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# Copyright (c) Meta Platforms, Inc. and affiliates. | |
# All rights reserved. | |
# This source code is licensed under the license found in the | |
# LICENSE file in the root directory of this source tree. | |
import numpy as np | |
import torch | |
import math | |
from copy import deepcopy | |
from itertools import product | |
from typing import Any, Dict, Generator, ItemsView, List, Tuple | |
class MaskData: | |
""" | |
A structure for storing masks and their related data in batched format. | |
Implements basic filtering and concatenation. | |
""" | |
def __init__(self, **kwargs) -> None: | |
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: | |
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: | |
del self._stats[key] | |
def __getitem__(self, key: str) -> Any: | |
return self._stats[key] | |
def items(self) -> ItemsView[str, Any]: | |
return self._stats.items() | |
def filter(self, keep: torch.Tensor) -> None: | |
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: | |
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: | |
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: | |
"""Filter masks at the edge of a crop, but not at the edge of the original image.""" | |
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: | |
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]: | |
assert len(args) > 0 and all( | |
len(a) == len(args[0]) for a in args | |
), "Batched iteration must have inputs of all the same size." | |
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]]: | |
""" | |
Encodes masks to an uncompressed RLE, in the format expected by | |
pycoco tools. | |
""" | |
# 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: | |
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: | |
"""Generates a 2D grid of points evenly spaced in [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)) | |
points = np.stack([points_x, points_y], axis=-1).reshape(-1, 2) | |
return points | |
def build_all_layer_point_grids( | |
n_per_side: int, n_layers: int, scale_per_layer: int | |
) -> List[np.ndarray]: | |
"""Generates point grids for all crop layers.""" | |
points_by_layer = [] | |
for i in range(n_layers + 1): | |
n_points = int(n_per_side / (scale_per_layer**i)) | |
points_by_layer.append(build_point_grid(n_points)) | |
return points_by_layer | |
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): | |
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: | |
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: | |
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: | |
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]: | |
""" | |
Removes small disconnected regions and holes in a mask. Returns the | |
mask and an indicator of if the mask has been modified. | |
""" | |
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 len(small_regions) == 0: | |
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 len(fill_labels) == 0: | |
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]: | |
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:] | |
if len(shape) > 2: | |
masks = masks.flatten(0, -3) | |
else: | |
masks = 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 | |
if len(shape) > 2: | |
out = out.reshape(*shape[:-2], 4) | |
else: | |
out = out[0] | |
return out | |