-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathmydemo2.py
175 lines (135 loc) · 7 KB
/
mydemo2.py
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
"""
Demo code
To run our method, you need a bounding box around the person. The person needs to be centered inside the bounding box and the bounding box should be relatively tight. You can either supply the bounding box directly or provide an [OpenPose](https://github.com/CMU-Perceptual-Computing-Lab/openpose) detection file. In the latter case we infer the bounding box from the detections.
In summary, we provide 3 different ways to use our demo code and models:
1. Provide only an input image (using ```--img```), in which case it is assumed that it is already cropped with the person centered in the image.
2. Provide an input image as before, together with the OpenPose detection .json (using ```--openpose```). Our code will use the detections to compute the bounding box and crop the image.
3. Provide an image and a bounding box (using ```--bbox```). The expected format for the json file can be seen in ```examples/im1010_bbox.json```.
Example with OpenPose detection .json
```
python demo.py --checkpoint=data/models/model_checkpoint_h36m_up3d_extra2d.pt --img=examples/im1010.png --openpose=examples/im1010_openpose.json
```
Example with predefined Bounding Box
```
python demo.py --checkpoint=data/models/model_checkpoint_h36m_up3d_extra2d.pt --img=examples/im1010.png --bbox=examples/im1010_bbox.json
```
Example with cropped and centered image
```
python demo.py --checkpoint=data/models/model_checkpoint_h36m_up3d_extra2d.pt --img=examples/im1010.png
```
Running the previous command will save the results in ```examples/im1010_{gcnn,smpl,gcnn_side,smpl_side}.png```. The files ```im1010_gcnn``` and ```im1010_smpl``` show the overlayed reconstructions of the non-parametric and parametric shapes respectively. We also render side views, saved in ```im1010_gcnn_side.png``` and ```im1010_smpl_side.png```.
"""
'''
Just for images in paper
'''
import torch
from torchvision.transforms import Normalize
import numpy as np
import cv2
import argparse
import json
from utils.imutils import crop
from utils.renderer import Renderer
import utils.config as cfg
from collections import namedtuple
from os.path import join, exists
from utils.objfile import read_obj
from models.TMR import build_model
from datasets.datasets import create_dataset, create_val_dataset
from utils.train_options import DDPTrainOptions
import datetime
import json
import random
import time
import numpy as np
import torch
import utils.misc as utils
import utils.samplers as samplers
from torch.utils.data import DataLoader
from train.train_one_epoch import train_one_epoch, evaluate
from pathlib import Path
from train.criterion import MeshLoss2, JointEvaluator, MeshLoss3, MeshLoss4
from models.TMR import build_model
from datasets.datasets import create_dataset, create_val_dataset
from utils.train_options import DDPTrainOptions
from tensorboardX import SummaryWriter
import os
from copy import deepcopy
from tqdm import tqdm
def notback(x):
IMAGENET_DEFAULT_MEAN = torch.tensor([0.485, 0.456, 0.406], device=x.device)
IMAGENET_DEFAULT_STD = torch.tensor([0.229, 0.224, 0.225], device=x.device)
x = x * IMAGENET_DEFAULT_STD + IMAGENET_DEFAULT_MEAN
return x.max().item() >= 1 / 255.0
def no_black_edge(img):
return notback(img[0, :, 0, 0]) \
and notback(img[0, :, 0, -1]) \
and notback(img[0, :, -1, 0]) \
and notback(img[0, :, -1, -1])
if __name__ == '__main__':
options = DDPTrainOptions().parse_args()
# Setup renderer for visualization
_, faces = read_obj('data/reference_mesh.obj')
renderer = Renderer(faces=np.array(faces) - 1)
device = torch.device('cuda')
model = build_model(options)
model.to(device)
model.eval()
if options.resume_from:
if os.path.exists(options.resume_from):
checkpoint = torch.load(options.resume_from, map_location='cpu')
missing_keys, unexpected_keys = model.load_state_dict(checkpoint['model'], strict=False)
unexpected_keys = [k for k in unexpected_keys if not (k.endswith('total_params') or k.endswith('total_ops'))]
if len(missing_keys) > 0:
print('Missing Keys: {}'.format(missing_keys))
if len(unexpected_keys) > 0:
print('Unexpected Keys: {}'.format(unexpected_keys))
print('resume finished.')
else:
print('NOTICE: ' + options.resume_from + ' not exists!')
evaluator = JointEvaluator(options, device)
dataset_val = create_val_dataset(options.val_dataset, options)
options_hr = deepcopy(options)
options_hr.img_res = 800
dataset_val_hr = create_val_dataset(options.val_dataset, options_hr)
evaluator.smpl = evaluator.male_smpl
# for idx in [4660, 4670, 4880, 5220, 6950, 10390, 10570, 10850, 17170]:
# for idx in [53, 63, 66, 71, 77, 102, 128, 137, 140, 183, 214, 217, 218, 219, 243, 254]: # lsp
for idx in tqdm(range(0, len(dataset_val), 10)):
data_batch = dataset_val[idx]
img = data_batch['img'].unsqueeze(0).to(device)
if not no_black_edge(img):
continue
with torch.no_grad():
pred_para = model(img)
pred_vertices = evaluator.apply_smpl(pred_para[0], pred_para[1])
pred_camera = pred_para[-1].detach().cpu()
camera_translation_all = torch.stack(
[pred_camera[:, 1], pred_camera[:, 2], 2 * cfg.FOCAL_LENGTH / (options_hr.img_res * pred_camera[:, 0] + 1e-9)],
dim=-1)
img = dataset_val_hr[idx]['img'].unsqueeze(0)
# img = data_batch['img'].detach()
img = img * torch.tensor([0.229, 0.224, 0.225], device=img.device).reshape(1, 3, 1, 1)
img = img + torch.tensor([0.485, 0.456, 0.406], device=img.device).reshape(1, 3, 1, 1)
img = img[0].permute(1, 2, 0).cpu().numpy()
camera_translation = camera_translation_all[0].cpu().numpy()
pred_vertices = pred_vertices[0].cpu().numpy()
# Render non-parametric shape
img_render = renderer.render(pred_vertices,
camera_t=camera_translation,
img=img, use_bg=True, body_color='pink')
# Render side views
aroundy = cv2.Rodrigues(np.array([0, np.radians(90.), 0]))[0]
center = pred_vertices.mean(axis=0)
center_smpl = pred_vertices.mean(axis=0)
rot_vertices = np.dot((pred_vertices - center), aroundy) + center
# Render non-parametric shape
img_render_side = renderer.render(rot_vertices,
camera_t=camera_translation,
img=np.ones_like(img), use_bg=True, body_color='pink')
# Render parametric shape
outfile = f'vis/{idx}'
# Save reconstructions
cv2.imwrite(outfile + '_input.png', 255 * img[:, :, ::-1])
cv2.imwrite(outfile + '_render.png', 255 * img_render[:, :, ::-1])
cv2.imwrite(outfile + '_render_side.png', 255 * img_render_side[:, :, ::-1])