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import numpy as np |
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import pycocotools.mask as mask_util |
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import torch |
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from mmengine.utils import slice_list |
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def split_combined_polys(polys, poly_lens, polys_per_mask): |
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"""Split the combined 1-D polys into masks. |
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A mask is represented as a list of polys, and a poly is represented as |
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a 1-D array. In dataset, all masks are concatenated into a single 1-D |
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tensor. Here we need to split the tensor into original representations. |
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Args: |
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polys (list): a list (length = image num) of 1-D tensors |
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poly_lens (list): a list (length = image num) of poly length |
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polys_per_mask (list): a list (length = image num) of poly number |
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of each mask |
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Returns: |
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list: a list (length = image num) of list (length = mask num) of \ |
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list (length = poly num) of numpy array. |
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""" |
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mask_polys_list = [] |
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for img_id in range(len(polys)): |
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polys_single = polys[img_id] |
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polys_lens_single = poly_lens[img_id].tolist() |
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polys_per_mask_single = polys_per_mask[img_id].tolist() |
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split_polys = slice_list(polys_single, polys_lens_single) |
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mask_polys = slice_list(split_polys, polys_per_mask_single) |
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mask_polys_list.append(mask_polys) |
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return mask_polys_list |
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def encode_mask_results(mask_results): |
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"""Encode bitmap mask to RLE code. |
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Args: |
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mask_results (list): bitmap mask results. |
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Returns: |
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list | tuple: RLE encoded mask. |
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""" |
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encoded_mask_results = [] |
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for mask in mask_results: |
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encoded_mask_results.append( |
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mask_util.encode( |
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np.array(mask[:, :, np.newaxis], order='F', |
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dtype='uint8'))[0]) |
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return encoded_mask_results |
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def mask2bbox(masks): |
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"""Obtain tight bounding boxes of binary masks. |
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Args: |
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masks (Tensor): Binary mask of shape (n, h, w). |
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Returns: |
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Tensor: Bboxe with shape (n, 4) of \ |
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positive region in binary mask. |
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""" |
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N = masks.shape[0] |
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bboxes = masks.new_zeros((N, 4), dtype=torch.float32) |
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x_any = torch.any(masks, dim=1) |
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y_any = torch.any(masks, dim=2) |
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for i in range(N): |
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x = torch.where(x_any[i, :])[0] |
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y = torch.where(y_any[i, :])[0] |
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if len(x) > 0 and len(y) > 0: |
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bboxes[i, :] = bboxes.new_tensor( |
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[x[0], y[0], x[-1] + 1, y[-1] + 1]) |
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return bboxes |
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