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# Copyright (c) Facebook, Inc. and its affiliates.
# Copyright (c) Meta Platforms, Inc. All Rights Reserved
import multiprocessing as mp
import numpy as np
from PIL import Image
try:
import detectron2
except:
import os
os.system('pip install git+https://github.com/facebookresearch/detectron2.git')
from detectron2.config import get_cfg
from detectron2.projects.deeplab import add_deeplab_config
from detectron2.data.detection_utils import read_image
from open_vocab_seg import add_ovseg_config
from open_vocab_seg.utils import VisualizationDemo
import gradio as gr
import gdown
ckpt_url = 'https://drive.google.com/uc?id=1cn-ohxgXDrDfkzC1QdO-fi8IjbjXmgKy'
output = './ovseg_swinbase_vitL14_ft_mpt.pth'
gdown.download(ckpt_url, output, quiet=False)
def setup_cfg(config_file):
# load config from file and command-line arguments
cfg = get_cfg()
add_deeplab_config(cfg)
add_ovseg_config(cfg)
cfg.merge_from_file(config_file)
cfg.freeze()
return cfg
def inference(class_names, input_img):
mp.set_start_method("spawn", force=True)
config_file = './ovseg_swinB_vitL_demo.yaml'
cfg = setup_cfg(config_file)
demo = VisualizationDemo(cfg)
class_names = class_names.split(',')
img = read_image(input_img, format="BGR")
_, visualized_output = demo.run_on_image(img, class_names)
return Image.fromarray(np.uint8(visualized_output.get_image())).convert('RGB')
examples = [['Oculus, Ukulele', './resources/demo_samples/sample_03.jpeg'],
['Saturn V, toys, blossom', './resources/demo_samples/sample_01.jpeg'],
['Golden gate, yacht', './resources/demo_samples/sample_02.jpeg'],]
output_labels = ['segmentation map']
title = 'OVSeg'
description = """
Gradio Demo for Open-Vocabulary Semantic Segmentation with Mask-adapted CLIP. \n
OVSeg could perform open vocabulary segmentation, you may input more classes (seperate by comma). You may click on of the examples or upload your own image. \n
It might take some time to process. Cheers!
<p>Don't want to wait in queue? <a href="https://colab.research.google.com/drive/1O4Ain5uFZNcQYUmDTG92DpEGCatga8K5?usp=sharing"><img data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" src="https://camo.githubusercontent.com/84f0493939e0c4de4e6dbe113251b4bfb5353e57134ffd9fcab6b8714514d4d1/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667"></a></p>
"""
article = """
<p style='text-align: center'>
<a href='https://arxiv.org/abs/2210.04150' target='_blank'>
Open-Vocabulary Semantic Segmentation with Mask-adapted CLIP
</a>
|
<a href='https://github.com/facebookresearch/ov-seg' target='_blank'>Github Repo</a></p>
"""
gr.Interface(
inference,
inputs=[
gr.inputs.Textbox(
lines=1, placeholder=None, default='', label='class names'),
gr.inputs.Image(type='filepath')
],
outputs=gr.outputs.Image(label='segmentation map'),
title=title,
description=description,
article=article,
examples=examples).launch(enable_queue=True)
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