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#!/usr/bin/env python
from __future__ import annotations
import argparse
import os
import pathlib
import subprocess
if os.getenv('SYSTEM') == 'spaces':
import mim
mim.uninstall('mmcv-full', confirm_yes=True)
mim.install('mmcv-full==1.5.2', is_yes=True)
subprocess.call('pip uninstall -y opencv-python'.split())
subprocess.call('pip uninstall -y opencv-python-headless'.split())
subprocess.call('pip install opencv-python-headless==4.5.5.64'.split())
subprocess.call('pip install pycocotools'.split())
subprocess.call('pip install detectron2==0.5 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu101/torch1.7/index.html'.split())
import cv2
import gradio as gr
import numpy as np
from mmdet.apis import init_detector, inference_detector
from utils import show_result
from mmcv import Config
DESCRIPTION = '''# OpenPSG
This is an official demo for [OpenPSG](https://github.com/Jingkang50/OpenPSG).
News: The PSG Challenge is NOW available on International Algorithm Case Competition and soon ECCV'22 SenseHuman Workshop! Prize pool 🤑 US$150K 🤑!
Check out our [GitHub repo](https://github.com/Jingkang50/OpenPSG) and [official website](http://psgdataset.org/) for more details.
<div class="row">
<div class="column">
<img id="logo" src="https://camo.githubusercontent.com/880346b66831a8212074787ba9a2301b4d700bd8f765ca11e4845ac0ab34c230/68747470733a2f2f6c6976652e737461746963666c69636b722e636f6d2f36353533352f35323139333837393637375f373531613465306237395f6b2e6a7067" alt="logo" style="width:100%">
</div>
<div class="column">
<img id="visualzation" src="https://github.com/Jingkang50/OpenPSG/blob/main/assets/psgtr_long.gif?raw=true" alt="visualzation" style="width:100%">
</div>
</div>
Inference takes about 10 second per image.
'''
FOOTER = '<img id="visitor-badge" src="https://visitor-badge.glitch.me/badge?page_id=c-liangyu.openpsg" alt="visitor badge" />'
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser()
parser.add_argument('--device', type=str, default='cpu')
parser.add_argument('--theme', type=str)
parser.add_argument('--share', action='store_true')
parser.add_argument('--port', type=int)
parser.add_argument('--disable-queue',
dest='enable_queue',
action='store_false')
return parser.parse_args()
def update_input_image(image: np.ndarray) -> dict:
if image is None:
return gr.Image.update(value=None)
scale = 1500 / max(image.shape[:2])
if scale < 1:
image = cv2.resize(image, None, fx=scale, fy=scale)
return gr.Image.update(value=image)
def set_example_image(example: list) -> dict:
return gr.Image.update(value=example[0])
class Model:
def __init__(self, model_name, device='cpu'):
model_ckt ='OpenPSG/checkpoints/epoch_60.pth'
cfg = Config.fromfile('OpenPSG/configs/psgtr/psgtr_r50_psg_inference.py')
self.model = init_detector(cfg, model_ckt, device=device)
def infer(self, input_image, num_rel):
result = inference_detector(self.model, input_image)
return show_result(input_image,
result,
is_one_stage=True,
num_rel=num_rel,
show=True
)
def main():
args = parse_args()
with gr.Blocks(theme=args.theme, css='style.css') as demo:
model = Model('psgtr', device=args.device)
gr.Markdown(DESCRIPTION)
with gr.Row():
with gr.Column():
with gr.Row():
input_image = gr.Image(label='Input Image', type='numpy')
with gr.Group():
with gr.Row():
num_rel = gr.Slider(
5,
100,
step=5,
value=20,
label='Number of Relations')
with gr.Row():
run_button = gr.Button(value='Run')
with gr.Column():
with gr.Row():
result = gr.Gallery(label='Result', type='numpy')
with gr.Row():
paths = sorted(pathlib.Path('images').rglob('*.jpg'))
example_images = gr.Dataset(components=[input_image],
samples=[[path.as_posix()]
for path in paths])
gr.Markdown(FOOTER)
input_image.change(fn=update_input_image,
inputs=input_image,
outputs=input_image)
run_button.click(fn=model.infer,
inputs=[
input_image, num_rel
],
outputs=result)
example_images.click(fn=set_example_image,
inputs=example_images,
outputs=input_image)
demo.launch(
enable_queue=args.enable_queue,
server_port=args.port,
share=args.share,
)
if __name__ == '__main__':
main()
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