示例#1
0
def test_SemanticKITTISCN():
    from xmuda.data.utils.visualize import draw_points_image_labels, draw_bird_eye_view
    preprocess_dir = '/datasets_local/datasets_mjaritz/semantic_kitti_preprocess/preprocess'
    semantic_kitti_dir = '/datasets_local/datasets_mjaritz/semantic_kitti_preprocess'
    # pselab_paths = ("/home/docker_user/workspace/outputs/xmuda/a2d2_semantic_kitti/xmuda_crop_resize/pselab_data/train.npy",)
    # split = ('train',)
    split = ('val',)
    dataset = SemanticKITTISCN(split=split,
                               preprocess_dir=preprocess_dir,
                               semantic_kitti_dir=semantic_kitti_dir,
                               # pselab_paths=pselab_paths,
                               merge_classes=True,
                               noisy_rot=0.1,
                               flip_y=0.5,
                               rot_z=2*np.pi,
                               transl=True,
                               bottom_crop=(480, 302),
                               fliplr=0.5,
                               color_jitter=(0.4, 0.4, 0.4)
                               )
    for i in [10, 20, 30, 40, 50, 60]:
        data = dataset[i]
        coords = data['coords']
        seg_label = data['seg_label']
        img = np.moveaxis(data['img'], 0, 2)
        img_indices = data['img_indices']
        # pseudo_label_2d = data['pseudo_label_2d']
        draw_points_image_labels(img, img_indices, seg_label, color_palette_type='SemanticKITTI', point_size=1)
        # draw_points_image_labels(img, img_indices, pseudo_label_2d, color_palette_type='SemanticKITTI', point_size=1)
        # assert len(pseudo_label_2d) == len(seg_label)
        draw_bird_eye_view(coords)
示例#2
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def test_A2D2SCN():
    from xmuda.data.utils.visualize import draw_points_image_labels, draw_bird_eye_view
    preprocess_dir = '/datasets_local/datasets_mjaritz/a2d2_preprocess'
    split = ('test',)
    dataset = A2D2SCN(split=split,
                      preprocess_dir=preprocess_dir,
                      merge_classes=True,
                      use_image=True,
                      noisy_rot=0.1,
                      flip_y=0.5,
                      rot_z=2*np.pi,
                      transl=True,
                      fliplr=0.5,
                      color_jitter=(0.4, 0.4, 0.4)
                      )
    for i in [10, 20, 30, 40, 50, 60]:
        data = dataset[i]
        coords = data['coords']
        seg_label = data['seg_label']
        img = np.moveaxis(data['img'], 0, 2)
        img_indices = data['img_indices']
        draw_points_image_labels(img, img_indices, seg_label, color_palette_type='SemanticKITTI', point_size=3)
        draw_bird_eye_view(coords)
示例#3
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def test_NuScenesSCN():
    from xmuda.data.utils.visualize import draw_points_image_labels, draw_points_image_depth, draw_bird_eye_view
    preprocess_dir = '/datasets_local/datasets_mjaritz/nuscenes_preprocess/preprocess'
    nuscenes_dir = '/datasets_local/datasets_mjaritz/nuscenes_preprocess'
    # split = ('train_singapore',)
    # pselab_paths = ('/home/docker_user/workspace/outputs/xmuda/nuscenes/usa_singapore/xmuda/pselab_data/train_singapore.npy',)
    split = ('train_night', )
    # pselab_paths = ('/home/docker_user/workspace/outputs/xmuda/nuscenes/day_night/xmuda/pselab_data/train_night.npy',)
    dataset = NuScenesSCN(
        split=split,
        preprocess_dir=preprocess_dir,
        nuscenes_dir=nuscenes_dir,
        # pselab_paths=pselab_paths,
        merge_classes=True,
        use_image=True,
        noisy_rot=0.1,
        flip_x=0.5,
        rot_z=2 * np.pi,
        transl=True,
        fliplr=0.5,
        color_jitter=(0.4, 0.4, 0.4))
    for i in [10, 20, 30, 40, 50, 60]:
        data = dataset[i]
        coords = data['coords']
        seg_label = data['seg_label']
        img = np.moveaxis(data['img'], 0, 2)
        img_indices = data['img_indices']
        draw_points_image_labels(img,
                                 img_indices,
                                 seg_label,
                                 color_palette_type='NuScenes',
                                 point_size=3)
        # pseudo_label_2d = data['pseudo_label_2d']
        # draw_points_image_labels(img, img_indices, pseudo_label_2d, color_palette_type='NuScenes', point_size=3)
        draw_bird_eye_view(coords)
        print('Number of points:', len(coords))