-
Notifications
You must be signed in to change notification settings - Fork 101
/
Copy pathdemo_gpt4v_som.py
226 lines (191 loc) · 8.95 KB
/
demo_gpt4v_som.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
# --------------------------------------------------------
# Set-of-Mark (SoM) Prompting for Visual Grounding in GPT-4V
# Copyright (c) 2023 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by:
# Jianwei Yang ([email protected])
# Xueyan Zou ([email protected])
# Hao Zhang ([email protected])
# --------------------------------------------------------
import io
import gradio as gr
import torch
import argparse
from PIL import Image
# seem
from seem.modeling.BaseModel import BaseModel as BaseModel_Seem
from seem.utils.distributed import init_distributed as init_distributed_seem
from seem.modeling import build_model as build_model_seem
from task_adapter.seem.tasks import interactive_seem_m2m_auto, inference_seem_pano, inference_seem_interactive
# semantic sam
from semantic_sam.BaseModel import BaseModel
from semantic_sam import build_model
from semantic_sam.utils.dist import init_distributed_mode
from semantic_sam.utils.arguments import load_opt_from_config_file
from semantic_sam.utils.constants import COCO_PANOPTIC_CLASSES
from task_adapter.semantic_sam.tasks import inference_semsam_m2m_auto, prompt_switch
# sam
from segment_anything import sam_model_registry
from task_adapter.sam.tasks.inference_sam_m2m_auto import inference_sam_m2m_auto
from task_adapter.sam.tasks.inference_sam_m2m_interactive import inference_sam_m2m_interactive
from task_adapter.utils.visualizer import Visualizer
from detectron2.data import MetadataCatalog
metadata = MetadataCatalog.get('coco_2017_train_panoptic')
from scipy.ndimage import label
import numpy as np
from gpt4v import request_gpt4v
from openai import OpenAI
from pydub import AudioSegment
from pydub.playback import play
import matplotlib.colors as mcolors
css4_colors = mcolors.CSS4_COLORS
color_proposals = [list(mcolors.hex2color(color)) for color in css4_colors.values()]
client = OpenAI()
'''
build args
'''
semsam_cfg = "configs/semantic_sam_only_sa-1b_swinL.yaml"
seem_cfg = "configs/seem_focall_unicl_lang_v1.yaml"
semsam_ckpt = "./swinl_only_sam_many2many.pth"
sam_ckpt = "./sam_vit_h_4b8939.pth"
seem_ckpt = "./seem_focall_v1.pt"
opt_semsam = load_opt_from_config_file(semsam_cfg)
opt_seem = load_opt_from_config_file(seem_cfg)
opt_seem = init_distributed_seem(opt_seem)
'''
build model
'''
model_semsam = BaseModel(opt_semsam, build_model(opt_semsam)).from_pretrained(semsam_ckpt).eval().cuda()
model_sam = sam_model_registry["vit_h"](checkpoint=sam_ckpt).eval().cuda()
model_seem = BaseModel_Seem(opt_seem, build_model_seem(opt_seem)).from_pretrained(seem_ckpt).eval().cuda()
with torch.no_grad():
with torch.autocast(device_type='cuda', dtype=torch.float16):
model_seem.model.sem_seg_head.predictor.lang_encoder.get_text_embeddings(COCO_PANOPTIC_CLASSES + ["background"], is_eval=True)
history_images = []
history_masks = []
history_texts = []
@torch.no_grad()
def inference(image, slider, mode, alpha, label_mode, anno_mode, *args, **kwargs):
global history_images; history_images = []
global history_masks; history_masks = []
_image = image['background'].convert('RGB')
_mask = image['layers'][0].convert('L') if image['layers'] else None
if slider < 1.5:
model_name = 'seem'
elif slider > 2.5:
model_name = 'sam'
else:
if mode == 'Automatic':
model_name = 'semantic-sam'
if slider < 1.5 + 0.14:
level = [1]
elif slider < 1.5 + 0.28:
level = [2]
elif slider < 1.5 + 0.42:
level = [3]
elif slider < 1.5 + 0.56:
level = [4]
elif slider < 1.5 + 0.70:
level = [5]
elif slider < 1.5 + 0.84:
level = [6]
else:
level = [6, 1, 2, 3, 4, 5]
else:
model_name = 'sam'
if label_mode == 'Alphabet':
label_mode = 'a'
else:
label_mode = '1'
text_size, hole_scale, island_scale=640,100,100
text, text_part, text_thresh = '','','0.0'
with torch.autocast(device_type='cuda', dtype=torch.float16):
semantic=False
if mode == "Interactive":
labeled_array, num_features = label(np.asarray(_mask))
spatial_masks = torch.stack([torch.from_numpy(labeled_array == i+1) for i in range(num_features)])
