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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 | |
🔥 2023/06/29: 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: [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](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="", | |
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) | |
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_e = 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): | |
# text_box = gr.Textbox(label="text prompt") | |
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) | |
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) | |
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_t = 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"], | |
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) | |
cond_img_p.select(get_points_with_draw, [cond_img_p, add_or_remove], cond_img_p) | |
segment_btn_e.click(segment_everything, | |
inputs=[ | |
cond_img_e, | |
input_size_slider, | |
iou_threshold, | |
conf_threshold, | |
mor_check, | |
contour_check_e, | |
retina_check, | |
], | |
outputs=segm_img_e) | |
segment_btn_p.click(segment_with_points, | |
inputs=[cond_img_p], | |
outputs=[segm_img_p, cond_img_p]) | |
segment_btn_t.click(segment_everything, | |
inputs=[ | |
cond_img_t, | |
input_size_slider_t, | |
iou_threshold, | |
conf_threshold, | |
mor_check, | |
contour_check_t, | |
retina_check, | |
text_box, | |
], | |
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() | |