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#include "open3d/t/geometry/VoxelBlockGrid.h"

#include "core/CoreTest.h"
#include "open3d/core/EigenConverter.h"
#include "open3d/core/Tensor.h"
#include "open3d/data/Dataset.h"
#include "open3d/io/PinholeCameraTrajectoryIO.h"
#include "open3d/io/TriangleMeshIO.h"
#include "open3d/t/io/ImageIO.h"
#include "open3d/t/io/NumpyIO.h"
#include "open3d/utility/FileSystem.h"
#include "open3d/visualization/utility/DrawGeometry.h"

namespace open3d {
namespace tests {

using namespace t::geometry;

class VoxelBlockGridPermuteDevices : public PermuteDevices {};
INSTANTIATE_TEST_SUITE_P(VoxelBlockGrid,
                         VoxelBlockGridPermuteDevices,
                         testing::ValuesIn(PermuteDevices::TestCases()));

static core::Tensor GetIntrinsicTensor() {
    camera::PinholeCameraIntrinsic intrinsic = camera::PinholeCameraIntrinsic(
            camera::PinholeCameraIntrinsicParameters::PrimeSenseDefault);
    auto focal_length = intrinsic.GetFocalLength();
    auto principal_point = intrinsic.GetPrincipalPoint();
    return core::Tensor::Init<double>(
            {{focal_length.first, 0, principal_point.first},
             {0, focal_length.second, principal_point.second},
             {0, 0, 1}});
}

static std::vector<core::Tensor> GetExtrinsicTensors() {
    data::SampleRedwoodRGBDImages redwood_data;

    // Extrinsics
    auto trajectory = io::CreatePinholeCameraTrajectoryFromFile(
            redwood_data.GetOdometryLogPath());

    std::vector<core::Tensor> extrinsics;
    for (size_t i = 0; i < trajectory->parameters_.size(); ++i) {
        Eigen::Matrix4d extrinsic = trajectory->parameters_[i].extrinsic_;
        core::Tensor extrinsic_t =
                core::eigen_converter::EigenMatrixToTensor(extrinsic);
        extrinsics.emplace_back(extrinsic_t);
    }

    return extrinsics;
}

static std::vector<core::HashBackendType> EnumerateBackends(
        const core::Device &device, bool include_slab = true) {
    std::vector<core::HashBackendType> backends;
    if (device.IsCUDA()) {
        if (include_slab) {
            backends.push_back(core::HashBackendType::Slab);
        }
        backends.push_back(core::HashBackendType::StdGPU);
    } else {
        backends.push_back(core::HashBackendType::TBB);
    }
    return backends;
}

static VoxelBlockGrid Integrate(const core::HashBackendType &backend,
                                const core::Dtype &dtype,
                                const core::Device &device,
                                const int resolution) {
    core::Tensor intrinsic = GetIntrinsicTensor();
    std::vector<core::Tensor> extrinsics = GetExtrinsicTensors();
    const float depth_scale = 1000.0;
    const float depth_max = 3.0;

    auto vbg = VoxelBlockGrid({"tsdf", "weight", "color"},
                              {core::Float32, dtype, dtype}, {{1}, {1}, {3}},
                              3.0 / 512, resolution, 10000, device, backend);

    data::SampleRedwoodRGBDImages redwood_data;
    for (size_t i = 0; i < extrinsics.size(); ++i) {
        Image depth =
                t::io::CreateImageFromFile(redwood_data.GetDepthPaths()[i])
                        ->To(device);
        Image color =
                t::io::CreateImageFromFile(redwood_data.GetColorPaths()[i])
                        ->To(device);

        core::Tensor frustum_block_coords = vbg.GetUniqueBlockCoordinates(
                depth, intrinsic, extrinsics[i], depth_scale, depth_max,
                /*trunc_multiplier=*/4.0);
        vbg.Integrate(frustum_block_coords, depth, color, intrinsic,
                      extrinsics[i], depth_scale, depth_max,
                      /*trunc multiplier*/ resolution * 0.5);
    }

