add local demo

SegGPT/README.md

@@ -24,7 +24,15 @@ Enjoy the [Demo](https://huggingface.co/spaces/BAAI/SegGPT)
 [[Paper]](https://arxiv.org/abs/2304.03284)
 [[Demo]](https://huggingface.co/spaces/BAAI/SegGPT)
 
-
+## **Run the demo**
+- We provide a UI with gradio for running the demo locally. Running the following command in a terminal will launch the demo: 
+ ```
+ python app_gradio.py
+ ```
+- This demo is also hosted on HuggingFace [here](https://huggingface.co/spaces/BAAI/SegGPT).
+- The current UI interface just unleashes a small part of the capabilities of SegGPT. Please stay tuned for more demonstrations.
+
+<image src="rainbow.gif" width="720px" />
 
 ## Citation
 

SegGPT/app_gradio.py

@@ -0,0 +1,91 @@
+# -*- 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://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(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,
+ description="<p> \
+ Choose an example below &#128293; &#128293; &#128293; <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='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/baaivision/Painter' target='_blank' rel='noopener'>[code]</a></h2> \
+<br> \
+<image src='file/rainbow.gif' 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)
+
+
+