import numpy as np
import os
import pytest
import tempfile
import torch
from mmcv.parallel import MMDataParallel
from os.path import dirname, exists, join

from mmdet3d.apis import (convert_SyncBN, inference_detector,
                          inference_mono_3d_detector,
                          inference_multi_modality_detector,
                          inference_segmentor, init_model, show_result_meshlab,
                          single_gpu_test)
from mmdet3d.core import Box3DMode
from mmdet3d.core.bbox import (CameraInstance3DBoxes, DepthInstance3DBoxes,
                               LiDARInstance3DBoxes)
from mmdet3d.datasets import build_dataloader, build_dataset
from mmdet3d.models import build_model


def _get_config_directory():
    """Find the predefined detector config directory."""
    try:
        # Assume we are running in the source mmdetection3d repo
        repo_dpath = dirname(dirname(dirname(__file__)))
    except NameError:
        # For IPython development when this __file__ is not defined
        import mmdet3d
        repo_dpath = dirname(dirname(mmdet3d.__file__))
    config_dpath = join(repo_dpath, 'configs')
    if not exists(config_dpath):
        raise Exception('Cannot find config path')
    return config_dpath


def _get_config_module(fname):
    """Load a configuration as a python module."""
    from mmcv import Config
    config_dpath = _get_config_directory()
    config_fpath = join(config_dpath, fname)
    config_mod = Config.fromfile(config_fpath)
    return config_mod


def test_convert_SyncBN():
    cfg = _get_config_module(
        'pointpillars/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d.py')
    model_cfg = cfg.model
    convert_SyncBN(model_cfg)
    assert model_cfg['pts_voxel_encoder']['norm_cfg']['type'] == 'BN1d'
    assert model_cfg['pts_backbone']['norm_cfg']['type'] == 'BN2d'
    assert model_cfg['pts_neck']['norm_cfg']['type'] == 'BN2d'


def test_show_result_meshlab():
    pcd = 'tests/data/nuscenes/samples/LIDAR_TOP/n015-2018-08-02-17-16-37+' \
              '0800__LIDAR_TOP__1533201470948018.pcd.bin'
    box_3d = LiDARInstance3DBoxes(
        torch.tensor(
            [[8.7314, -1.8559, -1.5997, 0.4800, 1.2000, 1.8900, 0.0100]]))
    labels_3d = torch.tensor([0])
    scores_3d = torch.tensor([0.5])
    points = np.random.rand(100, 4)
    img_meta = dict(
        pts_filename=pcd, boxes_3d=box_3d, box_mode_3d=Box3DMode.LIDAR)
    data = dict(points=[[torch.tensor(points)]], img_metas=[[img_meta]])
    result = [
        dict(
            pts_bbox=dict(
                boxes_3d=box_3d, labels_3d=labels_3d, scores_3d=scores_3d))
    ]
    tmp_dir = tempfile.TemporaryDirectory()
    temp_out_dir = tmp_dir.name
    out_dir, file_name = show_result_meshlab(data, result, temp_out_dir)
    expected_outfile_pred = file_name + '_pred.obj'
    expected_outfile_pts = file_name + '_points.obj'
    expected_outfile_pred_path = os.path.join(out_dir, file_name,
                                              expected_outfile_pred)
    expected_outfile_pts_path = os.path.join(out_dir, file_name,
                                             expected_outfile_pts)
    assert os.path.exists(expected_outfile_pred_path)
    assert os.path.exists(expected_outfile_pts_path)
    tmp_dir.cleanup()

