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  #include "test_precomp.hpp"
  #include "npy_blob.hpp"
  #include <opencv2/dnn/shape_utils.hpp>
  
  namespace opencv_test { namespace {
  
  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, DNNTarget> > 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());
      }
  
      net.setPreferableTarget(get<1>(GetParam()));
  
      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(/**/, Reproducibility_AlexNet, Combine(testing::Bool(), availableDnnTargets()));
  
  #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"));
      normAssertDetections(ref, out);
  }
  
  typedef testing::TestWithParam<DNNTarget> Reproducibility_MobileNet_SSD;
  TEST_P(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.setPreferableTarget(GetParam());
  
      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"));
      normAssertDetections(ref, out);
  
      // Check that detections aren't preserved.
      inp.setTo(0.0f);
      net.setInput(inp);
      out = net.forward();
      out = out.reshape(1, out.total() / 7);
  
      const int numDetections = out.rows;
      ASSERT_NE(numDetections, 0);
      for (int i = 0; i < numDetections; ++i)
      {
          float confidence = out.ptr<float>(i)[2];
          ASSERT_EQ(confidence, 0);
      }
  
      // Check batching mode.
      ref = ref.reshape(1, numDetections);
      inp = blobFromImages(std::vector<Mat>(2, sample), 1.0f / 127.5, Size(300, 300), Scalar(127.5, 127.5, 127.5), false);
      net.setInput(inp);
      Mat outBatch = net.forward();
  
      // Output blob has a shape 1x1x2Nx7 where N is a number of detection for
      // a single sample in batch. The first numbers of detection vectors are batch id.
      outBatch = outBatch.reshape(1, outBatch.total() / 7);
      EXPECT_EQ(outBatch.rows, 2 * numDetections);
      normAssert(outBatch.rowRange(0, numDetections), ref);
      normAssert(outBatch.rowRange(numDetections, 2 * numDetections).colRange(1, 7), ref.colRange(1, 7));
  }
  INSTANTIATE_TEST_CASE_P(/**/, Reproducibility_MobileNet_SSD, availableDnnTargets());
  
  typedef testing::TestWithParam<DNNTarget> Reproducibility_ResNet50;
  TEST_P(Reproducibility_ResNet50, Accuracy)
  {
      Net net = readNetFromCaffe(findDataFile("dnn/ResNet-50-deploy.prototxt", false),
                                 findDataFile("dnn/ResNet-50-model.caffemodel", false));
  
      int targetId = GetParam();
      net.setPreferableTarget(targetId);
  
      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);
  
      if (targetId == DNN_TARGET_OPENCL)
      {
          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");
      }
  }
  INSTANTIATE_TEST_CASE_P(/**/, Reproducibility_ResNet50, availableDnnTargets());
  
  typedef testing::TestWithParam<DNNTarget> Reproducibility_SqueezeNet_v1_1;
  TEST_P(Reproducibility_SqueezeNet_v1_1, Accuracy)
  {
      Net net = readNetFromCaffe(findDataFile("dnn/squeezenet_v1.1.prototxt", false),
                                 findDataFile("dnn/squeezenet_v1.1.caffemodel", false));
  
      int targetId = GetParam();
      net.setPreferableTarget(targetId);
  
      Mat input = blobFromImage(imread(_tf("googlenet_0.png")), 1.0f, Size(227,227), Scalar(), false);
      ASSERT_TRUE(!input.empty());
  
      Mat out;
      if (targetId == DNN_TARGET_OPENCL)
      {
          // Firstly set a wrong input blob and run the model to receive a wrong output.
          // Then set a correct input blob to check CPU->GPU synchronization is working well.
          net.setInput(input * 2.0f);
          out = net.forward();
      }
      net.setInput(input);
      out = net.forward();
  
      Mat ref = blobFromNPY(_tf("squeezenet_v1.1_prob.npy"));
      normAssert(ref, out);
  }
  INSTANTIATE_TEST_CASE_P(/**/, Reproducibility_SqueezeNet_v1_1, availableDnnTargets());
  
  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);
  }
  
  typedef testing::TestWithParam<tuple<std::string, DNNTarget> > opencv_face_detector;
  TEST_P(opencv_face_detector, Accuracy)
  {
      std::string proto = findDataFile("dnn/opencv_face_detector.prototxt", false);
      std::string model = findDataFile(get<0>(GetParam()), false);
      dnn::Target targetId = (dnn::Target)(int)get<1>(GetParam());
  
      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.setPreferableBackend(DNN_BACKEND_DEFAULT);
      net.setPreferableTarget(targetId);
  
      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, 7) << 0, 1, 0.99520785, 0.80997437, 0.16379407, 0.87996572, 0.26685631,
                                      0, 1, 0.9934696, 0.2831718, 0.50738752, 0.345781, 0.5985168,
                                      0, 1, 0.99096733, 0.13629119, 0.24892329, 0.19756334, 0.3310290,
                                      0, 1, 0.98977017, 0.23901358, 0.09084064, 0.29902688, 0.1769477,
                                      0, 1, 0.97203469, 0.67965847, 0.06876482, 0.73999709, 0.1513494,
                                      0, 1, 0.95097077, 0.51901293, 0.45863652, 0.5777427, 0.5347801);
      normAssertDetections(ref, out, "", 0.5, 1e-5, 2e-4);
  }
  INSTANTIATE_TEST_CASE_P(Test_Caffe, opencv_face_detector,
      Combine(
          Values("dnn/opencv_face_detector.caffemodel",
                 "dnn/opencv_face_detector_fp16.caffemodel"),
          Values(DNN_TARGET_CPU, DNN_TARGET_OPENCL)
      )
  );
  
  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, 7) << 0, 2, 0.949398, 99.2454, 210.141, 601.205, 462.849,
                                          0, 7, 0.997022, 481.841, 92.3218, 722.685, 175.953,
                                          0, 12, 0.993028, 133.221, 189.377, 350.994, 563.166),
                    (Mat_<float>(3, 7) << 0, 2, 0.90121, 120.407, 115.83, 570.586, 528.395,
                                          0, 7, 0.988779, 469.849, 75.1756, 718.64, 186.762,
                                          0, 12, 0.967198, 138.588, 206.843, 329.766, 553.176),
                    (Mat_<float>(2, 7) << 0, 7, 0.991359, 491.822, 81.1668, 702.573, 178.234,
                                          0, 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();
          normAssertDetections(refs[i], out, ("model name: " + models[i]).c_str(), 0.8);
      }
  }
  
  }} // namespace
  