if model_name == 'semantic-sam':
model = model_semsam
output, mask = inference_semsam_m2m_auto(model, _image, level, text, text_part, text_thresh, text_size, hole_scale, island_scale, semantic, label_mode=label_mode, alpha=alpha, anno_mode=anno_mode, *args, **kwargs)
elif model_name == 'sam':
model = model_sam
if mode == "Automatic":
output, mask = inference_sam_m2m_auto(model, _image, text_size, label_mode, alpha, anno_mode)
elif mode == "Interactive":
output, mask = inference_sam_m2m_interactive(model, _image, spatial_masks, text_size, label_mode, alpha, anno_mode)
elif model_name == 'seem':
model = model_seem
if mode == "Automatic":
output, mask = inference_seem_pano(model, _image, text_size, label_mode, alpha, anno_mode)
elif mode == "Interactive":
output, mask = inference_seem_interactive(model, _image, spatial_masks, text_size, label_mode, alpha, anno_mode)
# convert output to PIL image
history_masks.append(mask)
history_images.append(Image.fromarray(output))
return (output, [])
def gpt4v_response(message, history):
global history_images
global history_texts; history_texts = []
try:
res = request_gpt4v(message, history_images[0])
history_texts.append(res)
return res
except Exception as e:
return None
def highlight(mode, alpha, label_mode, anno_mode, *args, **kwargs):
res = history_texts[0]
# find the seperate numbers in sentence res
res = res.split(' ')
res = [r.replace('.','').replace(',','').replace(')','').replace('"','') for r in res]
# find all numbers in '[]'
res = [r for r in res if '[' in r]
res = [r.split('[')[1] for r in res]
res = [r.split(']')[0] for r in res]
res = [r for r in res if r.isdigit()]
res = list(set(res))
sections = []
for i, r in enumerate(res):
mask_i = history_masks[0][int(r)-1]['segmentation']
sections.append((mask_i, r))
return (history_images[0], sections)
'''
launch app
'''
demo = gr.Blocks()
image = gr.ImageMask(label="Input", type="pil", sources=["upload"], interactive=True, brush=gr.Brush(colors=["#FFFFFF"]))
slider = gr.Slider(1, 3, value=1.8, label="Granularity") # info="Choose in [1, 1.5), [1.5, 2.5), [2.5, 3] for [seem, semantic-sam (multi-level), sam]"
mode = gr.Radio(['Automatic', 'Interactive', ], value='Automatic', label="Segmentation Mode")
anno_mode = gr.CheckboxGroup(choices=["Mark", "Mask", "Box"], value=['Mark'], label="Annotation Mode")
image_out = gr.AnnotatedImage(label="SoM Visual Prompt", height=512)
runBtn = gr.Button("Run")
highlightBtn = gr.Button("Highlight")
bot = gr.Chatbot(label="GPT-4V + SoM", height=256)
slider_alpha = gr.Slider(0, 1, value=0.05, label="Mask Alpha") #info="Choose in [0, 1]"
label_mode = gr.Radio(['Number', 'Alphabet'], value='Number', label="Mark Mode")
title = "Set-of-Mark (SoM) Visual Prompting for Extraordinary Visual Grounding in GPT-4V"
description = "This is a demo for SoM Prompting to unleash extraordinary visual grounding in GPT-4V. Please upload an image and them click the 'Run' button to get the image with marks. Then chat with GPT-4V below!"
with demo:
gr.Markdown("<h1 style='text-align: center'><img src='https://som-gpt4v.github.io/website/img/som_logo.png' style='height:50px;display:inline-block'/> Set-of-Mark (SoM) Prompting Unleashes Extraordinary Visual Grounding in GPT-4V</h1>")
# gr.Markdown("<h2 style='text-align: center; margin-bottom: 1rem'>Project: <a href='https://som-gpt4v.github.io/'>link</a> arXiv: <a href='https://arxiv.org/abs/2310.11441'>link</a> Code: <a href='https://github.com/microsoft/SoM'>link</a></h2>")
with gr.Row():
with gr.Column():
image.render()
slider.render()
with gr.Accordion("Detailed prompt settings (e.g., mark type)", open=False):
with gr.Row():
mode.render()
anno_mode.render()
with gr.Row():
slider_alpha.render()
label_mode.render()
with gr.Column():
image_out.render()
runBtn.render()
highlightBtn.render()
with gr.Row():
gr.ChatInterface(chatbot=bot, fn=gpt4v_response)
runBtn.click(inference, inputs=[image, slider, mode, slider_alpha, label_mode, anno_mode],
outputs = image_out)
highlightBtn.click(highlight, inputs=[image, mode, slider_alpha, label_mode, anno_mode],
outputs = image_out)
demo.queue().launch(share=True,server_port=6092)