import math from copy import deepcopy from itertools import product from typing import Any, Dict, Generator, ItemsView, List, Tuple import numpy as np import torch class MaskData: """ A structure for storing masks and their related data in batched format. Implements basic filtering and concatenation. """ def __init__(self, **kwargs) -> None: """Initialize a MaskData object, ensuring all values are supported types.""" for v in kwargs.values(): assert isinstance( v, (list, np.ndarray, torch.Tensor)), 'MaskData only supports list, numpy arrays, and torch tensors.' self._stats = dict(**kwargs) def __setitem__(self, key: str, item: Any) -> None: """Set an item in the MaskData object, ensuring it is a supported type.""" assert isinstance( item, (list, np.ndarray, torch.Tensor)), 'MaskData only supports list, numpy arrays, and torch tensors.' self._stats[key] = item def __delitem__(self, key: str) -> None: """Delete an item from the MaskData object.""" del self._stats[key] def __getitem__(self, key: str) -> Any: """Get an item from the MaskData object.""" return self._stats[key] def items(self) -> ItemsView[str, Any]: """Return an ItemsView of the MaskData object.""" return self._stats.items() def filter(self, keep: torch.Tensor) -> None: """Filter the MaskData object based on the given boolean tensor.""" for k, v in self._stats.items(): if v is None: self._stats[k] = None elif isinstance(v, torch.Tensor): self._stats[k] = v[torch.as_tensor(keep, device=v.device)] elif isinstance(v, np.ndarray): self._stats[k] = v[keep.detach().cpu().numpy()] elif isinstance(v, list) and keep.dtype == torch.bool: self._stats[k] = [a for i, a in enumerate(v) if keep[i]] elif isinstance(v, list): self._stats[k] = [v[i] for i in keep] else: raise TypeError(f'MaskData key {k} has an unsupported type {type(v)}.') def cat(self, new_stats: 'MaskData') -> None: """Concatenate a new MaskData object to the current one.""" for k, v in new_stats.items(): if k not in self._stats or self._stats[k] is None: self._stats[k] = deepcopy(v) elif isinstance(v, torch.Tensor): self._stats[k] = torch.cat([self._stats[k], v], dim=0) elif isinstance(v, np.ndarray): self._stats[k] = np.concatenate([self._stats[k], v], axis=0) elif isinstance(v, list): self._stats[k] = self._stats[k] + deepcopy(v) else: raise TypeError(f'MaskData key {k} has an unsupported type {type(v)}.') def to_numpy(self) -> None: """Convert all torch tensors in the MaskData object to numpy arrays.""" for k, v in self._stats.items(): if isinstance(v, torch.Tensor): self._stats[k] = v.detach().cpu().numpy() def is_box_near_crop_edge(boxes: torch.Tensor, crop_box: List[int], orig_box: List[int], atol: float = 20.0) -> torch.Tensor: """Return a boolean tensor indicating if boxes are near the crop edge.""" crop_box_torch = torch.as_tensor(crop_box, dtype=torch.float, device=boxes.device) orig_box_torch = torch.as_tensor(orig_box, dtype=torch.float, device=boxes.device) boxes = uncrop_boxes_xyxy(boxes, crop_box).float() near_crop_edge = torch.isclose(boxes, crop_box_torch[None, :], atol=atol, rtol=0) near_image_edge = torch.isclose(boxes, orig_box_torch[None, :], atol=atol, rtol=0) near_crop_edge = torch.logical_and(near_crop_edge, ~near_image_edge) return torch.any(near_crop_edge, dim=1) def box_xyxy_to_xywh(box_xyxy: torch.Tensor) -> torch.Tensor: """Convert bounding boxes from XYXY format to XYWH format.""" box_xywh = deepcopy(box_xyxy) box_xywh[2] = box_xywh[2] - box_xywh[0] box_xywh[3] = box_xywh[3] - box_xywh[1] return box_xywh def batch_iterator(batch_size: int, *args) -> Generator[List[Any], None, None]: """Yield batches of data from the input arguments.""" assert args and all(len(a) == len(args[0]) for a in args), 'Batched iteration must have same-size inputs.' n_batches = len(args[0]) // batch_size + int(len(args[0]) % batch_size != 0) for b in range(n_batches): yield [arg[b * batch_size:(b + 1) * batch_size] for arg in args] def mask_to_rle_pytorch(tensor: torch.Tensor) -> List[Dict[str, Any]]: """Encode masks as uncompressed RLEs in the format expected by pycocotools.""" # Put in fortran order and flatten h,w b, h, w = tensor.shape tensor = tensor.permute(0, 2, 1).flatten(1) # Compute change indices diff = tensor[:, 1:] ^ tensor[:, :-1] change_indices = diff.nonzero() # Encode run length out = [] for i in range(b): cur_idxs = change_indices[change_indices[:, 0] == i, 1] cur_idxs = torch.cat([ torch.tensor([0], dtype=cur_idxs.dtype, device=cur_idxs.device), cur_idxs + 1, torch.tensor([h * w], dtype=cur_idxs.dtype, device=cur_idxs.device), ]) btw_idxs = cur_idxs[1:] - cur_idxs[:-1] counts = [] if tensor[i, 0] == 0 else [0] counts.extend(btw_idxs.detach().cpu().tolist()) out.append({'size': [h, w], 'counts': counts}) return out def rle_to_mask(rle: Dict[str, Any]) -> np.ndarray: """Compute a binary mask from an uncompressed RLE.""" h, w = rle['size'] mask = np.empty(h * w, dtype=bool) idx = 0 parity = False for count in rle['counts']: mask[idx:idx + count] = parity idx += count parity ^= True mask = mask.reshape(w, h) return mask.transpose() # Put in C order def area_from_rle(rle: Dict[str, Any]) -> int: """Calculate the area of a mask from its uncompressed RLE.""" return sum(rle['counts'][1::2]) def calculate_stability_score(masks: torch.Tensor, mask_threshold: float, threshold_offset: float) -> torch.Tensor: """ Computes the stability score for a batch of masks. The stability score is the IoU between the binary masks obtained by thresholding the predicted mask logits at high and low values. """ # One mask is always contained inside the other. # Save memory by preventing unnecessary cast to torch.int64 intersections = ((masks > (mask_threshold + threshold_offset)).sum(-1, dtype=torch.int16).sum(-1, dtype=torch.int32)) unions = ((masks > (mask_threshold - threshold_offset)).sum(-1, dtype=torch.int16).sum(-1, dtype=torch.int32)) return intersections / unions def build_point_grid(n_per_side: int) -> np.ndarray: """Generate a 2D grid of evenly spaced points in the range [0,1]x[0,1].""" offset = 1 / (2 * n_per_side) points_one_side = np.linspace(offset, 1 - offset, n_per_side) points_x = np.tile(points_one_side[None, :], (n_per_side, 1)) points_y = np.tile(points_one_side[:, None], (1, n_per_side)) return np.stack([points_x, points_y], axis=-1).reshape(-1, 2) def build_all_layer_point_grids(n_per_side: int, n_layers: int, scale_per_layer: int) -> List[np.ndarray]: """Generate point grids for all crop layers.""" return [build_point_grid(int(n_per_side / (scale_per_layer ** i))) for i in range(n_layers + 1)] def generate_crop_boxes(im_size: Tuple[int, ...], n_layers: int, overlap_ratio: float) -> Tuple[List[List[int]], List[int]]: """Generates a list of crop boxes of different sizes. Each layer has (2**i)**2 boxes for the ith layer.""" crop_boxes, layer_idxs = [], [] im_h, im_w = im_size short_side = min(im_h, im_w) # Original image crop_boxes.append([0, 0, im_w, im_h]) layer_idxs.append(0) def crop_len(orig_len, n_crops, overlap): """Crops bounding boxes to the size of the input image.""" return int(math.ceil((overlap * (n_crops - 1) + orig_len) / n_crops)) for i_layer in range(n_layers): n_crops_per_side = 2 ** (i_layer + 1) overlap = int(overlap_ratio * short_side * (2 / n_crops_per_side)) crop_w = crop_len(im_w, n_crops_per_side, overlap) crop_h = crop_len(im_h, n_crops_per_side, overlap) crop_box_x0 = [int((crop_w - overlap) * i) for i in range(n_crops_per_side)] crop_box_y0 = [int((crop_h - overlap) * i) for i in range(n_crops_per_side)] # Crops in XYWH format for x0, y0 in product(crop_box_x0, crop_box_y0): box = [x0, y0, min(x0 + crop_w, im_w), min(y0 + crop_h, im_h)] crop_boxes.append(box) layer_idxs.append(i_layer + 1) return crop_boxes, layer_idxs def uncrop_boxes_xyxy(boxes: