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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_sam(prompt, | |
| img, | |
| img_): | |
| files = { | |
| "useSam" : 1, | |
| "pimage" : resizeImg(prompt["image"]), | |
| "pmask" : resizeImg(prompt["mask"]), | |
| "img" : resizeImg(img), | |
| "img_" : resizeImg(img_) | |
| } | |
| 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[1:] # remove prompt image | |
| 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/ydt_2.jpg', './images/ydt_1.jpg', './images/ydt_3.jpg'], | |
| ] | |
| examples_sam = [ | |
| ['./images/nc_1.jpg', './images/nc_2.jpg', './images/nc_3.jpg'], | |
| ['./images/street_1.jpg', './images/street_2.jpg', './images/street_3.jpg'], | |
| ['./images/hmbb_1.jpg', './images/hmbb_2.jpg', './images/hmbb_3.jpg'], | |
| ['./images/earth_1.jpg', './images/earth_2.jpg', './images/earth_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=256, width=256), gr.Image(label="output2 (输出图2)").style(height=256, width=256)], | |
| #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>\ | |
| <br> \ | |
| 💎 The more accurate you annotate, the more accurate the model predicts. <br>\ | |
| 💎 Examples below were never trained and are randomly selected for testing in the wild. <br>\ | |
| 💎 Current UI interface only unleashes a small part of the capabilities of SegGPT, i.e., 1-shot case. \ | |
| </p>", | |
| cache_examples=False, | |
| allow_flagging="never", | |
| ) | |
| demo_mask_sam = gr.Interface(fn=inference_mask1_sam, | |
| inputs=[gr.ImageMask(brush_radius=4, 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=256, width=256), gr.Image(label="output2 (输出图2)").style(height=256, width=256)], | |
| #outputs=gr.Gallery(label="outputs (输出图)"), | |
| outputs=[gr.Image(label="SAM output (mask)").style(height=256, width=256),gr.Image(label="output1 (输出图1)").style(height=256, width=256), gr.Image(label="output2 (输出图2)").style(height=256, width=256)], | |
| # outputs=[gr.Image(label="output3 (输出图1)").style(height=256, width=256), gr.Image(label="output4 (输出图2)").style(height=256, width=256)], | |
| examples=examples_sam, | |
| #title="SegGPT for Any Segmentation<br>(Painter Inside)", | |
| description="<p> \ | |
| <strong>SAM+SegGPT: One touch for segmentation in all images or videos.</strong> <br>\ | |
| Choose an example below 🔥 🔥 🔥 <br>\ | |
| Or, upload by yourself: <br>\ | |
| 1. Upload images to be tested to 'img1' and 'img2'. <br>2. Upload a prompt image to 'prompt' and draw <strong>a point or line on the target</strong>. <br>\ | |
| <br> \ | |
| 💎 SAM segments the target with any point or scribble, then SegGPT segments all other images. <br>\ | |
| 💎 Examples below were never trained and are randomly selected for testing in the wild. <br>\ | |
| 💎 Current UI interface only unleashes a small part of the capabilities of SegGPT, i.e., 1-shot case. \ | |
| </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/rainbow2.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_sam, demo_mask], ['SAM+SegGPT (一触百通)', '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) | |
| # - | |