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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import json
import numpy as np
from functools import lru_cache
from typing import Dict, List, Optional, Tuple
import cv2
import torch
from detectron2.utils.file_io import PathManager
from densepose.modeling import build_densepose_embedder
from densepose.modeling.cse.utils import get_closest_vertices_mask_from_ES
from ..data.utils import get_class_to_mesh_name_mapping
from ..structures import DensePoseEmbeddingPredictorOutput
from ..structures.mesh import create_mesh
from .base import Boxes, Image, MatrixVisualizer
from .densepose_results_textures import get_texture_atlas
@lru_cache()
def get_xyz_vertex_embedding(mesh_name: str, device: torch.device):
if mesh_name == "smpl_27554":
embed_path = PathManager.get_local_path(
"https://dl.fbaipublicfiles.com/densepose/data/cse/mds_d=256.npy"
)
embed_map, _ = np.load(embed_path, allow_pickle=True)
embed_map = torch.tensor(embed_map).float()[:, 0]
embed_map -= embed_map.min()
embed_map /= embed_map.max()
else:
mesh = create_mesh(mesh_name, device)
embed_map = mesh.vertices.sum(dim=1)
embed_map -= embed_map.min()
embed_map /= embed_map.max()
embed_map = embed_map**2
return embed_map
class DensePoseOutputsVertexVisualizer:
def __init__(
self,
cfg,
inplace=True,
cmap=cv2.COLORMAP_JET,
alpha=0.7,
device="cuda",
default_class=0,
**kwargs,
):
self.mask_visualizer = MatrixVisualizer(
inplace=inplace, cmap=cmap, val_scale=1.0, alpha=alpha
)
self.class_to_mesh_name = get_class_to_mesh_name_mapping(cfg)
self.embedder = build_densepose_embedder(cfg)
self.device = torch.device(device)
self.default_class = default_class
self.mesh_vertex_embeddings = {
mesh_name: self.embedder(mesh_name).to(self.device)
for mesh_name in self.class_to_mesh_name.values()
if self.embedder.has_embeddings(mesh_name)
}
def visualize(
self,
image_bgr: Image,
outputs_boxes_xywh_classes: Tuple[
Optional[DensePoseEmbeddingPredictorOutput], Optional[Boxes], Optional[List[int]]
],
) -> Image:
if outputs_boxes_xywh_classes[0] is None:
return image_bgr
S, E, N, bboxes_xywh, pred_classes = self.extract_and_check_outputs_and_boxes(
outputs_boxes_xywh_classes
)
for n in range(N):
x, y, w, h = bboxes_xywh[n].int().tolist()
mesh_name = self.class_to_mesh_name[pred_classes[n]]
closest_vertices, mask = get_closest_vertices_mask_from_ES(
E[[n]],
S[[n]],
h,
w,
self.mesh_vertex_embeddings[mesh_name],
self.device,
)
embed_map = get_xyz_vertex_embedding(mesh_name, self.device)
vis = (embed_map[closest_vertices].clip(0, 1) * 255.0).cpu().numpy()
mask_numpy = mask.cpu().numpy().astype(dtype=np.uint8)
image_bgr = self.mask_visualizer.visualize(image_bgr, mask_numpy, vis, [x, y, w, h])
return image_bgr
def extract_and_check_outputs_and_boxes(self, outputs_boxes_xywh_classes):
densepose_output, bboxes_xywh, pred_classes = outputs_boxes_xywh_classes
if pred_classes is None:
pred_classes = [self.default_class] * len(bboxes_xywh)
assert isinstance(
densepose_output, DensePoseEmbeddingPredictorOutput
), "DensePoseEmbeddingPredictorOutput expected, {} encountered".format(
type(densepose_output)
)
S = densepose_output.coarse_segm
E = densepose_output.embedding
N = S.size(0)
assert N == E.size(
0
), "CSE coarse_segm {} and embeddings {}" " should have equal first dim size".format(
S.size(), E.size()
)
assert N == len(
bboxes_xywh