    return vbg;
}

TEST_P(VoxelBlockGridPermuteDevices, Construct) {
    core::Device device = GetParam();
    std::vector<core::HashBackendType> backends = EnumerateBackends(device);

    for (auto backend : backends) {
        auto vbg = VoxelBlockGrid({"tsdf", "weight", "color"},
                                  {core::Float32, core::UInt16, core::UInt8},
                                  {{1}, {1}, {3}}, 3.0 / 512, 8,
                                  /*  init capacity = */ 10, device, backend);

        auto tsdf_tensor = vbg.GetAttribute("tsdf");
        auto weight_tensor = vbg.GetAttribute("weight");
        auto color_tensor = vbg.GetAttribute("color");

        EXPECT_EQ(tsdf_tensor.GetShape(), core::SizeVector({10, 8, 8, 8, 1}));
        EXPECT_EQ(tsdf_tensor.GetDtype(), core::Dtype::Float32);

        EXPECT_EQ(weight_tensor.GetShape(), core::SizeVector({10, 8, 8, 8, 1}));
        EXPECT_EQ(weight_tensor.GetDtype(), core::Dtype::UInt16);

        EXPECT_EQ(color_tensor.GetShape(), core::SizeVector({10, 8, 8, 8, 3}));
        EXPECT_EQ(color_tensor.GetDtype(), core::Dtype::UInt8);
    }
}

TEST_P(VoxelBlockGridPermuteDevices, Exceptions) {
    core::Tensor intrinsic = GetIntrinsicTensor();
    std::vector<core::Tensor> extrinsics = GetExtrinsicTensors();
    float depth_scale = 1000.0;
    float depth_max = 3.0;

    data::SampleRedwoodRGBDImages redwood_data;
    Image depth = *t::io::CreateImageFromFile(redwood_data.GetDepthPaths()[0]);

    Image color = *t::io::CreateImageFromFile(redwood_data.GetColorPaths()[0]);

    auto vbg = VoxelBlockGrid();
    EXPECT_THROW(vbg.GetUniqueBlockCoordinates(depth, intrinsic, extrinsics[0],
                                               depth_scale, depth_max),
                 std::runtime_error);
    EXPECT_THROW(vbg.Integrate(core::Tensor(), depth, color, intrinsic,
                               extrinsics[0]),
                 std::runtime_error);

    EXPECT_THROW(vbg.ExtractTriangleMesh(), std::runtime_error);
    EXPECT_THROW(vbg.ExtractPointCloud(), std::runtime_error);
}

TEST_P(VoxelBlockGridPermuteDevices, Indexing) {
    core::Device device = GetParam();
    std::vector<core::HashBackendType> backends = EnumerateBackends(device);

    for (auto backend : backends) {
        auto vbg = VoxelBlockGrid({"tsdf", "weight", "color"},
                                  {core::Float32, core::UInt16, core::UInt8},
                                  {{1}, {1}, {3}}, 3.0 / 512, 2, 10, device,
                                  backend);

        auto hashmap = vbg.GetHashMap();

        // Unique Coordinates: (-1, 3, 2), (0, 2, 4), (1, 2, 3)
        core::Tensor keys = core::Tensor(
                std::vector<int>{-1, 3, 2, 0, 2, 4, -1, 3, 2, 0, 2, 4, 1, 2, 3},
                core::SizeVector{5, 3}, core::Dtype::Int32, device);

        core::Tensor buf_indices, masks;
        hashmap.Activate(keys, buf_indices, masks);
        buf_indices = buf_indices.IndexGet({masks});
        EXPECT_EQ(buf_indices.GetLength(), 3);

        // Non-flattened version, recommended for debugging
        int entries_per_block = 2 * 2 * 2;
        core::Tensor voxel_indices = vbg.GetVoxelIndices(buf_indices);
        EXPECT_EQ(voxel_indices.GetShape(),
                  core::SizeVector({4, 3 * entries_per_block}));

        core::Tensor voxel_coords = vbg.GetVoxelCoordinates(voxel_indices);
        EXPECT_EQ(voxel_coords.GetShape(),
                  core::SizeVector({3, 3 * entries_per_block}));

