init

README.md

@@ -1,10 +1,10 @@
 ---
 title: SegGPT
-emoji: 📚
-colorFrom: green
-colorTo: blue
+emoji: 🏢
+colorFrom: gray
+colorTo: indigo
 sdk: gradio
-sdk_version: 3.24.1
+sdk_version: 3.22.1
 app_file: app.py
 pinned: false
 license: mit

app.py

@@ -0,0 +1,89 @@
+# -*- 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)
+    a = json.loads(r.text)
+    res = []
+    for i in range(len(a)):
+        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(label="prompt (提示图)"), gr.Image(label="img1 (测试图1)"), gr.Image(label="img2 (测试图2)")], 
+                    outputs=gr.Gallery(label="outputs (输出图)"),
+                    examples=examples,
+                    #title="SegGPT for Any Segmentation<br>(Painter Inside)",
+                    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='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()
+#demo.launch(server_name="0.0.0.0", server_port=34311)
+# -
+
+