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| import os | |
| import torch | |
| import numpy as np | |
| from fairseq import utils,tasks | |
| from utils.checkpoint_utils import load_model_ensemble_and_task | |
| from models.polyformer import PolyFormerModel | |
| import cv2 | |
| import torch | |
| import numpy as np | |
| from fairseq import utils, tasks | |
| from fairseq import checkpoint_utils | |
| from utils.eval_utils import eval_step | |
| from tasks.refcoco import RefcocoTask | |
| from models.polyformer import PolyFormerModel | |
| from PIL import Image | |
| from torchvision import transforms | |
| import cv2 | |
| import gradio as gr | |
| import math | |
| from io import BytesIO | |
| import base64 | |
| import re | |
| from demo import visual_grounding | |
| title = "PolyFormer" | |
| description = """<p style='text-align: center'> <a href='https://polyformer.github.io/' target='_blank'>Project Page</a> | <a href='https://arxiv.org/pdf/2302.07387.pdf' target='_blank'>Paper</a> | <a href='https://github.com/amazon-science/polygon-transformer' target='_blank'>Github Repo</a></p> | |
| <p style='text-align: center'> Demo of PolyFormer for referring image segmentation and referring expression comprehension. Upload your own image or click any one of the examples, and write a description about a certain object. Then click \"Submit\" and wait for the result of grounding.</p> | |
| <p>For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings. | |
| <br/> | |
| <a href="https://huggingface.co/spaces/koajoel/PolyFormer?duplicate=true"> | |
| <img style="margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a> | |
| </p> | |
| """ | |
| examples = [['demo/bear.jpeg', 'a bear astronaut in the space'], | |
| ['demo/unicorn.jpeg', 'a unicorn doing computer vision research'], | |
| ['demo/pig.jpeg', 'a pig robot preparing a delicious meal'], | |
| ['demo/otta.png', 'a gentleman otter in a 19th century portrait'], | |
| ['demo/pikachu.jpeg', 'a pikachu fine-dining with a view to the Eiffel Tower'], | |
| ['demo/cabin.jpeg', 'a small cabin on top of a snowy mountain in the style of Disney art station'] | |
| ] | |
| io = gr.Interface(fn=visual_grounding, inputs=[gr.inputs.Image(type='pil'), "textbox"], | |
| outputs=[gr.outputs.Image(label="output", type='numpy'), gr.outputs.Image(label="predicted mask", type='numpy')], | |
| title=title, description=description, article=article, examples=examples, | |
| allow_flagging=False, allow_screenshot=False) | |
| io.launch(cache_examples=True) | |
| # io.launch() | |