        // Flattened version, recommended for performance
        std::tie(voxel_coords, voxel_indices) =
                vbg.GetVoxelCoordinatesAndFlattenedIndices();
        EXPECT_EQ(voxel_coords.GetShape(),
                  core::SizeVector({3 * entries_per_block, 3}));
        EXPECT_EQ(voxel_indices.GetShape(),
                  core::SizeVector({3 * entries_per_block}));
    }
}

TEST_P(VoxelBlockGridPermuteDevices, GetUniqueBlockCoordinates) {
    core::Device device = GetParam();
    std::vector<core::HashBackendType> backends = EnumerateBackends(device);

    core::Tensor intrinsic = GetIntrinsicTensor();
    std::vector<core::Tensor> extrinsics = GetExtrinsicTensors();
    const float depth_scale = 1000.0;
    const float depth_max = 3.0;
    const float trunc_voxel_multiplier = 4.0;
    for (auto backend : backends) {
        auto vbg = VoxelBlockGrid({"tsdf", "weight", "color"},
                                  {core::Float32, core::Float32, core::UInt16},
                                  {{1}, {1}, {3}}, 3.0 / 512, 8, 10000, device,
                                  backend);

        const int i = 0;
        data::SampleRedwoodRGBDImages redwood_data;
        Image depth =
                t::io::CreateImageFromFile(redwood_data.GetDepthPaths()[i])
                        ->To(device);
        core::Tensor block_coords_from_depth = vbg.GetUniqueBlockCoordinates(
                depth, intrinsic, extrinsics[i], depth_scale, depth_max,
                trunc_voxel_multiplier);

        PointCloud pcd = PointCloud::CreateFromDepthImage(
                depth, intrinsic, extrinsics[i], depth_scale, depth_max, 4);
        core::Tensor block_coords_from_pcd =
                vbg.GetUniqueBlockCoordinates(pcd, trunc_voxel_multiplier);

        // Hard-coded result -- implementation could change,
        // freeze result of test_data when stable.
        EXPECT_EQ(block_coords_from_depth.GetLength(), 4873);
        EXPECT_EQ(block_coords_from_pcd.GetLength(), 7491);
    }
}

TEST_P(VoxelBlockGridPermuteDevices, Integrate) {
    core::Device device = GetParam();
    std::vector<core::HashBackendType> backends = EnumerateBackends(device);

    // Again, hard-coded result
    std::unordered_map<int, int> kResolutionPoints = {{8, 225628},
                                                      {16, 254787}};
    std::unordered_map<int, int> kResolutionVertices = {{8, 223075},
                                                        {16, 254339}};
    std::unordered_map<int, int> kResolutionTriangles = {{8, 409271},
                                                         {16, 490301}};

    for (auto backend : backends) {
        for (int block_resolution : std::vector<int>{8, 16}) {
            for (auto &dtype :
                 std::vector<core::Dtype>{core::Float32, core::UInt16}) {
                auto vbg = Integrate(backend, dtype, device, block_resolution);

                // Allow numerical precision differences
                auto pcd = vbg.ExtractPointCloud();
                EXPECT_NEAR(pcd.GetPointPositions().GetLength(),
                            kResolutionPoints[block_resolution], 3);

                auto mesh = vbg.ExtractTriangleMesh();
                EXPECT_NEAR(mesh.GetVertexPositions().GetLength(),
                            kResolutionVertices[block_resolution], 3);
                EXPECT_NEAR(mesh.GetTriangleIndices().GetLength(),
                            kResolutionTriangles[block_resolution], 6);
            }
        }
    }
}

TEST_P(VoxelBlockGridPermuteDevices, IO) {
    core::Device device = GetParam();
    std::vector<core::HashBackendType> backends = EnumerateBackends(device);

    std::string file_name = "tmp.npz";
    for (auto backend : backends) {
        auto vbg = Integrate(backend, core::UInt16, device, 16);
        vbg.Save(file_name);

        EXPECT_TRUE(utility::filesystem::FileExists(file_name));
        auto pcd = vbg.ExtractPointCloud();

        auto vbg_loaded = VoxelBlockGrid::Load(file_name);
        auto pcd_loaded = vbg_loaded.ExtractPointCloud();

