Spaces:
kadirnar
/
Runtime error

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)