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 """ main.py How to run: python main.py """ def mainTest(inputpath, outpath): watermark = deep_nude_process(inputpath) watermark1 = cv2.cvtColor(watermark, cv2.COLOR_BGRA2RGBA) #cv2.imwrite(outpath, watermark1) 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 = "input_" + str(index) + ".jpg" cv2.imwrite(inputpath, bgra) outputpath = "out_" + str(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'], ] web = gr.Interface(inference, inputs="image", outputs="image", title=title, description=description, examples=examples, ) if __name__ == '__main__': web.launch( enable_queue=True )