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app_gradio.py
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from ultralytics import YOLO
import gradio as gr
import torch
from utils.tools_gradio import fast_process
from utils.tools import format_results, box_prompt, point_prompt, text_prompt
from PIL import ImageDraw
import numpy as np
# Load the pre-trained model
model = YOLO('./weights/FastSAM.pt')
device = torch.device(
"cuda"
if torch.cuda.is_available()
else "mps"
if torch.backends.mps.is_available()
else "cpu"
)
# Description
title = "<center><strong><font size='8'>🏃 Fast Segment Anything 🤗</font></strong></center>"
news = """ # 📖 News
🔥 2023/07/14: Add a "wider result" button in text mode (Thanks for [gaoxinge](https://github.com/CASIA-IVA-Lab/FastSAM/pull/95)).
🔥 2023/06/29: Support the text mode (Thanks for [gaoxinge](https://github.com/CASIA-IVA-Lab/FastSAM/pull/47)).
🔥 2023/06/26: Support the points mode. (Better and faster interaction will come soon!)
🔥 2023/06/24: Add the 'Advanced options" in Everything mode to get a more detailed adjustment.
"""
description_e = """This is a demo on Github project 🏃 [Fast Segment Anything Model](https://github.com/CASIA-IVA-Lab/FastSAM). Welcome to give a star ⭐️ to it.
🎯 Upload an Image, segment it with Fast Segment Anything (Everything mode). The other modes will come soon.
⌛️ It takes about 6~ 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)
"""
description_p = """ # 🎯 Instructions for points mode
This is a demo on Github project 🏃 [Fast Segment Anything Model](https://github.com/CASIA-IVA-Lab/FastSAM). Welcome to give a star ⭐️ to it.
1. Upload an image or choose an example.
2. Choose the point label ('Add mask' means a positive point. 'Remove' Area means a negative point that is not segmented).
3. Add points one by one on the image.
4. Click the 'Segment with points prompt' button to get the segmentation results.
**5. If you get Error, click the 'Clear points' button and try again may help.**
"""
examples = [["examples/sa_8776.jpg"], ["examples/sa_414.jpg"], ["examples/sa_1309.jpg"], ["examples/sa_11025.jpg"],
["examples/sa_561.jpg"], ["examples/sa_192.jpg"], ["examples/sa_10039.jpg"], ["examples/sa_862.jpg"]]
default_example = examples[0]
css = "h1 { text-align: center } .about { text-align: justify; padding-left: 10%; padding-right: 10%; }"
def segment_everything(
input,
input_size=1024,
iou_threshold=0.7,
conf_threshold=0.25,
better_quality=False,
withContours=True,
use_retina=True,
text="",
wider=False,
mask_random_color=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,)
if len(text) > 0:
results = format_results(results[0], 0)
annotations, _ = text_prompt(results, text, input, device=device, wider=wider)
annotations = np.array([annotations])
else:
annotations = results[0].masks.data
fig = fast_process(annotations=annotations,
image=input,
device=device,
scale=(1024 // input_size),
better_quality=better_quality,
mask_random_color=mask_random_color,
bbox=None,
use_retina=use_retina,
withContours=withContours,)
return fig
def segment_with_points(
input,
input_size=1024,
iou_threshold=0.7,
conf_threshold=0.25,
better_quality=False,
withContours=True,
use_retina=True,
mask_random_color=True,
):
global global_points
global global_point_label
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))
scaled_points = [[int(x * scale) for x in point] for point in global_points]
results = model(input,
device=device,
retina_masks=True,
iou=iou_threshold,
conf=conf_threshold,
imgsz=input_size,)
results = format_results(results[0], 0)
annotations, _ = point_prompt(results, scaled_points, global_point_label, new_h, new_w)
annotations = np.array([annotations])
fig = fast_process(annotations=annotations,
image=input,
device=device,
scale=(1024 // input_size),
better_quality=better_quality,
mask_random_color=mask_random_color,
bbox=None,
use_retina=use_retina,
withContours=withContours,)
global_points = []
global_point_label = []
return fig, None
def get_points_with_draw(image, label, evt: gr.SelectData):
global global_points
global global_point_label
x, y = evt.index[0], evt.index[1]
point_radius, point_color = 15, (255, 255, 0) if label == 'Add Mask' else (255, 0, 255)
global_points.append([x, y])
global_point_label.append(1 if label == 'Add Mask' else 0)
print(x, y, label == 'Add Mask')
# 创建一个可以在图像上绘图的对象
draw = ImageDraw.Draw(image)
draw.ellipse([(x - point_radius, y - point_radius), (x + point_radius, y + point_radius)], fill=point_color)
return image
cond_img_e = gr.Image(label="Input", value=default_example[0], type='pil')
cond_img_p = gr.Image(label="Input with points", value=default_example[0], type='pil')