        EXPECT_EQ(pcd.GetPointPositions().GetLength(),
                  pcd_loaded.GetPointPositions().GetLength());
        utility::filesystem::RemoveFile(file_name);
    }
}

TEST_P(VoxelBlockGridPermuteDevices, RayCasting) {
    core::Device device = GetParam();
    std::vector<core::HashBackendType> backends =
            EnumerateBackends(device, /* include_slab = */ false);

    core::Tensor intrinsic = GetIntrinsicTensor();
    std::vector<core::Tensor> extrinsics = GetExtrinsicTensors();
    const float depth_scale = 1000.0;
    const float depth_min = 0.1;
    const float depth_max = 3.0;

    for (auto backend : backends) {
        for (auto &dtype :
             std::vector<core::Dtype>{core::Float32, core::UInt16}) {
            auto vbg = Integrate(backend, dtype, device,
                                 /* block_resolution = */ 8);

            int i = extrinsics.size() - 1;

            data::SampleRedwoodRGBDImages redwood_data;
            Image depth =
                    t::io::CreateImageFromFile(redwood_data.GetDepthPaths()[i])
                            ->To(device);
            core::Tensor frustum_block_coords = vbg.GetUniqueBlockCoordinates(
                    depth, intrinsic, extrinsics[i], depth_scale, depth_max);

            // Select sets
            auto result_odometry =
                    vbg.RayCast(frustum_block_coords, intrinsic, extrinsics[i],
                                depth.GetCols(), depth.GetRows(),
                                {"vertex", "normal", "depth"}, depth_scale,
                                depth_min, depth_max, 1.0);
            EXPECT_TRUE(result_odometry.Contains("vertex"));
            EXPECT_TRUE(result_odometry.Contains("normal"));
            EXPECT_TRUE(result_odometry.Contains("depth"));

            auto result_rendering = vbg.RayCast(
                    frustum_block_coords, intrinsic, extrinsics[i],
                    depth.GetCols(), depth.GetRows(), {"depth", "color"},
                    depth_scale, depth_min, depth_max, 1.0);
            EXPECT_TRUE(result_rendering.Contains("depth"));
            EXPECT_TRUE(result_rendering.Contains("color"));

            auto result_diff_rendering = vbg.RayCast(
                    frustum_block_coords, intrinsic, extrinsics[i],
                    depth.GetCols(), depth.GetRows(),
                    {"index", "mask", "interp_ratio", "interp_ratio_dx",
                     "interp_ratio_dy", "interp_ratio_dz"},
                    depth_scale, depth_min, depth_max, 1.0);
            EXPECT_TRUE(result_diff_rendering.Contains("index"));
            EXPECT_TRUE(result_diff_rendering.Contains("mask"));
            EXPECT_TRUE(result_diff_rendering.Contains("interp_ratio"));

            EXPECT_TRUE(result_diff_rendering.Contains("interp_ratio_dx"));
            EXPECT_TRUE(result_diff_rendering.Contains("interp_ratio_dy"));
            EXPECT_TRUE(result_diff_rendering.Contains("interp_ratio_dz"));
        }
    }
}

TEST_P(VoxelBlockGridPermuteDevices, DISABLED_RayCastingVisualize) {
    core::Device device = GetParam();
    std::vector<core::HashBackendType> backends =
            EnumerateBackends(device, /* include_slab = */ false);

    core::Tensor intrinsic = GetIntrinsicTensor();
    std::vector<core::Tensor> extrinsics = GetExtrinsicTensors();
    const float depth_scale = 1000.0;
    const float depth_min = 0.1;
    const float depth_max = 3.0;

    for (auto backend : backends) {
        for (auto &dtype : std::vector<core::Dtype>{core::Float32}) {
            auto vbg = Integrate(backend, dtype, device,
                                 /* block_resolution = */ 8);

            int i = extrinsics.size() - 1;
            data::SampleRedwoodRGBDImages redwood_data;
            Image depth =
                    t::io::CreateImageFromFile(redwood_data.GetDepthPaths()[i])
                            ->To(device);
            core::Tensor frustum_block_coords = vbg.GetUniqueBlockCoordinates(
                    depth, intrinsic, extrinsics[i], depth_scale, depth_max);

            int width = depth.GetCols();
            int height = depth.GetRows();

