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*.pyc
*.pyo
*.pyd
.DS_Store
.idea
weights
build/
*.egg-info/
gradio_cached_examples
\ No newline at end of file
Inference.py 0 → 100644
View file @ eb807a19
import argparse
from fastsam import FastSAM, FastSAMPrompt
import ast
import torch
from PIL import Image
from utils.tools import convert_box_xywh_to_xyxy
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument(
"--model_path", type=str, default="./weights/FastSAM.pt", help="model"
)
parser.add_argument(
"--img_path", type=str, default="./images/dogs.jpg", help="path to image file"
)
parser.add_argument("--imgsz", type=int, default=1024, help="image size")
parser.add_argument(
"--iou",
type=float,
default=0.9,
help="iou threshold for filtering the annotations",
)
parser.add_argument(
"--text_prompt", type=str, default=None, help='use text prompt eg: "a dog"'
)
parser.add_argument(
"--conf", type=float, default=0.4, help="object confidence threshold"
)
parser.add_argument(
"--output", type=str, default="./output/", help="image save path"
)
parser.add_argument(
"--randomcolor", type=bool, default=True, help="mask random color"
)
parser.add_argument(
"--point_prompt", type=str, default="[[0,0]]", help="[[x1,y1],[x2,y2]]"
)
parser.add_argument(
"--point_label",
type=str,
default="[0]",
help="[1,0] 0:background, 1:foreground",
)
parser.add_argument("--box_prompt", type=str, default="[[0,0,0,0]]", help="[[x,y,w,h],[x2,y2,w2,h2]] support multiple boxes")
parser.add_argument(
"--better_quality",
type=str,
default=False,
help="better quality using morphologyEx",
)
device = torch.device(
"cuda"
if torch.cuda.is_available()
else "mps"
if torch.backends.mps.is_available()
else "cpu"
)
parser.add_argument(
"--device", type=str, default=device, help="cuda:[0,1,2,3,4] or cpu"
)
parser.add_argument(
"--retina",
type=bool,
default=True,
help="draw high-resolution segmentation masks",
)
parser.add_argument(
"--withContours", type=bool, default=False, help="draw the edges of the masks"
)
return parser.parse_args()
def main(args):
# load model
model = FastSAM(args.model_path)
args.point_prompt = ast.literal_eval(args.point_prompt)
args.box_prompt = convert_box_xywh_to_xyxy(ast.literal_eval(args.box_prompt))
args.point_label = ast.literal_eval(args.point_label)
input = Image.open(args.img_path)
input = input.convert("RGB")
everything_results = model(
input,
device=args.device,
retina_masks=args.retina,
imgsz=args.imgsz,
conf=args.conf,
iou=args.iou
)
bboxes = None
points = None
point_label = None
prompt_process = FastSAMPrompt(input, everything_results, device=args.device)
if args.box_prompt[0][2] != 0 and args.box_prompt[0][3] != 0:
ann = prompt_process.box_prompt(bboxes=args.box_prompt)
bboxes = args.box_prompt
elif args.text_prompt != None:
ann = prompt_process.text_prompt(text=args.text_prompt)
elif args.point_prompt[0] != [0, 0]:
ann = prompt_process.point_prompt(
points=args.point_prompt, pointlabel=args.point_label
)
points = args.point_prompt
point_label = args.point_label
else:
ann = prompt_process.everything_prompt()
prompt_process.plot(
annotations=ann,
output_path=args.output+args.img_path.split("/")[-1],
bboxes = bboxes,
points = points,
point_label = point_label,
withContours=args.withContours,
better_quality=args.better_quality,
)
if __name__ == "__main__":
args = parse_args()
main(args)
LICENSE 0 → 100644
View file @ eb807a19
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later version.
15. Disclaimer of Warranty.
THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
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17. Interpretation of Sections 15 and 16.
If the disclaimer of warranty and limitation of liability provided
above cannot be given local legal effect according to their terms,
reviewing courts shall apply local law that most closely approximates
an absolute waiver of all civil liability in connection with the
Program, unless a warranty or assumption of liability accompanies a
copy of the Program in return for a fee.
END OF TERMS AND CONDITIONS
How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to the public, the best way to achieve this is to make it
free software which everyone can redistribute and change under these terms.
To do so, attach the following notices to the program. It is safest
to attach them to the start of each source file to most effectively
state the exclusion of warranty; and each file should have at least
the "copyright" line and a pointer to where the full notice is found.
<one line to give the program's name and a brief idea of what it does.>
Copyright (C) <year> <name of author>
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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You should have received a copy of the GNU Affero General Public License
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Also add information on how to contact you by electronic and paper mail.
If your software can interact with users remotely through a computer
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You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU AGPL, see
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MORE_USAGES.md 0 → 100644
View file @ eb807a19
# MORE_USAGES
### Everything mode
Use --imgsz to change different input sizes.