), "number of bounding boxes {}" " should be equal to first dim size of outputs {}".format(
len(bboxes_xywh), N
)
assert N == len(pred_classes), (
"number of predicted classes {}"
" should be equal to first dim size of outputs {}".format(len(bboxes_xywh), N)
)
return S, E, N, bboxes_xywh, pred_classes
def get_texture_atlases(json_str: Optional[str]) -> Optional[Dict[str, Optional[np.ndarray]]]:
"""
json_str is a JSON string representing a mesh_name -> texture_atlas_path dictionary
"""
if json_str is None:
return None
paths = json.loads(json_str)
return {mesh_name: get_texture_atlas(path) for mesh_name, path in paths.items()}
class DensePoseOutputsTextureVisualizer(DensePoseOutputsVertexVisualizer):
def __init__(
self,
cfg,
texture_atlases_dict,
device="cuda",
default_class=0,
**kwargs,
):
self.embedder = build_densepose_embedder(cfg)
self.texture_image_dict = {}
self.alpha_dict = {}
for mesh_name in texture_atlases_dict.keys():
if texture_atlases_dict[mesh_name].shape[-1] == 4: # Image with alpha channel
self.alpha_dict[mesh_name] = texture_atlases_dict[mesh_name][:, :, -1] / 255.0
self.texture_image_dict[mesh_name] = texture_atlases_dict[mesh_name][:, :, :3]
else:
self.alpha_dict[mesh_name] = texture_atlases_dict[mesh_name].sum(axis=-1) > 0
self.texture_image_dict[mesh_name] = texture_atlases_dict[mesh_name]
self.device = torch.device(device)
self.class_to_mesh_name = get_class_to_mesh_name_mapping(cfg)
self.default_class = default_class
self.mesh_vertex_embeddings = {
mesh_name: self.embedder(mesh_name).to(self.device)
for mesh_name in self.class_to_mesh_name.values()
}
def visualize(
self,
image_bgr: Image,
outputs_boxes_xywh_classes: Tuple[
Optional[DensePoseEmbeddingPredictorOutput], Optional[Boxes], Optional[List[int]]
],
) -> Image:
image_target_bgr = image_bgr.copy()
if outputs_boxes_xywh_classes[0] is None:
return image_target_bgr
S, E, N, bboxes_xywh, pred_classes = self.extract_and_check_outputs_and_boxes(
outputs_boxes_xywh_classes
)
meshes = {
p: create_mesh(self.class_to_mesh_name[p], self.device) for p in np.unique(pred_classes)
}
for n in range(N):
x, y, w, h = bboxes_xywh[n].int().cpu().numpy()
mesh_name = self.class_to_mesh_name[pred_classes[n]]
closest_vertices, mask = get_closest_vertices_mask_from_ES(
E[[n]],
S[[n]],
h,
w,
self.mesh_vertex_embeddings[mesh_name],
self.device,
)
uv_array = meshes[pred_classes[n]].texcoords[closest_vertices].permute((2, 0, 1))
uv_array = uv_array.cpu().numpy().clip(0, 1)
textured_image = self.generate_image_with_texture(
image_target_bgr[y : y + h, x : x + w],
uv_array,
mask.cpu().numpy(),
self.class_to_mesh_name[pred_classes[n]],
)
if textured_image is None:
continue
image_target_bgr[y : y + h, x : x + w] = textured_image
return image_target_bgr
def generate_image_with_texture(self, bbox_image_bgr, uv_array, mask, mesh_name):
alpha = self.alpha_dict.get(mesh_name)
texture_image = self.texture_image_dict.get(mesh_name)
if alpha is None or texture_image is None:
return None
U, V = uv_array
x_index = (U * texture_image.shape[1]).astype(int)
y_index = (V * texture_image.shape[0]).astype(int)
local_texture = texture_image[y_index, x_index][mask]
local_alpha = np.expand_dims(alpha[y_index, x_index][mask], -1)
output_image = bbox_image_bgr.copy()
output_image[mask] = output_image[mask] * (1 - local_alpha) + local_texture * local_alpha
return output_image.astype(np.uint8)
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