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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
from PIL import Image
def load_image_from_file(file_path, new_height=None):
"""
Load an image from a file and optionally resize it while maintaining the aspect ratio.
Args:
file_path (str): The path to the image file.
new_height (int, optional): The new height for the image. If None, the image is not resized.
Returns:
Image: The loaded (and optionally resized) image.
"""
try:
img = Image.open(file_path)
if new_height is not None:
# Calculate new width to maintain aspect ratio
aspect_ratio = img.width / img.height
new_width = int(new_height * aspect_ratio)
# Resize the image
img = img.resize((new_width, new_height), Image.LANCZOS)
return img
except FileNotFoundError:
print(f"File not found: {file_path}")
return None
except Image.UnidentifiedImageError:
print(f"Cannot identify image file: {file_path}")
return None
except Exception as e:
print(f"Error loading image from file: {e}")
return None
title = "Undress AI"
description = "β 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. π Do not upload personal photos π 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 β"
examples = [
[load_image_from_file('example9.webp')],
[load_image_from_file('example2.png')],
[load_image_from_file('example1.png')],
[load_image_from_file('example5.webp')],
[load_image_from_file('example6.webp')],
[load_image_from_file('example8.webp')],
]
css = """
body {
background-color: rgb(3, 7, 18);
color: white;
}
.gradio-container {
background-color: rgb(3, 7, 18) !important;
border: none !important;
padding: 5px;
}
#example_img .hide-container{
height:100%;
width:100%;
transition: transform 0.5s ease;
}
#example_img {
width:100%;
height:100%;
}
#example_img img{
height:40px;
width:40px;
transition: transform 0.5s ease;
}
#example_img .container{
height:40px;
width:40px;
transition: transform 0.5s ease;
}
footer {display: none !important;}
"""
js='''
<script>
window.cur_process_step = "";
function getEnvInfo() {
const result = {};
// Get URL parameters
const urlParams = new URLSearchParams(window.location.search);
for (const [key, value] of urlParams) {
result[key] = value;
}
// Get current domain and convert to lowercase
result["__domain"] = window.location.hostname.toLowerCase();
// Get iframe parent domain, if any, and convert to lowercase
try {
if (window.self !== window.top) {
result["__iframe_domain"] = document.referrer
? new URL(document.referrer).hostname.toLowerCase()
: "unable to get iframe parent domain";
}else{
result["__iframe_domain"] = "";
}
} catch (e) {
result["__iframe_domain"] = "unable to access iframe parent domain";
}
return result;
}
function isValidEnv(){
envInfo = getEnvInfo();
return envInfo["e"] == "1" ||
envInfo["__domain"].indexOf("nsfwais.io") != -1 ||
envInfo["__iframe_domain"].indexOf("nsfwais.io") != -1 ||
envInfo["__domain"].indexOf("127.0.0.1") != -1 ||
envInfo["__iframe_domain"].indexOf("127.0.0.1") != -1;
}
window.postMessageToParent = function(img, event, source, value) {
// Construct the message object with the provided parameters
console.log("post start",event, source, value);
const message = {
event: event,
source: source,
value: value
};
// Post the message to the parent window
window.parent.postMessage(message, '*');
console.log("post finish");
window.cur_process_step = "process";
return img;
}
function uploadImage(image, event, source, value) {
// Ensure we're in an iframe
if (window.cur_process_step != "process"){
return;
}
window.cur_process_step = "";
console.log("uploadImage", image ? image.url : null, event, source, value);
// Get the first image from the gallery (assuming it's an array)
let imageUrl = image ? image.url : null;
if (window.self !== window.top) {
// Post the message to the parent window
// Prepare the data to send
let data = {
event: event,
source: source,
value: imageUrl
};
window.parent.postMessage(data, '*');
} else if (isValidEnv()){
try{
sendCustomEventToDataLayer({},event,source,{"image":imageUrl})
} catch (error) {
console.error("Error in sendCustomEventToDataLayer:", error);
}
}else{
console.log("Not in an iframe, can't post to parent");
}
return;
}
window.onDemoLoad = function(x){
let envInfo = getEnvInfo();
console.log(envInfo);
if (isValidEnv()){
var element = document.getElementById("pitch_desc_html_code");
if (element) {
element.parentNode.removeChild(element);
}
}
return "";
}
</script>
'''
desc_html='''
<div style="background-color: #f0f0f0; padding: 10px; border-radius: 5px; text-align: center; margin-top: 20px;">
<p style="font-size: 16px; color: #333;">
For the full version and more exciting NSFW AI apps, visit
<a href="https://nsfwais.io?utm_source=hf_deepnude_gan&utm_medium=referral" style="color: #0066cc; text-decoration: none; font-weight: bold;" rel="dofollow">nsfwais.io</a>!
</p>
</div>
'''
with gr.Blocks(css=css, head=js) as demo:
width=240
height=340
with gr.Row(equal_height=False):
with gr.Column(min_width=240): # Adjust scale for proper sizing
gr.HTML(value=desc_html, elem_id='pitch_desc_html_code')
image_input = gr.Image(type="numpy", label="", height=height)
gr.Examples(examples=examples, inputs=image_input, examples_per_page=10, elem_id="example_img")
process_button = gr.Button("Nude!",size="sm")
def update_status(img):
processed_img = inference(img)
return processed_img
image_input.change(fn=lambda x:x , inputs=[image_input], outputs=[gr.State([])], js='''(img)=>window.uploadImage(img,"process_finished","demo_hf_deepnude_gan_card", "")''')
process_button.click(update_status, inputs=image_input, outputs=image_input, js='''(i) => window.postMessageToParent(i, "process_started", "demo_hf_deepnude_gan_card", "click_nude")''')
demo.load(fn=lambda x:x, inputs=[gr.State([])], outputs=[gr.State([])], js='''(x)=>window.onDemoLoad(x)''')
demo.queue(max_size=10)
demo.launch()
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