Hugging Face's logo

Spaces:
wildoctopus
/
cloth-segmentation
Running

App Files Community
1
cloth-segmentation
File size: 4,767 Bytes 
896437a
 
 
1c6ea49
896437a
 
 
1c6ea49
896437a
 
1c6ea49
 
 
 
 
896437a
1c6ea49
896437a
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1c6ea49
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
7906bcb
 
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
 
import PIL
import torch
import gradio as gr
import os
from process import load_seg_model, get_palette, generate_mask



device = 'cpu'

def read_content(file_path: str) -> str:
    """read the content of target file
    """
    with open(file_path, 'r', encoding='utf-8') as f:
        content = f.read()

    return content

def initialize_and_load_models():

    checkpoint_path = 'model/cloth_segm.pth'
    net = load_seg_model(checkpoint_path, device=device)    

    return net

net = initialize_and_load_models()
palette = get_palette(4)


def run(img):

    cloth_seg = generate_mask(img, net=net, palette=palette, device=device)
    return cloth_seg

# Define input and output interfaces
input_image = gr.inputs.Image(label="Input Image", type="pil")

# Define the Gradio interface
cloth_seg_image = gr.outputs.Image(label="Cloth Segmentation", type="pil")

title = "Demo for Cloth Segmentation"
description = "An app for Cloth Segmentation"
inputs = [input_image]
outputs = [cloth_seg_image]

css = '''
.container {max-width: 1150px;margin: auto;padding-top: 1.5rem}
#image_upload{min-height:400px}
#image_upload [data-testid="image"], #image_upload [data-testid="image"] > div{min-height: 400px}
#mask_radio .gr-form{background:transparent; border: none}
#word_mask{margin-top: .75em !important}
#word_mask textarea:disabled{opacity: 0.3}
.footer {margin-bottom: 45px;margin-top: 35px;text-align: center;border-bottom: 1px solid #e5e5e5}
.footer>p {font-size: .8rem; display: inline-block; padding: 0 10px;transform: translateY(10px);background: white}
.dark .footer {border-color: #303030}
.dark .footer>p {background: #0b0f19}
.acknowledgments h4{margin: 1.25em 0 .25em 0;font-weight: bold;font-size: 115%}
#image_upload .touch-none{display: flex}
@keyframes spin {
    from {
        transform: rotate(0deg);
    }
    to {
        transform: rotate(360deg);
    }
}
#share-btn-container {
    display: flex; padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; width: 13rem;
}
#share-btn {
    all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.25rem !important; padding-bottom: 0.25rem !important;
}
#share-btn * {
    all: unset;
}
#share-btn-container div:nth-child(-n+2){
    width: auto !important;
    min-height: 0px !important;
}
#share-btn-container .wrap {
    display: none !important;
}
'''
example={}
image_dir='input'

image_list=[os.path.join(image_dir,file) for file in os.listdir(image_dir)]
image_list.sort()


image_blocks = gr.Blocks(css=css)
with image_blocks as demo:
    gr.HTML(read_content("header.html"))
    with gr.Group():
        with gr.Box():
            with gr.Row():
                with gr.Column():
                    image = gr.Image(source='upload', elem_id="image_upload", type="pil", label="Input Image")
                    

                with gr.Column():
                    image_out = gr.Image(label="Output", elem_id="output-img").style(height=400)
                    

                    
            
            
            with gr.Row():
                with gr.Column():
                    gr.Examples(image_list, inputs=[image],label="Examples - Input Images",examples_per_page=12)
                with gr.Column():
                    with gr.Row(elem_id="prompt-container").style(mobile_collapse=False, equal_height=True):
                        btn = gr.Button("Run!").style(
                            margin=False,
                            rounded=(False, True, True, False),
                            full_width=True,
                        )
                    
                    
            
            btn.click(fn=run, inputs=[image], outputs=[image_out])
            



            gr.HTML(
                """
                    <div class="footer">
                        <p>Model by <a href="" style="text-decoration: underline;" target="_blank">WildOctopus</a> - Gradio Demo by 🤗 Hugging Face
                        </p>
                    </div>
                    <div class="acknowledgments">
                        <p><h4>ACKNOWLEDGEMENTS</h4></p>
                        <p>
                        U2net model is from original u2net repo. Thanks to <a href="https://github.com/xuebinqin/U-2-Net" style="text-decoration: underline;" target="_blank">Xuebin Qin</a> for amazing repo.</p>
<p>Codes are modified from <a href="https://github.com/levindabhi/cloth-segmentation" style="text-decoration: underline;" target="_blank">levindabhi/cloth-segmentation</a>
        </p>
                """
            )

image_blocks.launch()