torch.Tensor, crop_box: List[int]) -> torch.Tensor: """Uncrop bounding boxes by adding the crop box offset.""" x0, y0, _, _ = crop_box offset = torch.tensor([[x0, y0, x0, y0]], device=boxes.device) # Check if boxes has a channel dimension if len(boxes.shape) == 3: offset = offset.unsqueeze(1) return boxes + offset def uncrop_points(points: torch.Tensor, crop_box: List[int]) -> torch.Tensor: """Uncrop points by adding the crop box offset.""" x0, y0, _, _ = crop_box offset = torch.tensor([[x0, y0]], device=points.device) # Check if points has a channel dimension if len(points.shape) == 3: offset = offset.unsqueeze(1) return points + offset def uncrop_masks(masks: torch.Tensor, crop_box: List[int], orig_h: int, orig_w: int) -> torch.Tensor: """Uncrop masks by padding them to the original image size.""" x0, y0, x1, y1 = crop_box if x0 == 0 and y0 == 0 and x1 == orig_w and y1 == orig_h: return masks # Coordinate transform masks pad_x, pad_y = orig_w - (x1 - x0), orig_h - (y1 - y0) pad = (x0, pad_x - x0, y0, pad_y - y0) return torch.nn.functional.pad(masks, pad, value=0) def remove_small_regions(mask: np.ndarray, area_thresh: float, mode: str) -> Tuple[np.ndarray, bool]: """Remove small disconnected regions or holes in a mask, returning the mask and a modification indicator.""" import cv2 # type: ignore assert mode in {'holes', 'islands'} correct_holes = mode == 'holes' working_mask = (correct_holes ^ mask).astype(np.uint8) n_labels, regions, stats, _ = cv2.connectedComponentsWithStats(working_mask, 8) sizes = stats[:, -1][1:] # Row 0 is background label small_regions = [i + 1 for i, s in enumerate(sizes) if s < area_thresh] if not small_regions: return mask, False fill_labels = [0] + small_regions if not correct_holes: fill_labels = [i for i in range(n_labels) if i not in fill_labels] # If every region is below threshold, keep largest if not fill_labels: fill_labels = [int(np.argmax(sizes)) + 1] mask = np.isin(regions, fill_labels) return mask, True def coco_encode_rle(uncompressed_rle: Dict[str, Any]) -> Dict[str, Any]: """Encode uncompressed RLE (run-length encoding) to COCO RLE format.""" from pycocotools import mask as mask_utils # type: ignore h, w = uncompressed_rle['size'] rle = mask_utils.frPyObjects(uncompressed_rle, h, w) rle['counts'] = rle['counts'].decode('utf-8') # Necessary to serialize with json return rle def batched_mask_to_box(masks: torch.Tensor) -> torch.Tensor: """ Calculates boxes in XYXY format around masks. Return [0,0,0,0] for an empty mask. For input shape C1xC2x...xHxW, the output shape is C1xC2x...x4. """ # torch.max below raises an error on empty inputs, just skip in this case if torch.numel(masks) == 0: return torch.zeros(*masks.shape[:-2], 4, device=masks.device) # Normalize shape to CxHxW shape = masks.shape h, w = shape[-2:] masks = masks.flatten(0, -3) if len(shape) > 2 else masks.unsqueeze(0) # Get top and bottom edges in_height, _ = torch.max(masks, dim=-1) in_height_coords = in_height * torch.arange(h, device=in_height.device)[None, :] bottom_edges, _ = torch.max(in_height_coords, dim=-1) in_height_coords = in_height_coords + h * (~in_height) top_edges, _ = torch.min(in_height_coords, dim=-1) # Get left and right edges in_width, _ = torch.max(masks, dim=-2) in_width_coords = in_width * torch.arange(w, device=in_width.device)[None, :] right_edges, _ = torch.max(in_width_coords, dim=-1) in_width_coords = in_width_coords + w * (~in_width) left_edges, _ = torch.min(in_width_coords, dim=-1) # If the mask is empty the right edge will be to the left of the left edge. # Replace these boxes with [0, 0, 0, 0] empty_filter = (right_edges < left_edges) | (bottom_edges < top_edges) out = torch.stack([left_edges, top_edges, right_edges, bottom_edges], dim=-1) out = out * (~empty_filter).unsqueeze(-1) # Return to original shape return out.reshape(*shape[:-2], 4) if len(shape) > 2 else out[0]