    # test multi-modality show
    # indoor scene
    pcd = 'tests/data/sunrgbd/points/000001.bin'
    filename = 'tests/data/sunrgbd/sunrgbd_trainval/image/000001.jpg'
    box_3d = DepthInstance3DBoxes(
        torch.tensor(
            [[-1.1580, 3.3041, -0.9961, 0.3829, 0.4647, 0.5574, 1.1213]]))
    img = np.random.randn(1, 3, 608, 832)
    K = np.array([[[529.5000, 0.0000, 365.0000], [0.0000, 529.5000, 265.0000],
                   [0.0000, 0.0000, 1.0000]]])
    Rt = torch.tensor([[[0.9980, 0.0058, -0.0634], [0.0058, 0.9835, 0.1808],
                        [0.0634, -0.1808, 0.9815]]])
    img_meta = dict(
        filename=filename,
        pcd_horizontal_flip=False,
        pcd_vertical_flip=False,
        box_mode_3d=Box3DMode.DEPTH,
        box_type_3d=DepthInstance3DBoxes,
        pcd_trans=np.array([0., 0., 0.]),
        pcd_scale_factor=1.0,
        pts_filename=pcd,
        transformation_3d_flow=['R', 'S', 'T'])
    calib = dict(K=K, Rt=Rt)
    data = dict(
        points=[[torch.tensor(points)]],
        img_metas=[[img_meta]],
        img=[img],
        calib=[calib])
    result = [dict(boxes_3d=box_3d, labels_3d=labels_3d, scores_3d=scores_3d)]
    tmp_dir = tempfile.TemporaryDirectory()
    temp_out_dir = tmp_dir.name
    out_dir, file_name = show_result_meshlab(
        data, result, temp_out_dir, 0.3, task='multi_modality-det')
    expected_outfile_pred = file_name + '_pred.obj'
    expected_outfile_pts = file_name + '_points.obj'
    expected_outfile_png = file_name + '_img.png'
    expected_outfile_proj = file_name + '_pred.png'
    expected_outfile_pred_path = os.path.join(out_dir, file_name,
                                              expected_outfile_pred)
    expected_outfile_pts_path = os.path.join(out_dir, file_name,
                                             expected_outfile_pts)
    expected_outfile_png_path = os.path.join(out_dir, file_name,
                                             expected_outfile_png)
    expected_outfile_proj_path = os.path.join(out_dir, file_name,
                                              expected_outfile_proj)
    assert os.path.exists(expected_outfile_pred_path)
    assert os.path.exists(expected_outfile_pts_path)
    assert os.path.exists(expected_outfile_png_path)
    assert os.path.exists(expected_outfile_proj_path)
    tmp_dir.cleanup()
    # outdoor scene
    pcd = 'tests/data/kitti/training/velodyne_reduced/000000.bin'
    filename = 'tests/data/kitti/training/image_2/000000.png'
    box_3d = LiDARInstance3DBoxes(
        torch.tensor(
            [[6.4495, -3.9097, -1.7409, 1.5063, 3.1819, 1.4716, 1.8782]]))
    img = np.random.randn(1, 3, 384, 1280)
    lidar2img = np.array(
        [[6.09695435e+02, -7.21421631e+02, -1.25125790e+00, -1.23041824e+02],
         [1.80384201e+02, 7.64479828e+00, -7.19651550e+02, -1.01016693e+02],
         [9.99945343e-01, 1.24365499e-04, 1.04513029e-02, -2.69386917e-01],
         [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.00000000e+00]])
    img_meta = dict(
        filename=filename,
        pcd_horizontal_flip=False,
        pcd_vertical_flip=False,
        box_mode_3d=Box3DMode.LIDAR,
        box_type_3d=LiDARInstance3DBoxes,
        pcd_trans=np.array([0., 0., 0.]),
        pcd_scale_factor=1.0,
        pts_filename=pcd,
        lidar2img=lidar2img)
    data = dict(
        points=[[torch.tensor(points)]], img_metas=[[img_meta]], img=[img])
    result = [
        dict(
            pts_bbox=dict(
                boxes_3d=box_3d, labels_3d=labels_3d, scores_3d=scores_3d))
    ]
    out_dir, file_name = show_result_meshlab(
        data, result, temp_out_dir, 0.1, task='multi_modality-det')
    tmp_dir = tempfile.TemporaryDirectory()
    temp_out_dir = tmp_dir.name
    expected_outfile_pred = file_name + '_pred.obj'
    expected_outfile_pts = file_name + '_points.obj'
    expected_outfile_png = file_name + '_img.png'
    expected_outfile_proj = file_name + '_pred.png'
    expected_outfile_pred_path = os.path.join(out_dir, file_name,
                                              expected_outfile_pred)
    expected_outfile_pts_path = os.path.join(out_dir, file_name,
                                             expected_outfile_pts)
    expected_outfile_png_path = os.path.join(out_dir, file_name,
                                             expected_outfile_png)
    expected_outfile_proj_path = os.path.join(out_dir, file_name,
                                              expected_outfile_proj)
    assert os.path.exists(expected_outfile_pred_path)
    assert os.path.exists(expected_outfile_pts_path)
    assert os.path.exists(expected_outfile_png_path)
    assert os.path.exists(expected_outfile_proj_path)
    tmp_dir.cleanup()
    # test mono-3d show
    filename = 'tests/data/nuscenes/samples/CAM_BACK_LEFT/n015-2018-' \
               '07-18-11-07-57+0800__CAM_BACK_LEFT__1531883530447423.jpg'
    box_3d = CameraInstance3DBoxes(
        torch.tensor(
            [[6.4495, -3.9097, -1.7409, 1.5063, 3.1819, 1.4716, 1.8782]]))
    img = np.random.randn(1, 3, 384, 1280)
    cam_intrinsic = np.array([[100.0, 0.0, 50.0], [0.0, 100.0, 50.0],
                              [0.0, 0.0, 1.0]])
    img_meta = dict(
        filename=filename,
        pcd_horizontal_flip=False,
        pcd_vertical_flip=False,
        box_mode_3d=Box3DMode.CAM,
        box_type_3d=CameraInstance3DBoxes,
        pcd_trans=np.array([0., 0., 0.]),
        pcd_scale_factor=1.0,
        cam_intrinsic=cam_intrinsic)
    data = dict(
        points=[[torch.tensor(points)]], img_metas=[[img_meta]], img=[img])
    result = [
        dict(
            img_bbox=dict(
                boxes_3d=box_3d, labels_3d=labels_3d, scores_3d=scores_3d))
    ]
    out_dir, file_name = show_result_meshlab(
        data, result, temp_out_dir, 0.1, task='mono-det')
    tmp_dir = tempfile.TemporaryDirectory()
    temp_out_dir = tmp_dir.name
    expected_outfile_png = file_name + '_img.png'
    expected_outfile_proj = file_name + '_pred.png'
    expected_outfile_png_path = os.path.join(out_dir, file_name,
                                             expected_outfile_png)
    expected_outfile_proj_path = os.path.join(out_dir, file_name,
                                              expected_outfile_proj)
    assert os.path.exists(expected_outfile_png_path)
    assert os.path.exists(expected_outfile_proj_path)
    tmp_dir.cleanup()

