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# -*- coding: utf-8 -*-
import sys
import io
import requests
import json
import base64
from PIL import Image
import numpy as np
import gradio as gr
def inference_mask1(prompt,
img,
img_):
files = {
"pimage" : resizeImg(prompt["image"]),
"pmask" : resizeImg(prompt["mask"]),
"img" : resizeImg(img),
"img_" : resizeImg(img_)
}
#r = requests.post("https://flagstudio.baai.ac.cn/painter/run", json = files)
r = requests.post("http://120.92.79.209/painter/run", json = files)
a = json.loads(r.text)
res = []
for i in range(len(a)):
#out = Image.open(io.BytesIO(base64.b64decode(a[i])))
#out = out.resize((224, 224))
#res.append(np.uint8(np.array(out)))
res.append(np.uint8(np.array(Image.open(io.BytesIO(base64.b64decode(a[i]))))))
return res
def resizeImg(img):
res, hres = 448, 448
img = Image.fromarray(img).convert("RGB")
img = img.resize((res, hres))
temp = io.BytesIO()
img.save(temp, format="WEBP")
return base64.b64encode(temp.getvalue()).decode('ascii')
def inference_mask_cat(
prompt,
img,
img_,
):
output_list = [img, img_]
return output_list
# define app features and run
examples = [
['./images/hmbb_1.jpg', './images/hmbb_2.jpg', './images/hmbb_3.jpg'],
['./images/rainbow_1.jpg', './images/rainbow_2.jpg', './images/rainbow_3.jpg'],
['./images/earth_1.jpg', './images/earth_2.jpg', './images/earth_3.jpg'],
['./images/obj_1.jpg', './images/obj_2.jpg', './images/obj_3.jpg'],
['./images/xray_1.jpg', './images/xray_2.jpg', './images/xray_3.jpg'],
['./images/ydt_2.jpg', './images/ydt_1.jpg', './images/ydt_3.jpg'],
]
demo_mask = gr.Interface(fn=inference_mask1,
inputs=[gr.ImageMask(brush_radius=8, label="prompt (提示图)"), gr.Image(label="img1 (测试图1)"), gr.Image(label="img2 (测试图2)")],
#outputs=[gr.Image(shape=(448, 448), label="output1 (输出图1)"), gr.Image(shape=(448, 448), label="output2 (输出图2)")],
outputs=[gr.Image(label="output1 (输出图1)").style(height=384, width=384), gr.Image(label="output2 (输出图2)").style(height=384, width=384)],
#outputs=gr.Gallery(label="outputs (输出图)"),
examples=examples,
#title="SegGPT for Any Segmentation<br>(Painter Inside)",
description="<p> \
Choose an example below 🔥 🔥 🔥 <br>\
Or, upload by yourself: <br>\
1. Upload images to be tested to 'img1' and/or 'img2'. <br>2. Upload a prompt image to 'prompt' and draw a mask. <br>\
Tips: The more accurate you annotate, the more accurate the model predicts.;) \
</p>",
cache_examples=False,
allow_flagging="never",
)
title = "SegGPT: Segmenting Everything In Context<br> \
<div align='center'> \
<h2><a href='https://arxiv.org/abs/2304.03284' target='_blank' rel='noopener'>[paper]</a> \
<a href='https://github.com/baaivision/Painter' target='_blank' rel='noopener'>[code]</a></h2> \
<br> \
<image src='file/seggpt_teaser.png' width='720px' /> \
<h2>SegGPT performs arbitrary segmentation tasks in images or videos via in-context inference, such as object instance, stuff, part, contour, and text, with only one single model.</h2> \
</div> \
"
demo = gr.TabbedInterface([demo_mask, ], ['General 1-shot', ], title=title)
#demo.launch(share=True, auth=("baai", "vision"))
demo.launch(enable_queue=False)
#demo.launch(server_name="0.0.0.0", server_port=34311)
# -
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