, 0.87996572, 0.26685631,
                                    0.9934696, 0.2831718, 0.50738752, 0.345781, 0.5985168,
                                    0.99096733, 0.13629119, 0.24892329, 0.19756334, 0.3310290,
                                    0.98977017, 0.23901358, 0.09084064, 0.29902688, 0.1769477,
                                    0.97203469, 0.67965847, 0.06876482, 0.73999709, 0.1513494,
                                    0.95097077, 0.51901293, 0.45863652, 0.5777427, 0.5347801);
    normAssert(out.reshape(1, out.total() / 7).rowRange(0, 6).colRange(2, 7), ref);
}

TEST(Test_Caffe, FasterRCNN_and_RFCN)
{
    std::string models[] = {"VGG16_faster_rcnn_final.caffemodel", "ZF_faster_rcnn_final.caffemodel",
                            "resnet50_rfcn_final.caffemodel"};
    std::string protos[] = {"faster_rcnn_vgg16.prototxt", "faster_rcnn_zf.prototxt",
                            "rfcn_pascal_voc_resnet50.prototxt"};
    Mat refs[] = {(Mat_<float>(3, 6) << 2, 0.949398, 99.2454, 210.141, 601.205, 462.849,
                                        7, 0.997022, 481.841, 92.3218, 722.685, 175.953,
                                        12, 0.993028, 133.221, 189.377, 350.994, 563.166),
                  (Mat_<float>(3, 6) << 2, 0.90121, 120.407, 115.83, 570.586, 528.395,
                                        7, 0.988779, 469.849, 75.1756, 718.64, 186.762,
                                        12, 0.967198, 138.588, 206.843, 329.766, 553.176),
                  (Mat_<float>(2, 6) << 7, 0.991359, 491.822, 81.1668, 702.573, 178.234,
                                        12, 0.94786, 132.093, 223.903, 338.077, 566.16)};
    for (int i = 0; i < 3; ++i)
    {
        std::string proto = findDataFile("dnn/" + protos[i], false);
        std::string model = findDataFile("dnn/" + models[i], false);

        Net net = readNetFromCaffe(proto, model);
        Mat img = imread(findDataFile("dnn/dog416.png", false));
        resize(img, img, Size(800, 600));
        Mat blob = blobFromImage(img, 1.0, Size(), Scalar(102.9801, 115.9465, 122.7717), false, false);
        Mat imInfo = (Mat_<float>(1, 3) << img.rows, img.cols, 1.6f);

        net.setInput(blob, "data");
        net.setInput(imInfo, "im_info");
        // Output has shape 1x1xNx7 where N - number of detections.
        // An every detection is a vector of values [id, classId, confidence, left, top, right, bottom]
        Mat out = net.forward();
        out = out.reshape(1, out.total() / 7);

        Mat detections;
        for (int j = 0; j < out.rows; ++j)
        {
            if (out.at<float>(j, 2) > 0.8)
              detections.push_back(out.row(j).colRange(1, 7));
        }
        normAssert(detections, refs[i], ("model name: " + models[i]).c_str(), 2e-4, 6e-4);
    }
}

}