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# Copyright (c) Facebook, Inc. and its affiliates.
from typing import Dict
import torch
from torch.nn import functional as F
from detectron2.structures.boxes import Boxes, BoxMode
from ..structures import (
DensePoseChartPredictorOutput,
DensePoseChartResult,
DensePoseChartResultWithConfidences,
)
from . import resample_fine_and_coarse_segm_to_bbox
from .base import IntTupleBox, make_int_box
def resample_uv_tensors_to_bbox(
u: torch.Tensor,
v: torch.Tensor,
labels: torch.Tensor,
box_xywh_abs: IntTupleBox,
) -> torch.Tensor:
"""
Resamples U and V coordinate estimates for the given bounding box
Args:
u (tensor [1, C, H, W] of float): U coordinates
v (tensor [1, C, H, W] of float): V coordinates
labels (tensor [H, W] of long): labels obtained by resampling segmentation
outputs for the given bounding box
box_xywh_abs (tuple of 4 int): bounding box that corresponds to predictor outputs
Return:
Resampled U and V coordinates - a tensor [2, H, W] of float
"""
x, y, w, h = box_xywh_abs
w = max(int(w), 1)
h = max(int(h), 1)
u_bbox = F.interpolate(u, (h, w), mode="bilinear", align_corners=False)
v_bbox = F.interpolate(v, (h, w), mode="bilinear", align_corners=False)
uv = torch.zeros([2, h, w], dtype=torch.float32, device=u.device)
for part_id in range(1, u_bbox.size(1)):
uv[0][labels == part_id] = u_bbox[0, part_id][labels == part_id]
uv[1][labels == part_id] = v_bbox[0, part_id][labels == part_id]
return uv
def resample_uv_to_bbox(
predictor_output: DensePoseChartPredictorOutput,
labels: torch.Tensor,
box_xywh_abs: IntTupleBox,
) -> torch.Tensor:
"""
Resamples U and V coordinate estimates for the given bounding box
Args:
predictor_output (DensePoseChartPredictorOutput): DensePose predictor
output to be resampled
labels (tensor [H, W] of long): labels obtained by resampling segmentation
outputs for the given bounding box
box_xywh_abs (tuple of 4 int): bounding box that corresponds to predictor outputs
Return:
Resampled U and V coordinates - a tensor [2, H, W] of float
"""
return resample_uv_tensors_to_bbox(
predictor_output.u,
predictor_output.v,
labels,
box_xywh_abs,
)
def densepose_chart_predictor_output_to_result(
predictor_output: DensePoseChartPredictorOutput, boxes: Boxes
) -> DensePoseChartResult:
"""
Convert densepose chart predictor outputs to results
Args:
predictor_output (DensePoseChartPredictorOutput): DensePose predictor
output to be converted to results, must contain only 1 output
boxes (Boxes): bounding box that corresponds to the predictor output,
must contain only 1 bounding box
Return:
DensePose chart-based result (DensePoseChartResult)
"""
assert len(predictor_output) == 1 and len(boxes) == 1, (
f"Predictor output to result conversion can operate only single outputs"
f", got {len(predictor_output)} predictor outputs and {len(boxes)} boxes"
)
boxes_xyxy_abs = boxes.tensor.clone()
boxes_xywh_abs = BoxMode.convert(boxes_xyxy_abs, BoxMode.XYXY_ABS, BoxMode.XYWH_ABS)
box_xywh = make_int_box(boxes_xywh_abs[0])
labels = resample_fine_and_coarse_segm_to_bbox(predictor_output, box_xywh).squeeze(0)
uv = resample_uv_to_bbox(predictor_output, labels, box_xywh)
return DensePoseChartResult(labels=labels, uv=uv)
def resample_confidences_to_bbox(
predictor_output: DensePoseChartPredictorOutput,
labels: torch.Tensor,
box_xywh_abs: IntTupleBox,
) -> Dict[str, torch.Tensor]:
"""
Resamples confidences for the given bounding box
Args:
predictor_output (DensePoseChartPredictorOutput): DensePose predictor
output to be resampled
labels (tensor [H, W] of long): labels obtained by resampling segmentation
outputs for the given bounding box
box_xywh_abs (tuple of 4 int): bounding box that corresponds to predictor outputs
Return:
Resampled confidences - a dict of [H, W] tensors of float
"""
x, y, w, h = box_xywh_abs
w = max(int(w), 1)
h = max(int(h), 1)
confidence_names = [
"sigma_1",
"sigma_2",
"kappa_u",
"kappa_v",
"fine_segm_confidence",
"coarse_segm_confidence",
]
confidence_results = {key: None for key in confidence_names}
confidence_names = [
key for key in confidence_names if getattr(predictor_output, key) is not None
]
confidence_base = torch.zeros([h, w], dtype=torch.float32, device=predictor_output.u.device)
# assign data from channels that correspond to the labels
for key in confidence_names:
resampled_confidence = F.interpolate(
getattr(predictor_output, key),
(h, w),
mode="bilinear",
align_corners=False,
)
result = confidence_base.clone()
for part_id in range(1, predictor_output.u.size(1)):
if resampled_confidence.size(1) != predictor_output.u.size(1):
# confidence is not part-based, don't try to fill it part by part
continue
result[labels == part_id] = resampled_confidence[0, part_id][labels == part_id]
if resampled_confidence.size(1) != predictor_output.u.size(1):
# confidence is not part-based, fill the data with the first channel
# (targeted for segmentation confidences that have only 1 channel)
result = resampled_confidence[0, 0]
confidence_results[key] = result
return confidence_results # pyre-ignore[7]
def densepose_chart_predictor_output_to_result_with_confidences(
predictor_output: DensePoseChartPredictorOutput, boxes: Boxes
) -> DensePoseChartResultWithConfidences:
"""
Convert densepose chart predictor outputs to results
Args:
predictor_output (DensePoseChartPredictorOutput): DensePose predictor
output with confidences to be converted to results, must contain only 1 output
boxes (Boxes): bounding box that corresponds to the predictor output,
must contain only 1 bounding box
Return:
DensePose chart-based result with confidences (DensePoseChartResultWithConfidences)
"""
assert len(predictor_output) == 1 and len(boxes) == 1, (
f"Predictor output to result conversion can operate only single outputs"
f", got {len(predictor_output)} predictor outputs and {len(boxes)} boxes"
)
boxes_xyxy_abs = boxes.tensor.clone()
boxes_xywh_abs = BoxMode.convert(boxes_xyxy_abs, BoxMode.XYXY_ABS, BoxMode.XYWH_ABS)
box_xywh = make_int_box(boxes_xywh_abs[0])
labels = resample_fine_and_coarse_segm_to_bbox(predictor_output, box_xywh).squeeze(0)
uv = resample_uv_to_bbox(predictor_output, labels, box_xywh)
confidences = resample_confidences_to_bbox(predictor_output, labels, box_xywh)
return DensePoseChartResultWithConfidences(labels=labels, uv=uv, **confidences)
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