Spaces:
Paused
Paused
File size: 8,422 Bytes
938e515 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 |
# 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)
|