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
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 = "⛔ 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 = [
[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')],
]
js='''
'''
with gr.Blocks(head=js, theme="Nymbo/Alyx_Theme") as demo:
width=240
height=340
with gr.Row(equal_height=False):
with gr.Column(min_width=240): # Adjust scale for proper sizing
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("Run", 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)''')
with gr.Row():
gr.HTML(
"""