    # test seg show
    pcd = 'tests/data/scannet/points/scene0000_00.bin'
    points = np.random.rand(100, 6)
    img_meta = dict(pts_filename=pcd)
    data = dict(points=[[torch.tensor(points)]], img_metas=[[img_meta]])
    pred_seg = torch.randint(0, 20, (100, ))
    result = [dict(semantic_mask=pred_seg)]
    tmp_dir = tempfile.TemporaryDirectory()
    temp_out_dir = tmp_dir.name
    out_dir, file_name = show_result_meshlab(
        data, result, temp_out_dir, task='seg')
    expected_outfile_pred = file_name + '_pred.obj'
    expected_outfile_pts = file_name + '_points.obj'
    expected_outfile_pred_path = os.path.join(out_dir, file_name,
                                              expected_outfile_pred)
    expected_outfile_pts_path = os.path.join(out_dir, file_name,
                                             expected_outfile_pts)
    assert os.path.exists(expected_outfile_pred_path)
    assert os.path.exists(expected_outfile_pts_path)
    tmp_dir.cleanup()


def test_inference_detector():
    pcd = 'tests/data/kitti/training/velodyne_reduced/000000.bin'
    detector_cfg = 'configs/pointpillars/hv_pointpillars_secfpn_' \
                   '6x8_160e_kitti-3d-3class.py'
    detector = init_model(detector_cfg, device='cpu')
    results = inference_detector(detector, pcd)
    bboxes_3d = results[0][0]['boxes_3d']
    scores_3d = results[0][0]['scores_3d']
    labels_3d = results[0][0]['labels_3d']
    assert bboxes_3d.tensor.shape[0] >= 0
    assert bboxes_3d.tensor.shape[1] == 7
    assert scores_3d.shape[0] >= 0
    assert labels_3d.shape[0] >= 0


def test_inference_multi_modality_detector():
    # these two multi-modality models both only have GPU implementations
    if not torch.cuda.is_available():
        pytest.skip('test requires GPU and torch+cuda')
    # indoor scene
    pcd = 'tests/data/sunrgbd/points/000001.bin'
    img = 'tests/data/sunrgbd/sunrgbd_trainval/image/000001.jpg'
    ann_file = 'tests/data/sunrgbd/sunrgbd_infos.pkl'
    detector_cfg = 'configs/imvotenet/imvotenet_stage2_'\
                   '16x8_sunrgbd-3d-10class.py'
    detector = init_model(detector_cfg, device='cuda:0')
    results = inference_multi_modality_detector(detector, pcd, img, ann_file)
    bboxes_3d = results[0][0]['boxes_3d']
    scores_3d = results[0][0]['scores_3d']
    labels_3d = results[0][0]['labels_3d']
    assert bboxes_3d.tensor.shape[0] >= 0
    assert bboxes_3d.tensor.shape[1] == 7
    assert scores_3d.shape[0] >= 0
    assert labels_3d.shape[0] >= 0

