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from run import process
import time
import subprocess
import os
import argparse
import cv2
import sys
from PIL import Image
import torch
import gradio as gr
TESTdevice = "cpu"
index = 1
def mainTest(inputpath, outpath):
watermark = deep_nude_process(inputpath)
watermark1 = cv2.cvtColor(watermark, cv2.COLOR_BGRA2RGBA)
return watermark1
def deep_nude_process(inputpath):
dress = cv2.imread(inputpath)
h = dress.shape[0]
w = dress.shape[1]
dress = cv2.resize(dress, (512, 512), interpolation=cv2.INTER_CUBIC)
watermark = process(dress)
watermark = cv2.resize(watermark, (w, h), interpolation=cv2.INTER_CUBIC)
return watermark
def inference(img):
global index
bgra = cv2.cvtColor(img, cv2.COLOR_RGBA2BGRA)
inputpath = f"input_{index}.jpg"
cv2.imwrite(inputpath, bgra)
outputpath = f"out_{index}.jpg"
index += 1
print(time.strftime("START!!!!!!!!! %Y-%m-%d %H:%M:%S", time.localtime()))
output = mainTest(inputpath, outputpath)
print(time.strftime("Finish!!!!!!!!! %Y-%m-%d %H:%M:%S", time.localtime()))
return output
title = "Undress AI"
description = "⛔ Input photos of people, similar to the test picture at the bottom, and undress pictures will be produced. You may have to wait 30 seconds for a picture. 🔞 Do not upload personal photos 🔞 There is a queue system. According to the logic of first come, first served, only one picture will be made at a time. Must be able to at least see the outline of a human body ⛔"
examples = [
['example1.png'],
['example2.png'],
['example3.png'],
['example5.webp'],
['example6.webp'],
]
css = """
body {
background-color: rgb(17, 24, 39);
color: white;
}
.gradio-container {
background-color: rgb(17, 24, 39) !important;
border: none !important;
}
#example_img .hide-container{
height:80px;
width:100%;
transition: transform 0.5s ease;
}
#example_img{
width:100%;
}
#example_img img{
height:80px;
width:50px;
transition: transform 0.5s ease;
}
footer {display: none !important;}
"""
with gr.Blocks(css=css) as demo:
width=320
height=240
with gr.Row():
with gr.Column(scale=2): # Adjust scale for proper sizing
image_input = gr.Image(type="numpy", label="Upload Image", width=width, height=height)
gr.Examples(examples=examples, inputs=image_input, examples_per_page=5, elem_id="example_img")
process_button = gr.Button("Nude!")
def update_status(img):
processed_img = inference(img)
return processed_img
process_button.click(update_status, inputs=image_input, outputs=image_input)
demo.queue(max_size=10)
demo.launch()
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