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# Copyright (c) Facebook, Inc. and its affiliates.
import numpy as np
import torch
from torch.nn import functional as F
from densepose.data.meshes.catalog import MeshCatalog
from densepose.structures.mesh import load_mesh_symmetry
from densepose.structures.transform_data import DensePoseTransformData
class DensePoseDataRelative:
"""
Dense pose relative annotations that can be applied to any bounding box:
x - normalized X coordinates [0, 255] of annotated points
y - normalized Y coordinates [0, 255] of annotated points
i - body part labels 0,...,24 for annotated points
u - body part U coordinates [0, 1] for annotated points
v - body part V coordinates [0, 1] for annotated points
segm - 256x256 segmentation mask with values 0,...,14
To obtain absolute x and y data wrt some bounding box one needs to first
divide the data by 256, multiply by the respective bounding box size
and add bounding box offset:
x_img = x0 + x_norm * w / 256.0
y_img = y0 + y_norm * h / 256.0
Segmentation masks are typically sampled to get image-based masks.
"""
# Key for normalized X coordinates in annotation dict
X_KEY = "dp_x"
# Key for normalized Y coordinates in annotation dict
Y_KEY = "dp_y"
# Key for U part coordinates in annotation dict (used in chart-based annotations)
U_KEY = "dp_U"
# Key for V part coordinates in annotation dict (used in chart-based annotations)
V_KEY = "dp_V"
# Key for I point labels in annotation dict (used in chart-based annotations)
I_KEY = "dp_I"
# Key for segmentation mask in annotation dict
S_KEY = "dp_masks"
# Key for vertex ids (used in continuous surface embeddings annotations)
VERTEX_IDS_KEY = "dp_vertex"
# Key for mesh id (used in continuous surface embeddings annotations)
MESH_NAME_KEY = "ref_model"
# Number of body parts in segmentation masks
N_BODY_PARTS = 14
# Number of parts in point labels
N_PART_LABELS = 24
MASK_SIZE = 256
def __init__(self, annotation, cleanup=False):
self.x = torch.as_tensor(annotation[DensePoseDataRelative.X_KEY])
self.y = torch.as_tensor(annotation[DensePoseDataRelative.Y_KEY])
if (
DensePoseDataRelative.I_KEY in annotation
and DensePoseDataRelative.U_KEY in annotation
and DensePoseDataRelative.V_KEY in annotation
):
self.i = torch.as_tensor(annotation[DensePoseDataRelative.I_KEY])
self.u = torch.as_tensor(annotation[DensePoseDataRelative.U_KEY])
self.v = torch.as_tensor(annotation[DensePoseDataRelative.V_KEY])
if (
DensePoseDataRelative.VERTEX_IDS_KEY in annotation
and DensePoseDataRelative.MESH_NAME_KEY in annotation
):
self.vertex_ids = torch.as_tensor(
annotation[DensePoseDataRelative.VERTEX_IDS_KEY], dtype=torch.long
)
self.mesh_id = MeshCatalog.get_mesh_id(annotation[DensePoseDataRelative.MESH_NAME_KEY])
if DensePoseDataRelative.S_KEY in annotation:
self.segm = DensePoseDataRelative.extract_segmentation_mask(annotation)
self.device = torch.device("cpu")
if cleanup:
DensePoseDataRelative.cleanup_annotation(annotation)
def to(self, device):
if self.device == device:
return self
new_data = DensePoseDataRelative.__new__(DensePoseDataRelative)
new_data.x = self.x.to(device)
new_data.y = self.y.to(device)
for attr in ["i", "u", "v", "vertex_ids", "segm"]:
if hasattr(self, attr):
setattr(new_data, attr, getattr(self, attr).to(device))
if hasattr(self, "mesh_id"):
new_data.mesh_id = self.mesh_id
new_data.device = device
return new_data
@staticmethod
def extract_segmentation_mask(annotation):
import pycocotools.mask as mask_utils
# TODO: annotation instance is accepted if it contains either
# DensePose segmentation or instance segmentation. However, here we
# only rely on DensePose segmentation
poly_specs = annotation[DensePoseDataRelative.S_KEY]
if isinstance(poly_specs, torch.Tensor):
# data is already given as mask tensors, no need to decode
return poly_specs
segm = torch.zeros((DensePoseDataRelative.MASK_SIZE,) * 2, dtype=torch.float32)
if isinstance(poly_specs, dict):
if poly_specs:
mask = mask_utils.decode(poly_specs)
segm[mask > 0] = 1
else:
for i in range(len(poly_specs)):
poly_i = poly_specs[i]
if poly_i:
mask_i = mask_utils.decode(poly_i)
segm[mask_i > 0] = i + 1
return segm
@staticmethod