    # outdoor scene
    pcd = 'tests/data/kitti/training/velodyne_reduced/000000.bin'
    img = 'tests/data/kitti/training/image_2/000000.png'
    ann_file = 'tests/data/kitti/kitti_infos_train.pkl'
    detector_cfg = 'configs/mvxnet/dv_mvx-fpn_second_secfpn_adamw_' \
                   '2x8_80e_kitti-3d-3class.py'
    detector = init_model(detector_cfg, device='cuda:0')
    results = inference_multi_modality_detector(detector, pcd, img, ann_file)
    bboxes_3d = results[0][0]['pts_bbox']['boxes_3d']
    scores_3d = results[0][0]['pts_bbox']['scores_3d']
    labels_3d = results[0][0]['pts_bbox']['labels_3d']
    assert bboxes_3d.tensor.shape[0] >= 0
    assert bboxes_3d.tensor.shape[1] == 7
    assert scores_3d.shape[0] >= 0
    assert labels_3d.shape[0] >= 0


def test_inference_mono_3d_detector():
    # FCOS3D only has GPU implementations
    if not torch.cuda.is_available():
        pytest.skip('test requires GPU and torch+cuda')
    img = 'tests/data/nuscenes/samples/CAM_BACK_LEFT/' \
          'n015-2018-07-18-11-07-57+0800__CAM_BACK_LEFT__1531883530447423.jpg'
    ann_file = 'tests/data/nuscenes/nus_infos_mono3d.coco.json'
    detector_cfg = 'configs/fcos3d/fcos3d_r101_caffe_fpn_gn-head_dcn_' \
                   '2x8_1x_nus-mono3d.py'
    detector = init_model(detector_cfg, device='cuda:0')
    results = inference_mono_3d_detector(detector, img, ann_file)
    bboxes_3d = results[0][0]['img_bbox']['boxes_3d']
    scores_3d = results[0][0]['img_bbox']['scores_3d']
    labels_3d = results[0][0]['img_bbox']['labels_3d']
    assert bboxes_3d.tensor.shape[0] >= 0
    assert bboxes_3d.tensor.shape[1] == 9
    assert scores_3d.shape[0] >= 0
    assert labels_3d.shape[0] >= 0


def test_inference_segmentor():
    # PN2 only has GPU implementations
    if not torch.cuda.is_available():
        pytest.skip('test requires GPU and torch+cuda')
    pcd = 'tests/data/scannet/points/scene0000_00.bin'
    segmentor_cfg = 'configs/pointnet2/pointnet2_ssg_' \
                    '16x2_cosine_200e_scannet_seg-3d-20class.py'
    segmentor = init_model(segmentor_cfg, device='cuda:0')
    results = inference_segmentor(segmentor, pcd)
    seg_3d = results[0][0]['semantic_mask']
    assert seg_3d.shape == torch.Size([100])
    assert seg_3d.min() >= 0
    assert seg_3d.max() <= 19


def test_single_gpu_test():
    if not torch.cuda.is_available():
        pytest.skip('test requires GPU and torch+cuda')
    cfg = _get_config_module('votenet/votenet_16x8_sunrgbd-3d-10class.py')
    cfg.model.train_cfg = None
    model = build_model(cfg.model, test_cfg=cfg.get('test_cfg'))
    dataset_cfg = cfg.data.test
    dataset_cfg.data_root = './tests/data/sunrgbd'
    dataset_cfg.ann_file = 'tests/data/sunrgbd/sunrgbd_infos.pkl'
    dataset = build_dataset(dataset_cfg)
    data_loader = build_dataloader(
        dataset,
        samples_per_gpu=1,
        workers_per_gpu=cfg.data.workers_per_gpu,
        dist=False,
        shuffle=False)
    model = MMDataParallel(model, device_ids=[0])
    results = single_gpu_test(model, data_loader)
    bboxes_3d = results[0]['boxes_3d']
    scores_3d = results[0]['scores_3d']
    labels_3d = results[0]['labels_3d']
    assert bboxes_3d.tensor.shape[0] >= 0
    assert bboxes_3d.tensor.shape[1] == 7
    assert scores_3d.shape[0] >= 0
    assert labels_3d.shape[0] >= 0
