Python visualize.display_instances() Examples

The following are 2 code examples of visualize.display_instances(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module visualize , or try the search function .
Example #1
Source File: instance_visualize.py    From SketchyScene with MIT License 6 votes vote down vote up 
def visualize_instance_segmentation(data_base_dir, dataset_type, image_id, save_path='', verbose=True):
    split_dataset = SketchDataset(data_base_dir)
    split_dataset.load_sketches(dataset_type)
    split_dataset.prepare()

    original_image = split_dataset.load_image(image_id - 1)
    gt_mask, gt_class_id = split_dataset.load_mask(image_id - 1)
    gt_bbox = utils.extract_bboxes(gt_mask)

    if verbose:
        log('original_image', original_image)
        log('gt_class_id', gt_class_id)
        log('gt_bbox', gt_bbox)
        log('gt_mask', gt_mask)

    visualize.display_instances(original_image, gt_bbox, gt_mask, gt_class_id,
                                split_dataset.class_names, save_path=save_path)
Example #2
Source File: segment_data_generation.py    From SketchyScene with MIT License 5 votes vote down vote up 
def debug_saved_npz(dataset_type, img_idx, data_base_dir):
    outputs_base_dir = 'outputs'
    seg_data_save_base_dir = os.path.join(outputs_base_dir, 'inst_segm_output_data', dataset_type)

    npz_name = os.path.join(seg_data_save_base_dir, str(img_idx) + '_datas.npz')
    npz = np.load(npz_name)

    pred_class_ids = np.array(npz['pred_class_ids'], dtype=np.int32)
    pred_boxes = np.array(npz['pred_boxes'], dtype=np.int32)
    pred_masks_s = npz['pred_masks']
    pred_masks = expand_small_segmentation_mask(pred_masks_s, pred_boxes)  # [N, H, W]

    pred_masks = np.transpose(pred_masks, (1, 2, 0))
    print(pred_class_ids.shape)
    print(pred_masks.shape)
    print(pred_boxes.shape)

    image_name = 'L0_sample' + str(img_idx) + '.png'
    images_base_dir = os.path.join(data_base_dir, dataset_type, 'DRAWING_GT')
    image_path = os.path.join(images_base_dir, image_name)
    original_image = Image.open(image_path).convert("RGB")
    original_image = original_image.resize((768, 768), resample=Image.NEAREST)
    original_image = np.array(original_image, dtype=np.float32)  # shape = [H, W, 3]

    dataset_class_names = ['bg']
    color_map_mat_path = os.path.join(data_base_dir, 'colorMapC46.mat')
    colorMap = scipy.io.loadmat(color_map_mat_path)['colorMap']
    for i in range(46):
        cat_name = colorMap[i][0][0]
        dataset_class_names.append(cat_name)

    visualize.display_instances(original_image, pred_boxes, pred_masks, pred_class_ids,
                                dataset_class_names, figsize=(8, 8))