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