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Upload app.py
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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 πŸ€— 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()