File size: 5,480 Bytes
5c80958 |
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 |
"""Visual Iterative Prompting Demo."""
import gradio as gr
import numpy as np
from vip_runner import vip_runner
from vlms import GPT4V
# Adjust radius of annotations based on size of the image
radius_per_pixel = 0.05
def run_vip(
im,
query,
n_samples_init,
n_samples_opt,
n_iters,
n_recurssion,
openai_api_key,
progress=gr.Progress(track_tqdm=True),
):
if not openai_api_key:
return [], 'Must provide OpenAI API Key'
if im is None:
return [], 'Must specify image'
if not query:
return [], 'Must specify description'
img_size = np.min(im.shape[:2])
print(int(img_size * radius_per_pixel))
# add some action spec
style = {
'num_samples': 12,
'circle_alpha': 0.6,
'alpha': 0.8,
'arrow_alpha': 0.0,
'radius': int(img_size * radius_per_pixel),
'thickness': 2,
'fontsize': int(img_size * radius_per_pixel),
'rgb_scale': 255,
'focal_offset': 1, # camera distance / std of action in z
}
action_spec = {
'loc': [0, 0, 0],
'scale': [0.0, 100, 100],
'min_scale': [0.0, 30, 30],
'min': [0, -300.0, -300],
'max': [0, 300, 300],
'action_to_coord': 250,
'robot': 'meta',
}
vlm = GPT4V(openai_api_key=openai_api_key)
ims, center, _ = vip_runner(
vlm,
im,
query,
style,
action_spec,
n_samples_init=n_samples_init,
n_samples_opt=n_samples_opt,
n_iters=n_iters,
recursion_level=n_recurssion,
)
return ims, f'Final selected coordinate: {np.round(center, decimals=0)}'
examples = [
{
'im_path': 'ims/aloha.png',
'desc': 'a point between the fork and the cup',
},
{
'im_path': 'ims/robot.png',
'desc': 'the toy in the middle of the table',
},
{
'im_path': 'ims/parking.jpg',
'desc': 'a place to park if I am handicapped',
},
{
'im_path': 'ims/tools.png',
'desc': 'what should I use pull a nail'
},
]
with gr.Blocks() as demo:
gr.Markdown("""
# Visual Iterative Prompting Demo
The demo below showcases the Visual Iterative Prompting (VIP) algorithm.
Given an image and a description of an object or region,
VIP leverages a Vision-Language Model (VLM) to iteratively search for the point in the image that best corresponds to the description.
This is done through visual prompting, where instead of reasoning with text, the VLM reasons over images annotated with sampled points,
in order to pick the best points.
In each iteration, we take the points previously selected by the VLM, resample new points around the their mean, and repeat the process.
To get started, you can use the provided example image and query pairs, or
upload your own images.
This demo uses GPT-4V, so it requires an OpenAI API key.
To use the provided example images, you can right click on the image -> copy image, then click the clipboard icon in the Input Image box.
Hyperparameters to set:
* N Samples for Initialization - how many initial points are sampled for the first VIP iteration.
* N Samples for Optimiazation - how many points are sampled for subsequent iterations.
* N Iterations - how many optimization iterations to perform.
* N Ensemble Recursions - how many ensembles for recursive VIP.
Note that each iteration takes about ~10s, and each additional ensemble adds a multiple number of N Iterations.
After VIP finishes, the image gallery below will visualize VIP results throughout all the iterations.
There are two images for each iteration - the first one shows all the sampled points, and the second one shows which one VIP picked.
The Info textbox will show the final selected pixel coordinate that VIP converged to.
""".strip())
gr.Markdown(
'## Example Images and Queries\n Drag images into the image box below'
)
with gr.Row(equal_height=True):
for example in examples:
gr.Image(value=example['im_path'], label=example['desc'])
gr.Markdown('## New Query')
with gr.Row():
with gr.Column():
inp_im = gr.Image(label='Input Image', type='numpy', show_label=True)
inp_query = gr.Textbox(label='Description', lines=1)
with gr.Column():
inp_openai_api_key = gr.Textbox(
label='OpenAI API Key (not saved)', lines=1
)
with gr.Group():
inp_n_samples_init = gr.Slider(
label='N Samples for Initialization',
minimum=10,
maximum=40,
value=25,
step=1,
)
inp_n_samples_opt = gr.Slider(
label='N Samples for Optimization',
minimum=3,
maximum=20,
value=10,
step=1,
)
inp_n_iters = gr.Slider(
label='N Iterations', minimum=1, maximum=5, value=3, step=1
)
inp_n_recurssions = gr.Slider(
label='N Ensemble Recursions', minimum=0, maximum=3, value=0, step=1
)
btn_run = gr.Button('Run')
with gr.Group():
out_ims = gr.Gallery(
label='Images with Sampled and Chosen Points',
columns=4,
rows=1,
interactive=False,
)
out_info = gr.Textbox(label='Info', lines=1)
btn_run.click(
run_vip,
inputs=[
inp_im,
inp_query,
inp_n_samples_init,
inp_n_samples_opt,
inp_n_iters,
inp_n_recurssions,
inp_openai_api_key,
],
outputs=[out_ims, out_info],
)
demo.launch()
|