```shell
python Inference.py --model_path ./weights/FastSAM.pt \
--img_path ./images/dogs.jpg \
--imgsz 720 \
```
![everything mode](assets/more_usages/everything_mode.png)
### Use more points
p
```shell
python Inference.py --model_path ./weights/FastSAM.pt \
--img_path ./images/dogs.jpg \
--point_prompt "[[520,360],[620,300],[520,300],[620,360]]" \
--point_label "[1,0,1,0]"
```
![points prompt](assets/more_usages/more_points.png)
### draw mask edge
Use `--withContours True` to draw the edge of the mask.
When `--better_quality True` is set, the edge will be more smooth.
```shell
python Inference.py --model_path ./weights/FastSAM.pt \
--img_path ./images/dogs.jpg \
--point_prompt "[[620,360]]" \
--point_label "[1]" \
--withContours True \
--better_quality True
```
![Draw Edge](assets/more_usages/draw_edge.png)
### use box prompt
Use `--box_prompt [x,y,w,h]` to specify the bounding box of the foreground object
```shell
python Inference.py --model_path ./weights/FastSAM.pt \
--img_path ./images/dogs.jpg \
--box_prompt "[[570,200,230,400]]"
```
![box prompt](assets/more_usages/box_prompt.png)
### use text prompt
Use `--text_prompt "text"` to specify the text prompt
```shell
python Inference.py --model_path ./weights/FastSAM.pt \
--img_path ./images/cat.jpg \
--text_prompt "cat" \
--better_quality True \
--withContours True
```
![text prompt](assets/more_usages/text_prompt_cat.png)
README.md 0 → 100644
View file @ eb807a19
# FastSAM
## 论文
[Fast Segment Anything](https://arxiv.org/pdf/2306.12156.pdf)
## 模型结构
以yolov8-seg的instance segmentation为基础，检测时集成instance segmentation分支,主要分为两步全实例分割(all instance Segmentation)和基于prompt的mask输出(Prompt-guided Selection)，仅使用了2%的SA-1B数据集便得到了差不多的精度但快几十倍的速度。
![Overview.png](assets%2FOverview.png)
## 算法原理
该算法采用yolov8-seg的instance segmentation为基础，检测时集成instance segmentation分支实现。
![11.png](resources%2F11.png)
## 环境配置
### Docker (方法一)
```
docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.1.0-ubuntu20.04-dtk24.04.1-py3.10
# <your IMAGE ID>为以上拉取的docker的镜像ID替换
docker run -it -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal:/opt/hyhal --shm-size=32G --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name docker_name imageID bash
cd /path/workspace/
pip3 install -r requirements.txt
pip3 install git+https://github.com/openai/CLIP.git
```
### Dockerfile (方法二)
```
cd ./docker
docker build --no-cache -t fastsam_pytorch:last .
# <your IMAGE ID>为以上拉取的docker的镜像ID替换
docker run -it -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal:/opt/hyhal --shm-size=32G --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name docker_name imageID bash
```
### Anaconda (方法三)
1、关于本项目DCU显卡所需的特殊深度学习库可从光合开发者社区下载安装： https://developer.hpccube.com/tool/
```
DTK软件栈：dtk24.04.1
python：python3.10
pytorch：2.1.0
torchvision:0.16.0
```
Tips：以上dtk软件栈、python、pytorch等DCU相关工具版本需要严格一一对应
2、其他非特殊库直接按照requirements.txt安装
```
pip3 install -r requirements.txt
pip3 install git+https://github.com/openai/CLIP.git
```
## 数据集
训练数据集为SA-1B的2%左右的数据量，由于数据量过大，且官方并未放出2%的具体数据集，以下数据集为SA-1B原始数据集，训练仅供参考。
[原始链接](https://ai.meta.com/datasets/segment-anything/) | [SCNet高速下载通道](http://113.200.138.88:18080/aidatasets/project-dependency/sa-1b)
## 推理
#### 单卡推理
[权重下载地址](https://pan.baidu.com/s/18KzBmOTENjByoWWR17zdiQ?pwd=0000)下载权重到当前文件夹，生成的图片默认存放在output文件夹下
```
HIP_VISIBLE_DEVICES=1 python Inference.py --model_path ./FastSAM_X.pt --img_path ./images/dogs.jpg
```
## result
此处以Fast-SAM-x模型进行推理测试<br>
| 输入 | 输出 |
|:--:|:-:|
| ![dogs.jpg](images%2Fdogs.jpg) |![dogs.jpg](output%2Fdogs.jpg)|
### 精度
#### Instance Segmentation On COCO 2017
| method | AP | APS | APM | APL |
| :------: | :--: | :--: | :--: | :--: |
| ViTDet-H | .510 | .320 | .543 | .689 |
| SAM | .465 | .308 | .510 | .617 |
| FastSAM | .379 | .239 | .434 | .500 |
## 应用场景
### 算法分类
图像分割
### 热点应用行业
制造,广媒,能源,医疗,家居,教育
## 源码仓库及问题反馈
https://developer.hpccube.com/codes/modelzoo/fastsam_pytorch
## 参考资料
[GitHub - Fast-SAM](https://github.com/CASIA-IVA-Lab/FastSAM?tab=readme-ov-file)
app_gradio.py 0 → 100644
View file @ eb807a19
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: [![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="",
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()
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