def validate_annotation(annotation):
for key in [
DensePoseDataRelative.X_KEY,
DensePoseDataRelative.Y_KEY,
]:
if key not in annotation:
return False, "no {key} data in the annotation".format(key=key)
valid_for_iuv_setting = all(
key in annotation
for key in [
DensePoseDataRelative.I_KEY,
DensePoseDataRelative.U_KEY,
DensePoseDataRelative.V_KEY,
]
)
valid_for_cse_setting = all(
key in annotation
for key in [
DensePoseDataRelative.VERTEX_IDS_KEY,
DensePoseDataRelative.MESH_NAME_KEY,
]
)
if not valid_for_iuv_setting and not valid_for_cse_setting:
return (
False,
"expected either {} (IUV setting) or {} (CSE setting) annotations".format(
", ".join(
[
DensePoseDataRelative.I_KEY,
DensePoseDataRelative.U_KEY,
DensePoseDataRelative.V_KEY,
]
),
", ".join(
[
DensePoseDataRelative.VERTEX_IDS_KEY,
DensePoseDataRelative.MESH_NAME_KEY,
]
),
),
)
return True, None
@staticmethod
def cleanup_annotation(annotation):
for key in [
DensePoseDataRelative.X_KEY,
DensePoseDataRelative.Y_KEY,
DensePoseDataRelative.I_KEY,
DensePoseDataRelative.U_KEY,
DensePoseDataRelative.V_KEY,
DensePoseDataRelative.S_KEY,
DensePoseDataRelative.VERTEX_IDS_KEY,
DensePoseDataRelative.MESH_NAME_KEY,
]:
if key in annotation:
del annotation[key]
def apply_transform(self, transforms, densepose_transform_data):
self._transform_pts(transforms, densepose_transform_data)
if hasattr(self, "segm"):
self._transform_segm(transforms, densepose_transform_data)
def _transform_pts(self, transforms, dp_transform_data):
import detectron2.data.transforms as T
# NOTE: This assumes that HorizFlipTransform is the only one that does flip
do_hflip = sum(isinstance(t, T.HFlipTransform) for t in transforms.transforms) % 2 == 1
if do_hflip:
self.x = self.MASK_SIZE - self.x
if hasattr(self, "i"):
self._flip_iuv_semantics(dp_transform_data)
if hasattr(self, "vertex_ids"):
self._flip_vertices()
for t in transforms.transforms:
if isinstance(t, T.RotationTransform):
xy_scale = np.array((t.w, t.h)) / DensePoseDataRelative.MASK_SIZE
xy = t.apply_coords(np.stack((self.x, self.y), axis=1) * xy_scale)
self.x, self.y = torch.tensor(xy / xy_scale, dtype=self.x.dtype).T
def _flip_iuv_semantics(self, dp_transform_data: DensePoseTransformData) -> None:
i_old = self.i.clone()
uv_symmetries = dp_transform_data.uv_symmetries
pt_label_symmetries = dp_transform_data.point_label_symmetries
for i in range(self.N_PART_LABELS):
if i + 1 in i_old:
annot_indices_i = i_old == i + 1
if pt_label_symmetries[i + 1] != i + 1:
self.i[annot_indices_i] = pt_label_symmetries[i + 1]
u_loc = (self.u[annot_indices_i] * 255).long()
v_loc = (self.v[annot_indices_i] * 255).long()
self.u[annot_indices_i] = uv_symmetries["U_transforms"][i][v_loc, u_loc].to(
device=self.u.device
)
self.v[annot_indices_i] = uv_symmetries["V_transforms"][i][v_loc, u_loc].to(
device=self.v.device
)
def _flip_vertices(self):
mesh_info = MeshCatalog[MeshCatalog.get_mesh_name(self.mesh_id)]
mesh_symmetry = (
load_mesh_symmetry(mesh_info.symmetry) if mesh_info.symmetry is not None else None
)
self.vertex_ids = mesh_symmetry["vertex_transforms"][self.vertex_ids]
def _transform_segm(self, transforms, dp_transform_data):
import detectron2.data.transforms as T
# NOTE: This assumes that HorizFlipTransform is the only one that does flip
do_hflip = sum(isinstance(t, T.HFlipTransform) for t in transforms.transforms) % 2 == 1
if do_hflip:
self.segm = torch.flip(self.segm, [1])
self._flip_segm_semantics(dp_transform_data)
for t in transforms.transforms:
if isinstance(t, T.RotationTransform):
self._transform_segm_rotation(t)
def _flip_segm_semantics(self, dp_transform_data):
old_segm = self.segm.clone()
mask_label_symmetries = dp_transform_data.mask_label_symmetries
for i in range(self.N_BODY_PARTS):
if mask_label_symmetries[i + 1] != i + 1:
self.segm[old_segm == i + 1] = mask_label_symmetries[i + 1]
def _transform_segm_rotation(self, rotation):
self.segm = F.interpolate(self.segm[None, None, :], (rotation.h, rotation.w)).numpy()
self.segm = torch.tensor(rotation.apply_segmentation(self.segm[0, 0]))[None, None, :]
self.segm = F.interpolate(self.segm, [DensePoseDataRelative.MASK_SIZE] * 2)[0, 0]
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