Hugging Face's logo

Spaces:
gradio
/
PIFu-Clothed-Human-Digitization
Runtime error

App Files Community
PIFu-Clothed-Human-Digitization
File size: 4,873 Bytes 
33000da
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
 
import os
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"  
os.environ["CUDA_VISIBLE_DEVICES"]="0"
try:
    os.system("pip install --upgrade  torch==1.11.0+cu113 torchvision==0.12.0+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html")
except Exception as e:
    print(e)

from pydoc import describe
from huggingface_hub import hf_hub_download
import gradio as gr
import os
from datetime import datetime
from PIL import Image
import torch
import torchvision
import skimage
import paddlehub
import numpy as np
from lib.options import BaseOptions
from apps.crop_img import process_img
from apps.eval import Evaluator
from types import SimpleNamespace
import trimesh
import glob

print(
    "torch: ", torch.__version__,
    "\ntorchvision: ", torchvision.__version__,
    "\nskimage:", skimage.__version__
)

print("EnV", os.environ)

net_C = hf_hub_download("radames/PIFu-upright-standing", filename="net_C")
net_G = hf_hub_download("radames/PIFu-upright-standing", filename="net_G")


opt = BaseOptions()
opts = opt.parse_to_dict()
opts['batch_size'] = 1
opts['mlp_dim'] = [257, 1024, 512, 256, 128, 1]
opts['mlp_dim_color'] = [513, 1024, 512, 256, 128, 3]
opts['num_stack'] = 4
opts['num_hourglass'] = 2
opts['resolution'] = 128
opts['hg_down'] = 'ave_pool'
opts['norm'] = 'group'
opts['norm_color'] = 'group'
opts['load_netG_checkpoint_path'] = net_G
opts['load_netC_checkpoint_path'] = net_C
opts['results_path'] = "./results"
opts['name'] = "spaces_demo"
opts = SimpleNamespace(**opts)
print("Params", opts)
evaluator = Evaluator(opts)
bg_remover_model = paddlehub.Module(name="U2Net")


def process(img_path):
    base = os.path.basename(img_path)
    img_name = os.path.splitext(base)[0]
    print("\n\n\nStarting Process", datetime.now())
    print("image name", img_name)
    img_raw = Image.open(img_path).convert('RGB')

    img = img_raw.resize(
        (512, int(512 * img_raw.size[1] / img_raw.size[0])),
        Image.Resampling.LANCZOS)

    try:
        # remove background
        print("Removing Background")
        masks = bg_remover_model.Segmentation(
            images=[np.array(img)],
            paths=None,
            batch_size=1,
            input_size=320,
            output_dir='./PIFu/inputs',
            visualization=False)
        mask = masks[0]["mask"]
        front = masks[0]["front"]
    except Exception as e:
        print(e)

    print("Aliging mask with input training image")
    print("Not aligned", front.shape, mask.shape)
    img_new, msk_new = process_img(front, mask)
    print("Aligned", img_new.shape, msk_new.shape)

    try:
        time = datetime.now()
        data = evaluator.load_image_from_memory(img_new, msk_new, img_name)
        print("Evaluating via PIFu", time)
        evaluator.eval(data, True)
        print("Success Evaluating via PIFu", datetime.now() - time)
        result_path = f'./{opts.results_path}/{opts.name}/result_{img_name}'
    except Exception as e:
        print("Error evaluating via PIFu", e)

    try:
        mesh = trimesh.load(result_path + '.obj')
        # flip mesh
        mesh.apply_transform([[-1, 0, 0, 0],
                              [0, 1, 0, 0],
                              [0, 0, -1, 0],
                              [0, 0, 0, 1]])
        mesh.export(file_obj=result_path + '.glb')
        result_gltf = result_path + '.glb'
        return [result_gltf, result_gltf]

    except Exception as e:
        print("error generating MESH", e)


examples = sorted(glob.glob('examples/*.png'))
description = '''
# PIFu Clothed Human Digitization
### PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization
<base target="_blank">

This is a demo for <a href="https://github.com/shunsukesaito/PIFu" target="_blank"> PIFu model </a>.
The pre-trained model has the following warning:
> Warning: The released model is trained with mostly upright standing scans with weak perspectie projection and the pitch angle of 0 degree. Reconstruction quality may degrade for images highly deviated from trainining data.

**The inference takes about 180seconds for a new image.**

<details>
<summary>More</summary>

#### Image Credits

* Julien and Clem
* [StyleGAN Humans](https://huggingface.co/spaces/hysts/StyleGAN-Human)
* [Renderpeople: Dennis](https://renderpeople.com)


#### More
* https://phorhum.github.io/
* https://github.com/yuliangxiu/icon
* https://shunsukesaito.github.io/PIFuHD/

</details>
'''

iface = gr.Interface(
    fn=process,
    description=description,
    inputs=gr.Image(type="filepath", label="Input Image"),
    outputs=[
        gr.Model3D(
            clear_color=[0.0, 0.0, 0.0, 0.0],  label="3D Model"),
        gr.File(label="Download 3D Model")
    ],
    examples=examples,
    allow_flagging="never",
    cache_examples=True
)

if __name__ == "__main__":
    iface.launch(debug=True, enable_queue=False)