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from ultralytics import YOLO
import gradio as gr
import torch
from tools import fast_process
# Load the pre-trained model
model = YOLO('checkpoints/FastSAM.pt')
# Description
title = "<center><strong><font size='8'>🏃 Fast Segment Anything 🤗</font></strong></center>"
news = """ # News
🔥 Add the 'Advanced options" in Everything mode to get a more detailed adjustment.
"""
# 🔥 Support the points mode and box mode, text mode will come soon.
description = """This is a demo on Github project 🏃 [Fast Segment Anything Model](https://github.com/CASIA-IVA-Lab/FastSAM).
🎯 Upload an Image, segment it with Fast Segment Anything (Everything mode). The other modes will come soon.
⌛️ It takes about 4~ seconds to generate segment results. The concurrency_count of queue is 1, please wait for a moment when it is crowded.
🚀 To get faster results, you can use a smaller input size and leave high_visual_quality unchecked.
📣 You can also obtain the segmentation results of any Image through this Colab: [](https://colab.research.google.com/drive/1oX14f6IneGGw612WgVlAiy91UHwFAvr9?usp=sharing)
😚 A huge thanks goes out to the @HuggingFace Team for supporting us with GPU grant.
🏠 Check out our [Model Card 🏃](https://huggingface.co/An-619/FastSAM)
"""
examples = [["assets/sa_8776.jpg"], ["assets/sa_414.jpg"], ["assets/sa_1309.jpg"], ["assets/sa_11025.jpg"],
["assets/sa_561.jpg"], ["assets/sa_192.jpg"], ["assets/sa_10039.jpg"], ["assets/sa_862.jpg"]]
default_example = examples[0]
css = "h1 { text-align: center } .about { text-align: justify; padding-left: 10%; padding-right: 10%; }"
def segment_image(
input,
input_size=1024,
iou_threshold=0.7,
conf_threshold=0.25,
better_quality=False,
mask_random_color=True,
withContours=True,
points=None,
bbox=None,
point_label=None,
use_retina=True,
):
input_size = int(input_size) # 确保 imgsz 是整数
# Thanks for the suggestion by hysts in HuggingFace.
w, h = input.size
scale = input_size / max(w, h)
new_w = int(w * scale)
new_h = int(h * scale)
input = input.resize((new_w, new_h))
results = model(input,
device=device,
retina_masks=True,
iou=iou_threshold,
conf=conf_threshold,
imgsz=input_size,)
fig = fast_process(annotations=results[0].masks.data,
image=input,
device=device,
scale=(1024 // input_size),
better_quality=better_quality,
mask_random_color=mask_random_color,
points=points,
bbox=bbox,
point_label=point_label,
use_retina=use_retina,
withContours=withContours,)
return fig
# input_size=1024
# high_quality_visual=True
# inp = 'assets/sa_192.jpg'
# input = Image.open(inp)
# device = 'cuda' if torch.cuda.is_available() else 'cpu'
# input_size = int(input_size) # 确保 imgsz 是整数
# results = model(input, device=device, retina_masks=True, iou=0.7, conf=0.25, imgsz=input_size)
# pil_image = fast_process(annotations=results[0].masks.data,
# image=input, high_quality=high_quality_visual, device=device)
device = 'cuda' if torch.cuda.is_available() else 'cpu'
cond_img = gr.Image(label="Input", value=default_example[0], type='pil')
segm_img = gr.Image(label="Segmented Image", interactive=False, type='pil')
input_size_slider = gr.components.Slider(minimum=512,
maximum=1024,
value=1024,
step=64,
label='Input_size (Our model was trained on a size of 1024)')
with gr.Blocks(css=css, title='Fast Segment Anything') as demo:
with gr.Row():
with gr.Column(scale=1):
# Title
gr.Markdown(title)
with gr.Column(scale=1):
# News
gr.Markdown(news)
# Images
with gr.Row(variant="panel"):
with gr.Column(scale=1):
cond_img.render()
with gr.Column(scale=1):
segm_img.render()
# Submit & Clear
with gr.Row():
with gr.Column():
input_size_slider.render()
with gr.Row():
contour_check = gr.Checkbox(value=True, label='withContours')
with gr.Column():
segment_btn = gr.Button("Segment Anything", variant='primary')
# with gr.Column():
# clear_btn = gr.Button("Clear", variant="primary")
gr.Markdown("Try some of the examples below ⬇️")
gr.Examples(examples=examples,
inputs=[cond_img],
outputs=segm_img,
fn=segment_image,
cache_examples=True,
examples_per_page=4)
with gr.Column():
with gr.Accordion("Advanced options", open=False):
iou_threshold = gr.Slider(0.1, 0.9, 0.7, step=0.1, label='iou_threshold')
conf_threshold = gr.Slider(0.1, 0.9, 0.25, step=0.05, label='conf_threshold')
mor_check = gr.Checkbox(value=False, label='better_visual_quality')
# Description
gr.Markdown(description)
segment_btn.click(segment_image,
inputs=[cond_img, input_size_slider, iou_threshold, conf_threshold, mor_check, contour_check],
outputs=segm_img)
# def clear():
# return None, None
# clear_btn.click(fn=clear, inputs=None, outputs=None)
demo.queue()
demo.launch()
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