import os.path as osp

import mmcv
import numpy as np

from mmdet3d.datasets.pipelines import Compose


def test_scannet_pipeline():
    np.random.seed(0)
    pipelines = [
        dict(
            type='IndoorLoadPointsFromFile',
            use_height=True,
            load_dim=6,
            use_dim=[0, 1, 2]),
        dict(type='IndoorLoadAnnotations3D'),
        dict(type='IndoorPointSample', num_points=5),
        dict(type='IndoorFlipData', flip_ratio_yz=1.0, flip_ratio_xz=1.0),
        dict(
            type='IndoorGlobalRotScale',
            use_height=True,
            rot_range=[-np.pi * 1 / 36, np.pi * 1 / 36],
            scale_range=None),
    ]
    pipeline = Compose(pipelines)
    info = mmcv.load('./tests/data/scannet/scannet_infos.pkl')
    results = dict()
    data_path = './tests/data/scannet/scannet_train_instance_data'
    results['data_path'] = data_path
    info = info[0]
    scan_name = info['point_cloud']['lidar_idx']
    results['pts_filename'] = osp.join(data_path, f'{scan_name}_vert.npy')
    if info['annos']['gt_num'] != 0:
        scannet_gt_bboxes_3d = info['annos']['gt_boxes_upright_depth']
        scannet_gt_labels = info['annos']['class'].reshape(-1, 1)
        scannet_gt_bboxes_3d_mask = np.ones_like(scannet_gt_labels)
    else:
        scannet_gt_bboxes_3d = np.zeros((1, 6), dtype=np.float32)
        scannet_gt_labels = np.zeros((1, 1))
        scannet_gt_bboxes_3d_mask = np.zeros((1, 1))
    scan_name = info['point_cloud']['lidar_idx']
    results['pts_instance_mask_path'] = osp.join(data_path,
                                                 f'{scan_name}_ins_label.npy')
    results['pts_semantic_mask_path'] = osp.join(data_path,
                                                 f'{scan_name}_sem_label.npy')
    results['gt_bboxes_3d'] = scannet_gt_bboxes_3d
    results['gt_labels'] = scannet_gt_labels
    results['gt_bboxes_3d_mask'] = scannet_gt_bboxes_3d_mask
    results = pipeline(results)
    points = results['points']
    gt_bboxes_3d = results['gt_bboxes_3d']
    gt_labels = results['gt_labels']
    pts_semantic_mask = results['pts_semantic_mask']
    pts_instance_mask = results['pts_instance_mask']
    expected_points = np.array(
        [[-2.9078157, -1.9569951, 2.3543026, 2.389488],
         [-0.71360034, -3.4359822, 2.1330001, 2.1681855],
         [-1.332374, 1.474838, -0.04405887, -0.00887359],
         [2.1336637, -1.3265059, -0.02880373, 0.00638155],
         [0.43895668, -3.0259454, 1.5560012, 1.5911865]])
    expected_gt_bboxes_3d = np.array([
        [-1.5005362, -3.512584, 1.8565295, 1.7457027, 0.24149807, 0.57235193],
        [-2.8848705, 3.4961755, 1.5268247, 0.66170084, 0.17433672, 0.67153597],
        [-1.1585636, -2.192365, 0.61649567, 0.5557011, 2.5375574, 1.2144762],
        [-2.930457, -2.4856408, 0.9722377, 0.6270478, 1.8461524, 0.28697443],
        [3.3114715, -0.00476722, 1.0712197, 0.46191898, 3.8605113, 2.1603441]
    ])
    expected_gt_labels = np.array([
        6, 6, 4, 9, 11, 11, 10, 0, 15, 17, 17, 17, 3, 12, 4, 4, 14, 1, 0, 0, 0,
        0, 0, 0, 5, 5, 5
    ])
    expected_pts_semantic_mask = np.array([3, 1, 2, 2, 15])
    expected_pts_instance_mask = np.array([44, 22, 10, 10, 57])
    assert np.allclose(points, expected_points)
    assert np.allclose(gt_bboxes_3d[:5, :], expected_gt_bboxes_3d)
    assert np.all(gt_labels.flatten() == expected_gt_labels)
    assert np.all(pts_semantic_mask == expected_pts_semantic_mask)
    assert np.all(pts_instance_mask == expected_pts_instance_mask)