-
Notifications
You must be signed in to change notification settings - Fork 0
/
demo.py
153 lines (125 loc) · 7.43 KB
/
demo.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
from pathlib import Path
import torch
import argparse
import os
import cv2
import numpy as np
from hmr2.configs import CACHE_DIR_4DHUMANS
from hmr2.models import HMR2, download_models, load_hmr2, DEFAULT_CHECKPOINT
from hmr2.utils import recursive_to
from hmr2.datasets.vitdet_dataset import ViTDetDataset, DEFAULT_MEAN, DEFAULT_STD
from hmr2.utils.renderer import Renderer, cam_crop_to_full
LIGHT_BLUE=(0.65098039, 0.74117647, 0.85882353)
def main():
parser = argparse.ArgumentParser(description='HMR2 demo code')
parser.add_argument('--checkpoint', type=str, default=DEFAULT_CHECKPOINT, help='Path to pretrained model checkpoint')
parser.add_argument('--img_folder', type=str, default='example_data/images', help='Folder with input images')
parser.add_argument('--out_folder', type=str, default='demo_out', help='Output folder to save rendered results')
parser.add_argument('--side_view', dest='side_view', action='store_true', default=False, help='If set, render side view also')
parser.add_argument('--top_view', dest='top_view', action='store_true', default=False, help='If set, render top view also')
parser.add_argument('--full_frame', dest='full_frame', action='store_true', default=False, help='If set, render all people together also')
parser.add_argument('--save_mesh', dest='save_mesh', action='store_true', default=False, help='If set, save meshes to disk also')
parser.add_argument('--batch_size', type=int, default=1, help='Batch size for inference/fitting')
args = parser.parse_args()
# Download and load checkpoints
download_models(CACHE_DIR_4DHUMANS)
model, model_cfg = load_hmr2(args.checkpoint)
# Setup HMR2.0 model
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
model = model.to(device)
model.eval()
# Load detector
from hmr2.utils.utils_detectron2 import DefaultPredictor_Lazy
from detectron2.config import LazyConfig
import hmr2
cfg_path = Path(hmr2.__file__).parent/'configs'/'cascade_mask_rcnn_vitdet_h_75ep.py'
detectron2_cfg = LazyConfig.load(str(cfg_path))
detectron2_cfg.train.init_checkpoint = "https://dl.fbaipublicfiles.com/detectron2/ViTDet/COCO/cascade_mask_rcnn_vitdet_h/f328730692/model_final_f05665.pkl"
for i in range(3):
detectron2_cfg.model.roi_heads.box_predictors[i].test_score_thresh = 0.25
detector = DefaultPredictor_Lazy(detectron2_cfg)
# Setup the renderer
renderer = Renderer(model_cfg, faces=model.smpl.faces)
# Make output directory if it does not exist
os.makedirs(args.out_folder, exist_ok=True)
# Iterate over all images in folder
for img_path in Path(args.img_folder).glob('*.jpg'):
img_cv2 = cv2.imread(str(img_path))
# Detect humans in image
det_out = detector(img_cv2)
det_instances = det_out['instances']
valid_idx = (det_instances.pred_classes==0) & (det_instances.scores > 0.5)
boxes=det_instances.pred_boxes.tensor[valid_idx].cpu().numpy()
# Run HMR2.0 on all detected humans
dataset = ViTDetDataset(model_cfg, img_cv2, boxes)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=8, shuffle=False, num_workers=0)
all_verts = []
all_cam_t = []
for batch in dataloader:
batch = recursive_to(batch, device)
with torch.no_grad():
out = model(batch)
pred_cam = out['pred_cam']
box_center = batch["box_center"].float()
box_size = batch["box_size"].float()
img_size = batch["img_size"].float()
scaled_focal_length = model_cfg.EXTRA.FOCAL_LENGTH / model_cfg.MODEL.IMAGE_SIZE * img_size.max()
pred_cam_t_full = cam_crop_to_full(pred_cam, box_center, box_size, img_size, scaled_focal_length).detach().cpu().numpy()
# Render the result
batch_size = batch['img'].shape[0]
for n in range(batch_size):
# Get filename from path img_path
img_fn, _ = os.path.splitext(os.path.basename(img_path))
person_id = int(batch['personid'][n])
white_img = (torch.ones_like(batch['img'][n]).cpu() - DEFAULT_MEAN[:,None,None]/255) / (DEFAULT_STD[:,None,None]/255)
input_patch = batch['img'][n].cpu() * (DEFAULT_STD[:,None,None]/255) + (DEFAULT_MEAN[:,None,None]/255)
input_patch = input_patch.permute(1,2,0).numpy()
regression_img = renderer(out['pred_vertices'][n].detach().cpu().numpy(),
out['pred_cam_t'][n].detach().cpu().numpy(),
batch['img'][n],
mesh_base_color=LIGHT_BLUE,
scene_bg_color=(1, 1, 1),
)
final_img = np.concatenate([input_patch, regression_img], axis=1)
if args.side_view:
side_img = renderer(out['pred_vertices'][n].detach().cpu().numpy(),
out['pred_cam_t'][n].detach().cpu().numpy(),
white_img,
mesh_base_color=LIGHT_BLUE,
scene_bg_color=(1, 1, 1),
side_view=True)
final_img = np.concatenate([final_img, side_img], axis=1)
if args.top_view:
top_img = renderer(out['pred_vertices'][n].detach().cpu().numpy(),
out['pred_cam_t'][n].detach().cpu().numpy(),
white_img,
mesh_base_color=LIGHT_BLUE,
scene_bg_color=(1, 1, 1),
top_view=True)
final_img = np.concatenate([final_img, top_img], axis=1)
cv2.imwrite(os.path.join(args.out_folder, f'{img_fn}_{person_id}.png'), 255*final_img[:, :, ::-1])
# Add all verts and cams to list
verts = out['pred_vertices'][n].detach().cpu().numpy()
cam_t = pred_cam_t_full[n]
all_verts.append(verts)
all_cam_t.append(cam_t)
# Save all meshes to disk
if args.save_mesh:
camera_translation = cam_t.copy()
tmesh = renderer.vertices_to_trimesh(verts, camera_translation, LIGHT_BLUE)
tmesh.export(os.path.join(args.out_folder, f'{img_fn}_{person_id}.obj'))
# Render front view
if args.full_frame and len(all_verts) > 0:
misc_args = dict(
mesh_base_color=LIGHT_BLUE,
scene_bg_color=(1, 1, 1),
focal_length=scaled_focal_length,
)
cam_view = renderer.render_rgba_multiple(all_verts, cam_t=all_cam_t, render_res=img_size[n], **misc_args)
# Overlay image
input_img = img_cv2.astype(np.float32)[:,:,::-1]/255.0
input_img = np.concatenate([input_img, np.ones_like(input_img[:,:,:1])], axis=2) # Add alpha channel
input_img_overlay = input_img[:,:,:3] * (1-cam_view[:,:,3:]) + cam_view[:,:,:3] * cam_view[:,:,3:]
cv2.imwrite(os.path.join(args.out_folder, f'{img_fn}_all.png'), 255*input_img_overlay[:, :, ::-1])
if __name__ == '__main__':
main()