| 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 = [ | |
| ['input.png', 'Test'], | |
| ['input.jpg', 'Test'], | |
| ] | |
| css = """ | |
| body { | |
| background-color: rgb(17, 24, 39); | |
| color: white; | |
| overflow: hidden; /* Prevent scrolling */ | |
| } | |
| .gradio-container { | |
| background-color: rgb(17, 24, 39) !important; | |
| border: none !important; | |
| max-width: 100%; /* Ensure it does not exceed the container's width */ | |
| max-height: 100%; /* Ensure it does not exceed the container's height */ | |
| overflow: hidden; /* Prevent internal scrolling */ | |
| } | |
| footer {display: none !important;} /* Hide footer */ | |
| """ | |
| with gr.Blocks(css=css) as demo: | |
| with gr.Column(): | |
| image_input = gr.Image(type="numpy", label="Upload Image", height=512, width=512) | |
| process_button = gr.Button("Process Image") | |
| status = gr.Markdown(value="") | |
| def update_status(img): | |
| return inference(img), gr.update(value="Processing complete!") | |
| process_button.click(update_status, inputs=image_input, outputs=[image_input, status]) | |
| demo.launch() | |