cond_img_t = gr.Image(label="Input with text", value="examples/dogs.jpg", type='pil')
segm_img_e = gr.Image(label="Segmented Image", interactive=False, type='pil')
segm_img_p = gr.Image(label="Segmented Image with points", interactive=False, type='pil')
segm_img_t = gr.Image(label="Segmented Image with text", interactive=False, type='pil')
global_points = []
global_point_label = []
input_size_slider = gr.components.Slider(minimum=512,
maximum=1024,
value=1024,
step=64,
label='Input_size',
info='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)
with gr.Tab("Everything mode"):
# Images
with gr.Row(variant="panel"):
with gr.Column(scale=1):
cond_img_e.render()
with gr.Column(scale=1):
segm_img_e.render()
# Submit & Clear
with gr.Row():
with gr.Column():
input_size_slider.render()
with gr.Row():
contour_check = gr.Checkbox(value=True, label='withContours', info='draw the edges of the masks')
with gr.Column():
segment_btn_e = gr.Button("Segment Everything", variant='primary')
clear_btn_e = gr.Button("Clear", variant="secondary")
gr.Markdown("Try some of the examples below ⬇️")
gr.Examples(examples=examples,
inputs=[cond_img_e],
outputs=segm_img_e,
fn=segment_everything,
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', info='iou threshold for filtering the annotations')
conf_threshold = gr.Slider(0.1, 0.9, 0.25, step=0.05, label='conf', info='object confidence threshold')
with gr.Row():
mor_check = gr.Checkbox(value=False, label='better_visual_quality', info='better quality using morphologyEx')
with gr.Column():
retina_check = gr.Checkbox(value=True, label='use_retina', info='draw high-resolution segmentation masks')
# Description
gr.Markdown(description_e)
segment_btn_e.click(segment_everything,
inputs=[
cond_img_e,
input_size_slider,
iou_threshold,
conf_threshold,
mor_check,
contour_check,
retina_check,
],
outputs=segm_img_e)
with gr.Tab("Points mode"):
# Images
with gr.Row(variant="panel"):
with gr.Column(scale=1):
cond_img_p.render()
with gr.Column(scale=1):
segm_img_p.render()
# Submit & Clear
with gr.Row():
with gr.Column():
with gr.Row():
add_or_remove = gr.Radio(["Add Mask", "Remove Area"], value="Add Mask", label="Point_label (foreground/background)")
with gr.Column():
segment_btn_p = gr.Button("Segment with points prompt", variant='primary')
clear_btn_p = gr.Button("Clear points", variant='secondary')
gr.Markdown("Try some of the examples below ⬇️")
gr.Examples(examples=examples,
inputs=[cond_img_p],
# outputs=segm_img_p,
# fn=segment_with_points,
# cache_examples=True,
examples_per_page=4)
with gr.Column():
# Description
gr.Markdown(description_p)
cond_img_p.select(get_points_with_draw, [cond_img_p, add_or_remove], cond_img_p)
segment_btn_p.click(segment_with_points,
inputs=[cond_img_p],
outputs=[segm_img_p, cond_img_p])
with gr.Tab("Text mode"):
# Images
with gr.Row(variant="panel"):
with gr.Column(scale=1):
cond_img_t.render()
with gr.Column(scale=1):
segm_img_t.render()
# Submit & Clear
with gr.Row():
with gr.Column():
input_size_slider_t = gr.components.Slider(minimum=512,
maximum=1024,
value=1024,
step=64,
label='Input_size',
info='Our model was trained on a size of 1024')
with gr.Row():
with gr.Column():
contour_check = gr.Checkbox(value=True, label='withContours', info='draw the edges of the masks')
text_box = gr.Textbox(label="text prompt", value="a black dog")
with gr.Column():
segment_btn_t = gr.Button("Segment with text", variant='primary')
clear_btn_t = gr.Button("Clear", variant="secondary")
gr.Markdown("Try some of the examples below ⬇️")
gr.Examples(examples=[["examples/dogs.jpg"]] + examples,
inputs=[cond_img_e],
# outputs=segm_img_e,
# fn=segment_everything,
# 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', info='iou threshold for filtering the annotations')
conf_threshold = gr.Slider(0.1, 0.9, 0.25, step=0.05, label='conf', info='object confidence threshold')
with gr.Row():
mor_check = gr.Checkbox(value=False, label='better_visual_quality', info='better quality using morphologyEx')
retina_check = gr.Checkbox(value=True, label='use_retina', info='draw high-resolution segmentation masks')
wider_check = gr.Checkbox(value=False, label='wider', info='wider result')
# Description
gr.Markdown(description_e)
segment_btn_t.click(segment_everything,
inputs=[
cond_img_t,
input_size_slider_t,
iou_threshold,
conf_threshold,
mor_check,
contour_check,
retina_check,
text_box,
wider_check,
],
outputs=segm_img_t)
def clear():
return None, None
def clear_text():
return None, None, None
clear_btn_e.click(clear, outputs=[cond_img_e, segm_img_e])
clear_btn_p.click(clear, outputs=[cond_img_p, segm_img_p])
clear_btn_t.click(clear_text, outputs=[cond_img_p, segm_img_p, text_box])
demo.queue()
demo.launch()