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import gradio as gr
from detectron2.data import MetadataCatalog
from segment_anything import SamAutomaticMaskGenerator
metadata = MetadataCatalog.get('coco_2017_train_panoptic')
print(metadata)
class ImageMask(gr.components.Image):
"""
Sets: source="canvas", tool="sketch"
"""
is_template = True
def __init__(self, **kwargs):
super().__init__(source="upload", tool="sketch", interactive=True, **kwargs)
def preprocess(self, x):
return super().preprocess(x)
demo = gr.Blocks()
image = ImageMask(
label="Input",
type="pil",
brush_radius=20.0,
brush_color="#FFFFFF")
slider = gr.Slider(
minimum=1,
maximum=3,
value=2,
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(
choices=['Automatic', 'Interactive', ],
value='Automatic',
label="Segmentation Mode")
image_out = gr.Image(label="Auto generation", type="pil")
slider_alpha = gr.Slider(
minimum=0,
maximum=1,
value=0.1,
label="Mask Alpha",
info="Choose in [0, 1]")
label_mode = gr.Radio(
choices=['Number', 'Alphabet'],
value='Number',
label="Mark Mode")
anno_mode = gr.CheckboxGroup(
choices=["Mask", "Box", "Mark"],
value=['Mask', 'Mark'],
label="Annotation Mode")
runBtn = gr.Button("Run")
title = "Set-of-Mark (SoM) Prompting for 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 try it on <a href='https://chat.openai.com/'>GPT-4V<a>!"
with demo:
gr.Markdown(f"<h1 style='text-align: center;'>{title}</h1>")
gr.Markdown("<h3 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></h3>")
gr.Markdown(f"<h3 style='margin-bottom: 1rem'>{description}</h3>")
with gr.Row():
with gr.Column():
image.render()
slider.render()
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()
# with gr.Row():
# example = gr.Examples(
# examples=[
# ["examples/ironing_man.jpg"],
# ],
# inputs=image,
# cache_examples=False,
# )
# example = gr.Examples(
# examples=[
# ["examples/ironing_man_som.png"],
# ],
# inputs=image,
# cache_examples=False,
# label='Marked Examples',
# )
# runBtn.click(inference, inputs=[image, slider, mode, slider_alpha, label_mode, anno_mode],
# outputs = image_out)
demo.queue().launch()
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