            // Select sets
            auto result =
                    vbg.RayCast(frustum_block_coords, intrinsic, extrinsics[i],
                                width, height,
                                {"vertex", "normal", "depth", "color", "index",
                                 "mask", "interp_ratio", "interp_ratio_dx",
                                 "interp_ratio_dy", "interp_ratio_dz"},
                                depth_scale, depth_min, depth_max, 1.0);

            auto to_legacy_ptr = [=](const Image &im_t) {
                return std::make_shared<open3d::geometry::Image>(
                        im_t.ToLegacy());
            };

            // Conventional rendering
            visualization::DrawGeometries(
                    {to_legacy_ptr(Image(result["vertex"]))});
            visualization::DrawGeometries(
                    {to_legacy_ptr(Image(result["normal"]))});
            visualization::DrawGeometries({to_legacy_ptr(
                    Image(result["depth"]).ColorizeDepth(1000.0, 0, 4))});
            visualization::DrawGeometries(
                    {to_legacy_ptr(Image(result["color"]))});

            // Differentiable rendering

            // Render color
            auto color_tensor = vbg.GetAttribute("color").Reshape({-1, 3});

            // (H * W * 8)
            core::Tensor nb_indices =
                    result["index"].Reshape(core::SizeVector({-1}));

            // (H * W * 8, 3)
            core::Tensor nb_colors = color_tensor.IndexGet({nb_indices});

            // (H * W * 8, 1)
            core::Tensor nb_interp_ratio =
                    result["interp_ratio"].Reshape(core::SizeVector({-1, 1}));

            // (H, W, 3)
            core::Tensor nb_sum_color =
                    (nb_colors * nb_interp_ratio)
                            .Reshape(core::SizeVector({height, width, 8, 3}))
                            .Sum({2});

            visualization::DrawGeometries(
                    {to_legacy_ptr(Image(nb_sum_color / 255.0))});

            // Render normal
            auto tsdf_tensor = vbg.GetAttribute("tsdf").Reshape({-1, 1});

            // (H * W * 8, 1)
            core::Tensor nb_tsdfs = tsdf_tensor.IndexGet({nb_indices});
            core::Tensor nb_interp_ratio_dx = result["interp_ratio_dx"].Reshape(
                    core::SizeVector({-1, 1}));
            core::Tensor nb_interp_ratio_dy = result["interp_ratio_dy"].Reshape(
                    core::SizeVector({-1, 1}));
            core::Tensor nb_interp_ratio_dz = result["interp_ratio_dz"].Reshape(
                    core::SizeVector({-1, 1}));

            // (H * W * 8, 1)
            core::Tensor nx = nb_interp_ratio_dx * nb_tsdfs;
            core::Tensor ny = nb_interp_ratio_dy * nb_tsdfs;
            core::Tensor nz = nb_interp_ratio_dz * nb_tsdfs;

            // (H * W) x 3
            nx = nx.Reshape(core::SizeVector({height * width, 8})).Sum({1});
            ny = ny.Reshape(core::SizeVector({height * width, 8})).Sum({1});
            nz = nz.Reshape(core::SizeVector({height * width, 8})).Sum({1});
            core::Tensor norm = (nx * nx + ny * ny + nz * nz).Sqrt();
            nx = nx / norm;
            ny = ny / norm;
            nz = nz / norm;

            core::Tensor normals = core::Tensor({3, height * width},
                                                core::Dtype::Float32, device);
            normals.SetItem({core::TensorKey::Index(0)}, nx);
            normals.SetItem({core::TensorKey::Index(1)}, ny);
            normals.SetItem({core::TensorKey::Index(2)}, nz);
            normals =
                    normals.T().Reshape({height, width, 3}).Contiguous().Neg_();
            visualization::DrawGeometries({to_legacy_ptr(normals)});
        }
    }
}

}  // namespace tests
}  // namespace open3d