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# Copyright (c) Facebook, Inc. and its affiliates.
from typing import Any
import torch
from torch.nn import functional as F
from detectron2.structures import BitMasks, Boxes, BoxMode
from .base import IntTupleBox, make_int_box
from .to_mask import ImageSizeType
def resample_coarse_segm_tensor_to_bbox(coarse_segm: torch.Tensor, box_xywh_abs: IntTupleBox):
"""
Resample coarse segmentation tensor to the given
bounding box and derive labels for each pixel of the bounding box
Args:
coarse_segm: float tensor of shape [1, K, Hout, Wout]
box_xywh_abs (tuple of 4 int): bounding box given by its upper-left
corner coordinates, width (W) and height (H)
Return:
Labels for each pixel of the bounding box, a long tensor of size [1, H, W]
"""
x, y, w, h = box_xywh_abs
w = max(int(w), 1)
h = max(int(h), 1)
labels = F.interpolate(coarse_segm, (h, w), mode="bilinear", align_corners=False).argmax(dim=1)
return labels
def resample_fine_and_coarse_segm_tensors_to_bbox(
fine_segm: torch.Tensor, coarse_segm: torch.Tensor, box_xywh_abs: IntTupleBox
):
"""
Resample fine and coarse segmentation tensors to the given
bounding box and derive labels for each pixel of the bounding box
Args:
fine_segm: float tensor of shape [1, C, Hout, Wout]
coarse_segm: float tensor of shape [1, K, Hout, Wout]
box_xywh_abs (tuple of 4 int): bounding box given by its upper-left
corner coordinates, width (W) and height (H)
Return:
Labels for each pixel of the bounding box, a long tensor of size [1, H, W]
"""
x, y, w, h = box_xywh_abs
w = max(int(w), 1)
h = max(int(h), 1)
# coarse segmentation
coarse_segm_bbox = F.interpolate(
coarse_segm,
(h, w),
mode="bilinear",
align_corners=False,
).argmax(dim=1)
# combined coarse and fine segmentation
labels = (
F.interpolate(fine_segm, (h, w), mode="bilinear", align_corners=False).argmax(dim=1)
* (coarse_segm_bbox > 0).long()
)
return labels
def resample_fine_and_coarse_segm_to_bbox(predictor_output: Any, box_xywh_abs: IntTupleBox):
"""
Resample fine and coarse segmentation outputs from a predictor to the given
bounding box and derive labels for each pixel of the bounding box
Args:
predictor_output: DensePose predictor output that contains segmentation
results to be resampled
box_xywh_abs (tuple of 4 int): bounding box given by its upper-left
corner coordinates, width (W) and height (H)
Return:
Labels for each pixel of the bounding box, a long tensor of size [1, H, W]
"""
return resample_fine_and_coarse_segm_tensors_to_bbox(
predictor_output.fine_segm,
predictor_output.coarse_segm,
box_xywh_abs,
)
def predictor_output_with_coarse_segm_to_mask(
predictor_output: Any, boxes: Boxes, image_size_hw: ImageSizeType
) -> BitMasks:
"""
Convert predictor output with coarse and fine segmentation to a mask.
Assumes that predictor output has the following attributes:
- coarse_segm (tensor of size [N, D, H, W]): coarse segmentation
unnormalized scores for N instances; D is the number of coarse
segmentation labels, H and W is the resolution of the estimate
Args:
predictor_output: DensePose predictor output to be converted to mask
boxes (Boxes): bounding boxes that correspond to the DensePose
predictor outputs
image_size_hw (tuple [int, int]): image height Himg and width Wimg
Return:
BitMasks that contain a bool tensor of size [N, Himg, Wimg] with
a mask of the size of the image for each instance
"""
H, W = image_size_hw
boxes_xyxy_abs = boxes.tensor.clone()
boxes_xywh_abs = BoxMode.convert(boxes_xyxy_abs, BoxMode.XYXY_ABS, BoxMode.XYWH_ABS)
N = len(boxes_xywh_abs)
masks = torch.zeros((N, H, W), dtype=torch.bool, device=boxes.tensor.device)
for i in range(len(boxes_xywh_abs)):
box_xywh = make_int_box(boxes_xywh_abs[i])
box_mask = resample_coarse_segm_tensor_to_bbox(predictor_output[i].coarse_segm, box_xywh)
x, y, w, h = box_xywh
masks[i, y : y + h, x : x + w] = box_mask
return BitMasks(masks)
def predictor_output_with_fine_and_coarse_segm_to_mask(
predictor_output: Any, boxes: Boxes, image_size_hw: ImageSizeType
) -> BitMasks:
"""
Convert predictor output with coarse and fine segmentation to a mask.
Assumes that predictor output has the following attributes:
- coarse_segm (tensor of size [N, D, H, W]): coarse segmentation
unnormalized scores for N instances; D is the number of coarse
segmentation labels, H and W is the resolution of the estimate
- fine_segm (tensor of size [N, C, H, W]): fine segmentation
unnormalized scores for N instances; C is the number of fine
segmentation labels, H and W is the resolution of the estimate
Args:
predictor_output: DensePose predictor output to be converted to mask
boxes (Boxes): bounding boxes that correspond to the DensePose
predictor outputs
image_size_hw (tuple [int, int]): image height Himg and width Wimg
Return:
BitMasks that contain a bool tensor of size [N, Himg, Wimg] with
a mask of the size of the image for each instance
"""
H, W = image_size_hw
boxes_xyxy_abs = boxes.tensor.clone()
boxes_xywh_abs = BoxMode.convert(boxes_xyxy_abs, BoxMode.XYXY_ABS, BoxMode.XYWH_ABS)
N = len(boxes_xywh_abs)
masks = torch.zeros((N, H, W), dtype=torch.bool, device=boxes.tensor.device)
for i in range(len(boxes_xywh_abs)):
box_xywh = make_int_box(boxes_xywh_abs[i])
labels_i = resample_fine_and_coarse_segm_to_bbox(predictor_output[i], box_xywh)
x, y, w, h = box_xywh
masks[i, y : y + h, x : x + w] = labels_i > 0
return BitMasks(masks)
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