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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
"""
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
)
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